Increasing concerns about global warming have led to the exploration of options to abate CO2 emissions. Recent studies have identified the energy sector as the largest emitting source worldwide. Therefore, the transition towards low-carbon power systems has incorporated larger volumes of renewable generation. This situation is prompting the necessity of improving current strategies to operate power systems, as more variability is introduced in the decision making process. This thesis contributes in two aspects to manage the generation mix of future power systems. Firstly, it addresses the question of how many scenarios are enough to represent the variability of wind power. Results obtained indicate that a balance should be pursued between quality of solution and computational burden, as more scenarios does not significantly change the operational cost. Secondly, an original method to narrow down the number of scenarios is proposed. The so-called severe scenarios outperform typical reductions in the sense that fewer adjustments are required to the generation scheduling programme. Despite the growing renewable generation capacity, the operation of the electric system is likely to continue its reliance on thermal plants. Hence, the need to curb CO2 emissions in the existing thermal plants has led to the development of technologies such as carbon capture. The technical maturity of this technology is still in its early stages, since its application to thermal plants is under development. This thesis bridges the gap of current knowledge on carbon capture in three aspects. Firstly, it presents an innovative methodology to quantify the value of flexibility provided by carbon capture in the context of the British system. Secondly, the role of retrofitted generators as reserve providers is addressed. Finally, the synergy between carbon capture and wind power is assessed. The evaluation considers CO2 pricing, two strategies to manage CO2 capture rate, variability and different levels of wind integration.

The contributions of this research are a scalable formulation and solution method for decentralized unit commitment, experimental results comparing decentralized unit commitment solution times to conventional unit commitment methods, a demonstration of the benefits of faster unit commitment computation time, and extensions of decentralized unit commitment to handle system network security constraints. We begin with a discussion motivating the shift from centralized power system control architectures to decentralized architectures and describe the characteristics of such an architecture. We then develop a formulation and solution method to solve decentralized unit commitment by adapting an existing approach for separable convex optimization problems to the nonconvex domain of unit commitment. The potential computational speed benefits of the novel decentralized unit commitment approach are then further investigated through a rolling-horizon framework that represents how system operators make decisions and adjustments online as new information is revealed. Finally, the decentralized unit commitment approach is extended to include network contingency constraints, a crucial function for the maintenance of system security. The results indicate decentralized unit commitment holds promise as a way of coordinating system operations in a future decentralized grid and also may provide a way to leverage parallel computing resources to solve large-scale unit commitment problems with greater speed and model fidelity than is possible with conventional methods.

Keeping a contingency reserve in power systems is necessary to preserve the security of real-time operations. This work studies two different approaches to the optimal allocation of energy and reserves in the day-ahead generation scheduling process. Part I presents a stochastic security-constrained unit commitment model to co-optimize energy and the locational reserves required to respond to a set of uncertain generation contingencies, using a novel state-based formulation. The model is applied in an offer-based electricity market to allocate contingency reserves throughout the power grid, in order to comply with the N-1 security criterion under transmission congestion. The objective is to minimize expected dispatch and reserve costs, together with post contingency corrective redispatch costs, modeling the probability of generation failure and associated post contingency states. The characteristics of the scheduling problem are exploited to formulate a computationally efficient method, consistent with established operational practices. We simulated the distribution of locational contingency reserves on the IEEE RTS96 system and compared the results with the conventional deterministic method. We found that assigning locational spinning reserves can guarantee an N-1 secure dispatch accounting for transmission congestion at a reasonable extra cost. The simulations also showed little value of allocating downward reserves but sizable operating savings from co-optimizing locational nonspinning reserves. Overall, the results indicate the computational tractability of the proposed method. Part II presents a distributed generation scheduling model to optimally allocate energy and spinning reserves among competing generators in a day-ahead market. The model is based on the coordination between individual generators and a market entity. The proposed method uses forecasting, augmented pricing and locational signals to induce efficient commitment of generators based on firm posted prices. It is price-based but does not rely on multiple iterations, minimizes information exchange and simplifies the market clearing process. Simulations of the distributed method performed on a six-bus test system showed that, using an appropriate set of prices, it is possible to emulate the results of a conventional centralized solution, without need of providing make-whole payments to generators. Likewise, they showed that the distributed method can accommodate transactions with different products and complex security constraints.

