Identification of the key factors and development of strategies for promoting residential customer participation in demand response programs

Abstract

Effective Demand Response (DR) design plays a crucial role in mitigating peak demand increases and price volatility. Understanding diverse customer behavior, particularly in the residential sector, is pivotal for successful DR implementation. Recently, there has been a growing focus on price-based demand response programs, offering increased flexibility and potential for enhanced demand responsiveness. The success of these programs hinges on encouraging greater residential customer participation, necessitating the formulation of more effective incentive schemes. DR programs, including direct load control, empower utilities to manage peak loads by temporarily reducing electricity demand. While the efficacy of direct load control on individual appliances is well- established, utility demand-side management strategies often integrate multiple DR programs to optimally reduce demand during peak events. Power distribution transformers are one of the key assets to be managed by the utility. The aging of transformers causes operational and financial burdens on the utility. Insulation failure due to overloading is the primary cause of the aging of the transformer. In Sri Lanka high peak demand appears at nighttime due to the residential loads are increasing. Therefore, the transformers are overloading at nighttime. Instead, the concept of demand response can be used to reduce the transformer overloading at the nighttime peak considering the requirements of the load. In this, I have demonstrated how Demand Response can positively affect the lifespan of the transformer. To characterize the psychological behaviors of the customer by analyzing the conduct of a customer survey among the residential customers of the overloaded feeder. From this, the available capacity of DR can be measured. The consumer willingness to participate in DR is also measured by identifying the key factors the customer expects to participate in such program. After that, analyze the customer load profiles and transformer load profiles to find the necessary load reduction in the night peak.Finally, based on the available capacity, a DR scheduling model is developed. Then, according to the scheduling results, an incentive scheme is proposed to evaluate the impact of DR on generation adequacy and analyze the benefits to the utility and the customers who participated in DR.