Distribution Power Flow for Smart Grid
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智慧储能系统英文简称是设计方案The English abbreviation for Smart Energy Storage System is SESS.As the global demand for clean and sustainable energy sources continues to grow, the development of innovative energy storage solutions becomes increasingly crucial. The Smart Energy Storage System (SESS) is a cutting-edge technology that aims to efficiently manage and store energy from renewable sources such as solar and wind. The SESS consists of advanced software, hardware, and controls that allow for optimal energy storage and utilization.The SESS combines various energy storage technologies such as batteries, pumped hydro storage, and compressed air energy storage to create a flexible and reliable system. By integrating these different storage technologies, the SESS can provide a continuous and stable power supply, even when renewable energy sources are intermittent or unavailable.One of the main features of the SESS is its intelligent control system. This system uses advancedalgorithms and machine learning techniques to predict and optimize energy usage patterns. The SESS continuously monitors and analyzes energy production and consumption trends, allowing for the efficient allocation and utilization of stored energy. Through real-time data analysis and feedback, the SESS can automatically adjust energy storage and distribution to meet changing demand and reduce waste.Another important aspect of the SESS is its remote monitoring and management capabilities. With the help of internet connectivity and smart grid technology, the SESS can be monitored and controlled remotely. This allows for proactive maintenance, quick troubleshooting, and effective load balancing. By remotely managing the SESS, energy providers can ensure maximum efficiency and reliability, as well as reduce operational costs.The SESS also incorporates energy management and conservation features to further optimize energy usage. By monitoring and controlling devices and appliances connected to the system, the SESS can identify energy-hungry appliances and provide recommendations for energy conservation. Thisenables users to make informed decisions about their energy consumption, leading to reduced energy bills and a more sustainable lifestyle.Additionally, the SESS supports grid integration, enabling bidirectional power flow between the system and the grid. This means that excess energy stored in the SESS can be supplied back to the grid during peak demand periods. This not only helps to stabilize the power grid but also allows energy providers to monetize excess energy and incentivize the adoption of renewable energy sources.In conclusion, the Smart Energy Storage System (SESS) is a comprehensive and intelligent solution for managing and optimizing energy storage and utilization. With its advanced control system, remote monitoring capabilities, energy management features, and grid integration support, the SESS offers significant benefits in terms of efficiency, reliability, cost-effectiveness, and sustainability. As the demand for clean energy continues to rise, the SESS will play a critical role in facilitating the transition to a renewable energy future.。
基于混合整数二阶锥规划的主动配电网有功无功协调多时段优化运行一、本文概述Overview of this article随着可再生能源的大规模接入和电力电子设备的广泛应用,主动配电网(Active Distribution Network, ADN)的运行和管理面临着前所未有的挑战。
有功功率和无功功率的协调优化是保障ADN安全、经济、高效运行的关键。
本文提出了一种基于混合整数二阶锥规划(Mixed-Integer Second-Order Cone Programming, MISOCP)的主动配电网有功无功协调多时段优化运行方法。
该方法旨在通过综合考虑ADN中的多种约束条件和运行目标,实现有功和无功功率的协同优化,提高配电网的运行效率和稳定性。
With the large-scale integration of renewable energy and the widespread application of power electronic devices, the operation and management of Active Distribution Network (ADN) are facing unprecedented challenges. The coordinated optimization of active and reactive power is the key to ensuring the safe, economical, and efficient operation of ADN. Thisarticle proposes a multi period optimization operation method for active and reactive power coordination in active distribution networks based on Mixed Integer Second Order Cone Programming (MISOCP). This method aims to achieve collaborative optimization of active and reactive power by comprehensively considering various constraints and operational objectives in ADN, and improve the operational efficiency and stability of the distribution network.本文首先介绍了ADN的特点和面临的挑战,然后详细阐述了有功无功协调优化的重要性。
电力系统接入能力英语Power System Integration Capability.The integration capability of a power system refers to its ability to seamlessly accommodate and distribute electrical power from various sources while maintaining stability, reliability, and efficiency. This capability is crucial in ensuring the smooth operation of the grid, as it must be able to handle fluctuations in demand, variationsin power generation from renewable sources, and unexpected outages.To understand the intricacies of power system integration capability, it's essential to delve into the components and operation of the electric grid. The electric grid is a complex network of power plants, transformers, transmission lines, distribution lines, and end-user equipment. It's responsible for delivering electricity from generators to consumers, and its efficiency depends on the seamless integration of all these components.One of the key factors determining the integration capability of a power system is its ability to handle changes in power flow. Power flow refers to the direction and magnitude of electrical power moving through the grid. As power generation from renewable sources such as solar and wind becomes more prevalent, the grid must be able to accommodate intermittent and variable power output. This requires intelligent monitoring and control systems that can quickly adjust power flows to maintain stability.Another crucial aspect is the integration of distributed generation, which refers to small-scale power generation units located close to the end-users. These units, such as rooftop solar panels or wind turbines, can provide power to the grid during peak demand periods, reducing the burden on central power plants. However, integrating distributed generation into the grid requires careful planning and management to ensure it doesn't disrupt the balance of the system.The transmission and distribution systems within thegrid also play a vital role in integration capability. Transmission lines carry power from generation sources to distribution centers, while distribution lines deliver power to end-users. These systems must be designed and operated efficiently to minimize losses and maximize the amount of power delivered to consumers.To enhance the integration capability of power systems, several strategies can be employed. One such strategy is the use of smart grids, which utilize advanced sensing, communication, and control technologies to monitor and manage power flows more efficiently. Smart grids can identify and respond to changes in demand and generation in real-time, ensuring the grid remains stable and reliable.Another strategy is the development of flexible and efficient power plants that can quickly adapt to changes in power demand. These plants, often referred to as "flexible resources," can ramp up or down their generation output to match demand, providing necessary support to the grid during peak periods.Additionally, energy storage technologies such as batteries and pumped-hydro systems can help balance thegrid by storing excess power during low-demand periods and releasing it during peak demand. These technologies provide a buffer between supply and demand, reducing the need for additional generation capacity and enhancing the overall reliability of the system.In conclusion, the integration capability of power systems is crucial in ensuring the reliable and efficient operation of the electric grid. It requires a comprehensive understanding of the grid's components and operation, as well as the implementation of advanced technologies and strategies to manage and optimize power flows. By enhancing integration capability, we can ensure that power systems can meet the growing demand for electricity while maintaining stability, reliability, and sustainability.。
2010 International Conference on Power System TechnologyNew Challenges to Power System Planning and Operation of Smart Grid Development in ChinaZhang Ruihua, Du Yumei, Liu YuhongAbstract--The future development trend of electric power gridis smart grid, which includes such features as secure and reliable, efficientandeconomical,cleanandgreen,flexibleandcompatible, open and interactive, integrated and so on. The concept and characteristics of smart grid are introduced in this paper.On the basis of practical national situation, thedevelopment plans of smart grid in china with Chinese characteristics are proposed. Smart grid development in china is bases on information technology, communication technology, computertechnologywiththehighintegrationwithinfrastructure of generating, transmission and distribution power system. Besides, smart grid development in china brings forward many new challenge and requirements for power system planning and operation in 9 key technologies as below:1. Planning and construction of strong ultra high voltage (UHV) power grid2. Large-scale thermal power, hydropower and nuclear power bases integration of power grid3. Large-scale renewable energy sources integration of power grid4. Distributed generation and coordinated development of the grids of various voltage ratings5. Study on smart grid planning and developing strategy6. Improve the controllability of the power grid based on power electronics technology.7. Superconductivity, energystorageand othernewtechnologies widely used in power system8.Powersystemsecuritymonitoring,fastsimulation,intelligent decision-making and comprehensive defense technology9. The application of emergency and restoration control technology in power systemIn response to the challenge, this paper presents the mainresearch contents, detailed implementation plan and anticipated goals ofabove 9 key technologies.Some measures andsuggestions for power system planning and operation of smart grid development in China are given in this paper.Index Terms--smart grid, power system planning, powersystem operation, key technologies, large-scale power bases, information andcommunicationtechnology,computertechnology.Zhang Ruihua is with the Institute of Electrical Engineering, Chinese Academy of Sciences(CAS), Beijing 100190, China (E-mail: ruihuazh @mail .iee ).DU Yumei is with the Institute of Electrical Engineering, Chinese Academy of S ciences(CAS), Beij ing 100190, ChinaLiu Yuhong is with the Institute of Electrical Engineering, Chinese Academy ofSciences(CAS), Beijing 100190, China978-1-4244-5940-7/10/$26.00©20 1 0 IEEEI. INTRODUCTIONWITH the increasing pressure on environmental protection, energy conserving and persistence develops improves gradually required for society. At the same time, power market-oriented development consistently and provide higher electric energy reliability and quality are required for consumer_ It require that the future smart grid must can to provide secure, reliable, clean, high quality power supply, is able to adapt to various of electric power generation, need being able to adapt to highly become market-oriented electric power exchange especially, acting on selfs own being able to adapt to customer especially chooses need, further, improve the ample power grid assets utilization efficiency and beneficial result, provide higher quality service. For this purpose, many countries without exception look upon smart grid as future development direction of power grid [1-4].On the basis of present situation and practical condition, the development plans of smart grid in china with Chinese characteristics are proposed. The imbalance in the distribution of energy resources and the development of regional economic requires the high efficient development of energy resource in western region to satisfy the electricity demand of whole country. Besides, the limitation of environmental capacity confines conventional coal-fired thermal power in East China, which requires a new model of power supply, which will carry out large-scale power flows and balance between regions [5].The power system condition in different areas of China is very different. The condition of China's energy and electricity load distribution to determine the long-distance large scale power transmission will be the direction of the development of China's power system_ So, this determined the smart grid of China with the common characters of smart grid, it with the unique characters of large sending ends, large receiving ends, large power transmission grid [6-9].Smart grid