Essays on Energy Economics and Environmental Policies

This dissertation contains three distinct empirical chapters in applied energy and environmental economics. Each chapter focuses on a unique set of research questions, methods, and data. The unifying motivation therein concerns the development of renewable or alternative low-carbon energy sources as a policy response to the challenges of climate change mitigation, local and regional environmental quality issues, and energy security concerns. Economic and environmental evaluation of the energy policies coupled with understanding energy use patterns is of paramount importance. Together, the empirical chapters focus on demand, supply, and policy aspects of energy markets in the United States (US). First, Chapter 2 evaluates the impacts of the Renewable Portfolio Standard (RPS) on renewable electricity capacity. RPS is a state-level policy that requires electricity suppliers to include a certain proportion (or quantity) of renewable electricity in their total electricity sales over a specified time period. The chapter employs a fixed-effects panel regression model and a spatial econometric methodology using panel data spanning 47 states between 1990 and 2014. Thus, and importantly, the analyses incorporate salient spatial and temporal heterogeneities of RPS (i.e., varying RPS features across states and years). The results illustrate that the RPS has driven a 194 MW increase in overall renewable capacity (representing more than one third of the average electricity capacity developed between 1990 and 2014 in 47 states). The results also suggest that the impacts of RPS, while exhibiting spatial dependencies, vary depending on the renewable energy source. RPS positively impacts renewable electricity capacity, the share of renewable electricity capacity in total electricity capacity, as well as the shares of solar and wind capacity in total electricity capacity (the impacts become 1.3 times larger for solar and about two thirds fold larger for wind with reference to their average counterparts). However, the impacts of RPS are not statistically significant for biomass or geothermal resources. With the consistent patterns of the impacts of RPS across modeling scenarios, RPS adoption or lack thereof in different states, policy age, provision of renewable energy certificates (REC), and annually mandated obligations for renewable electricity in the overall electricity mix are among the critical factors which determine the efficacy of RPS. The positive impacts of RPS on solar and wind capacity are consistent with the relatively emphasized focus of RPS legislation across states which serves to prioritize these two renewable energy sources. Notwithstanding limitations in the available data (and the possibility that improvements in this respect over time would enable a more nuanced and higher-resolution investigation), the current findings provide guidance on how RPS is performing. The significantly positive impact of flexible REC provisions (allowing REC to be generated in any state), coupled with spatial spillover effects indicate the interstate marketing possibilities of renewable energy (and energy credits). The results (with respect to the significant contribution of different RPS attributes) suggest that the critical role the state level policies can make to meet national level goals about climate change and energy mix. More specifically, the results imply that scaling up RPS proliferation across the states (guided by policy treatment effects, coupled with spatial dependencies of both the RPS and renewable electricity) and specifying RPS mandates by renewable energy sources (guided by significantly positive impacts for solar and wind), at least up to the point where renewable energy sector obtains efficiency gains (economies of scales and allocative efficiency) or to the situation where better alternative to the RPS becomes available (e.g., market based carbon pricing policy, which can be least-cost carbon mitigation mechanism), can play an important role in generating transformative advances in renewable electricity sector. Next, Chapter 3 reports on an economic and environmental assessment to determine the optimal manure management strategy for large dairies. More specifically, a cost-benefit analysis and a life cycle assessment are carried out based on publicly available secondary data, motivated by the fact that improper management of dairy manure can result in adverse environmental and public health impacts. The results illustrate the comparatively high economic and environmental benefits associated with an integrated framework of bioenergy production as an alternative approach to manure management. Analyses are conducted under several scenarios (exploring the potential market for nutrients and greenhouse gases), all of which confirm that co-producing bioenergy in this context is more profitable than traditional on-site management approaches. The results imply that the livestock sector can maximize economic and environmental gains by integrating nutrient recovery and bioenergy production in alternative manure management considerations (rather than simply considering dairy manure as a waste disposal problem). The final empirical investigation, Chapter 4, explores the temporal and spatial variation of sectoral natural gas demand in the US. A fixed-effects panel regression model is configured to analyze monthly data between 2001 and 2015. The results demonstrate the inelastic price responses at state, regional, and national levels across natural gas consumption sectors in the US, reflecting the importance of natural gas in contemporary energy systems. The implication is that price based policies, such as energy efficiency standards or energy saving targets in building codes, in the natural gas sector may not be effective (but, since the magnitudes of price elasticity vary across economic sectors, states and regions, efficacy of such price based policies will vary across these different dimensions). On the other hand, the inelastic price responses may reveal resiliency (i.e., stable market) of natural gas market to the changes in natural gas prices that may be driven by policy changes in other segment of the energy market (e.g., renewable energy supporting policies may increase natural gas prices). The resulting implication can be that natural gas that holds critical significance in the contemporary energy system from both environmental and economic perspectives can also serve as a transition fuel. The statistically significant weather impacts in terms of heating degree days (HDD) and cooling degree days (CDD) revealed in this analysis are consistent with the extant energy demand literature, where higher HDD stimulates greater consumption of natural gas in the residential sector while CDD appears to increase natural gas consumption for electricity production. The impacts with regard to weather attributes (HDD and CDD) also help to design informed policies to achieve various energy management goals (e.g., attaining energy efficiency or promoting alternative clean energy by quantifying the repercussions of changes in consumers’ responses to natural gas demand across climatic seasons in the energy market stability). Collectively, these empirical chapters offer novel and important implications concerning energy market structures (supply and demand aspects), the environmental and economic assessment for renewable energy production potentials, and the policy responses, which have been or should be designed, to ensure the multi-dimensional sustainability of complex energy systems

