Genetic algorithm methodology applied to intelligent house control

Fernandes, Filipe; Sousa, Tiago; Silva, M.; Morais, Hugo; Vale, Zita; Faria, Pedro

doi:10.1109/ciasg.2011.5953341

Cited by 42 publications

(22 citation statements)

References 7 publications

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“…The positive flow of energy to the system, including that of the mRES at the house (P prod ), should be equal to the negative flow of energy from the system, i.e. the house consumptions P demand , as shown in (1) or equivalently in (2).…”

Section: Control Of the Battery Management Systemmentioning

confidence: 99%

“…In this case, a practical and efficient solution consists of a device capable of monitoring the electricity generation and consumption in the house and the electricity prices from the grid in order to make all the energy management decisions required to maximize the profit for the prosumer. Towards this direction, recent research efforts have focused on energy management for residential settings having distributed generation (DG) units [2], grid-connected photovoltaic (PV) systems coupled with batteries [3] and plug-in hybrid electric vehicles (PHEV) as electricity loads [4]. The minimization of the operational cost in a DC "smart house" with PVs and electricity storage from a fixed battery and an electric vehicle (EV) is considered in [5] using hourly time steps under a fixed time-of-day electricity pricing scheme.…”

Section: Introductionmentioning

confidence: 99%

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A non-myopic approach for a domotic battery management system

Genikomsakis

Eliasstam

Weingraber

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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The foreseeable dynamic pricing of electricity combined with the emergence of the so-called electricity "prosumers" that not only consume, but also have the ability to produce and store electricity, necessitates the development of energy management systems even at residential level. In this context, the present work considers that in a modern residence, or a "smart house", electricity is generated from microrenewable energy sources, while the electricity storage options include a fixed battery as well as the battery from a plug-in electric vehicle. This paper presents the theoretical framework for the implementation of a domotic battery management system with the key characteristic that energy management decisions are made every 10 min, based on the forecasted conditions for the next 24 h. The results obtained from the simulation of the proposed system highlight the contribution not only of the fixed battery, but also of the electric vehicle's battery to the maximization of electricity cost savings.

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Section: Control Of the Battery Management Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A non-myopic approach for a domotic battery management system

Genikomsakis

Eliasstam

Weingraber

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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“…Esse dispositivo também possibilita o corte via comando remoto, agendamento ou sobrecarga. Fernandes et al (2011) apresentou uma abordagem para gerenciar diretamente o consumo de cargas em uma residência utilizando algoritmos genéticos e fazendo uso de um sistema SCADA desenvolvido pelos autores. Essa gestão do consumo foi feita reduzindo ou limitando cargas para manter o consumo de energia abaixo de um limite determinado de acordo com a estratégia do consumidor, e também levando em conta a geração local baseada em fontes renová-veis de energia, preços, solicitações de fornecedores e consumidores.…”

Section: Introductionunclassified

Proposta de algoritmo para gerenciamento pelo lado demanda em residências através do corte de smart plugs

Custódio¹,

Ferreira²,

Siebert³

et al. 2013

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Abstract Due to the increase of electricity demand every year and rising problems in its distribution, it is a need for the utilities to find ways to supply this demand with techniques beyond the usual infrastructural growth. One possibility is to use demand side management, including actions concerning load shifting, e.g. actions to change the time pattern consumption of equipment from peak to off-peak hours encouraged by Time-Of-Use tariffs. To support the customers in these time pattern consumption changes, aiming benefits to both utility and customers, it is being lately often discussed the use of smart plug as a measuring and shedding tool, supported by algorithms that provide the need automation and optimization. The algorithm presented in this paper was able to provide satisfactory results, indicating when the customer should avoid using each smart plug, as well as avoiding energy waste, through the shed of energy consumption in standby.Keywords Demand Side Management, Load Shifting, Smart Plugs, White Tariff, Power Systems.Resumo Com o aumento da demanda de energia a cada ano e consequentes dificuldades no processo de distribuição da mesma, é necessidade das concessionárias encontrar saídas para conseguir suprir esse aumento, procurando técnicas além do aumento da infraestrutura. Uma dessas saídas é o gerenciamento pelo lado da demanda, que engloba diversas técnicas incluindo o deslocamento de carga, ou seja, ações para realizar o deslocamento da utilização de equipamentos do horário de pico para outro horário, incentivadas através de tarifas variáveis no tempo. Para dar suporte ao cliente nessas ações, visando benefícios para a concessionária e também reduções de gastos de energia do cliente, ultimamente vem sido bastante discutida a utilização de smart plugs como ferramenta de medição e de corte, auxiliada por algoritmos que possibilitem a automação e otimização das mesmas. O algoritmo desenvolvido no presente trabalho apresentou resultados satisfatórios, indicando os momentos que o consumidor deve evitar o consumo de energia em cada smart plug assim como auxiliar na redução do desperdício de energia, atuando no corte do consumo de aparelhos em standby.Palavras-chave Gerenciamento pelo Lado da Demanda, Deslocamento de Carga, Tomadas Inteligentes, Tarifa Branca, Sistemas de Potência. IntroduçãoConforme Falcão (2010), smart grid deve ser entendido mais como um conceito do que uma tecnologia ou equipamento específico. O smart grid carrega a ideia da utilização intensiva de tecnologia de informação e comunicação na rede elétrica, através da possibilidade de comunicação do estado dos diversos componentes da rede, o que permitirá a implantação de estratégias de controle e otimização da rede de forma muito mais eficiente que as atualmente em uso.O monitoramento em tempo real do desempenho da rede pode melhorar a confiabilidade do sistema, reduzir apagões e aumentar o retorno financeiro sobre os investimentos na rede. Essas mudanças na demanda e oferta exigem sistemas inteligentes para gerenciar de forma e...

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“…However, these techniques have very slow convergence rate, and in some cases, they are unable to handle a large number of appliances. So, the heuristic algorithms such as GA [33], BPSO [34], WDO [35] and BFO [36] are introduced to overcome these problems. The heuristic optimizations are used where it is very difficult to find the exact optimal/feasible points.…”

Section: Problem Statementmentioning

confidence: 99%

An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources

et al. 2017

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Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.

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Genetic algorithm methodology applied to intelligent house control

Cited by 42 publications

References 7 publications

A non-myopic approach for a domotic battery management system

A non-myopic approach for a domotic battery management system

Proposta de algoritmo para gerenciamento pelo lado demanda em residências através do corte de smart plugs

An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources

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