A new optimized demand management system for smart grid-based residential buildings adopting renewable and storage energies

Demand Side Management (DSM) implies intelligently managing load appliances in a Smart Grid (SG). DSM programs help customers save money by reducing their electricity bills, minimizing the utility’s peak demand, and improving load factor. To achieve these goals, this paper proposes a new load shifting-based optimal DSM model for scheduling residential users’ appliances. The proposed system effectively handles the challenges raised in the literature regarding the absence of using recent, easy, and more robust optimization techniques, a comparison procedure with well-established ones, using Renewable Energy Resources (RERs), Renewable Energy Storage (RES), and adopting consumer comfort. This system uses recent algorithms called Virulence Optimization Algorithm (VOA) and Earth Worm Optimization Algorithm (EWOA) for optimally shifting the time slots of shiftable appliances. The system adopts RERs, RES, as well as utility grid energy for supplying load appliances. This system takes into account user preferences, timing factors for each appliance, and a pricing signal for relocating shiftable appliances to flatten the energy demand profile. In order to figure out how much electricity users will have to pay, a Time Of Use (TOU) dynamic pricing scheme has been used. Using MATLAB simulation environment, we have made effectiveness-based comparisons of the adopted optimization algorithms with the well-established meta-heuristics and evolutionary algorithms (Genetic Algorithm (GA), Cuckoo Search Optimization (CSO), and Binary Particle Swarm Optimization (BPSO) in order to determine the most efficient one. Without adopting RES, the results indicate that VOA outperforms the other algorithms. The VOA enables 59% minimization in Peak-to-Average Ratio (PAR) of consumption energy and is more robust than other competitors. By incorporating RES, the EWOA, alongside the VOA, provides less deviation and a lower PAR. The VOA saves 76.19% of PAR, and the EWOA saves 73.8%, followed by the BPSO, GA, and CSO, respectively. The electricity consumption using VOA and EWOA-based DSM cost 217 and 210 USD cents, respectively, whereas non-scheduled consumption costs 273 USD cents and scheduling based on BPSO, GA, and CSO costs 219, 220, and 222 USD cents.publishedVersio

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature- inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field

Golden Search Optimization Algorithm

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Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

An improved multi-strategy beluga whale optimization for global optimization problems

This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO. It was inspired by the group hunting behavior of beluga whales in nature. The GAS keeps individual belugas whales together, allowing them to hide together from the threat posed by their natural enemy, the tiger shark. It also enables the exchange of location information between individual belugas whales to enhance the balance between local and global lookups. On this basis, the dynamic pinhole imaging strategy (DPIS) and quadratic interpolation strategy (QIS) are added to improve the global optimization ability and search rate of IBWO and maintain diversity. In a comparison experiment, the performance of the optimization algorithm (IBWO) was tested by using CEC2017 and CEC2020 benchmark functions of different dimensions. Performance was analyzed by observing experimental data, convergence curves, and box graphs, and the results were tested using the Wilcoxon rank sum test. The results show that IBWO has good optimization performance and robustness. Finally, the applicability of IBWO to practical engineering problems is verified by five engineering problems

Aplicação e análise de métodos estocásticos de otimização ao modelo de múltiplas fontes pontuais ponderadas para a determinação da radiação em chamas

