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2023, Misan Journal of Engineering Sciences
Residential consumers can efficiently engage in demand response programs with the help of home energy management systems (HEMSs). The best scheduling of gas, oil, coal, and uranium appliances has yet to be considered by traditional HEMSs, which exclusively control domestic electric devices to lower the cost of electricity usage. The associated demand response (DR) for smart homes is then adjusted to lessen the difference between predicted and actual demand after comparing the actual price to this forecast. Data acquired by Nordpool and publicly available energy information have been used to test the proposed strategy about price demand forecast and DR management. The function is to reduce energy costs for the home and the dissatisfaction brought on by moving, cutting, and replacing loads by changing prices over time. Case studies with accurate data show that the suggested strategy can return on investment bills by up to 85% while maintaining their satisfaction.
The Home Energy Management System (HEMS) is an important part of the smart grid that enables the residential customers to execute demand response programs autonomously. This study presents theoutcome of a new system architecture and control algorithm that can use both battery storage andmanage the temperature of thermal appliances. The proposed algorithm receives the price information from the utility company in advance and purchases the electricity at off-peak hours and utilizes thebattery as well as manages the temperature of the thermal appliances during peak hours. The proposed algorithm assures that the power consumption of the electrical appliances is always less than certainlevel. The proposed house is supported by the battery system and Photovoltaic system as to increase thegreen index by utilizing alternative energy resource. The amount of the power that can be drained fromthe battery is limited by the algorithm to remain more during a day. The simulation results indicate t...
2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4)
Home energy management systems (HEMS) are set to play a key role in the future smart grid (SG). HEMS concept enables residential customers to actively participate in demand response programs (DR) to control their energy usage, reduce peak demand and therefore contribute to improve the performance and reliability of the grid. The aim of this paper is to propose an energy management strategy for residential endconsumers. In this framework, a demand response strategy is developed to reduce home energy consumption. The proposed algorithm seeks to minimise peak demand by scheduling household appliances operation and shifting controllable loads during peak hours, when electricity prices are high, to off-peak periods, when electricity prices are lower without affecting the customer's preferences. The overall system is simulated using MATLAB/Simulink and the results demonstrate the effectiveness of the proposed control strategy in managing the daily household energy consumption.
Sustainable Cities and Society, 2021
Smart thermostats and home energy management systems (HEMSs) are generally studied separately. However, their joint use can provide a greater benefit. Therefore, this study primarily aims to combine a smart thermostat with a HEMS. The mixed-integer linear programming (MILP)-based HEMS performs day-ahead load scheduling for cost-minimization and provides optimal demand response (DR) and photovoltaic (PV) self-consumption, and the fuzzy logic-based thermostat aims efficient DR of airconditioning and maintenance of thermal comfort. In the first stage, unlike conventional fixed set-point thermostats, the proposed thermostat defines different setpoints for each time interval, by fuzzifying input parameters of electricity prices, solar radiation, and occupant presence, to be used by HEMS. In the second stage, the HEMS schedules the operation of time-shiftable, thermostatically controlled, and power-shiftable (battery energy storage system (BESS), electric vehicle (EV)) loads. The HEMS considers bi-directional power flow between home, BESS, EV, and grid, as well as battery degradation to avoid unnecessary energy arbitrage. The simulation results show that a daily cost reduction of 53.2 % is achieved under time-of-use (TOU) and feed-in tariff rates of Turkey. AC cost is reduced by 24 % compared to conventional thermostats. In a future scenario of real-time pricing (RTP) and dynamic feed-in tariff, vehicle-to-grid (V2G) becomes possible.
International Journal of Electrical and Computer Engineering (IJECE), 2021
Aim of demand response (DR) programs are to change the usage pattern of electricity in such a way that, beneficial to the consumers as well as to the distributors by applying some methods or technology. This way additional cost to erect new energy sources can be postponed in power grid. Best method to implement demand response (DR) program is by influencing consumer through the implementation of real time pricing scheme. To harness the benefit of DR, automated home energy management system is essential. This paper presents a comprehensive demand response system with real time pricing. The real time price is determined after considering price elasticity of various classes of consumers and their load profiles. A real time clustering algorithm suitable for big data of smart grid is devised for the segmentation of consumers. This paper is novel in its design for real time pricing and modelling and automatic scheduling of appliances for home energy management. Simulation results showed that this new real time pricing method is suitable for DR programs to reduce the peak load of the system as well as reducing the energy expenditure of houses, while ensuring profit for the retailer.
