A Survey on Home Energy Management <s> B. CRITERIA <s> In order to keep a proper functional electricity grid and to prevent large investments in the current grid, the creation, transmission and consumption of electricity needs to be controlled and organized in a different way as done nowadays. Smart meters, distributed generation and -storage and demand side management are novel technologies introduced to reach a sustainable, more efficient and reliable electricity supply. Although these technologies are very promising to reach these goals, coordination between these technologies is required. It is therefore expected that ICT is going to play an important role in future smart grids. In this paper, we present the results of our three step control strategy designed to optimize the overall energy efficiency and to increase the amount of generation based on renewable resources with the ultimate goal to reduce the CO2 emission resulting from generation electricity. The focus of this work is on the control algorithms used to reshape the energy demand profile of a large group of buildings and their requirements on the smart grid. In a use case, steering a large group of freezers, we are able to reshape a demand profile full of peaks to a nicely smoothed demand profile, taking into the account the amount of available communication bandwidth and exploiting the available computation power distributed in the grid. <s> BIB001 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> This paper proposes a novel load management solution for coordinating the charging of multiple plug-in electric vehicles (PEVs) in a smart grid system. Utilities are becoming concerned about the potential stresses, performance degradations and overloads that may occur in distribution systems with multiple domestic PEV charging activities. Uncontrolled and random PEV charging can cause increased power losses, overloads and voltage fluctuations, which are all detrimental to the reliability and security of newly developing smart grids. Therefore, a real-time smart load management (RT-SLM) control strategy is proposed and developed for the coordination of PEV charging based on real-time (e.g., every 5 min) minimization of total cost of generating the energy plus the associated grid energy losses. The approach reduces generation cost by incorporating time-varying market energy prices and PEV owner preferred charging time zones based on priority selection. The RT-SLM algorithm appropriately considers random plug-in of PEVs and utilizes the maximum sensitivities selection (MSS) optimization. This approach enables PEVs to begin charging as soon as possible considering priority-charging time zones while complying with network operation criteria (such as losses, generation limits, and voltage profile). Simulation results are presented to demonstrate the performance of SLM for the modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs. <s> BIB002 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> The electricity and transportation industries are the main sources of greenhouse gas emissions on Earth. Renewable energy, mainly wind and solar, can reduce emission from the electricity industry (mainly from power plants). Likewise, next-generation plug-in vehicles, which include plug-in hybrid electric vehicles (EVs) and EVs with vehicle-to-grid capability, referred to as “gridable vehicles” (GVs) by the authors, can reduce emission from the transportation industry. GVs can be used as loads, energy sources (small portable power plants), and energy storages in a smart grid integrated with renewable energy sources (RESs). Smart grid operation to reduce both cost and emission simultaneously is a very complex task considering smart charging and discharging of GVs in a distributed energy source and load environment. If a large number of GVs is connected to the electric grid randomly, peak load will be very high. The use of traditional thermal power plants will be economically and environmentally expensive to support the electrified transportation. The intelligent scheduling and control of GVs as loads and/or sources have great potential for evolving a sustainable integrated electricity and transportation infrastructure. Cost and emission reductions in a smart grid by maximum utilization of GVs and RESs are presented in this paper. Possible models for GV applications, including the smart grid model, are given, and results are presented. The smart grid model offers the best potential for maximum utilization of RESs to reduce cost and emission from the electricity industry. <s> BIB003 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> The paper outlines energy management concepts and the smart grid evolution. The necessity of considering energy management as a crucial innovation in load supplying to permit a more powerful penetration of renewable energy usage at the building and city level and to perform energy savings and CO2 emissions reduction is pointed out. The driving factors to enhance the current power distribution are presented, and the benefits concerning smart grids are underlined. In the paper, a specific energy management analysis is reported by considering all the electric value chain, and the demand-side management and distributed on site control actions are described. To verify the benefit of energy management control actions, a house simulator and a grid simulator are here presented and the results discussed in three different scenarios. Moreover, in the paper, the evaluation of ecological benefits are reported, and a cost benefit analysis of the energy management system is performed. Results pointed out that with the standard control actions, the system is not economic for the end user, and only by using