Optimal control of switched capacitor banks in Vietnam distribution network using integer genetic algorithm

In distribution network, power and energy losses can be reduced by using switched capacitor banks. The capacitor banks can be switched on or off based on voltage profile or power factor or using timers. Due to variation of load, it is necessary to control the capacitor banks switching in function of load curve. This paper presents the application of an integer genetic algorithm to determine the optimal number of banks corresponding with hourly load to minimize total active power losses of distribution feeders. The problem constraints include voltage profile and heat conditions which are taken into account to the objective function by a penalty function. In this application, the structure of chromosomes is a set of numbers of the capacitor banks hourly connected to the grid. The proposed formulation is validated by a feeder. The result shows that in some cases, the active power losses at maximum compensation are greater than the ones of optimal control compensation, and the voltage reaches a higher level than the maximum voltage limit. The optimal control of switched capacitor banks can reduce power and energy losses as well as ensure maximum voltage profile within the limit

Techno-economic Assessment of Wireless Charging Systems for Airport Electric Shuttle Buses

Data Access Statement: Data supporting this study are included within the article....DTE Network+ funded by EPSRC grant reference EP/S032053/1

Techno-economic assessment of wireless charging systems for airport electric shuttle buses

Flightpath 2050, the European Commission's vision for aviation, requires that the aviation industry achieves a 75 % reduction in CO2 emissions per passenger mile and airports become emission-free by 2050. Airport shuttle buses in the airfields are going to be electrified to reduce ground emissions. Simultaneously, the airfield movement space and time schedules are becoming more limited for adopting stationary charging facilities for electrified ground vehicles. Therefore, the dynamic wireless charging technology becomes a promising technology to help improve the stability of electrification of the airfield transport network. This paper proposes a techno-economic assessment of wireless charging, wired charging, and conventional technologies for electrifying airport shuttle buses. A bi-level planning optimisation approach combines the multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-III) and mixed integer linear programming (MILP) algorithm to handle a large number of decision variables and constraints generated from the investigated problem. The airport shuttle bus transport is simulated through a multi-agent-based model (MABM) approach. Four case studies are analysed for illustrating the techno-economic feasibility of wireless charging technology for airport electric shuttle buses. The results show that the wireless charging technology enables the electric shuttle buses to carry smaller batteries while conducting the same as tasks conventional diesel/petrol vehicles and the bi-directional wireless charging technology could help mitigate the impact of electrification of shuttle buses on the distribution network.Engineering and Physical Sciences Research Council (EPSRC): EP/S032053/

Fluxo de Potência para Redes de Distribuição Radiais, Ativas e Ilhadas

O presente trabalho tem por objetivo principal apresentar uma ferramenta de análise de redes de distribuição radiais ativas conectadas a rede básica e funcionando de maneira ilhada. Os objetivos específicos são avaliar o impacto de fontes renováveis em sistemas radiais de distribuição, considerando impactos da pequena variação a da frequência nos elementos do sistema. A metodologia utilizada é baseada no modelo da varredura considerando droop, sendo o modelo da varredura responsável pelo cálculo do fluxo de potência e o método droop responsável pela inserção de geradores despacháveis de energia. Inicialmente é apresentado objetivo, a motivação, a estrutura da dissertação, seguida da contextualização da geração distribuída no Brasil. Apresenta-se o estado da arte do fluxo de potência para redes de distribuição bem como uma bibliografia auxiliar a respeito de controle para redes de distribuição. Em seguida o método estático da varredura direta e inversa é explicado. Então, a inserção da variação dos parâmetros de rede em função de pequenas variações da frequência é explicada. E por fim, as simulações e suas respectivas análises são feitas em diferentes cenários. Chegou-se à conclusão que a metodologia proposta é eficiente e apresenta resultados coerentes, considerando a dependência com a variação da frequência, a topologia e impactos da geração distribuída

Optimal Operation and Maximal Hosting Capacity of High-Renewable Islanded Microgrids

