Systematic Review of Advanced Optimization Techniques and Multi-Asset Integration in Home Energy Management Systems

With rising energy costs and concerns about environmental sustainability, there is a growing need to deploy Home Energy Management Systems (HEMS) that can efficiently manage household energy consumption. This paper proposes a new supervised-learning-based strategy for optimal energy scheduling of an HEMS that considers the integration of energy storage systems (ESS) and electric vehicles (EVs). The proposed supervised-learning-based HEMS framework aims to optimize the energy costs of households by forecasting the energy demand and simultaneously scheduling the charging and discharging operations of ESS and EV. From the scenarios extracted from historical data, the HEMS optimization problem is solved using a mixed-integer linear programming (MILP) solver to collect the datasets on the optimal actions of the ESS and EV. Accordingly, a supervised learning method is used to learn the optimal actions of the MILP solver using deep neural networks (DNNs). Well-trained DNNs act as decision-making tools that are subsequently applied to predict near-optimal actions for ESS and EV based on real-time data. The effectiveness of the proposed method is demonstrated through simulation results and compared with deep reinforcement learning-based and forecasting-based methods. The results show that the proposed method can significantly reduce energy costs and improve the efficiency of ESS and EV operations. Overall, the proposed supervised-learning-based HEMS offers a practical and effective solution for residential energy management.

Electric vehicles (EVs) are expected to drastically increase residential electricity consumption and could provide a significant source of flexible demand. Aggregating smart EV charge controllers with other smart home devices through a home energy management system can lead to more optimal outcomes that benefit homeowners, utilities, and grid operators. Control strategies should consider occupant convenience by accounting for the need for fully charged EVs near the EV departure time. In this paper, we develop an EV charging framework that accounts for occupant convenience using OCHRE, a residential energy model, and foresee, a home energy management system. We simulate a community with high EV penetration and show that integrated, smart EV charging reduces peak demand and smooths night-time energy consumption. Simulation results show that the proposed control strategy nearly eliminates peak period EV charging and reduces the daily peak demand from EVs by 23%.

The proliferation of intermittent renewable-energy-based power systems and the emergence of new types of loads are likely to introduce new powe-quality and power-demand management challenges in a smart grid. An additional level of complication gets added when the system deals with a mass penetration of uncontrolled mobile energy sources and loads, that is, electric vehicles (EVs), to the grid. However, the use of an advanced EV management technique can overcome the challenges through an intelligent bidirectional energy transfer process. This chapter highlights various control and optimization techniques to manage the power demand of both single and multiple customers in a smart grid using EVs. The techniques cover both the energy resource and load-management approaches. Energy-resource management technique for single customer coordinates between EV, photovoltaics (PV) and battery storages based on the peak and off-peak load conditions, to minimize the peak load and electricity cost with an increased efficiency. Likewise, the energy-resource management for multiple customers, controls the aggregated PVs, battery storage and aggregated EVs in a parking lot to flatten the energy demand curve, and reduce the peak load energy costs. In this process, a controller reads the real-time household power consumption data through smart meter, PV power generation under real environment, the state of-charge (SOC) for both EVs and battery storage, and the EV availability, to intelligently control the power flow from/to the energy sources to reduce the grid load demand. Additionally, an advanced charge management technique for both aggregated EVs and single EV is developed. On the contrary, the load management technique models a load-scheduling technique for a demand response (DR)-based home energy management systems (HEMS) that minimizes the electricity cost for the consumer and incorporates operational constraints for individual loads and energy sources. A Mixed-Integer Linear Programming (MILP)-based optimization model is formulated to determine the optimal scheduling of operation for residential loads and DERs according to a day-ahead time-of-use (TOU) electricity tariff. Peak load constraint is also incorporated into the optimization model to address grid reliability issues such as demand peaks, rebound peaks and congestion in the grid. The fmdings in this chapter suggest that an intelligent management technique can substantially reduce the power demand of the grid using EVs and reduce the impact of intermittent sources and thus improve the load factor.

The home energy management system (HEMS) plays an important role in enabling residential customers to participate in demand response (DR) programs autonomously via appliance-level dispatch with smart plugs, and the flexibility analysis could help the HEMS to dispatch appliances efficiently without harming user's comfort level. This paper presents a bottom-up method to obtain the flexibility of appliances. With the use of smart plugs in the HEMS, the type of the appliance attached to each smart plug is inferred with consideration of measurement uncertainty as well as user behavior. Then, the flexibility of an appliance is analyzed based on the extrapolated power consumption of the appliance as well as the owner's usage behavior. In the case study, the types of appliances recognized for individual smart plugs as well as the corresponding flexibilities are used as reference for appliance dispatching in the HEMS under various DR applications. The case study demonstrates the performance of the proposed appliance load monitoring method and the application of the proposed flexibility analysis.

