Optimal day-ahead trading and storage of renewable energies—an approximate dynamic programming approach

Abstract

A renewable power producer who trades on a day-ahead market sells electricity under supply and price uncertainty. Investments in energy storage mitigate the associated financial risks and allow for decoupling the timing of supply and delivery. This paper introduces a model of the optimal bidding strategy for a hybrid system of renewable power generation and energy storage. We formulate the problem as a continuous-state Markov decision process and present a solution based on approximate dynamic programming. We propose an algorithm that combines approximate policy iteration with Least Squares Policy Evaluation (LSPE) which is used to estimate the weights of a polynomial value function approximation. We find that the approximate policies produce significantly better results for the continuous state space than an optimal discrete policy obtained by linear programming. A numerical analysis of the response surface of rewards on model parameters reveals that supply uncertainty, imbalance costs and a negative correlation of market price and supplies are the main drivers for investments in energy storage. Supply and price autocorrelation, on the other hand, have a negative effect on the value of storage.

Computing the value of flexibility in electricity retail, ahead and balancing markets

“…Multiple value propositions for storage and DR technologies have been evaluated previously: self-sufficiency under feedin tariffs (FIT) [7], ahead market participation [4]- [6], [8], providing ancillary services to system operators [9], [10], flexibility as substitute to conventional grid reinforcements [11] or to curtailment of wind energy [12], [13]. Some papers evaluate the cumulative value of multiple value propositions [14], [15].…”

Prosumer bidding and scheduling in electricity markets

“…They state that "From a theoretical point of view, the optimal bidding strategy will be found by taking all subsequent markets into account when bidding in the first market". First, they review the bidding models, all covering day-ahead market in different combinations with intraday, balancing and ancillary services 18 Local Electricity Retail Markets For Prosumer Smart Grid Power Services, www.empowerh2020.eu 19 Statnett is the Norwegian transmission system operator, see www.statnett.no/en 20 www.nordpoolspot.com markets (Boomsma, Juul, & Fleten, 2014;Faria & Fleten, 2009;Löhndorf & Minner, 2010;Plazas, Conejo, & Prieto, 2005;Triki, Beraldi, & Gross, 2005). Coordinated bidding is the term they use when taking subsequent markets into account, while the contrary is denoted separate bidding.…”

Mean-Conditional Value-at-Risk Optimal Energy Storage Operation in the Presence of Transaction Costs

“…The problem is formulated as a two-stage stochastic programming problem. An optimal bidding strategy in a day-ahead market, to maximize the (expected) discounted rewards from the bidding process for the owner of a hybrid system of renewable power generation and energy storage is investigated in [8]. This problem is modeled as a continuous-state Markov decision process and solved using approximate dynamic programming.…”