development in china is bases on information technology, communication technology, computer technology with the high integration with infrastructure of generating, transmission and distribution power system [10-13]. Smart grid development in china addresses many new challenge and requirements for power system planning and operation in 9 key technical aspects. To response the challenge, the paper presents main research contents and key technologies in the area of power system planning and operation, and proposed detailed implementation procedure and anticipated goals.Finally, some measures and suggestions for power system planning and operation about China smart grid development are given in the paper.II. DEFINITION AND CHARACTERISTICS OF SMART GRIDA. The Definition of Smart GridBased on physical power grid, smart grid is a new type power grid which highly integrates modern advanced information techniques, communication techniques, computer science and techniques with physical grids. It has many advantages, such as improving energy efficiency, reducing the impact to environment, enhancing the security and reliability of power supply and reducing the power loss of the electricity transmission network and so on.The objectives of smart grid are: fully satisfy customer requirements for electrical power, optimize resources allocation, ensure the security, reliability and economic of power supply, satisfy environment protection constraints, guarantee power quality and adapt to power market development. Smart grid can provide customer with reliable, economical, clean and interactive power supply and valueadded services.B. The Characteristics of Smart GridSmart grid holds the promise that the power sector can go "green" by not simply reducing the use of dirty power generation methods but instead become a system that can take more aggressive measures to lower greenhouse gas emissions through efficient integration of renewable energy sources. Smart grid that focus on improving demand-side management for energy and promoting renewable energy could be a transformational force that redefines the way people view energy generation, transmission and consumption, in that such grids would encourage active engagement by the broader society, not just power sector specialists.Smart grid mainly has features as secure and reliable, efficient and economical, clean and green, flexible and compatible, open and interactive, integrated and so on [14-15].( 1) Secure and Reliable: The power grid is still to maintain the power supply capacity to the users, rather than a large area power outage when big disturbances on the power grid, faults, natural disasters and extreme weather conditions, or man-made damage happen.( 2) Efficient and Economical: The power grid can improve the economic benefits through technological innovation, energy efficient management, orderly market competition and related policies. The power grid is in support of the electricity market and power transactions effectively to achieve the rational allocation of resources and reduce power losses and finally to improve the efficiency of energy.(3) Clean and Green: a large-scale of renewable energy sources can be fed into the grid which will reduce the potential impact on the environment.(4) Optimization: The power grid can improve power supply reliability and security to meet electricity demand in digital age. The optimal cost to provide qualified electricity to the community. Smart grid can optimize utilization of assets, reduce investment costs and operation and maintenance costs. Quality of power meets industry standards and consumer needs. Provide various level of power quality for the range of needs.(5) Interactive: interaction and real-time response to the power market and consumers, which improves service. Mature wholesale market operations in place, well integrated nationwide and integrated with reliability coordinators. Retail markets flourishing where appropriate. Minimize transmission congestion and constraints.(6) Self-healing: The power grid has capabilities such as real-time & on-line security assessment and analysis, powerful control system for early warning and prevention control, automatic fault diagnosis, automatic fault isolation and system self-recovery capability. Self-Healing and adaptive to correct problems before they become emergencies. Predictive rather than reactive, to prevent emergencies ahead rather than solve after. Resilient to attack and natural disasters with rapid restoration capabilities.(7) Flexible and Compatible: The power grid can support correct, reasonable integration of renewable energy sources and it is suitable for integration of distributed generation and micro power grid. Besides, it can improve and enhance the function of demand side management to achieve the efficient interaction capability with users. Accommodate all generation and storage options. Very large numbers of diverse distributed generation and storage devices deployed to complement the large generating plants.(8)Integrated: Unified platform and models are used on the power grid. It can achieve a high degree of integration and information sharing of power grid, and to achieve standard, normative and refined management, which integrates the infrastructure, processes, devices, information and market structure so that energy can be generated, distributed, and consumed more efficiently and cost effectively. Thereby achieving a more resilient, secure and reliable energy system. Integrated to merge all critical information.III. SMART GRID DEVELOPMENT IN CHINAA. Necessities of Constructing China's Smart grid(1) Rapid growth of economy and society require to construct strong and reliable, efficient and economical power gridPower grid is the important infrastructure of energy. Chinese economy will remain high-growth in the future, China's energy and electricity demand over a longer period of time to maintain a rapid growth in the basic pattern, as well as the distribution of primary energy resources, uneven distribution and productivity of the basic national conditions, objectively determine the need to implement long-distance,large-scale transmission, walking across the country optimization resource allocation path. Therefore, there is need to construct strong and reliable, efficient and economical power grid.(2) Global resource environment pressure require to construct resource-saving and environmentally-friendly power gridA smart grid is an inevitable choice for China to address issues in its power industry and develop a lower-carbon economy. Much of China's power is generated by dirty coal plants. The government has stated that it wants to clean up its act by boosting renewable power generation to 15 percent of the total power supply by 2020. Chinese smart grid proposals call for the integration of renewable power sources, including wind and solar. The current power grid isn't able to efficiently integrate intermittent power generation from wind turbines or solar panels.In order to optimize the energy structure, improve energy efficiency and improve the climate adaptability, the state has intensified the development on wind, solar and other renewable energy. Especially for the large-scale renewable energy base in the "Three North" area, the local demand is not large enough to consume all local electricity, it's necessary to transmit the electricity through long-distance grid to load center. Generally, due to the intermittence and fluctuation of renewable energy, formulation and implementation of accurate power generation plan is impossible, which challenge the request the present ability on power acceptance and optimizing resource allocation.(3) Various generation options require to construct open and transparent, friendly and interactive power grid With the improving of future Chinese electrification level, power generation enterprises and customers will have higher requirements for service quality and principles. In order to guarantee the power production and transmission, power generation enterprises require power grid to provide reliable, efficient and flexible power integration. Electrical power customers will be able to flexibly choose power supply modes, need interaction between power grid to realize high efficient economical power utilization, and be capable to send distributed energy power to power grid in the right time to realize clean and efficient energy utilization.(4) The development of power and relative industry require to construct power grid with leading technology and equipmentDepending on technology innovation, constructing unified strong smart grid is the development direction of power grid of china. Many advanced technologies and advanced equipment will be applied in constructing smart grid, a substantial platform can be established for the stable and secure operation of grids and improve the strength of the grids' primary systems. It can upgrade the manufacture technology of power equipment and control technology of power grid. The development of smart grid involved technology and products in many fields of information, communication, power equipment manufacture, intelligent home electricity machine and so on. It will promote not only the development of relative industry but also the technology innovation and equipment creation for intelligent building, intelligent home and intelligent transportation.B. Basis oj C onstructing China's Smart gridThe basic development goal of power grid is to form a security and economical power grid. Constructing smart grid firstly depend on strong physical power grid. China speeding up the construction the power grid with UHV grid as backbone and subordinate grids coordinated development at all levels. In the technical and institutional, equipment manufacturing and project put into practice aspects has laid down solid basis for the development of smart grid [16].China pays more attention to research and project implementation, many achievements in smart grid have been accomplished in China. To be specific, China has already research and implementation in following technical aspects: Generation link: In the power generation link includes distributed generation, renewable energy generation, generator and power system coordinate operation, and energy-saving oriented dispatching technology and auto-generation control.Transformation link: In the power transformation link includes UHV AC and U HV DC transmission, FACTS, digital substation technology, PMU-based W A MS, DMS, stateoriented maintenance and so on.Distribution and supply link: In the power distribution and supply link includes distribution automation system and feeder automation system, custom power, auto-metering, Automation measurement technology and electric automobile charge power station construction and so on.Dispatching link: In the Dispatching link, much research and application have been carried out, such as next generation dispatch technology supporting system, four main dispatch application platforms, dispatch technology of energy-saving generation, online early warning and coordinated security and defense technology, integrated model management, massive information process technology, intelligent visualization, dispatch defense technology for extreme disaster.Information building link: In the information building link includes construction of system information collection, load management system, automatic meter reading system and other related systems. After promoting of marketing information work for many years, the coverage of users with electricity collected automatically improves every year, scope and effect of the system is in gradual expansion, it has played an active role in the company's marketing, production and safety management. Many electricity companies are making themselves more digital and information-wise, which also contributes to smart grid construction.C. Development Goals oJ China's Smart gridThe general development goals of China smart grid is speed up construction of a strong power grid with U HV power grid as backbone, coordinated development of power grid at all voltage levels, with information technology, digitization, automation, interactive features into independent innovation,the world's leading strong smart grid.To achieve this goal, the State Grid Corporation of China in accordance with unified planning, unified standard, pilot first, as a whole to promote the principle of speeding up the construction by the UHV AC transmission lines and ±800kV, ±1000kV DC transmission lines constitute a UHV backbone power grid to achieve coordinated development of the power grid at all voltage levels around the power generation, transmission, substations, power distribution, supply, dispatching and other major links and information building, in phases to promote the development of strong smart grid.D. Characteristics of China's Smart GridChinese smart grid framework could be different from the rest of the world. This is due to the relatively primitive structure at the distribution ends, the extensive development ofUHV transmission in recent years, and also the unique asset ownership and management structure in China.China's specific national conditions determined the smart grid of China with the common characters of smart grid, besides, it has own unique characters. These characteristics as below:(1) Large sending ends. Based on intensive exploitation of large-scale thermal power, hydro power, nuclear power and renewable energy base, build a strong and smart guide constructed of UHV power networks as the backbone according to the general requirements of a reliable efficient self-adjustable grid. The strong and smart grid will greatly optimize the allocation of resources, improve the service quality and achieve flexible integration of different sources and loads.