The future smart grid is envisioned as a large scale cyber-physical system encompassing advanced power, computing, communications and control technologies. This work provides comprehensive accounts of the application with optimization methods, probability theory, commitment and dispatching technologies for addressing open problems in three emerging areas that pertain to the smart grid: unit commitment, service restoration problems in microgrid systems, and charging services for the plug-in hybrid electric vehicle (PHEV) markets. The work on the short-term scheduling problem in renewable-powered islanded microgrids is to determine the least-cost unit commitment (UC) and the associated dispatch, while meeting electricity load, environmental and system operating requirements. A novel probability-based concept, {\em probability of self-sufficiency}, is introduced to indicate the probability that the microgrid is capable of meeting local demand in a self-sufficient manner. Furthermore, we make the first attempt in approaching the mixed-integer UC problem from a convex optimization perspective, which leads to an analytical closed-form characterization of the optimal commitment and dispatch solutions. The extended research of the renewable-powered microgrid in the connection mode is the second part of this work. In this situation, the role of microgrid is changed to be either an electricity provider selling energy to the main grid or a consumer purchasing energy from the main grid. This interaction with the main grid completes work on the scheduling schemes. Third, a microgrid should be connected with the main grid most of the time. However, when a blackout of the main grid occurs, how to guarantee reliability in a microgrid as much as possible becomes an immediate question, which motivates us to investigate the service restoration in a microgrid, driven islanded by an unscheduled breakdown from the main grid. The objective is to determine the maximum of the expected restorative loads by choosing the best arrangement of the power network configurations immediately from the beginning of the breakdown all the way to the end of the island mode. Lastly, the work investigating the pricing strategy in future PHEV markets considers a monopoly market with two typical service classes. The unique characteristics of battery charging result in a piecewise linear quality of service model. Resorting to the concept of subdifferential, some theoretical results, including the existence and uniqueness of the subscriber equilibrium as well as the convergence of the corresponding subscriber dynamics are established. In the course of developing revenue-maximizing pricing strategies for both service classes, a general tradeoff has been identi ed between monetization and customer acquisition. / Graduate

The deployment of Renewable Energy (RE)-based generation has experienced a sustained global growth in the recent decades, driven by many countries' interest in reducing greenhouse gas emissions and dependence on fossil fuel for electricity generation. This trend is also observed in remote off-grid systems (isolated microgrids), where local communities, in an attempt to reduce fossil fuel dependency and associated economic and environmental costs, and to increase availability of electricity, are favouring the installation of RE-based generation. This practice has posed several challenges to the operation of such systems, due to the intermittent and hard-to-predict nature of RE sources. In particular, this thesis addresses the problem of reliable and economic dispatch of isolated microgrids, also known as the energy management problem, considering the uncertain nature of those RE sources, as well as loads. Isolated microgrids feature characteristics similar to those of distribution systems, in terms of unbalanced power flows, significant voltage drops and high power losses. For this reason, detailed three-phase mathematical models of the microgrid system and components are presented here, in order to account for the impact of unbalanced system conditions on the optimal operation of the microgrid. Also, simplified three-phase models of Distributed Energy Resources (DERs) are developed to reduce the level of complexity in small units that have limited impact on the optimal operation of the system, thus reducing the number of equations and variables of the problem. The proposed mathematical models are then used to formulate a novel energy management problem for isolated microgrids, as a deterministic, multi-period, Mixed-Integer Nonlinear Programming (MINLP) problem. The multi-period formulation allows for a proper management of energy storage resources and multi-period constraints associated with the commitment decisions of DERs. In order to obtain solutions of the energy management problem in reasonable computational times for real-time, realistic applications, and to address the uncertainty issues, the proposed MINLP formulation is decomposed into a Mixed-Integer Linear Programming (MILP) problem, and a Nonlinear programming (NLP) problem, in the context of a Model Predictive Control (MPC) approach. The MILP formulation determines the unit commitment decisions of DERs using a simplified model of the network, whereas the NLP formulation calculates the detailed three-phase dispatch of the units, knowing the commitment status. A feedback signal is generated by the NLP if additional units are required to correct reactive power problems in the microgrid, triggering a new calculation MINLP problem. The proposed decomposition and calculation routines are used to design a new deterministic Energy Management System (EMS) based on the MPC approach to handle uncertainties; hence, the proposed deterministic EMS is able to handle multi-period constraints, and account for the impact of future system conditions in the current operation of the microgrid. In the proposed methodology, uncertainty associated with the load and RE-based generation is indirectly considered in the EMS by continuously updating the optimal dispatch solution (with a given time-step), based on the most updated information available from suitable forecasting systems. For a more direct modelling of uncertainty in the problem formulation, the MILP part of the energy management problem is re-formulated as a two-stage Stochastic Programming (SP) problem. The proposed novel SP formulation considers that uncertainty can be properly modelled using a finite set of scenarios, which are generated using both a statistical ensembles scenario generation technique and historical data. Using the proposed SP formulation of the MILP problem, the deterministic EMS design is adjusted to produce a novel stochastic EMS. The proposed EMS design is tested in a large, realistic, medium-voltage isolated microgrid test system. For the deterministic case, the results demonstrate the important connection between the microgrid's imbalance, reactive power requirements and optimal dispatch, justifying the need for detailed three-phase models for EMS applications in isolated microgrids. For the stochastic studies, the results show the advantages of using a stochastic MILP formulation to account for uncertainties associated with RE sources, and optimally accommodate system reserves. The computational times in all simulated cases show the feasibility of applying the proposed techniques to real-time, autonomous dispatch of isolated microgrids with variable RE sources.

