Towards sustainable regional energy solutions: An optimized operational model for integrated energy systems with price-responsive planning

With dynamic changes in the energy market and continuous advances in smart energy technology, regional integrated energy systems (RIESs) have emerged as a vital direction in the evolution of energy systems. They are increasingly gaining significance in the realm of energy supply (ES) and demand response initiatives. In this context, this article introduces a novel optimal operational model for an integrated energy system (IES) that incorporates energy price responsiveness. The paper commences by delivering a comprehensive overview of the typical structure of an IES, presenting detailed models for each system module, encompassing the electrical energy supplier, heating, and cooling components. The proposed system model comprises three distinct subsystems, each catering to specific energy requirements. The primary emphasis is on devising an efficient planning scheme tailored to the energy consumption patterns and operational aspects of the IES. Through the presented model, there is substantial potential for significantly reducing the overall system cost without inducing a noteworthy upsurge in environmental pollution. Moreover, the energy efficiency of the system can experience considerable enhancement. The desired optimization problem has been solved using the proposed multi-objective Horse Herd Optimization Algorithm (MOHHOA). Additionally, in this strategy, Pareto front and fuzzy selection are employed to make better decisions among all Pareto solutions. The optimization strategy devised in this research not only enables the integrated operation of the IES but also ensures its compatibility with ongoing energy development initiatives. The proposed method yields a diverse set of Pareto solutions across the objective space, providing various trade-offs between economic, environmental, and reliability considerations. Utilizing MOHHOA, the algorithm efficiently balances exploration and exploitation during optimization, resulting in informed decision-making. In optimal planning, Scenario 2 exhibits lower operational costs (8.2 % reduction) and a dramatic improvement in LESP (1.24 %–1.176 %) compared to Scenario 1. Sensitivity analysis reveals an 11.2 % increase in environmental costs in Scenario 1 with a 36 % rise in electricity prices. Conversely, in Scenario 2, a 10 % reduction in natural gas prices leads to a 24.5 % increase in system running costs. These findings underscore the significance of considering energy price fluctuations for optimizing integrated energy system performance.