With the advancement of technology, renewable power generators such as solar photovoltaics and wind turbines have become cost-effective and competitive compared to traditional generators. On the other hand, carbon emission issues have been globally focused, promoting development of renewable energy. Meanwhile, microgrids have been widely constructed with increasing installation of distributed generators including microturbines and renewable power generators. Challenges from intermittent and uncertain renewable sources, low operating efficiency as well as system stability in the islanded mode still exist for microgrid operation and renewable hosting capacity assessment. To address these unsolved issues, it is worth developing advanced optimal operation and hosting capacity maximization approaches for high-renewable microgrids, which are presented in this thesis. For microgrid operation, economic efficiency, solution robustness and system stability are major concerns to be addressed. In order to achieve cost-effective operation, firstly a new stochastic optimal power flow (OPF) is proposed for islanded microgrids. A linear network operating model which can be used in the OPF problem is specifically developed, while uncertainties of photovoltaic power and loads are addressed by Monte Carlo simulation. Secondly, an improved OPF method with a new iterative solution algorithm is proposed to enhance the accuracy of network operating model and the computing speed. Besides, an advanced probabilistic modelling method is adapted to present real-time uncertainties in the OPF method. Thirdly, a novel stochastic OPF method with consideration of tie-line switching from the grid-connected to the islanded mode while the main grid in contingency is proposed. Security constraints to guarantee the system stability in the islanded mode are formulated. Moreover, a Benders decomposition based solution algorithm is developed, to efficiently solve the OPF problem with a master problem and a sub-problem which formulate the grid-connected and the islanded modes, respectively. Fourthly, a renewable hosting capacity maximization approach for an islanded microgrid, considering system frequency deviation, is proposed. An advanced sensitivity region based optimization method is proposed to address the uncertainties of wind power and loads, thus obtaining a robust solution. The proposed methods have been successfully demonstrated and compared with existing works. Simulation results have verified their feasibility and effectiveness

Grid flexibility by electrifying energy systems for sustainable aviation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDecarbonisation of aviation goals set by Flightpath 2050 Europe’s Vision for Aviation requires that the airports become emission-free by 2050. This thesis original contribution to knowledge is to explore the incorporation of aviation electrification technologies, including electric aircraft (EA), electrified ground support equipment (GSE), and airport parking electric vehicles (EVs), into power systems, evaluating their influence on grid infrastructure and operations, as well as their potential to support the grid operation. A comprehensive review of aviation electrification technologies revealed a research gap in the integration of these technologies into the power systems. The thesis contributes to electricity network infrastructure planning for electrification of aviation and airport-based distributed energy resources (DER) that provide ancillary services to the power grid. A multi-objective airport microgrid planning framework is developed, comparing EA charging strategies and revealing that battery swap performs better. Vehicle-to-grid (V2G) strategy with parking EVs improves the microgrid's performance. A techno-economic assessment of wireless charging systems for electric airport shuttle buses shows better economic performance than conventional buses and other charging options. A novel Aviation-to-Grid (A2G) flexibility concept provides frequency response services to the GB power system using EA battery charging systems, with typical A2G service capacity showing significant variation across eight UK airports. A deep reinforcement learning (DRL)-based A2G dispatch approach evaluates the impact of EA charger capacity on energy dispatch results, with higher capacities leading to higher revenue and lower operation costs. To summarise, this thesis addresses the research gaps in integrating aviation electrification technologies into power systems, offering valuable insights for airport operators aiming to decarbonise air transport activities through the adoption of these technologies. The study also provides an understanding of the impacts on grid operators in terms of infrastructure planning and operations. This comprehensive approach ensures a cohesive understanding of the challenges and opportunities presented by aviation electrification and its integration into power systems

Allocation of dump load in islanded microgrid using the mixed-integer distributed ant colony optimization with robust backward\forward sweep load flow

