A Survey on Home Energy Management <s> A. APPLIANCES CATEGORISATION <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> BIB001 </s> A Survey on Home Energy Management <s> A. APPLIANCES CATEGORISATION <s> In this paper, for efficient energy consumption through the residential demand response in the smart grid, an optimization algorithm, which can provide a schedule plan for the home appliance usages, is proposed. In order to minimize the average electricity price based on the time-varying electricity price in conjunction with the peak hourly load, which decides the capacity of the electric supply facilities, we establish a mixed integer linear programming problem considering various energy consumption patterns of home appliances. In addition, a photovoltaic system and an energy storage are added to the residential side to achieve further efficient schedule plans. By measuring the power consumptions of the home appliances with respect to the time, we constructed the power consumption patterns of each appliance and numerically analyzed the performance of our algorithm by using a real time-varying electricity price and the solar cell power profile obtained through a mathematical model. <s> BIB002 </s> A Survey on Home Energy Management <s> A. APPLIANCES CATEGORISATION <s> The objective of this paper is to study the impact of scheduling flexibility on both demand profile flatness and user inconvenience in residential smart grid systems. Temporal variations in energy consumption by end users result in peaks and troughs in the aggregated demand profile. In a residential smart grid, some of these peaks and troughs can be eliminated through appropriate load balancing algorithms. However, load balancing requires user participation by allowing the grid to re-schedule some of their loads. In general, more scheduling flexibility can result in more demand profile flatness, however the resulting inconvenience to users would also increase. In this paper, our objective is to help the grid determine an appropriate amount of scheduling flexibility that it should demand from users, based on which, proper incentives can be designed. We consider three different types of scheduling flexibility (delay, advance scheduling and flexible re-scheduling) in flexible loads and develop both optimal and sub-optimal scheduling algorithms. We discuss their implementation in centralized and distributed manners. We also identify the existence of a saturation point. Beyond this saturation point, any increase in scheduling flexibility does not significantly affect the flatness of the demand profile while user inconvenience continues to increase. Moreover, full participation of all the households is not required since increasing user participation only marginally increases demand profile flatness. <s> BIB003 </s> A Survey on Home Energy Management <s> A. APPLIANCES CATEGORISATION <s> The advancement of renewable energy technologies has seen the emergence of customer owned grid tied wind and solar microgrids. These microgrids offer an opportunity to energy users to lower their energy costs as well as enabling the power suppliers to regulate the utility grid. However, the integration of the renewable energy based sources into the smart grid increases the complexity of the main grid. The success of this scheme will be heavily reliant on accurate real-time information exchange between the microgrid, the main grid, and the consumers. The communication between these agents will be critical in implementation of intelligent decisions by the smart grid. The microgrids will be required to relay energy forecasts information to the utility grid. Similarly, customers will be expected to submit energy demand schedules, to actively monitor energy price signals, to participate in energy bids, and to respond to energy management signals in real time. This kind of grid-user interaction will be overwhelming and could result in consumer apathy. There is therefore a need to develop smart systems that will autonomously execute all these tasks without the prompting of the customers. This paper presents one such approach. In this study, we proposed a demand side energy management for a grid connected household with a locally generated photovoltaic energy. To ensure efficient household energy management, smart scheduling of electrical appliances has also been presented. <s> BIB004 </s> A Survey on Home Energy Management <s> A. APPLIANCES CATEGORISATION <s> Abstract Various forms of demand side management (DSM) programs are being deployed by utility companies for load flattening amongst the residential power users. These programs are tailored to offer monetary incentives to electricity customers so that they voluntarily consume electricity in an efficient way. Thus, DSM presents households with numerous opportunities to lower their electricity bills. However, systems that combine the various DSM strategies with a view to maximizing energy management benefits have not received sufficient attention. This study therefore proposes an intelligent energy management framework that can be used to implement both energy storage and appliance scheduling schemes. By adopting appliance scheduling, customers can realize cost savings by appropriately scheduling their power consumption during the low peak hours. More savings could further be achieved through smart electricity storage. Power