Power systems generation scheduling and optimisation using evolutionary computation techniques

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.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.Overseas Development Agenc

Long term hydrothermal scheduling of the brazilian integrated system based on model predictive control

Orientador: Secundino Soares FilhoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: O planejamento da operação energética do Sistema Interligado Nacional (SIN) é uma tarefa complexa realizada por meio de uma cadeia de modelos de médio, curto e curtíssimo prazo acoplados entre si, cada um com considerações pertinentes à etapa que aborda. A proposta deste trabalho é apresentar uma alternativa para o planejamento da operação energética de médio prazo. Foi desenvolvida uma metodologia baseada em modelo de controle preditivo, abordando os aspectos estocásticos do problema de forma implícita pela utilização de valores esperados das vazões, e fazendo uso de um modelo determinístico de otimização a usinas individualizadas, que possibilita uma representação mais precisa do sistema hidrotérmico. A análise de desempenho é feita através de simulações da operação, considerando os parques hidrelétrico e termelétrico que compõem o SIN, com restrições operativas reais, em configuração dinâmica, com plano de expansão e a possibilidade de intercâmbio e importação de mercados vizinhos. Os resultados são comparados aos fornecidos pela metodologia em vigor no setor elétrico brasileiro, notadamente o modelo NEWAVE, que determina as decisões de geração por subsistema, e o modelo Suishi-O, que as desagrega por usinas individualizadasAbstract: The long term hydrothermal scheduling of the Brazilian Integrated System (SIN) is a complex task solved by a chain of long, medium and short term coupled models, each one with considerations pertinent to the stage of operation that it deals with. The proposal of this work is to present an alternative for the long term hydrothermal scheduling. A methodology based on model predictive control was developed, implicitly handling stochastic aspects of the problem by the use of inflows expected values, and making use of a deterministic optimization model to obtain the optimal dispatch for individualized plants, what makes possible a more accurate representation of the hydrothermal system. The performance analysis is made through simulations of the operation, taking into consideration all the hydro and thermal plants that compose the SIN, with real operative constraints, in dynamic configuration, with its expansion plan and the possibility of interchange and importation from neighboring markets. The results are compared with those provided by the approach actually in use by the Brazilian electric sector, specifically the NEWAVE model, which defines the generation decisions for the subsystems, and the Suishi-O model, that disaggregates them for the individualized plantsDoutoradoEnergia EletricaDoutor em Engenharia Elétric

Optimisation de la planification court-terme d’un système de production hydroélectrique de grande taille