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonReliable planning and operation of droop-controlled islanded microgrids (DCIMGs) is fundamental to expand microgrids (MGs) scalability and maximize renewable energy potential. Employing dump loads (DLs) is a promising solution to absorb excess generation during off-peak hours while keeping voltage and frequency within acceptable limits to meet international standards. Considering wind power and demand forecast uncertainties in DCIMG during off-peak hours, the allocation of DL problem was modelled as two problems, viz., deterministic and stochastic. The former problem was tackled using four highly probable deterministic generation and demand mismatch scenarios, while the latter problem was formulated within scenario based stochastic framework for uncertainty modelling. The mixed-integer distributed ant colony optimization (MIDACO) was introduced as a novel application in microgrids to find the optimal location and size of DL as well as the optimal droop setting for distributed generation (DG). Furthermore, to enhance the convergence of the proposed optimization technique, three robust and derivative free load flow methods were developed as novel extensions of the original backward\forward sweep (BFS) for grid-connected MGs. The three load flow methods are called special BFS, improved special BFS, and general BFS. The first two methods rely on one global voltage variable distributed among all DGs, while the latter has more general approach by adopting local voltage at each generating bus. The deterministic multi-objective optimization problem was formulated to minimize voltage and frequency deviation as well as power losses. Inversely, the stochastic multi-objective problem with uncertainty was formulated to minimize total microgrid cost, maximum voltage error, frequency deviation, and total energy loss. The proposed method was applied to the IEEE 33-, 69-, and 118-test systems as modelled in MATLAB environment and further validated against competitive swarm and evolutionary metaheuristics. Various convergence tests were considered to demonstrate the efficacy of the proposed load flow methods with MIDACO’s non-dominated solution. Likewise, different optimization parameters were utilized to investigate their impact on the solution. Moreover, the advantage of multi-objective optimization against single objective was provided for the deterministic optimization problem, while the effect of load model and droop response were also investigated. The obtained results in chapter 5 and 6 further demonstrate the fundamental role of DL in voltage and frequency regulation while minimizing costs and energy losses associated with DCIMG operation. Accordingly, an improved voltage and frequency profiles for the system after DL inclusion were attained in Figure 6.9 and Figure 6.10, respectively. To demonstrate the competitiveness of DL-based energy management system (EMS) against storage-based EMS, a brief cost benefit analysis considering hot water demand was also provided

