Optimal generation scheduling attempts to minimise the cost of power production while satisfying the various operation constraints and physical limitations on the power system components. The thermal generation scheduling problem can be considered as a power system control problem acting over different time frames. The unit commitment phase determines the optimum pattern for starting up and shutting down the generating units over the designated scheduling period, while the economic dispatch phase is concerned with allocation of the load demand among the on-line generators. In a hydrothermal system the optimal scheduling of generation involves the allocation of generation among the hydro electric and thermal plants so as to minimise total operation costs of thermal plants while satisfying the various constraints on the hydraulic and power system network. This thesis reports on the development of genetic algorithm computation techniques for the solution of the short term generation scheduling problem for power systems having both thermal and hydro units. A comprehensive genetic algorithm modelling framework for thermal and hydrothermal scheduling problems using two genetic algorithm models, a canonical genetic algorithm and a deterministic crowding genetic algorithm, is presented. The thermal scheduling modelling framework incorporates unit minimum up and down times, demand and reserve constraints, cooling time dependent start up costs, unit ramp rates, and multiple unit operating states, while constraints such as multiple cascade hydraulic networks, river transport delays and variable head hydro plants, are accounted for in the hydraulic system modelling. These basic genetic algorithm models have been enhanced, using quasi problem decomposition, and hybridisation techniques, resulting in efficient generation scheduling algorithms. The results of the performance of the algorithms on small, medium and large scale power system problems is presented and compared with other conventional scheduling techniques.

El objetivo general del presente trabajo de título es realizar, mediante la aplicación de criterios técnicos de ingeniería, una evaluación técnica del desempeño de los algoritmos de Relajación Lagrangeana (RL) y Branch and Bound (B&B) en la búsqueda de soluciones para el problema de optimización de corto plazo en el sistema eléctrico interconectado del norte grande (SING). En la primera parte de la memoria se muestra el planteamiento general del problema de optimización de la operación de corto plazo, el cual corresponde a un problema de optimización entero-mixto y un conjunto de restricciones lineales mediante las cuales se establecen las características técnicas del sistema. Por otra parte, la función objetivo de dicho problema de optimización corresponde a la minimización de los costos asociados a la operación de las unidades en el horizonte de tiempo evaluado. Posteriormente, se muestra una revisión del estado del arte presentando algunas de las principales técnicas utilizadas para resolver este tipo de problema: Lista de Prioridad, Programación Dinámica, Unit Decommitment, RL, Método de Benders, B&B y Algoritmos Genéticos. Para realizar la evaluación sobre los algoritmos de RL y B&B, se realizan programas en Matlab de dichos métodos con el objeto de realizar pruebas que permitan efectuar un análisis comparativo de los rendimientos de ambos algoritmos. Se aplican dichos programas para resolver problemas de predespacho en un modelo reducido del SING. De esta forma se puede observar el rendimiento de cada algoritmo respecto de su capacidad de obtener soluciones factibles, calidad de las soluciones, uso de heurística para generar soluciones y tiempos de ejecución requeridos. Adicionalmente, se puede estudiar la flexibilidad de ambos algoritmos para considerar restricciones de mayor complejidad y sus limitaciones para resolver predespacho en sistemas de dimensiones reales. Se concluye que el algoritmo que presenta un rendimiento que permite resolver de manera más eficiente el problema de predespacho en el SING corresponde al algoritmo RL, lo anterior debido principalmente a los tiempos de ejecución requeridos para su aplicación en sistemas de dimensiones reales y a que las soluciones generadas presentan una precisión del orden del 99% respecto a las soluciones generadas por el otro algoritmo. Adicionalmente, se puede acotar que las actuales políticas de operación aplicadas en el SING no representan una gran complejidad de programación y por lo tanto, la heurística requerida no presenta una complejidad adicional.

