Load Curtailment Optimization Using the PSO Algorithm for Enhancing the Reliability of Distribution Networks
Laura M. Cruz,
David L. Alvarez,
Ameena S. Al-Sumaiti and
Sergio Rivera
Additional contact information
Laura M. Cruz: Department of Electric and Electronic Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia
David L. Alvarez: Department of Electric and Electronic Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Ameena S. Al-Sumaiti: Advanced Power and Energy Center, Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi 127788, UAE
Sergio Rivera: Department of Electric and Electronic Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Energies, 2020, vol. 13, issue 12, 1-15
Abstract:
Power systems are susceptible to disturbances due to their nature. These disturbances can cause overloads or even contingencies of greater impact. In case of an extreme situation, load curtailment is considered the last resort for reducing the contingency impact, its activation being necessary to avoid the collapse of the system. However, load shedding systems seldom work optimally and cause either excessive or insufficient reduction of the load. To resolve this issue, the present paper proposes a methodology to enhance the load curtailment management in medium voltage distribution systems using Particle Swarm Optimization (PSO). This optimization seeks to minimize the amount of load to be cut off. Restrictions on the optimization problem consist of the security operation margins of both loading and voltage of the system elements. Heuristic optimization algorithms were chosen, since they are considered an online basis (allowing a short processing time) to solve the formulated load curtailment optimization problem. Best performances regarding optimal value and processing time were obtained using a PSO algorithm, qualifying the technique as the most appropriate for this study. To assess the methodology, the CIGRE MV distribution network benchmark was used, assuming dynamic load profiles during an entire week. Results show that it is possible to determine the optimal unattended power of the system. This way, improvements in the minimization of the expected energy not supplied (ENS) as well as the System Average Interruption Frequency Index (SAIDI) at specific hours of the day were made.
Keywords: contingency assessment; load curtailment; load forecasting; particle swarm optimization (PSO) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:12:p:3236-:d:374918
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