(2) Large power transmission grid. The Smart Grid initially proposed in the world is to promote intelligence and automation for distribution system. The shortage of electric power supply in China is still a challenge, so construction for a strong national transmission networks to realize the electric power transmission from the west to the east and the mutual supply between the south and the north is still the main task. In China, to develop a smart transmission grid should be ranked in a priority. Smart transmission grid includes both the construction of a strong U HV grid and the development of the smart dispatch and control technologies.(3) Large receiving ends. In China, the electricity price was not opened to follow the electricity market, so the room for demand side management and costumer participation is limited. Therefore Smart Grid in China has a much different connotation compared with that used in west countries.The smart grid with Chinese characteristics are the means and modes to realize grid asset efficient management, enlarge grids' capability to serve both electricity producers and electricity users, make rational developing planning strategies and optimize system operation under the conditions of continuously lowering costs, improving efficiency and benefits and bettering the reliability and availability of the whole power systems, with U HV power grid as backbone and the coordinated development of the power grid of various voltage levels and in combination of advanced information, communication and control technologies and the advanced managerial philosophy [17-18].IV. NEW CHALLENGES TO POWER SYSTEM PLANNING OFSMART GRID DEVELOPMENT IN CHI NA The development of smart grid in china bring forward many new challenges and requirements for power system planning in 5 key technical aspects, which are analyzed in this section, detailed implementation plan and anticipated goals are proposed. 5 key technical aspects are as follows:A. Planning and Construction of Strong UHV Power GridResearch content: Construct the UHV power grid structure to meet the requirements of smart grid development. The structure must have strong adaptive ability, high reliability and security, strong ability to resist failure for the integration of the multifarious large-scale power generation, and can provides a flexible and easy network infrastructure conditions for the stability control system. Study of the smart power grid structure with the flexible energy exchange ability and the operating conditions adjust ability that can achieve the effective management and efficient use of resources by adjusting power network, and can continuously improve the economic benefits of the power grid.Study the HVDC planning for the receiving-end of the power system, propose the configuration principles for the intelligent dynamic reactive power compensation devices and the planning indices of the HVDC that can improve the voltage stability in the multi-infeed HVDC power system. Forecasting the load, the installed capacity and the power flow scale on the base of the analysis to economic and social development and the energy resources distribution in our country. Demonstrate the major technical problems that should be considered during the construction process of the strong and reasonable UHV network structure. Study the various factors which will affect the development of UHV network with the current technology and the current development status of the power network.Implementation Plan: The first stage will focus mainly on the UHV power development strategy, and the rational structure of UHV power network. The second stage will fully research the way of the large power base integration to UHV power network, the main factors which will affect the multiinfeed HVDC power system, the planning for the receivingend of multi-infeed HVDC power transmission system, and other pivotal technologies. The third stage will fully build the strong UHV network that can meet the demand of the smart grid.Targets: Present the particular configuration of the UHV network that can meet the special needs of the future smart grid. Guide the coordinated and sustainable development to the power grid in our country.B. Large-Scale Ordinary Power Bases Integration of Power SystemResearch content: Smart grid development in china require to study on security and stability, control measures and integration patterns of large-scale hydropower or thermal power bases connecting to power systems. Study the security stability and control technology of the HVDC islanded sending mode. Study coordinated control strategy of AC/DC system to improve system stability and the interactions between the integrated huge wind farms and the power grid. The factors which impact on large power supplies integration of power system are analyzed.Implementation Plan: The first stage will focus mainly on compare the various integration patterns of large power supplies to power grid. The second stage will fully research coordinated control strategy of AC/DC system to improve system stability. The third stage will propose integration patterns and control measures of large power supplies to power grid satisfied to the requirement of smart grid.Targets: Propose the principles optimized integration patterns of large power supply integration to power grid. Enhance generators and power grid coordinate operation, to ensure power system safely and economical operation.C. Large-Scale Renewable Energy Sources Integration of Power SystemResearch content: Study and summarize the electricity production features of various renewable energy sources (such as wind power, photovoltaic power generation). Analyze the influence, the interaction and the technologies that must be considered when the large-scale renewable energy production with different characteristics integration to the power grid.Implementation Plan: The first stage will focus mainly on the influence when the large-scale renewable energy production with different characteristics integration to the power grid. The second stage will fully study the interaction and the technologies that must be considered when the largescale renewable energy production integration to the power grid. The third stage will study the reasonable delivery scale of the renewable energy base and the reasonable delivery proportion of the renewable energy and the conventional energy and other storage systems such as pumped storage device and flywheel energy storage device.Targets: propose the system planning methods and the technologies that can meet the demands when the large renewable energy integration to the power grid.D. Distributed Generation and Coordinated Development of Transmission and Distribution NetworkResearch content: Study the operating characteristics of different distributed power generation and power supply system, study the interaction mechanism between the distributed power supply system and the power grid. Study the coordinated development at all levels of power transmission and distribution under the smart grid goals, and propose the design principles about the coordinated development of the power transmission and distribution planning at all levels; Study the planning method for the coordinated development of UHV I EHV power grid; study the planning principles for regional power grid that are adapt to the development ofUHV power grid; study the influence of HVDC power in-feed and the development of regional EHV power grid; study the principles and the time of looping-off for UHV I EHV electromagnetic loop; study the coordinated planning for UHV I EHV power grid that can improve grid stability and inhibit the short circuit current.Implementation Plan: The first stage will focus mainly on the analysis methods for the distributed power supply system performance, and the coordinated development of the power transmission and distribution at all levels. The second stage will fully research the interaction mechanism between the distributed power supply system and the power grid, and the planning method for the coordinated development of UHV / EHV power grid. The third stage will propose the standards and test specifications for the distributed power gridconnection running.Targets: Propose the planning methods for the coordinated development of the transmission and distribution network, optimize the network resources and improve the safety and reliability of the power supply, and promote the orderly development of distributed power supply, and promote the coordinated development of the power grid at all levels.E. Study on Smart Grid Planning and Developing StrategyResearch content: After fully knowing the characteristics of smart grid development at home and abroad, and the development needs and supporting capacity for intelligence, development direction and problems of our smart grid will be analyzed, investigated and researched further, in order to identify development goals and recognize obstacles in the development process of smart grid. At the same time, we should pay more attention to the study of developing strategy for smart grid in combination with China's specific national conditions. In the strategic planning, the positions, developing frameworks and measures will be clarified. The development roadmap and goals of China's smart grid adaptable to the marketization, especially in the scenario of power market, should be studied. Study on Power System Planning based on Reliability. The procedure of power system planning based on reliability .Implementation Plan: The first stage will mainly focus on the developing strategy for china future power grid, theory and method of strong smart grid planning, Power System Planning based on reliability evaluation method and principle. The second stage will fully research the procedure of power system planning based on reliability. The third stage will develop theory and method structure of the smart grid planning that can meet the requirement of smart grid.Targets: Propose the concept and physical configuration of advanced power grid, propose the planning methods for the coordinated development of the power grid, and promote the coordinated development of the power grid at all levels.。
共用直流母线原理The principle of sharing a common DC bus is a critical aspect of power distribution and management in electrical systems. 共用直流母线的原理是电力系统中电力分配和管理的关键方面。
It involves the sharing of a single DC bus among multiple loads or sources, which brings about numerous benefits in terms of cost-effectiveness, efficiency, and flexibility. 它涉及在多个负载或源之间共享单个直流母线,这在成本效益、效率和灵活性方面带来了许多好处。
By understanding the principles and applications of a shared DC bus, engineers and system designers can optimize the performance and reliability of their electrical systems. 通过理解共用直流母线的原理和应用,工程师和系统设计师可以优化其电气系统的性能和可靠性。
This article will explore the concept of a shared DC bus from different perspectives, including its benefits, challenges, applications, and future developments. 本文将从不同的角度探讨共用直流母线的概念,包括其优点、挑战、应用和未来发展。
One of the key benefits of a shared DC bus is its ability to improve overall system efficiency. 共用直流母线的一个关键优点是它能够提高整个系统的效率。