energy management systems with renewable energy, in site production remunerative energy savings can be reached. Finally the evolution of smart grids is presented, focusing on potential benefits and technical problems. The active grids, microgrids and virtual utility are described, and final consideration on hypothetical scenarios is presented in the conclusion. <s> BIB004 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> In this paper, we propose a novel optimization-based real-time residential load management algorithm that takes into account load uncertainty in order to minimize the energy payment for each user. Unlike most existing demand side management algorithms that assume perfect knowledge of users' energy needs, our design only requires knowing some statistical estimates of the future load demand. Moreover, we consider real-time pricing combined with inclining block rate tariffs. In our problem formulation, we take into account different types of constraints on the operation of different appliances such as must-run appliances, controllable appliances that are interruptible, and controllable appliances that are not interruptible. Our design is multi-stage. As the demand information of the appliances is gradually revealed over time, the operation schedule of controllable appliances is updated accordingly. Simulation results confirm that the proposed energy consumption scheduling algorithm can benefit both users, by reducing their energy expenses, and utility companies, by improving the peak-to-average ratio of the aggregate load demand. <s> BIB005 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> Demand side management encourages the users in a smart grid to shift their electricity consumption in response to varying electricity prices. In this paper, we propose a distributed framework for the demand response based on cost minimization. Each user in the system will find an optimal start time and operating mode for the appliances in response to the varying electricity prices. We model the cost function for each user and the constraints for the appliances. We then propose an approximate greedy iterative algorithm that can be employed by each user to schedule appliances. In the proposed algorithm, each user requires only the knowledge of the price of the electricity, which depends on the aggregated load of other users, instead of the load profiles of individual users. In order for the users to coordinate with each other, we introduce a penalty term in the cost function, which penalizes large changes in the scheduling between successive iterations. Numerical simulations show that our optimization method will result in lower cost for the consumers, lower generation costs for the utility companies, lower peak load, and lower load fluctuations. <s> BIB006 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> In this paper, we investigate the joint optimization of electric vehicle (EV) and home energy scheduling. Our objective is to minimize the total electricity cost while considering user comfort preference. We take both household occupancy and EV travel patterns into account. The novel contributions of this paper lie in the exploitation of EVs as dynamic storage facility as well as detailed modeling of user comfort preference, thermal dynamics, EV travel, and customer occupancy patterns in a concrete optimization framework. Extensive numerical results are presented to illustrate the efficacy of the proposed design. Specifically, we show that the proposed design can achieve significant saving in electricity cost, allow more flexibility in setting the tradeoff between cost and user comfort, and enable to reduce energy demand during peak hours. We also demonstrate the benefits of applying the proposed framework to a residential community compared to optimization of individual household separately. <s> BIB007 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> By offering appropriate incentives, electricity users can be incentivized to alter their power consumption patterns so as to achieve the electricity supplier's intended profile. In this paper we have proposed a day-ahead demand-side bidding approach to realize the desired demand response. In our mechanism, customers submit their day-ahead electricity demands to the utility company through a secure communication infrastructure. The electricity supplier then generates the customers' hourly demand profile by aggregation of the individual requests. This information then forms the basis of procuring power in the wholesale market as well as developing a dynamic electricity pricing scheme. However, as expected, the actual electricity supply and the actual electricity consumption may not match. This could be due to the inability of the utility company to secure enough power at the wholesale market or due to changes in consumer power requirements. In the event the demand outstrips the supply, the customers willingly offer to shed some of their flexible appliances' loads but bid for prices at which they would like to pay for the curtailed load. We assume that customers are rational but selfish and are only concerned with maximizing their own benefits. In this study, this demand- side bidding problem is mathematically formulated and solved. Simulation studies are also carried out to verify the effectiveness of the proposed method. <s> BIB008 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> In a smart grid network, demand-side management plays a significant role in allowing consumers, incentivized by utilities, to manage their energy consumption. This can be done through shifting consumption to off-peak hours and thus reducing the peak-to-average ratio (PAR) of the electricity system. In this paper, we begin by proposing