MULTI-DIMENSIONAL MODELING FOR ENVIRONMENTAL IMPACT ASSESSMENT AT INTERSECTIONS OF THE FOOD-ENERGY-WATER NEXUS

This dissertation uses multi-dimensional modeling for environmental impact assessment at intersections of the Food-Energy-Water (FEW) Nexus, including life cycle assessment (LCA) modeling for quantification of environmental impacts due to household FEW consumption, a linear regression framework for quantification of water-use impacts of marginal electricity generation, and a multi-objective optimization model to assess monetization of water withdrawals for electricity generation and impacts to water stress due to electricity dispatch schemes. Chapter 2 of this dissertation summarizes the development of an LCA model that quantifies the direct and indirect environmental impacts of household FEW consumption. The model is executed through a novel household consumption tracker called the HomeTracker. The result of this work is an open-source software application that has been used to support experimental research taking place in suburban households in the midwestern United States for identification of effective interventions to inform household consumption behavior change. Chapter 3 addresses the need to quantify the water-use impacts of marginal electricity generation. A linear regression methodology is used to quantify water withdrawal and consumption impacts due to marginal generation, and a case study is presented to demonstrate how the framework can be applied to generate marginal water factors (MWFs) at multiple temporal resolutions. Results illustrate that MWFs vary in space and time and are lower when renewables are deployed on the margin. Chapter 4 investigates the effect of implementing a dispatch cost per unit water withdrawals for electricity generation on water stress at the watershed scale. Impacts to water stress are assessed using a freshwater withdrawal to availability ratio, which quantifies water stress at the watershed level. Adding a dispatch cost per unit water withdrawal decreases water withdrawals up to 92% with a 45% increase in generation cost. The key contribution of this work is an advancement of knowledge of FEW Nexus systems at multiple spatial and temporal scales through life cycle assessment modeling, statistical modeling, and optimization modeling. Future work will include spatial and temporal improvements to models including expansion of geographic coverage and increased temporal resolution as data becomes available

Clustering based assessment of cost, security and environmental tradeoffs with possible future electricity generation portfolios

