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.

For the power market participators, there are competition and more trade opportunities in the power industry under the deregulation. In the electricity market, the bidding model is adopted instead of the cost model. GenCos try to maximize the profit under bidding model according to the power demand. Electricity becomes commodity and its price varies with power demand, bidding strategy and the grid. GenCos perform the unit commitment in a price volatile environment to reach the maximal profit. In a deregulation environment, Independent System Operator (ISO) is very often responsible for the electricity auction and secured power scheduling. The ISO operation may involve all kinds of risks. These risks include price volatility risk, bidding risk, congestion risk, and so on. For some markets, it is very important how GenCos determine the optimal unit commitment schedule considering risk management. A good risk analysis will help GenCo maximize profit and purse sustainable development. In this study, price forecasting is developed to provide information for power producers to develop bidding strategies to maximize profit. Profit-Based Unit Commitment (PBUC) model was also derived. An Enhanced Immune Algorithm (EIA) is developed to solve the PBUC problem. Finally, the Value-at-Risk (VAR) of GenCos is found with a present confident level. Simulation results provide a risk management rule to find an optimal risk control strategy to maximize profit and raise its compatibility against other players.

Unit commitment is one of the most important subjects with respect to the economical operation of power systems, which attempts to minimize the total thermal generating cost while satisfying all the necessary restrictive conditions. ¡@¡@This thesis proposes a short-term thermal generating unit commitment by genetic algorithm and back propagation network. Genetic algorithm is based on the optimization theory developed from natural evolution principles, and in the optimization process, seeks a set of solutions simultaneously rather than any single one by adopting stochastic movement rule from one solution to another, which prevents restriction to fractional minimal values. Neural networks method outperforms in speed and stability. This thesis uses back propagation network method to complete neural networks and sets the optimal unit combination derived from genetic algorithm as the target output. ¡@¡@Under fixed electrical systems, instant responsiveness can be calculated by neural networks. When the systematical architecture changes, genetic algorithm can be applied to re-evaluation of the optimal unit commitment, hoping to improve the pitfalls of traditional methods. ¡@¡@This thesis takes the power system of six units for example to conduct performance assessment. The results show that genetic algorithm provides solutions closer to the overall optimal solution than traditional methods in optimizing unit commitment. On the other hand, neural networks method can not only approximate the solution obtained by genetic algorithm but also process faster than any other methods.

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

The United State’s renewable portfolio standards call for a large increase of renewable energy and improved conservation efforts over today’s current system. Wind will play a ma jor role in meeting the renewable portfolio standards. As a result, the amount of wind capacity and generation has been growing exponentially over the past 10 to 15 years. The proposed unit commitment method integrates wind energy into a scheduable resource while keeping the formulation simple using mixed integer programming. A reserve constraint is developed and added to unit commitment giving the forecasted wind energy an effective cost. The reserve constraint can be scaled based on the needs of the system: cost, reliability, or the penetration of wind energy. The results show that approximately 24% of the load can be met in the given test system, while keeping a constant reliability before and after wind is introduced. This amount of wind will alone meet many of the renewable portfolio standards in the United States.