A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <s> Abstract This paper presents a new three-layer household energy control system capable both to satisfy the maximum available electrical energy constraint and to maximize user comfort criteria. These layers are composed by: an equipment layer, with local and fast control mechanism, a protection layer, which is triggered when energy constraints are violated, and an anticipation mechanism, which adjusts future set-points of equipments in order to tackle energy events that can be forseen. The scheduling problem of the set-points of equipments is modelled by a graph and solved by the Bellman-Ford's algorithm. This control mechanism makes it possible to have a more flexible control of the overall power consumption in housing in exploiting natural thermal energy accumulation. <s> BIB001 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <s> It is widely accepted that renewable energy sources are the key to a sustainable energy supply infrastructure since they are both inexhaustible and nonpolluting. A number of renewable energy technologies are now commercially available, the most notable being wind power, photovoltaic, solar thermal systems, biomass, and various forms of hydraulic power. In this paper, a methodology has been proposed for optimally allocating different types of renewable distributed generation (DG) units in the distribution system so as to minimize annual energy loss. The methodology is based on generating a probabilistic generation-load model that combines all possible operating conditions of the renewable DG units with their probabilities, hence accommodating this model in a deterministic planning problem. The planning problem is formulated as mixed integer nonlinear programming (MINLP), with an objective function for minimizing the system's annual energy losses. The constraints include the voltage limits, the feeders' capacity, the maximum penetration limit, and the discrete size of the available DG units. This proposed technique has been applied to a typical rural distribution system with different scenarios, including all possible combinations of the renewable DG units. The results show that a significant reduction in annual energy losses is achieved for all the proposed scenarios. <s> BIB002 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <s> This paper proposes the design of a Smart Home Controller strategy providing efficient management of electric energy in a domestic environment. The problem is formalized as a binary linear programming problem, the output of which specifies the best time to run of Smart Household Appliances, under a Virtual Power Threshold constraint, taking into account the real power threshold and the forecast of consumption from not plannable loads. This problem formulation allows to analyze relevant scenarios from consumer and energy retailer point of view: here optimization of economic saving in case of multi-tariff contract and Demand Side Management have been discussed and simulated. Simulations have been performed on relevant test cases, based on real load profiles provided by the smart appliance manifacturer Electrolux S.p.A. and on energy tariffs suggested by the energy retailer Edison. Results provide a proof of concept about the consumers benefits coming from the use of local energy management systems and the relevance of automated Demand Side Management for the general target of efficient and cost effective operation of electric networks. <s> BIB003 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <s> With the Smart Grid revolution and the increasing interest in renewable distributed sources, house energy consumption will play a significant role in the energy system: the whole energy generation and distribution system performance can be improved by optimizing the house energy management. Beside the energy bill reduction for single users, another advantage can be obtained for the overall system by jointly managing the energy consumption of a set of users, thus reducing their peak absorption. In this paper we propose optimization models which allow to manage every day energy load for both single and multi-users cases, taking into account distributed energy sources and batteries. Computational results, obtained applying models on real life data, are provided and discussed. <s> BIB004 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <s> Intelligent consumer energy management systems will become important elements at the delivery points of the smart grid inside homes, buildings, and industrial plants. The end users will be able to better monitor and manage their energy consumption, while utilities will gain more flexible mechanisms for management of peak demands that will extend beyond demand response initiatives as they are implemented today. With a broader use of distributed generation many buildings and campuses will become microgrids interconnecting multiple generation, storage, and consumption devices of one or several end users. We discuss how energy management and control for such facilities can be viewed as a large-scale optimization problem. Specific supply-side and demand-side aspects include on-site renewable generation, storage technologies, electric cars, dynamic pricing, and load management. Technical challenges related to the optimization formulation are noted - in general, mixed-integer, nonlinear, constrained optimization is needed. We also describe an implementation of optimization-based energy management solution for a hospital in the Netherlands, providing economic details and an analysis of the savings achieved. <s> BIB005 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <s> Abstract Building energy simulations are key to studying energy efficiency in buildings. The state-of-the art building energy simulation tools requires a high level of multi disciplinary domain expertise from the user and many technical data inputs that curb the usability of such programs. In this paper an IT tool is