Energy and Buildings, 2014
The existing electricity systems have been substantially designed to allow only centralized power generation and unidirectional power flow. Therefore, the objective of improving the conventional power systems with the capabilities of decentralized generation and advanced control has conflicted with the present power system infrastructure and thus a profound change has necessitated in the current power grids. To that end, the concept of smart grid has been introduced at the last decades in order to accomplish the modernization of the power grid while incorporating various capabilities such as advanced metering, monitoring and self-healing to the systems. Among the various advanced components in smart grid structure, "smart home" is of vital importance due to its handling difficulties caused by the stochastic behaviors of inhabitants. However, limited studies concerning the implementation of smart homes have so far been reported in the literature. Motivated by this need, this paper investigates an experimental smart home with various renewable energy sources and storage systems in terms of several aspects such as in-home energy management, appliances control and power flow. Furthermore, the study represents one of the very first attempts to evaluate the contribution of power forecasting of renewable energy sources on the performance of smart home concepts.
A home energy management (HEM) system is an integral part of a smart grid that can potentially enable demand response applications for residential customers. This paper presents an intelligent HEM algorithm for managing high power consumption household appliances with simulation for demand response (DR) analysis. The proposed algorithm manages household loads according to their preset priority and guarantees the total household power consumption below certain levels. A simulation tool is developed to showcase the applicability of the proposed algorithm in performing DR at an appliance level. This paper demonstrates that the tool can be used to analyze DR potentials for residential customers. Given the lack of understanding about DR potentials in this market, this work serves as an essential stepping-stone toward providing an insight into how much DR can be performed for residential customers.
2019
The advent of smart grid provided ample opportunities for consumers to adopt small-scale renewable energy generation and become prosumers. In addition to this, advancement in information, communication and control technologies has equipped prosumers with smart home appliances. To extract energy saving and lesser cost of electricity, residential prosumers perform energy management in accordance with renewable energy generation, energy storage, responsive appliances, and electricity price. This requires optimal scheduling of prosumer demand with their operational preferences of appliances in order to perform energy saving. In this regard, this paper proposes a novel optimization based control of different (characteristics) appliances to schedule electricity for residential prosumers. Prosumer demand preferences for appliances are considered with operational constraints of appliances. Time-of-use tariff and dayahead real time pricing is used for electricity scheduling and its impact is...
IEEE Access
Domestic load profiles in the residential sectors are being modified with the adoption of smart home management systems and solar generation. In addition, houses with rooftop PV behave like local generators, contributing to the growth of the penetration of PV energy. Hence, the demand for power is declining day by day. However, the increasing PV penetration causes technical challenges for the power system, such as the ''duck curve''. This can be addressed through home energy management (HEM) techniques including peak shaving, load shifting with smart home devices. In this regard, electric water heaters (EWH), with high thermal mass and being ubiquitous, are attractive and low-cost energy storage systems. In this article, a case study for one of the largest rural field smart energy technology demonstrators involving business, industries, and more than 5,000 residences, located in Glasgow, KY, US, is presented. Furthermore, a HEM system, which aims to minimize the total energy usage and peak demand by regulating the heating, ventilation, and airconditioning (HVAC) systems, water heaters, and batteries, thereby benefiting both the utility and the consumer is proposed. This work also demonstrates the ability of EWH to provide ancillary services while maintaining customer comfort. The minimum participation rates for EWH and batteries are calculated and compared with respect to different peak reduction targets. Long term load prediction by considering different fractions of smart homes for the utility is also provided. INDEX TERMS Battery energy storage system (BESS), demand response (DR), electric water heater (EWH), grid service, home energy management (HEM), home energy model, power system, smart home.