storage allows electricity consumers to purchase power during off-peak hours when electricity prices are low and satisfy their demands when prices are high by discharging the batteries. For optimal cost savings, the customers must constantly monitor the price fluctuations in order to determine when to switch between the utility grid and the electricity storage devices. However, with a high penetration of consumer owned storage devices, the charging of the batteries must be properly coordinated and appropriately scheduled to avoid creating new peaks. This paper therefore proposes an autonomous smart charging framework that ensures both the stability of the power grid and customer savings. <s> BIB005 </s> A Survey on Home Energy Management <s> A. APPLIANCES CATEGORISATION <s> Innovations in the residential sector are required to reduce environmental impacts, as the sector is a contributor to greenhouse gas emissions. The increasing demand for electricity and the emergence of smart grids have presented new opportunities for home energy management systems (HEMS) in demand response markets. HEMS are demand response tools that shift and curtail demand to improve the energy consumption and production profile of a dwelling on behalf of a consumer. HEMS usually create optimal consumption and productions schedules by considering multiple objectives such as energy costs, environmental concerns, load profiles and consumer comfort. The existing literature has presented several methods, such as mathematical optimization, model predictive control and heuristic control, for creating efficient operation schedules and for making good consumption and production decisions. However, the effectiveness of the methods in the existing literature can be difficult to compare due to diversity in modelling parameters, such as appliance models, timing parameters and objectives. The present chapter provides a comparative analysis of the literature on HEMS, with a focus on modelling approaches and their impact on HEMS operations and outcomes. In particular, we discuss a set of HEMS challenges such as forecast uncertainty, modelling device heterogeneity, multi-objective scheduling, computational limitations, timing considerations and modelling consumer well-being. The presented work is organized to allow a reader to understand and compare the important considerations, approaches, nomenclature and results in prominent and new literary works without delving deeply into each one. <s> BIB006 </s> A Survey on Home Energy Management <s> A. APPLIANCES CATEGORISATION <s> With the arrival of smart grid era and the advent of advanced communication and information infrastructures, bidirectional communication, advanced metering infrastructure, energy storage systems and home area networks would revolutionize the patterns of electricity usage and energy conservation at the consumption premises. Coupled with the emergence of vehicle-to-grid technologies and massive distributed renewable energy, there is a profound transition for the energy management pattern from the conventional centralized infrastructure towards the autonomous responsive demand and cyber-physical energy systems with renewable and stored energy sources. Under the sustainable smart grid paradigm, the smart house with its home energy management system (HEMS) plays an important role to improve the efficiency, economics, reliability, and energy conservation for distribution systems. In this paper, a brief overview on the architecture and functional modules of smart HEMS is presented. Then, the advanced HEMS infrastructures and home appliances in smart houses are thoroughly analyzed and reviewed. Furthermore, the utilization of various building renewable energy resources in HEMS, including solar, wind, biomass and geothermal energies, is surveyed. Lastly, various home appliance scheduling strategies to reduce the residential electricity cost and improve the energy efficiency from power generation utilities are also investigated. <s> BIB007 </s> A Survey on Home Energy Management <s> A. APPLIANCES CATEGORISATION <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> BIB008 </s> A Survey on Home Energy Management <s> A. APPLIANCES CATEGORISATION <s> This work is focused on the optimal management of electricity and heat generation and demand in microgrids. The objective of the proposed mathematical model is to adjust energy and heat availability profiles resulting from the use of renewable energy sources and flexible energy and heat demands. The optimisation of the resulting short-term problem is addressed through a Mixed-Integer Linear Programming (MILP) mathematical model to minimise the operational cost of the microgrid. Delays in the energy demands are allowed to tackle flexible demand profiles, under penalties in the objective function. An additional characteristic was the consideration of non-constant profiles in the considered tasks. Also, this model takes into account eventual interruptions in the tasks, applying penalties in the economic objective function. The main decisions to be made includes the schedule of tasks, as well as energy and heat generation levels, purchases from and exportation to the power grid, and storage levels. <s> BIB009 </s> A Survey on Home Energy Management <s> A. APPLIANCES CATEGORISATION <s> Abstract Demand Side Management (DSM) has been a subject of much research due