Improving Performance Assessment for Technologies of Energy Transition: Emissions, Economics, and Policy Implications

University of Minnesota Ph.D. dissertation. August 2018. Major: Natural Resources Science and Management. Advisors: Timothy Smith, Elizabeth Wilson. 1 computer file (PDF); vii, 104 pages.Global climate change requires immediate actions to mitigate emissions from energy related sectors. Specifically, the electricity system plays a pivotal role in achieving the global emission reduction goals that many countries have publicly committed to. In the United States (U.S.), energy policies have focused on increasing electricity production from renewables, decreasing electricity consumption by improving energy efficiency, and shifting demand by using energy storage technology. This dissertation explores the specific challenges and information gaps that confront practitioners in three separate case studies, consequently contributing to electricity system and energy policy literature. It is the hope of the author that information provided helps to inform policy makers, electricity system operators, and private investors toward critical transition and transformation of the U.S. energy system. The studies, taking the form of independent chapters, are summarized as follows. The first study presents an improved methodology for estimating the marginal emission factors (MEFs) of electricity generation in the Midcontinent Independent System Operator (MISO) system. Findings highlight the importance of including emitting and nonemitting resources in MEFs calculation in regions with high and growing renewables penetration and compare this approach to competing conventional approaches within the context of energy storage technologies. The second study demonstrates a multi-regional energy and emissions assessment of the ground source heat pump (GSHP) technology in comparison to the conventional heating and cooling technologies in residential houses. Findings indicate that applying EFs with higher spatial and temporal resolutions and using MEFs instead of average emission factors (AEFs) both give more accurate emission estimates. The third study assesses economics and emissions of grid-scale battery storage that arbitrages as a price taker in the MISO wholesale electricity market. Findings demonstrate specific locations where battery storage might initially be most profitable under historical pricing dynamics and reveal the heterogeneity in storage’s economics and emissions throughout the MISO grid

Computational intelligence techniques for energy storage management

Ph. D. ThesisThe proliferation of stochastic renewable energy sources (RES) such as photovoltaic and wind power in the power system has made the balancing of generation and demand challenging for the grid operators. This is further compounded with the liberalization of electricity market and the introduction of real-time electricity pricing (RTP) to reflect the dynamics in generation and demand. Energy storage sources (ESS) are widely seen as one of the keys enabling technology to mitigate this problem. Since ESS is a costly and energy-limited resource, it is economical to provide multiple services using a single ESS. This thesis aims to investigate the operation of a single ESS in a grid-connected microgrid with RES under RTP to provide multiple services. First, artificial neural network is proposed for day-ahead forecasting of the RES, demand and RTP. After the day-ahead forecast is obtained, the day-ahead schedule of energy storage is formulated into a mixed-integer linear programming and implemented in AMPL and solved using CPLEX. This method considers the impact of forecasting errors in the day-ahead scheduling. Empirical evidence shows that the proposed nearoptimal day-ahead scheduling of ESS can achieve a lower operating cost and peak demand. Second, a fuzzy logic-based energy management system (FEMS) for a grid-connected microgrid with RES and ESS is proposed. The objectives of the FEMS are energy arbitrage and peak shaving for the microgrid. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state-of-charge (SoC) of ESS, the power difference between RES and demand, and RTP. Instead of using a forecasting-based approach, the proposed FEMS is designed with the historical data of the microgrid. It determines the charge and discharge rate of the ESS in a rolling horizon. A comparison with other controllers with the same objectives shows that the proposed controller can operate at a lower cost and reduce the peak demand of the microgrid. Finally, the effectiveness of the FEMS greatly depends on the membership functions. The fuzzy membership functions of the FEMS are optimized offline using a Pareto based multi-objective evolutionary algorithm, nondominated sorting genetic algorithm- II (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy logic controller. A comparison was made against other control strategies with similar objectives are carried out at a simulation level. Empirical evidence shows that the proposed methodology can find more solutions on the Pareto front in a single run. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at Newcastle University, UK. Furthermore, reserve service is added on top of energy arbitrage and peak shaving to the energy management system (EMS). As such multi-service of a single ESS which benefit the grid operator and consumer is achieved

Service Revenue Evaluation Methodologies to Maximize the Benefits of Energy Storage

