Many electric utilities worldwide have been forced to change their ways of doing business, from vertically integrated mechanisms to open market systems. We are facing urgent issues about how we design the structures of power market systems. In order to settle down these issues, many studies have been made with market models of various characteristics and regulations. The goal of modeling analysis is to enrich our understanding of fundamental process that may appear. However, there are many kinds of modeling methods. Each has drawback and advantage about validity and versatility. This paper presents two kinds of methods to construct multi-agent market models. One is based on game theory and another is based on reinforcement learning. By comparing the results of the two methods, they can advance in validity and help us figure out potential problems in electricity markets which have oligopolistic generators, demand fluctuation and inelastic demand. Moreover, this model based on reinforcement learning enables us to consider characteristics peculiar to electricity markets which have plant unit characteristics, seasonable and hourly demand fluctuation, real-time regulation market and operating reserve market. This model figures out importance of the share of peak-load-plants and the way of designing operating reserve market.
SUMMARYThe player in deregulated electricity markets can be categorized into three groups of GENCO (Generator Companies), TRANSCO (Transmission Companies), and DISCO (Distribution Companies). This research focuses on the role of Distribution Companies, which purchase electricity from the market at randomly fluctuating prices, and provide it to their customers at given fixed prices. Therefore, Distribution Companies have to take the risk stemming from price fluctuation of electricity instead of the customers. This entails the necessity of developing a certain method to make an optimal strategy for electricity procurement. In such a circumstance, this research has the purpose of proposing a mathematical model based on stochastic dynamic programming to evaluate the value of a long-term bilateral contract for electricity trade, and also a project of combination of the bilateral contract and power generation with their own generators for procuring electric power in a deregulated market.
The player in deregulated electricity markets can be categorized into three groups of GENCO (Generator Companies), TRNASCO (Transmission Companies), DISCO (Distribution Companies). This research focuses on the role of Distribution Companies, which purchase electricity from market at randomly fluctuating prices, and provide it to their customers at given fixed prices. Therefore Distribution companies have to take the risk stemming from price fluctuation of electricity instead of the customers. This entails the necessity to develop a certain method to make an optimal strategy for electricity procurement.In such a circumstance, this research has the purpose for proposing the mathematical method based on stochastic dynamic programming to evaluate the value of a long-term bilateral contract of electricity trade, and also a project of combination of the bilateral contract and power generation with their own generators for procuring electric power in deregulated market.Keywords: electricity market, uncertainty of electric power price, bilateral contract, own generator, procurement cost 1.
In deregulated electricity markets, the role of a distribution company is to purchase electricity from the wholesale electricity market at randomly fluctuating prices and to provide it to its customers at a given fixed price. Therefore, the company has to take risk stemming from the uncertainties of electricity prices and/or demand fluctuation instead of the customers. The way to avoid the risk is to make a bilateral contract with generating companies or install its own power generation facility. This entails the necessity to develop a certain method to make an optimal strategy for electric power procurement. In such a circumstance, this research proposes a mathematical method based on stochastic dynamic programming and considers the characteristics of the start‐up cost of an electric power generation facility to evaluate strategies of combination of the bilateral contract and power auto‐generation with its own facility for procuring electric power in a deregulated electricity market. In the beginning we proposed two approaches to solve the stochastic dynamic programming, and they are a Monte Carlo simulation method and a finite difference method to derive the solution of a partial differential equation of the total procurement cost of electric power. Finally we discussed the influences of the prime uncertainty on optimal strategies of power procurement. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 160(2): 20–29, 2007; Published online in Wiley InterScience (http://www.interscience.wiley.com). DOI 10.1002/eej.20296
In deregulated electricity markets, the role of a distribution company is to purchase electricity from the wholesale electricity market at randomly fluctuating prices and to provide it to its customers at a given fixed price. Therefore the company has to take risk stemming from the uncertainties of electricity prices and/or demand fluctuation instead of the customers. The way to avoid the risk is to make a bilateral contact with generating companies or install its own power generation facility. This entails the necessity to develop a certain method to make an optimal strategy for electric power procurement.In such a circumstance, this research has the purpose for proposing a mathematical method based on stochastic dynamic programming and additionally considering the characteristics of the start-up cost of electric power generation facility to evaluate strategies of combination of the bilateral contract and power auto-generation with its own facility for procuring electric power in deregulated electricity market. In the beginning we proposed two approaches to solve the stochastic dynamic programming, and they are a Monte Carlo simulation method and a finite difference method to derive the solution of a partial differential equation of the total procurement cost of electric power. Finally we discussed the influences of the price uncertainty on optimal strategies of power procurement.
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