In this thesis, the Hybrid Genetic Algorithm-Ant Colony Optimization (GACO) approach is presented to solve the unit commitment problem (UC), and comparison with the results obtained using literature methods. Then this thesis applied the ability of the Genetic Algorithm (GA) operated after Ant Colony Optimization (ACO) can promote the ACO efficiency. The objective of GA is to improve the searching quality of ants by optimizing themselves to generate a better result, because the ants produced randomly by pheromone process are not necessary better. This method can not only enhance the neighborhood search, but can also search the optimum solution quickly to advance convergence. The other objective of this thesis is to investigate an influence of emission constraints on generation scheduling. The motivation for this objective comes from the efforts to reduce negative trends in a climate change. In this market structure, the independent power producers have to deal with several complex issues arising from uncertainties in spot market prices, and technical constraints which need to be considered while scheduling generation and trading for the next day. In addition to finding dispatch and unit commitment decisions while maximizing its profit, their scheduling models should include trading decisions like spot-market buy and sell. The model proposed in this thesis build on the combined carbon finance and spot market formulation, and help generators in deciding on when these commitments could be beneficial.

In recent years the deregulation of energy markets and expansion of volatile renewable energy supplies has triggered an increased interest in stochastic optimization models for thermal and hydro-thermal scheduling. Several studies have modelled this as stochastic linear or mixed-integer optimization problems. Although a variety of efficient solution techniques have been developed for these models, little is published about the added value of stochastic models over deterministic ones. In the context of day-ahead and intraday unit commitment under wind uncertainty, we compare two-stage and multi-stage stochastic models to deterministic ones and quantify their added value. We show that stochastic optimization models achieve minimal operational cost without having to tune reserve margins in advance, and that their superiority over deterministic models grows with the amount of uncertainty in the relevant wind forecasts. We present a modification of the WILMAR scenario generation technique designed to match the properties of the errors in our wind forcasts, and show that this is needed to make the stochastic approach worthwhile. Our evaluation is done in a rolling horizon fashion over the course of two years, using a 2020 central scheduling model of the British National Grid with transmission constraints and a detailed model of pump storage operation and system-wide reserve and response provision. Solving stochastic problems directly is computationally intractable for large instances, and alternative approaches are required. In this study we use a Dantzig-Wolfe reformulation to decompose the problem by scenarios. We derive and implement a column generation method with dual stabilisation and novel primal and dual initialisation techniques. A fast, novel schedule combination heuristic is used to construct an optimal primal solution, and numerical results show that knowing this solution from the start also improves the convergence of the lower bound in the column generation method significantly. We test this method on instances of our British model and illustrate that convergence to within 0.1% of optimality can be achieved quickly.

As compared to short-term forecasting (e.g., 1 day), it is often challenging to accurately forecast the volume of precipitation in a medium-term horizon (e.g., 1 week). As a result, fluctuations in water inflow can trigger generation shortage and electricity price spikes in a power system with major or predominant hydro resources. In this paper, we study a two-stage robust scheduling approach for a hydrothermal power system. We consider water inflow uncertainty and employ a vector autoregressive (VAR) model to represent its seasonality and accordingly construct an uncertainty set in the robust optimization approach. We design a Benders' decomposition algorithm to solve this problem. Results are presented for the proposed approach on a real-world case study.

In the effort to reduce the release of harmful gases associated with the transportation sector, Plug-in Electric Vehicles (PEV) have been deployed on the account of zero-tail pipe emissions. With electrification of transport it is imperative to address the electrical grid emissions during vehicle charging by motivating the use of distributed generation. This thesis employs optimal charging strategies based on solar availability and electrical grid tariffs to minimize the cost of retrofitting an existing parking lot with photovoltaic (PV) and PEV infrastructure. The optimization is cast as a unit-commitment problem using the CPLEX optimization tool to determine the optimal charge scheduling. The model determines the optimal capacity of system components and assesses the techno-economic feasibility of PV infrastructure in the microgrid by minimizing the net present cost (NPC) in two case studies: Victoria, BC and Los Angeles, CA. It was determined that due to a relatively low grid tariff and scarcity of solar irradiation, it is not economically feasible to install solar panels and coordination of charging reduces the operating cost by 11% in Victoria. Alternatively, with a high grid tariff and abundance of solar radiation, it shown that Los Angeles is a promising candidate for PV installations. With the implementation of a charging coordination scheme in this region, NPC savings of 8-16% are simulated with the current prices of solar infrastructure. Additionally, coordinated charging was assessed in conjunction with various commercial buildings posing as a base load and it was determined that the effects of coordination were more prominent with smaller base loads. / Graduate

