Multi-Agent Model for Power System Simulation

Transcription

1 Multi-Agent Model for Power System Simulation A.A.A. ESMIN A.R. AOKI C.R. LOPES JR. G. LAMBERT-TORRES Institute of Electrical Engineering Federal School of Engineering at Itajubá Av. BPS, 1303 Itajubá/MG BRAZIL Abstract: - This paper describes a Multi-Agent model developed for Power System simulation based on the Intelligent Agent technology (IA). The system model architecture is composed by two main packages: an Agent Decision Package and an Agent Tool Package. Together with these packages there are two support agents working on integration of the model with the real world. The first one is a Communication Agent who is responsible by agents interconnection, and the other one is an Interface Agent who is responsible by accessing the SCADA database and data pre-processing for the system. Based on this model a new software tool is being developed. Key-Words: - Multi-Agent System, Power System Simulation, Artificial Intelligence 1 Introduction There is a permanent demand for new application and simulation software required for different purposes such as research, planning and operation. These systems become larger and increasingly complex. As a consequence creating system software is more difficult to complete on time and within the constraints of budget. It has become very difficult to create these new applications with traditional software development technology. When finally finished, they are difficult to understand. Thus they are hard to maintain, integrating with the old application and to modify for new requirements. Studies in computer science have shown that reusability can improve software development productivity and quality. Productivity increases as previously developed assets can be used in current applications, which saves new development time. Quality can be increased as frequently reused assets have been tested and corrected in different study cases. A generic power system can be modeled based on the Object-Oriented concepts. Reuse is done by specializing derived classes and by using template classes. The main objective of this work is the development of a generic simulation tool with Graphical User Interface (GUI) that can simulate the real power systems. This tool was modeled by using an Object- Oriented approach integrated with the intelligent agent technology that gives more flexibility to create simulation software. This system will be used for training, design, simulation as well as several electrical studies. The implementation is done using the Java-based Framework, which provides a generic methodology for developing Multi-Agent Systems (MAS) architecture, and a set of classes to support these implementation. The development methodology follows five stages: (i) identifying the agents, (ii) identifying the agent conversations, (iii) identifying the conversation rules, (iv) analyzing the conversation model, and (v) MAS implementation. The system developed provides communication, linguistic and coordination support through Java classes. Communication support is provided for both directed communication and subject-based broadcast communication. This feature enables the development of scalable, fault-tolerant, self-configurable and flexible MAS. Linguistic support used is KQML Knowledge Query and Manipulation Language [5, 6]. MAS is a way to artificially reproduce real-life system through a model made of autonomous and interacting objects, called agents [4]. The main advantage of multi-agent simulation is to allow the modeling of individual behavior and the facility to get more real simulation systems. The behavior of the power system components can be simulated by agents that act in the same way. The system model architecture is composed by two main packages: an Agent Decision Package, an Agent Tool Package, and it has two agents working on

