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Wind and solar energy are clean, free of fuel cost and likely to have great potential in the future. However, besides the technical difficulties associated with integrating variable sources of generation with the electric grid, high capital cost and other indirect costs to power system operations, such as ancillary service requirements, delay more widespread investment in wind and solar power plants. Current energy policies, especially renewable incentives and CO2 emission regulations, remain controversial and uncertain. Pumped-hydroelectric energy storage has proven to be valuable as bulk energy storage for energy arbitrage coordinating with conventional thermal generators. In the future grid, there are uncertainties, in terms of modeling and optimization, of assessing the value of bulk energy storage coordinating less with thermal generators and more with wind and solar. Moreover, the price of natural gas is predicted to have large variations in the next several decades. It is therefore necessary to construct a generation planning model with comprehensive modeling of wind, solar and energy storage under multiple scenarios of energy policies and natural gas prices. This dissertation presents such an optimal planning model using a multi-period optimization formulation and its implementation in the MATPOWER's extensible optimal power flow structure. A 3-bus test system is constructed to test the sensitivity of the planning model. This model is further applied to the reduced 240-bus Western Electric Coordinating Council (WECC) system to study more practical planning results.
This research is dedicated to the study of electric power system generation expansion planning considering uncertainty of climate change. Policymakers across the world are increasingly concerned about the effects of climate change and its impact on human systems when making decisions. Electric power Generation Expansion Planning (GEP) problems that determine the optimal expansion capacity and technology under particular technical constraints, given cost and policy assumptions are undoubtedly among those decisions. Now and in the future, climate change is and will be affecting new power plant investment decisions and the electricity generation system in more uncertain ways. The power system needs to be more reliable, cost-effective and environmentally friendly when exposed to higher temperature, less precipitation and more intense and frequent extreme events. However, incorporating the climate change effects into a GEP model has rarely been attempted before in the literature. The best approach to comprehensively model those uncertainties into electricity generation, and to optimize the generation planning under uncertainty needs be studied in a more specific way. In this research, a preliminary GEP model is proposed with available input data from various resources. Discrete scenarios and robust optimization are adopted to specifically model uncertainty. Relationships between climate change and GEP parameters are defined and considered in each scenario. The preliminary GEP model is then solved under each scenario to identify the climate change impact on the generation expansion planning decision. Two robust optimization models are presented and solved to find the optimal results under uncertainty: Model 1 is expected total cost minimization and Model 2 is maximum regret minimization. Both models find a compromise solution that is good for all scenarios, which avoids the possible risk associated with a poor decision that is only optimal for one particular scenario. The results suggest recommendations for further power system uncertainty modeling and risk management.
This book presents a panoramic look at the transformation of the transmission network in the context of the energy transition. It provides readers with basic definitions as well as details on current challenges and emerging technologies. In-depth chapters cover the integration of renewables, the particularities of planning large-scale systems, efficient reduction and solution methods, the possibilities of HVDC and super grids, distributed generation, smart grids, demand response, and new regulatory schemes. The content is complemented with case studies that highlight the importance of the power transmission network as the backbone of modern energy systems. This book will be a comprehensive reference that will be useful to both academics and practitioners.
This dissertation develops a set of analytical tools and conceptual frameworks to explore the socio-technical implications of transitioning to a low carbon energy future. The chapters here investigate the energy challenges in Sub-Saharan Africa and analyze power expansion pathways in Nigeria and Kenya, outline the development of a novel electricity modeling tool, and conceptualize an energy sovereignty framework to enable people-centered energy planning approaches. Chapter 2 presents an overview of Africa’s energy systems and the role renewable energy can play in supporting sustainable development in Africa, with a main focus on the challenges in Sub-Saharan Africa. I synthesize the most prominent papers in the past five years. I review the literature concerning the scale of generation expansion needed to achieve universal access in the region, the challenges of power sector finance, and the need for people-centered planning paradigms. Through an extensive literature review, I assess the capacity expansion needs of the region and highlight the policy lessons that enable private power sector investment such as transparent regulatory and procurement policies. I also present a critique of the socio-political implications of increased foreign investment in the region’s power sector. Finally, I present several studies that explore the need for people-centered planning approaches in order to achieve more equitable energy systems for all. I argue that renewable energy presents opportunities to achieve power systems expansion in an economically, environmentally and socially sustainable manner. To do this, Sub-Saharan Africa must adapt its planning strategies to holistically address the technical, economic and socio-political challenges it faces. Chapter 3 takes a deep-dive from an overview of Sub-Saharan Africa to a focus on Nigeria. I develop a first-order capacity expansion model to analyze power expansion scenarios in Nigeria. Nigeria serves as a case of countries with significant electricity demand growth that is constrained by under-developed grid infrastructure. I illustrate how the dependence on natural gas for generation has stifled the nation’s power supply, assess the role of renewable energy in meeting the nation’s