RÉSUMÉ: La planification de la production hydroélectrique vise à optimiser l’utilisation future d’un ensemble de centrales, afin de maximiser son efficacité tout en satisfaisant la charge électrique et de très nombreuses contraintes opérationnelles. A court terme, l’horizon étudié est typiquement de 7 à 15 jours au pas de temps horaire. Les décisions concernent l’état de fonctionnement de chaque groupe turbine-alternateur dans les centrales, les débits turbinés par les groupes en fonctionnement et les débits déversés par les ouvrages d’évacuation. Les contraintes opérationnelles se rapportent notamment à la sécurité des équipements et des personnes, à la fiabilité du système de production en énergie, en puissance et en transport, aux maintenances des équipements, aux transactions sur le marché de l’énergie et aux accords touristiques et environnementaux. Ces contraintes proviennent de très nombreux agents avec des objectifs parfois contradictoires. Ce problème est complexe à résoudre, car il est combinatoire et de très grande taille. De plus, les vallées hydrauliques lient les décisions dans le temps et dans l’espace. Il existe de très nombreuses méthodes de résolution pour ce problème, mais aucune ne permet de résoudre des problèmes réels de grande taille dans des temps opérationnels. L’objectif de cette thèse est de développer des méthodes de résolution rapides et précises pour des systèmes de production de grande taille, comme celui d’Hydro-Québec. Afin de répondre à cet objectif, nous proposons trois modèles et méthodes de résolution qui exploitent la structure du problème de planification court-terme dans chacun de ses contextes d’utilisation. La première méthode de résolution est dédiée aux analyses d’impact, qui permettent aux planificateurs d’évaluer le coût d’une décision de gestion. Les décisions évaluées sont par exemple des retraits d’équipement pour maintenance. La méthode de résolution doit fournir des solutions quasi optimales, ainsi qu’une mesure de la distance à la solution optimale. Nous avons donc développé un modèle de programmation linéaire en nombres entiers (PLNE) ainsi qu’une méthode de décomposition qui tire parti du petit nombre de contraintes liantes entre les vallées hydrauliques. Le système de production est partitionné en sous-systèmes géographiques, sans restriction sur la partition. La méthode de résolution est basée sur la relaxation lagrangienne et se déroule en trois phases. Tout d’abord, la relaxation linéaire est résolue, afin d’estimer la valeur duale des contraintes liant les sous-systèmes. Ensuite, les sousproblèmes de planification associés aux sous-systèmes sont résolus en conservant les variables entières. Enfin, le problème global est résolu en fixant les variables entières avec les résultats obtenus dans la résolution des sous-problèmes. De plus, la valeur de la relaxation linéair permet de calculer une borne sur l’écart à la solution optimale. Les résultats numériques montrent que notre méthode fournit des solutions extrêmement proches de l’optimum dans des temps opérationnels. La deuxième méthode vise à ajuster rapidement un plan de production suite à une perturbation dans les intrants. L’objectif est ici de permettre aux planificateurs de valider rapidement la faisabilité du plan de production moyen-terme en considérant toutes les contraintes opérationnelles. Nous proposons un modèle mathématique précis et une méthode de résolution basée sur la recherche tabou. Afin de rendre la recherche efficace, nous avons développé de nouveaux voisinages larges qui exploitent les écarts entre l’offre et la demande, ainsi que la configuration des centrales dans les vallées. De plus, nous avons décomposé l’horizon par journée pour paralléliser la recherche tabou. Ce parallélisme permet de gérer de longs horizons, ce qui est essentiel pour attacher la planification court-terme aux décisions prises à moyen terme. Nous avons comparé notre méthode à un modèle de PLNE résolu avec un solveur générique. Même avec la solution initiale la plus naïve, notre recherche tabou parvient à des solutions proches de l’optimum dans des temps opérationnels, et jusqu’à 235 fois plus vite qu’un solveur générique. La troisième méthode considère le processus opérationnel de planification court-terme à Hydro-Québec, qui a pour but de fournir des consignes de production aux répartiteurs. Afin de gérer l’incertitude sur les intrants, les planificateurs fournissent des règles de gestion plutôt qu’un plan de production. Nous avons donc développé une nouvelle forme de règles de gestion, adaptée aux vallées hydrauliques complexes et aux réservoirs très contraints. Ces règles sont évaluées sur un arbre de scénarios, représentant l’incertitude sur les apports en eau et la charge électrique. Nous avons optimisé l’ordre d’engagement des centrales défini dans ces règles à l’aide d’une recherche tabou. De plus, nous avons étudié la nécessité de prendre en compte cette incertitude dans l’optimisation. Les résultats numériques montrent que l’optimisation stochastique permet de réduire significativement la valeur objectif de la solution. Afin de répondre aux contraintes de temps opérationnelles, nous proposons une optimisation hybride : déterministe durant les premières itérations de la recherche tabou, puis stochastique. Cette méthode hybride permet d’améliorer la valeur objective moyenne de 10% lorsque les apports prévus sont moyens ou élevés.----------ABSTRACT: Hydropower production planning aims at determining the future use of a set of hydro plants to maximize their efficiency, while satisfying the electrical load and a large number of operational constraints. At short-term, the horizon ranges from 7 to 15 days with hourly time steps. Decisions concern the operating status of each generating unit in hydro plants, the turbined flow in the functioning units, and the spilled flow in spillways. Operational constraints involve for instance safety of equipment and people, reliability of the production system, regulations on environment and tourism, equipment maintenance and transactions in the energy market. All these constraints are requests from many stakeholders with sometimes contradictory objectives. The short-term hydropower production planning is hard to solve, because it is of large scale and combinatorial. Moreover, hydro-valleys link decision variables both in time and space. One can find many solution approaches in the literature, but they are not adapted to realworld large-scale production systems. The purpose of this thesis is to develop fast and accurate solution approaches for large-scale hydropower production systems, in particular Hydro-Québec’s. We propose two new models and three new solution methods, which exploit the problem structure in each context of use. The first approach is dedicated to impact analysis. Planners make this type of analysis to evaluate the cost of a given decision, for example the some equipment outage for maintenance. Therefore, the solution approach must provide near-optimal solutions and a measure of the gap to the optimum. We developed a new mixed-integer linear problem (MILP) and a new solution method. Our three-phase method approach exploits the small number of constraints between hydro valleys and is based on lagrangian decomposition. First, we solve the linear relaxation of the problem to estimate the dual value of the linking constraints. Then, the production system is partitioned into subsystems and the associated MILP subproblems are solved. Finally, the initial planning problem is solved with the commitment decisions fixed as in the subproblems solutions. Numerical experiments show that our solution approach leads to near-optimal solutions within operational computation time. The second approach aims at quickly adjusting a given production planning after some perturbation in the data. Short-term planners will then be able to quickly validate the feasibility of the medium-term planning decisions while considering all operational constraints. We developed a new non-restrictive mathematical model and a solution method based on tabu search. To find good solutions quickly, we proposed new neighborhoods which consider the current solution flexibility to satisfy the total load and reservoir volume bounds. Moreover, tabu search is performed in parallel for each day of the horizon. Hence, our approach can handle a long horizon. We compared our method with a classical MILP approach. Even with the most naive initial solution, our tabu search obtain solutions close to the optimum within operational time. The third approach concerns the operational planning process from the short term to real time at Hydro-Québec. Short-term planners must provide daily instructions to real-time operators. To tackle uncertainty, planners provide operating rules instead of production schedules. We developed a new form of operating rules specially designed to handle large hydropower production systems with complex hydro-valleys and tightly bounded reservoirs. These rules are evaluated on a scenario tree, representing uncertain water inflows and electrical load, and optimized in a tabu search framework. We also evaluated the value of stochastic optimization for operating rules. Numerical results show that stochastic optimization has a significant value on scenarios with moderate or high inflows. However, it must be coupled with deterministic optimization to obtain good solutions when computational time is limited