Three problems that should be stressed for China to constructsmart grids1.The smart grid with Chinese characteristics isboth strong and smart.China’s grids, as an important part in the national energy strategy and a vital link in energy sector and the important component of the national comprehensive transportation system, are in the phase of rapid development. The country has to stick to the road of constructing large grids and UHV transmission systems because of the basic conditions of the country on energy resources. However, the requirement on the level to which the grids are smart is very high, which can be ascribed to the diversification of energy sources and electricitydemandsand p eople’s concerns about environmental protection and sustainable development. On one hand, a strong property of grid is the base of safe and reliable operation of the grid and immune to natural disasters and even attacks from outside. And on the other hand, the introduction of advanced technology and equipment, scientific managerial philosophy enables flexible operation and controllable flows of power. Therefore, the grid, both strong and smart, is the direction for China’s future grids. The construction and planning of China’s smart grids should be fully considered with the construction and planning of its UHV grids.2. Scientifically planning the temporal orders ofmaking T&D systems smartAs the US and Europe have evolved into a relatively mature phase which has limited room left for electricity demands to grow, their smart grids start with distribution systems and emphasize the importance of electricity users. Power balance is usually maintained in local regions. But in China, at the beginning of constructing smart grids, more importance should be attached to making large-capacity trans-area power delivery more efficient, more reliable and more cost-effective. In middle stage, with the maturity of electricity market, the function of DSM will become more prominent. More enthusiasm will be displayed by users to participate the market. Meanwhile, the smart transmission systems willaccelerate the installing of advanced metering systems that will enable the two-way flow of data. Therefore, the whole system can be made smart under the condition of electricity market. By that time, electricity users will have enjoyed more options and decision-making power and electricity will have taken up more part of terminal energy consumptions, which display the flexibility, interactivity and environmentally friendly quality of smart grids. So, the flexibility and openness of smart grid planning and the temporal orders of making T&D systems smart should be stressed. A network with rational structure, flexible operation and high adaptability will guarantee the security, flexibility and efficiency of the grids.3.The integration of smart grid and informationproject should be considered in advanceWith introducing advanced managerialphilosophies into system management, smart gridsneed an ocean of data from all sections of the system(power generation, power T&D, power consumptions)and should process them in smart ways. From theviewpoint of information, constructing smart grids isequal to building a communication platform, aframework, and a decision-making system, ahierarchical system of agreements, which will makean efficient managerial platform to realize automationof production, modernization of management andscientification of decision making process.For instance, currently the State GridCorporation of China (SGCC) is devoted to realizingthe whole-process information project that covers itsstaff, money and materials and business. Therefore,when SGCC constructs its smart grids, advanceconsideration should be made on the consistence oftheir information structures, decision-making process,communication frameworks and the system ofagreement to settle the mutual integration of theto-be-used and obtained information and data, withoutproducing contradictory data, congestion or mutuallyrejected data. Only this way can smart grids promotefurther development of the information projects andintegrate all kinds of databases scientifically,rationally, and efficiently.。
Research on Dependable Distributed Systems forSmart GridQilin LiProduction and Technology Department, Sichuan Electric Power Science and Research Institute, Chengdu, P.R.ChinaEmail: li_qi_lin@Mingtian ZhouSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu,P.R.ChinaEmail: mtzhou@Abstract—Within the last few years, smart grid has been one of major trends in the electric power industry and has gained popularity in electric utilities, research institutes and communication companies. As applications for smart grid become more distributed and complex, the probability of faults undoubtedly increases. This fact has motivated to construct dependable distributed systems for smart grid. However, dependable distributed systems are difficult to build. They present challenging problems to system designers. In this paper, we first examine the question of dependability and identify major challenges during the construction of dependable systems. Next, we attempt to present a view on the fault tolerance techniques for dependable distributed systems. As part of this view, we present the distributed tolerance techniques for the construction of dependable distributed applications in smart grid. Subsequently, we propose a systematic solution based on the middleware that supports dependable distributed systems for smart grid and study the combination of reflection and dependable middleware. Finally, we draw our conclusions and points out the future directions of research. Index Terms—smart grid, dependability, dependable middleware, fault-tolerance, fault, error, failure, error processing, fault treatment, replication, distributed recovery, partitioning, open implementation, reflection, inspection, adaptationI.I NTRODUCTIONWithin the last few years, smart grid has been one of major trends in the electric power industry and has gained popularity in electric utilities, research institutes and communication companies. The main purpose of smart grid is to meet the future power demands and to provide higher supply reliability, excellent power quality and satisfactory services. Although smart grid brings great benefits to electric power industry, such a new grid introduces new technical challenges to researchers and engineering practioners.As applications for smart grid become more distributed and complex, the probability of faults undoubtedly increases. Distributed systems are defined as a set of geographically distributed components that must cooperate correctly to carry out some common work. Each component runs on a computer. The operation of one component generally depends on the operation of other components that run on different computers[1] [2]. Although the reliability of computer hardware has improved during the last few decades, the probability of component failure still exists. Furthermore, as the number of interdependent components in a distributed system increases, the probability that a distributed service can easily be disrupted if any of the components involved should fail also increases[2]. This fact has motivated to construct dependable distributed systems for smart grid.Fault tolerance is needed in many different dependable distributed applications for smart grid. However, dependable distributed systems are difficult to build. They present challenging problems to system designers. System designers must face the daunting requirement of having to provide dependability at the application level, as well as to deal with the complexities of the distributed application itself, such as heterogeneity, scalability, performance, resource sharing, and the like. Few system designers have these skills. As a result, a systematic approach to achieving the desired dependability for distributed applications in smart grid is needed to simplify the difficult task.Recently, middleware has emerged as an important architectural component in supporting the construction of dependable distributed systems. Dependable middleware can render building blocks to be exploited by applications for enforcing non-functional properties, such as scalability, heterogeneity, fault-tolerance, performance, security, and so on[3]. These attractive features have made middleware a powerful tool in the construction of dependable distributed systems for smart grid [3].This paper makes three contributions to the construction of dependable distributed systems for smart grid. First of all, we examine the question of dependability and identify major challenges during the construction of dependable systems. Subsequently, we attempt to present a view on the fault tolerance techniques for dependable distributed systems. As part of© 2012 ACADEMY PUBLISHER doi:10.4304/jsw.7.6.1250-1257this view, we present the distributed tolerance techniques for building dependable distributed applications in smart grid. Finally, we propose a systematic solution based on the middleware that supports dependable distributed systems for smart grid and study the combination of reflection and dependable middleware.The remainder of this paper is organized as follows: SectionⅡstudies dependability matters for distributed systems in smart grid and identifies the major challenges for the construction of dependable systems. Section Ⅲintroduces basic concepts and key approaches related to fault-tolerance. In SectionⅣ, we discusses distributed fault-tolerant techniques for building dependable systems in smart grid. SectionⅤintroduces dependable middleware to address the ever increasing complexity of distributed systems for smart grid in a reusable way. Finally, SectionⅥdraws our conclusions and points out the future directions of research.Ⅱ.D EPENDABLILITY M ATTERSDistributed systems are intended to form the backbone of emerging applications for smart grid, including supervisory control, data acquisition system and distribution management system, and so on. An obvious benefit of distributed systems is that they reflect the global business and social environments in which electric utilities operate. Another benefit is that they can improve the quality of service in terms of scalability, reliability, availability, and performance for complex power systems.Dependability is an important quality in power distributed applications. In general terms, a system's dependability is defined as the degree to which reliance can justifiably be placed on the service it delivers [4]. The service delivered by a system is its behavior as it is perceived by its user(s); a user is another system (physical, human) which interacts with the former[4]. More specifically, dependability is a global concept that encapsulates the attributes of reliability (continuity of service), availability (readiness for usage), safety (avoidance of catastrophes), and security (prevention of unauthorized handling of information)[2] [4]. In power distributed environments, even small amounts of downtime can annoy customers, hurt sales, or endanger human lives. This fact has made it necessary to build dependable distributed systems for electric utilities.Fault tolerance is an important aspect of dependability. It is referred to as the ability for a system to provide its specified service in spite of component failure[2] [4]. Fault-tolerant system‘s behavior is predictable despite of partial failures, asynchrony, and run-time reconfiguration of the system. Moreover, fault-tolerant applications are highly available. The application can provide its essential services despite the failure of computing nodes, software object crash, communication network partition, value fault for applications [5]. However, building dependable distributed systems is complex and challenging. On the one hand, system designers have to deal explicitly with problems related to distribution, such as heterogeneity, scalability, resource sharing, partial failures, latency, concurrency control, and the like. On the other hand, system developers must have a deep knowledge of fault tolerance and must write fault-tolerant application software from scratch[2]. As consequence, they have to face a daunting and error-prone task of providing fault tolerance at the application level [2].Certain aspects of distributed systems make dependability more difficult to achieve. Distribution presents system developers with a number of inherent problems. For instance, partial failures are an inherent problem in distributed systems. A distributed service can easily be disrupted if any of the nodes involved should fail. As the number of computing nodes and communication links that constitute the system increases, the reliability of components in a distributed system rapidly decreases.Another inherent problem is concurrency control. System developers must address complex execution states of concurrent programs. Distributed systems consist of a collection of components, distributed over various computers connected via a computer network. These components run in parallel on heterogeneous operating systems and hardware platforms and are therefore prone to race conditions, the failure of communication links, node crashes, and deadlocks. Thus, dependable distributed systems are often more difficult to develop, applications developers must cope explicitly with the complexities introduced by distribution.In theory, the fault tolerance mechanisms of a dependable distributed system can be achieved with either software or hardware solution. However, the cost of custom hardware solution is prohibitive. In the meantime, software can provide more flexibility than its counterpart[2]. As a result, software is a better choice for implementing the fault tolerance‘s mechanisms and policies of dependable distributed systems[2]. However, the software solution for the construction of dependable is also difficult. This is particularly true if distributed systems‘ dependability requirements dynamically change during the execution of an application. Further complicating matters are accidental problems such as the lack of widely reused higher level application frameworks, primitive debugging tools, and non-scalable, unreliable software infrastructures. In that case, fault tolerance can be achieved using middleware [2]. Middleware can be devised to address these problems and to hide heterogeneity and the details of the underlying system software, communication protocols, and hardware. Built-in mechanisms and policies for fault-tolerant can be achieved by middleware and provide solutions to the problem of detecting and reacting to partial failures and to network partitioning. Middleware can render a reusable software layer that supports standard interfaces and protocols to construct a fault-tolerance distributed systems. Dependable middleware shields the underlying distributed environment‘s complexity by separating applications from explicit© 2012 ACADEMY PUBLISHERprotocol handling, disjoint memories, data replication, and facilitates the construction of dependable application [6].