electricity demand growth, and compare the cost of its current power generation expansion pathways to cost-optimized pathways. Using the capacity expansion model, I find that Nigeria’s current energy policy, known as Vision 30:30:30, perpetuates this heavy reliance on natural gas and significantly underestimates the role of solar energy in the future electricity mix. I also identify and assess lower cost alternative pathways which do not require any coal and nuclear generation expansion unlike the Vision 30:30:30 pathway. The results show that Nigeria will have to install at least an additional 38 GW by 2030 to keep up with grid-based demand growth alone - about eight times the current operational capacity. This chapter reveals Nigeria’s need for an energy policy reform that reduces its dependency on natural gas, eschews coal and nuclear expansion, and harnesses its abundant solar potential using centralized and distributed renewable energy technologies. Chapter 4 outlines my development of a novel open-access electricity modeling tool known as PROGRESS (Programmable Resource Optimization for Growth in Renewable Energy and Sustainable Systems). PROGRESS enables generation expansion modeling for countries with low availability and access to power systems data. The design of sustainable electricity systems needed to fuel development in regions with low electrification rates (such as Sub-Saharan Africa) requires context-specific power system modeling. Modeling data requirements for these regions, however, can be challenging for researchers and other stakeholders to access. This chapter presents a proof-of-concept description to show how PROGRESS works and then presents preliminary results for generation capacity expansion using the case of Kenya. Chapter 5 presents what is, for me, the most critical aspect of this dissertation. I explore how transitioning to low carbon energy systems and achieving universal electricity access will require not only an extensive redesign of the existing energy infrastructure but also a rethinking of energy planning approaches. I argue that innovation in decentralized and distributed energy technology transforms people from mere consumers to prosumers by empowering them to plan for their energy autonomously. I aim to connect the rise of prosumers with long-standing social movements that call for just, fair and sustainable energy systems. I draw from a rich literature of socio-energy concepts that aim to incorporate social and human dimensions into energy planning. I focus on energy justice, energy democracy, and I introduce energy sovereignty. I synthesize how these concepts together emphasize critical considerations for energy planning: “energy for whom, for what, and at whose costs?” I also introduce an additional consideration: “energy by whom?” and I conceptualize its framework in relation to electricity provision. I propose that “energy by whom?” is an essential question for re-envisioning a new energy paradigm and designing a low-carbon energy future. Overall, this dissertation contributes analytical and conceptual tools for low carbon energy systems, which together provide novel socio-technical approaches for planning towards a low carbon energy future, and urge on the paradigm shift to just and sustainable energy for all.
"Increasing penetration of renewable energy in the power grid is creating new challenges for generation planning. High levels of renewable energy - primarily wind and solar - results in high levels of uncertainty and variability at time scales as fast as seconds to minutes. As a result, conventional generation needs to have adequate flexibility to maintain the balance between supply and demand at fast time scales. Therefore, while planning the future conventional generation mix, a problem known as generation expansion planning (GEP), it is important for power system planners to incorporate flexibility. In particular, it is valuable to include high temporal resolution operational details into GEP in order to ensure that conventional generators will have sufficient flexibility to be able to counteract the intra-hour fluctuations in renewable generation. The main challenge for incorporating intra-hour operational details into GEP is that for existing formulations the problem size scales dramatically as the temporal resolution increases, leading to prohibitively high computational cost. To address this challenge, this dissertation proposes a novel, computationally efficient GEP formulation which ensures the selected generation mix has sufficient capacity and intra-hour flexibility to balance the variations in renewable generation. The method proceeds by using historical data to develop time-independent bounds on ramp (MW/5 min) as a function of load and renewable generation (MW). The solution quality of the proposed approach is validated using the IEEE RTS test case by comparison with two existing GEP techniques - unit clustering and representative weeks - and by assessing its sensitivity to generation parameters. The method yields generation mixes with costs as low or lower than the alternatives, but with much faster run times and including higher temporal resolution. The proposed approach is then extended to a novel problem: including investment in renewable generation as a decision variable, so that planners can make the optimal selection of both conventional and renewable generation. Results are shown for both wind and solar generation. A novel load shaping technique is developed to use demand response to contribute to countering the effect of renewable variability, and its impact on the displacement of conventional generation and on renewable energy integration is illustrated. Finally, the GEP with renewable investment is extended to a 25 year planning horizon which includes the retiring of existing units at the ends of their lifetimes and bringing new generation online in the years it is needed, while still ensuring sufficient intra-hour (5 minute) flexibility. Its performance is demonstrated on the IEEE RTS test case"--
The scope of this book covers the modeling and forecast of renewable energy and operation and planning of power system with renewable energy integration.The first part presents mathematical theories of stochastic mathematics; the second presents modeling and analytic techniques for renewable energy generation; the third provides solutions on how to handle the uncertainty of renewable energy in power system operation. It includes advanced stochastic unit commitment models to acquire the optimal generation schedule under uncertainty, efficient algorithms to calculate the probabilistic power, and an efficient operation strategy for renewable power plants participating in electricity markets.