Today the modern power system is more dynamic and its operation is a subject to a number of constraints that are reflected in various management and planning tools used by system operators. In the case of hourly generation planning, Economic Dispatch (ED) allocates the outputs of all committed generating units, which are previously identified by the solution of the Unit Commitment (UC) problem. Thus, the accurate solutions of the ED and UC problems are essential in order to operate the power system in an economic and efficient manner. A number of computation techniques have progressively been proposed to solve these critical issues. One of them is a Particle Swarm Optimisation (PSO), which belongs to the evolutionary computation techniques, and it has attracted a great attention of the research community since it has been found to be extremely effective in solving a wide range of engineering problems. The attractive characteristics of PSO include: ease of implementation, fast convergence compared with the traditional evolutionary computation techniques and stable convergence characteristic. Although the PSO algorithms can converge very quickly towards the optimal solutions for many optimisation problems, it has been observed that in problems with a large number of suboptimal areas (i.e. multi-modal problems), PSO could get trapped in those local minima, including ED and UC problems. Aiming at enhancing the diversity of the traditional PSO algorithms, this thesis proposes a method of combining the PSO algorithms with a real-valued natural mutation (RVM) operator to enhance the global search capability and investigate the performance of the proposed algorithm compared with the standard PSO algorithms and other algorithms. Prior to applying to ED and UC problems, the proposed method is tested with some selected mathematical functions where the results show that it can avoid being trapped in local minima. The proposed methodology is then applied to ED and UC problems, and the obtained results show that it can provide solutions with good accuracy and stable convergence characteristic with simple implementation and satisfactory calculation time. Furthermore, the sensitivity analysis of PSO parameters has been studied so as to investigate the response of the proposed method to the parameter variations, especially in both ED and UC problems. The outcome of this research shows that the proposed method succeeds in dealing with the PSO' s drawbacks and also shows the superiority over the traditional PSO algorithms and other methods in terms of high quality solutions, stable convergence characteristic, and robustness.

abstract: Traditional deterministic reserve requirements rely on ad-hoc, rule of thumb methods to determine adequate reserve in order to ensure a reliable unit commitment. Since congestion and uncertainties exist in the system, both the quantity and the location of reserves are essential to ensure system reliability and market efficiency. The modeling of operating reserves in the existing deterministic reserve requirements acquire the operating reserves on a zonal basis and do not fully capture the impact of congestion. The purpose of a reserve zone is to ensure that operating reserves are spread across the network. Operating reserves are shared inside each reserve zone, but intra-zonal congestion may block the deliverability of operating reserves within a zone. Thus, improving reserve policies such as reserve zones may improve the location and deliverability of reserve. As more non-dispatchable renewable resources are integrated into the grid, it will become increasingly difficult to predict the transfer capabilities and the network congestion. At the same time, renewable resources require operators to acquire more operating reserves. With existing deterministic reserve requirements unable to ensure optimal reserve locations, the importance of reserve location and reserve deliverability will increase. While stochastic programming can be used to determine reserve by explicitly modelling uncertainties, there are still scalability as well as pricing issues. Therefore, new methods to improve existing deterministic reserve requirements are desired. One key barrier of improving existing deterministic reserve requirements is its potential market impacts. A metric, quality of service, is proposed in this thesis to evaluate the price signal and market impacts of proposed hourly reserve zones. Three main goals of this thesis are: 1) to develop a theoretical and mathematical model to better locate reserve while maintaining the deterministic unit commitment and economic dispatch structure, especially with the consideration of renewables, 2) to develop a market settlement scheme of proposed dynamic reserve policies such that the market efficiency is improved, 3) to evaluate the market impacts and price signal of the proposed dynamic reserve policies. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2015