2 integration of the model with the real world. The first one is a Communication Agent who is responsible by agents interconnection, and the other one is an Interface Agent who is responsible by accessing the SCADA database and data pre-processing for the system. The current studies point to the growing interest on MAS technology to development of complex industrial systems. Activity Planner This paper is organized in this way: first the agent technology is presented, then the model architecture is presented, then the implementation process is discussed and finally we presented the conclusion and the future work. 2 Agent Technology Agent Technology has been used over the last decade of a number of different application in order to understand, model and develop complex distributed systems by viewing then as a computational organization consisting of various interacting components [1]. Controller Interface Inputs Outputs Knowledge Base Intelligent Agent These applications are founded in different areas, like finance market, internet, robotics and power systems. In a simulation problem, the agents represent interactive and autonomous independent entities, reproducing real-life phenomenon artificially [2]. An Intelligent Agent is a computer object that has the following properties: autonomy, social ability, reactivity and own initiative [3], and besides processes its inputs accordingly to its own intelligence producing some outputs, Fig.1. The agent has the feature of temporal continuity, because it monitors the environment, awaiting the occurrences that asks for actions. By analyzing an occurred facts sequence registration the agent makes the right decision. The autonomy feature is guaranteed by its acquired knowledge basedbehavior, decision-making and actions control to achieve its goals. Multi-Agent System (MAS) is a system that performs collaborative tasks using many intelligent agents in a distributed environment. The Distributed Artificial Intelligence introduces the concept of agent society to the traditional Artificial Intelligence, using the Oriented-Object approach. In this way, a great problem is fragmented in a group of small problems, and each small problem is studied by an Agent or by a small society of Agents. Environment Fig.1 The architecture of the proposed agent 3 Multi-Agent Model Architecture The Multi-Agent Model developed is shown in Fig.2, and its architecture is composed by two main packages: an Agent Decision Package, an Agent Tool Package, and it has two agents working on integration of the model with the real world. The first one is a Communication Agent who is responsible by agents interconnection, and the other one is an Interface Agent who is responsible by accessing the SCADA database and data pre-processing for the system. The Agent Decision Package is responsible for developing plans according to its virtual model using the information provided by event identification agent. It is composed by: an event identification agent, a planning agent and a model agent. The Agent Tool Package is responsible for providing specific support for the decision agents, which includes: a restoration agent, a power flow agent, a optimal power flow, a load shedding agent, a load forecasting agent, an alarm processing agent and an unit commitment & economic dispatching agent. This package uses conventional developed programs integrating numerical and symbolical routines. This

3 Agent Decision Package Agent Tool Package Event Identification Planning Model Restoration Power Flow Optimal Power Flow Load Shedding Load Forecasting Alarm Processing Unit Commitment & Economic Dispatching Communication Agent INTERFACE AGENT SCADA DATABASE Fig.2 The Architecture of the proposed model integration can be done in two possible ways. The first possibility is to join on all executable programs through a batch file. The second possibility is to encapsulate this programs using an agent architecture Integrating by batch file needs to carry about parameters changes and computer virtual memory. Instead of this possibility, the integration process using encapsulation is more interesting to keep an uniformed model. 3.1 Agent Decision Package This section describes all agents involved in decisionmaking process: Event Identification Agent Event Identification Agent provides expertise to establish the cause-effect relationship between faults and actions of protective relays and circuit breakers. The implemented strategy detects initially a set of fault section candidates, and then, a study about the chronological arrival of relay signals is developed using the last data set before the fault. This agent contains a special data where the adjustments of each relay are saved. These data are important to find what section started the problem Planning Agent Planning Agent is responsible by determining actions to solve operation tasks using any tool available in the Agent Tool Package. This agent contains an expert system that analysis the event identification agent information and call specific tools to achieve its goals. After that it analysis the commands provided by the tools and provides a assigned plan to the Communication Agent Model Agent Model Agent contains the object-oriented model and continuously compare the power system model with the information provided by the Interface Agent. If a difference between the model and the real world is detected the agent immediately inform the communication agent that is responsible for transferring this for the appropriate agent [9]. 3.2 Agent Tool Package This section describes all agents involved supporting decision-making process:

