Optimal Management of an Integrated Electric Vehicle Charging Station under Weather Impacts

The focus of this Dissertation is on developing an optimal management of what is called the “Integrated Electric Vehicle Charging Station” (IEVCS) comprising the charging stations for the Plug-in Electric Vehicles (PEVs), renewable (solar) power generation resources, and fixed battery energy storage in the buildings. The reliability and availability of the electricity supply caused by severe weather elements are affecting utility customers with such integrated facilities. The proposed management approach allows such a facility to be coordinated to mitigate the potential impact of weather condition on customers electricity supply, and to provide warnings for the customers and utilities to prepare for the potential electricity supply loss. The risk assessment framework can be used to estimate and mitigate such impacts. With proper control of photovoltaic (PV) generation, PEVs with mobile battery storage and fixed energy storage, customers’ electricity demand could be potentially more flexible, since they can choose to charge the vehicles when the grid load demand is light, and stop charging or even supply energy back to the grid or buildings when the grid load demand is high. The PV generation capacity can be used to charge the PEVs, fixed battery energy storage system (BESS) or supply power to the grid. Such increased demand flexibility can enable the demand response providers with more options to respond to electricity price changes. The charging stations integration and interfacing can be optimized to minimize the operational cost or support several utility applications

Improving electricity network utilisation with distributed energy storage systems

Network capacity utilisation is the ratio of the average energy demand to the installed capacity required to meet peak demand. Network capacity utilisation is one of the biggest problems faced by network operators in Australia and around the world. As a response to high peak demands, network operators expand the generation and network capacity. This results in large investments in infrastructure that only operates a couple of hours annually. Investment and operation costs of the underutilised infrastructure are passed on to customers through increased energy prices. Accordingly, there is a need to control peak demand, and distributed energy storage systems hold promise for this application. The immediate objective of this research project is to improve utilisation of network assets in an urban area with distributed energy storage systems. The NSW network was analysed under both winter and summer conditions to determine the size of the peak demand and the unused network capacity during the off peak period that could be used for charging energy storage systems without creating a peak. The minimum number of households required to be programmed to use energy storage systems during peak periods in order to avoid the network peak demand and the maximum number of households that the network could charge in the off peak period without creating a peak demand were determined. A model was developed to evaluate the effectiveness of distributed energy storage systems on the NSW network. A power flow analysis was conducted to analyse the voltage regulation capabilities of distributed energy storage systems at demand nodes on the network. Analysis and simulation results showed that distributed energy storage systems are a viable solution to improving network capacity utilisation

Energy Optimization and Management of Demand Response Interactions in a Smart Campus

The proposed framework enables innovative power management in smart campuses, integrating local renewable energy sources, battery banks and controllable loads and supporting Demand Response interactions with the electricity grid operators. The paper describes each system component: the Energy Management System responsible for power usage scheduling, the telecommunication infrastructure in charge of data exchanging and the integrated data repository devoted to information storage. We also discuss the relevant use cases and validate the framework in a few deployed demonstrators

Convex Optimization Approach to the Optimal Power Flow Problem in DC-Microgrids with Energy Storage

Humanity is currently facing a global energy crisis. This is due to the shortage in the conventional energy resources while the demand for energy is rising. In response to this crisis, research in designing more energy efficient systems has gained significant importance. The Microgrids (MGs) are one of the main key elements in giving significant momentum to efficient decentralized energy generation. From the perspective of MGs power management, economical scheduling for generators, energy storage, and demand loads are critical. Performance optimization processes are needed to minimize the operating costs while considering operational constraints. In this thesis, the optimal power flow problem for managing energy sources with storage devices is presented for dc microgrids. The power management model has been examined in various scenarios. One of them is based on a network of a six-bus power system, including an energy storage device coupling at a certain bus. The other scenario is based on the same model but including more energy storage devices. After analyzing the results of these scenarios, several conclusions have been made such as when the energy storage should charge/discharge to minimize costs. The study shows the feasibility of optimal power flow operation in DC microgrids

Battery SOC management strategy for enhanced frequency response and day-ahead energy scheduling of BESS for energy arbitrage

