Effect of Demand Response and Energy Storage on Power System Operation
Mohamed Abdallah Mahmoud Shaheen;
Abstract
The penetration level of Renewable Energy Resources (RESs) in the electrical power system is increasing as they provide a cleaner and a cheaper alternative as compared to conventional electricity generators. The main challenge to the spread of these RESs is that they are not dispatchable due to their intermittent nature. Hence, their coincidence with demand is not guaranteed, and this affects system reliability.
The main aim of this research is to assess the effectiveness of utilizing new metaheuristic optimization algorithms; namely the SunFlower Optimization (SFO) algorithm, the Hybrid Firefly and Particle Swarm Optimization (HFPSO) technique, and the Harris Hawks Optimization (HHO) in solving a constrained Optimal Power Flow (OPF) problem. The principle target is to minimize the generating units’ fuel cost under the power system’ practical constraints. At initial stage, the objective function is solved to find the optimal locations of photovoltaic (PV) generators and/or wind generators within the system under study. Then, different scenarios are performed to solve the OPF problem including and excluding renewable energy sources. The generators’ real output power defines the exploration field for the OPF problem. The SFO, the HFPSO, and the HHO algorithms are applied, one at a time, to minimize the fitness function and yield the best solutions of the problem. The suggested techniques are applied to four standard test systems to check the validity of the proposed algorithms. These test systems are the IEEE 14-bus, 30-bus, 57-bus, and 118-bus networks respectively. Simulations, with different scenarios, are implemented on these networks. To obtain a realistic result, real daily load curves are considered in this study. The results of simulations are investigated and analyzed. Results confirm the feasibility, effectiveness, and superiority of the introduced SFO, HFPSO, and HHO -based OPF methodologies, especially when compared with the genetic algorithm and particle swarm optimization.
LIST OF CONTENTS
The main aim of this research is to assess the effectiveness of utilizing new metaheuristic optimization algorithms; namely the SunFlower Optimization (SFO) algorithm, the Hybrid Firefly and Particle Swarm Optimization (HFPSO) technique, and the Harris Hawks Optimization (HHO) in solving a constrained Optimal Power Flow (OPF) problem. The principle target is to minimize the generating units’ fuel cost under the power system’ practical constraints. At initial stage, the objective function is solved to find the optimal locations of photovoltaic (PV) generators and/or wind generators within the system under study. Then, different scenarios are performed to solve the OPF problem including and excluding renewable energy sources. The generators’ real output power defines the exploration field for the OPF problem. The SFO, the HFPSO, and the HHO algorithms are applied, one at a time, to minimize the fitness function and yield the best solutions of the problem. The suggested techniques are applied to four standard test systems to check the validity of the proposed algorithms. These test systems are the IEEE 14-bus, 30-bus, 57-bus, and 118-bus networks respectively. Simulations, with different scenarios, are implemented on these networks. To obtain a realistic result, real daily load curves are considered in this study. The results of simulations are investigated and analyzed. Results confirm the feasibility, effectiveness, and superiority of the introduced SFO, HFPSO, and HHO -based OPF methodologies, especially when compared with the genetic algorithm and particle swarm optimization.
LIST OF CONTENTS
Other data
| Title | Effect of Demand Response and Energy Storage on Power System Operation | Other Titles | تأثير برامج استجابة الطلب و نظم تخرين الاقة على تشغيل نظم القوى | Authors | Mohamed Abdallah Mahmoud Shaheen | Issue Date | 2020 |
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