Managing Wind‐Based Electricity Generation in the Presence of Storage and Transmission Capacity

Abstract

W e 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.Note: All experiments are run on a computer with Intel(R) Core(TM) i7-3770K 3.40 GHz CPU and 8 GB RAM.Zhou, Scheller-Wolf, Secomandi, and Smith: Managing Wind-Based Electricity Generation Production and Operations Management 28(4), pp. 970-989,

Impact of Electricity Pricing Policies on Renewable Energy Investments and Carbon Emissions

“…Zhou et al (2016) study electricity storage with possibly negative electricity prices and derive the optimal disposal strategy. Zhou et al (2014) propose an easily implementable policy for operating wind farms in the presence of storage facilities. Hu et al (2015) focus on energy investments of a DG without considering utility firms and determine the optimal investment level for the DG.…”

Merchant Energy Trading in a Network

“…Our MDP extends the energy merchant operations literature (Secomandi and Seppi 2014), in which energy conversion assets are modeled as real options (Dixit andPindyck 1994, Trigeorgis 1996): It generalizes to a setting with multiple energy storage and transport assets energy trading models with one storage asset and no transport assets (Scott et al 2000, Maragos 2002, Sinha et al 2004, Boogert and De Jong 2008, Lai et al 2010, Secomandi 2010b, Thompson 2012, Wu et al 2012, Mazières and Boogert 2013, Bäuerle and Riess 2014, Jiang and Powell 2015, Zhou et al 2015, Nadarajah et al 2017 or one or more transport assets and no storage assets (Deng et al 2001, Secomandi 2010a, Secomandi and Wang 2012. Bannister and Kaye (1991), Löhndorf and Minner (2010), Devalkar et al (2011), Kim andPowell (2011), Lai et al (2011), Grillo et al (2012), Arvesen et al (2013), Denault et al (2013), Nascimento andPowell (2013), Zhou et al (2013), Jiang et al (2014), Salas and Powell (2014), Moazeni et al (2015), and Powell and Miesel (2016) jointly optimize energy/commodity production/procurement and storage assets (Arvesen et al 2013 model linepack storage in a natural gas pipeline). In contrast to ours, their models do not feature a network of energy storage and transport assets.…”

Parallel Nonstationary Direct Policy Search for Risk-Averse Stochastic Optimization

“…The authors formulate the problem as a discrete-time stochastic dynamic programming problem over an infinite horizon and establish the existence of an optimal stationary policy. Zhou et al (2014) consider the colocation of wind generation and electricity storage facilities with uncertain wind and stochastic prices, limited transmission capacity, and the option of buying power from the market. They model the wind-storage management problem as a finite-horizon Markov decision process and establish some structural results.…”