The objective of this research is to develop novel methodologies and tools for service revenue evaluation of electrical energy storage systems. Energy storage systems can provide a wide range of services and benefits to the entire value chain of the electricity industry and, therefore, are becoming a favorable technology among stakeholders. The U.S. Government and various states have set initiatives and mandated energy storage deployment as part of their grid modernization roadmap. The key to an increased deployment of energy storage projects is their economic viability. Because of the significant potential value of energy storage as well as the complexity of the decision-making problem, sophisticated service evaluation methodologies and service optimization tools are highly needed. The maximum potential value of energy storage cannot be captured with the evaluation methodologies that have been developed for conventional generators or other distributed energy resources. Previous research studies mostly operational strategies for energy storage coupled with renewable energy sources and the benefits and business models of privately-owned energy storage systems are not well understood. Most of the existing literature focuses on evaluating energy storage systems providing a single service while multiservice operation and evaluation is often not considered. The few available methods for multiservice evaluation study a limited number of services and cannot be readily implemented into a computational tool due to complexity and scalability issues. Accordingly, this research proposes novel service evaluation methodologies with two main objectives: a. Discover the maximum value of energy storage systems for single and multiservice applications, b. Provide flexibility, scalability and tractability of implementation. In order to meet these objectives, various methodologies based on statistical analysis, dynamic control, mixed integer linear programming, convex optimization and decomposition have been proposed. The challenges, complexities, and the benefits of modeling energy services using a scalable approach are analyzed, solutions are proposed and simulated with realistic data in three main chapters of this research: a) energy storage in wholesale energy markets, b) generic multiservice revenue analysis of energy storage, and c) temporal complexities of energy storage optimization models: value and decomposition. Simulation results show the feasibility of the proposed approaches, and significant added values to the economic viability of energy storage projects using the proposed methodologies. Energy storage decision makers including public utility commissioners, transmission/distribution system operators, aggregators, private energy storage owners/investors, and end-use customers (residential and commercial loads) can benefit from the proposed methodologies and simulation results. A software tool has been developed for multiservice benefit cost analysis of energy storage projects. It is hoped that with the significant unlocked value of energy storage systems using the proposed tools and methodologies, more of these technologies be deployed in the future grids to help communities with their sustainability and environmental goals.Ph.D

Investigation of energy storage system and demand side response for distribution networks

PhD ThesisThe UK government has a target of achieving an 80% reduction in CO2 emissions with respect to the values from 1990 by 2050. Therefore, renewables based distributed generations (DGs) coupled with substantial electrification of the transport and heat sectors though low carbon technologies (LCTs), will be essential to achieve this target. The anticipated proliferation of these technologies will necessitate major opportunities and challenges to the operation and planning of future distribution networks. Smartgrid technologies and techniques, such as energy storage systems (ESSs), demand side response (DSR) and real time thermal ratings (RTTRs), provide flexible, economic and expandable solutions to these challenges without resorting to network reinforcement. This research investigates the use of ESS and DSR in future distribution networks to facilitate LCTs with a focus on the management and resolution of thermal constraints and steady state voltage limit violation problems. Firstly, two control schemes based on sensitivity factors and cost sensitivity factors are proposed. Next, the impacts of a range of sources of uncertainties, arising from existing and future elements of the electrical energy system, are studied. The impacts of electric vehicle charging are investigated with Monte Carlo simulation (MCS). Furthermore, to deal with uncertainties efficiently, a scheduling scheme based on robust optimization (RO) is developed. Two approaches have been introduced to estimate the trade-off between the cost and the probability of constraint violations. Finally, the performance of this scheme is evaluated. The results of this research show the importance of dealing with uncertainties appropriately. Simulation results demonstrate the capability and effectiveness of the proposed RO based scheduling scheme to facilitate DG and LCTs, in the presence of a range of source of uncertainties. The findings from this research provide valuable solution and guidance to facilitate DG and LCTs using ESS, DSR and RTTR in future distribution networks

The design and operation optimization of liquid air energy storage within multi-vector energy systems

