Optimal DG Planning Incorporating Energy Management for an Economical and Resilient Smart Distribution System

• Published in: IEEE transactions on industry applications Vol. 60; no. 1; pp. 1 - 12
• Main Authors: Prakash, Ram, Lokeshgupta, B., Sivasubramani, S., Kobaku, Tarakanath, Agarwal, Vivek
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Costs; DG cost; Distributed generation; Distributed generation (DG); Energy management; energy management (EM) system; expected outage cost (ECOST); Game theory; game theory approach; Indexes; multi-objective optimization; Multiple objective analysis; Optimization; Outage costs; Planning; Power system reliability; Renewable energy sources; System reliability; Wind turbines
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2023.3327991

In this study, a novel application of multi-objective (MO) optimization methodology is presented for distributed generation (DG) planning incorporating energy management (EM). The MO problem considers the DG cost reduction, system reliability enhancement, and power loss minimization objectives to determine the optimal capacity and position of renewable and dispatchable DG. The initial capital, maintenance and operation, emission, and fuel costs make up the proposed long-term DG cost. Expected outage cost (ECOST) due to interrupted power supply is evaluated to estimate the reliability of the distribution network. The proposed MO optimization problem is optimized using a cooperative game theory technique. The technique is based on the super-criterion and bargaining model concept. In this game process, each objective acts as a participant, and ultimately, all stakeholders are able to achieve win-win outcomes through collective negotiations. Various scenarios and case studies are performed on the modified IEEE 33-bus system with an hourly profile of solar photovoltaic arrays, wind turbines, and load demand. Simulation results illustrate the efficacy of the proposed approach. Moreover the real-time applicability of the proposed model is validated through an experimental setup on the OPAL-RT platform.