Optimal demand response with energy storage management (original) (raw)
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2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, 2014
In this paper, the benefits that can be derived from the installation of an energy storage system in an industrial facility are analyzed, and an optimal control strategy of the storage system is proposed. The proposed control strategy is based on a two-step procedure and aims at (i) reducing the electricity costs sustained by an industrial customer that provides demand response and (ii) satisfying technical constraints for the maximization of the efficiency and lifecycle of the storage system. The procedure was performed based on two different time periods, i.e., day ahead and very short time. The day-ahead scheduling evaluates the peak value of the power that the facility must request from the grid and the periods in which the battery is allowed to charge and discharge; the very short-time control evaluates the battery's charging/discharging power to minimize costs. This paper reports the theoretical aspects of the optimization models for the two-step procedure, whereas the companion paper "Part II: numerical applications" proposes the results of numerical applications of the proposed approach on an actual industrial facility.
Optimal control of storage under time varying electricity prices
2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2017
End users equipped with storage may exploit time variations in electricity prices to earn profit by doing energy arbitrage, i.e., buying energy when it is cheap and selling it when it is expensive. We propose an algorithm to find an optimal solution of the energy arbitrage problem under given time varying electricity prices. Our algorithm is based on discretization of optimal Lagrange multipliers of a convex problem and has a structure in which the optimal control decisions are independent of past or future prices beyond a certain time horizon. The proposed algorithm has a run time complexity of O(N 2) in the worst case, where N denotes the time horizon. To show the efficacy of the proposed algorithm, we compare its runtime performance with other algorithms used in MATLAB's constrained optimization solvers. Our algorithm is found to be at least ten times faster, and hence has the potential to be used for in real-time. Using the proposed algorithm, we also evaluate the benefits of doing energy arbitrage over an extended period of time for which price signals are available from some ISO's in USA and Europe.
Energy Storage Optimization Strategies for Smart Grids
The efficient management of the supply and demand in electricity networks is becoming a pivotal issue with important fallbacks both in the technological and financial domains. An interesting topic in this domain is the use of large batteries at the end users premises to reduce the average cost of energy supply, by storing energy when its cost is low and releasing it when the cost is high. In this paper, we wish to gain insights on the impact of some system model parameters, such as battery capacity, charge/discharge rate, power request process, and cost functions, on the cost saving that can be achieved by some selected energy storage algorithms. The study shows that the battery capacity has a direct and rather linear impact on cost reduction, while the effect of charge/discharge rates is less straightforward to predict. Furthermore, we show that, with piecewise, convex and nondecreasing cost functions, the optimal energy storage strategy has a threshold structure, where the number of thresholds depend on the shape of the cost function and the constraints of the battery.
Optimal demand-side management with a multi-technology battery storage system
Renewable Energy and Power Quality Journal, 2018
Demand-side management (DSM) is considered as a key solution for more energy system flexibility, which is needed for the transition to low-carbon electricity generation based on variable renewable resources. Increased flexibility reduces energy bills for customers and congestions in electricity transport and distribution networks, which reduces costs for network operators, as demand is matched with available renewable generation. Recently, smart-meter deployment, real-time pricing and cost reductions for electricity storage opened new opportunities for dynamic DSM optimization tools. This paper describes software and hardware tools and a low-cost energy storage system (ESS) to elaborate demand management programs, which reduce the energy bill of industrial customers. These tools operate at two levels: remotely, to calculate the economically optimal consumption and ESS operation program and locally to adapt the economic program to the real-time user state. The described tools have been developed within a national Spanish research project called EV-OPTIMANAGER, which was co-funded by the Spanish Government through the "Retoscolaboración 2015" research program.
Optimized Control of Price-Based Demand Response With Electric Storage Space Heating
The increased uncertainty of the electric grid due to the penetration of renewable energy sources and deregulation of the electric market is aimed to be alleviated by demand response (DR) in the future smart grid. The demand-side resources can be incentivized to alter their consumption patterns by varying their electricity price over time. A major residential energy demand contribution is from electric heating, which, when combined with smart energy storage using water heaters, could be utilized to defer consumption to more inexpensive periods without affecting the customer's thermal quality of service. The objective is to optimize the consumer electricity price of electric storage space heating customers, in order to maximize the profit of the retailer. This approach of varying the customer electricity prices leads to a game-theoretic scenario, where the procurement and consumption profiles of the retailer and consumer agents are based on the set electricity price. The optimization of the consumer electricity price is shown to offer lesser expense for the retailer. In addition, hourly load-following can be improved by offering further discounts for the consumers.
