FarmConners market showcase results: wind farm flow control considering electricity prices

Abstract

Abstract. The EU and UK have made ambitious commitments under the net-zero plans to decarbonise their economies by 2050. For this, offshore wind will play a major role, significantly contributing to a paradigm shift in the power generation and greater volatility of electricity prices. The operating strategy of wind farms should therefore move from power maximisation to profit maximisation which includes income from providing power system services and the reduction of maintenance costs. Wind farm flow control (WFFC) is a key enabler for this shift through mitigation of wake effects in the design and operation phases. The results of the FarmConners market showcases presented here are the first attempt to economically assess WFFC strategies with respect to electricity market prices. Here, we present a conceptual simulation study starting from individual turbine control and extend it to layouts with 10 and 32 turbines operated with WFFC based on the results of five participants. Each participant belonged to a different research group with their respective simulation environments, flow models and WFFC strategies. Via a comparative analysis of relative WFFC benefits estimated per participant, the implications of wind farm size, the applied control strategy and the overall model fidelity are discussed in zero-subsidy scenarios. For all the participants, it is seen that the income gain can differ significantly from the power gain depending on the electricity price under the same inflow, and a favourable control strategy for dominant wind directions can pay off even for low electricity prices. However, a strong correlation between income and power gain is also observed for the analysed high-electricity-price scenarios, underlining the need for additional modelling capabilities to carry out a more comprehensive value optimisation including lower prices and system requirements driven cases.

The value of wake steering wind farm flow control in US energy markets

“…In practice, there are often contractual reasons for a plant to avoid curtailing its output during negatively priced hours, but the contractual considerations are beyond the scope of this analysis. Note that ARP is similar to the "income gain" reported by Kölle et al (2022) and the "energy value" (EV) metric presented by Millstein et al (2021), except ARP is expressed in units of USD per year, whereas EV represents the revenue per unit of energy produced (USD per MWh).…”

“…This volatility in pricing and electricity markets in general suggests that careful analysis is required to understand the additional revenue that can be achieved with wind farm flow control, as total revenue may be sensitive to the ability to provide energy gains during a subset of high-priced hours. An initial assessment of the impact of wind farm flow control on the value of the electricity generated was performed by Kölle et al (2022). The authors evaluated the energy and revenue increases for different wind turbine and wind farm flow control strategies using two reference offshore wind plants, 1 year of simulated wind speed and wind direction time series off the western coast of Denmark, and corresponding simulated hourly electricity price time series representing market scenarios in 2020 and 2030.…”

“…In general, the authors found that the revenue increase for a particular wind plant depends strongly on the distribution of electricity prices for different wind speeds and directions, the power increase from wind farm flow control for each wind condition, and the type of wind farm flow control strategy used. Further, Kölle et al (2022) optimized the control system of a single wind turbine considering revenue and structural loads in the objective function, finding that revenue could be boosted without increasing structural loads by generating more than the turbine's intended rated power when electricity prices are high and derating the turbine to reduce damage when prices are low.…”

Maximizing value through optimized annual selection of Pareto-optimal wind turbine operating strategies

“…In particular, fatigue damage depends on the loads applied and thus on the operation [1,2]. For future operation of wind farms, integrated control approaches considering market and grid interaction will become more and more important [3,4]. For an optimal operation over the entire lifetime, an operational planning which considers turbine control, lifetime consumption and economic aspects is required.…”

Stochastic gradient descent for wind farm optimization

“…This study benchmarks the performance of the proposed SGD approach when compared to conventional gradientbased optimization within the TOPFARM framework (DTU Wind Energy Systems, 2023b), considering wind farms with different shapes and sizes. We examine the open-source SLSQP algorithm (Kraft, 1988), which is employed in many engineering frameworks (King et al, 2017;Allen et al, 2020;Wu et al, 2020;Zilong and Wei, 2022;Kölle et al, 2022;Clark et al, 2022;Simley et al, 2023) and has been used in previous comparisons of optimization algorithms (Lam et al, 2018;Li and Zhang, 2021;Fleming et al, 2022). The TOPFARM framework has been used with SLSQP in several wind farm optimization studies (Riva et al, 2020;Ciavarra et al, 2022;Criado Risco et al, 2023;Rodrigues et al, 2023).…”