Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers

Shareef, Hussain; Ahmed, Maytham S.; Mohamed, Azah; Hassan, Eslam Al

doi:10.1109/access.2018.2831917

Cited by 336 publications

(206 citation statements)

References 110 publications

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“…Since the customer is empowered and plays a key role in smart grid, EMS at the distribution level can monitor control and optimize the local system performance. At the customer end, some of the commonly implemented EMS are SHEMS, 24,57 HEMS, 9,24,29,[57][58][59][60][61] BEMS, 15,[62][63][64][65][66] and plant energy management system (PEMS). 67 At the end users level, the ultimate role of EMS is to minimize energy usage by properly scheduling the devices in specified time horizons.…”

Section: Types Of Ems At the Distribution Levelmentioning

confidence: 99%

Energy management system for smart grid: An overview and key issues

2020

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Summary Energy crisis and the global impetus to “go green” have encouraged the integration of renewable energy resources, plug‐in electric vehicles, and energy storage systems to the grid. The presence of more than one energy source in the grid necessitates the need for an efficient energy management system to guide the flow of energy. Moreover, the variability and volatile nature of renewable energy sources, uncertainties associated with plug‐in electric vehicles, the electricity price, and the time‐varying load bring new challenges to the power engineers to achieve demand‐supply balance for stable operation of the power system. The energy management system can effectively coordinate the energy sharing/trading among all available energy resources, and supply loads economically in all the conditions for the reliable, secure, and efficient operation of the power system. This paper reviews the framework, objectives, architecture, benefits, and challenges of the energy management system with a comprehensive analysis of different stakeholders and participants involved in it. The review paper gives a critical analysis of the distributed energy resources behavior and different programs such as demand response, demand‐side management, and power quality management implemented in the energy management system. Different uncertainty quantification methods are also summarized. This review paper also presents a comparative and critical analysis of the main optimization techniques used to achieve different energy management system objectives while satisfying multiple constraints. Thus, the review offers numerous recommendations for research and development of the cutting‐edge optimized energy management system applicable for homes, buildings, industries, electric vehicles, and the whole community.

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Section: Types Of Ems At the Distribution Levelmentioning

confidence: 99%

Energy management system for smart grid: An overview and key issues

2020

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“…1 shows the component layout used in this paper. It is assumed that the building is equipped with the home energy management system (HEMS) [13]. In order to satisfy the building's energy demand, HEMS can provide electricity from any combination of PV generation, battery storage, and electricity purchased from the retail electricity provider [14].…”

Section: Photovoltaic (Pv) Generator Modelmentioning

confidence: 99%

“…and constraints (1)- (2) and (5)- (11) The minimization of the electricity usage cost is given by (12), where P k T is the electric power purchased from the retail electricity provider; W is the prediction window, and P rice k is the electricity tariff at the sampling time k. The desired temperature range, HVAC power consumption limits, air mass flow limits, the thermal building model, and the total consumed power by HVAC are presented in (13), (14), (15), (1)-(2), and (5)- (7), respectively; where, at sampling time k, variables T k M in , T k M ax , P k H M ax , u k M in and u k M ax are minimum and maximum range of the temperature ( • C), maximum HVAC power consumption limits, minimum and maximum limits for HVAC mass flow rate, respectively. Constraint (16) determines the total power consumption where P k l is the consumed power by inflexible loads of the building at sampling time k. Constraint (17) states that the total power consumption cannot be negative, in other words, the surplus of energy cannot be sold back to the power grid.…”

Section: Photovoltaic (Pv) Generator Modelmentioning

confidence: 99%

Smart Building Energy Management using Nonlinear Economic Model Predictive Control

Ostadijafari

Dubey

Liu

et al. 2019

2019 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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Owing to the call for energy efficiency, the need to optimize the energy consumption of commercial buildingsresponsible for over 40% of US energy consumption-has recently gained significant attention. Moreover, the ability to participate in the retail electricity markets through proactive demand-side participation has recently led to development of economic model predictive control (EMPC) for building's Heating, Ventilation, and Air Conditioning (HVAC) system. The objective of this paper is to develop a price-sensitive operational model for building's HVAC systems while considering inflexible loads and other distributed energy resources (DERs) such as photovoltaic (PV) generation and battery storage for the buildings. A Nonlinear Economic Model Predictive Controller (NL-EMPC) is presented to minimize the net cost of energy usage by building's HVAC system while satisfying the comfort-level of building's occupants. The efficiency of the proposed NL-EMPC controller is evaluated using several simulation case studies.