a demand-side energy consumption scheduling scheme for household appliances that considers a PAR constraint. An initial optimization problem is formulated to minimize the energy cost of the consumers through the determination of the optimal usage power and operation time of throttleable and shiftable appliances, respectively. We realize that the acceptance of consumers of these load management schemes is crucial to its success. Hence, we then introduce a multi-objective optimization problem which not only minimizes the energy cost but also minimizes the inconvenience posed to consumers. In addition to solving the proposed optimization problems in a centralized manner, two distributed algorithms for the initial and the multi-objective optimization problems are also proposed. Simulation results show that the proposed demand-side energy consumption schedule can provide an effective approach to reducing total energy costs while simultaneously considering PAR constraints and consumers' preferences. <s> BIB009 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> One of the most challenging problems associated with operation of smart micro-grids is the optimal energy management of residential buildings with respect to multiple and often conflicting objectives. In this paper, a multiobjective mixed integer nonlinear programming model is developed for optimal energy use in a smart home, considering a meaningful balance between energy saving and a comfortable lifestyle. Thorough incorporation of a mixed objective function under different system constraints and user preferences, the proposed algorithm could not only reduce the domestic energy usage and utility bills, but also ensure an optimal task scheduling and a thermal comfort zone for the inhabitants. To verify the efficiency and robustness of the proposed algorithm, a number of simulations were performed under different scenarios using real data, and the obtained results were compared in terms of total energy consumption cost, users' convenience rates, and thermal comfort level. <s> BIB010 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> Abstract Energy management strategies are instrumental in the performance and economy of smart homes integrating renewable energy and energy storage. This article focuses on stochastic energy management of a smart home with PEV (plug-in electric vehicle) energy storage and photovoltaic (PV) array. It is motivated by the challenges associated with sustainable energy supplies and the local energy storage opportunity provided by vehicle electrification. This paper seeks to minimize a consumer's energy charges under a time-of-use tariff, while satisfying home power demand and PEV charging requirements, and accommodating the variability of solar power. First, the random-variable models are developed, including Markov Chain model of PEV mobility, as well as predictive models of home power demand and PV power supply. Second, a stochastic optimal control problem is mathematically formulated for managing the power flow among energy sources in the smart home. Finally, based on time-varying electricity price, we systematically examine the performance of the proposed control strategy. As a result, the electric cost is 493.6% less for a Tesla Model S with optimal stochastic dynamic programming (SDP) control relative to the no optimal control case, and it is by 175.89% for a Nissan Leaf. <s> BIB011 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> Over the last few years, the fast-growing energy needs across the world have intensified a central challenge: how to reduce the generation and operation costs in power systems and, in parallel, to minimize the hydrocarbon emissions. Moreover, one-quarter of world's population still lacks access to electricity, as the cost of building conventional power grids is not affordable by third world countries. On the other hand, behind-the-meter (BTM) energy systems offer cost-effective solutions to aforementioned challenges, as they enable end-users to satisfy their energy needs with distributed energy generation and storage technologies. To that end, this paper presents a detailed survey of BTM energy management systems. The paper starts with the classification of the electrical loads with respect to their physical properties, priority ranking, and sizes. Next, the literature on BTM energy management systems is systematically classified into three main categories: technology layer, economic layer, and social layer. The technology layer spans the studies related to power systems including distributed generation and storage technologies, whereas the economic layer shows how economic incentives along with optimization and scheduling techniques are employed to shape the energy consumption. The social layer, on the other hand, presents the recent studies on how to employ social sciences to reduce the energy consumption without requiring any technological upgrades. This paper also provides an overview of the enabling technologies and standards for communication, sensing, and monitoring purposes. In the final part, a case study is provided to illustrate an implementation of the system. <s> BIB012 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> In recent years, increasing interest in developing small-scale fully integrated energy resources in distributed power networks and their production has led to the emergence of smart Microgrids (MG), in particular for distributed renewable energy resources integrated with wind turbine, photovoltaic and energy storage assets. In this paper, a sustainable day-ahead scheduling of the grid-connected home-type Microgrids (H-MG) with the integration of non-dispatchable/dispatchable distributed energy resources and responsive