Multi-objective grasshopper optimization algorithm for optimal energy scheduling by considering heat as integrated demand response

Due to the challenges associated with grid management, it is crucial to develop energy management systems that consider optimal resource performance within an integrated system and enable control of power exchange with the grid. Simultaneously, addressing the demand for multiple energy sources is a critical step towards achieving an economically and efficiently integrated energy system (IES), which plays a pivotal role in promoting sustainable IES development. Building upon these considerations, this article proposes a two-objective optimization model that incorporates the concept of demand response and integrated energy systems, while considering comprehensive economic and energy efficiency benefits. To ensure realism, a range of linear and nonlinear constraints are employed. To address such a problem, a two-stage modeling and solution approach is proposed. Initially, flexible electrical and thermal loads are combined to establish a mechanism for effectively responding to system behavior. Subsequently, a multi-objective model is presented to enhance energy generation and transfer efficiency, taking into account both demand response and economic aspects. Given the distinct properties of these objectives, a multi-objective grasshopper optimization algorithm (GOA) is developed. The Pareto concept is utilized to enhance search performance, and fuzzy theory is employed to select the optimal solution from the set of solutions. Finally, the proposed model and algorithm are evaluated under various operating conditions on the studied system. The results demonstrate that this model effectively improves economic aspects, enhances efficiency, and reduces pollutant emissions to an acceptable level.