An Optimistic Approach with a Distribution Generator for Demand-Side Resource Management Considering a Deregulated Electricity Market
Year: 2025
Type: article
Abstract: Distribution Generator (DG) technology mainly uses solar photovoltaics and wind turbines, popular for reducing greenhouse gas emissions. Connecting renewable-based distributed generators offers technical and environmental benefits, leading to their increased adoption over time. Higher penetration of distributed resources negatively affects the distribution system's performance. This makes demand-side management more complex as renewable-based DGs provide variable electricity generation depending on the state of the environment. The excess generation of electricity from DGs causes a harmful impact on the entire system's performance. Therefore, it is necessary to curtail the generation under light loading conditions. The proposed strategy presents an energy management system under a deregulated electricity market for the optimal operation of renewable-based DGs. A Genetic Algorithm (GA) is used to satisfy multiple technical and economic criteria by reducing the weighted objectives to identify the optimal solution to the problem statement. This analysis incorporates the hourly variations in the generation of all four types of renewable (DG) based on environmental data for a single day. The results show good power loss reduction and voltage profile improvement, yielding 50% RE penetration. It has been tested on an IEEE-33 bus standard distribution network with three different load profiles and has shown satisfactory results. Results indicate that considerable line loss reduction and voltage profile improvement have been achieved in each hour due to the energy curtailment of various DG models. The proposed methodology curtails the generation based on each DG's impact on the network performance. Comparative results demonstrate that PV-based SG1 and wind-based WG3 models operate at full capacity throughout the day, while PV-based SG3 and wind-based WG4 operate at their optimal capacity based on the proposed MOF.
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411065208