The integration of variable renewable energy sources (VRE) is likely to cause fundamental and structural changes to the operation of future power systems. In the United Kingdom (UK), large amounts of price-insensitive and variable-output wind generation is expected to be deployed to contribute towards renewable energy and carbon dioxide (CO2) emission targets. Wind generation, with near-zero marginal costs, limited predictability, and a limited ability to provide upward dispatch, displaces price-setting thermal power plants, with higher marginal costs, changing flexibility and reserve requirements. New-build, commercial-scale, and low-carbon generation capacity, such as CO2 capture and storage (CCS) and nuclear, may impact power system flexibility and ramping capabilities. Low-carbon generation portfolios with price-sensitive thermal power plants and energy storage are therefore likely to be required to manage increased levels of variability and uncertainty at operational timescales. This work builds on a high-resolution wind reanalysis dataset of UK wind sites. The locations of existing and proposed wind farms are used to produce plausible and internally consistent wind deployment scenarios that represent the spatial distribution of future UK wind capacity. Temporally consistent electricity demand data is used to characterise and assess demand-wind variability and net demand ramp events. A unit commitment and economic dispatch (UCED) model is developed to evaluate the likely operating regimes of thermal power plants and CCS-equipped units across a range of future UK wind scenarios. Security constraints for reserve and power plant operating constraints, such as power output limits, ramp rates, minimum up/down times, and start-up times, ensure the operational feasibility of dispatch schedules. The load factors, time spent at different loads, and the ramping and start-up requirements of thermal power plants are assessed. CO2 duration curves are developed to assess the impacts of increasing wind capacity on the distribution of CO2 emissions. A sensitivity analysis investigates the impacts of part-load efficiency losses, ramp rates, minimum up/down times, and start-up/shut-down costs on power plant operating regimes and flexibility requirements. The interactions between a portfolio of energy storage units and flexible CO2 capture units are then explored. This multi-disciplinary research presents a temporally-explicit and detailed assessment of operational flexibility requirements at full 8760 hour resolution, highlighting the non-linear impacts of increasing wind capacity. The methodological framework presented here uses high spatial-and temporal-resolution wind data but is expected to provide useful insights for other VREbased power systems to mitigate the implications of inadequate flexibility.

La chaine d’approvisionnement énergétique a fortement évolué aux cours des 20 dernières années. La libéralisation des marchés de l’électricité et les nouvelles technologies ont fortement influencé la manière d’envisager la production et la transmission d’électricité. Les modèles mathématiques classiques utilisés dans les problèmes lié à l’énergie ont besoin d’être revus pour intégrer les contraintes pratiques modernes.Un problème classique pour un Compagnie Génératrice (CG) est le problème de Unit Commitment (UC) qui consiste à établir un plan de production pour une demande en électricité connue. Lorsque ce problème fut considéré, le prix de l’électricité et la demande étaient relativement simple à estimer comme une seule CG nationale avait le monopole du marché. Ce problème a été étudié de manière extensive en utilisant de la Programmation Mathématique (PM). Aujourd’hui, le prix de l’électricité est relativement volatile à cause de l’introduction de marchés dérégulés et la demande du marché est répartie entre plusieurs CGs en compétition sur divers marchés. Une CG ne peut se limiter à considérer un problème de UC seul pour envisager sa production. Il y a un besoin d’intégrer les incertitudes liées au marché de l’électricité et aux quantités à produire aux modèles utilisés pour qu’une CG puisse établir un plan de production rentable.La technologie a aussi permis d’envisager de nouveaux concept tel que les Micro-Grilles (MGs). Une MG est composée d’un ensemble de consommateurs reliés à travers un réseau de transmission, possédant des générateurs d’électricité et optimisant leur consommation interne. Ce concept est possible grâce à l’utilisation croissante d’énergies renouvelables locales ainsi que l’utilisant croissante d’appareils interconnectés. Cependant, étant donné que les énergies renouvelables ont un faible rendement, sont intermittentes et que les appareils de stockage d’énergie sont encore peu efficaces, les MGs ne peuvent pas envisager d’être pleinement autonome en électricité. Il y a donc une nécessité d’avoir un fournisseur d’électricité externe pour avoir suffisamment d’électricité disponible à tout moment. Une CG jouant le rôle de fournisseur auprès d’une MG fait face énormément d’incertitude concernant la demande à cause de la gestion interne de la MG sur laquelle elle n’a pas de contrôle.Dans cette thèse, des problèmes d’optimisation intégrant de nouvelles contraintes modernes liés à l’approvisionnement énergétique sont étudiés via la PM. Plusieurs problèmes considèrant des interactions entre plusieurs acteurs sont modélisés via des formulations bi-niveau. Nous illustrons comment les difficultés liées aux contraintes modernes peuvent être exploitées pour obtenir des propriétés permettant de reformuler les problèmes étudiés en formulation linéaire en nombre entiers. Des heuristiques performantes sont obtenus à partir des formulations exactes dont certaines sont applicables à des problèmes plus généraux. Une analyse extensive de la performance des méthodes de résolution ainsi que de l’influence des contraintes modernes sont présentées dans diverses expériences numériques. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

