Simulation of producers behaviour in the electricity market by evolutionary games

Menniti, Daniele; Pinnarelli, Anna; Sorrentino, N.

doi:10.1016/j.epsr.2007.04.005

Cited by 36 publications

(26 citation statements)

References 5 publications

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“…Several methods and theories have been introduced and utilized to model different aspects of deregulated pool-based electricity markets. Agent-based modeling [1][2][3], statistical methods [4,5], artificial intelligence based approaches [6][7][8], optimization theory [9], and of course game-theoretic approaches [10][11][12][13][14] have been all frequently used in the literature. Among these approaches, game theoretic methods not only have better ways of realistically simulating the oligopolistic competition in electric power markets, but also are perfectly applicable to a wide range of market models such as Bertrand, Cournot, Stackelberg and supply function models [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Direct approach in computing robust Nash strategies for generating companies in electricity markets

Langary

Sadati

Ranjbar

2014

International Journal of Electrical Power & Energy Systems

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Section: Introductionmentioning

confidence: 99%

Direct approach in computing robust Nash strategies for generating companies in electricity markets

Langary

Sadati

Ranjbar

2014

International Journal of Electrical Power & Energy Systems

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“…In Reference [20], every GenCO and DisCO was assumed to have imperfect information about ongoing strategies of its rivals, but complete information about historical ones so that the parameters in optimization model of every GenCO or DisCO were estimated as the historical strategies of its rivals. In the study by Menniti et al [21], an evolutionary game model only to simulate the behaviors of the generation-side was proposed, and the modeling approaches of this paper can also be extended to the consumption-side issue. The classical evolutionary game theory can only solve problems with a discrete strategy set, which is not in line with the actual situation in the day-ahead electricity market.…”

Section: Literature Review and Main Contributionsmentioning

confidence: 99%

“…Taking [8,12,13,[20][21][22] as references, we only consider one (negotiation) time interval for the next day. Hence, maximizing Equations (5) and (6) are the objectives of GenCO i and DisCO j in the double-side day-ahead electricity market, respectively.…”

Section: Participants' Bidding Modelmentioning

confidence: 99%

“…Taking provisions (1)-(4) into consideration, the methods both in [8][9][10][11][12][13][14][15][16][17][18][20][21][22][23] and in [19,[24][25][26][27][28][29][30][31][32][33] are not quite suitable for modeling and simulating the practical day-ahead electricity market exactly, the reason of which is that in the real day-ahead electricity market, every participant can adjust its bidding strategy within a continuous interval of values, but the literatures suitable for provisions (1)- (3) have assumed that the sets for alternative actions (e.g., bidding strategies) or potential states (e.g., historical MCP) are discrete.…”

Section: Literature Review and Main Contributionsmentioning

confidence: 99%

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Application of a Gradient Descent Continuous Actor-Critic Algorithm for Double-Side Day-Ahead Electricity Market Modeling

et al. 2016

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An important goal of China's electric power system reform is to create a double-side day-ahead wholesale electricity market in the future, where the suppliers (represented by GenCOs) and demanders (represented by DisCOs) compete simultaneously with each other in one market. Therefore, modeling and simulating the dynamic bidding process and the equilibrium in the double-side day-ahead electricity market scientifically is not only important to some developed countries, but also to China to provide a bidding decision-making tool to help GenCOs and DisCOs obtain more profits in market competition. Meanwhile, it can also provide an economic analysis tool to help government officials design the proper market mechanisms and policies. The traditional dynamic game model and table-based reinforcement learning algorithm have already been employed in the day-ahead electricity market modeling. However, those models are based on some assumptions, such as taking the probability distribution function of market clearing price (MCP) and each rival's bidding strategy as common knowledge (in dynamic game market models), and assuming the discrete state and action sets of every agent (in table-based reinforcement learning market models), which are no longer applicable in a realistic situation. In this paper, a modified reinforcement learning method, called gradient descent continuous Actor-Critic (GDCAC) algorithm was employed in the double-side day-ahead electricity market modeling and simulation. This algorithm can not only get rid of the abovementioned unrealistic assumptions, but also cope with the Markov decision-making process with continuous state and action sets just like the real electricity market. Meanwhile, the time complexity of our proposed model is only O(n). The simulation result of employing the proposed model in the double-side day-ahead electricity market shows the superiority of our approach in terms of participant's profit or social welfare compared with traditional reinforcement learning methods.

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“…Ref. [16] used evolutionary games and near Nash equilibrium to model competition in electricity markets and predict local marginal prices. Additionally, the authors in [17] implemented the agent-based reasoning through a Dynamic Bayesian Network (DBN) representation and proposed an online Bayesian learning algorithm to predict loads and Residual Demand Curves (RDC).…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Inference Approach to Unveil Supply Curves in Electricity Markets

Mitridati

2018

IEEE Trans. Power Syst.

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Abstract-With increased competition in wholesale electricity markets, the need for new decision-making tools for strategic producers has arisen. Optimal bidding strategies have traditionally been modeled as stochastic profit maximization problems. However, for producers with non-negligible market power, modeling the interactions with rival participants is fundamental. This can be achieved through equilibrium and hierarchical optimization models. The efficiency of these methods relies on the strategic producer's ability to model rival participants' behavior and supply curve. But a substantial gap remains in the literature on modeling this uncertainty. In this study we introduce a Bayesian inference approach to reveal the aggregate supply curve in a day-ahead electricity market. The proposed algorithm relies on Markov Chain Monte Carlo and Sequential Monte Carlo methods. The major appeal of this approach is that it provides a complete model of the uncertainty of the aggregate supply curve, through an estimate of its posterior distribution. We show on a small case study that we are able to reveal accurately the aggregate supply curve with no prior information on rival participants. Finally we show how this piece of information can be used by a price-maker producer in order to devise an optimal bidding strategy.

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Simulation of producers behaviour in the electricity market by evolutionary games

Cited by 36 publications

References 5 publications

Direct approach in computing robust Nash strategies for generating companies in electricity markets

Direct approach in computing robust Nash strategies for generating companies in electricity markets

Application of a Gradient Descent Continuous Actor-Critic Algorithm for Double-Side Day-Ahead Electricity Market Modeling

A Bayesian Inference Approach to Unveil Supply Curves in Electricity Markets

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