Currently, for developing optimal bidding strategy considering carbon credit trading, Generating Company (GENCO) separately participates in electric power and emission market. Recently, researchers proposed a new scheme in which emission trading is facilitated within Power Exchange (PX). Developing optimal GENCO's bidding strategy considering carbon credit trading within PX is a complex single‐objective optimization problem with generator's surplus as the main objective function. Obtaining its solution using a simple evolutionary optimization algorithm is difficult and time‐consuming. Therefore, this paper presents an application of Intelligent Programmed Genetic Algorithm (IPGA) equipped with advanced deterministic diversity creating operator for solving optimal GENCO's bidding strategy problem with minimum number of objective function evaluations. The performance of IPGA is first tested on three different categories of standard test functions by comparing its simulation results with that obtained using binary‐coded GA, simulated annealing, particle swarm optimization, biogeography‐based optimization, teaching learning‐based optimization, and differential evolution‐based hybrid GA. A system comprising of six GENCOs and two buyers is used to validate the applicability of IPGA. IPGA along with other algorithms was used to obtain optimal bidding curve for 24 hours for GENCO GC‐1. Simulation results clearly show much faster convergence for IPGA in terms of the number of objective function evaluations required to reach optima. Result also shows that GENCO GC‐1 can obtain much higher GC‐1 surplus, carbon credit limit, and social welfare by utilizing the obtained bidding strategy using IPGA as compared with the bidding strategy obtained by other algorithms.

中文翻译：

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GENCO在电力交换中的最优投标策略，可使用智能程序遗传算法促进电力和排放交易的联合

当前，为了制定考虑碳信用额交易的最佳投标策略，发电公司（GENCO）分别参与了电力和排放市场。最近，研究人员提出了一种新的方案，其中在Power Exchange（PX）中促进了排放交易。考虑到PX内的碳信用交易，制定最佳GENCO的竞标策略是一个复杂的单目标优化问题，其中发电机盈余为主要目标函数。使用简单的进化优化算法来获得其解决方案既困难又费时。因此，本文提出了一种具有高级确定性分集创建算子的智能程序遗传算法（IPGA）在求解最优GENCO'中的应用。目标函数评估次数最少的出价策略问题。首先，通过将IPGA的仿真结果与使用二进制编码的GA，模拟退火，粒子群优化，基于生物地理的优化，基于教学学习的优化以及差分进化获得的仿真结果进行比较，首先对IPGA的性能进行了三种不同类别的测试基于混合GA。使用由六个GENCO和两个买方组成的系统来验证IPGA的适用性。IPGA和其他算法一起用于获得GENCO GC-1的24小时最佳投标曲线。仿真结果清楚地表明，就达到最佳状态所需的目标函数评估而言，IPGA的收敛速度更快。结果还表明，GENCO GC-1可以获得更高的GC-1盈余，碳信用额度，