scholarly journals Household Energy Demand Management Strategy Based on Operating Power by Genetic Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 96414-96423 ◽  
Author(s):  
Xin Jiang ◽  
Chunyan Xiao
Keyword(s):  
Genetic Algorithm ◽  
Energy Demand ◽  
Management Strategy ◽  
Demand Management ◽  
Household Energy

Demand Management of Distributed Energy Loads Based on Genetic Algorithm Optimization

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Jiaming Li ◽  
Glenn Platt ◽  
Geoff James
Keyword(s):  
Genetic Algorithm ◽  
Information Exchange ◽  
Energy Demand ◽  
Demand Management ◽  
Energy Resource ◽  
Electricity Consumption ◽  
Central Processor ◽  
Algorithm Optimization ◽  
Distributed Energy ◽  
Multi Agent

Management of a very large number of distributed energy resources, energy loads, and generators, is a hot research topic. Such energy demand management techniques enable appliances to control and defer their electricity consumption when price soars and can be used to cope with the unpredictability of the energy market or provide response when supply is strained by demand. We consider a multi-agent system comprising multiple energy loads, each with a dedicated controller. This paper introduces our latest research in self-organization of coordinated behavior of multiple agents. Energy resource agents (RAs) coordinate with each other to achieve a balance between the overall consumption by the multi-agent collective and the stress on the community. In order to reduce the overall communication load while permitting efficient coordinated responses, information exchange is through indirect communications between RAs and a broker agent (BA). This gives a decentralized coordination approach that does not rely on intensive computation by a central processor. The algorithm presented here can coordinate different types of loads by controlling their set-points. The coordination strategy is optimized by a genetic algorithm (GA) and a fast coordination convergence has been achieved.

scholarly journals The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin Pullinger ◽  
Jonathan Kilgour ◽  
Nigel Goddard ◽  
Niklas Berliner ◽  
Lynda Webb ◽  
...  
Keyword(s):  
Survey Data ◽  
Energy Demand ◽  
Secondary Data ◽  
Hot Water ◽  
Power Measurement ◽  
Sensor Data ◽  
Energy Awareness ◽  
Household Energy ◽  
Wide Range ◽  
The Ideal

AbstractThe IDEAL household energy dataset described here comprises electricity, gas and contextual data from 255 UK homes over a 23-month period ending in June 2018, with a mean participation duration of 286 days. Sensors gathered 1-second electricity data, pulse-level gas data, 12-second temperature, humidity and light data for each room, and 12-second temperature data from boiler pipes for central heating and hot water. 39 homes also included plug-level monitoring of selected electrical appliances, real-power measurement of mains electricity and key sub-circuits, and more detailed temperature monitoring of gas- and heat-using equipment, including radiators and taps. Survey data included occupant demographics, values, attitudes and self-reported energy awareness, household income, energy tariffs, and building, room and appliance characteristics. Linked secondary data comprises weather and level of urbanisation. The data is provided in comma-separated format with a custom-built API to facilitate usage, and has been cleaned and documented. The data has a wide range of applications, including investigating energy demand patterns and drivers, modelling building performance, and undertaking Non-Intrusive Load Monitoring research.

scholarly journals Multilayer modeling of adoption dynamics in energy demand management

2020 ◽  
Vol 30 (1) ◽  
pp. 013153 ◽  
Author(s):  
Iacopo Iacopini ◽  
Benjamin Schäfer ◽  
Elsa Arcaute ◽  
Christian Beck ◽  
Vito Latora
Keyword(s):  
Energy Demand ◽  
Demand Management ◽  
Adoption Dynamics

Rural household energy demand modelling

1990 ◽  
Vol 12 (4) ◽  
pp. 279-288 ◽  
Author(s):  
Kamal Rijal ◽  
N.K. Bansal ◽  
P.D. Grover
Keyword(s):  
Energy Demand ◽  
Rural Household ◽  
Household Energy ◽  
Demand Modelling

scholarly journals Proposal and Implementation of a User-Centered Design Approach for Smart Grid Technologies: Example of the VERTPOM® Project

2021 ◽  
Vol 14 (4) ◽  
pp. 57
Author(s):  
Helios Raharison ◽  
Emilie Loup-Escande
Keyword(s):  
Distribution System ◽  
Smart Grids ◽  
Energy Demand ◽  
Demand Management ◽  
Real Life ◽  
Positive Energy ◽  
User Centered Design ◽  
Electrical Networks ◽  
Evaluation Approach ◽  
User Centered

Acting to preserve our planet as much as possible is no longer optional in today's world. To do so, Smart Grids within the framework of electrical networks - involving not only Distribution System Operators (DSOs), but also consumers in their Energy Demand Management (EDM) activity - represent an innovative and sustainable solution. However, the integration of Smart Grids into network management or into consumers' homes implies changes at several levels: organizational, social, psychological, etc. This is why it is essential to consider the human factor in the design of the technologies used in these Smart Grids. This paper proposes the integration of DSO operators and consumers within a user-centered evaluation approach in order to design Smart Grids that are sufficiently acceptable to users to enable Positive Energy Territories that produce more energy than they consume. This demonstration will be illustrated by the VERTPOM® project aiming at facilitating the use of renewable energies specific to each territory in order to contribute to the reduction of greenhouse gases and make the territories less dependent on traditional energies, and thus make Picardy (in France) a Positive Energy Territory. This paper presents the user-centered evaluation approach applied to three technologies (i.e., the VERTPOM-BANK® supervision tool intended for DSO operators, the private web portal and the IBox smart meter intended for households) from the upstream design phase to the implementation of the technologies in real-life situations.

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