Ⅲ.F AULT T OLERANCEA. Failure, Error and FaultIn order to construct a dependable distributed system, it is important to understand the concepts of failure, error, and fault. In a distributed system, a failure occurs when the delivered service of a system or a component deviates from its specification[4]. An error is that part of the system state that is liable to lead to subsequent failure. An error affecting the service is an indication that a failure occurs or has occurred[4]. A fault is the adjudged or hypothesized cause of an error [4].In general terms, we think that an error is the manifestation of a fault in the distributed system, while a failure is the effect of an error on the service. As a result, faults are potential sources of system failures.Whether or not an error will actually lead to a failure depends on three major factors. One factor is the system composition, and especially the nature of the existing redundancy [4]. Another factor is the system activity. An error may be overwritten before creating damage[4]. A third factor is t he definition of a failure from the user‗s viewpoint. What is a failure for a given user may be a bearable nuisance for another one [4].Faults and their sources are extremely diversified. They can be categorized according to five main perspectives that are their phenomenological cause, their nature, their phase of creation or of occurrence, their situation with respect to the system boundaries, and their persistence [4].B. Fault modelsWhen designing a distributed fault-tolerant system, we can not to tolerate all faults. As consequence, we must define what types of faults the system is intended to tolerate. The definition of the types of faults to tolerate is referred to as the fault model, which describes abstractly the possible behaviors of faulty components[2] [4]. A system may not, and generally does not, always fail in the same way. The ways a system can fail are its fault modes. As a result, the fault model is an assumption about how components can fail [2] [4].In distributed systems, a fault model is characterized by component and communication failures[2] [4]. It is common to acknowledge that communication failures can only result in lost or delayed messages, since checksums can be used to detect and discard garbled messages[2] [4]. However, duplicated or disordered messages are also included in some models [2] [4].For a component, the most commonly assumed fault models are (in increasing order of generality): stopping failures or crashes, timing fault model, value fault model and arbitrary fault model[2] [4]. Stopping failures or crashes is the simplest and most common assumption about faulty components[2] [4]. This model always assumes that the only way a component can fail is by stopping the delivery of messages and that its internal state is lost [2] [4].The timing fault model assumes that a component will respond with the correct value, but not within a given time specification [2] [4]. A timing fault model can result in events arriving too soon or too late. A timing fault model includes delay and omission faults[2] [4]. A delay fault occurs when the message has the right content but arrives late[2] [4]. An omission fault occurs when no message is received. Sometimes, delay faults are called performance faults[2] [4]. In the value fault model, the value of delivered service does not comply with the specification [2] [4].Arbitrary fault model is the most general fault model, in which components can fail in an arbitrary way [2] [4]. As a result, if arbitrary faults are considered, no restrictive assumption will be made[2] [4]. An arbitrarily faulty component might even send contradictory messages to different destinations (a so-called byzantine fault)[2] [4]. This model can include all possible causes of fault, such as messages arriving too early or too late, messages with incorrect values, messages never sent at all, or malicious faults [2] [4].C. Error Processing and Fault TreatmentFault tolerance is system‘s ability to continue to provide service in spite of faults [2] [4]. It can be achieved by two main forms: error processing and fault treatment [2] [4]. The purpose of error processing is to remove errors from the computational state before a failure occurs, if possible before failure occurrence, whereas the purpose of fault treatment is to prevent faults from being activated again [2] [4].In error processing, error detection, error diagnosis, and error recovery are commonly used approaches[2] [4]. Error detection and diagnosis is an approach that first identifies an erroneous state in the system, and then assesses the damages caused by the detected error or by errors propagated before detection[2] [4]. After error detection and diagnosis, error recovery substitutes an error-free state for the erroneous state [2] [4].Error recovery may take on three forms: backward recovery, forward recovery, and compensation[2] [4]. In backward recovery, the erroneous state transformation consists of bringing the system back to a state already occupied prior to error occurrence[2] [4]. This entails the establishment of recovery points, which are points in time during the execution of a process for which the then current state may subsequently need to be restored [2] [4].In forward recovery, the erroneous state transformation consists of finding a new state, from which the system can operate[2] [4]. Error compensation renders enough redundancy so that a system is able to deliver an error-free service from the erroneous state [2] [4].The goal of fault treatment determines the cause of observed errors and prevents faults from being activated again[2] [4]. The first step in fault treatment is fault diagnosis, which consists of determining the cause(s) of error(s), in terms of both location and nature [2] [4]. Then it© 2012 ACADEMY PUBLISHERtakes actions aimed at making it (them) passive[2] [4]. This is achieved by preventing the component(s)identified as being faulty from being invoked in further executions[2] [4]. Fault treatment can be used to reconfigure a system to restore the level of redundancy so that the system is able to tolerate further faults [2] [4].Ⅳ.D ISTRIBUTED T OLERANCE T ECHNIQUESA. ReplicationIn order to mask the effects of faults, distributed fault tolerance always requires some form of redundancy. Replication is a classic example of space redundancy. Itexploits additional resources beyond what is needed for normal system operation to implement a distributed fault-tolerant service[2] [4]. The metaphor of replication is to manage the group of processes or replicas so as to maskfailures of some members of the group[2] [4]. By coordinating a group of components replicated on different computing nodes, distributed systems can provide continuity of service in the presence of failednodes [2] [4].There are three well-known replication schemes: active replication, passive replication, and semi-active replication. In active replication scheme, every replicaexecutes the same operations[2] [4]. Input messages are atomically multicasted to all replicas, who all process them and update their internal states. All replicas generate output messages [2] [4].Passive replication is a technique in which only one of the replicas (the primary) actively executes the operation, updates its internal state and sends output messages [2] [4]. The other replicas (the standby replicas) do not processinput messages; however, their internal state must be updated periodically by information sent by the primary [2] [4]. If the primary should fail, one of the standby replicas is elected to take its place [2] [4].Semi-active replication is a technique which is similar to active replication[2] [4]. In semi-active replication, all replicas will receive and process input messages. However, unlike active replication, the processing of messages is asymmetric in that one replica (the leader) takes responsibility for certain decisions (e.g., concerning message acceptance) [2] [4]. The leader replica can enforce its choice on the other replicas (the followers) without resorting to a consensus protocol [2] [4]. One alternative for semi-active replication is that the leader replica may take sole responsibility for sending output messages[2] [4]. Semi-active replication primarily targeted at crash failures. However, under certain conditions, this strategy can also be extended to deal with arbitrary or byzantine failures [2] [4].Continuity of service in the presence of failed nodesrequires replication of processes or objects on multiple nodes[2] [4]. Replication can provide high-available service for a dependable distributed system. By replicating their constituent objects and distributing their replicas across different computers connected by the network, distributed applications can be made dependable [5]. The major challenge of replication technique is to maintain replica consistency [7] [8] [9]. Replication will fail in its purpose if the replicas are not true copies of each other, both in state and in behavior [5] [10] [11] [12].B. Distributed RecoveryIn a dependable distributed system, some form of recovery is required to minimize the negative impact of a failed process or replica on the availability of a distributed service [4]. In its simplest form, this can be just a local recovery of the failed process or replica. However, distributed recovery will occurs if the recovery of one process or replica requires remote processes or replicas also to undergo recovery[4]. In this case, processes or replica must rollback to a set of checkpoints that together constitute a consistent global state [4].In order to create checkpoints, there are several major approaches. One way is asynchronous checkpointing[4]. In asynchronous checkpointing, checkpoints are created independently by each process or replica, and then when a failure occurs, a set of checkpoints must be found that represents a consistent global state [4]. This approach aims to minimize timing overheads during normal operation at the expense of a potentially large overhead when a global state is sought dynamically to perform the recovery[4]. The price to be paid for asynchronous checkpointing is domino effect. If no other global consistent state can be found, it might be necessary to roll all processes back to the initial state[4]. As a result, in order to avoid the domino effect, checkpoints can be taken in some coordinated fashion.Another way is to structure process or replica interactions in conversations[4]. In a conversation, processes or replicas can communicate freely between themselves but not with other processes external to a conversation[4]. If processes or replicas all take a checkpoint when entering or leaving a conversation, recovery of one process or replica will only propagate to other processes or replica in the same conversation [4].A third alternative is synchronous checkpointing [4] [13]. In this approach, dynamic checkpoint coordination