2017
Smart home energy management: An analysis of a novel dynamic Smart home energy management: An analysis of a novel dynamic pricing and demand response aware control algorithm for pricing and demand response aware control algorithm for households with distributed renewable energy generation and households with distributed renewable energy generation and storage storage
Sustainability, 2017
The role of Demand Side Management (DSM) with Distributed Energy Storage (DES) has been gaining attention in recent studies due to the impact of the latter on energy management in the smart grid. In this work, an Energy Scheduling and Distributed Storage (ESDS) algorithm is proposed to be installed into the smart meters of Time-of-Use (TOU) pricing consumers possessing in-home energy storage devices. Source of energy supply to the smart home appliances was optimized between the utility grid and the DES device depending on energy tariff and consumer demand satisfaction information. This is to minimize consumer energy expenditure and maximize demand satisfaction simultaneously. The ESDS algorithm was found to offer consumer-friendly and utility-friendly enhancements to the DSM program such as energy, financial, and investment savings, reduced/eliminated consumer dissatisfaction even at peak periods, Peak-to-Average-Ratio (PAR) demand reduction, grid energy sustainability, socio-economic benefits, and other associated benefits such as environmental-friendliness. Sustainability 2017, 9, 120 2 of 13 work intends, through its proposed DSM algorithm, to offer PDR benefits to the utility with reduced or negligible peak period demand dissatisfaction to consumers, by optimizing energy supply and demand in consumer premises through the incorporation of an in-home DES device. The proposed Energy Scheduling and Distributed Storage (ESDS) algorithm will carry out energy consumption, storage, and expenditure optimization in the smart homes equipped with an in-home DES device. The ESDS algorithm optimizes energy demand and supply in the home between the grid and battery depending on grid energy price and consumer preferences. The ESDS optimization problem was formulated using convex programming and can be installed into smart meters on consumers' premises.
Demand Response (DR) and Time-of-Use (TOU) pricing refer to programs offering incentives to customers who reduce their energy use during times of peak demand. A demand response system coordinates the demand and supply between the energy provider and the customer. In this study, a turn-key solution is developed to offer demand response services for residential customers. A cognitive residential ANFIS-based Master Energy Controller (MEC) is developed to forecast future power demand based on user's life style and environmental/social factors affecting power consumption. The Master Energy Controller implements a branch and bound scheduling algorithm for managing appliance runtime schedules. The MEC is developed to provide users with functionality to schedule appliances at home and automate demand response strategies without the burden of learning demand response strategies. The MEC monitors and controls appliances through intelligent appliance controllers connected by a selforganizing zero configuration network --also developed in this study. This paper discusses the system architecture for this energy management system and shows that such a system is a viable platform for demand response.
Journal of Electrical Engineering and Technology, 2014
Application of residential demand response (DR) programs are currently realized up to a limited extent due to customers' difficulty in manually responding to the time-differentiated prices. As a solution, this paper proposes an automatic home load management (HLM) framework to achieve the household minimum payment as well as meet the operational constraints to provide customer's comfort. The projected HLM method controls on/off statuses of responsive appliances and the charging/discharging periods of plug-in hybrid electric vehicle (PHEV) and battery storage at home. This paper also studies the impacts of different time-varying tariffs, i.e., time of use (TOU), real time pricing (RTP), and inclining block rate (IBR), on the home load management (HLM). The study is effectuated in a smart home with electrical appliances, a PHEV, and a storage system. The simulation results are presented to demonstrate the effectiveness of the proposed HLM program. Peak of household load demand along with the customer payment costs are reported as the consequence of applying different pricings models in HLM.
International Journal of Engineering Research and, 2015
The objective of this work is to design and implement an architectural framework for a webbased demand management system that allows an electric utility to reduce system peak load by automatically managing end-use appliances based on homeowners' preferences. The proposed framework comprises the following components: human user interface, home energy management (HEM) algorithms, web services for demand response communications, selected ZigBee and smart energy profile features for appliance interface, and security aspects for a webbased HEM system. The proposed web-based HEM system allows homeowners to be more aware about their electricity consumption by allowing visualization of their real-time and historical electricity consumption data. The HEM system enables customers to monitor and control their household appliances from anywhere with an Internet connection. It offers a user-friendly and attractive display panel for a homeowner to easily set his/her preferences and comfort settings. An algorithm to autonomously control appliance operation is incorporated in the proposed webbased HEM system, which makes it possible for residential customers to participate in demand response programs. In this work, the algorithm is demonstrated to manage power-intensive appliances in a single home, keeping the total household load within a certain limit while satisfying preset comfort settings and user preferences. Furthermore, an extended version of the algorithm is demonstrated to manage power-intensive appliances for multiple homes within a neighborhood.
Energies, 2020
Electricity demand is increasing, as a result of increasing consumers in the electricity market. By growing smart technologies such as smart grid and smart energy management systems, customers were given a chance to actively participate in demand response programs (DRPs), and reduce their electricity bills as a result. This study overviews the DRPs and their practices, along with home energy management systems (HEMS) and load management techniques. The paper provides brief literature on HEMS technologies and challenges. The paper is organized in a way to provide some technical information about DRPs and HEMS to help the reader understand different concepts about the smart grid, and be able to compare the essential concerns about the smart grid. The article includes a brief discussion about DRPs and their importance for the future of energy management systems. It is followed by brief literature about smart grids and HEMS, and a home energy management system strategy is also discussed in detail. The literature shows that storage devices have a huge impact on the efficiency and performance of energy management system strategies.