4 3.2.1 Restoration Agent Restoration Agent contains some advice about the best strategy to reclose switches, and circuit breakers. Two main ideas provide the ways for problem solving. The first one is to try a restoration by a previously energized feeder. The second idea is to find parallel circuits to provide this restoration. The first idea is possible to apply in radial systems or in temporary unavailable circuits (e.g. temporary faults). In the case where a partial black-out occurs, the system contains a strategy to feed in the first place the boundary buses of the black-out system. The idea is to reduce the affected area step-by-step Power Flow Agent Power Flow Agent provides the voltage drop on each feeder branch, the voltage on each bus, the projected power flow through the power system, and the looses in the power systems. This is a numerical application encapsulated in an agent Optimal Power Flow Optimal Power Flow Agent is designed to achieve economic operation. This agent encapsulates an optimal power flow application that is part of the standard tools of the supervisory, control and data acquisition (SCADA) and energy management system (EMS). It schedules power system controls to optimize an objective function while satisfying nonlinear equality and linear equality constraints Load Shedding Agent Load Shedding Agent is designed to avoid a frequency or voltage power system collapse. This agent contains a knowledge base about the load shedding strategies of the power system under analyzing and encapsulates a standard numerical program Load Forecasting Agent Load Forecasting Agent contains three knowledge bases (KB) to provide expertise about standard load, recent performance of the load, and the special characteristics of the load. The first KB represents the historical data set, i.e., it contains information about the load shapes in the last years. The second one, recent load performance, contains a multiple linear model which represents the variation of the load in function of the weather parameters. The third KB contains rules of thumb about load forecasting evolution Alarm Processing Agent Alarm Processing Agent provides expertise about the possible occurred problem. When a disturbance occurs in the system, there all be many alarms provided by the SCADA system. Usually, 90% of these alarms are redundant and happen due to secondary problems caused by the primary problem. The main idea of this agent is to detect the primary problem, and to send two kinds of alarms to the MAS. The first kind are the alarms of the primary problem, while the second kind are the main alarms for secondary problems. The first kind is very useful for the MAS to know the problem and provide a solution. The second kind (sometimes, more important than the first one) is useful to decide the degree of the contingency. So, this agent contains two main parts, one for detection problems and another for evaluation of the disturbance. The first part is composed of production rules, which read information about the relays and other sensors to define where the disturbance started. The second part makes an evaluation using the data from the files and some rules based on operation conditions Unit Commitment & Economic Dispatch Unit Commitment & Economic Dispatch Agent determines the start-up and shut-down times for the generators over a period of time to satisfy some requirements of demand and operative restrictions at the lowest possible cost. This agent encapsulates a standard program and a knowledge base of costs. 4 Conclusion In this work, the analysis and design of a novel model system has been described where the introduction of agent technology and Multi-agent approach gives more flexibility in the development process. The new model is divided into packages of agents that have the responsibility to act in the same process. This packaging is organized to facilitate the development process, that can be done by different developer groups. This study has shown too, a framework of application of the multi-agent system to the power system in achieving the integration capability with the conventional SCADA system by using specific agents that act in the intercommunication process. Based on this new model a software tool is being developed using the Object-Oriented language Java [7, 8]. The implementation process staring by the development of the specific Classes for agent creation, agent communication and coordination. These classes will be used to construct the other system agents classes. The other system agents with

5 the Graphical User Interface (GUI) still under development. References: [1] D. Cockburn and N. Jennings, ARCHON: A Distributed Artificial Intelligence System for Industrial Applications, Foundations of Distributed Artificial Intelligence, O Hare, G. and Jennings, N., Wiley, [2] F.-R. Monclair and R. Quatrain, Simulation of Eletricity Markets: A Multi-Agent Approach, IEEE ISAP2001 Conference, Budapest, Hungary, June18-21, [3] M. Wooldridge and N. Jennings, Intelligent Agents: Theory and Practice, Berlin, Germany: Springer-Verlag, 1994 [4] J. Feber, Multi-Agent Systems, An Introduction to Distributed Artificial Intelligence, Addison- Wesley [5] T. Finin, R. Fritzson, D. McKay and R. McEntrie, KQML as an Agent Communication Language, Third International Conference on Information and Knowledge Management (CIJM 94), ACM Press, November [6] M.-S. Tsai, Conceptual Design Distributed Rule Base Expert System for Distributed Automation, Proc. of Intelligent System Application to Power System (ISAP 99), April 4-8, 1999, Rio de Janeiro, Brazil, pp [7] Sun MicroSystems, Implementing Java Computing Solutions White Paper, [8] J.P. Bigus an J. Bigus, Constructing intelligent agents with Java: a programmer s guide to smarter applications, Wiley Computer Publishing, [9] A.R. Aoki, G. Lambert-Torres and L.E. de Souza, Planning Knowledge Acquisition for Restoration of Substation Using Functional Modeling, Proc. of Intelligent System Application to Power System (ISAP 99), April 4-8, 1999, Rio de Janeiro, Brazil, pp