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Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage
Kabulo Loji1,2, Sachin Sharma3, Gulshan Sharma2
1Department of Electrical Power Engineering, Durban University of Technology, Durban, 4001, South Africa.
Scientific Reports
|January 3, 2025
Summary
Demand response significantly enhances solar photovoltaic hosting capacity in distribution systems. This optimization minimizes network losses and maintains voltage stability, showcasing the effectiveness of the modified crow search optimization algorithm.
Area of Science:
- Electrical Engineering
- Power Systems
- Renewable Energy Integration
Background:
- The integration of solar photovoltaic (PV) systems into distribution networks is crucial for renewable energy adoption.
- Assessing the hosting capacity of solar PV is essential for grid stability and reliable power supply.
- Demand response programs offer a flexible approach to manage electricity consumption and support grid integration.
Purpose of the Study:
- To investigate the impact of demand response on the hosting capacity of solar PV in distribution systems.
- To develop and present a tri-objective optimization model for maximizing solar PV hosting capacity, minimizing network losses, and maintaining voltage deviation.
- To evaluate the simultaneous and individual optimization of these objectives under various case studies.
Main Methods:
- Formulation of a non-linear, non-convex multi-objective optimization problem.
- Proposal and application of the modified crow search optimization (MCSO) algorithm to solve the complex problem.
- Scrutiny of different multi-objective case studies combining hosting capacity, network losses, and voltage deviation, incorporating demand response effects.
Main Results:
- The MCSO algorithm achieved optimal integration of distributed generation with minimal network loss (0.0714 MW).
- Simulations demonstrated a solar PV hosting capacity of 3322.31 kW, voltage deviation of 0.4982 p.u., and system losses of 1314.86 kWh with demand response when all objectives were simultaneously optimized.
- The MCSO algorithm proved superior to other established optimization techniques.
Conclusions:
- Demand response plays a vital role in enhancing solar PV hosting capacity while improving grid performance.
- The proposed tri-objective optimization model and MCSO algorithm effectively address the challenges of integrating high levels of solar PV.
- The study highlights the practical benefits and efficacy of the proposed research for smart grid applications.
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