Assessing electricity prices and their driving mechanisms in Brazil with neural networks

In general, electricity prices are very volatile and derive from many external variables. In Brazil, this price is determined by computer models developed and operated by government organizations. The supply and demand relationships are not enough to determine prices in Brazilian submarkets. Due to the particularities of the predominance of hydroelectric production in the country and many regulatory factors, electricity prices in Brazil carry a high level of uncertainty to be managed by market participants. The Brazilian electricity Settlement Price is defined by the composition of three models: NEWAVE, DECOMP and DESSEM; for long-, mid- and short-term predictions, respectively. The prices are based on the Operational Marginal Cost, which those models aim to minimize especially by outputting the cheapest hydrothermal operational settings that can attend the electricity demand. To minimize the prices uncertainty, this research proposes investigating the feasibility of developing a predictive model supported by the time series Machine Learning technique, the Long Short-Term Memory (LSTM). This tool is part of a theoretical framework called Recurrent Neural Networks (RNNs). The raw material for this work is the combination of literature on the history of the Brazilian Energy Market and its particularities, in addition to studies on Neural Network technologies and LSTM applications, as well as real historical data related to electricity price in the country. Accordingly, this work compiles data from June 2001 to April 2023, weekly and by submarket, which represents the input variables of the proposed model. The product of this work revolves around a predictive model programmed in Python with support from the Keras library, capable of predicting 4 weekly prices ahead. In addition, a comparative analysis is registered between the results of the LSTM and DECOMP models, which is the one already widely used on the Brazilian market. For this evaluation, performance indicators were used on the assertiveness of the predicted absolute values, the direction of the predicted price, and the predicted volatility. The results show that the LSTM model was significantly more accurate with respect to direction and volatility and less accurate with respect to the absolute values of the predicted prices

Forecasting tools and probabilistic scheduling approach incorporatins renewables uncertainty for the insular power systems industry