to the demand of strategies which enable the development of power generation, transmission and distribution in Smart Grids (SG). Throughout the years, many authors have applied mathematical programming and, or, metaheuristic methods for obtaining a satisfactory solution for load management, mainly considering only one performance function in their respective optimization approaches. However, in addition to maximum load peak minimization (an often used performance function), it may be interesting to deal with some other merit functions, such as energy production cost, costumer’s preferences and constraints, among others. Due to this multi-criteria nature of this problem, in this paper it is proposed an exact multi-objective methodology to the optimal Demand Side Management. Essentially, the DSM problem is addressed by using an ϵ-constraint approach, in which one of the objectives is handled with Quadratic Programming whilst the others are considered as linear constrains. This paper also discusses the behavior of the solutions under the presence of uncertainties on the problem parameters due to possible unpredictable variations of the consumption pattern, energy price and number of appliances. The results, regarding the case studies analyzed, have shown promising solutions with interesting trade-off relations between power consumption and energy production cost. Moreover, the robustness analysis has indicated not sensitive solutions even when some parameters of the problem are varied, such as the energy price and the number of types of appliances, which can be regarded as a useful information for the utility. <s> BIB010
From a load scheduling point of view, household appliances can be categorised based on how they are managed by HEMSs. In BIB010 appliances are distinguished between controllable, according to which appliance operations can be scheduled over a given time horizon, and non-controllable appliances, for which scheduling is not available. Only the ability to define appliances' start time is possbile, while interrupting their operation or reducing their energy consumption are not available. A broader categorisation is proposed in BIB006 , comprising the following six classes aiming to model different groups of devices: (a) Uncontrollable loads -they cannot be changed or re-scheduled by a HEMS. This class considers loads that provide an added value to residents, sometimes completely controlled by users. Examples include, but are not restricted to, entertainment equipments such as tv sets, computers or sound systems. (b) Curtailable loads -energy consumption can be adjusted mid-operation, usually with no significant impact to residents' comfort. Such adjustments restrict energy consumption by changing the underlying settings, with no subsequent compensation. One example concerns dimming indoor artificial illuminance during day-time as a function of daylighting. (c) Uninterruptible loads -once started, they should run a complete cycle. Hence, the underlying HEMS, or residents, are only able to schedule the corresponding starting time. Dishwasher, clothes washing or dryer machines are typically included in this category. batteries that store energy for subsequent use. It should be stressed that no household appliance categorisation scheme is currently globally accepted. Different terms are used to characterise identical concepts. As an example, in BIB007 household appliances are classified as schedulable or non-schedulable based on deferment flexibility, while in BIB010 , BIB001 , the classes controllable and noncontrollable are analogous, even as elastic and inelastic loads in BIB008 . The latter work also suggests a new class for smart loads, further categorised in elastic and inelastic loads, resulting from their ability to adjust power consumption mid-operation. It should be pointed out that even among authors following the categorisation proposed in BIB006 , a consensus has not been reached yet on which appliances are assigned to each category. Refrigerators, for instance, are such an example due to their relatively short cycling characteristics. At hourly resolution, consumption cannot be adjusted, and thus they are regarded as uncontrollable devices BIB002 , BIB003 . For lower time resolutions, consumption can in principle be adjustable, allowing them to be classified either as interruptible BIB004 , BIB005 or uninterruptible loads BIB001 , BIB009 . Finally, loads can also be categorised as thermostatically controlled, in the case they are related to maintaining indoor temperature close to a given reference value. Table 1 presents the most common controlled appliances in the context of HEMSs found in the literature. As can be observed, there is a clear predominance for some types of appliances. Washing, dryer machines, dishwasher and charging of electric vehicles appear on the top of this list, with over 20 references each. Indoor temperature management is also common, and associated to heating or cooling systems. This finding is, to some extent, expected, as these appliances are responsible for a high share of overall household energy consumption. In comparison to other appliances equally important for comfort and lifestyle, such as phone chargers, personal computers, tv sets or entertainment systems, those devices are more prone to external management, making them top candidates for HEMS-based supervision.