The electricity sector has a key role to play in the sustainable energy transition. The falling costs of wind and solar PV have added to both the opportunities yet also challenges of balancing sometimes competing industry objectives of costs, security, and environmental impacts. This paper presents novel techniques for assessing possible future industry generation portfolios in three ways: (1) incorporating explicit metrics for energy trilemma objectives into modelling, (2) using the optimization process of evolutionary programming to map the solution space of �high performing�, near least-cost, portfolio solutions, and (3) applying boundary min�max cases and clustering to categorize these varied portfolios to better facilitate planning and policy making. We use an open-source evolutionary programming tool, National Electricity Market Optimiser, to assess possible future generation portfolios for Indonesia�s Java-Bali interconnected power system. Our findings highlight the wide range of possible portfolios that might potentially deliver similar total industry costs, and their different security and environmental implications. In particular, additional solar photovoltaic deployment appears a low-risk opportunity to reduce costs and emissions compared to more fossil-fuel oriented mixes. Our novel techniques may be useful for the energy modelling community seeking to better understand and communicate complex, uncertain, and multi-dimensional choices for electricity industry planning

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Deep Reinforcement Learning Based Optimal Energy Management of Multi-energy Microgrids with Uncertainties

Multi-energy microgrid (MEMG) offers an effective approach to deal with energy demand diversification and new energy consumption on the consumer side. In MEMG, it is critical to deploy an energy management system (EMS) for efficient utilization of energy and reliable operation of the system. To help EMS formulate optimal dispatching schemes, a deep reinforcement learning (DRL)-based MEMG energy management scheme with renewable energy source (RES) uncertainty is proposed in this paper. To accurately describe the operating state of the MEMG, the off-design performance model of energy conversion devices is considered in scheduling. The nonlinear optimal dispatching model is expressed as a Markov decision process (MDP) and is then addressed by the twin delayed deep deterministic policy gradient (TD3) algorithm. In addition, to accurately describe the uncertainty of RES, the conditional-least squares generative adversarial networks (C-LSGANs) method based on RES forecast power is proposed to construct the scenarios set of RES power generation. The generated data of RES is used for scheduling to obtain caps and floors for the purchase of electricity and natural gas. Based on this, the superior energy supply sector can formulate solutions in advance to tackle the uncertainty of RES. Finally, the simulation analysis demonstrates the validity and superiority of the method.Comment: Accepted by CSEE Journal of Power and Energy System

Integration of Renewables in Power Systems by Multi-Energy System Interaction

This book focuses on the interaction between different energy vectors, that is, between electrical, thermal, gas, and transportation systems, with the purpose of optimizing the planning and operation of future energy systems. More and more renewable energy is integrated into the electrical system, and to optimize its usage and ensure that its full production can be hosted and utilized, the power system has to be controlled in a more flexible manner. In order not to overload the electrical distribution grids, the new large loads have to be controlled using demand response, perchance through a hierarchical control set-up where some controls are dependent on price signals from the spot and balancing markets. In addition, by performing local real-time control and coordination based on local voltage or system frequency measurements, the grid hosting limits are not violated

Planning framework and methods to assess possible future high renewable penetrations in emerging economy electricity industries and security, affordability, and environmental implications for Indonesia’s Java-Bali grid

Electricity industries worldwide are transitioning away from fossil-fuels towards wind and solar generation. While these technologies are now often cost-competitive as well as environmentally preferrable alternatives to coal and gas options, their highly variable output does raise challenges for delivering secure, affordable, and clean energy. This is particularly challenging for the electricity industries of emerging economies giving growing demand and limited financial resources. This thesis aims to address some of the limitations with existing frameworks, methods, and tools for assisting policymakers to plan electricity industry development, with a particular focus on better assessing future electricity generation options for emerging economies. It uses an open-source evolutionary programming-based optimisation model, National Electricity Market Optimiser (NEMO), to assess future generation options for the case study of Indonesia’s Java-Bali electricity grid. NEMO can model geographically and temporally variable wind and solar resources and solve least cost generation mixes in a highly configurable and transparent manner. A first study assessed the potential industry costs savings possible by recognising the reality of lower reliability standards in emerging economies than often assumed for modelling exercises. Accepting lower reliability outcomes not only reduces industry costs but also supports greater solar and wind deployment, hence better environmental outcomes. Next, the underlying evolutionary programming optimisation of NEMO was used to assess not just the least cost generation mix but the wider solution space, including generation portfolios that deliver total industry costs within 5% of the least cost solution highlighted the wide range of possible technology mixes that could potentially deliver a low cost future industry. Finally, NEMO was used to explore the potential implications of high variable renewable penetrations for operating reserves and hence power system security. The inevitability of some periods with both low wind and solar availability means that high renewables portfolios still feature significant dispatchable generation capacity. This means that the power system will generally have greater levels of operating reserves to cover possible plant failures than mixes with predominantly dispatchable generation. In summary, this thesis contributes to better understanding of the challenges and opportunities of deploying possible future high renewables in emerging economy electricity industries