is allowed so that a set of checkpoints can represent global consistent states [4] [13]. As consequence, the domino effect problem can be transparently avoided for the software developers even if the processes or replicas are not deterministic[4]. At each instant, each process or replica possesses one or two checkpoints: a permanent checkpoint (constituting a global consistent state) and another temporary checkpoint[4]. The temporary checkpoints may be undone or transformed into a permanent checkpoint. The creation of temporary checkpoints, and their transformation into permanent ones, is coordinated by a two-phase commit protocol to ensure that all permanent checkpoints effectively constitute a global consistent state [4].C. Partitioning ToleranceA distributed system may partition into a finite number of components. The processes or replicas in different© 2012 ACADEMY PUBLISHERcomponents can not communicate each other[11]. Partitioning may occur due to normal operations, such as in mobile computing, or due to failures of processes or inter-process communication. Performance failures due to overload situations can cause ephemeral partitions that are difficult to distinguish from physical partitioning [4]. Partitioning is a very real concern and a common event in wide area networks[4]. If the network partitions, different operations may be performed on the processes or replicas in different components, leading to inconsistencies that must be resolved when communication is re-established and the components remerge[5]. One strategy for achieving this is to allow components of a partition to continue some form of operation until the components can re-merge [4] [11]. Once the components of a partitioned remerge, the processes or replicas in the merged components must communicate their states, perform state transfer and reach a global consistent state [5].As another example, certain distributed fault-tolerance techniques are aimed at adopting dynamic linear voting protocol to ensure replica consistency in partitioned networks[5]. Voting protocols are based on quorums. In voting protocols, each node is assigned a number of votes. When a network is partitioned or remerged, if a majority of the last installed quorum is connected, a new quorum is established and updates can be performed within this partition [5].Ⅴ. DEPENDABLE M IDDLEWAREIn the past decade, middleware has emerged as a major building block in supporting the construction of distributed applications[14]. The development of distributed applications has been greatly enhanced by middleware. Middleware provides application developers with a reusable software layer that relieve them from dealing with frequently encountered problems related to distribution, such as heterogeneity, interoperability, security, scalability, and so on[14][15][16][17]. Implementation details are encapsulated inside the middleware itself and are shielded from both users and application develop ers‘, so that the infrastructure‘s diversities are homogenized by middleware [18] [19] [20] [21]. These attractive features have made middleware an important architectural component in the distributed system development practice. Further, with applications becoming increasingly distributed and complex, middleware appears as a powerful tool for the development of software systems [14].Recently, a strong incentive has been given to research community to develop middleware to provide fault tolerance to distributed applications[2]. Middleware support for the construction of dependable distributed systems has the potential to relieve application developers from the burden by making development process faster and easier and significantly enhancing software reuse. Hence, such middleware can render building blocks to be exploited by applications for enforcing dependability property [2].However, building such software infrastructure that achieves dependable goal is not an easy task. Neither the standard nor conventional implementations of middleware directly address complex problems related to dependable computing, such as partial failures, detection of and recovery from faults, network partitioning, real-time quality of service or high-speed performance, group communication, and causal ordering of events[9]. In order to cope with these limitations, many research efforts have been focused on designing new middleware systems capable of supporting the requirements imposed by dependability [5].A first issue that needs to be addressed by dependable middleware is interoperability[2]. Interoperability allows different software systems to exchange data via a common set of exchange formats, to read and write the same file formats, and to use the same protocols. As a result, in order to be useful, dependable middleware should be interoperable[2]. Through interoperability, dependable middleware can provide a platform-independent way for applications to interact with each other[2]. In other words, two systems running on the different middleware platforms can interoperate with each other even when implemented in different programming languages, operating systems, or hardware facilities [2].Another important problem concerns transparency. Dependable middleware should provide some form of transparency to applications[2]. It allows dynamically to add to an existing distributed application and to interfere as little as possible with applications at runtime. Therefore, many existing applications can benefit from the dependable middleware [2]. Traditional middleware is built adhering to the metaphor of the black box. Application developers do not have to deal explicitly with problems introduced by distribution. Middleware developed upon network operating systems provides application developers with a higher level of abstraction. The infrastructure‘s diversities are hidden from both users and application developers, so that the system appears as a single integrated computing facility [16].Although transparency philosophy has been proved successful in supporting the construction of traditional distributed systems, it cannot be used as the guiding principle to develop the new abstractions and mechanisms needed by dependable middleware to foster the development of dependable distributed systems when applied to the today‘s computing settings[15][18][19]. As a result, it is important to adopt an open implementation approach to the engineering of dependable middleware platforms in terms of allowing inspection and adaptation of underlying components at runtime[22][23][24][25].With networks becoming increasingly pervasive, major system requirements posed by today‘s networking infrastructure relate to openness and context-awareness [14]. This leads to investigate new approaches formiddleware with support for dependability and context-aware adaptability. However, in order to provide transparency, traditional middleware must make decisions on behalf of the application. This is inevitably© 2012 ACADEMY PUBLISHER。
智能电网英语作文English:The smart grid is a modern electricity distribution system that integrates advanced technologies such as artificial intelligence, sensors, and communication networks. It enables two-way communication between the utility provider and the consumers, allowing for real-time monitoring and control of energy flow. Smart grids have the ability to optimize energy utilization, reduce wastage, and enhance grid reliability and resilience. By leveraging data analytics and machine learning, these intelligent systems can predict demand patterns, detect abnormalities, and respond proactively to potential issues. Additionally, smart grids support the integration of renewable energy sources like solar and wind power, facilitating a more sustainable and environmentally-friendly energy ecosystem. Overall, the smart grid represents a significant step towards a more efficient, secure, and sustainable energy infrastructure for the future.中文翻译:智能电网是一种现代电力分配系统,集成了人工智能、传感器和通讯网络等先进技术。
Abstract —Smart Grid technologies hold the promise of being able to solve many of the problems currently facing in the electric power industry. However, th e large scale deployment of th esenew tech nologies h as been limited due to an inability toaccurately model th eir effects or to quantify th eir potentialbenefits. GridLAB-D is a new open source power systemmodeling and simulation environment developed by th e United States Department of Energy specifically to integrate detailed power systems and end-use models. In order to effectively modelth e vast array of possible smart grid tech nologies GridLAB-Dwas developed as a general simulation environment. This paperdescribes th e basic design concept, th e power flow solutionsimplemented, and a detailed example of the type of analysis that can be performed within the simulation environment in order to support the evaluation of smart grid technologies.Index Terms — current injection meth od, distribution systemanalysis, forward-backward sweep meth od, power simulation,power modeling, Gauss-Seidel, smart grid.I. I NTRODUCTION ridLAB-D is the first of a new generation of distributionsystem simulation technologies developed by the U.S.Department of Energy (DOE) at the Pacific NorthwestNational Laboratory (PNNL) in collaboration with Industry and Academia. GridLAB-D has been developed as an enabling tool to facilitate the evaluation of smart grid technologies. Historically the inability to effectively model smart grid technologies has been identified as a significant barrier to their adoption; GridLAB-D is an attempt to address this problem. GridLAB-D is a complete simulation environment which incorporates advanced modeling techniques, high performance computing capabilities, andintegration tools to other existing software platforms. GridLAB-D is continually under development, by PNNL as well as additional collaborators, and versions exist that can currently be downloaded [1]. The most important new capabilities slated for GridLAB-D include:This work was funded by the Office of Electricity Delivery and EnergyReliability of the U.S. Department of Energy (DOE) under contract DE-AC05-76RLO1830.K. P. Schneider, D. Chassin and Y. Chen are with Pacific Northwest National Laboratory in Richland, Washington 99352. They may be contacted byemail at kevin.schneider@ , david.chassin@ andYousu.chen@ respectively.J. Fuller is with Washington State University Tri-Cities in Richland Washington. He may be contacted at jcfuller@ .• Extended quasi-steady state time-series solutions;•Detailed end-use models including consumer appliances and distribution utility equipment models, all implemented with the latest agent-based simulationmethods; • Distributed energy resource models, including appliance-based load shedding technology and distributed generator and storage models; • Integration of transmission and distribution systemmodels including unified solvers; • Retail market modeling tools, including contract selection, business and operations simulation tools, models of SCADA controls, and metering technologies; • The ability to run efficiently on multicore andmultiprocessor machines; and • External links to Matlab®, MySQL®, Microsoft® Excel® and Access®, and text-based tools, as well asbeing able to convert models from the SynerGEE® andWindmil® power distribution modeling systems. GridLAB-D is capable of studying distribution utility system behaviors ranging from a few seconds to decades, simulating the interaction between physical phenomena, business systems, markets and regional economics, and consumer behaviors. The results include many power system statistics, including selected reliability indices from IEEE 1366 [2] (e.g., CAIDI, SAIDI, SAIFI), and business metrics such a profitability, revenue rates of return, and per customer or per line-mile cost metrics are planned.GridLAB-D has been validated with both existing end-use simulation and standard distribution analysis tools such as the IEEE Radial Test Feeders [3]. II. T HE G RID LAB-D S YSTEMA. What is GridLAB-D?GridLAB-D is a flexible simulation environment that can be integrated with a variety of third-party data management and analysis tools. At its core, GridLAB-D has an advanced algorithm that can determine the simultaneous state of millions of independent devices, each of which can be described by multiple differential or difference equations solved locally for both state and time. The advantages of this algorithm over traditional finite difference-based simulatorsDistribution Power Flow for Smart GridTechnologiesK. P. Schneider, Senior Member, IEEE , D. Chassin, Senior Member, IEEE, Y. Chen , Member, IEEEand J. C. Fuller, Student Member, IEEEG978-1-4244-3811-2/09/$25.00 ©2009 IEEEare: 1) it is much more accurate; 2) it can handle widely disparate time scales, ranging from sub-second to many years; and 3) it is very easy to integrate with new modules and third-party systems. The advantage over tradition differential algebraic solvers is that it is not necessary to integrate all the device’s behaviors into a single set of equations that must be solved. The GridLAB-D system also includes modules to perform the following system simulation functions:•Power flow calculations and device controls, including distributed generation and energy storage•End-use appliance technologies, equipment and controls•Data collection on every property of every object in the system, and boundary condition management includingweather and electrical boundaries.Additional planned modules are being developed to provide additional functionality, including:•Consumer behavior including daily, weekly, and seasonal demand profiles, price response, and contractchoice•Energy operations such as distribution automation, load-shedding programs, and emergency operations. •Business operations such as retail rate, billing, and market-based incentive programs.B. How does GridLAB-D work?GridLAB-D includes an extensive suite of tools to build and manage studies, and analyze results. Existing and planned tools include:•Agent-based and information-based modeling tools that allow you to create detailed models of how newend-use technologies, distributed energy resources,distribution automation, and retail markets interact andevolve over time.