An intelligent negotiator agent design for bilateral contracts of electrical energy |
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| Affiliation: | 1. SNL/CIPCE, School of ECE, College of Eng., Univ. of Tehran, Tehran, Iran;2. CIPCE, School of ECE, College of Eng., Univ. of Tehran, Tehran, Iran;1. Department of Business and Entrepreneurial Management, Kainan University, 1, Kainan Road, Luchu Shiang, Taoyuan 33857, Taiwan;2. Graduate Institute of Management Science, National Chiao Tung University, 1001, Ta-Hsueh Road, Hsinchu 300, Taiwan;3. Graduate Institute of Urban Planning, College of Public Affairs, National Taipei University, 151, University Road, San Shia 237, Taiwan;1. School of Management Science and Engineering, Dongbei University of Finance & Economics, Jianshan Street 217, Dalian 116025, PR China;2. Graduate School of Management, Clark University, 950 Main Street, Worcester, MA 01610-1477, USA;3. School of Business, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280, USA;1. School of Telecommunication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, PR China;2. School of Computer Science, Shaanxi Normal University, Xi’an, PR China;1. Graduate Program in Computer Science, PPGI, UFES Federal University of Espirito Santo, Av. Fernando Ferrari, 514, CEP 29075-910 Vitória, Espírito Santo, ES, Brazil;2. Department of Production Engineering & Graduate Program in Computer Science, PPGI, UFES Federal University of Espirito Santo, Av. Fernando Ferrari, 514, CEP 29075-910 Vitória, Espírito Santo, ES, Brazil |
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| Abstract: | In this paper, an intelligent agent (using the Fuzzy SARSA learning approach) is proposed to negotiate for bilateral contracts (BC) of electrical energy in Block Forward Markets (BFM or similar market environments). In the BFM energy markets, the buyers (or loads) and the sellers (or generators) submit their bids and offers on a daily basis. The loads and generators could employ intelligent software agents to trade energy in BC markets on their behalves. Since each agent attempts to choose the best bid/offer in the market, conflict of interests might happen. In this work, the trading of energy in BC markets is modeled and solved using Game Theory and Reinforcement Learning (RL) approaches. The Stackelberg equation concept is used for the match making among load and generator agents. Then to overcome the negotiation limited time problems (it is assumed that a limited time is given to each generator–load pairs to negotiate and make an agreement), a Fuzzy SARSA Learning (FSL) method is used. The fuzzy feature of FSL helps the agent cope with continuous characteristics of the environment and also prevents it from the curse of dimensionality. The performance of the FSL (compared to other well-known traditional negotiation techniques, such as time-dependent and imitative techniques) is illustrated through simulation studies. The case study simulation results show that the FSL based agent could achieve more profits compared to the agents using other reviewed techniques in the BC energy market. |
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| Keywords: | Bilateral contracts Negotiation Game theory Reinforcement learning Fuzzy SARSA learning |
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