A novel power management and control design framework for resilient operation of microgrids

This thesis concerns the investigation of the integration of the microgrid, a form of future electric grids, with renewable energy sources, and electric vehicles. It presents an innovative modular tri-level hierarchical management and control design framework for the future grid as a radical departure from the ‘centralised’ paradigm in conventional systems, by capturing and exploiting the unique characteristics of a host of new actors in the energy arena - renewable energy sources, storage systems and electric vehicles. The formulation of the tri-level hierarchical management and control design framework involves a new perspective on the problem description of the power management of EVs within a microgrid, with the consideration of, among others, the bi-directional energy flow between storage and renewable sources. The chronological structure of the tri-level hierarchical management operation facilitates a modular power management and control framework from three levels: Microgrid Operator (MGO), Charging Station Operator (CSO), and Electric Vehicle Operator (EVO). At the top level is the MGO that handles long-term decisions of balancing the power flow between the Distributed Generators (DGs) and the electrical demand for a restructure realistic microgrid model. Optimal scheduling operation of the DGs and EVs is used within the MGO to minimise the total combined operating and emission costs of a hybrid microgrid including the unit commitment strategy. The results have convincingly revealed that discharging EVs could reduce the total cost of the microgrid operation. At the middle level is the CSO that manages medium-term decisions of centralising the operation of aggregated EVs connected to the bus-bar of the microgrid. An energy management concept of charging or discharging the power of EVs in different situations includes the impacts of frequency and voltage deviation on the system, which is developed upon the MGO model above. Comprehensive case studies show that the EVs can act as a regulator of the microgrid, and can control their participating role by discharging active or reactive power in mitigating frequency and/or voltage deviations. Finally, at the low level is the EVO that handles the short-term decisions of decentralising the functioning of an EV and essential power interfacing circuitry, as well as the generation of low-level switching functions. EVO level is a novel Power and Energy Management System (PEMS), which is further structured into three modular, hierarchical processes: Energy Management Shell (EMS), Power Management Shell (PMS), and Power Electronic Shell (PES). The shells operate chronologically with a different object and a different period term. Controlling the power electronics interfacing circuitry is an essential part of the integration of EVs into the microgrid within the EMS. A modified, multi-level, H-bridge cascade inverter without the use of a main (bulky) inductor is proposed to achieve good performance, high power density, and high efficiency. The proposed inverter can operate with multiple energy resources connected in series to create a synergized energy system. In addition, the integration of EVs into a simulated microgrid environment via a modified multi-level architecture with a novel method of Space Vector Modulation (SVM) by the PES is implemented and validated experimentally. The results from the SVM implementation demonstrate a viable alternative switching scheme for high-performance inverters in EV applications. The comprehensive simulation results from the MGO and CSO models, together with the experimental results at the EVO level, not only validate the distinctive functionality of each layer within a novel synergy to harness multiple energy resources, but also serve to provide compelling evidence for the potential of the proposed energy management and control framework in the design of future electric grids. The design framework provides an essential design to for grid modernisation

Modelling scenarios for enhancing the effective implementation of secure, affordable and sustainable electricity on the Greek islands

