This is the first part of a two-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part I of this paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part II of this paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented.

/pdf/multi-agent-systems-for-power-engineering-applications-part-485uxyyn3d.pdf

Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges

The objective of this paper is to discuss the design and implementation of a multi-agent system that provides intelligence to a distributed smart grid — a smart grid located at a distribution level. A multi-agent application development will be discussed that involves agent specification, application analysis, application design and application realization. The message exchange in the proposed multi-agent system is designed to be compatible with an IP-based network (IP = Internet Protocol) which is based on the IEEE standard on Foundation for Intelligent Physical Agent (FIPA). The paper demonstrates the use of multi-agent systems to control a distributed smart grid in a simulated environment. The simulation results indicate that the proposed multi-agent system can facilitate the seamless transition from grid connected to an island mode when upstream outages are detected. This denotes the capability of a multi-agent system as a technology for managing the microgrid operation.

Multi-agent systems in a distributed smart grid: Design and implementation

Dynamic economic dispatch (DED) is one of the main functions of power generation operation and control. It determines the optimal settings of generator units with predicted load demand over a certain period of time. The objective is to operate an electric power system most economically while the system is operating within its security limits. This paper proposes a new hybrid methodology for solving DED. The proposed method is developed,in such a way that a simple evolutionary programming (EP) is applied as a based level search, which can give a good direction to the optimal global region, and a local search sequential quadratic programming (SQP) is used as a fine tuning to determine the optimal solution at the final. A ten-unit test system with nonsmooth fuel cost function is used to illustrate the effectiveness of the proposed method compared with those obtained from EP and SQP alone.

A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function

Dynamic economic dispatch (DED) is one of the main functions of power generation operation and control. It determines the optimal settings of generator units with predicted load demand over a certain period of time. The objective is to operate an electric power system most economically while the system is operating within its security limits. This paper proposes a new hybrid methodology for solving DED. The proposed method is developed in such a way that a simple evolutionary programming (EP) is applied as a based level search, which can give a good direction to the optimal global region, and a local search sequential quadratic programming (SQP) is used as a fine tuning to determine the optimal solution at the final. A ten-unit test system with nonsmooth fuel cost function is used to illustrate the effectiveness of the proposed method compared with those obtained from EP and SQP alone. Index Terms—Dynamic economic dispatch (DED), evolutionary programming (EP), global solution, power generation operation and control, sequential quadratic programming (SQP), stochastic optimization techniques. solutions for the optimization problem. This method is similar to the local search technique in optimization, which can only guarantee a local optimum solution. However, the SA method in addition employs a probabilistic approach in accepting can- didate solutions in its solution process such that it can jump out from the local optimum solutions. The SA technique has been applied in many power system problems. However, appropriate setting of the control parameters of the SA based algorithm is a difficult task and the speed of the algorithm is slow when ap- plied to a real power system. GA, invented by Holland (19) in the early 1970s, is a stochastic global search method that mimics the metaphor of natural biological evaluation. GA operates on a population of candidate solutions encoded to finite bit string called chromosome. In order to obtain optimality, each chromo- some exchanges information by using operators borrowed from natural genetic to produce the better solution. The GA uses only objective function information, not derivative or other auxiliary knowledge. Therefore, GA can deal with the nonsmooth, non- continuous and nondifferentiable functions, which actually exist in a practical optimization problem. On the other hand, EP is also a global search method starting from a population of can- didate solutions, and finds solution in parallel using evaluation process. Both GA and EP can provide a near global solution. However, the encoding and decoding schemes essential in the GA approach are not needed in DED problem. Therefore, EP is faster in speed than GA in this case. Although GA and EP seem to be good methods to solve opti- mization problem, sometimes the solutions obtained from both methods are just near global optimum ones. And also GA and EP take long computation times in order to obtain the solutions. Therefore, the combine method of GA/EP and another optimiza- tion techniques were applied in power system application such as optimal power flow problem (20), unit commitment problem (21), and economic dispatch in (22)-(25). However, all the pre- vious works mentioned above neglected the nonsmooth char- acteristic of generator, which actually exist in the real power system. In this paper, the application of EP combined with SQP for solving DED is proposed. The hybrid proposed method uses the property of EP, which can provide a good solution even the problem has many local optimum solutions at the beginning. Then, the local searching property of SQP is used to obtain a final solution. Basically, the hybrid method can be divided into two parts. The first part employs EP to obtain a near global so- lution, while the second part employs SQP to determine the op- timal solution. In order to show the effectiveness of the proposed method over EP and SQP alone, ten units test system with non- smooth fuel cost function is used in this paper. The results of the

