Enhancing Day-Ahead Electricity Market Planning with a Novel Probabilistic Strategy for Wind Power and Uncertain Customers

Document Type : Article

Authors

1 Department of Electrical Engineering, University of Guilan, Rasht, Iran

2 - Department of Electrical Engineering, University of Guilan, Rasht, Iran - Center of Excellence for Mathematical Modeling, Optimization and Combinatorial Computing (MMOCC), University of Guilan, Rasht, Iran

10.24200/sci.2024.63947.8673

Abstract

Nowadays, the participation of wind power plants in electricity markets has become a severe challenge due to their intermittent nature for decision makers of market. In the presence of uncertainties, some sellers and buyers experience a reduction in their satisfaction. This paper presents a new method for the participation of wind power plants and uncertain customers in a day-ahead electricity market based on the local marginal pricing mechanism to maximize the total profits of sellers and buyers considering their importance level through a two-level optimization problem. For this purpose, using the empirical cumulative distribution function and the Monte Carlo method, the uncertainties are modeled. Then, by defining some economic indices to evaluate participants' satisfaction and using the analytic hierarchy process, a new objective function is proposed to optimize the mentioned indices. Simulations are implemented on a realistic 8-bus sample system, and the results confirm the efficiency of the proposed method in significantly reducing the costs of producers and customers, and consequently their total profits. Based on the results obtained from the presented method, the expected ranges for total cost fall between 1,270.91$ and 1,719.50$, while the expected ranges for total payment range from 2,151.41$ to 2,192.58$.

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Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 26 May 2024

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