Optimal Bidding Strategy for Social Welfare Maximization in Wind Farm Integrated Deregulated Power System Using Artificial Gorilla Troops Optimizer Algorithm

Nitesh Kumar Singh; Sadhan Gope; Chaitali Koley; Subhojit Dawn; Hassan Haes Alhelou

doi:10.1109/ACCESS.2022.3186519

IEEE Access (Jan 2022)

Optimal Bidding Strategy for Social Welfare Maximization in Wind Farm Integrated Deregulated Power System Using Artificial Gorilla Troops Optimizer Algorithm

Nitesh Kumar Singh,
Sadhan Gope,
Chaitali Koley,
Subhojit Dawn,
Hassan Haes Alhelou

Affiliations

Nitesh Kumar Singh: ORCiD; Department of Electronics and Communication Engineering, National Institute of Technology Mizoram, Aizawl, India
Sadhan Gope: ORCiD; Department of Electrical Engineering, Mizoram University, Aizawl, India
Chaitali Koley: ORCiD; Department of Electronics and Communication Engineering, National Institute of Technology Mizoram, Aizawl, India
Subhojit Dawn: ORCiD; Department of Electrical and Electronics Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India
Hassan Haes Alhelou: ORCiD; Department of Electrical Power Engineering, Tishreen University, Lattakia, Syria

DOI: https://doi.org/10.1109/ACCESS.2022.3186519
Journal volume & issue: Vol. 10
pp. 71450 – 71461

Abstract

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PoolCo electricity trading is one of the most capable bidding practices for executing a centralized energy market model. In the PoolCo market model, each seller and buyer submit their bid price and bid quantity to the independent market operator, which they are ready to sell and buy from the market respectively. The market operator regulates the equilibrium market price and volume by considering the acquiesced bid price and bid quantity to settle the market. To maximize the social welfare of market participants, the optimal bidding strategy of a wind farm integrated system is represented as a centralized power market model. Initially, the bid price and bid quantity for consumers and suppliers have been calculated using the Monte-Carlo simulation (MCS) approach. Secondly, a wind farm is incorporated into the system with the help of locational marginal price (LMP). The market operator determines market clearing price (MCP) and market clearing volume (MCV) based on the submitted bid price and bid quantity of suppliers and buyers in order to find the eligible buyers and suppliers. After obtaining MCP and MCV, the market operator reschedules the supplier’s bid quantity with the help of an artificial gorilla troops optimizer (AGTO) algorithm to maximize social welfare by pleasing the system constraints. The AGTO algorithm is used here for the first time to solve the market-clearing power simulation (MCPS) problem with the integration of wind farm. To show the feasibility and effectiveness of the proposed bidding strategy, modified IEEE 14-bus and modified IEEE 30-bus test systems are used here along with a wind farm of 5 MW and 30 MW rated capacity, respectively. Results obtained by using the AGTO algorithm have been compared with those obtained by other optimization algorithms like honey badger algorithm (HBA), artificial bee colony (ABC), particle swarm optimizer (PSO), and slime mould optimizer (SMO) algorithms.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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