As rooftop solar panels and batteries are becoming more viable in the residential area, home energy management system (HEMS) is rapidly gaining popularity. Its operation should consider possible interactions with utility regulators to make benefit from pricing policies. To reduce house energy expenses, as an objective of scheduling electrical appliances at home, this research proposes a HEMS controlling method to take a connecting agreement and the expected active power curtailment into account. Such a curtailment comes from a generation-load balance requirement planned by system operators, a preset volt-watt function in the solar-battery inverter, or from both. In addition, effects of HEMS synchronization on the loading condition of a pole-transformer are investigated. Through simulations of a low-voltage network, effectiveness of the proposed HEMS control is well illustrated in different scenarios of electricity prices.

Home Energy Management System (HEMS) comprises many types of sensors that collect a variety of data. In this study, a product recommendation system based on the user's indoor comfort preference, which is a function of HEMS, is proposed as a secondary use of HEMS data. The proposed system utilizes the user's indoor comfort preference based on data collected by HEMS sensors, and recommends items that enhance their comfort in summer. The system has two main functions to facilitate the recommendation items: (i) collection of environmental and physical data to generate the predicted mean value (PMV), which is an indicator of indoor comfort, and (ii) identification of the appropriate preference for each user and provision of the collected information via e-mail and the webpage. The information of the product recommendation and instructions pertaining the effectiveness of the products for maintaining the indoor comfort are provided to the user. To test the system's applicability, we installed the system in three households, and then conducted the ABAB test, which shows recommends random items during period A and the selected items during period B. Results show that the willingness of buying the recommended products increased by approximately 34.2% when the user's indoor comfort preference was considered as compared to when indoor comfort preference was not considered.

Finding optimal schedules in a home energy management system

This paper proposes an electric vehicle (EV) charge-discharge management framework for the effective utilization of photovoltaic (PV) output through coordination based on information exchange between home energy management system (HEMS) and grid energy management system (GEMS). In our proposed framework, the HEMS determines an EV charge discharge plan for reducing the residential operation cost and PV curtailment without disturbing EV usage for driving, on the basis of voltage constraint information in the grid provided by the GEMS and forecasted power profiles. Then, the HEMS controls the EV charge-discharge according to the determined plan and real-time monitored data, which is utilized for mitigating the negative effect caused by forecast errors of power profiles. The proposed framework was evaluated on the basis of the Japanese distribution system simulation model. The simulation results show the effectiveness of our proposed framework from the viewpoint of reduction of the residential operation cost and PV curtailment.

This paper developed a new Home Energy Management System (HEMS) tool that integrates multi-objective day-ahead optimization with a real-time HEMS operation. The approach begins by applying the Long Short-Term Memory neural network to perform day-ahead predictions. With these results, a day-ahead multi-objective HEMS optimization aimed at minimizing energy costs and enhancing user comfort is solved by the Non-dominated Sorting Genetic Algorithm III, integrating an optimal Load Scheduling (LS) and Electric Vehicle (EV) energy management. Finally, with the LS defined by the NSGA-III, the real-time SH operation using a Model Predictive Controller (MPC) is the main contribution of the paper, which utilizes the flexibility of the EV battery via a bidirectional charger to address prediction errors and minimize energy costs. The integration of multi-objective day-ahead optimization with real time HEMS operation results in a substantial cost reduction of 19.91% compared to the baseline scenario. Furthermore, the annual case study demonstrates an additional 1.78% cost reduction when this integrated approach is compared to the use of day-ahead optimization alone. Therefore, the inclusion of the proposed MPC is justified by its ability to generate further savings without necessitating additional investments or compromising user comfort.

With the advent of smart grid (SG) and the emergence of information and communication technology, smart meters, bidirectional communication, smart homes and storage systems the energy consumption patterns at the consumer premises have been revolutionized. Moreover, with the rise of renewable energy sources (RESs), storage systems and electric vehicles (EVs) a profound amelioration in the energy management systems has been observed. Home energy management systems (HEMSs) help to control, manage and optimize the energy in smart homes. In this paper, we present a HEMS using multi-agent system (MAS) for smart homes. The HEMS uses priority techniques with the integration of electrical supply system (ESS). Furthermore, a bioinspired technique, binary particle swarm optimization (BPSO), is used for the optimal scheduling of appliances in a smart home. Simulation results illustrate the effectiveness of the HEMS in terms of electricity cost, demand, user comfort and peak to average ratio (PAR).

