Multi-Criteria Decision-Making Framework for Sustainable Energy Scheduling and Demand Response Strategies in Smart Grids: An Economic, Environmental, and Technical Perspective

Abstract

Sustainable energy scheduling combined with demand response (DR) is increasingly recognized as a critical approach in smart grids to balance economic, environmental, and technical objectives. This paper proposes a novel Multi-Criteria Decision-Making (MCDM) framework that integrates cost minimization, emission reduction, and technical constraints (like reliability, load balancing, and peak shaving) to optimize both energy scheduling and DR strategy selection. The framework incorporates hybrid MCDM methods (AHP for weight elicitation, fuzzy TOPSIS for ranking, and scenario‐based multi‐objective optimization) to evaluate alternatives. A case study on a regional smart grid under multiple scenarios (with and without DR, different renewable penetration levels) demonstrates that the proposed framework reduces operational cost by up to 15.6%, CO₂ emissions by 12.8%, and improves load factor and peak load reduction significantly compared to baseline scheduling without DR. Sensitivity analyses verify robustness of results under varying weights and uncertainties. The findings provide actionable insights for utilities and policymakers aiming to implement economically efficient, environmentally friendly, and technically acceptable scheduling with demand response in smart grids.