Abstract
Energy storage systems are imperative in tackling the regulation of electricity supply and demand mismatch to avoid curtailment of wind energy. Co-locating energy storage in the same operating region as the offshore wind farms allows for an unobtrusive environment while also steering away from rising land prices. The presented work involves an offshore Hydro-Pneumatic Energy Storage (HPES) system made up of a subsea accumulator pre-charged with compressed air. The Energy Conversion Unit (ECU) of the system consists of a megawatt-scale hydraulic pump and a turbine. This paper discusses a novel numerical model developed in Python™ for simulating the operation of the ECU pump and turbine of the offshore HPES system by using a simple moving average, based on a time window of wind data, whilst also introducing a one-step-ahead forecasting model. The results of the research are split into two parts; Firstly, the analysis and validation of the Seasonal Auto Regressive Integrated Moving Average (SARIMA) forecasting model is performed. Secondly, results on the pump and turbine performance based on the smoothened power are shown. The research found that despite the centrifugal pump limitations due to variable head operation, a smoothened power output is efficiently (> 90 %) attained in retaining the intermittent power generated.
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