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We consider the problem of minimizing costs in the generation unit commitment problem, a cornerstone in electric power system operations, while enforcing an -- reliability criterion. This reliability criterion is a generalization of the well-known - criterion and dictates that at least fraction of the total system demand (for ) must be met following the failure of or fewer system components. We refer to this problem as the contingency-constrained unit commitment problem, or CCUC. We present a mixed-integer programming formulation of the CCUC that accounts for both transmission and generation element failures. We propose novel cutting plane algorithms that avoid the need to explicitly consider an exponential number of contingencies. Computational studies are performed on several IEEE test systems and a simplified model of the Western US interconnection network. These studies demonstrate the effectiveness of our proposed methods relative to current state-of-the-art.

Output power of hydro power plant was modelled and an optimization algorithm was implemented in a tool for optimizing hydro power plants. The tool maximizes power output of a hydro power plant by distributing water over a set of active units in the power plant which will be used in planning of electricity production. This tool was built in a MATLAB environment, using the optimization toolbox, and a GUI was developed for Vattenfall. The optimization tool was based on the same architecture as the current tool used for this kind of optimization which is to be replaced by the work presented in this thesis. Therefore, the goal was to achieve the same optimal results as the current optimization tool. Power output of three of Vattenfall’s hydro power plants were computed and two of these plants were optimized. These power output results were compared to results from the optimization tool currently used. This showed differences within the inaccuracy of measurements of ≤ 0.3%. These three power plants proved that the new tool is sufficient to replace the current tool but further testing is recommended to be conducted on more of Vattenfall’s hydro power plants to prove its consistency.

This PhD thesis proposes a bottom-up approach that accurately addresses the operational flexibility embedded in each part of a multi-energy system (MES). Several models which cover the simulations from replicating domestic electrified demands to power system scheduling are proposed. More specifically, a domes-tic multi-energy consumption model is firstly developed to simulate one minute resolution energy profiles of individual dwellings with the installation of prospec-tive technologies (i.e., electric heat pumps (EHPs), electric vehicles (EVs)). After-wards, a fast linear programming (LP) unit commitment (UC) model is devel-oped with the consideration of characteristics of generators and a full set of ancil-lary services (i.e., frequency response and reserves). More importantly, the fre-quency response requirements in low inertia systems are assessed with the con-sideration of three grid frequency regulations (i.e., rate of change of frequency, Nadir and quasi-steady state). Furthermore, the UC model has integrated vari-ous flexibility contributors in MES to provide ancillary and flexibility services, which include pumped hydro storages (PHSs), interconnectors, batteries and demand side resources (i.e., individual EHPs, heat networks, electrolysers). More importantly, the fast frequency response (FFR) provision from nonsynchronous resources is implemented and the demand response application of electrolysers is taken as an example to provide FFR in the UC model. By using the integrated UC model with the consideration of flexibility services provided by resources in the MES, the advantages of multi-energy operation can be clearly identified which can be used to inform system operators and policy makers to design and operate energy systems in a more economic and environment-friendly way.

The reliability issue of the infrastructure systems has become one of the major concerns of the system operators. This dissertation is a collection of four published and working papers that address the specific reliable design and operations problems from three different application settings: transportation/telecommunications network, distribution network, and power plant. In these four projects, key random factors like site disruption and uncertain demand are explicitly considered and proper research tools including stochastic programming, robust optimization, and variants of robust optimization are applied to formulate the problems based on which the important and challenging modelling elements (nonlinear congestion, disruption caused demand variation, etc.) can be introduced and studied. Besides, for each of the optimization models, we also develop advanced solution algorithms that can solve large-scale instances within a short amount of time and devise comprehensive numerical experiments to derive insights. The modelling techniques and solution methods can be easily extended to study reliability issues in other applications.