The electricity system has to balance demand and supply every second, a task that is becoming evermore challenging due to the increased penetration of renewable energy sources and subsequent inertial levels. In the UK, a number of grid frequency support services are available, which are developed to provide a real-time response to changes in the grid frequency. The National Grid Electricity Transmission (NGET) - the primary electricity transmission network operator in the UK - has introduced a new faster frequency response service, called the Enhanced Frequency Response (EFR), which requires a response time of under one second. Battery energy storage systems (BESSs) are ideal choice for delivering such a service. In this paper a control algorithm is presented which supplies a charge/discharge power output with respect to deviations in the grid frequency and the ramp-rate limits imposed by NGET, whilst managing the state-of-charge (SOC) of the BESS to maximise the utilisation of the available energy capacity. Using the real UK market clearing prices, a forecasted battery state of charge (SOC) management strategy has been also developed to deliver EFR service whilst scheduling throughout the day for energy arbitrage. Simulation results demonstrate that the proposed algorithm delivers an EFR service within the specification whilst generating arbitrage revenue. A comparative study is also presented to compare the yearly arbitrage revenue obtained from the model of the Willenhall and an experimental Leighton Buzzard battery storage system. Simulation results on a 2MW/1MWh lithium-titanate BESS are provided to verify the proposed algorithm based on the control of an experimentally validated battery model

Nano-engineering and Simulating Electrostatic Capacitors for Electrical Energy Storage

Electrical energy storage solutions with significantly higher gravimetric and volumetric energy densities and rapid response rates are needed to balance the highly dynamic, time-variant supply and demand for power. Nanoengineering can provide useful structures for electrical energy storage because it offers the potential to increase efficiency, reduce size/weight, and improve performance. While several nanostructured devices have shown improvements in energy and/or power densities, this dissertation focuses on the nanoengineering of electrostatic capacitors (ESC) and application of these high-power electrostatic capacitors in electrical energy storage systems. A porous nano-template with significant area enhancement per planar unit area coated with ultra-thin metal-insulator-metal (MIM) layers has shown significant improvements in areal capacitance. However, sharp asperities inherent to the initial nano-template localized electric fields and caused premature (low field) breakdown, limiting the possible energy density (E = ½ CV2/m). A nanoengineering strategy was identified for rounding the template asperities, and this showed a significant increase in the electrical breakdown strength of the device, providing rapid charging and discharging and an energy density of 1.5 W-h/kg - which compares favorably with the best state-of-the-art devices that provide 0.7 W-h/kg. The combination of the high-power ESC with a complementary high-energy-density electrochemical capacitor (ECC) was modeled to evaluate methods resulting in the combined power-energy storage capabilities. While significant improvements in the ESC's energy density were reported, the nanodevices display nonlinear leakage resistance, which directly relates to charge retention. The ECC has distinctly different nonlinearities, but can retain a greater density of charge for significantly longer, albeit with slower inherent charging and discharging rates than the ESC. The experimentally derived dynamic model simulating the nonlinear performance of the ESC and ECC devices indicated this hybrid-circuit reduces the time required to charge the ECC to near-maximum capacity by a factor of up to ~ 12

Optimal Management of an Integrated Electric Vehicle Charging Station under Weather Impacts

The focus of this Dissertation is on developing an optimal management of what is called the “Integrated Electric Vehicle Charging Station” (IEVCS) comprising the charging stations for the Plug-in Electric Vehicles (PEVs), renewable (solar) power generation resources, and fixed battery energy storage in the buildings. The reliability and availability of the electricity supply caused by severe weather elements are affecting utility customers with such integrated facilities. The proposed management approach allows such a facility to be coordinated to mitigate the potential impact of weather condition on customers electricity supply, and to provide warnings for the customers and utilities to prepare for the potential electricity supply loss. The risk assessment framework can be used to estimate and mitigate such impacts. With proper control of photovoltaic (PV) generation, PEVs with mobile battery storage and fixed energy storage, customers’ electricity demand could be potentially more flexible, since they can choose to charge the vehicles when the grid load demand is light, and stop charging or even supply energy back to the grid or buildings when the grid load demand is high. The PV generation capacity can be used to charge the PEVs, fixed battery energy storage system (BESS) or supply power to the grid. Such increased demand flexibility can enable the demand response providers with more options to respond to electricity price changes. The charging stations integration and interfacing can be optimized to minimize the operational cost or support several utility applications

Fast transactive control for frequency regulation in smart grids with demand response and energy storage

