A multi-timescale smart grid energy management system based on adaptive dynamic programming and Multi-NN Fusion prediction method

Jun, Yuan; Zhang, Guidong; Yu, Samson Shenglong; Chen, Zhe; Zhong, Li; Zhang, Yun

doi:10.1016/j.knosys.2022.108284

Cited by 14 publications

(2 citation statements)

References 19 publications

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“…Topic 15- “Energy Optimization” is concerned with business problems related to energy management. The soft computing approaches are widely applied in the optimization of energy power plants (Tan et al ., 2022), smart grid energy management (Yuan et al ., 2022), energy-efficient scheduling in the industry (Chen et al ., 2022d), investment planning in energy systems (Aloini et al ., 2021), optimization of energy efficiency of buildings (Boulmaiz et al ., 2022) and energy management in electric buses (Li et al ., 2021).…”

Section: Resultsmentioning

confidence: 99%

Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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PurposeThe primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.Design/methodology/approachThis research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.FindingsThis study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.Practical implicationsThis analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.Originality/valueThis study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

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Section: Resultsmentioning

confidence: 99%

Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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“…More specifically, the ratio of residential to commercial consumers influences the final net consumption of buildings, as commercial buildings have higher energy requirements (especially supermarkets) [37]. In addition, the electric load distribution of residential buildings is significantly different from that of non-residential buildings, with the residential load being higher during morning and evening hours [38]. Additionally, potential EV charging schedules also depend on the residential share of buildings, as cars are usually available for charging at night in residential districts and during working hours in non-residential districts.…”

Section: Input Parameters For District Categorisationmentioning

confidence: 99%

Creating Comparability among European Neighbourhoods to Enable the Transition of District Energy Infrastructures towards Positive Energy Districts

et al. 2022

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Planning the required energy infrastructure for the energy transition is a crucial task for various neighbourhood concepts, such as positive energy districts. However, energy planning often comes with the challenges of data shortages and a lack of comparability among solutions for different districts. This work aims to enable this comparability by introducing an approach for categorising districts according to parameters that are relevant for the planning of neighbourhood energy infrastructures. Four parameters (climate, floor space index, heating demand and share of residential buildings) and their respective ranges (bands) were derived from the literature. Additionally, this work visualised the combination of all parameter bands across Europe to conveniently showcase districts that are comparable according to the selected parameters. This approach and its visualisation could be used in urban planning to share knowledge from existing energy district projects with those planned in comparable districts.

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A Hybrid Wild Horses Optimization (WHO) and Dwarf Mongoose Optimization (DMO) method for optimum energy management in SG system

Ganesan,

Arockia Edwin Xavier

2024

Optim Control Appl Methods

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A hybrid technique is proposed for the energy management (EM) of smart grid (SG) systems. The proposed method integrates the Wild Horse Optimization (WHO) and dwarf mongoose optimization (DMO) methods; hence, it is named the WHO–DMO approach. The Micro‐grid (MG)‐tied system is a combination of battery, micro turbine (MT), photovoltaic (PV), and Wind Turbine (WT). The key aim of the proposed approach is to manage the resources and power of the SG model and reduce the cost of electricity. The objective of the system is to improve load demand. The WHO method is enhanced by the DMO method, which minimizes the objective of the system. Access to power demand, state of charge (SOC), and renewable energy sources (RESs) for storing elements are considered constraints of the system. The unit of renewable power systems relies on batteries as energy sources to stabilize and sustain stable and consistent output power throughout the operation. The proposed technique is done in MATLAB platform and its implementation is calculated using the existing methods. From the simulation, the proposed method has less cost and higher power than the existing methods.

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A multi-timescale smart grid energy management system based on adaptive dynamic programming and Multi-NN Fusion prediction method

Cited by 14 publications

References 19 publications

Soft computing in business: exploring current research and outlining future research directions

Soft computing in business: exploring current research and outlining future research directions

Creating Comparability among European Neighbourhoods to Enable the Transition of District Energy Infrastructures towards Positive Energy Districts

A Hybrid Wild Horses Optimization (WHO) and Dwarf Mongoose Optimization (DMO) method for optimum energy management in SG system

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