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“…The EWH has been considered as a special SA due to its thermal inertia and therefore has its own power balance equation as it is apparent from (11). From left to right, this balance involves the energy stored inside the EWH tank characterized by the current and previous average water temperature T wh (t) and T wh (t − 1) (in • C, as the rest of temperatures hereafter), the tank capacity C wh (in m 3 ) and the parameters that model essential features of the supply water like its density ρ (in kg/m 3 ) and its specific heat C p (in kJ/kg· • C).…”

Section: An Optimization Model For Demand-side Managementmentioning

confidence: 99%

“…By 2013, most systems only offered home monitoring, either local or remote and rarely some manual control over switches or dimmable loads [10].Currently, on the contrary, a wide variety of control systems are available ranging from the simple automatic scheduling of applications to the optimization of energy resources, through advanced algorithms that consider the state of numerous external variables such as energy prices or weather conditions. What is more, they are even able to learn from users thanks to the incorporation of artificial intelligence [11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Direct Load Control Testbed for Smart Appliances

et al. 2019

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The effort to continuously improve and innovate smart appliances (SA) energy management requires an experimental research and development environment which integrates widely differing tools and resources seamlessly. To this end, this paper proposes a novel Direct Load Control (DLC) testbed, aiming to conveniently support the research community, as well as analyzing and comparing their designs in a laboratory environment. Based on the LabVIEW computing platform, this original testbed enables access to knowledge of major components such as online weather forecasting information, distributed energy resources (e.g., energy storage, solar photovoltaic), dynamic electricity tariff from utilities and demand response (DR) providers together with different mathematical optimization features given by General Algebraic Modelling System (GAMS). This intercommunication is possible thanks to the different applications programming interfaces (API) incorporated into the system and to intermediate agents specially developed for this case. Different basic case studies have been presented to envision the possibilities of this system in the future and more complex scenarios, to actively support the DLC strategies. These measures will offer enough flexibility to minimize the impact on user comfort combined with support for multiple DR programs. Thus, given the successful results, this platform can lead to a solution towards more efficient use of energy in the residential environment.Energies 2019, 12, 3336 2 of 16 since 2010 [3]. Although the electricity demand for major appliances has slightly decreased since 2007, mainly due to improvements in their energy efficiency, the rapid proliferation of small appliances and brown goods has absorbed these savings. The energy consumption due to these small loads has grown twice as fast as that of large appliances in the last decade. In addition, only one-third of domestic appliances consumption is under regulatory protection, particularly in emerging markets. This may become even more relevant in the near future as the demand for electricity in buildings increases due to the impact of the charging infrastructure for electric vehicles. While it is true that there is a need to increase the rigor of existing policies by extending regulatory coverage to a broader range of devices, on the other hand, user awareness may be the key factor. However, to achieve this, consumers should be rewarded to some extent when changing their behavior. The availability of information and communication technologies (ICT) on SG can be decisive in meeting this commitment through the widespread adoption of DR strategies.In other areas, such as power electronics, it is common to find a complete chain of modeling, development, testing, optimization, virtual validation, and rapid prototyping commercial tools that integrate seamlessly into a convenient testing and development environment such as these tools of Typhoon (Typhon, Somerville, USA) [4] and dSPACE (dSPACE, Paderborn, Germany) [5]. It is possible to find testbed...

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Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers

Cited by 336 publications

References 110 publications

Energy management system for smart grid: An overview and key issues

Energy management system for smart grid: An overview and key issues

Smart Building Energy Management using Nonlinear Economic Model Predictive Control

A Novel Direct Load Control Testbed for Smart Appliances

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