ترغب بنشر مسار تعليمي؟ اضغط هنا

The optimal control of storage for arbitrage and buffering, with energy applications

67   0   0.0 ( 0 )
 نشر من قبل Stan Zachary
 تاريخ النشر 2015
  مجال البحث
والبحث باللغة English




اسأل ChatGPT حول البحث

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.



قيم البحث

اقرأ أيضاً

Large scale electricity storage is set to play an increasingly important role in the management of future energy networks. A major aspect of the economics of such projects is captured in arbitrage, i.e. buying electricity when it is cheap and selling it when it is expensive. We consider a mathematical model which may account for nonlinear---and possibly stochastically evolving---cost functions, market impact, input and output rate constraints and both time-dependent and time-independent inefficiencies or losses in the storage process. We develop an algorithm which is maximally efficient in the sense that it incorporates the result that, at each point in time, the optimal management decision depends only a finite, and typically short, time horizon. We give examples related to the management of a real-world system. Finally we consider a model in which the associated costs evolve stochastically in time. Our results are formulated in a perfectly general setting which permits their application to other commodity storage problems.
Reduced installation and operating costs give energy storage systems an opportunity to participate actively and profitably in electricity markets. In addition to providing ancillary services, energy storage systems can also arbitrage temporal price d ifferences. Congestion in the transmission network often accentuates these price differences and will under certain circumstances enhance the profitability of arbitrage. On the other hand, congestion may also limit the ability of a given storage device to take advantage of arbitrage opportunities. This paper analyzes how transmission congestion affects the profitability of arbitrage by storage devices in markets with perfect and imperfect competition. Imperfect competition is modeled using a bilevel optimization where the offers and bids submitted by the storage devices can alter the market outcome. Price-taker and price-maker assumptions are also investigated through market price duration curves. This analysis is based on simulating an entire year of market operation on the IEEE Reliability Test system.
111 - James Cruise , Stan Zachary 2018
It is likely that electricity storage will play a significant role in the balancing of future energy systems. A major challenge is then that of how to assess the contribution of storage to capacity adequacy, i.e. to the ability of such systems to mee t demand. This requires an understanding of how to optimally schedule multiple storage facilities. The present paper studies this problem in the cases where the objective is the minimisation of expected energy unserved (EEU) and also a form of weighted EEU in which the unit cost of unserved energy is higher at higher levels of unmet demand. We also study how the contributions of individual stores may be identified for the purposes of their inclusion in electricity capacity markets.
65 - Xiao Yanchi , Bruce Vargas , 2018
Increasing wind turbines (WT) penetration and low carbon demand can potentially lead to two different flow peaks, generation and load, within distribution networks. This will not only constrain WT penetration but also pose serious threats to network reliability. This paper proposes energy storage (ES) to reduce system congestion cost caused by the two peaks by sending cost-reflective economic signals to affect ES operation in responding to network conditions. Firstly, a new charging and discharging (C/D) strategy based on Binary Search Method is designed for ES, which responds to system congestion cost over time. Then, a novel pricing method, based on Location Marginal Pricing, is designed for ES. The pricing model is derived by evaluating ES impact on the network power flows and congestion from the loss and congestion components in Location Marginal Pricing. The impact is then converted into an hourly economic signal to reflect ES operation. The proposed ES C/D strategy and pricing methods are validated on a real local Grid Supply Point area. Results show that the proposed Location Marginal Pricing-based pricing is efficient to capture the feature of ES and provide signals for affecting its operation. This work can further increase network flexibility and the capability of networks to accommodate increasing WT penetration.
193 - Yue Chen , Wei Wei , Tongxin Li 2021
Energy storage is expected to play an increasingly important role in mitigating variations that come along with the growing penetration of renewable energy. In this paper, we study the optimal bidding of an energy storage unit in a semi-centralized m arket. The energy storage unit offers its available storage capacity and maximum charging/ discharging rate to the operator; then the operator clears the real-time market by minimizing the total cost. The energy storage unit is paid/ charged at locational marginal price (LMP). The problem casts down to a bilevel optimization problem with a mixed-integer lower-level. An improved surrogate-based method with the combined spatial-temporal entropy term is developed to solve this problem. Numerical examples demonstrate the scalability, efficiency, and accuracy of the proposed method.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا