Customer directrix load-based large-scale demand response for integrating renewable energy sources

Abstract

Large-scale demand response (DR) is a critical enabler to integrate significant renewable energy sources (RES) into power systems. Current customer baseline load (CBL)-based DR schemes face obstacles in large-scale deployments due to their centralized form, unfair DR performance measurement, and poor effect on decision making approach of customers. To bridge the gaps, this paper proposes the concept of customer directrix load (CDL), which is the desired load profile for customers from the view of the entire DR program, and a novel CDL-based DR scheme. Additionally, an optimization problem considering time-coupling constraints is formulated to help customers respond to the CDL. The computationally intensive problem is then translated into a quadratic programming problem in each time slot using Lyapunov optimization approach. A closed-form solution exists and ensures that the optimal decision is reached in real-time efficiently. Test systems are generated using data from PJM and Open Energy Information. The online algorithm and fairness performance of the proposed scheme are validated in a small system through benchmark comparisons. Further tests on a large-scale system show that the CDL-based DR scheme can help the power system integrate considerably more RES.