On the operation and value of storage in consumer demand response

Abstract

We study the optimal operation and economic value of energy storage operated by a consumer who faces (possibly random) fluctuating electricity prices and seeks to reduce its energy costs. The value of storage is defined as the consumer's net benefit obtained by optimally operating the storage. We formulate the operation problem as a dynamic program. Under the assumption that consumer utility (received from electricity consumption) is additively separable over time, we establish a threshold structure of the optimal operation policy for a consumer who faces random electricity prices and stochastic demand. For a more general setting that incorporates the inter-temporal substitution effect in consumer demand, if the consumer always faces the same (realized) purchasing and selling price, then it is optimal for her to use the storage only for arbitrage, and therefore the VoS does not depend on the consumer's demand.

Energy-cost-aware flow shop scheduling considering intermittent renewables, energy storage, and real-time electricity pricing

“…Demand‐side power procurement that considers intermittent renewable energy generation and storage systems has been a challenging research topic in the power system domain. Modeling sequential control of power system operations and finding the optimal strategy of real‐time power procurement using the Markov decision process and Lyapunov optimization have been the central topic of recent studies. Kwon et al proposed a sequential power procurement model to meet real‐time electricity demand by using the grid, in‐house renewable supply, and a storage system.…”

Schedule-Based Operation Method Using Market Data for an Energy Storage System of a Customer in the Ontario Electricity Market

“…By using an ESS, electrical energy can be stored and subsequently discharged quickly within its capacity limits. Thus, ESSs can be utilized for frequency regulation [1], renewable energy integration [2], power quality enhancement [3], and electricity cost reduction [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Although ESSs provide various capabilities for generation and network operators and customers, they are currently not widely used due to their high cost.…”

“…Various methods have been proposed to reduce the EUC determined by hourly electricity consumption and corresponding price, e.g., time-of-use (TOU) [4][5][6][7][8][9][10] and real-time pricing [9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Since the energy capacity of ESSs is finite, most of these approaches are schedule-based methods where the operation schedule of the ESS is determined from various forecasted data.…”

Cooperation of Storage Operation in a Power Network With Renewable Generation

“…We note that the TESM algorithms have a similar structure as the optimal threshold policies characterized in the literature that applies dynamic programming (DP) on the operation of a single energy storage device [10], [13], [14], [15]. However, unlike DP based approaches, the decisions made by TESM algorithms can be computed by solving simple oneshot optimization problem(s); as a result, TESM algorithms are much less computationally demanding than the optimal threshold policies that have to be computed through backward induction using a DP based approach.…”

“…The scheduling of energy storage systems is studied in [7], [8], [9] to maximize the joint profit of wind farms and energy storage systems. As a natural methodology for sequential decision making under uncertainty, dynamic programming (DP) has been adopted to study the optimal operation of a single energy storage device [10], [11], [12], [13], [14], [15], where a variety of optimal threshold based control policies are characterized under different settings (on renewable generation, consumer demand, and electricity prices). The authors of [16], [17] conduct DP based approaches to estimate the capacity value of energy storage.…”