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)

A linear stochastic formulation for distribution energy management systems considering lifetime extension of battery storage devices

Recently, the number of Battery Energy Systems (BESs) connected to the grid has grown significantly. These assets can alleviate some operational issues such as demand surges and occasional power fluctuations associated with the Renewable Energy Sources (RESs) connected to the grid. Nonetheless, both overcharging and frequent usage severely affect their health status and shorten their life expectancy. In this paper, an Energy Management System (EMS) framework with a linearised algorithm and in-depth analysis on BES life extension is presented, which optimises the techno-economic aspects of an Active Distribution Network (ADN) connected to RESs. By applying a mathematical linearisation formulation, a Mixed-Integer Linear Programming (MILP) model is proposed for linearising the Optimal Power Flow (OPF) problem. This technique, which has the merit of fair accuracy while having high speed, is used for scheduling BESs to increase their durability and decrease grid costs. To consider the inherent uncertainty associated with demand and RES generation, a two-stage Stochastic Programming (SP) method is implemented in the proposed model. In terms of battery Loss of Health (LoH) assessment, a linearised battery lifetime method is introduced. Ultimately, a modified 33-bus radial distribution test system with a day-ahead Real-Time Pricing (RTP) program was chosen to apply the proposed algorithm and assess its efficiency

Second-life electric vehicle batteries as a wind energy storage system to avoid power reductions. A case study in Tenerife, Spain.

Increasing energy demand leads to environmental challenges such as global warming and climate change. This situation requires a paradigm shift to take place in the ways of generating energy. Sustainable carbon-free energy sources, such as wind or solar, must increase rapidly to replace the generation systems based on conventional sources that predominate today. However, the increase in the use of renewable energy systems has produced an instability of the grid, due to the stochastic nature of this type of energy, especially wind energy. These challenges require storage systems that provide viable power system operation solutions. In this work, the use of second-life electric vehicle batteries has been proposed to design electrical energy storage systems at a lower cost, so that surplus wind energy can be stored at times of low electricity demand and high wind resources, and thus, being able to avoid power reductions, with the main objective of reducing energy waste and making intelligent use of stored energy, in order to obtain an additional economic benefit. Firstly, the role of wind energy in the electricity generation structure of the island of Tenerife, Spain, has been studied. Second, research has been carried out on electric mobility on the island under study, so that the capacity of second-life electric vehicle batteries that may be available in the future can be estimated. And, finally, a technical and economic feasibility study has been carried out for the introduction of these storage systems in a wind farm in Tenerife. In the future, the proposed technology has the potential to become a low-cost battery energy storage system that is essential for increasing the integration of renewable energy into the grid, as well as reducing the environmental footprint by prolonging the useful life of the batteries for electric vehicles, which will offer added value to the entire system

Planning for Integration of Wind Power Capacity in Power Generation Using Stochastic Optimization

The demand for energy is constantly rising in the world while most of the conventional sources of energy are getting more scarce and expensive. Additionally, environmental issues such as dealing with excessive greenhouse gas emissions (especially CO2) impose further constraints on energy industry all over the globe. Therefore, there is an increasing need for the energy sector to raise the share of clean and renewable sources of energy in power generation. Wind power has specifically attracted large scale investment in recent years since it is ample, widely distributed and has minimal environmental impact. Wind flow and consequently wind-generated power have a stochastic nature. Therefore, wind power should be used in combination with more reliable and fuel-based power generation methods. As a result, it is important to investigate how much capacity from each source of energy should be installed in order to meet electricity demand at the desired reliability level while considering cost and environmental implications. For this purpose, a probabilistic optimization model is proposed where demand and wind power generation are both assumed stochastic. The stochastic model uses a combination of recourse and chance-constrained approaches and is capable of assigning optimal production levels for different sources of energy while considering the possibility of importation, exportation and storage of electricity in the network

Control and Optimization of Future Electric Grid Integrating Plug-In Electric Vehicles and Wind Power.

This dissertation studies the integration and control problems that will arise when plug-in electric vehicles (PEVs) and wind power are introduced to the electric grid. This dissertation harnesses the synergy between them via various control and optimization techniques. This dissertation first presents a PEV charging control algorithm. The algorithm adopts a partially-decentralized structure to address the different battery state of charge and plug-off time of individual PEVs. This allows most PEVs to be fully charged. Also, “valley filling” and grid frequency regulation are achieved. Secondly, this dissertation adopts model predictive control (MPC) on battery energy storage system (BESS) to mitigate wind intermittency. The MPC controller is derived using realistic objective functions that capture the reserve costs to cover wind intermittency. The capacity sizing of BESS is also investigated. Next, a three-level hierarchical control algorithm is proposed to integrate PEVs and wind power on the grid. The top-level controller solves a scheduling optimization problem to minimize the costs of electricity generation. The middle- and bottom-level controllers are based on the control algorithms previously developed for PEV charging and wind power scheduling. The hierarchical structure allows the features in the different control algorithms to be preserved. Exerting on the scheduling optimization framework, the scope of study is expended to consider grid CO2 emissions. A carbon disincentive policy is proposed to promote the use of low-carbon power plants for electricity generation. The tradeoff between the generation costs and grid CO2 emissions is investigated using optimal Pareto fronts. Lastly, a cost evaluation is proposed for generation planning. The evaluation considers the evolutions in both the supply and demand on the electric grid. The wind intermittency and reserve-related costs are also considered. The evaluation show that wind power will still be expensive in the next two decades owing to the high construction cost, although the wind intermittency can be addressed by BESS or PEVs on the operation stage. This dissertation shows more than one piece of evidence that PEVs and wind power are good complements to each other, and a proper integration is needed to bring the best out of them.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99824/1/ctli_1.pd