A Hybrid EP and SQP for Dynamic Economic

There is huge expectation from smart power grid to provide sustainable energy services using bi-directional flow of data and power enabled by advanced information, communication and control infrastructure. An important element of such a smart grid is prosumers i.e. the consumers who also produce and share surplus energy with grid and other users. Prosumers are not only an important stakeholder of the future smart grids but also have a vital role in peak demand management. Therefore, it is needed to investigate and review the Prosumers based Energy Management and Sharing (PEMS) along with associated challenges. It will help in understanding and analyzing the impact of prosumers in future smart grids. In order to achieve these objectives, this paper presents a comprehensive review of PEMS in smart grid environment and associated impact on power system reliability and energy sustainability. The process of energy sharing among prosumers involves two key elements: information and communication technologies and optimization techniques. These two elements have been discussed in detail to cover the PEMS implementation requirements. The relevant communications technologies presented in the paper include wired, wireless, short and long range options while linear and nonlinear optimization techniques, in context of PEMS, are described. Various technologies, methodologies and mechanisms adopted for PEMS are comprehensively discussed in order to enhance readers’ intuition. Challenges and issues faced by prosumer communities and energy sharing have also been elaborated in detail.

Prosumer based energy management and sharing in smart grid

This paper proposes a multi-agent approach to power system restoration. The proposed system consists of several bus agents (BAGs) and a single facilitator agent (FAG). A BAG is developed to decide a sub-optimal target configuration after faults occur by interacting with other BAGs, while a FAG is developed to act as a manager for the decision process. The interaction of several simple agents leads to a dynamic system allowing efficient approximation of a solution. It is shown from simulation results that this method is able to obtain sub-optimal target configurations which are the same as ones obtained by a mathematical programming approach.

A multi-agent approach to power system restoration

When electric power supply interruption is caused by a fault, it is imperative to restore the power system promptly to an optimal target configuration after the fault. The problem of obtaining a target system is referred to as power system restoration. Both mathematical programming (MP) and expert systems (ES) have been used to solve restoration problems. However, existing approaches based on either MP and ES alone have inherent limitations stemming from their own paradigms. Mathematical progressing can obtain an optimal configuration under specified operational constraints, but requires a relatively long solution time. Although ES are effective in that they can utilize expert knowledge, maintenance of large-scale ES requires inordinate effort.



This paper proposes a new approach to power system restoration that utilizes both methodologies so as to exploit both systems' advantages. That is, a system under study is decomposed into a set of subsystems based on the knowledge of restoration experts, which is realized as an expert system. Then, MP is applied to each decomposed subsystem to obtain an optimal target configuration. A feasible operation sequence leading to the target configuration is generated by ES. This approach reduces significantly the computation time required to obtain target systems and is far less than would be the case if the total system is solved (as a unity). Moreover, the number of rules in the knowledge-base are greatly decreased.

Power system restoration by joint usage of expert system and mathematical programming approach

This paper proposes a multiagent approach to decentralized power system restoration for a distribution system network. The proposed method consists of several feeder agents (FAGs) and load agents (LAGs). A LAG corresponds to the customer load, while a FAG is developed to act as a manager for the decision process. From the simulation results, it can be seen the proposed multiagent system could reach the right solution by making use of only local information. This means that the proposed multiagent system is a promising approach to more large-scale distribution networks.

A multi-agent approach to distribution system restoration

This paper proposes an efficient and computationally fast solution algorithm for distribution system restoration based on a mathematical programming approach. The computation time is improved greatly by dividing the problem into two stages and incorporating restoration strategies, which are derived from an operator's knowledge and experience. It is shown from simulation results that this method is able to obtain an optimal target configuration in all cases. In addition, the computation time has been reduced to within 30% of our previous method.

http://ieeexplore.ieee.org/iel5/7205/19429/00898201.pdf

Takeshi Nagata

Papers

A multi-agent approach to power system restoration

Power system restoration by joint usage of expert system and mathematical programming approach

A multi-agent approach to distribution system restoration

An efficient method for power distribution system restoration based on mathematical programming and operation strategy

A solution for unit commitment using Lagrangian relaxation combined with evolutionary programming