A home energy management (HEM) system is an integral part of a smart grid that can potentially enable demand response applications for residential customers. This paper presents an intelligent HEM algorithm for managing high power consumption household appliances with simulation for demand response (DR) analysis. The proposed algorithm manages household loads according to their preset priority and guarantees the total household power consumption below certain levels. A simulation tool is developed to showcase the applicability of the proposed algorithm in performing DR at an appliance level. This paper demonstrates that the tool can be used to analyze DR potentials for residential customers. Given the lack of understanding about DR potentials in this market, this work serves as an essential stepping-stone toward providing an insight into how much DR can be performed for residential customers.
ETRI Journal, 2013
The state of the art of old-age grids into smart grids provides residents the opportunity to schedule their appliances to consume the energy optimally that leads to potentially balance the demand side as well as the supply side more effectively and minimizes the power Peak-to-Average Ratio (PAR), which ultimately provides benefit to the residents in the form of reduction cost and expense. The Energy Management System (EMS) in the home receives the market and system signals and controls the loads, Heating, Ventilation and Air Conditioning systems (HVAC), storages and local generation units according to the user preferences. This survey encompasses novel home energy management techniques including different shiftable and nonshiftable load scheduling methods and peak shaving strategies. Several Pricing strategies have been suggested for smart grid such as, Real-Time Pricing (RTP), Time of Use (ToU), Inclining Block Rates (IBR), Critical Peak Pricing (CPP), etc. Moreover, this paper discusses the HEM architecture and reveals that the different energy management techniques intelligently schedule the appliances in order to satisfy the maximum resident’s comfort level and consume the energy optimally.
Renewable and Sustainable Energy Reviews, 2016
Advances in information and communication technologies (ICT) enable a great opportunity to develop the residential demand response that is relevant in smart grid applications. Demand response (DR) aims to manage the required demand to match the available energy resources without adding new generation capacity. Expanding the DR to cover the residential sector in addition to the industrial and commercial sectors gives rise to a wide range of challenges. This study presents an overview of the literature on residential DR systems, load-scheduling techniques, and the latest ICT that supports residential DR applications. Furthermore, challenges are highlighted and analyzed, which are likely to become relevant research topics with regard to the residential DR of smart grid. The literature review shows that most DR schemes suffer from an externality problem that involves the effect of high-level customer consumption on the price rates of other customers, especially during peak period. A recommendation for using adaptive multi-consumption level pricing scheme is presented to overcome this challenge.
Energies
The steady increase in energy demand for residential consumers requires an efficient energy management scheme. Utility organizations encourage household applicants to engage in residential energy management (REM) system. The utility’s primary goal is to reduce system peak load demand while consumer intends to reduce electricity bills. The benefits of REM can be enhanced with renewable energy sources (RESs), backup battery storage system (BBSS), and optimal power-sharing strategies. This paper aims to reduce energy usages and monetary cost for smart grid communities with an efficient home energy management scheme (HEMS). Normally, the residential consumer deals with numerous smart home appliances that have various operating time priorities depending on consumer preferences. In this paper, a cost-efficient power-sharing technique is developed which works based on priorities of appliances’ operating time. The home appliances are sorted on priority basis and the BBSS are charged and dis...
IEEE Access, 2018
The increasing demand for electricity and the emergence of smart grids have presented new opportunities for a home energy management system (HEMS) that can reduce energy usage. The HEMS incorporates a demand response (DR) tool that shifts and curtails demand to improve home energy consumption. This system commonly creates optimal consumption schedules by considering several factors, such as energy costs, environmental concerns, load profiles, and consumer comfort. With the deployment of smart meters, performing load control using the HEMS with DR-enabled appliances has become possible. This paper provides a comprehensive review on previous and current research related to the HEMS by considering various DR programs, smart technologies, and load scheduling controllers. The application of artificial intelligence for load scheduling controllers, such as artificial neural network, fuzzy logic, and adaptive neural fuzzy inference system, is also reviewed. Heuristic optimization techniques, which are widely used for optimal scheduling of various electrical devices in a smart home, are also discussed. INDEX TERMS Home energy management system, demand response, smart technologies, integrated wireless technology, intelligent scheduling controller.
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