Modified particle swarm optimization for day-ahead distributed energy resources scheduling including vehicle-to-grid

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de EnergiaThis thesis proposes a modified Particle Swarm Optimization (PSO) approach for the day-ahead scheduling of Distributed Energy Resources (DER) in smart grids, considering Electric Vehicles (EVs) with gridable capability (vehicle-to-grid). The proposed methodology introduces several changes in traditional PSO meta-heuristic to solve effectively the scheduling problem of DER with EVs. This thesis proposes an intelligent mechanism for adjusting the velocity limits of the swarm to alleviate violations of problem constraints and to improve the quality of the solution, namely the value of the objective function. In addition, a hybridization of PSO method is used, which combines this meta-heuristic with an exact method, a full ac power flow in order to validate network constraints of the solutions explored by the swarm. This thesis proposes a trip reduce demand response program for EVs users. A datamining based methodology is used to support the network operator in the definition of this program and to estimate how much demand response is adequate for a certain operation condition. The case studies included in the thesis aim to demonstrate the effectiveness of the modified PSO approach to the problem of DER scheduling considering EVs. An application named EV Scenario Simulator (EVeSSi) has been developed. EVeSSi allows creating scenarios considering EVs in distribution networks. A case study comparison of the modified PSO with an accurate mixed integer non-linear programming is presented. Furthermore, it is also compared with other variants of PSO, and the traditional PSO. Addionatly, different methods of EV battery management, namely uncontrolled charging, smart charging and vehicle-to-grid, are compared. Finally, a test case is presented to illustrate the use of the proposed demand response program for EVs and the data-mining methodology applied to a large database of operation scenarios.Esta tese apresenta uma aplicação modificada e adaptada da meta-heurística Particle Swarm Optimization (PSO) para o escalonamento de recursos energéticos em redes de distribuição inteligentes vulgo smart grids, considerando a utilização de veículos eléctricos. Este conceito em que os veiculos podem carregar e descarregar energia para a rede eléctrica é denominado na giria anglo-saxónica por vehicle-to-grid. Esta tese apresenta várias modificações na meta-heuristica PSO original para resolver mais eficazmente o problema do escalonamento de recursos energéticos com veículos eléctricos. Realça-se nesta tese a prosposta de um mecanismo inteligente para o ajustamento do limite das velocidades do swarm com vista a aliviar violações de restrições do problema e a melhorar a qualidade da solução, isto é, o valor da função objectivo. Adicionalmente, refere-se a hibridização desta meta-heurística com um método exacto, nomeadamente um trânsito de potências com o objectivo de verificar o cumprimento das restrições da rede eléctrica das soluções exploradas pelo swarm. Um programa de demand response para veículos eléctricos é apresentado na tese. Além disso, uma metodologia baseada em técnicas de data-mining é proposta para suportar as decisões do operator de sistema na definição e na estimativa do uso desse programa. Os casos de estudo incluídos nesta tese pretendem demonstrar a eficácia do PSO modificado no problema do escalonamento de recursos energéticos considerando os veículos eléctricos. Uma aplicação com a designação de EVeSSi foi desenvolvida e apresentada nesta tese para criar cenários de penetração de veículos eléctricos e simular os movimentos dos veículos ao longo dos nós das redes de distribuição. Um caso de estudo de comparação com um método exacto de programação não linear inteira mista é apresentado. Além disso, a aplicação proposta é comparada com outras variantes do PSO, incluindo a versão original. São ainda incluídos casos de estudo que abordam diferentes metodologias de interação do veículo com a rede, nomeadamente uncontrolled charging, smart charging e vehicle-to-grid. Por fim, é apresentado um caso de estudo com o programa de demand response e a metodologia de data-mining

Demand response in future power systems management – a conceptual framework and simulation tool

