Improving optimal control of grid-connected lithium-ion batteries through more accurate battery and degradation modelling

The increased deployment of intermittent renewable energy generators opens up opportunities for grid-connected energy storage. Batteries offer significant flexibility but are relatively expensive at present. Battery lifetime is a key factor in the business case, and it depends on usage, but most techno-economic analyses do not account for this. For the first time, this paper quantifies the annual benefits of grid-connected batteries including realistic physical dynamics and nonlinear electrochemical degradation. Three lithium-ion battery models of increasing realism are formulated, and the predicted degradation of each is compared with a large-scale experimental degradation data set (Mat4Bat). A respective improvement in RMS capacity prediction error from 11\% to 5\% is found by increasing the model accuracy. The three models are then used within an optimal control algorithm to perform price arbitrage over one year, including degradation. Results show that the revenue can be increased substantially while degradation can be reduced by using more realistic models. The estimated best case profit using a sophisticated model is a 175% improvement compared with the simplest model. This illustrates that using a simplistic battery model in a techno-economic assessment of grid-connected batteries might substantially underestimate the business case and lead to erroneous conclusions

Stochastic Optimal Control of Grid-Level Storage

The primary focus of this dissertation is the design, analysis and implementation of stochastic optimal control of grid-level storage. It provides stochastic, quantitative models to aid decision-makers with rigorous, analytical tools that capture high uncertainty of storage control problems. The first part of the dissertation presents a $p$-periodic Markov Decision Process (MDP) model, which is suitable for mitigating end-of-horizon effects. This is an extension of basic MDP, where the process follows the same pattern every $p$ time periods. We establish improved near-optimality bounds for a class of greedy policies, and derive a corresponding value-iteration algorithm suitable for periodic problems. A parallel implementation of the algorithm is provided on a grid-level storage control problem that involves stochastic electricity prices following a daily cycle. Additional analysis shows that the optimal policy is threshold policy. The second part of the dissertation is concerned with grid-level battery storage operations, taking battery aging phenomenon (battery degradation) into consideration. We still model the storage control problem as a MDP with an extra state variable indicating the aging status of the battery. An algorithm that takes advantage of the problem structure and works directly on the continuous state space is developed to maximize the expected cumulated discounted rewards over the life of the battery. The algorithm determines an optimal policy by solving a sequence of quasiconvex problems indexed by a battery-life state. Computational results are presented to compare the proposed approach to a standard dynamic programming method, and to evaluate the impact of refinements in the battery model. Error bounds for the proposed algorithm are established to demonstrate its accuracy. A generalization of price model to a class of Markovian regime-switching processes is also provided. The last part of this dissertation is concerned with how the ownership of energy storage make an impact on the price. Instead of one player in most storage control problems, we consider two players (consumer and supplier) in this market. Energy storage operations are modeled as an infinite-horizon Markov Game with random demand to maximize the expected discounted cumulated welfare of different players. A value iteration framework with bimatrix game embedded is provided to find equilibrium policies for players. Computational results show that the gap between optimal policies and obtained policies can be ignored. The assumption that storage levels are common knowledge is made without much loss of generality, because a learning algorithm is proposed that allows a player to ultimately identify the storage level of the other player. The expected value improvement from keeping the storage information private at the beginning of the game is then shown to be insignificant

Demand Management Storage Project (DMSP) – an application of grid scale battery energy storage systems

Grid scale BESS (battery energy storage system) has been identified as one of the key technologies in the utility network of the future. There are significant benefits associated with their ability to store energy. This study aims to use economic models to evaluate grid scale BESS benefits and to sum them up into value propositions. DMSP project is planning to install one of the largest BESS systems at a 22kV distribution feeder in Australia. According to (Eyer & Corey, 2010) guide, energy storage systems could have 17 electric grid related applications which across 5 categories: electrical supply, ancillary services, grid system, end user/utility customer and renewable integration. Among all the applications, DMSP project focuses on two major applications: using grid scale BESS for energy time-shift and feeder construction deferral applications. In order to quantify the economic feasibility of the DMSP BESS system, studies were done to analyse the distribution system, energy market and BESS system. Two data models had been created to quantify the two BESS applications with the factors such as energy prices, feeder load data and battery parameters. With the data models, methods were found out about how to simulate electrical and economic performance of the battery energy storage system and quantify these performances into market value. The simulation results had been presented and analysed in the document. From the simulation, it concluded that economic feasibility of BESS energy time-shift application is depended on active level of energy market and also the BESS system cost; Feeder construction deferral application can bring significant benefits if the feeder upgrade construction costs are high. Further in the research an optimal battery control scheme was developed using the forward dynamic programming approach. Based on the data models, this scheme provided the optimal battery control strategy to achieve the maximum benefits from BESS application. The research shows that BESS can bring positive benefits for combined energy storage applications. The potentials of using BESS systems in Australian utility network shall be extended specially with the system costs decreased in the future