presented, which has the capability of predicting a building's energy utilization configuration based on the reported annual energy and a few non-technical inputs from the user; and correspondingly generates cost effective energy conservation measures for the intended savings. The approach first identifies the system variables that are critical to a building's energy consumption and searches for the combination of these parameters that would give rise to the annual energy consumption as reported by the facility. Genetic algorithms are utilized to generate this database. A statistical fit is formulated between the system variables and the annual energy consumption from the database. Using this correlation, system configuration for the target energy efficiency is determined with corresponding energy conservation measures. A cost analysis is carried out to prescribe the most cost effective energy conservation measures. Competency of the tool is demonstrated in the paper through case studies on three geographies with different climate conditions. <s> BIB006 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <s> The problem of causally scheduling power consumption to minimize the expected cost at the consumer side is considered. The price of electricity is assumed to be time-varying. The scheduler has access to past and current prices, but only statistical knowledge about future prices, which it uses to make an optimal decision in each time period. The scheduling problem is naturally cast as a Markov decision process. Algorithms to find decision thresholds for both noninterruptible and interruptible loads under a deadline constraint are then developed. Numerical results suggest that incorporating the statistical knowledge into the scheduling policies can result in significant savings, especially for short tasks. It is demonstrated with real price data from Commonwealth Edison that scheduling with mismatched modeling and online parameter estimation can still provide significant economic advantages to consumers. <s> BIB007 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <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> BIB008 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <s> This paper explores electric privacy issues that may occur along with the residential appliance demand response (DR) energy management in smart meters. Three metrics are introduced to quantitatively measure the spatial and/or temporal similarity of metered power profiles. The online stochastic optimization adopts the scenario-based approach via Monte Carlo (MC) simulation for minimizing the sum of the expected electricity payment and the weighted difference among metered power profiles for the entire day, which are measured by the three similarity metrics, in order to balance the tradeoff between the electricity payment and the electric privacy protection. In addition, batteries are employed to disguise the actual appliance power profile along with the scheduling horizon and enhance the electric privacy protection. Numerical case studies illustrate the effectiveness of the proposed approach for protecting the electric privacy in residential appliance DR energy management. <s> BIB009 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <s> The problem of modeling and stochastic optimization for home energy management is considered. Several different types of load classes are discussed, including heating, ventilation, and air conditioning unit, plug-in hybrid electric vehicle, and deferrable loads such as washer and dryer. A first-order thermal dynamic model is extracted and validated using real measurements collected over an eight months time span. A mixed integer multi-time scale stochastic optimization is formulated for the scheduling of loads of different characteristics. A model predictive control based heuristic is proposed. Numerical simulations coupled with real data measurements are used for performance evaluation and comparison studies. <s> BIB010 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <s> High quality demand side management has become indispensable in the smart grid infrastructure for enhanced energy reduction and system control. In this paper, a new demand side management technique, namely, a new energy efficient scheduling algorithm, is proposed to arrange the household appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model. The proposed algorithm takes into account the uncertainties in household appliance operation time and intermittent renewable generation. Moreover, it considers the variable frequency drive and capacity-limited energy storage. Our technique first uses the linear programming to efficiently compute a deterministic scheduling solution without considering uncertainties. To handle the uncertainties in household appliance operation time and energy consumption, a stochastic scheduling technique, which involves an energy consumption adaptation variable , is used to model the stochastic energy consumption patterns for various household appliances. To handle the intermittent behavior of the energy generated from the renewable resources, the offline static operation schedule is adapted to the runtime dynamic scheduling considering variations in renewable energy. The simulation results demonstrate the effectiveness of our approach. Compared to a traditional scheduling scheme which models typical household appliance operations in the traditional home scenario, the proposed deterministic linear programming based scheduling scheme achieves up to 45% monetary expense reduction, and the proposed stochastic design scheme achieves up to 41% monetary expense reduction. Compared to a worst case design where an appliance is assumed to consume the maximum amount of energy, the proposed stochastic design which considers the stochastic energy consumption patterns achieves up to 24% monetary expense reduction without violating the target trip rate of 0.5%. Furthermore, the proposed energy consumption scheduling algorithm can always