Domestic energy management methodology for optimizing efficiency in Smart Grids

Molderink, Albert; Bakker, Vincent; Bosman, M.G.C.; Hurink, Johann L.; Smit, Gerard J. M.

doi:10.1109/ptc.2009.5281849

Cited by 122 publications

(81 citation statements)

References 10 publications

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“…However, UC is not considered in most of these techniques, like [8,10,[12][13][14][15][16][17]. In these works, [11,18,19] aim to reduce the electricity cost, and [20,21] focus on minimizing the aggregated power consumption using integer linear programming and mixed integer linear programming. Similarly, electricity bills and aggregated power consumption are reduced in [22] by using mixed integer non-linear programming.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

et al. 2017

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Abstract:In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

et al. 2017

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“…The most common renewable energy sources connected in power networks are large wind turbines and PV (photovoltaic) parks as well as wide range of distributed sources like photovoltaic power plants on roofs and micro CHP (combined heat and power) on a domestic level [1,2,3]. Although distributed generation located in the distribution grid (near point of consumption) has many advantages for the grid itself (negligible distribution losses, increased reliability of generation, less need for transmission and distribution network capacity, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Demand side load management can increase the generation efficiency in two ways: by peak shaving and by shifting load to more beneficial periods [1]. Some of the home appliances (air conditioners, washing machines and dryers, freezers, heaters and refrigerators) can be temporarily switched off or postponed, without any strong impact on residents.…”

Section: Introductionmentioning

confidence: 99%

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Demand side management in the distribution system with photovoltaic generation

2015

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Original scientific paper Recently, there has been made a great effort to include electricity generation from the renewable energy sources into the power system. Random renewable generation creates the imbalance between electricity production and consumption, which requires power plants with fast response or energy storage systems. Generally accepted solution for load balancing is the concept of smart grids and one of the elements of smart grid efficiency is the ability of real-time demand-supply balancing. In this paper, the model of the part of power distribution network of the city of Osijek has been created based on results of the power measurements of total electricity consumption in a family house in Osijek, air conditioning system consumption and PV power plant production. Also, algorithm for real-time load management is proposed. It assumes coordinated control of air conditioning system units depending on the production of PV power plants and electricity consumption of distribution network, in order to reduce peak demand in the distribution network.Keywords: demand side load management; distribution system; photovoltaic generation; real-time demand-supply balancing; smart grid Upravljanje potražnjom u distribucijskom sustavu s fotonaponskim elektranamaIzvorni znanstveni članak Posljednjih godina učinjen je znatan napor na uključivanju obnovljivih izvora električne energije u elektroenergetski sustav. Nepredvidiva prozvodnja obnovljivih izvora električne energije stvara neravnotežu između proizvodnje i potrošnje, što onda zahtijeva elektrane s brzim odzivom, ili sustave skladištenja električne energije. Opće prihvaćeno rješenje za uravnoteženje potrošnje je koncept naprednih mreža, a jedan od elemenata učinkovitosti naprednih mreža je sposobnost za uravnoteženje potražnje i opskrbe u stvarnom vremenu. U ovom radu razvijen je model dijela distribucijske mreže grada Osijeka i to na temelju rezultata mjerenja ukupne potrošnje obiteljske kuće u Osijeku, potrošnje klimatizacijskog uređaja te proizvodnje fotonaponske elektrane. Također, predložen je algoritam za upravljanje potražnjom u stvarnom vremenu. Algoritam sadrži usklađeno upravljanje klimatizacijskim uređajima ovisno o proizvodnji fotonaponskih elektrana te o potrošnji u distribucijskoj mreži, a u cilju snižavanja vršne potražnje u distribucijskoj mreži.Ključne riječi: distribucijski sustav; napredne mreže; proizvodnja fotonaponske elektrane; upravljanje potražnjom; uravnoteženje potražnje i opskrbe u stvarnom vremenu

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“…On the demand side, the transition to all-electrification in consideration of economy, safety, and use of heat pump water heater (HP) and electric vehicle (EV) those are suitable for the environment. Thus, power conditioning ability is increasing by these large capacity load connected to power system in the demand side [5,6]. In addition, increase of equipment capacity for frequency and voltage control are not used for the electric power supplier.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Frequency and Voltage Control by Decentralized Controllable Loads with Smart Grid

Kinjyo¹,

Yona

Urasaki

et al. 2012

Journal of International Council on Electrical Engineering

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-In recent years, amount of renewable energy facilities using wind turbine generator and photovoltaic power system have been increased due to natural environment and resource depletion. Moreover, all electrification apartment houses or residences and electric vehicles have been increased. However, due to the fluctuating power from renewable energy sources and loads, fluctuations of grid frequency and distributed voltage become problematic. This paper presents a methodology of control system frequency and distributed voltage by distributed controllable loads such as heat pump and electric vehicles. By applying power consumption controller using distribution control and H ∞ control, fluctuations of grid frequency and voltage are suppressed around a desired value. In order to verify the effectiveness of the proposed system, MATLAB ® /Simulink ® is used for computed simulations.

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Domestic energy management methodology for optimizing efficiency in Smart Grids

Cited by 122 publications

References 10 publications

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

Demand side management in the distribution system with photovoltaic generation

Autonomous Frequency and Voltage Control by Decentralized Controllable Loads with Smart Grid

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