This paper proposes a home energy management system able to achieve optimized load scheduling for the operation of appliances within a given household. The system, based on the genetic algorithm, provides recommendations for the user to improve the way the energy needs of the home are handled. These recommendations not only take into account the dynamic pricing of electricity, but also the optimization for solar energy usage as well as user comfort. Historical data regarding the times at which the appliances have been used is leveraged through a statistical method to integrate the user’s preference into the algorithm. Based on real life appliance consumption data collected from a household in Morocco, three scenarios are established to assess the performance of the proposed system with each scenario having different parameters. Running the scenarios on the developed MATLAB script shows a cost saving of up to 63.48% as compared to a base scenario for a specific day. These results demonstrate that significant cost saving can be achieved while maintaining user comfort. The addition of supplementary shiftable loads (i.e., an electric vehicle) to the household as well as the limitations of such home energy management systems are discussed. The main contribution of this paper is the real data and including the user comfort as a metric in in the home energy management scheme.

Demand Response (DR) applications via Home Energy Management System (HEMS) at the building level in a strategically integrated RES-based electricity grid can help reduce the building peak demand as well as energy consumption and the power inefficiency in the grid respectively. Distributed Energy Generation (DEG) based on Renewable Energy Sources (RES) are seen as a reliable alternative and more efficient grid optimization to the traditional fossil energy sources-based grid. But a proper combination of demand response and the DEG can significantly make a revolutionary change in the electricity grid. This paper focuses on the impact of integrated control of Solar PV and the Wind Turbine on improving the supply-side and coordinated control strategy for automated demand response by ensuring efficient use of home appliances including Electric Vehicle (EV), air cooling, water heating etc. based on HEMS in order to reduce the peak demand and overall power inefficiency in the grid. For that purpose and analysis, a highly accurate aggregated model of Solar PV, Wind turbine and the home appliances has been developed in the Simulink (MATLAB) and an iterative algorithm based on Load Shifting technique has been established for HEMS controller with embedded MATLAB code. The obtained simulation result illustrates the effectiveness of the proposed system.

It is essential for Home Energy Management Systems (HEMSs) to minimize the system operating cost while maintaining the user comfort under forecasting uncertainties of solar and electricity load demand. However, the existing HEMS excessively relies on a single battery system and may not effectively assess user comfort. To this end, a hierarchical HEMS, i.e., system- and local-level, is proposed in this article to coordinate the dispatch of home resources including battery energy storages and supercapacitors (SC). The system-level HEMS consists of long-term (LT) and short-term (ST) optimization based on Model Predictive Control (MPC). The LT optimization optimizes resource dispatch by using forecasted load and solar generation to minimize house operating costs and maximize the user comfort. The ST layer one is proposed to track the optimal power scheduling to minimize the cost error, refine the dispatch of resources and ensure a safe operational level of hybrid energy storage systems including the SC. The SC is employed to compensate the transient power and alleviate the battery degradation effects. The local-level HEMS is used to achieve DC voltage restoration, power sharing, voltage recovery of SC and state of charge (SoC) balance between batteries. The interaction between system- and local-level is also discussed. By using the dataset from NREL and Ameren Illinois Company, the test results show that this methodology can potentially reduce the system operating cost by 4.3500%, 7.7600%, and 37.7253% compared to the other single and multi-layer HEMSs.

Distributed coordination of electric vehicles charging station and home energy management systems in residential neighborhood

Smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining balance between demand and supply by implementing demand response (DR). In SG the main focus of the researchers is on home energy management (HEM) system, that is also called demand side management (DSM). DSM includes all responses, which adjust the consumer’s electricity consumption pattern, and make it match with the supply. If the main grid cannot provide the users with sufficient energy, then the smart scheduler (SS) integrates renewable energy source (RES) with the HEM system. This alters the peak formation as well as minimizes the cost. Residential users basically effect the overall performance of traditional grid due to maximum requirement of their energy demand. HEM benefits the end users by monitoring, managing and controlling their energy consumption. Appliance scheduling is integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances or shifting the load from peak to off peak hours. Recently different techniques based on artificial intelligence (AI) are being used to meet aforementioned objectives. In this paper, three different types of heuristic algorithms are evaluated on the basis of their performance against cost saving, user comfort and peak to average ratio (PAR) reduction. Two techniques are already existing heuristic techniques i.e. harmony search (HS) algorithm and enhanced differential evolution (EDE) algorithm. On the basis of aforementioned two algorithms a hybrid approach is developed i.e. harmony search differential evolution (HSDE). We have done our problem formulation through multiple knapsack problem (MKP), that the maximum consumption of electricity of consumer must be in the range which is bearable for utility and also for consumer in sense of electricity bill. Finally simulation of the proposed techniques will be conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms of minimum cost, reduced peak to average ratio (PAR), waiting time and equally distributed energy consumption pattern in each hour of a day to benefit both utility and end users.