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

Abstract

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. INDEX TERMSWind farm, locational marginal price, Monte-Carlo simulation, artificial gorilla troops optimizer algorithm, slime mould optimizer algorithms.

Profit enhancement and grid frequency control by energy level scheduling of CAES system in wind-connected electrical system

“…The proposed method also identifies the parameters for operating the CAES in generating or pumping mode while considering the energy level for charging or discharging. The cost coefficient for all generators in power plants is obtained from Singh et al (2022). The optimum power flow offers the least generation pricing for the generation re-scheduling in a thermal power plant.…”

“…1 is chosen as the slack bus and the MVA limit of this system is 100 MVA. It has six generators, 19 loads, and 41 transmission lines, and all the bus, branch, and generator data are taken from Singh et al (2022). The intended system consists of 20 wind farms each having a capacity of 3.5 MW, so a total capacity of 70 MW wind farms is considered in the chosen system.…”

Cooperative optimal operation of hybrid energy integrated system considering multi‐objective dragonfly algorithm

“…In the present scenario, the process of reducing emissions through the utilization of the DA is executed, and the obtained outcomes are documented in Table 4. The analysis of Table 4 reveals that the application of DA leads to a lower emission level in comparison to other algorithms, namely ACS, 33 ESDE, 34 MOEA/D, 36 MOPOSO, 36 and NSGA-II. 36 T A B L E 5 Optimal generation cost and emission using a multi-objective optimization algorithm.…”

“…On the other hand, the variable P cg represents the real power delivered by the CAES, while the variable P cc signifies the real power absorbed by the CAES. It is possible to express P wp in the form of an equation, as indicated by the reference [33].…”

Improved artificial gorilla troops optimizer with chaotic adaptive parameters - application to the parameter estimation problem of mixed additive and multiplicative random error models

“…In GTO, the fitness value of candidate points in the feasible domain is abstracted as the food source on which the gorilla population depends, and the higher the fitness value, the more abundant their food is. Although GTO has better performance in different test functions as well as engineering applications compared to existing popular algorithms [18], while it has been widely used in several fields [19][20][21][22][23][24][25][26][27], it still suffers from local convergence and premature maturity. In response to the above problems, domestic and foreign scholars have improved GTO.…”