Integrated Management of Residential Energy Resources

Soares, Ana; Gomes, Álvaro; Antunes, Carlos Henggeler

doi:10.1051/epjconf/20123305005

Cited by 6 publications

(5 citation statements)

References 12 publications

(17 reference statements)

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“…the controllable groups and the non-controllable groups (Abushnaf et al 2015). Another more detailed categorization based on degree of control consists of four groups (Soares et al 2012;Soares et al 2014). ( 1) Non-controllable loads: Loads when reduced may result in discomfort to users or interruption on ongoing activities (lighting, cooking appliances, etc.).…”

Section: Categorization Of Home Appliances For Drmentioning

confidence: 99%

Building demand response and control methods for smart grids: A review

Shan

Wang

Yan

et al. 2016

Science and Technology for the Built Environment

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Demand response (DR) provides a solution to the grid imbalance problems which restrict the use of renewable energy. Since buildings possess high amount of flexible load, they could contribute to smart grid stability and achieve cost savings for buildings and the other participants in the smart grid. However, the research and application of building DR are still at the beginning stage. Majority of the existing DR studies are from the view point of the grid side, rather than the demand side. This paper therefore provides an overview on the studies on building DR from the view point of buildings at the demand side. It mainly consists of two parts: (1) an overview of different types of DR programs, and the status of the DR programs in several countries and regions; (2) a review on the control methods for DR in commercial and residential buildings. This review intends to support the further development of DR methods for future smart grid applications and their implementation in buildings.

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Section: Categorization Of Home Appliances For Drmentioning

confidence: 99%

Building demand response and control methods for smart grids: A review

Shan

Wang

Yan

et al. 2016

Science and Technology for the Built Environment

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“…In the energy sector, EAs have been applied to resolve problems such as network planning, unit commitment and economic dispatch, scheduling appliances operational cycles, and minimization of power losses. [20][21][22]…”

Section: Optimization Methods Applied To Renewable and Sustainable Enmentioning

confidence: 99%

“…EAs have been used as a robust approach to solve combinatorial problems with multiple conflicting objectives. In the energy sector, EAs have been applied to resolve problems such as network planning, unit commitment and economic dispatch, scheduling appliances operational cycles, and minimization of power losses 20–22 …”

Section: Introductionmentioning

confidence: 99%

Modelling demand flexibility and energy storage to support increased penetration of renewable energy resources on Porto Santo

et al. 2020

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This paper assesses the contribution of a controllable load (a reverse osmosis [RO] seawater desalination plant), together with an energy storage system in Porto Santo's small islanded electric power system. The controllable load and storage system are used to (i) smooth the net-demand fluctuations and adapt it to the availability of renewable energy sources (RES), thus avoiding possible curtailments and contributing to a higher dissemination of RES, (ii) minimize the overall operational cost associated with the production of electricity and potable-water, and (iii) reduce the environmental pollutants associated with the electric power systems on the island. The nonlinear nature of the problem makes it difficult to quickly obtain a robust solution through conventional mathematical tools. Therefore, an evolutionary algorithm is developed to find feasible solutions for dispatching the resources for a one-week simulation period. The proposed algorithm determined the power output of the conventional thermal power plant, the RO desalination plant operating periods, and the storage charging and discharging periods and powers. In the proposed scenario, through a seven-day simulation, 50% of the demand is supplied by renewable sources. The numerical results illustrate a reduction in the average total electricity-peak demand of the island. The obtained diagrams are compared with the data gathered on Porto Santo's energy system. They display that the proposed solution is economically beneficial for the management of the electric power grid of the island of Porto Santo, while reducing the global warming potential (GWP) of the electric power system. Furthermore, it reveals that in a scenario with 50% penetration of renewable sources, through the proposed solution, a more efficient and predictable operation of the conventional electricity generators and the RO desalination plants can be achieved.

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“…The smart home can be controlled from remote monitoring and control module which will communicate with the home server through internet as indicated in Fig. (1) [9,10].…”

Section: Overview Of Smart Home Architecturementioning

confidence: 99%

Lithium-ion energy storage battery in PV-smart building application

El-Sayed¹

2019

REPQJ

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Photovoltaic (PV) panels with energy storage batteries represents a feasible solution for powering domestic loads. The service life of the batteries and the power management are the main challenges by developing the energy supply system of smart homes. Therefore, in this paper an efficient algorithm is developed in order to power a house by extracting the maximum power from the PV panels, enhancing the battery service life and minimizing the power supply from the smart grid. The smart metering, advanced inverter and rule-based energy management strategy ensure safe and optimal operation of the energy supply system. Matlab/Simulink model of typical PV array, lithium-ion battery, inverter with the associated controllers are developed to evaluate the performance of the proposed system. Starting from the actual solar irradiance data, the maximum power of PV array will be extracted as a function of the available irradiance and ambient temperature. The daily battery state of charge (SOC) and its internal temperature are calculated depending on the load, PV power and the battery charge/discharge modes. Simulation results show that the proposed algorithm can enhance the performance of energy supply system, extract the maximum solar power and minimize the power from smart grid.

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Integrated Management of Residential Energy Resources

Cited by 6 publications

References 12 publications

Building demand response and control methods for smart grids: A review

Building demand response and control methods for smart grids: A review

Modelling demand flexibility and energy storage to support increased penetration of renewable energy resources on Porto Santo

Lithium-ion energy storage battery in PV-smart building application

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