The advent of smart grid technologies due to the explosive increase in the electricity demand, has necessitated utilities around the globe in establishing intelligent demand response programs (DRPs) to influence customers' consumption patterns. The successful implementation of DRPs together with dynamic pricing strategies not only reduces energy prices in electricity markets but also improves network reliability and overall system efficiency. This study proposes a game theory-based DRP (GTDRP) which merges the incentive- and price-based DRP concepts with a focus on residential, commercial, and industrial sectors. Three pricing strategies are structured and compared, that is, fixed pricing, time-of-use pricing (for both utility- and customer-side), and real-time pricing along with their combination. Also, an enthusiasm-aided teaching and the learning-based optimization algorithm are developed to solve the GTDRP model through a self-adaptive power violation criterion. To validate the practicality of the presented model, three case studies through seven different scenarios are investigated. Results of the case studies demonstrate that the formulated multi-criteria security-constrained GTDRP can create a win-win situation for the utility and customers in the electricity markets, such that the utility profits increase, the customers' related costs reduce, and the load curve is flattened.

由于电力需求的爆炸性增长，智能电网技术的出现迫使全球公用事业公司建立智能需求响应程序 (DRP) 以影响客户的消费模式。DRP 的成功实施以及动态定价策略不仅降低了电力市场的能源价格，还提高了网络可靠性和整体系统效率。本研究提出了一种基于博弈论的 DRP (GTDRP)，它融合了基于激励和价格的 DRP 概念，重点关注住宅、商业和工业部门。三种定价策略被构建和比较，即固定定价、使用时间定价（对于公用事业和客户方）和实时定价以及它们的组合。还，开发了热情辅助教学和基于学习的优化算法，通过自适应功率违规准则求解GTDRP模型。为了验证所提出模型的实用性，研究了七个不同场景的三个案例研究。案例研究结果表明，制定的多准则安全约束 GTDRP 可以为电力市场中的公用事业和客户创造双赢的局面，从而提高公用事业利润，降低客户相关成本，并减轻负荷。曲线变平。