Optimal control of storage for arbitrage, with applications to energy systems

We study the optimal control of storage which is used for arbitrage, i.e. for buying a commodity when it is cheap and selling it when it is expensive. Our particular concern is with the management of energy systems, although the results are generally applicable. We consider a model which may account for nonlinear cost functions, market impact, input and output rate constraints and inefficiencies or losses in the storage process. We develop an algorithm which is maximally efficient in then 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.Comment: 7 pages, 6 figure

Optimal Allocation of Energy Storage and Wind Generation in Power Distribution Systems

The advent of energy storage technologies applications for the electric power system gives new tools for planners to cope with the operation challenges that come from the integration of renewable generation in medium voltage networks. This work proposes and implements an optimization model for Battery Energy Storage System (BESS) and distributed generation allocation in radial distribution networks. The formulation aims to assist distribution system operators in the task of making decisions on energy storage investment, BESSs\u27 operation, and distributed generation penetration\u27s level to minimize electricity costs. The BESSs are required to participate in energy arbitrage and voltage control. In addition, due to the complexity of the model formulated, a genetic algorithm combined with an AC multi-period optimal power flow implementation is used to solve the problem. The methodology provides the optimal connection points and size of a predetermined number of BESSs and wind generators, and the BESS\u27s operation. The model considers the BESSs\u27 charging/discharging efficiency, depth of discharge level, and the network\u27s operation constraints on the nodal voltage and branches power flow limits. The proposed methodology was evaluated in the IEEE 33-bus system. The results show that BESSs investment in radial distribution systems facilitates the deployment of distributed generation and favors the reduction of generation costs despite its still high capital cost. Adviser: Fred Choobine

Online Modified Greedy Algorithm for Storage Control under Uncertainty

This paper studies the general problem of operating energy storage under uncertainty. Two fundamental sources of uncertainty are considered, namely the uncertainty in the unexpected fluctuation of the net demand process and the uncertainty in the locational marginal prices. We propose a very simple algorithm termed Online Modified Greedy (OMG) algorithm for this problem. A stylized analysis for the algorithm is performed, which shows that comparing to the optimal cost of the corresponding stochastic control problem, the sub-optimality of OMG is bounded and approaches zero in various scenarios. This suggests that, albeit simple, OMG is guaranteed to have good performance in some cases; and in other cases, OMG together with the sub-optimality bound can be used to provide a lower bound for the optimal cost. Such a lower bound can be valuable in evaluating other heuristic algorithms. For the latter cases, a semidefinite program is derived to minimize the sub-optimality bound of OMG. Numerical experiments are conducted to verify our theoretical analysis and to demonstrate the use of the algorithm.Comment: 14 page version of a paper submitted to IEEE trans on Power System

Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains

We consider using a battery storage system simultaneously for peak shaving and frequency regulation through a joint optimization framework which captures battery degradation, operational constraints and uncertainties in customer load and regulation signals. Under this framework, using real data we show the electricity bill of users can be reduced by up to 15\%. Furthermore, we demonstrate that the saving from joint optimization is often larger than the sum of the optimal savings when the battery is used for the two individual applications. A simple threshold real-time algorithm is proposed and achieves this super-linear gain. Compared to prior works that focused on using battery storage systems for single applications, our results suggest that batteries can achieve much larger economic benefits than previously thought if they jointly provide multiple services.Comment: To Appear in IEEE Transaction on Power System