Managing wind-based electricity generation in the presence of storage and transmission capacity

• Main Authors: ZHOU, Yangfang (Helen), SCHELLER-WOLF, Alan, SECOMANDI, Nicola, SMITH, Stephen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Markov decision process; wind-based electricity generation; energy storage; negative electricity prices; real options; Environmental Sciences; Operations and Supply Chain Management
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/4958; https://ink.library.smu.edu.sg/context/lkcsb_research/article/5957/viewcontent/Zhou__et_al_2018__SMU_Ink__Managing_wind_based_electricity_generation_in_the_presence_of_storage_and_transmission_capacity___Copy.pdf
• Institution: Singapore Management University

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 stage‐ and partial‐state‐dependent‐threshold 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 price‐dependent‐threshold 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.