AC microgrids analysis, optimization and planning for resilience enhancement

Face à des événements météorologiques violents, un système de distribution électrique peut souffrir de la perte ou de la défaillance d'un ou de plusieurs de ses composants. Ce phénomène est connu sous le nom de contingence de système. Néanmoins, en tirant parti des systèmes de protection, de l'électronique de puissance et de la pénétration des ressources énergétiques décentralisées dans le réseau électrique, un système de distribution électrique a la possibilité d'être reconfiguré en micro-réseaux. Cela permet de résister contre de telles éventualités en gardant au minimum la possibilité d'une interruption de l'alimentation. Poussé par des facteurs techniques, économiques et environnementaux, ainsi que par le déploiement rapide d'un grand nombre de ressources de production décentralisées, le micro-réseau est récemment devenu un concept important dans un système de distribution actif et rapidement reconfigurable. Les micro-réseaux ont la capacité de fonctionner à la fois en mode connecté au réseau et en mode isolé. En raison de cet avantage, le micro-réseau est devenu un élément clé du futur réseau intelligent. Bien que le concept de micro-réseau puisse apporter différents avantages aux services de distribution et aux clients, à savoir une amélioration de l'économie, de l'environnement et de la résilience, il offre toujours un défi au niveau de la planification et la gestion opérationnelle. Les défis de la planification des micro-réseaux proviennent de : 1) la nature intermittente et incertaine des ressources décentralisées et des charges des systèmes distribués, ainsi que l'incertitude relative aux contingences auxquelles le réseau de distribution est confronté, 2) la charge de calcul que la prise en compte des incertitudes du micro-réseau implique, et 3) le grand nombre de compromis entre les différents objectifs d'optimisation possibles du micro-réseau qui doivent être pris en compte dans la phase de planification. Motivée par ces défis, cette recherche propose le développement de nouvelles méthodologies d'analyse et de planification qui peuvent assurer l'efficacité du processus de création du micro-réseau en tenant compte des caractéristiques particulières et de la philosophie opérationnelle du micro-réseau. Dans un premier temps, les modèles d'étude d'écoulement de puissance linéaires et non linéaires sont développés en prenant compte des caractéristiques réelles d'un micro-réseau insulaire équilibré et déséquilibré, c'est-à-dire l'absence de bus infini, la présence d'une fréquence du système variable et de certains générateurs fonctionnant en mode de contrôle « statisme ». Tout d'abord, nous présentons un algorithme non linéaire basé sur la méthode des mailles et la matrice Z[indice bus] pour un micro-réseau opéré en mode « droop ». Cet algorithme sans inversion est particulièrement adapté aux grandes dimensions des systèmes de distribution pratiques comprenant des milliers de nœuds électriques, lorsqu'ils fonctionnent comme des micro-réseaux insulaires. Deuxièmement, un modèle linéaire pour étudier l'écoulement de puissance (LPF) basé sur la méthode des nœuds est proposé. Étant basé sur la méthode des nœuds, le modèle proposé est utilisable dans différents problèmes d'optimisation de micro-réseaux. Des études de cas numériques sont développées et utilisées pour démontrer la précision des modèles proposés et garantir leur application réussie pour modéliser avec précision l'écoulement d'énergie dans un micro-réseau. Cela permet son application dans un processus d'optimisation présenté à l'étape suivante de cette recherche. Dans la deuxième étape, cette recherche propose un modèle d'écoulement de puissance optimal (OPF) pour le fonctionnement optimal des micro-réseaux à courant alternatif, équilibrés et déséquilibrés, avec un contrôle hiérarchique (c'est-à-dire, un contrôle primaire et secondaire). Le modèle proposé a d'abord été formulé comme un modèle non linéaire en nombres entiers (MINLP), puis il a été linéarisé et converti en un modèle linéaire en nombres entiers (MILP) en utilisant le modèle LPF développé dans la première étape de cette recherche. La philosophie de fonctionnement du micro-réseau, en mode connecté au réseau et en mode îloté, a été prise en compte. De plus, plusieurs types de générateurs distribués, y compris ceux qui sont disptachables et non disptachables, ainsi que des ressources de stockage d'énergie, ont été pris en compte dans le modèle MILP proposé. Plusieurs études de cas numériques ont été menées pour valider et prouver l'efficacité et la précision du modèle MILP développé. Les résultats de ces études de cas ont démontré la précision et la supériorité de calcul du modèle MILP proposé Enfin, un cadre de planification pour les micro-réseaux dans les réseaux de distribution actifs est proposé. Ce cadre de planification vise à améliorer la résilience des systèmes de distribution d'électricité face à des événements de faible probabilité, à fort impact. Dans le cadre proposé, le problème de planification a été présenté comme un problème d'optimisation stochastique à deux niveaux. Tout d'abord, le niveau externe traite du placement optimal des éléments de planification du système de distribution (c.-à-d. les ressources répartissables ou non répartissables, les unités de stockage d'énergie et les interrupteurs d'isolement). Ce problème a été formulé en utilisant une formulation d'optimisation multi-objectifs et ensuite l'algorithme métaheuristique bien connu NSGA-II est adopté pour la recherche d'une solution optimale. Cette approche permet de déterminer les solutions qui impliquent le meilleur compromis entre plusieurs objectifs éventuellement conflictuels du problème de planification, à savoir le coût, la résilience et l'impact environnemental. Deuxièmement, le niveau interne du cadre de planification traite du problème d'optimisation relatif au fonctionnement optimal des micro-réseaux qui peuvent être créés par les éléments de planification du système de distribution alloués dans le niveau externe. Le problème du fonctionnement optimal du micro-réseau est présenté comme un problème d'étude de l'écoulement de puissance optimal linéaire (LOPF). À cette fin, le modèle MILP développé dans la deuxième étape est adopté. Néanmoins, il est nécessaire de prendre en compte différents scénarios stochastiques dans le niveau interne pour tenir compte des différentes incertitudes du système. Il faut aussi considérer la nature métaheuristique du niveau externe ce qui demande la résolution du modèle LOPF pour chacun