GENCO Optimal Bidding Strategy and Profit based Unit Commitment using Evolutionary Particle Swarm Optimization Illustrating the Effect of GENCO Market Power

Abstract

<p>In deregulated electricity markets, generation companies (GENCOs) make unit commitment (UC) decisions based on a profit maximization objective in what is termed profit based unit commitment (PBUC). PBUC is done for the GENCOs demand which is a summation of its bilateral demand and allocations from the spot energy market. While the bilateral demand is known, allocations from the spot energy market depend on the GENCOs bidding strategy. A GENCO thus requires an optimal bidding strategy (OBS) which when combined with a PBUC approach would maximize operating profits. In this paper, a solution of the combined OBS-PBUC problem is presented. An evolutionary particle swarm optimization (EPSO) algorithm is implemented for solving the optimization problem. Simulation results carried out for a test power system with GENCOs of differing market strengths show that the optimal bidding strategy depends on the GENCOs market power. Larger GENCOs with significant market power would typically bid higher to raise market clearing prices while smaller GENCOs would typically bid lower to capture a larger portion of the spot market demand. It is also illustrated that the proposed EPSO algorithm has a better performance in terms of solution quality than the classical PSO algorithm.</p>

Bilateral bidding strategy in joint day-ahead energy and reserve electricity markets considering techno-economic-environmental measures

“…Despite the evolutionary game approach to address the dynamics of bidding strategies, it can only be implemented for the simplest of models. Bikeri et al 16 utilize a single-level profit maximization framework with hour-ahead market data to characterize both optimal bidding strategies and operating schedules of the GenCos. They represent how the market power can be integrated into the bidding strategy problem without taking into account techno-environmental measures.…”

“…The first type of modeling deals with the unilateral generation-side bidding strategy problem within energy-only electricity markets. 6–17 In Aien and Fotuhi-Firuzabad, 6 an unscented transformation-based model is presented for the bidding strategy of the price-taker GenCos in an oligopoly market. In this simple, single-level methodology, the market-clearing mechanism is considered as uniform pricing and techno-economic-environmental measures are neglected; subsequently, in such a situation, the market competitive level and profitability are largely diminished.…”

“…13 16 19 22 25 28 31 34 37 40 43 46 49 52 Strategic offering factors of GenCos GenCos No.Second strategic offeri ng factor under case 2 First strategic offering factor under case 1 Second strategic offeri ng factor under case 1…”

A review of optimization techniques applied to solve unit commitment problem in microgrid

“…Application results reveal that for the proposed unit commitment problem, the scheduling of the distributed generations and the best sizes of BBs would be completely different when the accessibility of wind energy was taken into consideration in consequence of using HN method. In [84][85][86][87][88][89][90] unit commitment problem was elucidated by considering different hybrid renewable energy systems.…”

Application of Ahp Method for Solving the Unit Commitment Problem in a Day Ahead Market

“…There have been many solution techniques presented in the literature for solving PBUC problems. Some of them are Lagrange Relaxation-Differential Evolution [2], Binary fireworks algorithm [3], Particle Swarm Optimization (PSO) [4], Genetic Algorithm [5], hybrid Lagrangian Relaxation-Particle Swarm Optimization [6], Memetic Algorithm [7], Shuffled Frog Leaping Algorithm [8], Artificial Immune System [9], hybrid Binary Successive Approximation (BSA) and Civilized Swarm Optimization (CSO) [10], hybrid Lagrangian relaxation (LR)secant method-invasive weed optimization (IWO) [11], evolutionary particle swarm optimization (EPSO) [12]. In this study, AHP is developed to solve PBUC problem that aims to maximize GENCOs profit.…”