A generic constant-efficiency energy flow model is commonly used in techno-economic analyses of grid energy storage systems. In practice, charge and discharge efficiencies of energy storage systems depend on state of charge, temperature, and charge/discharge powers. Furthermore, the operating characteristics of energy storage devices are technology specific. Therefore, generic constant-efficiency energy flow models do not accurately capture the system performance. In this work, we propose to use technology-specific nonlinear energy flow models based on nonlinear operating characteristics of the storage devices. These models are incorporated into an optimization problem to find the optimal market participation of energy storage systems. We develop a dynamic programming method to solve the optimization problem and perform two case studies for maximizing the revenue of a vanadium redox flow battery (VRFB) and a Li-ion battery system in Pennsylvania New Jersey Maryland (PJM) interconnection's energy and frequency regulation markets.
Ingalalli, Aravind; Luna, Andre; Durvasulu, Venkat; Hansen, Timothy M.; Tonkoski, Reinaldo; Copp, David C.; Nguyen, Tu A.
Different Federal Energy Regulator Commission (FERC) orders have provided the opportunity for battery energy storage systems (ESSs) to participate in markets. The ability to be a fast-ramping generator or load allows ESSs to provide different grid services. This paper discusses opportunities for ESSs to participate in multiple existing and future electricity markets. The economic value of ESSs can be further increased by pragmatically participating in markets and services considering operational and degradation aspects. The impact of ESS on grid resilience is discussed, including resilience-as-a -service. ESSs can restore the grid to its 100% resilient state during system events, and may also reduce the resilience degradation time during extreme events.
Energy storage systems are flexible and controllable resources that can provide a number of services for the electric power grid. Many technologies are available, and corresponding models vary greatly in level of detail and tractability. In this work, we propose an adaptive optimal control and estimation approach for real-time dispatch of energy storage systems that neither requires accurate state-of-energy measurements nor knowledge of an accurate state-of-energy model. Specifically, we formulate an online optimization problem that simultaneously solves moving horizon estimation and model predictive control problems, which results in estimates of the state-of-energy, estimates of the charging and discharging efficiencies, and future dispatch signals. We present a numerical example in which the plant is a nonlinear, time-varying Lithium-ion battery model and show that our approach effectively estimates the state-of-energy and dispatches the system without accurate knowledge of the dynamics and in the presence of significant measurement noise.
IEEE International Symposium on Industrial Electronics
Tamrakar, Ujjwol; Hansen, Timothy M.; Tonkoski, Reinaldo; Copp, David C.
In isolated power systems with low rotational inertia, fast-frequency control strategies are required to maintain frequency stability. Furthermore, with limited resources in such isolated systems, the deployed control strategies have to provide the flexibility to handle operational constraints so the controller is optimal from a technical as well as an economical point-of-view. In this paper, a model predictive control (MPC) approach is proposed to maintain the frequency stability of these low inertia power systems, such as microgrids. Given a predictive model of the system, MPC computes control actions by recursively solving a finite-horizon, online optimization problem that satisfies peak power output and ramp-rate constraints. MATLAB/Simulink based simulations show the effectiveness of the controller to reduce frequency deviations and the rate-of-change-of-frequency (ROCOF) of the system. By proper selection of controller parameters, desired performance can be achieved while respecting the physical constraints on inverter peak power and/or ramp-rates.
Copp, David C.; Vamvoudakis, Kyriakos G.; Hespanha, João P.
We propose a distributed output-feedback model predictive control approach for achieving consensus among multiple agents. Each agent computes a distributed control action based on an output-feedback measurement of a local neighborhood tracking error and communicates information only to its neighbors, according to a communication network modeled as a directed graph. Each agent computes its distributed control action by solving a local min–max optimization problem that simultaneously computes a local state estimate and control input under worst-case assumptions on unmeasured input disturbances and measurement noise. Under easily verified controllability and observability assumptions, this distributed output-feedback model predictive control approach provides an upper bound on the group consensus error, thereby ensuring practical consensus in the presence of unmeasured disturbances and noise. A numerical example with four agents connected in a directed graph is given to illustrate the results.
Power system inter-area oscillations can be damped using distributed control of multiple power injections within the interconnection. This type of control traditionally requires system-wide measurements which are transmitted from dispersed, sometimes remote, locations and are subject to delays. This paper evaluates the effect that delayed feedback signals have on the stability of a two-area power system and presents delay-dependent criteria for stability using two different implementations of a damping controller. The controllers are based on a uniform proportional control action and use two feedback signals one from each area of the two-area power system. Each of these signals is subject to an independent delay. Using a Lyapunov-based approach, sufficient conditions for stability that depend on each time delay are found for a range of proportional control gains. Numerical results show that the regions of time delays for which the system is stable are reduced as the proportional gain increases. Time domain simulations validate these stability regions and show the varying responses for the two control implementations and different values of the proportional gain.
Energy storage systems are flexible resources that accommodate and mitigate variability and uncertainty in the load and generation of modern power systems. We present a stochastic optimization approach for sizing and scheduling an energy storage system (ESS) for behind-the-meter use. Specifi-cally, we investigate the use of an ESS with a solar photovoltaic (PV) system and a generator in islanded operation tasked with balancing a critical load. The load and PV generation are uncertain and variable, so forecasts of these variables are used to determine the required energy capacity of the ESS as well as the schedule for operating the ESS and the generator. When the forecasting uncertainties can be fit to normal distributions, the probabilistic load balancing constraint can be reformulated as a linear inequality constraint, and the resulting optimization problem can be solved as a linear program. Finally, we present results from a case study considering the balancing of the critical load of a water treatment plant in islanded operation.
Energy storage is a unique grid asset in that it is capable of providing a number of grid services. In market areas, these grid services are only as valuable as the market prices for the services provided. This paper formulates the optimization problem for maximizing energy storage revenue from arbitrage (day-ahead and real-time markets) in the California Independent System Operator (CAISO) market. The optimization algorithm was then applied to three years of historical market data (2014-2016) at 2200 nodes to quantify the locational and time-varying nature of potential revenue. The optimization assumed perfect foresight, so it provides an upper bound on the maximum expected revenue. Since California is starting to experience negative locational marginal prices (LMPs) because of increased renewable generation, the optimization includes a duty cycle constraint to handle negative LMPs. Two additional trading algorithms were tested that do not require perfect foresight. The first sets a buy price threshold and a sell price threshold (e.g., limit orders) for participation in the real time market, subject to the constraints of the energy storage system. The second uses the day-ahead prices as an estimate for the real time prices and performs an optimization on a rolling time horizon. The simple threshold algorithm performed the best, but both fell well short of the potential revenue identified by the optimization with perfect foresight.
Inter-area oscillations are present in all power systems dispersed over large areas and can have detrimental effects limiting transmission capacity or even causing blackouts. The availability of wide-area measurements in power systems has enabled damping of inter-area oscillations using distributed control methods and system components, such as energy storage devices. We investigate the performance of damping control enabled by energy storage devices distributed throughout an example two-area power system assuming the availability of wide-area measurements of generator machine speeds. The energy storage devices are capable of injecting active power into the system in order to damp inter-area oscillations that occur after a fault in the system. An analysis of the linearized system and several simulations of the nonlinear system with multiple combinations of controlled power injections from energy storage devices are performed. From the results, we quantify and discuss how damping performance depends on the sizes and locations of injections.