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de EnergiaIn competitive electricity markets with deep efficiency concerns, demand response gains significant importance. Moreover, demand response can play a very relevant role in the context of power systems with an intensive use of distributed energy resources, from which renewable intermittent sources are a significant part. More active consumers’ participation can help improving the system reliability and decrease or defer the required investments. From the consumers’ point of view, it can result in reduced costs while guaranteeing adequate comfort levels. As demand response levels have decreased after the introduction of competition in the power industry, new approaches are required to take full advantage of demand response opportunities. DemSi, a demand response simulator, designed and implemented in the scope of this thesis, allows studying demand response actions and schemes in distribution networks. It undertakes the technical validation of the solution using realistic network simulation based on PSCAD. DemSi is able to support decision making concerning demand response programs design and use. DemSi considers the players involved in demand response actions, and the results can be analyzed from each specific player point of view. Five types of players are considered: electricity consumers, electricity retailers/suppliers, distribution network operators (DNO), Curtailment Service Providers, and Virtual Power Players (VPPs). Each model considers the minimization of the operation costs or the maximization of the profits. Several models were developed covering a diversity of demand response programs. Each model is defined by a number of items such as the program event trigger (mostly based on Locational Marginal Prices), the response characterization, and the aggregated participation of players, namely consumers and DG owners.No âmbito dos mercados competitivos de energia eléctrica, fortemente direccionados para a eficiência, a demand response torna-se especialmente importante. Espera-se que a demand response assuma um papel relevante nos sistemas eléctricos de energia com uso intensivo da produção distribuída, muito baseada em fontes de energia renováveis com a sua inerente intermitência. Uma participação mais activa dos consumidores poderá melhorar a fiabilidade do sistema e diminuir ou diferir investimentos em unidades de produção de energia eléctrica e outras infraestruturas do sistema de energia. Do ponto de vista dos consumidores, tal deverá resultar em redução dos custos, garantido sempre os requisitos mínimos de conforto. Uma vez que os níveis de demand response diminuíram desde a introdução dos mercados competitivos de energia eléctrica, são necessários novos modelos para tirar pleno partido da demand response. O DemSi, um simulador de demand response, concebido e implementado no âmbito desta tese, permite o estudo das acções e programas de demand response relativos aos consumidores existentes nas redes de distribuição. Este simulador efectua a validação técnica das soluções obtidas através de uma simulação de rede efectuada em PSCAD. O DemSi apoia a decisão, no que diz respeito ao desenvolvimento e uso adequado dos programas de DR. O simulador DemSi tem em conta as entidades envolvidas nas acções de DR, sendo os resultados analisados sob o ponto de vista de cada uma delas. Consideram-se cinco tipos de entidades: consumidores, fornecedores/retalhistas, operadores de rede de distribuição, Curtailment Service Providers (CSPs) e Virtual Power Players (VPPs). Os modelos permitem a minimização dos custos de operação ou a maximização dos lucros. Foram desenvolvidos vários modelos, cobrindo uma ampla gama de programas de demand response com características diversas. Cada modelo é definido por diversas características, incluindo o trigger, a caracterização da resposta dos consumidores e a participação agregada de consumidores e produtores distribuídos

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches

Optimal Model Reduction of Lithium-Ion Battery Systems Using Particle Swarm Optimization

