Optimal storage policies with wind forecast uncertainties

Gast, Nicolas; Tomozei, Dan-Cristian; Boudec, Jean-Yves Le

doi:10.1145/2425248.2425255

Cited by 18 publications

(36 citation statements)

References 5 publications

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“…The use of storage can also compensate for forecast uncertainties. Generation scheduling policies that minimize energy losses and the use of fast-ramping generators are developed in [3,12].…”

Section: Related Workmentioning

confidence: 99%

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Impact of storage on the efficiency and prices in real-time electricity markets

Gast

Boudec

Proutière

et al. 2013

Proceedings of the Fourth International Conference on Future Energy Systems

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We study the effect of energy-storage systems in dynamic real-time electricity markets. We consider that demand and renewable generation are stochastic, that real-time production is affected by ramping constraints, and that market players seek to selfishly maximize their profit. We distinguish three scenarios, depending on the owner of the storage system: (A) the supplier, (B) the consumer, or (C) a stand-alone player. In all cases, we show the existence of a competitive equilibrium when players are price-takers (they do not affect market prices). We further establish that under the equilibrium price process, players' selfish responses coincide with the social welfare-maximizing policy computed by a (hypothetical) social planner. We show that with storage the resulting price process is smoother than without.We determine empirically the storage parameters that maximize the players' revenue in the market. In the case of consumer-owned storage, or a stand-alone storage operator (scenarios B and C), we find that they do not match socially optimal parameters. We conclude that consumers and the stand-alone storage operator (but not suppliers) have an incentive to under-dimension their storage system. In addition, we determine the scaling laws of optimal storage parameters as a function of the volatility of demand and renewables. We show, in particular, that the optimal storage energy capacity scales as the volatility to the fourth power.

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Section: Related Workmentioning

confidence: 99%

“…For example, let us consider the scenario envisioned for the UK in 2020 [3,12] where wind power is used to cover 20% of the total electricity consumption -this corresponds to 26GW of peak power. At the scale of the country, the variability of the demand is small compared to the variability of the wind generation.…”

Section: Numerical Example and Energy Unitsmentioning

confidence: 99%

Impact of storage on the efficiency and prices in real-time electricity markets

Gast

Boudec

Proutière

et al. 2013

Proceedings of the Fourth International Conference on Future Energy Systems

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“…Such a delay can however be incorporated in a straightforward manner, e.g., see [5] where production is planned a fixed number of slots ahead. We also ignore plant start-up times and minimum run times; they could also be included easily as additional model constraints.…”

Section: B the System Dynamicsmentioning

confidence: 99%

“…Although we follow [2], [5] in taking the production offset as the control variable, the planned production p(s + i|s) is certainly another possible and valid control variable.…”

Section: B the System Dynamicsmentioning

confidence: 99%

“…At every time slot, storage level predictions are used to determine the slowramping power production level for the next time slot. Gast et al [5] consider an MDP model whose state is the storage level, and a simple random variable is used to capture renewable unpredictability. Planning decisions are made at every slot but actualized with a delay.…”

Section: Introductionmentioning

confidence: 99%

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Optimal planning of slow-ramping power production in energy systems with renewables forecasts and limited storage

Richmond

Jacko

Makowski

2014

2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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Abstract-We address the cost-efficient operation of an energy production system under renewables uncertainty. We develop an MDP model for an idealized system with the following features: (1) perfectly predictable power demand, (2) a renewable power source subject to uncertain forecast, (3) limited energy storage, (4) an unlimited fast-ramping power source, and (5) a slow-ramping power source which requires (optimal) planning. A finite-horizon stochastic optimization problem is introduced to minimize the overall cost of operating the system, and then solved numerically using standard approaches (based on backward induction) and available data. In contrast with the unit commitment problem which is traditionally optimized for a single planning frame, we show in simple scenarios that it may be beneficial to optimize over a few planning frames, and that there is no benefit to considering longer (e.g., infinite) horizons. We discretize the state space in an attempt to mitigate the curse of dimensionality usually associated with numerically solving MDPs. We note that few discretization states already yield a significant decrease in the total cost.

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The Optimal Control of Storage for Arbitrage and Buffering, with Energy Applications

Cruise

Zachary

2018

Springer Proceedings in Mathematics &Amp; Statistics

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We study the optimal control of storage which is used for both arbitrage and buffering against unexpected events, with particular applications to the control of energy systems in a stochastic and typically time-heterogeneous environment. Our philosophy is that of viewing the problem as being formally one of stochastic dynamic programming, but of using coupling arguments to provide good estimates of the costs of failing to provide necessary levels of buffering. The problem of control then reduces to that of the solution, dynamically in time, of a deterministic optimisation problem which must be periodically re-solved. We show that the optimal control then proceeds locally in time, in the sense that the optimal decision at each time t depends only on a knowledge of the future costs and stochastic evolution of the system for a time horizon which typically extends only a little way beyond t. The approach is thus both computationally tractable and suitable for the management of systems over indefinitely extended periods of time. We develop also the associated strong Lagrangian theory (which may be used to assist in the optimal dimensioning of storage), and we provide characterisations of optimal control policies. We give examples based on Great Britain electricity price data.

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Optimal storage policies with wind forecast uncertainties

Cited by 18 publications

References 5 publications

Impact of storage on the efficiency and prices in real-time electricity markets

Impact of storage on the efficiency and prices in real-time electricity markets

Optimal planning of slow-ramping power production in energy systems with renewables forecasts and limited storage

The Optimal Control of Storage for Arbitrage and Buffering, with Energy Applications

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