The Unit Commitment Problem (UCP) poses a significant challenge in optimizing powergeneration schedules within complex and dynamic energy systems. This study explores theapplication of Genetic Algorithms (GAs) as a promising approach to address UCP, their ability tonavigate complex solution spaces and adapt to changing operational conditions. The work provides a broad exploration of their effectiveness, challenges, and future prospects. The objective of UCP is to efficiently optimize power generation schedules within complex energy systems, seeking cost-effective and reliable solutions while accommodating various operational constraints. Various encoding techniques and GA operations are implemented and evaluated incomparison to the solutions obtained from a commercial Mixed-Integer Linear Programming (MILP) solver. The key findings point to the potential for achieving high quality solutions and robustness in the application of these techniques. However, it is important to acknowledge and address challenges such as encoding complexity, extensive computation times, the risk of premature convergence, and the complications of handling complex constraints that continue to exist in this domain. The future scope lies in hybrid approaches, scalability enhancement and incorporation of multi-objective optimization, offering unrealized potential for the efficient andsustainable operation of modern energy systems.

This operations research thesis should be situated in the field of the power generation industry. The general objective of this work is to efficiently solve the Generalized Unit Commitment (GUC) problem by means of specialized software. The GUC problem generalizes the Unit Commitment (UC) problem by simultane-ously solving the associated Optimal Power Flow (OPF) problem. There are many approaches to solve the UC and OPF problems separately, but approaches to solve them jointly, i.e. to solve the GUC problem, are quite scarce. One of these GUC solving approaches is due to professors Batut and Renaud, whose methodology has been taken as a starting point for the methodology presented herein.This thesis report is structured as follows. Chapter 1 describes the state of the art of the UC and GUC problems. The formulation of the classical short-term power planning problems related to the GUC problem, namely the economic dispatching problem, the OPF problem, and the UC problem, are reviewed. Special attention is paid to the UC literature and to the traditional methods for solving the UC problem. In chapter 2 we extend the OPF model developed by professors Heredia and Nabona to obtain our GUC model. The variables used and the modelling of the thermal, hydraulic and transmission systems are introduced, as is the objective function. Chapter 3 deals with the Variable Duplication (VD) method, which is used to decompose the GUC problem as an alternative to the Classical Lagrangian Relaxation (CLR) method. Furthermore, in chapter 3 dual bounds provided by the VDmethod or by the CLR methods are theoretically compared.Throughout chapters 4, 5, and 6 our solution methodology, the Radar Multiplier (RM) method, is designed and tested. Three independent matters are studied: first, the auxiliary problem principle method, used by Batut and Renaud to treat the inseparable augmented Lagrangian, is compared with the block coordinate descent method from both theoretical and practical points of view. Second, the Radar Sub- gradient (RS) method, a new Lagrange multiplier updating method, is proposed and computationally compared with the classical subgradient method. And third, we study the local character of the optimizers computed by the Augmented Lagrangian Relaxation (ALR) method when solving the GUC problem. A heuristic to improve the local ALR optimizers is designed and tested.Chapter 7 is devoted to our computational implementation of the RM method, the MACH code. First, the design of MACH is reviewed brie y and then its performance is tested by solving real-life large-scale UC and GUC instances. Solutions computed using our VD formulation of the GUC problem are partially primal feasible since they do not necessarily fulfill the spinning reserve constraints. In chapter 8 we study how to modify this GUC formulation with the aim of obtaining full primal feasible solutions. A successful test based on a simple UC problem is reported. The conclusions, contributions of the thesis, and proposed further research can be found in chapter 9.