generate the scheduling solution within 10 seconds, which is fast enough for household appliance applications. <s> BIB011 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <s> In this paper, we design and evaluate the feasibility of a system which minimizes residential electricity cost of individual homes by shifting demand over a daily forecast price cycle. Ideally, our system will accept use-time preferences from consumers and optimize their appliances' operation around those given patterns. However, using the system to recommend optimum use-times to consumers is also possible by accepting ideal use preferences from an external load manager and computing the cost savings of these preferences relative to the cost of the consumer's current use patterns. We implement the optimization problem in two stages by using Mixed Integer Linear Programming (MILP). In the first stage, we obtain the optimum scheduling for appliances to be connected to the outlet; the output of this stage only shows the hours that we can use the appliance and does not reflect the actual consumption hours of each appliance. In the second stage, we model the random behavior of users via Monte Carlo simulation by running the appliances within the times they receive power, specified in the first stage. Finally, we evaluate the effectiveness of the proposed model in terms of cost savings by considering three appliances and four pricing schemes. <s> BIB012 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <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> BIB013 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <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> BIB014 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <s> Abstract This paper discusses an experimental study of the home appliances scheduling problem that incorporates realistic aspects. The residential load scheduling problem is solved while considering consumer’s preferences. The objective function minimizes the weighted sum of electricity cost by earning relevant incentives, and the scheduling inconvenience. The objective of this study is five-fold. First, it sought to develop and solve a binary integer linear programming optimization model for the problem. Second, it examined the factors that might affect the obtained schedule of residential loads. Third, it aimed to test the performance of a developed optimization model under different experimental scenarios. Fourth, it proposes a conceptual definition of a new parameter in the problem, the so-called “flexibility ratio”. Finally, it adds a data set for use in the literature on the home appliance scheduling problem, which can be used to test the performance of newly-developed approaches to the solution of this problem. This paper presents the results of experimental analysis using four factors: problem size, flexibility ratio, time slot length and the objective function weighting factor. The experimental results show the main and interaction effects, where these exist, on three performance measures: the electricity cost, inconvenience and the optimization model computation time. <s> BIB015 </s> A Survey on Home Energy Management <s> 1) MATHEMATICAL OPTIMISATION <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> BIB016
HEMSs define appliance operation schedules over a given predefined time horizon, so that some particular criteria are optimised, while taking into account underlying constraints. A common approach to find feasible solutions relies on deterministic optimisation-based methodologies. The corresponding problem formulation can be grouped into the following categories: (a) Linear Programming (LP) problems -the objective function and constraints are strictly expressed by linear relationships, being binary programming BIB015 , BIB003 and mixed-integer linear programming (MILP) BIB012 , BIB008 , BIB013 , BIB016 , BIB004 the most predominant methods. LP problems are appealing due to their relatively low computation burden and the availability of specific software packages. Algorithms such as branch and bound, simplex or interior point can be employed. Commercial and non-commercial solvers with support for linear programming problems are also available, namely GLPK  , CPLEX  or GAMS  . (b) Non-Linear Programming problems -either the underlying criteria or constraints, or even both, are expressed by non-linear functions. These techniques are more powerful than LPs, but on the other hand the computation burden is larger. Common solvers with support for non-linear problems include SCIP  ,  , GAMS or LINGO  . Mixed-integer non-linear programming (MINLP) problems are commonly formulated in the context of HEMSs BIB005 ,  , BIB002 . (c) Convex Programming problems -consider convex objective functions, linear equality constraints and concave inequality constraints. Convex programming problems can be solved by least squares, conic programming, geometric optimisation and Lagrange multiple methods BIB014 . (d) Dynamic Programming -the optimisation problem is structured into multiple stages, being scheduling decisions made sequentially, one at a time, and not independently at each time interval BIB001 , BIB006 . A drawback of deterministic formulations, in the context of HEMS scheduling, is the lack of precise knowledge concerning demand, local micro generation or grid prices over the scheduling horizon. Although scheduling could be found by solving LP or MILP problems, by considering demand and production forecasts, optimal solutions can only be found if future uncertain realizations match forecasts. For uncertainty sources modelled by probability distributions, a stochastic optimisation approach can be used BIB009 , BIB010 , BIB007 . This methodology was followed in BIB011 , leading to household energy bill cuts of around 41%, when compared to traditional deterministic optimisation approaches, with uncertainties were considered for household appliance consumption, operation times, and with respect to renewable electricity generation.