An Integrated Chaos Search Genetic Algorithm (CGA) /Fuzzy System (FS), Tabu Search (TS) and Neural Fuzzy Network (NFN) method for load forecasting is presented in this paper. A Fuzzy Hyper-Rectangular Composite Neural Networks (FHRCNNs) was used for the initial load forecasting. Then we used CGAFS and TS to find the optimal solution of the parameters of the FHRCNNs, instead of Back-Propagation (BP). First the CGAFS generates a set of feasible solution parameters and then puts the solution into the TS. The CGAFS has good global optimal search capabilities, but poor local optimal search capabilities. The TS method on the other hand has good local optimal search capabilities. We combined both methods to try and obtain both advantages, and in doing so eliminate the drawback of the traditional ANN training by BP. This thesis presents a hybrid Chaos Search Immune Algorithm (IA)/Genetic Algorithm (GA) and Fuzzy System (FS) method (CIGAFS) for solving short-term thermal generating unit commitment problems (UC). The UC problem involves determining the start-up and shutdown schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy other constraints such as the generating limits per unit, reserve and individual units. We combined IA and GA, then added chaos search and fuzzy system approach in it. Then we used the hybrid system to solve UC. Numerical simulations were carried out using four cases; ten, twenty and thirty thermal units power systems over a 24-hour period.

Carbon dioxide (CO₂) capture and sequestration (CCS) at fossil-fueled power plants is a critical technology for CO₂ emissions mitigation during the transition to a sustainable energy system. Post-combustion amine scrubbing is a relatively mature CO₂ capture technology, but barriers to implementation include high capital costs and energy requirements that reduce net power output by 20-30%. Capture energy requirements are typically assumed constant, but work investigates whether flexibly operating amine scrubbing systems in response to electricity market conditions can add value to CO₂ capture facilities while maintaining environmental benefits. Two versatile optimization models have been created to study the electricity system implications of flexible CO₂ capture. One model assesses the value of flexible capture at a single facility in response to volatile electricity prices, while the other represents a full electricity system to study the ability of flexible capture to meet electricity demand and reliability (ancillary) service requirements. Price-responsive flexible CO₂ capture has limited value at market conditions that justify CO₂ capture investments. Solvent storage can add value for price arbitrage by allowing flexible operation without additional CO₂ emissions, but only with favorable capital costs. The primary advantage of flexible CO₂ capture is an increased ability to provide grid reliability services and improve grid resiliency at minimum and maximum electricity demand. Flexibility mitigates capacity shortages because capture energy requirements need not be replaced, and variable capture at low demand helps respond to intermittent renewable generation. / text

In this thesis, I present four research papers related by their focus on environmental and economic effects of low-carbon policies and technologies in electric power. The papers address a number of issues related to the operation and design of CCS-equipped plants with solvent storage and bypass, the effect of Renewable Portfolio Standards (RPS) on cycling of coal-fired power plants, and the EPA’s proposed CO2 emissions rule for existing power plants. In Chapter 2, I present results from a study of the design and operation of power plants equipped with CCS with flue gas bypass and solvent storage. I considered whether flue gas bypass and solvent storage could be used to increase the profitability of plants with CCS. Using a pricetaker profit maximization model, I evaluated the increase in NPV at a pulverized coal (PC) plant with an amine-based capture system, a PC plant with an ammonia-based capture system, and a natural gas combined-cycle plant with an amine-based capture system when these plants were equipped with an optimally sized solvent storage vessel and regenerator. I found that while flue gas bypass and solvent storage increased profitability at low CO2 prices, they ceased to do so at CO2 prices high enough for the overall plant to become NPV-positive. In Chapter 3, I present results from a Unit Commitment and Economic Dispatch model of the PJM West power system. I quantify the increase in cycling of coal-fired power plants that results when complying with a 20% RPS using wind power, accounting for cycling costs not usually included in power plant bids. I find that while additional cycling does increase cycling-related production costs and emissions of CO2, SO2, and NOX, these increases are small compared to the overall reductions in production costs and air emissions that occur with high levels of wind. In proposing its existing power plant CO2 emissions standard, the Environmental Protection Agency determined that significant energy efficiency would be available to aid in compliance. In Chapter 4, I use an expanded version of the model of Chapter 3 to evaluate compliance with the standard with and without this energy efficiency, as well as under several other scenarios. I find that emissions of CO2, SO2, and NOX are relatively insensitive to the amount of energy efficiency available, but that production costs increase significantly when complying without efficiency. In complying with the EPA’s proposed existing power plant CO2 emissions standard, states will have the choice of whether to comply individually or in cooperation with other states, as well as the choice of whether to comply with a rate-based standard or a mass-based standard. In Chapter 5, I present results from a linear dispatch model of the power system in the continental U.S. I find that cooperative compliance reduces total costs, but that certain states will prefer not to cooperate. I also find that compliance with a mass-based standard increases electricity prices by a larger margin than does compliance with a rate-based standard, with implications for the distribution of surplus changes between producers and consumers.