Lithium-ion batteries (LIBs) have been widely used as an energy storage mechanism among all the types of rechargeable batteries owing to their high energy and power density. Because of the vast applications of LIBs in several dynamic operations, the development of a robust model to simulate the battery’s dynamic behavior and performance for control and system design is paramount. Several modeling efforts have been invested into the development of electrochemical models for simulation of LIB systems ranging from a full-order model, the so-called Doyle-Fuller-Newman (DFN) model to several reduced-order models. This thesis work involves the development of a reduced-order electrochemical model based on single particle approach with electrolyte dynamics (SPMe). The partial differential equations (PDEs) that capture the dynamic behavior and performance characteristics of the LIB systems were solved numerically through a finite difference method in MATLAB environment. For model reduction purpose, a constrained optimization problem was formulated to determine the optimal uneven discretization node points needed to numerically solve the battery PDEs for both solid and electrolyte phase concentration predictions. The optimization problem was solved using a particle swarm optimization (PSO) by minimizing the errors between the reference model, a SPMe with even discretization and the reduced model, a SPMe with uneven discretization. The proposed approach is similar to that proposed by Lee T.K. and Filipi Z., but differs because of the inclusion of electrolyte dynamics. The battery voltage was computed based on the optimal uneven discretization nodes under three different charging/discharging conditions. The proposed model demonstrates that as the number of optimal uneven discretization nodes applied to the model increases, the fidelity of the model increase. However, no significant improvement of prediction accuracy is observed after a certain level of uneven discretization. The proposed model demonstrates that in comparison to the evenly discretized model, the complexity in terms of the number of states can be reduced by 7 times without loss of physical interpretation of the diffusion and migration dynamics in the solid particles and electrolyte. This reduction in the number of discretization allows for faster computation for the purpose of control and system design.Master of Science in EngineeringEnergy Systems Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/148848/1/Isaiah Oyewole Final Thesis Draft .pdfDescription of Isaiah Oyewole Final Thesis Draft .pdf : Thesi

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Utilizzo di tecnologie avanzate per applicazioni di "high speed cooking"

The microwave (MW) technology has become crucial in "high speed cooking" equipment. This new generation of cooker is still being refined and still finding its place in today's kitchens, but it is clear that these new high speed ovens can cook a wide variety of products and cook them faster than anything previously on the market. In ordinary cooking, heat is applied to the outside of food and it gradually penetrates to the inside. In MW cooking, the heat is generated within the food. Thus, a shorter heating time and a higher efficiency are some of the benefits of this technology. MW cooking is rapid, but non-uniform. MW heating is non-uniform mainly because of the inherently uneven distribution of the electromagnetic (EM) field inside the oven cavity. As the energy penetrates a lossy material, it is absorbed and less of it remains to penetrate further. Thus, energy absorption is non-uniform. Moreover, the energy absorption process is strongly affected by shape, size, dielectric properties of materials, position of the workload, as well as by the cavity geometry and dimensions. MW heating systems must provide uniform heating to obtain high quality products and avoid the so-called hot spots and cold spots. Traditionally, the temperature uniformity is accomplished by moving parts within the applicator, using mode stirrers, employing turntable or a combination of these techniques. Unfortunately, these techniques are not applicable within all type of resonant cavities. Other techniques which do not involve moving parts are: the pulsing MW energy or the phase shifting for different sources of MW energy. The phase shift technique and its effects on the EM field distribution and heating of a workload is discussed in the Thesis. The study of MW power sources has increased popularity among researchers in the field of cooking systems. In the last few years the innovative high frequency power solid state devices has gained much attention in place of magnetron due to their higher performances. In particular, a more careful control of the cooking process, which guarantees a more uniform heating of the foodstuff, can be achieved by using the solid state devices. This result can be reached, for instance, by using a properly phase shift for different sources of MW energy and it is discussed in the Thesis. Another solution, that is examined, involves the use of slots in the waveguide wall, which radiate EM energy from the waveguide. Since a multi slotted waveguide can be considered as an antenna array, a proper design of the slotted waveguide antennas, which feed the launch box and the MW applicator, is proposed in order to attain a more uniform temperature distribution without the need of moving parts. The effectiveness of the aforementioned technical solutions has been verified by means of numerical simulations on a test case model of practical interest, named "panini grill". The use of MW technology ensures a sandwich is heated through without a cold centre while reducing cooking time significantly. In the 3D numerical model two physical phenomena, i.e. EM wave propagation and heat transport, are coupled together by the thermal effects of MW energy deposition and the temperature-dependent material parameters. The coupled problem is solved by means of a FEA commercial software (COMSOL). In order to achieve the design parameters for the slotted waveguide feeding system and the phase shifting, which guarantee the more uniform hating, an optimization problem has been solved. More specifically, a metamodels-based optimization method has been set up. The metamodels can significantly reduce the problem complexity and simulation time. The optimization procedure has been characterized by pre-processing, programming, and post-processing coupling COMSOL Multiphysics and Matlab software