Multi-agent based experimental analysis on bidding mechanism in electricity auction markets

Liu, Zhen; Yan, Jianming; Shi, Yuren; Zhu, Kaiwei; Pu, Gangqing

doi:10.1016/j.ijepes.2012.05.056

Cited by 28 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. Nojavan et al [15], used the information gap decision theory to model the market price with severe uncertainty so that an optimal bidding strategy can be determined for the day-ahead market.…”

Section: Literature Review and Main Contributionsmentioning

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%

See 1 more Smart Citation

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

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Bunn and Martoccia [16] analyzed generator market power in the Electricity Pool of England and Wales. Other papers that used ABM to study bidding strategy and coalition in electricity market are presented by [17][18][19]. A study by [20] developed an Agent-based Modeling of Electricity Supply (AMES) using Repast to model the wholesale electricity market run by the New England Independent System Operator and the Midwest Independent System Operator (MISO).…”

Section: Application Of Abm In Liberalized Electricity Marketmentioning

confidence: 99%

Agent-Based Modeling for Studying the Impact of Capacity Mechanisms on Generation Expansion in Liberalized Electricity Market

Dahlan

2015

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-This paper presents an approach to solve the long-term generation expansion planning problem of the restructured electricity industry using an agent-based environment. The proposed model simulates the generation investment decisions taken by a particular agent (i.e. a generating company) in a market environment taking into account its competitors' strategic investment. The investment decision of a particular company is modeled taking into account that such company has imperfect foresight on the future system development hence electricity prices. The delay in the construction of new plants is also explicitly modeled, in order to compute accurately the yearly revenues of each agent. On top of a conventional energy market, several capacity incentive mechanisms including capacity payment and capacity market are simulated, so as to assess their impact on the investment promotion for generation expansion. Results provide insight on the investment cycles as well as dynamic system behavior of long-term generation expansion planning in a competitive electricity industry.

show abstract

“…They argued that such mechanisms did not encourage generators to practice economic withholding, therefore market power and price spikes were suppressed. Liu et al 14 used swarm simulation methods to analyze the bidding processes of generators in the spot electricity market. Their results illustrated that the volatility of quantities and price of each generator with the PAB mechanism is less than it is with the MCP system.…”

Section: Introductionmentioning

confidence: 99%

The design of a Probability Bidding Mechanism in electricity auctions by considering trading constraints

Han

Sun

2015

SIMULATION

View full text Add to dashboard Cite

In the electricity trading exchange, we need to consider electricity price, transmission costs, network congestion and technology constraints. Therefore, some electricity trading may be forced to be canceled because of limits on power transmission lines. The most challenging issue is how to make an appropriate auction bidding mechanism in electricity trading. According to the bipartite network and evolutionary game theory with the Paid-as-Bid auction mechanism as research background, we explore a Probability Bidding Mechanism (PBM). The designed mechanism could significantly disrupt the learning process and weaken the unconscious collusion behaviors of generators during the auction process. The simulation results of the proposed mechanism are compared with the High-Low Matching bidding mechanism and the comparison shows that the PBM could minimize the average transaction price and maximize the average trading quantities effectively. Further investigation reveals that the parameter, which is used to adjust trading price, has more influence on generators than purchasers. Some suggestions for reducing the generator's offer and increasing the market trading quantities are recommended at the end of the paper.

show abstract

Multi-agent based experimental analysis on bidding mechanism in electricity auction markets

Cited by 28 publications

References 21 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

Agent-Based Modeling for Studying the Impact of Capacity Mechanisms on Generation Expansion in Liberalized Electricity Market

The design of a Probability Bidding Mechanism in electricity auctions by considering trading constraints

Contact Info

Product

Resources

About