Integrate multiple energy sources of the microgrid: Enhancing performance and sustainability in multi-energy systems

Abstract

This paper introduces a novel hybrid optimization framework for Multi-Energy Systems that jointly addresses cost efficiency, uncertainty, and demand-side flexibility. The proposed model uniquely integrates electric and thermal Load Response Plans within a unified structure and incorporates a Negative Risk Limit to explicitly control downside financial exposure under volatile conditions. A key innovation lies in the combination of scenario-based stochastic modeling and robust optimization to manage uncertainties in renewable generation, market prices, and consumer demand. The Flower Pollination Algorithm, a nature-inspired metaheuristic, is employed to efficiently solve the resulting high-dimensional problem. A residential-scale case study, involving photovoltaic panels, wind turbines, combined heat and power, boilers, electric vehicles, thermal storage, and heat pumps, demonstrates the framework’s applicability. Four simulation scenarios assess the individual and combined effects of Load Response Plans and risk constraints. Results indicate that energy purchases from upstream networks are reduced with coordinated load shifting, lowering peak hour procurement by 15–30 % compared to baseline operation. Electric vehicles exhibit active charge/discharge behavior in up to 75 % of daily time slots under joint Load Response Plan and Negative Risk Limit conditions, enhancing flexibility.