Particle Swarm Optimization for Optimal Frequency Response with High Penetration of Photovoltaic and Wind Generation

Abstract

As the installation of solar-photovoltaic and wind-generation systems continue to grow, the location must be strategically selected to maintain a reliable grid. However, such strategies are commonly subject to system adequacy constraints, while system security constraints (e.g., frequency stability, voltage limits) are vaguely explored. This may lead to inaccuracies in the optimal placement of the renewables, and thus maximum benefits may not be achieved. In this context, this paper proposes an optimization-based mathematical framework to design a robust distributed generation system, able to keep system stability in a desired range under system perturbance. The optimum placement of wind and solar renewable energies that minimizes the impact on system stability in terms of the standard frequency deviation is obtained through particle swarm optimization, which is developed in Python and executed in PowerFactory-DIgSILENT. The results reveal that the proposed approach has the potential to reduce the influence of disturbances, enhancing critical clearance time before frequency collapse and supporting secure power system operation.

Optimal Design of a Hybrid Solar–Battery–Diesel System: A Case Study of Galapagos Islands

“…Therefore, the cost function denoted by f is formulated as presented in (14). Then, the optimization problem focuses on minimizing the total cost of the system based on the optimal number and power of diesel generators, solar panels, and batteries, as indicated in (15). The system constraints are defined by Equations ( 4), (10), (11), ( 13), (16), and (17).…”

“…A challenge in this context is to optimize the distribution of energy from different sources to meet the islands' electricity demands while ensuring a stable and reliable energy supply. A hybrid renewable energy system typically includes multiple sources of power, such as solar, wind, and diesel generators, which are integrated and controlled to provide electricity to the grid, and their design can be further optimized to achieve the best results [15][16][17]. The challenge lies in balancing the contribution of each energy source to meet the islands' energy demand while ensuring a stable and reliable supply of electricity.…”

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“…Such is the case of the Institute of Electrical and Electronics Engineers (IEEE), which provides users with model networks for implementation or study to obtain results applicable to future electrical network designs. In the literature, there are many studies that have used IEEE test feeders for their research: Scheneider et al (2018) analyzed the different IEEE test feeders, Alvarez-Alvarado et al (2022) used IEEE 39-node test feeder and DIgSILENT PowerFactory® to optimize the location and size of new solar and wind power plants by meeting frequency requirements in a distribution network, Alzahrani et al (2020) verified the optimization of the location of battery energy storage systems in distribution networks with photovoltaics minimizing losses in the IEEE 37-node test feeder, Kaur et al (2010) optimized generation economically to meet the operating requirements of the 30-node IEEE test feeder, Yadav et al (2019) used DIgSILENT PowerFactory® in the IEEE 39-node test feeder in the transmission network of India for the classification of events in networks with integration of renewables, Montoya-Bueno et al (2016) used the IEEE 34-node test feeder to test a new approach to manage uncertainty in distribution networks with integration of renewables minimizing distribution costs.…”

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