An evolutionary game approach to analyzing bidding strategies in electricity markets with elastic demand

Wang, Jianhui; Zhou, Zhi; Botterud, Audun

doi:10.1016/j.energy.2011.03.050

Cited by 93 publications

(52 citation statements)

References 19 publications

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“…Gao, et al [11] researched on how to find the optimal bidding strategy of a GenCO in the single-side day-ahead electricity market, based on the parametric linear programming method and with the assumption that all GenCOs in the day-ahead market pursue profit maximization. In the papers by Kumar et al [12] and Wang [13], every GenCO in the single-side market optimizes its bidding strategy by evaluating the strategy probability distributions of its rivals with the information about their cost functions (complete information) and their strategies from last game iteration (but imperfect information). The dynamic evolution process of GenCOs' bidding strategy was simulated by shuffled frog leaping algorithm (SFLA) [12] and genetic algorithm (GA) [13], respectively.…”

Section: Literature Review and Main Contributionsmentioning

confidence: 99%

“…In the papers by Kumar et al [12] and Wang [13], every GenCO in the single-side market optimizes its bidding strategy by evaluating the strategy probability distributions of its rivals with the information about their cost functions (complete information) and their strategies from last game iteration (but imperfect information). The dynamic evolution process of GenCOs' bidding strategy was simulated by shuffled frog leaping algorithm (SFLA) [12] and genetic algorithm (GA) [13], respectively. Liu et al [14] reported an incentive bidding mechanism in which the semi-randomized approach is applied to model the information disturbance in the electricity auction markets.…”

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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Section: Literature Review and Main Contributionsmentioning

confidence: 99%

Section: Literature Review and Main Contributionsmentioning

confidence: 99%

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

et al. 2016

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“…Traditional bidding models may possibly fail to reflect the process during which bidders observed and learned from other bidders; however, evolutionary game-based models have potential in dynamically and retrospectively simulating bidding strategies [65]. On this account, an imperfect information evolutionary game is developed to discuss bidding strategies in the electricity market with price elastic demands [66]. In recent years, the theory has begun to be applied in an international climate change negotiation and greenhouse gas (GHG) emission mitigation burden sharing [67][68][69].…”

Section: Literature Review On Resource Allocation Under Incomplete Anmentioning

confidence: 99%

Innovative Carbon Allowance Allocation Policy for the Shenzhen Emission Trading Scheme in China

Jiang

Liu

et al. 2015

Sustainability

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Abstract:The initial allocation of tradable carbon emission allowances is among the most contentious issues in developing an emission trading scheme (ETS). China faces serious dilemmas of system complexity and information incompleteness and asymmetry in allocating carbon allowance among enterprises. As one of the pilot ETS regions, Shenzhen has launched the first regional cap-and-trade ETS (SZ ETS) in China. Adhering to the overall plan and classification analysis, SZ ETS intends to solve the aforementioned dilemmas by developing innovative allowance allocation policies. A fundamental principle is to allocate allowances based on carbon intensity and actual output, according to which a two-step allocation procedure is constructed. A competitive game mechanism is introduced for allowance allocation among manufacturing enterprises. Empirical results indicate the following: (1) Carbon allowance allocation based on carbon intensity and actual output can mitigate carbon emission growth by reducing CO 2 emitted per unit output, and, thus, buffer the shocks of unexpected economic fluctuations to ETS stability; (2) Competitive game allocation may contribute to improving the use of scattered information to enhance the efficiency of information and emission resource allocation. Exploring SZ ETS may provide a reference for formulating future national carbon allowance allocation policies in China and other developing regions.

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“…Gross and Finlay adopted a Lagrangian relaxation-based approach for strategic bidding in the England-Wales pool-type electricity market [3]. Jainhui et al [4] used an evolutionary * Correspondence: jvkeee@gmail.com game approach to analyze the bidding strategies by considering the elastic demand. Ebrahim and Galiana developed a Nash equilibrium-based bidding strategy in electricity markets [5].…”

Section: Introductionmentioning

confidence: 99%

Bidding strategy of generation companies in a competitive electricity market using the shuffled frog leaping algorithm

Jonnalagadda¹,

Mallesham²

2013

Turk J Elec Eng & Comp Sci

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Abstract:In a competitive electricity market, generation companies need suitable bidding models to maximize their profit. Therefore, each supplier will bid strategically for choosing the bidding coefficients to counter the competitors' bidding strategies. In this paper, the optimal bidding strategy problem is solved using a novel algorithm based on the shuffled frog leaping algorithm (SFLA). It is a memetic metaheuristic that is designed to seek a global optimal solution by performing a heuristic search. It combines the benefits of the genetic-based memetic algorithm (MA) and the social behavior-based particle swarm optimization (PSO). This allows it to have a more precise search that avoids the premature convergence and selection of the operators. Therefore, the proposed method overcomes the short comings of the selection of operators and premature convergence of the genetic algorithm (GA) and PSO method. The most important merit of the proposed method is its high convergence speed. The proposed method is numerically verified through computer simulations on an IEEE 30-bus system consisting of 6 suppliers and a practical 75-bus Indian system consisting of 15 suppliers. The results show that the SFLA takes less computational time and produces higher profits compared to PSO and the GA.

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An evolutionary game approach to analyzing bidding strategies in electricity markets with elastic demand

Cited by 93 publications

References 19 publications

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

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

Innovative Carbon Allowance Allocation Policy for the Shenzhen Emission Trading Scheme in China

Bidding strategy of generation companies in a competitive electricity market using the shuffled frog leaping algorithm

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