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This paper presents a model for transactive energy management within microgrids (MGs) that include smart homes and buildings. The model focuses on peer-to-peer (P2P) transactive energy management among these homes, establishing a collaborative use of a cloud energy storage system (CESS) to reduce daily energy costs for both smart homes and MGs. This research assesses how smart homes and buildings can effectively utilize CESS while implementing P2P transactive energy management. Additionally, it explores the potential of a solar rooftop parking lot facility that offers charging and discharging services for plug-in electric vehicles (PEVs) within the MG. Controllable and non-controllable appliances, along with air conditioning (AC) systems, are managed by a home energy management (HEM) system to optimize energy interactions within daily scheduling. A linear mathematical framework is developed across three scenarios and solved using General Algebraic Modeling System (GAMS 24.1.2) software for optimization. The developed model investigates the operational impacts and optimization opportunities of CESS within smart homes and MGs. It also develops a transactive energy framework in a P2P energy trading market embedded with CESS and analyzes the cost-effectiveness and arbitrage driven by CESS integration. The results of the comparative analysis reveal that integrating CESS within the P2P transactive framework not only opens up further technical opportunities but also significantly reduces MG energy costs from $55.01 to $48.64, achieving an 11.57% improvement. Results are further discussed.

This article proposes a generalized energy storage model for battery energy storage systems, electric water heaters (EWH), and heating, ventilation, and air-conditioning (HVAC) systems to enable demand response control complying to Energy Star and CTA-2045 standards. The demand response control has been implemented in the DER integration testbed, which was originally developed by Electric Power Research Institute, to demonstrate that the “energy content” and “energy take” for BESS and EWH with mixing valve technology are comparable for typical residential ratings. A distribution power system was modeled using the modified IEEE 123-bus feeder system, measured residential loads, and EWH power simulated based on realistic hot water draws from CBECC-Res software. The demand response control, which complies to CTA-2045 standards was implemented to the EWHs considering the energy take values. Results demonstrate that the EWHs can be controlled to postpone the peak power at the distribution system level and provide a large amount of energy storage, while maintaining system robustness. The impact on occupant comfort was also analyzed. [ABSTRACT FROM AUTHOR]

Intelligent home energy management system (IHEMS) manages various home appliances depending on user preferences in order to save energy cost and assure user satisfaction. To realize cooperation between the distribution system operator (DSO) and end-user, a two-stage scheduling optimization model is developed based on the flexibility quantification, with the user privacy in demand-side management in a specific cluster taken into account. It also aids to reduce the network operation cost, and the distribution system's uncertainty caused by wind power is represented using chance constraints. The user uploads the IHEMS-calculated offered flexibility to DSO via cluster; DSO optimizes the optimal flexibility to minimize the operation cost; then, clusters distribute the flexibility requirement based on the flexibility index; finally, the user updates the optimal schedule. The numerical results using MATLAB are provided to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

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 air-conditioning (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.

Heating, ventilation, and air-conditioning (HVAC) systems use the most electricity of any household appliance in residential communities. HVAC system modeling facilitates the study of demand response (DR) at both the residential and power system levels. In this article, the equivalent thermal model of a reference house is proposed. Parameters for the reference house were determined based on the systematic study of experimental data obtained from fully instrumented field demonstrators. Energy storage capacity of HVAC systems is calculated and an equivalent state-of-charge is defined. The uniformity between HVAC systems and battery energy storage system is demonstrated by DR control. The aggregated HVAC load model is based on the reference house and considers a realistic distribution of HVAC parameters derived from one of the largest smart grid field demonstrators in rural America. A sequential DR scheme as part of a virtual power plant control is proposed to reduce both ramping rate and peak power at the aggregated level, while maintaining human comfort according to ASHRAE standards. [ABSTRACT FROM AUTHOR]

Net zero energy (NZE) houses purchase zero net metered electricity from the grid over a year. Technical challenges brought forth by NZE homes are related to the intermittent nature of solar generation, and are due to the fact that peak solar generation and load are not coincident. This leads to a large rate of change of load, and in case of high PV penetration communities, often requires the installation of gas power plants to service this variability. This article proposes a hybrid energy storage system including batteries and a variable power electric water heater which enables the NZE homes to behave like dispatchable generators or loads, thereby reducing the rate of change of the net power flow from the house. A co-simulation framework, INSPIRE+D, which enables the dynamic simulation of electricity usage in a community of NZE homes, and their connection to the grid is enabled. The calculated instantaneous electricity usage is validated through experimental data from a field demonstrator in southern Kentucky. It is demonstrated that when the operation of the proposed hybrid energy storage system is coordinated with solar PV generation, the required size and ratings of the battery would be substantially reduced while still maintaining the same functionality. Methodologies for sizing the battery and solar panels are developed. [ABSTRACT FROM AUTHOR]

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.

This paper proposes a home energy management (HEM) strategy to not only reduce the customer’s billing cost but also to compensate the reactive power at the point of grid integration. The developed HEM enables the home owner to manage different components and appliances including electric vehicle (EV), energy storage system (ESS), and shiftable loads (SLs). Optimal scheduling of consumption times of SLs and charging/discharging cycles of EV and ESS ends in sensible reduction in daily operation cost. Then, satisfying the obtained minimum operation cost within the constraints, the remaining capacity of EV and ESS inverters is dedicated to reactive power compensation of the home appliances. Accordingly, the power factor (PF) of home-to-grid connection is sensibly improved. The proposed HEM is modeled as a mixed-integer linear programming (MILP) and is solved with general algebraic modeling system (GAMS). The obtained results approve both the economic and technical successes. [ABSTRACT FROM AUTHOR]