•Tools to create and validate rate structures, examine consumer reaction, and verify the interaction anddependence of programs with other technologies andwholesale markets.•Interfaces to industry-standard power systems tools and analysis systems.•Extensive data collection tools to permit a wide variety of analyses.GridLAB-D is capable of examining, in detail, the interplay of every part of a distribution system with every other. GridLAB-D does not require the use of reduced-order models, so the danger of erroneous assumption is averted. The ability to use fully detailed models, with no simplifications, will be necessary to the effective evaluation of smart grid technologies. GridLAB-D is therefore specifically suited to address the many issues pertaining to the integration and adoption of smart grid technologies.III. P OWER F LOW M ODELINGThe power flow component of GridLAB-D is separated into a distribution module and a transmission module. While distribution systems were the original focus of GridLAB-D, the transmission module was included so that in the future interactions between the two modules can be examined. When the integration of these two modules is complete it will be possible to examine how “smart devices” on the distribution system affect the transmission system. Traditionally the ability to examine interactions on this level has been limited by computational power; the use of multiple processors is how this is addressed in GridLAB-D.A. Transmission Systems Component modelsAs previously stated, the transmission system is not the primary focus of GridLAB-D. The primary purpose of including a transmission module is to allow for the interconnection of multiple distribution substations. If a transmission module was not included each substation, and the associated feeders, would need to be solved independently. While distribution feeders can be solved independently, as is common in current commercial software packages, GridLAB-D will also have the ability to generate a power flow solution for multiple distributions feeders at substations which are interconnected via a transmission or sub-transmission network.In the current version of GridLAB-D the power flow algorithm used for the transmission system is the Gauss-Seidel (GS) method. There are 2 primary reasons why the GS method was selected over other algorithms for the initial transmission module. The first was that GS method is able to solve a single power flow iteration more efficiently than other methods such as the Newton Raphson (NR) method. This is especially important since GridLAB-D will only start from a “flat start” at the first time step and all other solutions will use the solution from the previous time step. This is one of the key differences in GridLAB-D; it is not designed to solve only a single power flow problem but instead to generate a series of power flow solutions that approximate a time varying condition. The ability to examine time-series issues is central to many of the questions surrounding smart grid technologies.The second reason for the use of the GS solver is that it has many desirable characteristics when implemented in an object orient environment on multiple processors. For example, NR solvers do not lend themselves to parallelization because of the need to invert the Jacobian, which is not an intrinsically parallel process. The GS method, while converging linearly, does so in an intrinsically parallel way [4]. Because GridLAB-D solves a time-series of quasi-steady solution, the number of iteration for either solver is generally very low, and the convergence performance advantage of NR is lost when compared with the high degree of parallelism possible with GS. In addition, the GS solver is more robust when starting from a poor initial “guess” solution. This means that when a large disturbance is evaluated, the solver is more likely to find a solution, even if it requires substantially more iterations.In the current version of GridLAB-D, the transmission module is capable of solving balanced 3-phase models. Future modules will be capable of solving unbalanced conditions allowing integration of the transmission model with unbalanced distribution models. B. Forward Backward SweepIn order to accurately represent the distribution system thedistribution feeders are expressed in terms of their conductortypes, conductor placement on poles, underground conductororientation, phasing, and grounding [5]. GridLAB-D does notsimplify the component models of the distribution system.The existing distribution module of GridLAB-D utilizes thetraditional Forward and Backward Sweep (FBS) method for solving the power flow problem. This method was selected in lieu of newer methods such as the Newton-Raphson based Three-Phase Current Injection Method (TCIM) methods of [6] for the same reasons that the GS method was selected for the transmission module; converging in the fewest number of iterations is not the primary goal. Just as with the transmission module the distribution modules will only startwith a “flat start” at initialization and all subsequent solutionswill be from the previous time step.Even though the FBS method was chosen as the initialpower flow algorithm, the modular nature of GridLAB-D doesnot prevent the creation of a power flow module that implements other algorithms, as in the case of the transmission module. Section C will describe a new power flow module that is currently being developed.While there is discussion over what constitutes the forward sweep and what constitutes the backwards sweep, for the purposes of this paper the conventions of [5] will be used. Equations for the forward and backward sweep are shownbelow:Forward Sweep[][][][][]212abc abc abc I B V A V ⋅−⋅= (1)Backward Sweep[][][][][]221abc abc abc I b V a V ⋅+⋅= (2)[][][][][]221abc abc abc I d V c I ⋅+⋅= (3)The [a], [b], [c], [d], [A], and [B] generalized matrices are developed using characteristics of the individual series components. The matrices are 3x3 and represent three-phase components, but can also represent two-phase and single-phase components by filling in the unused rows and columnswith zeroes. At this time the following components are modeled and available for use: • Over ead and Underground Lines . Multiple configurations, bare conductor, concentric neutral underground cables, and triplex lines.• Transformers. Single-phase or three-phase in most common configurations, and center-tapped. • Voltage Regulators . Three-phase Load Tap Changer (LTC) type, 3 - single-phase in Wye and Delta configurations, support for Line Drop Compensation (LDC). • Fuses . Simplified over-current model. • Switches . Single-phase and three-phase ganged. • Sh unt Capacitor Banks . Modeled similar to a load; static, switched, and automatic control are supported. Metering is supported for both single phase center-tapped customers and three phase customers. Metering is also available to support distributed resource operations including distributed generation and energy storage. Support forreclosers, islanding, and overbuilt lines is anticipated incoming versions.The ability to model overbuilt lines will be essential in simulations where multiple feeders originate from a single substation. When multiple feeders originate from a single substation it is common for them to share the same supportstructures, resulting in mutual coupling. The mutual coupling means that the simulation of two feeders, sharing a support structure, cannot be completely decoupled. This result is the necessity to be able to model an n-phase system. C. Gauss Seidel Three Phase Current Injection MethodWhile the power flow problem can be solved effectively and efficiently using the FBS method, there are certain limitations. The most notable of these limitations is the inability to handle networked distributions systems. While the vast majority of distribution feeders in the United States areradial there are a substantial number that are not, especially in urban centers. For this reason PNNL has begun work on anew power flow module that is based on the well establishedTCIM [6]. Since GridLAB-D was constructed with a modulardesign the implementation of a new power flow algorithm willnot require modifications to any other modules. Additionally all of the existing models for power system equipment within the power flow module are valid; this is a key design feature of GridLAB-D.The method discussed in [6] is an effective method to solve the power flow problem but it does not readily adapt to the GridLAB-D environment, so a variation of it is being implemented. Instead of solving the three phase current injection based power flow problem with a NR technique, the GS solution method will be used. The primary justification for this is that the NR method cannot be inherently performed on multiple processors, while the GS method can. This is the same reason that the transmission module utilizes the GS method. The basic formulation of the GS method, extended to an unbalanced representation, is shown in (4):[Vabc]1 2()()[]∑∑=∈⎥⎦⎤⎢⎣⎡⋅⋅=nk p t t k mk jt j mj mV y V S 1** (4)where j indicates phase a, b, or c, m indicates the bus number, n indicates the number of buses, and p is the set of phase a, b, and c.Equation 4 can be further expanded to give the GS formulation for the voltage at any PQ bus, as is shown in (5):()()()[]()()[]⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡⋅−⋅−⎟⎟⎠⎞⎜⎜⎝⎛+=∑∑∑≠=≠∈∈n m k k j t p t t kmk jt pt t kmk jt j m j m j m mm jj j m V y V y V Q P y V 1*****1 (5)In (5) both the real and reactive powers at a given bus are known values, but this is not true for a PV bus where the reactive power can vary based on bus voltage. Equation 6 shows how the reactive power injection at a PV bus is calculated.