Optimal bidding strategy for GENCOs based on parametric linear programming considering incomplete information

Gao, Feng; Sheblé, G.B.; Hedman, Kory W.; Yu, Chien-Ning

doi:10.1016/j.ijepes.2014.10.053

Cited by 21 publications

(12 citation statements)

References 24 publications

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“…A simulated market model based on double auction is presented in [146,147]. The market model matches consumers and generator bids pairwise [148]. The focus of this simulator is on the learning process of agents' bidding behavior, with the application of the Roth-Erev reinforcement learning algorithm [147] and genetic algorithms [146].…”

Section: Complex Large-scale Simulation Inmentioning

confidence: 99%

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

et al. 2018

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This survey provides a comprehensive analysis on recent research related to optimization and simulation in the new paradigm of power systems, which embraces the so-called smart grid. We start by providing an overview of the recent research related to smart grid optimization. From the variety of challenges that arise in a smart grid context, we analyze with a significance importance the energy resource management problem since it is seen as one of the most complex and challenging in recent research. The survey also provides a discussion on the application of computational intelligence, with a strong emphasis on evolutionary computation techniques, to solve complex problems where traditional approaches usually fail. The last part of this survey is devoted to research on large-scale simulation towards applications in electricity markets and smart grids. The survey concludes that the study of the integration of distributed renewable generation, demand response, electric vehicles, or even aggregators in the electricity market is still very poor. Besides, adequate models and tools to address uncertainty in energy scheduling solutions are crucial to deal with new resources such as electric vehicles or renewable generation. Computational intelligence can provide a significant advantage over traditional tools to address these complex problems. In addition, supercomputers or parallelism opens a window to refine the application of these new techniques. However, such technologies and approaches still need to mature to be the preferred choice in the power systems field. In summary, this survey provides a full perspective on the evolution and complexity of power systems as well as advanced computational tools, such as computational intelligence and simulation, while motivating new research avenues to cover gaps that need to be addressed in the coming years.

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Section: Complex Large-scale Simulation Inmentioning

confidence: 99%

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

et al. 2018

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“…When compared with the traditional PSO, the proposed method gave better results, proving that it can be applied to other optimization problems. As in the case of the UC problem, the PCs also solve the ED problem, with the goal of increasing profits in an electricity market environment [31]. In [32], a variation of a traditional PSO was also proposed to solve the ED problem, in which a more realistic swarm behavior was introduced, promoting better communication among several solutions through the inertia of the swarm weights.…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling Model for Renewable Energy Sources Integration in an Insular Power System

et al. 2018

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Insular power systems represent an asset and an excellent starting point for the development and analysis of innovative tools and technologies. The integration of renewable energy resources that has taken place in several islands in the south of Europe, particularly in Portugal, has brought more uncertainty to production management. In this work, an innovative scheduling model is proposed, which considers the integration of wind and solar resources in an insular power system in Portugal, with a strong conventional generation basis. This study aims to show the benefits of increasing the integration of renewable energy resources in this insular power system, and the objectives are related to minimizing the time for which conventional generation is in operation, maximizing profits, reducing production costs, and consequently, reducing greenhouse gas emissions.

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“…In the study performed by Alberto et al [10], the Nash equilibrium of the single-side day-ahead market was analyzed with a static game model considering the transmission constraints. 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).…”

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%

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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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Optimal bidding strategy for GENCOs based on parametric linear programming considering incomplete information

Cited by 21 publications

References 24 publications

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

Scheduling Model for Renewable Energy Sources Integration in an Insular Power System

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

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