The last fifteen years have seen a boom in global wind‐based electricity production. However, wind-based electricity generation can be intermittent due to varying weather conditions. To support this projected growth, wind farm merchants have been considering co‐locating electricity generation and grid‐scale storage facilities, such as industrial batteries.
Find out how operations can be better optimised, with Assistant Professor of Operations Management, Zhou Yangfang, Helen.
Energy and sustainable operations, Agriculture operations
Zhou, Y., Scheller‐Wolf, A., Secomandi, N., & Smith, S. (2019). Managing Wind‐Based Electricity Generation in the Presence of Storage and Transmission Capacity. Production and Operations Management, 28(4), 970-989.
We investigate the management of a merchant wind energy farm co‐located with a grid‐level storage facility and connected to a market through a transmission line. We formulate this problem as a Markov decision process (MDP) with stochastic wind speed and electricity prices. Consistent with most deregulated electricity markets, our model allows these prices to be negative. As this feature makes it difficult to characterize any optimal policy of our MDP, we show the optimality of a policy when prices can only be positive. We extend this structure when prices can also be negative to develop heuristic one (H1) that approximately solves a stochastic dynamic program. We then simplify H1 to obtain heuristic two (H2) that relies on a policy and derivative‐free deterministic optimization embedded within a Monte Carlo simulation of the random processes of our MDP. We conduct an extensive and data‐calibrated numerical study to assess the performance of these heuristics and variants of known ones against the optimal policy, as well as to quantify the effect of negative prices on the value added by and environmental benefit of storage. We find that (i) H1 computes an optimal policy and on average is about 17 times faster to execute than directly obtaining an optimal policy; (ii) H2 has a near optimal policy (with a 2.86% average optimality gap), exhibits a two orders of magnitude average speed advantage over H1, and outperforms the remaining considered heuristics; (iii) storage brings in more value but its environmental benefit falls as negative electricity prices occur more frequently in our model.
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