Optimal energy storage system control in a smart grid including renewable generation units
The paper deals with energy storage systems applications in Smart Grids. Several services can be performed thanks to the energy storage systems use, with objectives aimed at meeting needs internal or external to the Smart Grid. Optimal nonlinear constrained problems can be formulated and properly solved in order to perform the services in the best economical and technical way. In this paper, optimal control strategies are proposed in order to allow the Smart Grid to minimize internal losses and to sell energy and ancillary services during high power prices periods. The procedure involves the formulation of optimal power flow problems; proper objective functions and constraints are imposed to satisfying the services that have to be carried out. A numerical application on a 30-bus low voltage Smart Grid shows the effectiveness of the proposed procedure.
Economic Impact of Demand-Side Energy Storage System in Electricity Markets
2013
In the electricity market, market clearing price (MCP) changes depending upon the demand profile. The MCP is high in high-demand time periods and lower during off-peak periods. Due to this volatility of MCP, Load service entities (LSEs) and their customers face a risk of higher cost. Demand-side management (DSM) is an efficient method to reduce MCP at peak periods and is helpful in load leveling. One of the DSM techniques is to use energy storage (ES) systems on demand-side in order to change demand profile. This paper evaluates the influence of demand-side energy storage on MCP when the operating pattern of ES system is governed by LSE with consideration of MCP variations. The market dispatch problem in the pool-based day-ahead electricity market is formulated so as to maximize the social welfare of market participants subject to operational and security constraints. The Proposed methodology is applied on IEEE 30-bus system to demonstrate the effect of energy storage system on MCP....
Joint supply, demand, and energy storage management towards microgrid cost minimization
2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2014
The problem of real-time power balancing in a gridconnected microgrid is studied. We consider a microgrid powered by a conventional generator (CG) and multiple renewable generators (RGs) each co-located with one distributed storage (DS) unit. An aggregator operates the microgrid and aims to minimize the long-term system cost, including all RGs' cost, the CG's cost, and the cost for exploiting external energy markets. We jointly manage the supply side, demand side, and DS units, taking into account the randomness of the system, and incorporating the ramping constraint of the CG. A real-time algorithm is proposed, which does not require any statistics of the system. We analytically characterize the gap between the system cost under our algorithm and the minimum cost, demonstrating that our algorithm is asymptotically optimal as the DS energy capacity increases and the CG ramping constraint loosens. In simulation, we compare the proposed algorithm with a greedy algorithm as well as a lower bound on the optimum. Simulation shows that our algorithm outperforms the greedy one and its performance can be close to the optimum even with small DS units.
Optimal Day-Ahead Power Procurement With Renewable Energy and Demand Response
IEEE Transactions on Power Systems, 2017
This study proposes the demand-side power procurement problem to optimally reduce consumer's energy cost. The motivation stems from pressing issues on an increase of energy cost in an industrial section. From an energy consumer's perspective, there exists an opportunity to reduce energy cost by adjusting purchase and consumption of energy in response to time-varying electricity price while utilizing renewable energy, which is called demand response. In this case, energy storage can be used to mitigate fluctuation of intermittent renewable supply and volatile electricity price. Although it is anticipated to serve a significant amount of energy consumption from renewable energy and to avoid peak electricity price, variability and uncertainty in power demand, renewable supply, and electricity price, make it challenging to determine an optimal power procurement. The main objective of this study is to suggest a decision-making methodology that enables energy consumers to optimally determine power procurement against time-varying and stochastic electricity price and renewable supply. Specifically, this study formulates an optimal day-ahead power procurement as a twostage stochastic mixed integer program and proposes a solution approach based on Benders decomposition. The proposed methodology can be successfully applied to energy-intensive industries, such as data centers.
2020
The purpose of present research is an analysis of currently promoted energy storage systems based on high-capacity electric batteries from the standpoint of algorithms for intelligent control of their charge and discharge processes. It is discussed the reduction the cost of electricity consumed by the enterprise by the redistributing of energy depending on the variation in tariffs over time. It is based on the use of the Energy Storage System (ESS) and optimal battery charge/discharge schedule. An estimation of savings in consumed energy costs is carried out depending on the power, capacity of ESS, as well as of the period of planned schedule calculating. On base of numerical simulation of battery’s charge/discharge control by linear programming optimisation method the efficiency of ESS usage was estimated in the range 10–15% for different periods from 1 up to 5 days of scheduling (planning horizon) respectively.