()()[]⎥⎥⎦⎤⎢⎢⎣⎡⎥⎦⎤⎢⎣⎡⋅⋅=∑∑=∈n k p t t k mkjt j m jmV y V Q 1**Im (6)Equations 5 and 6 form the core equations that allow for the implementation of a GS solution to the TCIM; a method that is inherently parallel. This work is currently in progress and a paper detailing the GS solution to the TCIM is being written and a new GridLAB-D module implementing this solution method is in development. At the present all GridLAB-D calculations use the FBS method.IV. EXAMPLE CASE In order to fully demonstrate the effects of modeling large distribution systems, their end-use loads, and distributed resources, an example case has been developed. The examplecase presented in this section is a 12.47 kV feeder with 1,400 nodes and is representative of a typical West Coast feeder that is a mix of residential and commercial loads.Connected to this feeder are three wind turbine generators (WTG). To increase the level of model complexity 50 of the residential loads were replaced with complex load models. While most of the residential loads were kept as constant power loads supplied through a triplex cable from a center-tapped transformer, 50 loads were expanded to include models for the various appliances within the residence. These appliances included hot water heaters, lighting, microwaves, ranges, dishwashers, clothes washers, refrigerators, and heating, ventilating and air conditioning (HVAC). The following subsections explain in detail how the WTGs and the details residential loads were modeled.A. Wind Turbine Generator ModelA simple model for a generator can be described as a constant power or constant power factor source, injecting power or current into the system solely as a function of voltage. The WTG models implemented in this example are far more complex. While the level of detail shown in this example may not always be necessary or practical, it is illustrative of the capability to model generators in GridLAB-D. Additionally, the evaluation of future smart grid technologies may require this level of modeling detail.Terminal voltage and meteorological data, including temperature, wind speed, and air pressure, are used as inputs into the WTG model, while the final output is the current injections at the generator terminals. With the inputs and outputs defined the WTG can then be modeled by breaking the system into two main components, the mechanical model and the electrical model.The mechanical model determines the amount of power extracted from the wind, what percentage of that is transmitted through the drive train, and the final amount of power that is delivered to the electrical generator. By using the meteorological data, and design specifications supplied by WTG manufacturers, the mechanical power extracted, P m , canwhere ρtemperature and pressure, r is the radius of the blade, C p is the coefficient of performance, u is the wind velocity, and ηgear is the efficiency of the drive train.The coefficient of performance, C p , is blade specific to each turbine type and must be extrapolated from manufacturers’ tables when available, or approximated through the methods described in [7] and [8]. Fig. 1 shows a set of example results from an approximation of a General Electric 2.5 MW machineusing the methods of [7] and [8]. Through the use of higherorder polynomials the GridLAB-D output can be made tomatch the manufactures’ data to any desired level of accuracy, at the cost of computational speed.Fig. 1. GridLAB-D active power output for a General Electric 2.5 MW wind turbine compared to manufacturer’s data.One the mechanical power available at the output of the drive train is calculated it is then converted to electrical power through a synchronous generator. The generators for this example were modeled using balanced Y-grounded circuit models, but GridLAB-D has the ability to implement models with unbalanced generator parameters if the user desires. The equivalent circuits are solved through an iterative process, where the inputs are terminal voltage and mechanical power, and the output is injected current.GridLAB-D is able to incorporate complex meteorological data, aerodynamic characteristics, and mechanical parameters into the power flow solution. It is up to the user to determine what level of complexity is necessary for their particular application.B. Residential House ModelFor this example the majority of residential, and all commercial, loads were modeled as time varying constants power values, i.e. the load was treated as a constant power at each time step, but allowed to vary between time steps. To increase the level of model complexity 50 of the residential house models were treated as complex time-varying loads, i.e. the power consumed is not an operator defined input. This model simulates power demand in a single-family home by incorporating models for end-use products, such as dishwashers, refrigerators, water heaters, lights, ranges, microwaves, plug loads, and HVAC. Each end-use device is modeled individually, and the power demands are then aggregated and incorporated into the system through the parent house model.An example of a relatively simple individual appliance model can be seen in the electric ranges, incorporated in each of the 50 residential house models. Each is modeled as a purely resistive load, where 100% of the power is converted to radiant heat, which then contributes to heat gains within the house that must be compensated for by the HVAC system.A more complicated individual appliance model can be seen in the hot water heater. These are modeled as two-element systems where cold water flows from the bottom through the larger first element, to the second, smaller element. The water heater is a multi-state model with states for full, partial, or empty. Corresponding to the multiple states there are various power consumption levels which are based on variables such as tank size, thermal mass of water in the tank, flow rate, ambient temperature, thermostatic set-points, control dead bands, and tank shell losses. The complexity of the residential model can be varied as desired or based on available computational resources.C. Analysis of Radial Distribution SystemTo highlight the level of modeling detail necessary to perform smart grid evaluations, a 1,400 node radial feeder was created. The feeder model contains three 1.5 MW wind turbines, 50 residential house models, each with unique, time-varying load distributions, 50 polyphase and 400 single-phase constant power loads, and over 600 transformers, all feed through a 12.47 kV substation. The houses were connected through center-tapped pole-top transformers and triplex cabling. The WTGs were connected to three equidistant nodes along the radial feeder. Wind speed data was imported from a Typical Meteorological Year 2 data set (TMY2) produced by the National Renewable Energy Laboratory (NREL) [9], then semi-randomized at one-minute intervals to provide gust-like conditions. Voltage measurements were made at five minute intervals at a residential house approximately halfway along the feeder. Fig. 2 shows the results for 2 days at 1 minute time intervals and Fig. 3 shows the results for 8 days at 5 minute time intervals. In Fig. 2 and Fig. 3 the regulator at the substation was held constant so that the voltage effects of the WTG can be seen.Fig. 2. Nominal end use voltage over a 2 day periodFig. 3. Nominal end use voltage over an 8 day periodFrom Fig. 2 and Fig. 3 it can be seen that the outputs of the WTGs have a pronounced effect on the voltage level at the house, so much so that the normal diurnal variations are masked. This is not a completely unexpected result for three 1.5 MW turbines on a 12.47 kV feeder. These WTGs are much large than those that are generally connected to a 12.47 kV distribution feeder.The point of interest in Fig. 2 and Fig. 3 is that the detailed residential models and the detailed WTG models were combined into a single simulation environment. In this environment the meteorological effects were used to determine the power output of the WTGs and the heating/cooling requirements of the house while at the same time electrical models were coupled to thermal building models. All of these effects were combined to determinewhat the actual electrical load of the houses would be and what would be the resultant voltage seen at the outlets. As previously mentioned this level of detail may not be necessary for feeder capacity studies but it critical for the evaluation of new smart grid technologies.For the radial example system of 1,400 nodes with 3 WTGs and 50 detailed residential models the system solved at the rate of approximately 5 minutes simulation time per second, i.e. 9.6 minutes for a 2 day simulation. This speed was achieved on an early model Pentium IV desktop. Newer desktop machines, especially those with multiple processors complete these simulations in significantly less time.V. M ODELING OF D ISTRIBUTION F EEDERSDue to the size and complexity of distribution feeders it is rarely practical to generate new distribution feeder models. For this reason GridLAB-D has incorporated the ability to convert files from other software formats as well as providing a set of distribution feeder models that may be used openly. A. Importing ModelsMost distributions utilities utilize some form of software to model their distribution feeders. Generally these models are created by importing information from their Geographic Information System (GIS). Depending on the utility these models may be combined with their billing system or other databases to indicate the type of customer served by each transformer, e.g. residential, commercial, industrial, or agricultural. While distribution utilities often only utilize these models for peak capacity studies, and a limited range of other studies, the models contain much of the information necessary to generate the distribution feeder models in GridLAB-D.To gain the ability to access the information in these models scripting has been written to import distribution feeder models from the SynerGEE® and Windmil® software packages. These feeder models can then be combined with detailed residential and commercial models to accurately model existing distribution feeders. While scripting exists for importing models it is not a fully automated process because of the varying quality of model data. Scripting for importing distribution feeder models from other software packages are pending.B. Feeder TaxonomyWhile it is desirable to obtain distribution feeders models directly from a utility, it is not always possible. For various well founded reasons distribution utilities generally do not make copies of their system models available for general use. Without meaningful distribution feeder models it is not possible to effectively analyze smart grid technologies. To address this issue the DOE Modern Grid Initiative (MGI) [10] undertook the task of developing a set of prototypical distribution feeders that could be disseminated to the general public, for use in GridLAB-D. The goal was to generate a set of distribution feeder models that was representative of those used in the 48 states of the contiguous United States. At present 25 radial distribution feeders have been constructed in GridLAB-D. Each feeder model contains all system elements from the substation regulator to the end-use meters, the same level of detail displayed in the example of section IV. Each feeder was designed to represent a specific voltage level andload composition within the various climate regions of the United States. A complete description of this work will be presented in a future paper which is currently being written.VI. C ONCLUSIONThis paper has shown how the traditional power flow analysis can be enhanced through the use of detailed end useand generator models, and implemented in the GridLAB-D simulation environment. It has also discussed the level of modeling detail that will be necessary to determine the impacts of smart grid technologies. The ability to model these effects will be essential in increasing the penetrations of thesenew technologies and modernizing the electricity infrastructure.VII. R EFERENCES[1]/[2]IEEE 1366: IEEE Guide for Electric Power Distribution ReliabilityIndices, available online at: [3]IEEE Radial Test Feeders- Available through the IEEE DistributionSystem Analysis Subcommittee.:/soc/pes/dsacom/testfeeders.html[4] D. P. Chassin, P. R. Armstrong, D. G. Chavarria-Miranda, R. T.Guttromson, “Gauss-Seidel accelerated: implementing flow solvers onfield programmable gate arrays”, IEEE PES GM 2006,[5]W. H. Kersting, 2nd ed., Distribution System Modeling and Analysis.Boca Raton: CRC Press, 2007[6]P. A. N. Garcia, J. L. R. Pereira, S. Carneiro Jr., V. M. Da Costa, and N.Martins, “Three-Phase Power Flow Calculations using the CurrentInjection Method”, IEEE Transaction on Power Systems, Vol. 15, Issue 4,May 2000, pp. 508-514[7] C.G. Justus, “Winds and Wind System Performance”, Philadelphia, PA:Franklin Institute Press, 1978.[8] B. Malinga, J.E. Sneckenberger, and A. Feliachi, “Modeling and Controlof a Wind Turbine as a Distributed Resource,” Proc. 35th SoutheasternSymp. System Theory, Atlanta, GA, 2003, pp. 108-112.[9]TMY2weather data, available online at:/solar/old_data/nsrdb/tmy2/[10]/moderngrid/VIII. B IOGRAPHIESKevin P. Schneider (M’06, SM’08) received his B.S. degreein Physics and his M.S. and Ph.D. degrees in ElectricalEngineering from the University of Washington. His mainareas of research are power system operations and visualanalytics. He is currently a research engineer at the PacificNorthwest National Laboratory (PNNL), working at theBattelle Seattle Research Center in Seattle Washington. Dr.Schneider is also an Adjunct Faculty member at the Washington State University Tri-Cities campus and a licensed Professional Engineer in Washington State.David P. Ch assin (M’03, SM’05) received his BS ofBuilding Science from Rensselaer Polytechnic Institute inTroy, New York. He is a staff scientist with the EnergyScience and Technology Division at Pacific NorthwestNational Laboratory where he has worked since 1992. Hewas Vice-President of Development for Image SystemsTechnology from 1987 to 1992, where he pioneered a hybridraster/vector computer aided design (CAD) technology called。