Abstract

Harnessing wind energy is one of the fastest-growing areas in the energy industry. However, wind power still faces challenges, such as output intermittency due to its nature and output reduction as a result of the wake effect. Moreover, the current practice uses the available renewable energy resources as a fuel-saver simply to reduce fossil-fuel consumption. This is related mainly to the inherently variable and non-dispatchable nature of renewable energy resources, which poses a threat to power system reliability and requires utilities to maintain power-balancing reserves to match the supply from renewable energy resources with the real-time demand levels. Thus, further efforts are needed to mitigate the risk that comes with integrating renewable resources into the electricity grid. Hence, an integrated strategy is being created to determine the optimal size of the hybrid wind-solar photovoltaic power systems (HWSPS) using heuristic optimization with a numerical iterative algorithm such that the output fluctuation is minimized. The research focuses on sizing the HWSPS to reduce the impact of renewable energy resource intermittency and generate the maximum output power to the grid at a constant level periodically based on the availability of the renewable energy resources. The process of determining HWSPS capacity is divided into two major steps. A genetic algorithm is used in the initial stage to identify the optimum wind farm. A numerical iterative algorithm is used in the second stage to determine the optimal combination of photovoltaic plant and battery sizes in the search space, based on the reference wind power generated by the moving average, Savitzky–Golay, Gaussian and locally weighted linear regression techniques. The proposed approach has been tested on an existing wind power project site in the southern part of the Sultanate of Oman using a real weather data. The considered land area dimensions are 2 × 2 km. The integrated tool resulted in 39 MW of wind farm, 5.305 MW of PV system, and 0.5219 MWh of BESS. Accordingly, the estimated cost of energy based on the HWSPS is 0.0165 EUR/kWh.

Highlights

  • Introduction iationsIn most countries, the demand for electricity is growing rapidly

  • Most of the power suppliers depend on fossil fuels as the primary energy source due to their ready availability and lower cost compared to other resources

  • The overall velocity loss owing to the turbine wake effect is approximately 2.72% of the available wind speed

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Summary

Introduction

The demand for electricity is growing rapidly. One of the challenges in the electricity sector is to meet this demand while supplying customers with reliable and stable power simultaneously. Conventional power resources are being supplemented with renewable resources. Most of the power suppliers depend on fossil fuels as the primary energy source due to their ready availability and lower cost compared to other resources. The increase in demand, along with increased oil and gas production costs, drive the use of other energy resources [1]. Political integration via a common energy policy or climate-change mitigation is one of the motives to use renewable energy sources

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