This paper proposes a framework for controlling grid frequency by engaging the generation-side and demand-side resources simultaneously, via a fast transactive control approach. First, we use a proportional frequency-price relation to build and analyze a transactive frequency droop controller for a single-area power grid. Then, we develop a transactive demand response system by incorporating a large population of thermostatically controlled air conditioning loads. A proportional-integral controller is used to adjust the setpoint temperature of the air conditioners based on price variations. A battery storage system is then developed and augmented to the system to capture the energy arbitrage effects. A nonlinear price-responsive battery management system is developed to enable effective charging and discharging operations within the battery’s state-of-charge and power constraints. Simulation results indicate that the proposed transactive control system improves the steady-state and transient response of the grid to sudden perturbations in the supply and demand equilibrium. To decouple frequency from price during daily operation and maintain frequency near the nominal value, we propose adding a feedforward price broadcast signal to the control loop based on the net demand measurement. Through various simulations, we conclude that a combination of feedback transactive controller with feedforward price broadcast scheme provides an effective solution for the simultaneous generation-side and demand-side energy management and frequency control in smart power grids

Optimal sizing design and operation of electrical and thermal energy storage systems in smart buildings

Photovoltaic (PV) systems in residential buildings require energy storage to enhance their productivity; however, in present technology, battery storage systems (BSSs) are not the most cost-effective solutions. Comparatively, thermal storage systems (TSSs) can provide opportunities to enhance PV self-consumption while reducing life cycle costs. This paper proposes a new framework for optimal sizing design and real-time operation of energy storage systems in a residential building equipped with a PV system, heat pump (HP), thermal and electrical energy storage systems. For simultaneous optimal sizing of BSS and TSS, a particle swarm optimization (PSO) algorithm is applied to minimize daily electricity and life cycle costs of the smart building. A model predictive controller is then developed to manage energy flow of storage systems to minimize electricity costs for end-users. The main objective of the controller is to optimally control HP operation and battery charge/discharge actions based on a demand response program. The controller regulates the flow of water in the storage tank to meet designated thermal energy requirements by controlling HP operation. Furthermore, the power flow of battery is controlled to supply all loads during peak-load hours to minimize electricity costs. The results of this paper demonstrate to rooftop PV system owners that investment in combined TSS and BSS can be more profitable as this system can minimize life cycle costs. The proposed methods for optimal sizing and operation of electrical and thermal storage system can reduce the annual electricity cost by more than 80% with over 42% reduction in the life cycle cost. Simulation and experimental results are presented to validate the effectiveness of the proposed framework and controller

Towards Better Understanding of Failure Modes in Lithium-Ion Batteries: Design for Safety

In this digital age, energy storage technologies become more sophisticated and more widely used as we shift from traditional fossil fuel energy sources to renewable solutions. Specifically, consumer electronics devices and hybrid/electric vehicles demand better energy storage. Lithium-ion batteries have become a popular choice for meeting increased energy storage and power density needs. Like any energy solution, take for example the flammability of gasoline for automobiles, there are safety concerns surrounding the implications of failure. Although lithium-ion battery technology has existed for some time, the public interest in safety has become of higher concern with media stories reporting catastrophic cellular phone- and electric vehicle failures. Lithium-ion battery failure can be dangerously volatile. Because of this, battery electrochemical and thermal response is important to understand in order to improve safety when designing products that use lithium-ion chemistry. The implications of past and present understanding of multi-physics relationships inside a lithium-ion cell allow for the study of variables impacting cell response when designing new battery packs. Specifically, state-of-the-art design tools and models incorporate battery condition monitoring, charge balancing, safety checks, and thermal management by estimation of the state of charge, state of health, and internal electrochemical parameters. The parameters are well understood for healthy batteries and more recently for aging batteries, but not for physically damaged cells. Combining multi-physics and multi-scale modeling, a framework for isolating individual parameters to understand the impact of physical damage is developed in this work. The individual parameter isolated is the porosity of the separator, a critical component of the cell. This provides a powerful design tool for researchers and OEM engineers alike. This work is a partnership between a battery OEM (Johnson Controls, Inc.), a Computer Aided Engineering tool maker (ANSYS, Inc.), and a university laboratory (Advanced Manufacturing and Design Lab, University of Wisconsin-Milwaukee). This work aims at bridging the gap between industry and academia by using a computer aided engineering (CAE) platform to focus battery design for safety