Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value

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

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Real-time energy resources scheduling considering intensive wind penetration

The use of distributed energy resources, based on natural intermittent power sources, like wind generation, in power systems imposes the development of new adequate operation management and control methodologies. A short-term Energy Resource Management (ERM) methodology performed in two phases is proposed in this paper. The first one addresses the day-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. The ERM scheduling is a complex optimization problem due to the high quantity of variables and constraints. In this paper the main goal is to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixedinteger non-linear programming approach. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units and 1000 electric vehicles has been implemented in a simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior

Smart Microgrids: Overview and Outlook

The idea of changing our energy system from a hierarchical design into a set of nearly independent microgrids becomes feasible with the availability of small renewable energy generators. The smart microgrid concept comes with several challenges in research and engineering targeting load balancing, pricing, consumer integration and home automation. In this paper we first provide an overview on these challenges and present approaches that target the problems identified. While there exist promising algorithms for the particular field, we see a missing integration which specifically targets smart microgrids. Therefore, we propose an architecture that integrates the presented approaches and defines interfaces between the identified components such as generators, storage, smart and \dq{dumb} devices.Comment: presented at the GI Informatik 2012, Braunschweig Germany, Smart Grid Worksho

Enhancement of Metaheuristic Algorithm for Scheduling Workflows in Multi-fog Environments

Whether in computer science, engineering, or economics, optimization lies at the heart of any challenge involving decision-making. Choosing between several options is part of the decision- making process. Our desire to make the "better" decision drives our decision. An objective function or performance index describes the assessment of the alternative's goodness. The theory and methods of optimization are concerned with picking the best option. There are two types of optimization methods: deterministic and stochastic. The first is a traditional approach, which works well for small and linear problems. However, they struggle to address most of the real-world problems, which have a highly dimensional, nonlinear, and complex nature. As an alternative, stochastic optimization algorithms are specifically designed to tackle these types of challenges and are more common nowadays. This study proposed two stochastic, robust swarm-based metaheuristic optimization methods. They are both hybrid algorithms, which are formulated by combining Particle Swarm Optimization and Salp Swarm Optimization algorithms. Further, these algorithms are then applied to an important and thought-provoking problem. The problem is scientific workflow scheduling in multiple fog environments. Many computer environments, such as fog computing, are plagued by security attacks that must be handled. DDoS attacks are effectively harmful to fog computing environments as they occupy the fog's resources and make them busy. Thus, the fog environments would generally have fewer resources available during these types of attacks, and then the scheduling of submitted Internet of Things (IoT) workflows would be affected. Nevertheless, the current systems disregard the impact of DDoS attacks occurring in their scheduling process, causing the amount of workflows that miss deadlines as well as increasing the amount of tasks that are offloaded to the cloud. Hence, this study proposed a hybrid optimization algorithm as a solution for dealing with the workflow scheduling issue in various fog computing locations. The proposed algorithm comprises Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO). In dealing with the effects of DDoS attacks on fog computing locations, two Markov-chain schemes of discrete time types were used, whereby one calculates the average network bandwidth existing in each fog while the other determines the number of virtual machines existing in every fog on average. DDoS attacks are addressed at various levels. The approach predicts the DDoS attack’s influences on fog environments. Based on the simulation results, the proposed method can significantly lessen the amount of offloaded tasks that are transferred to the cloud data centers. It could also decrease the amount of workflows with missed deadlines. Moreover, the significance of green fog computing is growing in fog computing environments, in which the consumption of energy plays an essential role in determining maintenance expenses and carbon dioxide emissions. The implementation of efficient scheduling methods has the potential to mitigate the usage of energy by allocating tasks to the most appropriate resources, considering the energy efficiency of each individual resource. In order to mitigate these challenges, the proposed algorithm integrates the Dynamic Voltage and Frequency Scaling (DVFS) technique, which is commonly employed to enhance the energy efficiency of processors. The experimental findings demonstrate that the utilization of the proposed method, combined with the Dynamic Voltage and Frequency Scaling (DVFS) technique, yields improved outcomes. These benefits encompass a minimization in energy consumption. Consequently, this approach emerges as a more environmentally friendly and sustainable solution for fog computing environments

Study of an Optimized Micro-Grid’s Operation with Electrical Vehicle-Based Hybridized Sustainable Algorithm

Recently, the expansion of energy communities has been aided by the lowering cost of storage technologies and the appearance of mechanisms for exchanging energy that is driven by economics. An amalgamation of different renewable energy sources, including solar, wind, geothermal, tidal, etc., is necessary to offer sustainable energy for smart cities. Furthermore, considering the induction of large-scale electric vehicles connected to the regional micro-grid, and causes of increase in the randomness and uncertainty of the load in a certain area, a solution that meets the community demands for electricity, heating, cooling, and transportation while using renewable energy is needed. This paper aims to define the impact of large-scale electric vehicles on the operation and management of the microgrid using a hybridized algorithm. First, with the use of the natural attributes of electric vehicles such as flexible loads, a large-scale electric vehicle response dispatch model is constructed. Second, three factors of micro-grid operation, management, and environmental pollution control costs with load fluctuation variance are discussed. Third, a hybrid gravitational search algorithm and random forest regression (GSA-RFR) approach is proposed to confirm the method’s authenticity and reliability. The constructed large-scale electric vehicle response dispatch model significantly improves the load smoothness of the micro-grid after the large-scale electric vehicles are connected and reduces the impact of the entire grid. The proposed hybridized optimization method was solved within 296.7 s, the time taken for electric vehicle users to charge from and discharge to the regional micro-grid, which improves the economy of the micro-grid, and realizes the effective management of the regional load. The weight coefficients λ1 and λ2 were found at 0.589 and 0.421, respectively. This study provides key findings and suggestions that can be useful to scholars and decisionmakers

Residential Demand Side Management model, optimization and future perspective: A review

The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints