An Overview of the Building Energy Management System Considering the Demand Response Programs, Smart Strategies and Smart Grid

Shakeri, Mohammad Taghi; Pasupuleti, Jagadeesh; Amin, Nowshad; Rokonuzzaman, Md.; Low, Foo Wah; Yaw, Chong Tak; Samsudin, Nurul Asma; Tiong, Sieh Kiong; Chong, K. H.; Lai, Chin Wei

doi:10.3390/en13133299

Cited by 40 publications

(24 citation statements)

References 95 publications

(59 reference statements)

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“…However, the authors did not explore the optimization objectives, constraints related to comfort index parameters, energy consumption and scheduling. Shakeri et al [46] presented the BEMS considering the demand response programs, load management techniques, smart grid, different AI and optimization-based home energy management systems (HEMS). However, the authors did not provide the categorization of controllers and optimizations.…”

Section: Comfort Index Optimizationmentioning

confidence: 99%

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.INDEX TERMS Building energy management, controller, optimization, scheduling, sustainable development goals I. INTRODUCTIONPresently, buildings take the lead in consuming a substantial amount of energy, indicating about 40% of global energy consumption, which is responsible to release one-third of greenhouse gas (GHG) emissions [1], [2]. Another report demonstrates that buildings hold 49% of the total energy worldwide in which 60% of the energy is consumed for heating and cooling purposes [3], [4]. The poor management and ineffective control approach of appliances used in the building may result in a significant loss of energy in a

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Section: Comfort Index Optimizationmentioning

confidence: 99%

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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“…However, in 2020, South Africa improved its performance to 74 th position [46]. The supply of South Africa's energy is dominated by coal, which represents about 70% of the total installed capacity; crude oil, natural gas and nuclear power contribute about 29%, with less than 2% from renewables [49,50], as indicated in Figure 3. A further check-in [47,51] discloses that coal contributes about 90% of the power generated, 3% of nuclear, Natural gas 5%, and other RES such as biomass, wind, solar, bagasse, and landfill gas together contribute only 2% of the total energy mix, which falls below the global standard.…”

Section: Overview Of the Energy Sectormentioning

confidence: 99%

“…The IEP is, however, optimistic that South Africa still stands a chance of hitting an incomparable jackpot with coal or shale gas or both. The largest cost component of the entire energy system for 2014 to 2050 is imported fuel, but slightly cheaper than the current fuel and oil import cost [48][49][50][51].…”

Section: Supplymentioning

confidence: 99%

South Africa’s integrated energy planning framework, 2015–2050

Akom

Shongwe

Joseph

2021

J. energy South. Afr.

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The Integrated Energy Plan (IEP) was designed to consider South Africa’s energy needs from 2015 to 2050, as a guide for energy structural savings and the development of energy policy. The main aim of the Department of Energy is to ensure the security of energy supply. The current energy situation in the country has its gains and challenges. With the growing population and infrastructural development, the country requires prudent measures to meet the country’s energy needs for 2020–2050. The country’s energy is currently dominated by coal-fired plants, which represent about 70% of the total installed capacity, crude oil contributes about 21%, with only 9% from all other energy sources, including renewables. This paper examines the scope of the IEP framework, key objectives of the IEP, the methodology applied to achieve those objectives, and the projections made for attaining the framework target. The paper further reviews the energy requirements for the key sectors of the economy and analyses the effects of CO2 emissions and the benefits of job creation for the entire period. Despite substantial renewable potential in South Africa, at present it contributes as little as 2% of the energy mix. The global renewable energy policy on CO2 emissions reduction, improvement of energy efficiency and deployment of renewable development are not met in the IEP framework.

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“…The bidirectional data and energy flows are the most important features of smart grid technology. The incorporation of renewable energy sources (RESs), such as solar and wind, along with battery energy storage systems (BESSs) in residential premises can significantly decrease electricity prices and energy consumption [4][5][6]. However, the development of efficient home energy management (HEM) is a challenging task to efficiently handle the energy consumption inside a residence while exchanging energy with the utility grid.…”

Section: Introductionmentioning

confidence: 99%

Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm

et al. 2021

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From a residential point of view, home energy management (HEM) is an essential requirement in order to diminish peak demand and utility tariffs. The integration of renewable energy sources (RESs) together with battery energy storage systems (BESSs) and central battery storage system (CBSS) may promote energy and cost minimization. However, proper home appliance scheduling along with energy storage options is essential to significantly decrease the energy consumption profile and overall expenditure in real-time operation. This paper proposes a cost-effective HEM scheme in the microgrid framework to promote curtailing of energy usage and relevant utility tariff considering both energy storage and renewable sources integration. Usually, the household appliances have different runtime preferences and duration of operation based on user demand. This work considers a simulator designed in the C++ platform to address the domestic customer’s HEM issue based on usages priorities. The positive aspects of merging RESs, BESSs, and CBSSs with the proposed optimal power sharing algorithm (OPSA) are evaluated by considering three distinct case scenarios. Comprehensive analysis of each scenario considering the real-time scheduling of home appliances is conducted to substantiate the efficacy of the outlined energy and cost mitigation schemes. The results obtained demonstrate the effectiveness of the proposed algorithm to enable energy and cost savings up to 37.5% and 45% in comparison to the prevailing methodology.

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An Overview of the Building Energy Management System Considering the Demand Response Programs, Smart Strategies and Smart Grid

Cited by 40 publications

References 95 publications

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

South Africa’s integrated energy planning framework, 2015–2050

Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm

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