Climate changes call for the construction of a net-zero-carbon energy system across the globe. Such a massive need become more urgent due to the recent war on Ukraine, which has led to energy poverty, sharp rise in living costs and economic challenges particularly in Europe. Renewable energy represents a critical pathway towards the decarbonisation. A high share of renewable could trigger multiple problems due to the intrinsic intermittency and variability. Energy storage technologies offer the major solution to resolve such problems. There are many energy storage technologies at different development stages; among which, Liquid Air Energy Storage (LAES) is considered as a promising large-scale energy storage technology. The key advantages of the LAES include high scalability, no geographical constraints, cost-effectiveness, and capability of providing multi-vector energy services, which is expected to play an increasingly crucial role in future energy systems with a high renewable penetration. However, there are few studies working on the optimization and discussing the functions and benefits of LAES when it is applied into net-zero carbon energy systems. This forms the main motivation of this Ph.D. work, to address the research gaps. In the first and second parts of the thesis, the thermo-economic and dynamic simulation and optimization of the LAES system were conducted, which can provide the basis for discussing its key roles in distributed and grid-scale multi-vector energy systems. The given results can provide evidence for the optimal design, operation and improvement of LAES integrated systems. Meantime, the outcome can provide the enlightening views on the business investment decisions, and on developing renewable energy policies and storage expansion plans, to help achieve carbon mitigation ambitions in the UK by 2050. The following is a brief summary of the work and major conclusions: In the first part of this work, the multi-objective thermo-economic optimization of a stand-alone LAES system by using a Genetic algorithm was conducted, taking the round-trip efficiency (RTE) and economic indicators as the optimization objectives. The optimization has lead to a 9%~14% of increase in energy efficiency and a 14% of decrease in exergy destruction. The optimal design and operational parameters of LAES under different configurations and scenarios can be determined, including the optimal charging and discharging pressure, heat transfer areas, and mass flow rates of hot and cold storage media etc. Meantime, the design and operational guidelines of LAES can be derived. A LAES system with lower machine efficiencies requires lower charging and discharging pressure, while a system with worse heat transfer performance needs higher charging pressure but lower discharging pressure. Finally, the Pareto Front of capital costs, efficiencies and the occupied space energy density (OSDE) was obtained to provide system operators good investment advice of LAES. It indicated that a higher capital cost lead to a higher RTE, NPV and OSDE. Specifically, when the RTE increases by 1%, the optimized capital investment increases by 0.5-1%. If the investment budget is over 48 M£, a LAES system with three-stage compressors and four-stage turbines can produce better RTE than three-stage and four-stage LAES systems. In the second part of this work, the dynamic simulation and analysis of the LAES discharging unit were conducted to investigate its dynamic characteristic and response time when integrated with wind power. The results revealed that the LAES discharging unit is more suitable for responding to the wind power component at a time scale more than its start-up time, which can help compensate the wind power deficiency and reduce the motor fatigue. Meanwhile, the combined storage scheme with LAES and battery was proposed to smooth the varying wind power. The economic comparison among different storage schemes indicated the suitable storage system for wind power integration. The annual cost of solely battery storage is more than two times higher than that of the combined LAES and battery storage system, meantime, the larger the wind farm, the more obvious the economic advantages of the combined storage system. In the third part of this work, the multiple functions of LAES in decarbonizing a hybrid renewable micro-grid with high share of wind power were investigated. A mixed-integer linear programming (MILP)-based system design framework with the decoupled model of LAES was developed, which can determine the optimal sizes and operation of the micro-grid components and the LAES units. Specifically, the optimal charge/discharge energy to power ratio (27/14 h) and the storage tank size (608 t) of LAES in a micro-grid with 75% of wind power were obtained, leading to ~60% of carbon emission reduction on the 2016 level. The results also revealed the key roles of LAES in supporting a micro-grid with high share of wind power by providing multiple functions. The total benefits were split into six explicit revenue streams for the first time, including the time shifting (13.2%), renewable firming (11.4%), peak shaving (28%), flexibility (21%) and reserve value (20.4%), as well as the waste heat recovery (6%). It also indicated that a higher renewable percentage (over 50%) would be the major driving force to increase the attractiveness of LAES in micro-grids than the mildly reduced LAES capital cost and the enlarged electricity price differences. In the fourth part of this work, the cost-effective pathways and the storage needs for the transition to a net-zero carbon energy system in the UK by 2050 were assessed. A MILP-based energy expansion model was developed to achieve the optimal design and operation of the system. Firstly, the results revealed that a future 100% renewable or net-zero carbon power system is feasible with levelised cost of energy (LCOE) at 65~80 £/MWh, and a net-zero carbon heat system is affordable with the levelised cost of heat (LCOH) at 45~63 £/MWh. The major expansions are onshore wind power (94.5 GW) in power sector and air-source heat pump (~80 - 90 GW) in heat sector. Secondly, storage technologies would play crucial roles in a net-zero carbon system, only ~10-12% of investments in electric storages would reduce the total annual costs by ~15.1% - 28%. The major storage expansions lie in LAES (384 GWh) in power sector and the short-term heat storage (330 GWh) in heat sector. Thirdly, the newly deployed capacities of renewables and storages in different zones are correlated with each other, the LAES and renewable capacity ratio is around 20%. It also indicated that the LAES with the charge durations at 8~10 h and discharge durations at 14~15 h is more suitable for the wind-dominated case in the UK than short-duration batteries (~4/5h)

Thermo-mechanical energy storage applications for energy system decarbonisation

This research explores the prospective application of thermo-mechanical energy storage technologies for energy system decarbonisation. It characterises, first, the techno-economic performance of one such technologies, Liquid Air Energy Storage (LAES), when operated within the power system to supply energy and reserve services. Then, Liquid Air Energy Storage operation as a multi-energy asset is studied. To conclude, the potential of six between established and novel thermo-mechanical energy storage concepts is cross-compared and benchmarked with incumbent storage technologies for long-duration energy storage applications