Low‐carbon demand response program for power systems considering uncertainty based on the data‐driven distributionally robust chance constrained optimization

Abstract

AbstractThe demand response (DR) program is an effective solution to promote the low‐carbon operation of power systems with increasing penetration of renewable energy sources (RESs). This paper proposes a low‐carbon DR program for power systems to enhance both the environmental friendliness and uncertainty resilience of the system operation. The system operator aims to minimize both the system's operation cost and carbon trading cost. To handle the uncertainty associated with stochastic RES generation power and load consumption power, a data‐driven method named the two‐sided distributionally robust chance‐constrained (TS‐DRCC) approach is adopted to enhance the system's uncertainty resilience. A ladder‐type carbon trading scheme is utilized to calculate the carbon emission cost of the system. Comprehensive analyses of case studies have been conducted to validate that the proposed strategy can effectively reduce the total carbon emissions and total operation costs with good uncertainty resilience performance. The proposed low‐carbon DR program is verified to achieve 63.64% more carbon emission reduction compared with the conventional DR program. Besides this, the proposed low‐carbon DR program can also achieve 4.39% carbon‐intensive generation power reduction and 5.52% RES power consumption compared with the conventional DR program.