Stochastic unit commitment in microgrids based on model predictive control

This article deals with the problem of Stochastic Unit Commitment (SUC), considering the stochastic nature of demand and meteorological phenomena. This paper shows the optimal operation of a hybrid microgrid composed of the following generation units: wind turbine (WT), photovoltaic solar panel (PV), diesel engine generator (DE), micro-turbine (MT), as well as storage devices such as Battery Energy Storage (BES), considering its constraints and the requirements of the reserve generation. For this purpose, a Model-based Predictive Control (MPC), which uses dynamic models of prediction of renewable power and demand in real time, is developed, allowing feedback at each step of time, which corrects the uncertainty of the models. A comparison with a classic UC formulation has been made. The results reach a lower cost solution

Optimization-Based Power Management of Hybrid Power Systems with Applications in Advanced Hybrid Electric Vehicles and Wind Farms with Battery Storage

Modern hybrid electric vehicles and many stationary renewable power generation systems combine multiple power generating and energy storage devices to achieve an overall system-level efficiency and flexibility which is higher than their individual components. The power or energy management control, ``brain\u27 of these ``hybrid\u27 systems, determines adaptively and based on the power demand the power split between multiple subsystems and plays a critical role in overall system-level efficiency. This dissertation proposes that a receding horizon optimal control (aka Model Predictive Control) approach can be a natural and systematic framework for formulating this type of power management controls. More importantly the dissertation develops new results based on the classical theory of optimal control that allow solving the resulting optimal control problem in real-time, in spite of the complexities that arise due to several system nonlinearities and constraints. The dissertation focus is on two classes of hybrid systems: hybrid electric vehicles in the first part and wind farms with battery storage in the second part. The first part of the dissertation proposes and fully develops a real-time optimization-based power management strategy for hybrid electric vehicles. Current industry practice uses rule-based control techniques with ``else-then-if\u27 logic and look-up maps and tables in the power management of production hybrid vehicles. These algorithms are not guaranteed to result in the best possible fuel economy and there exists a gap between their performance and a minimum possible fuel economy benchmark. Furthermore, considerable time and effort are spent calibrating the control system in the vehicle development phase, and there is little flexibility in real-time handling of constraints and re-optimization of the system operation in the event of changing operating conditions and varying parameters. In addition, a proliferation of different powertrain configurations may result in the need for repeated control system redesign. To address these shortcomings, we formulate the power management problem as a nonlinear and constrained optimal control problem. Solution of this optimal control problem in real-time on chronometric- and memory- constrained automotive microcontrollers is quite challenging; this computational complexity is due to the highly nonlinear dynamics of the powertrain subsystems, mixed-integer switching modes of their operation, and time-varying and nonlinear hard constraints that system variables should satisfy. The main contribution of the first part of the dissertation is that it establishes methods for systematic and step-by step improvements in fuel economy while maintaining the algorithmic computational requirements in a real-time implementable framework. More specifically a linear time-varying model predictive control approach is employed first which uses sequential quadratic programming to find sub-optimal solutions to the power management problem. Next the objective function is further refined and broken into a short and a long horizon segments; the latter approximated as a function of the state using the connection between the Pontryagin minimum principle and Hamilton-Jacobi-Bellman equations. The power management problem is then solved using a nonlinear MPC framework with a dynamic programming solver and the fuel economy is further improved. Typical simplifying academic assumptions are minimal throughout this work, thanks to close collaboration with research scientists at Ford research labs and their stringent requirement that the proposed solutions be tested on high-fidelity production models. Simulation results on a high-fidelity model of a hybrid electric vehicle over multiple standard driving cycles reveal the potential for substantial fuel economy gains. To address the control calibration challenges, we also present a novel and fast calibration technique utilizing parallel computing techniques. The second part of this dissertation presents an optimization-based control strategy for the power management of a wind farm with battery storage. The strategy seeks to minimize the error between the power delivered by the wind farm with battery storage and the power demand from an operator. In addition, the strategy attempts to maximize battery life. The control strategy has two main stages. The first stage produces a family of control solutions that minimize the power error subject to the battery constraints over an optimization horizon. These solutions are parameterized by a given value for the state of charge at the end of the optimization horizon. The second stage screens the family of control solutions to select one attaining an optimal balance between power error and battery life. The battery life model used in this stage is a weighted Amp-hour (Ah) throughput model. The control strategy is modular, allowing for more sophisticated optimization models in the first stage, or more elaborate battery life models in the second stage. The strategy is implemented in real-time in the framework of Model Predictive Control (MPC)

Digitalization Platform for Mechanistic Modeling of Battery Cell Production

he application of batteries in electric vehicles and stationary energy-storage systems is widely seen as a promising enabler for a sustainable mobility and for the energy sector. Although significant improvements have been achieved in the last decade in terms of higher battery performance and lower production costs, there remains high potential to be tapped, especially along the battery production chain. However, the battery production process is highly complex due to numerous process–structure and structure–performance relationships along the process chain, many of which are not yet fully understood. In order to move away from expensive trial-and-error operations of production lines, a methodology is needed to provide knowledge-based decision support to improve the quality and throughput of battery production. In the present work, a framework is presented that combines a process chain model and a battery cell model to quantitatively predict the impact of processes on the final battery cell performance. The framework enables coupling of diverse mechanistic models for the individual processes and the battery cell in a generic container platform, ultimately providing a digital representation of a battery electrode and cell production line that allows optimal production settings to be identified in silico. The framework can be implemented as part of a cyber-physical production system to provide decision support and ultimately control of the production line, thus increasing the efficiency of the entire battery cell production process