Nowadays, the paradigm shift in the electricity sector and the advent of the smart grid, along with the growing impositions of a gradual reduction of greenhouse gas emissions, pose numerous challenges related with the sustainable management of power systems. The insular power systems industry is heavily dependent on imported energy, namely fossil fuels, and also on seasonal tourism behavior, which strongly influences the local economy. In comparison with the mainland power system, the behavior of insular power systems is highly influenced by the stochastic nature of the renewable energy sources available. The insular electricity grid is particularly sensitive to power quality parameters, mainly to frequency and voltage deviations, and a greater integration of endogenous renewables potential in the power system may affect the overall reliability and security of energy supply, so singular care should be placed in all forecasting and system operation procedures. The goals of this thesis are focused on the development of new decision support tools, for the reliable forecasting of market prices and wind power, for the optimal economic dispatch and unit commitment considering renewable generation, and for the smart control of energy storage systems. The new methodologies developed are tested in real case studies, demonstrating their computational proficiency comparatively to the current state-of-the-art

Simulated Annealing

The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine

Flatness-Based Control Methodologies to Improve Frequency Regulation in Power Systems with High Penetration of Wind

To allow for high penetration of distributed generation and alternative energy units, it is critical to minimize the complexity of generator controls and to minimize the need for close coordination across regions. We propose that existing controls be replaced by a two-tier structure of local control operating within a global context of situational awareness. Flatness as an extension of controllability for non-linear systems is a key to enabling planning and optimization at various levels of the grid in this structure. In this study, flatness-based control for: one, Automatic Generation Control (AGC) of a multi-machine system including conventional generators; and two, Doubly fed Induction Machine (DFIG) is investigated. In the proposed approach applied to conventional generators, the local control tracks the reference phase, which is obtained through economic dispatch at the global control level. As a result of applying the flatness-based method, an $n$ machine system is decoupled into n linear controllable systems in canonical form. The control strategy results in a distributed AGC formulation which is significantly easier to design and implement relative to conventional AGC. Practical constraints such as generator ramping rates can be considered in designing the local controllers. The proposed strategy demonstrates promising performance in mitigating frequency deviations and the overall structure facilitates operation of other non-traditional generators. For DFIG, the rotor flux and rotational speed are controlled to follow the desired values for active and reactive power control. Different control objectives, such as maximum power point tracking (MPPT), voltage support or curtailing wind to contribute in secondary frequency regulation, can be achieved in this two-level control structure

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Electric generation production scheduling using a quasi-optimal sequential technique

Prepared in association with Electric Power Systems Engineering Laboratory and Dept. of Civil Engineering, M.I.TA quasi-optimal technique ('quasi' in that the technique discards unreasonable optimums), realized by a dynamically evolving mixed integer program, is used to develop regional electric power maintenance and production schedules for a two to five year planning horizon. This sophisticated, yet computationally feasible, method is used to develop the bulk dispatch schedules required to meet electric power demands at a given reliability level while controlling the associated dollar costs and environmental impacts. The electric power system considered is a power exchange pool of closely coupled generation facilities supplying a region approximately the size of New England. Associated with a tradeoff between a given cost of production and the relevant ecological factors, an optimum production schedule is formulated which considers fossil, nuclear, hydroelectric, gas turbine and pumped storage generation facilities; power demands, reliabilities, maintenance and nuclear refueling requisites; labor coordination, geographic considerations, as well as various contracts such as interregional power exchanges, interruptible loads, gas contracts and nuclear refueling contracts. A prerequisite of the model was that it be flexible enough for use in the evaluation of the optimum system performance associated with hypothesized expansion patterns. Another requirement was that the effects of changed scheduling factors could be predicted, and if necessary corrected with a minimum computational effort. A discussion of other possible optimization techniques is included, however, this study was primarily intended as a development of a static procedure; a dynamic technique counterpart with a more probabilistic. approach is being undertaken as a Part II of this study and at its conclusion the two techniques will be compared. Although the inputs are precisely defined, this paper does not deal explicitly with any of the fabrications of the required inputs to the model. Rather, it is meant as a method of incorporating those inputs into the optimum operation schedule process