load demand is co-investigated, in particular to study the simultaneously existed uncontrollable and controllable production resources despite the existence of responsive and non-responding loads. An efficient energy management system (EMS) optimization algorithm based on mixed-integer linear programming (MILP) (termed as EMS-MILP) using the GAMS implementation for producing power optimization with minimum hourly power system operational cost and sustainable electricity generation of within a H-MG. The day-ahead scheduling feature of electric power and energy systems shared with renewable resources as a MILP problem characteristic for solving the hourly economic dispatch-constraint unit commitment is also modelled to demonstrate the ability of an EMS-MILP algorithm for a H-MG under realistic technical constraints connected to the upstream grid. Numerical simulations highlight the effectiveness of the proposed algorithmic optimization capabilities for sustainable operations of smart H-MGs connected to a variety of global loads and resources to postulate best power economization. Results demonstrate the effectiveness of the proposed algorithm and show a reduction in the generated power cost by almost 21% in comparison with conventional EMS. <s> BIB013 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> Abstract In the design of low-energy buildings, mathematical optimisation has proven to be a powerful tool for minimising energy consumption. Simulation-based optimisation methods are widely employed due to the nonlinear thermal behaviour of buildings. However, finding high-quality solutions with reasonable computational cost remains a significant challenge in the building industry. In this paper, Ant Colony Optimisation for continuous domain (ACOR) is developed and applied to optimise a commercial building in Australia. The results for a typical commercial building showed that optimisation can achieve an additional energy savings of more than 11.4%, even after some common energy saving measures were implemented (e.g. double pane windows). The performance of ACOR was compared to three benchmark optimisation algorithms: Nelder-Mead (NM) algorithm, Particle Swarm Optimisation with Inertia Weight (PSOIW) and the hybrid Particle Swarm Optimisation and Hooke-Jeeves (PSO-HJ). This comparison showed that ACOR was able to consistently find better solutions in less time than the benchmark algorithms. The findings demonstrate that ACOR can further facilitate the design of low-energy buildings. <s> BIB014 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> In this work we monitor and study electrical and water consumptions in a home environment in order to reduce its energy costs. To this end, we apply a population-based optimisation algorithm to determine optimal operation schedules for certain appliances in a domestic environment. We propose a criteria function that assesses the performance of an operation schedule for controllable appliances in the household. An optimisation problem is, then, formulated and solved in order to compute the desired operation schedule that minimises this criteria. Particle Swarm Optimisation (PSO) was the algorithm chosen to solve this problem, mainly because of its simple implementation and relatively low computational burden, allowing it to generate (near-optimal) solutions in a feasible time. A fine-tuning of its parameters was promoted. The best computed solutions for a time horizon of one day revealed not only the ability to take advantage of night periods (when energy is cheaper) but also that a positive balance in the electrical bill can be obtained, benefiting from excess photovoltaic energy being sold to the public grid. <s> BIB015 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> Abstract We present an intelligent system, based on the particle swarm optimization (PSO) technique, to solve a power consumption minimization problem which is commonly encountered at the industrial factories or workshops. The power minimization problem is concerned with adjusting the settings of a number of lighting devices in real time in a working environment, subject to the requirements of minimizing the power consumption of the lighting devices as well as producing sufficient illuminance over all the specified working spots in the working area. Usually, the search space involved is too huge and solving the problem with traditional methods, e.g., brute force or least squares, is out of the question. In this paper we describe a distributive-PSO (DPSO) based algorithm to solve the problem. We show that by dividing the whole population of particles into a number of groups, PSO can be done distributively on each group and the best settings for the lighting devices, which meet the requirements, can be efficiently obtained. DPSO is very suitable to be parallelized. Parallel implementations in GPU and Hadoop MapReduce are developed. Simulation results show that our developed system is effective for a variety of working environments. We believe our work facilitates developing an efficient tool for energy conservation as well as other optimization applications. <s> BIB016 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> Abstract Home energy management (HEM) is one of the crucial requirements for future smart grid environment. Usually, utility companies motivate residential consumers to participate in household demand side management (DSM) techniques. The residential consumers’ main intention is to reduce their electricity bills whereas utilities’ primary goal is to reduce their system peak load demand. The benefits of DSM can be enhanced with smart energy storage systems. In this paper, an intelligent multi-objective household DSM with battery storage systems is