ACE (Agent-based Computational Economics) simulation method of electricity market by adopting cooperative particle swarm algorithm

A particle swarm algorithm and power market technology, applied in computing, data processing applications, instruments, etc., can solve difficult problems such as power market simulation

Inactive Publication Date: 2010-12-15
CHINA ELECTRIC POWER RES INST +1
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Problems solved by technology

At present, it is difficult to simulate the

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  • ACE (Agent-based Computational Economics) simulation method of electricity market by adopting cooperative particle swarm algorithm
  • ACE (Agent-based Computational Economics) simulation method of electricity market by adopting cooperative particle swarm algorithm
  • ACE (Agent-based Computational Economics) simulation method of electricity market by adopting cooperative particle swarm algorithm

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Embodiment Construction

[0027] The modeling of electricity market members requires agents to perform perception, analysis, reasoning and judgment as well as self-learning and self-adaptation according to the market operation, so as to formulate their own trading strategies and schemes. In this case, a power market ACE simulation method using the collaborative particle swarm algorithm is tested from the perspective of the agent process. The test organization is as follows: through the power market simulation modeling platform, the test scene of the Northeast regional power market is established, and the transaction type is selected as monthly concentration. Bidding transactions. In the test, 28 power plants participated in the monthly centralized bidding in the market, with a total capacity of 12.14 million MWh.

[0028] The process of co-evolution not only gives full play to the autonomous initiative of each agent, but also learns from each other and improves itself through cooperation or confrontatio...

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Abstract

The invention relates to an ACE (Agent-based Computational Economics) simulation method of an electricity market by adopting a cooperative particle swarm algorithm. A multiple agent system (MAS) is adopted in the ACE simulation of the electricity market, and intelligent behavior is coordinated among independent agents so as to further embody the social intelligence of the human beings. An agent module adopting the cooperative particle swarm algorithm aims at maximizing self benefits and selects a bidding strategy according to market information and proposes to a trading center, a trading center module confirms the generating capacities and the electricity prices of all the agent modules according to market needs and feeds back the needed information to each agent module which respectively adjusts the self strategy according to a feedback result, and the process is continued until reaching a balance state. The ACE simulation method of the electricity market by adopting the cooperative particle swarm algorithm is characterized in that the agent modules adopt a cooperative particle swarm intelligent method and have respective autonomy, initiative, responsiveness and sociality. The invention provides a new method for the ACE simulation of the electricity market.

Description

technical field [0001] The invention belongs to the field of electric power system and its automation, and in particular relates to an ACE simulation method of electric power market using cooperative particle swarm algorithm. Background technique [0002] The methods of power market simulation research are mainly divided into Agent-Based Computational Economics (Agent-Based Computational Economics, ACE for short) simulation and experimental economics experiments. Experimental economics experiments are carried out by relevant personnel. Although they can more realistically simulate the actual situation of electricity market transactions, they are susceptible to interference, and the results are often not convergent, and some factors cannot be qualitatively explained; while agent-based The simulation method mainly uses intelligent algorithms to act as an agent for power generation companies to choose bidding strategies, and then studies various problems in the power market acc...

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Application Information

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IPC IPC(8): G06Q10/00G06Q50/00G06Q50/06
Inventor 陈乃仕王海宁周海明李伟刚王文史述红
Owner CHINA ELECTRIC POWER RES INST
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