A determinação da emissão de radiação em chamas é um problema frequente em projetos de caldeiras, queimadores e equipamentos similares, sendo sua modelagem fundamental para controle de processos, para redução de custos e para prevenção de falhas e acidentes. Alguns modelos foram propostos, entre eles o chamado WMP, mas poucos estudos se dedicaram a desenvolvê-los e avaliá-los. Estudos anteriores buscaram relacionar os parâmetros do modelo WMP com o fenômeno da combustão e otimizá-los com a Otimização Extrema Generalizada. Existe contudo a possibilidade de que outros métodos sejam mais e cientes sem perda de qualidade. Com isso em mente, este estudo busca aplicar e avaliar o desempenho de diferentes algoritmos estocásticos de otimização ao modelo WMP. É feita uma revisão do estado da arte, sendo escolhidos cinco algoritmos para análise: Otimização de Lobos Cinzentos, Otimização de Manadas Egoístas, Algoritmo Genético, Algoritmo de Dentes-de-Leão e Otimização de Bactérias. Também é proposto um procedimento de calibração desses algoritmos baseada na metodologia de Projeto de Experimentos. O Algoritmo de Lobos Cinzentos se mostrou como o de melhor desempenho, sendo seus resultados médios e desvios padrões satisfatórios em comparação com os demais. O Algoritmo Genético e o Algoritmo de Dentes-de-Leão mostraram desempenho satisfatório, mas necessitando mais execuções para a con rmação da resposta. A Otimização de Manadas Egoístas e a Otimização de Bactérias exibiram desempenhos inferiores, com altas médias e desvios. A metodologia de Projeto de Experimentos se mostrou adequada para algoritmos com poucos parâmetros, mas perde qualidade à medida que o número de parâ- metros aumenta. Os melhores resultados foram encontrados com maiores quantidades de fontes emissoras e maiores comprimentos de distribuição, concordando com a tendência apresentada por trabalhos anteriores. O melhor resultado foi encontrado pelo Algoritmo de Dentes-de-Leão, na con guração com sete fontes emissoras e comprimento de distribui ção das fontes equivalente a 2,5 vezes o comprimento visível da chama, com um valor de função objetivo de 0,216 kW/m2.The determination of radiation emission on ames is a frequent problem designing boilers, burners and similar equipments, its modelling being therefore fundamental for process control, cost reduction and prevention of failures and accidents. Some models have been proposed, among them the WMP model, however little study has been dedicated to solve and evaluate them. Some previous researches sought to correlate WMP model parameters to the phenomenon of combustion and to optimise them with the Generalised Extreme Optimisation. However there is the possibility of other optimisation methods being more e cient without loss of quality. Having this in mind, this study aims to apply and evaluate the performance of di erent stochastic optimisation algorithms applied to the WMP model. A bibliographical review of the state of the art is made, being chosen ve methods to be analysed: Grey Wolf Optimiser, Sel sh Herds Optimiser, Genetic Algorithms, Dandelion Algorithm and Bacterial Foraging Optimisation. Also, a procedure for the calibration of these methods is proposed, based on the Design of Experiments methodology. The Grey Wolf Optimizer presents the best performance, with satisfactory mean results and standard deviations. The Genetic Algorithm and the Dandelion Algorithm showed good satisfactory results, but also the necessity of a deeper conference of results. The Sel sh Herds Optimiser and the Bacterial Foraging Optimisation had inferior performance, with higher means and deviations. The Design of Experiments methodology was satisfactory for algorithms with few parameters, but lost quality as the number of parameters increased. The best results were found for higher quantities of sources and larger distribution lengths, which agrees with tendencies presented by previous works. The best result was found by the Dandelion Algorithm, employing 7 point sources and a distribution length equivalent to 2.5 times the visible ame length, presenting an objective function value of 0.216 kW/m2

Consensus-based dispatch optimization of a microgrid considering meta-heuristic-based demand response scheduling and network packet loss characterization

The uncertainty inherent in power load forecasts represents a major factor in the mismatches between supply and demand in renewables-rich electricity networks, which consequently increases the energy bills and curtailed generation. As the transition to a power grid founded on the so-called grid-of-grids becomes more evident, the need for distributed control algorithms capable of handling computationally challenging problems in the energy sector does so as well. In this light, the consensus-based distributed algorithm has recently been shown to provide an effective platform for solving the complex energy management problem in microgrids. More specifically, in a microgrid context, the consensus-based distributed algorithm requires reliable information exchange with customers to achieve convergence. However, packet losses remain an important issue, which can potentially result in the failure of the overall system. In this setting, this paper introduces a novel method to effectively characterize such packet losses during information exchange between the customers and the microgrid operator, whilst solving the microgrid scheduling optimization problem for a multi-agent-based microgrid. More specifically, the proposed framework leverages the virulence optimization algorithm and the earth-worm optimization algorithm to optimally shift the energy consumption during peak periods to lower-priced off-peak hours. The effectiveness of the proposed method in minimizing the overall active power mismatches in the presence of packet losses has also been demonstrated based on benchmarking the results against the business-as-usual iterative scheduling algorithm. Also, the robustness of the overall meta-heuristic- and multi-agent-based method in producing optimal results is confirmed based on comparing the results obtained by several well-established meta-heuristic optimization algorithms, including the binary particle swarm optimization, the genetic algorithm, and the cuckoo search optimization