proposed to minimize both the objectives simultaneously. Generally, the residential DSM deals with various controllable appliances that normally have different operating time priorities according to the consumers’ preferences. Because of this, the DSM problem is modelled as a mixed integer optimization problem. In this paper, the multi-objective mixed integer linear programming technique (MOMILP) is used to solve the proposed residential DSM problem. The proposed model is tested on a system having four different residential consumers. The benefits of the battery energy storage systems are analysed by taking six different scenarios. An economic analysis of each residential consumer's battery storage system with load uncertainty modelling is also done in this paper. Simulation results of various scenarios with different objectives demonstrate the effectiveness of the proposed model. <s> BIB017 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> Abstract In this paper, a novel approach is proposed to optimize the behavior of household appliances towards retail electricity price. At the supply-side, a distributed generation-owning retailer participates in the wholesale electricity market, i.e. day-ahead and intraday trading floors. Considering the uncertainties associated with electricity price and wind/solar power, the retailer determines the retail price using stochastic programming. At the demand-side, smart household prosumers take the advantage of two kinds of storage capacities: (1) thermal storage capacity of thermostatic devices and (2) electrical storage capacity of batteries integrated with roof-top photovoltaic panels. The Home Energy Management System (HEMS) determines the operational strategies of appliances, including thermostatically controlled, uninterruptible and curtailable appliances, in response to the retail price. The HEMS uses a heuristic Forward-Backward Algorithm (F-BA) to minimize the energy cost of the thermal appliances satisfying the residents’ comfort. To prevent from creating peak demands in the power system, a Peak Flattening Scheme (PFS) is suggested. Investigating the interaction between household consumption and network security, the household demands are located at different buses of a distribution network relieving congestion in weak lines. Finally, the proposed approach is implemented as a case for Danish sector of Nordic Electricity Market. <s> BIB018 </s> A Survey on Home Energy Management <s> B. CRITERIA <s> Abstract A hierarchical control framework is proposed that enables the provision of flexibility services in power systems through aggregation entities. The focus is on both wholesale energy trade functions, i.e. day-ahead market optimisation, and ancillary services to the operators of the transmission and distribution systems, i.e. provision of automatic frequency restoration reserves and peak shaving services. The control framework is generic, scalable, and is effectively configured to address all those diverse needs originating from the lower level, i.e. the device or the user, to the distribution and transmission levels. The work includes the establishment of procedures to identify and solve possible conflicts between the operators of the transmission and distribution systems when procuring ancillary services with competing objectives. The framework is applied on a case study focusing on an aggregation of residential buildings in the Netherlands which are equipped with photovoltaic installations and battery-based energy storage systems. The annual costs and benefits are calculated by using historical market data, whereas the simulation scenarios address the impact of different levels of generation and demand forecasting errors in the economic performance. The outcome of computer simulations provides an informative insight into the differences between the strategies of aggregator companies, and the potential of the investigated markets. The economic evaluation includes the value estimation of peak shaving services at the distribution level. An increase in procurement costs for such services is expected to stimulate the distribution system operators to invest into grid capacity enhancement. <s> BIB019
In the following, some of the criteria commonly considered for scheduling appliances are listed BIB012 : 1) Electricity bill -is the most common objective, as the main motivation of residential consumers is to minimise the underlying bill, while taking into account available tariffs and renewable micro-generation BIB006 , BIB017 ,  , BIB010 , BIB015 , BIB018 , BIB007 , BIB011 , BIB013 , BIB019 . 2) Distribution system losses -due to Joule effect in power lines and other equipment deployed along the grid network, namely transformers, a fraction of generated power is lost. A common approach to deal with this problem includes deploying generation sources along the power line, regulated by an optimal dispatch strategy BIB004 ,  , BIB002 . 3) Peak load -utility companies encourage customers to minimise peak load demand or even to achieve a particular load profile, which benefits grid management BIB017 , BIB014 , BIB016 , BIB008 ,  . By defining individual target loads, utility companies promote a balanced use of power grid resources, expressed as peak-to-average load ratio (PALR). The closer this ratio is to one, the flatter consumption load is throughout the day BIB005 , BIB009 . 4) Carbon emission -taking into account the environmental impact of energy consumption, HEMSs can incorporate carbon and other GHG emissions as additional criteria BIB003 , BIB001 . Penalty fees can also be charged to consumers based on GHG emission levels, as considered in  , where pollutant emissions are indirectly reduced through electricity cost minimisation.