Comparative study of pricing mechanisms and settlement methods in electricity spot energy market based on multi-agent simulation

Wang, Mei; Song, Yuhui; Bo, Simona; Wu, Haibo; Zhu, Jie; Jing, Zhaoxia; Rong, Yuxia

doi:10.1016/j.egyr.2022.02.078

Cited by 15 publications

(5 citation statements)

References 3 publications

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“…Where, is then assuming Gaussian average filter   g l M has been found and that the missing temporal h to be analyzed is represented as   w L . The moving-average technique that follows can be used to get the instantaneous mean in DAGAF and shown in equation (5).…”

Section: B Data Preprocessing Using Data-adaptive Gaussian Average Fi...mentioning

confidence: 99%

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Optimization Research on Provincial Users' Participation in Regional Electricity Spot Market Bidding Based on Hierarchically Gated Recurrent Neural Network

Jinqing Luo

2024

jes

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The creation of strategic pricing models is crucial in the highly competitive centralized energy markets in order to maximize revenues. In order to achieve maximum profit, market participants must act intelligently while bidding on electrical energy purchases or sales. Notwithstanding the many restructuring changes of the Chinese electricity sector, market participants are nonetheless exposed to risks associated with volatility in market prices and uncertainty over demand behavior. The data are collected from New South Wales electricity market. Data-adaptive Gaussian average filtering (DAGAF) is used to pre-process the data during the pre-processing step. Hierarchically Gated Recurrent Neural Networks (HGRNNs) are successfully used to classify both long-term and spot transactions. The neural network's weight parameter is optimized by the Lotus Effect Optimization Algorithm (LEA), which enhances the HGRNN. The RESMB-HGRNN-LEA proposed is implemented on the Python working platform. To calculate the suggested approach, performance measures including accuracy, precision, sensitivity, computation time, and recall were looked at.In comparison to the existing technique, the proposed RESMB-HGRNN-LEA method yields better results in terms of accuracy (16.65% and 18.85%), sensitivity (16.34% and 12.23%), precision (14.89% and 16.89%), and computing time (82.37% and 94.47%).

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Section: B Data Preprocessing Using Data-adaptive Gaussian Average Fi...mentioning

confidence: 99%

“…In August 2018, the province of Guangdong officially launched its pilot electricity spot market. The Guangdong spot market signaled the start of China's spot electricity market when, after two years, it successfully concluded its trial operations [4][5][6]. In China, the cost of power was tightly controlled prior to 2015.…”

Section: Introductionmentioning

confidence: 99%

Optimization Research on Provincial Users' Participation in Regional Electricity Spot Market Bidding Based on Hierarchically Gated Recurrent Neural Network

Jinqing Luo

2024

jes

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“…With the reform of market mechanisms, many scholars have gradually begun to pay attention to and extend the design theory of economic mechanisms. For example, considering the characteristics of the electricity system and the strategic behavior of electricity providers triggered by incomplete information problems [ 158 , 184 ], which affects the security and economy of the electricity market. Some scholars applied mechanism design theory, supply function, Cuomo model, Stackelberg model to explore the strategic behavior of electricity suppliers and incentive bidding mechanism and other issues.…”

Section: Challenges In Mechanism Designmentioning

confidence: 99%

Breaking the traditional: a survey of algorithmic mechanism design applied to economic and complex environments

Chen

Wang

Jiang

et al. 2023

Neural Comput & Applic

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The mechanism design theory can be applied not only in the economy but also in many fields, such as politics and military affairs, which has important practical and strategic significance for countries in the period of system innovation and transformation. As Nobel Laureate Paul said, the complexity of the real economy makes it difficult for “Unorganized Markets” to ensure supply-demand balance and the efficient allocation of resources. When traditional economic theory cannot explain and calculate the complex scenes of reality, we require a high-performance computing solution based on traditional theory to evaluate the mechanisms, meanwhile, get better social welfare. The mechanism design theory is undoubtedly the best option. Different from other existing works, which are based on the theoretical exploration of optimal solutions or single perspective analysis of scenarios, this paper focuses on the more real and complex markets. It explores to discover the common difficulties and feasible solutions for the applications. Firstly, we review the history of traditional mechanism design and algorithm mechanism design. Subsequently, we present the main challenges in designing the actual data-driven market mechanisms, including the inherent challenges in the mechanism design theory, the challenges brought by new markets and the common challenges faced by both. In addition, we also comb and discuss theoretical support and computer-aided methods in detail. This paper guides cross-disciplinary researchers who wish to explore the resource allocation problem in real markets for the first time and offers a different perspective for researchers struggling to solve complex social problems. Finally, we discuss and propose new ideas and look to the future.

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“…In the overall reform framework, the spot market, as a key connection part between the medium-and long-term electricity transactions and the real-time operation of the power grid, plays the role of restoring the commodity attributes of electricity, guiding the discovery of power prices, and promoting the optimal allocation of resources [1]. Compared with the power system operating conditions in the traditional centralized dispatching pattern, in the electricity spot market, the power system planning will be affected by several uncertain factors such as node electricity price fluctuation [2] and unit output adjustment [3]. Whether the existing power grid planning scheme can maintain a good operation state in a market-oriented environment needs to be verified urgently.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market Environment

et al. 2023

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The electricity spot market plays a significant role in promoting the self-improvement of the overall resource utilization efficiency of the power system and advancing energy conservation and emission reduction. This paper analyzes and compares the potential impacts of spot market operations on system planning, considering the differences between planning methods in traditional and spot market environments through theoretical analysis and model comparison. Furthermore, we conduct research and analysis on grid planning methods under the spot market environment with the goal of maximizing social benefits. Unlike the pricing approach based on historical price data in traditional market simulation processes, a data-driven approach that combines experimental economics and machine learning is proposed, specifically using mixed empirical learning to simulate unit bidding strategies in market transactions. A simulation model for electricity spot market trading is constructed to analyze the performance of the planning results in the spot market environment. The case study results indicate that the proposed planning methods can enable the grid to operate well in the spot market environment, maintain relatively stable nodal prices, and ensure the integration of a high proportion of clean energy.

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Comparative study of pricing mechanisms and settlement methods in electricity spot energy market based on multi-agent simulation

Cited by 15 publications

References 3 publications

Optimization Research on Provincial Users' Participation in Regional Electricity Spot Market Bidding Based on Hierarchically Gated Recurrent Neural Network

Optimization Research on Provincial Users' Participation in Regional Electricity Spot Market Bidding Based on Hierarchically Gated Recurrent Neural Network

Breaking the traditional: a survey of algorithmic mechanism design applied to economic and complex environments

Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market Environment

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