Posters

Abstract

Countries with high levels of deployed renewable energy have seen increases in price volatility and grid congestion. With increasing decentralized electricity consumption and generation, local energy availability and demand diverge. Co-located energy plants with BESS and wind energy can play an important role by supplying energy in a more adaptive way. In addition to BESS, wind farm owners can use wind farm control techniques to increase the generation of the existing plant. Wake steering will be the focus technique due to its higher technology readiness level and its demonstrable yield increase. Its most significant contribution will be by increasing the generation profile of the wind farm for lower wind speeds. The co-optimization of wake steering and energy storage in wind farm planning is addressed in this work. This multi-faceted planning problem poses a complex decision-making process for developers, establishing an economically viable business case involving the capacity sizing of BESS and the impact of wake steering. The development process studied in this work starts from an existing wind farm and expands that plan to include or extend the plant with BESS. In this work, we propose a time-series-based optimization for BESS dimensioning, considering utilization mechanisms. The optimization objectives are the plant's revenue and net present value, following industry-standard project financing metrics. Below, the plant's two elements, the BESS and the wind farm, are respectively elaborated. First, BESS operations are constrained by multiple electrical design limitations and the degradation properties of lithium-ion storage technology. The maximum site capacity equals the highest C-rate multiplied by the maximum site power. The considered C-rates are 1, 2, and 4, whereas the maximum power will be the wind farm’s rated power. Arbitrage trading is the primary mechanism. Second, the wind field and power generation are modeled using static wake, turbulence, and deflection models in PyWake. The effect of wake steering is considered in two aspects: first, its increase in yield, and second, its impact on the wind farm's loads. Both are translated into techno-economic inputs using a price time series and a load surrogate coupled to an operations and maintenance cost function. The optimization uses non-linear programming and is applied to the Haringvliet-Zuid, existing co-located wind farm with solar and BESS, and the Danish Energy Island Cluster, a planned co-located plant. The time-series-based results are analyzed and processed to convert the optimal operations into performance metrics. Additionally, the performance is extrapolated to quantify lifetime business case effects. The main contribution of this work is a framework for the optimization of the co-located BESS dimensioning problem that determines the optimal C-rate and quantifies the effect of wake steering on the business case of BESS co-location.