des scénarios stochastiques et aussi pour chaque individu de la population. La prise en compte de ces facteurs présente un défi au niveau du calcul. Par conséquent, une nouvelle méthodologie utilisant un modèle de réseau de neurones (DNN) est proposée. Cette méthode permet de dériver rapidement l'information requise des solutions LOPF pour les scénarios stochastiques considérés. Enfin, l'efficacité du cadre proposé est validée par des résultats de simulation numérique.Facing severe weather events, a distribution system may suffer from the loss or failure of one or more of its components, known as N-K contingencies. Nevertheless, taking advantage of the system's isolate switches and the penetration of the distributed energy resources in the electrical grid, a distribution system has the possibility to be clustered into microgrids in order to with stand such contingencies with minimal power interruption. Driven by technical, economic and environmental factors, as well as by the rapid deployment of a large number of distributed generation resources, the microgrid has recently become an important concept in the active distribution system. Microgrids have the ability to operate in both grid-connected and islanded modes. The benefits that the microgrid concept can bring to the operation of the distribution grids make the microgrid a key component of the future smart grid. While, the microgrid concept can bring different benefits to both distribution utilities and customers i.e., economic, environmental and resilience enhancement; the planning and operational management of microgrids still present several challenges for the decision maker and the distribution network operator. The challenges with the planning of microgrids arise from: 1) the intermittent and uncertain nature of the distributed energy resources and loads as well as the uncertainty pertaining to the contingencies facing the distribution network, 2) the computational burden that considering the microgrid's uncertainties entails, and 3) the large number of trade-offs between the different possible microgrid optimization objectives that need to be considered in the planning stage. Motivated by these challenges, this research proposes the development of new analysis and planning methodologies that can ensure the efficacy of the microgrid creation process considering the microgrids special features and operational philosophy. Initially, nonlinear and linear power flow models are developed to cope with the real characteristics of balanced and unbalanced islanded microgrid i.e. the absence of the slack bus, the system frequency being a variable and some DGs operating in droop-control mode. First, a non-linear branch-based Z[subscript bus] algorithm for the droop-controlled islanded microgrid is introduced. This algorithm is inversion free and is particularly suited for the large dimensions of practical distribution systems comprising up to thousands of electrical node, i.e., when operated as islanded microgrids. Secondly, a node-based linear power flow (LPF) model for droop-controlled islanded microgrids is proposed. The node-based nature of the proposed LPF model, allows this model to be integrated in different microgrid optimization models. Numerical case studies are developed and are used to demonstrate the accuracy of the proposed power flow models and guarantee its successful application to accurately model the microgrid power flow in the optimization application in the next stage of this research. In the second stage, this research proposes an optimal power flow (OPF) model for the optima operation of balanced and unbalanced AC microgrids with hierarchical control (i.e., primary droop and secondary control). The proposed model has been first formulated as a mixed integer nonlinear programing (MINLP) model, then it was linearized and converted into a mixed integer linear programing (MILP) model by adapting the LPF model developed in the first stage of this research. The operating philosophy of the microgrid, in both grid-connected and islanded modes of operation, was considered. Additionally, several types of distributed generators, including dispatchable and non-dispatchable, as well as energy storage resources, were considered in the proposed MILP model. Several numerical case studies were conducted to validate, and prove the effectiveness and the accuracy of the developed MILP model. The results from the developed case studies demonstrated the accuracy and the computational superiority of the proposed MILP model. Finally, a planning framework for microgrids in active distribution networks is proposed. The proposed planning framework is aimed at enhancing the resilience of power distribution systems facing high impact low probability events. In the proposed framework, the planning problem has been casted as a stochastic bi-level optimization problem. First, the outer level deals with the optimal placement of the distribution system planning elements (i.e., dispatchable/non-dispatchable resources, energy storage units and isolating switches). This problem has been formulated using a multi-objective optimization formulation and the well know metaheuristic Non-dominated Sorting Genetic Algorithm (NSGA-II) algorithm is adopted for its solution. This approach allows for determining the solutions that entail the best trade-off between the possibly conflicting multi-objectives of the planning problem, namely, cost, resilience and environmental impact. Second, the inner level of the planning framework handles the optimization problem pertaining to the optimal operation of the microgrids that can be created by the distribution system planning elements allocated in the outer level. The problem of the microgrid's optimal operation is casted as a Linear Optimal Power Flow (LOPF) problem. To this end, the proposed MILP model developed in the second stage is adopted. Despite using a LOPF model, considering different stochastic scenarios in the inner level, to account for the different system uncertainties, along with the metaheuristic nature of the outer level make solving the LOPF model for each of the stochastic scenarios for each individual in the metaheuristic optimization's population, using a numerical optimization solver computationally challenging. Motivated by this challenge, a novel methodology using a deep neural network (DNN) model is proposed for deriving the information required from the LOPF solutions for the stochastic scenarios under consideration. The effectiveness of the proposed framework is finally validated by numerical simulation results