The Greek islands’ power system is fragmented into 29 autonomous electrical systems relying on oil-fired generators to supply 82% of their electricity demand. Local power grids are only allowed to absorb a maximum renewable energy share of approximately 30% to secure the stability of the network and avoid abrupt frequency alterations. Inevitably, fossil-fuel dominated, isolated systems lead to increased generation costs, high carbon intensity and frequent power cuts. A novel integrated methodological approach has been developed to address these challenges consisting of: I) Long and short-term modelling considering interconnections and energy storage in the form of batteries versus the current energy autonomy, using the PLEXOS integrated energy model (Energy Exemplar, 2019) for a projection horizon extending between 2020 and 2040. II) ISLA demand model (Spataru, 2013), adapted to the Greek islands (ISLA_EGI), preceded by an extensive data processing, to anticipate annual demand scenarios. The two models inform each other and support the analysis of 35 scenarios. III) The development of methods to simulate electromobility in PLEXOS considering various charging strategies. This analysis contextualises the impact of innovative technologies in providing feasible solutions on the Greek islands in line with the Energy Trilemma Index (security, affordability, sustainability). It was concluded that when combining submarine interconnections and batteries (Scenario IB.x.1.0.a), generation prices were reduced by 42% at the regional and 10% at the national level compared to a BAU scenario (A.y.1.0.a), while carbon dioxide equivalent (CO2eq) emissions are reduced by 99% and 74% respectively. Also, power outage events are abolished. The benefits of a High-Efficiency demand scenario produced by ISLA_EGI show further reductions of 2.5% in emissions between 2020 and 2040. The results unveil that certain small, remote systems should remain autonomous, supported by battery storage. The operation of EVs highlights that primarily V2G scenarios and occasionally, scheduled unidirectional charging bring the ultimate benefits

Investigation on electricity market designs enabling demand response and wind generation

Demand Response (DR) comprises some reactions taken by the end-use customers to decrease or shift the electricity consumption in response to a change in the price of electricity or a specified incentive payment over time. Wind energy is one of the renewable energies which has been increasingly used throughout the world. The intermittency and volatility of renewable energies, wind energy in particular, pose several challenges to Independent System Operators (ISOs), paving the way to an increasing interest on Demand Response Programs (DRPs) to cope with those challenges. Hence, this thesis addresses various electricity market designs enabling DR and Renewable Energy Systems (RESs) simultaneously. Various types of DRPs are developed in this thesis in a market environment, including Incentive-Based DR Programs (IBDRPs), Time-Based Rate DR Programs (TBRDRPs) and combinational DR programs on wind power integration. The uncertainties of wind power generation are considered through a two-stage Stochastic Programming (SP) model. DRPs are prioritized according to the ISO’s economic, technical, and environmental needs by means of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The impacts of DRPs on price elasticity and customer benefit function are addressed, including the sensitivities of both DR parameters and wind power scenarios. Finally, a two-stage stochastic model is applied to solve the problem in a mixed-integer linear programming (MILP) approach. The proposed model is applied to a modified IEEE test system to demonstrate the effect of DR in the reduction of operation cost.A Resposta Dinâmica dos Consumidores (DR) compreende algumas reações tomadas por estes para reduzir ou adiar o consumo de eletricidade, em resposta a uma mudança no preço da eletricidade, ou a um pagamento/incentivo específico. A energia eólica é uma das energias renováveis que tem sido cada vez mais utilizada em todo o mundo. A intermitência e a volatilidade das energias renováveis, em particular da energia eólica, acarretam vários desafios para os Operadores de Sistema (ISOs), abrindo caminho para um interesse crescente nos Programas de Resposta Dinâmica dos Consumidores (DRPs) para lidar com esses desafios. Assim, esta tese aborda os mercados de eletricidade com DR e sistemas de energia renovável (RES) simultaneamente. Vários tipos de DRPs são desenvolvidos nesta tese em ambiente de mercado, incluindo Programas de DR baseados em incentivos (IBDRPs), taxas baseadas no tempo (TBRDRPs) e programas combinados (TBRDRPs) na integração de energia eólica. As incertezas associadas à geração eólica são consideradas através de um modelo de programação estocástica (SP) de dois estágios. Os DRPs são priorizados de acordo com as necessidades económicas, técnicas e ambientais do ISO por meio da técnica para ordem de preferência por similaridade com a solução ideal (TOPSIS). Os impactes dos DRPs na elasticidade do preço e na função de benefício ao cliente são abordados, incluindo as sensibilidades dos parâmetros de DR e dos cenários de potência eólica. Finalmente, um modelo estocástico de dois estágios é aplicado para resolver o problema numa abordagem de programação linear inteira mista (MILP). O modelo proposto é testado num sistema IEEE modificado para demonstrar o efeito da DR na redução do custo de operação