Contributing to a flattened load profile by smart home owners could effectively assists the microgrid operator (MGO) in coping with technical hurdles and assuring an economic operation. To attain such functionality, a home energy management (HEM) approach is proposed which interrogates two objectives, sequentially. The first objective assures a minimum energy payment cost of home appliances, as the main purpose of the home owners. In the sequel, the obtained minimum cost is assigned as an optimization constraint and the minimum load profile deviation is pursued. Besides the fixed-load and shiftable appliances, plug-in hybrid electric vehicle (PHEV), small-scale energy storage system, and renewable energy resources are also contemplated. Efficient linearization techniques are applied to avert non-linear nature of the established model ending to a linear one. Detailed simulation studies are conducted to assess performance of the proposed approach. Results are discussed in terms of reduced energy payment costs of home owners and a flattened load profile. [ABSTRACT FROM AUTHOR]

Home energy management (HEM) systems enable residential consumers to participate in demand response programs (DRPs) more actively. However, HEM systems confront some practical difficulties due to the uncertainty related to renewable energies as well as the uncertainty of consumers’ behavior. Moreover, the consumers aim for the highest level of comfort and satisfaction in operating their electrical appliances. In addition, technical limits of the appliances must be considered. Furthermore, DR providers aim at keeping the participation of consumers in DRPs and minimize the “response fatigue” phenomenon in the long-term period. In this paper, a stochastic model of an HEM system is proposed by considering uncertainties of electric vehicles availability and small-scale renewable energy generation. The model optimizes the customer's cost in different DRPs, while guarantees the inhabitants’ satisfaction by introducing a response fatigue index. Different case studies indicate that the implementation of the proposed stochastic HEM system can considerably decrease both the customers’ cost and response fatigue. [ABSTRACT FROM PUBLISHER]

The residential sector is a major contributor to the global energy balance. So far, the residential users demand has been largely uncontrollable and inelastic with respect to the power grid conditions. Demand Side Management (DSM) is an important function in smart grid that allows consumers to make informed decision regarding energy consumption, and helps energy providers to reshape the load profile and to reduce peak load demand. DSM can be mathematically formulated either to maximize the system total peak demand or to maximize overall system load factor and utility׳s revenue and to minimize customer electricity bill. This paper reviews the various optimization techniques applied to DSM as contrasting characteristics like individual users versus cooperative users, deterministic versus stochastic and day-ahead versus real time DSM. This paper reviews a survey on residential DSM, which can help general readers to have an outlook of the topic which includes the architecture, formulation of optimization problems and its various approaches. The issues, existing solutions and approaches are presented. In addition, the future research directions are also discussed to enhance the work in this domain. [ABSTRACT FROM AUTHOR]

As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest in the literature recently, especially for residential areas. As a new type of consumer load in the electric power system, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options instead of peak power procurement from the grid. In this paper, as the main contribution to the literature, a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies with a bi-directional utilization possibility for EV and energy storage system (ESS) is realized. A mixed-integer linear programming (MILP) framework-based modeling of a home energy management (HEM) structure is provided for this purpose. A distributed small-scale renewable energy generation system, the V2H and V2G capabilities of an EV together with two-way energy trading of ESS, and different DR strategies are all combined in a single HEM system for the first time in the literature. The impacts of different EV owner consumer preferences together with the availability of ESS and two-way energy trading capabilities on the reduction of total electricity prices are examined with case studies. [ABSTRACT FROM PUBLISHER]

Abstract: Strong and huge interests on smart grid have increased extensively in recent years around the world. This scenario could be a promising reason for future research in this area. This next form of electricity grid will be able to manage various parts of power production from power plants to the customers. Smart grid has become a major challenge in developed nations in both research and utilization aspects. On the other side, application of smart grid in developing countries is still lagging behind as compared to the developed ones. However, most of developing nations are currently investigating potentials of some pilot projects or few research works. In this article, the applied activities in developing countries for smart grid are reviewed and categorized into two major groups: group of pioneer developing countries in smart grid and other developing countries are placed in another group. The findings demonstrate that a few countries such as China, India and Brazil have had proper planning and development in this technology. In some cases like China, the efforts are considered comparable with developed nations like U.S. Therefore, according to the development progress for smart grid in China, India and Brazil, a pattern of reference for other developing countries is suggested. [Copyright &y& Elsevier]

A Home Energy Management (HEM) system plays a crucial role in realizing residential Demand Response (DR) programs in the smart grid environment. It provides a homeowner the ability to automatically perform smart load controls based on utility signals, customer's preference and load priority. This paper presents the hardware demonstration of the proposed HEM system for managing end-use appliances. The HEM's communication time delay to perform load control is analyzed, along with its residual energy consumption. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

Buildings with solar photovoltaic (PV) generation and a stationary battery energy storage system (BESS) may self-sustain an uninterrupted full-level electricity supply during power outages. The duration of off-grid operation is dependent on the time of the power fault and the capabilities of the home energy management system (HEMS). In this paper, building resilience is quantified by analyzing the self-sustainment duration for all possible power outages throughout an entire year. An evaluation method is proposed and exercised on a reference house in California climate zone 9 for which the detailed electricity usage is simulated using the EnergyPlus software. The influence of factors such as energy use behavioral patterns, energy storage capacity from the BESS, and an electric vehicle (EV) battery on the building resilience is evaluated. Varying combinations of energy storage and controllable loads are studied for optimally improved resilience based on user preferences. It is shown that for the target home and region with a solar PV system of 7.2 kW, a BESS with a capacity of 11 kWh, and an EV with a battery of 80 kWh permanently connected to the home, off-grid self-sustained full operation is guaranteed for at least 72 h. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]