A novel modeling framework for demand response-based energy management systems in smart electricity markets, using optimization and multi-criteria decision making techniques

Modern power grids require adaptive demand-side management, yet existing mechanisms often rely on static strategies that fail to reflect consumer heterogeneity. This study develops a demand response program (DRP)-based Energy Management System (ESM) as a novel framework designed to overcome these limitations. The innovation lies in a hybrid methodology that integrates a tri-actor welfare optimization model, capturing generators, service providers, and consumers, alongside a dynamic Multi-Criteria Decision Making (MCDM) approach. The proposed DRP-based ESM autonomously allocates the most suitable program between a Price-Based Program (PBP) and an Incentive-Based Program (IBP) on an hourly basis. Empirical validation uses the Japanese electricity market data, disaggregated across four consumer segments: residential, industrial, commercial offices, and wholesale/retail facilities. Results confirm that no single DRP is universally optimal; rather, effectiveness depends on temporal and sectoral contexts. Sensitivity analysis indicates that, PBPs dominate during valley hours, accounting for up to 75 % of allocations due to lower tariffs that enhance affordability and improve load factor recovery. During off-peak periods, the DRP-based ESM alternates between PBPs and IBPs depending on hourly demand conditions. On low-demand days, IBPs represent 66.67 % of selections, while during the Obon holiday, PBPs are chosen for roughly 75 % of operating hours, effectively managing heightened demand variability. Findings demonstrate that the proposed DRP-based ESM adapts effectively to changes in demand magnitude, elasticity, and operator priorities. The results underscore the importance of behavioral context in shaping DRP performance and highlight the potential of dynamic, data-driven program selection to enhance grid flexibility and consumer welfare.