Recently, the concept of Microgrids (MG) has been introduced in the distribution network. Microgrids are defined as small power systems that consist of various distributed micro generators that are capable of supplying a significant portion of the local demand. Microgrids can operate in grid-connected mode, in which they are connected to the upstream grid, or in isolated mode, where they are disconnected from the upstream grid and the local generators are the only source of power supply. In order to maximize the benefits of the resources available in a microgrid, an optimal scheduling of the power generation is required. Renewable resources have an intermittent nature that causes uncertainties in the system. These added uncertainties must be taken into consideration when solving the generation scheduling problem in order to obtain reliable solutions. This research studies the scheduling of power generation in a microgrid that has a group of dispatchable and non-dispatchable generators. The operation of a microgrid during grid-connected mode and isolated mode is analyzed under variable demand profiles. Two mixed integer linear programming (MILP) models for the day-ahead unit commitment problem in a microgrid are proposed. Each model corresponds to one mode of operation. Uncertainty handling techniques are integrated in both models. The models are solved using the General Algebraic Modeling System (GAMS). A number of study cases are examined to study the operation of the microgrid and to evaluate the effects of uncertainties and spinning reserve requirement on the microgrid’s expenses.

Wind power is variable, uncertain, intermittent and site specific. The operating capacity credit associated with a wind farm is therefore considerably different from that assigned to a conventional generating unit and as wind penetrations in conventional power systems increase, it is vital that wind power be fully integrated in power system planning and operating protocols.<p> The research described in this thesis is focused on the determination of the operating capacity benefits associated with adding wind power to a conventional power system. Probabilistic techniques are used to quantify the risk and operating capacity benefits under various risk criteria. A short term wind speed probability distribution and short term wind power probability distribution forecasting model is presented and a multi-state model of a wind farm is utilized to determine several operating performance indices. The concepts and developed model are illustrated by application to two published test systems. The increase in peak load carrying capability attributable to added wind power is examined under a range of system operating conditions that include the effects of seasonality, locality and wind parameter trends. The operating capacity credit associated with dependent and independent wind farms is also examined. The dependent and independent conditions provide boundary values that clearly indicate the effects of wind speed correlation. Well-being analyses which incorporate the accepted deterministic criterion in an evaluation of the system operating state probabilities is applied to the wind integrated test systems using a novel approach to calculate the operating state probabilities. Most modern power systems are interconnected to one or more other power systems and therefore have increased access and exposure to wind power. This thesis examines the risk benefits associated with wind integrated interconnected power systems under various conditions using the two test systems.<p> The research described in this thesis clearly illustrates that the operating capacity benefits associated with wind power can be quantified and used in making generating capacity scheduling decisions in a wind integrated power system.

Wind power is variable, uncertain, intermittent and site specific. The operating capacity credit associated with a wind farm is therefore considerably different from that assigned to a conventional generating unit and as wind penetrations in conventional power systems increase, it is vital that wind power be fully integrated in power system planning and operating protocols.<p> The research described in this thesis is focused on the determination of the operating capacity benefits associated with adding wind power to a conventional power system. Probabilistic techniques are used to quantify the risk and operating capacity benefits under various risk criteria. A short term wind speed probability distribution and short term wind power probability distribution forecasting model is presented and a multi-state model of a wind farm is utilized to determine several operating performance indices. The concepts and developed model are illustrated by application to two published test systems. The increase in peak load carrying capability attributable to added wind power is examined under a range of system operating conditions that include the effects of seasonality, locality and wind parameter trends. The operating capacity credit associated with dependent and independent wind farms is also examined. The dependent and independent conditions provide boundary values that clearly indicate the effects of wind speed correlation. Well-being analyses which incorporate the accepted deterministic criterion in an evaluation of the system operating state probabilities is applied to the wind integrated test systems using a novel approach to calculate the operating state probabilities. Most modern power systems are interconnected to one or more other power systems and therefore have increased access and exposure to wind power. This thesis examines the risk benefits associated with wind integrated interconnected power systems under various conditions using the two test systems.<p> The research described in this thesis clearly illustrates that the operating capacity benefits associated with wind power can be quantified and used in making generating capacity scheduling decisions in a wind integrated power system.