Combined heat and power (CHP) is a technology that decreases total fuel consumption and related greenhouse gas emissions by producing both electricity and useful thermal energy from a single energy source. In the industrial and commercial sectors, a typical CHP site relies upon the electricity distribution network for significant periods, i.e., for purchasing power from the grid during periods of high demand or when off-peak electricity tariffs are available. On the other hand, in some cases, a CHP plant is allowed to sell surplus power to the grid during on-peak hours when electricity prices are highest while all operating constraints and local demands are satisfied. Therefore, if the plant is connected with the external grid and allowed to participate in open energy markets in the future, it could yield significant economic benefits by selling/buying power depending on market conditions. This is achieved by solving the power system generation scheduling problem using mathematical programming. In this work, we present the application of mixed-integer nonlinear programming (MINLP) approach for scheduling of a CHP plant in the day-ahead wholesale energy markets. This work employs first principles models to describe the nonlinear dynamics of a CHP plant and its individual components (gas and steam turbines, heat recovery steam generators, and auxiliary boilers). The MINLP framework includes practical constraints such as minimum/maximum power output and steam flow restrictions, minimum up/down times, start-up and shut-down procedures, and fuel limits. We provide case studies involving the Hal C. Weaver power plant complex at the University of Texas at Austin to demonstrate this methodology. The results show that the optimized operating strategies can yield substantial net incomes from electricity sales and purchases. This work also highlights the application of a nonlinear model predictive control scheme to a heavy-duty gas turbine power plant for frequency and temperature control. This scheme is compared to a classical PID/logic based control scheme and is found to provide superior output responses with smaller settling times and less oscillatory behavior in response to disturbances in electric loads. / text

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-12-21T10:50:23Z No. of bitstreams: 1 flaviarodriguesdonascimento.pdf: 1211614 bytes, checksum: ab9ba99ac0572dc9242451b399b808c5 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-12-22T12:00:31Z (GMT) No. of bitstreams: 1 flaviarodriguesdonascimento.pdf: 1211614 bytes, checksum: ab9ba99ac0572dc9242451b399b808c5 (MD5) / Made available in DSpace on 2016-12-22T12:00:31Z (GMT). No. of bitstreams: 1 flaviarodriguesdonascimento.pdf: 1211614 bytes, checksum: ab9ba99ac0572dc9242451b399b808c5 (MD5) Previous issue date: 2011-09-15 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A programação diária da operação de sistemas termoelétricos de geração consiste em determinar uma estratégia de despacho das unidades geradoras para atender a demanda de energia, satisfazendo as restrições operacionais e funcionais do sistema elétrico de potência. O problema pode ser dividido em dois subproblemas: (i) referente à determinação das unidades que devem estar em operação mediante a demanda solicitada, “Thermal Unit Commitment” e (ii) referente à determinação da potência gerada por cada uma das unidades colocadas em serviço, “Despacho Econômico”. Devido à variação de carga ao longo do tempo, a programação da operação envolve decisões do sistema de geração a cada hora, dentro do horizonte de um dia a duas semanas. Os estudos relacionados às técnicas de otimização bio-inspiradas, utilizadas na resolução da programação diária da operação de sistemas termoelétricos de geração, apontam que a combinação entre os métodos computacionais biologicamente inspirados com outras técnicas de otimização tem papel importante na obtenção de melhores soluções em um menor tempo de processamento. Seguindo esta linha de pesquisa, o presente trabalho faz uso de uma metodologia baseada na otimização por colônia de formiga para a minimização do custo da programação diária de operação de unidades termoelétricas. O modelo proposto utiliza uma Matriz de Sensibilidade (MS) baseada nas informações fornecidas pelos multiplicadores de Lagrange a fim de melhorar o processo de busca bio-inspirado. Desta forma, um percentual dos indivíduos da colônia faz uso destas informações no processo evolutivo da colônia. Os resultados alcançados através das simulações indicam que a utilização da MS resulta em soluções de qualidade com um número reduzido de indivíduos. / The daily schedule of thermoelectric systems consists of determining the strategy to set the generation units to be put in operation to meet the load, meeting also the operational and functional constraints of the respective power system. This problem can be split into two subproblems: (i) schedule of units that must operate in accordance with a given load, or Thermal Unit Commitment and (ii) set the power generation for each committed unit, or Economical Schedule. Due to load variations the schedule involves hourly generation decisions, in a horizon that varies from one day to two weeks. Researches related to bio-inspired optimization strategies applied to the daily thermal system operation show that the combination between bio-inspired computing techniques and other optimization methods has an important role in order to obtain better solutions in a shorter computing time. Following this, the present work makes use of a methodology based on Ant Colony Optimization to minimize the costs of the thermal system daily scheduling. This proposed method uses a Sensitivity Matrix (SM) based on information from Lagrange Multipliers related to the problem in order to improve the bio-inspired process. In this way, a percentage of the individuals make use of the provided information in the colony evolution process. The results obtained through those simulations indicate that the use of the SM presents better quality solutions with a reduced number of individuals.