A Survey on Home Energy Management <s> B. DEMAND RESPONSE <s> Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain. <s> BIB001 </s> A Survey on Home Energy Management <s> B. DEMAND RESPONSE <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> BIB002 </s> A Survey on Home Energy Management <s> B. DEMAND RESPONSE <s> The scope of this paper is to provide a review on the topic of demand side management. A historical overview provides a critical insight to applied cases, while the discovery of new evidence calls for reconsideration of the design of demand response control schemes. The developments at the demand side are analyzed, and philosophical considerations are made with respect to envisioned energy management systems. Finally, the authors classify demand response control schemes based on the formulation of the rate structures and the employed technology. This classification follows a historical order, and addresses both applied cases and research developments. <s> BIB003 </s> A Survey on Home Energy Management <s> B. DEMAND RESPONSE <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> BIB004 </s> A Survey on Home Energy Management <s> B. DEMAND RESPONSE <s> The present study uses the DHW (domestic hot water) electric backup from solar thermal systems to optimize the total electricity dispatch of an isolated mini-grid. The proposed approach estimates the hourly DHW load, and proposes and simulates different DR (demand response) strategies, from the supply side, to minimize the dispatch costs of an energy system. <s> BIB005 </s> A Survey on Home Energy Management <s> B. DEMAND RESPONSE <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> BIB006 </s> A Survey on Home Energy Management <s> B. DEMAND RESPONSE <s> In this paper we consider the problem of optimizing the operation of a building heating system under the hypothesis that the building is included as an active consumer in a demand response program. Demand response requests to the building operational system come from an external market player or a grid operator. Requests assume the form of price–volume signals specifying a maximum volume of energy to be consumed during a given time slot and a monetary reward assigned to the participant in case it fulfills the conditions. A receding horizon control approach is adopted for the minimization of the energy bill, by exploiting a simplified model of the building. Since the resulting optimization problem is a mixed integer linear program which turns out to be manageable only for buildings with very few zones, a heuristics is devised to make the algorithm applicable to realistic size problems as well. The derived control law is tested on the realistic simulator EnergyPlus to evaluate pros and cons of the proposed algorithm. The performance of the suboptimal control law is evaluated on small- and large-scale test cases. <s> BIB007 </s> A Survey on Home Energy Management <s> B. DEMAND RESPONSE <s> Display Omitted Hybrid heating system is used to optimize energy consumption.In order to minimize cost of energy, control methods are used.A PID controller and a MPC controller are used to control hybrid system.These controllers are used to control the temperature of indoor volume.The GA has been applied to tune optimal gains of PID controller. Today, the buildings' energy consumption is considerable amount of whole. Therefore, optimizing energy in buildings leads to a noticeable decrease in total energy consumption of the world. Energy-efficient buildings have developed by carrying out great research effort. The control procedures serve as a privileged method to help new buildings to comply with the most optimal system as an energy consumer and thus meet 'nearly zero-energy'.The purpose of this paper is to present a method of controlling the building temperature and simultaneously reducing the cost of providing the hybrid heating systems with sufficient energy. Investigating a room in Tehran city on a day as an example, methods of (a) Model Predictive Control (MPC) with economic optimization (MPC consecutively with On-Off), (b) MPC without economic optimization, (c) Proportional-Integral-Derivative (PID) controller optimized by Genetic Algorithm (GA) in presence of gas thermal source, (d) PID controller optimized with GA in presence of electric thermal source and (e) PID controller optimized with multi-objective GA in the presence of two gas and electric thermal sources have been designed and implemented in this research. Furthermore, the effect of each of these methods on cost reduction and temperature regulation of inside of the room has been studied. Eventually it has been specified that using MPC method with economical optimization has the highest influence on cost reduction and keeps the temperature of inside of the room in the predefined range. This method achieved cost saving of 50% compared to the MPC and GA. But the main targets of this study are both of regulating inside temperature and cost optimization. According to the main targets of this study, using MPC methods without economical optimization and multi-objective genetic algorithm would be more effective. <s> BIB008 </s> A Survey on Home Energy Management <s> B. DEMAND RESPONSE <s> Demand-side resources like demand response (DR) and energy efficiency (EE) can contribute to the capacity adequacy underpinning power system reliability. Forward capacity markets are established in many liberalised markets to procure capacity, with a strong interest in procuring DR and EE. With case studies of ISO New England, PJM and Great Britain, this paper examines the process and trends of procuring DR and EE in forward capacity markets, and the design for integration mechanisms. It finds that the contribution of DR and EE varies wildly across these three capacity markets, due to a set of factors regarding mechanism design, market conditions and regulatory provisions, and the offering of EE is more heavily influenced by regulatory utility EE obligation. DR and EE are complementary in targeting end-uses and customers for capacity resources, thus highlighting the value of procuring them both. System needs and resources’ market potential need to be considered in defining capacity products. Over the long-term, it is important to ensure the removal of barriers for these demand-side resources and the capability of providers in addressing risks of unstable funding and forward planning. For the EDR Pilot in the UK, better coordination with forward capacity auction needs to be achieved. <s> BIB009 </s> A Survey on Home Energy Management <s> B. DEMAND RESPONSE <s> Abstract This paper classifies and discusses the energy flexibility improvement strategies for demand responsive control in grid-interactive buildings based on a comprehensive study of the literature. Both supply and demand sides are considered. The flexibility measures range from renewable energy such as photovoltaic cells (PV) and wind to heating, ventilation, and air conditioning (HVAC) systems, energy storage, building thermal mass, appliances, and occupant behaviors. Currently, owing to the highly developed smart appliances and sensing communication techniques, DR is considered as an essential measure for improving energy flexibility in buildings without much additional investment. With the help of advanced demand response (DR) control strategies and measures, buildings can become more flexible in terms of power demand from the power grid. In this way, buildings achieve a better ability to balance differences in energy supply and demand. Furthermore, a synergistic approach with various measures is advisable, e.g., the use of energy storage technologies with PV and passive DR methods. This paper summarizes the measures for improving the flexibility of commercial and residential buildings, and develops a systematic methodology framework to evaluate energy demand flexibility in buildings. <s> BIB010 </s> A Survey on Home Energy Management <s> B. DEMAND RESPONSE <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> BIB011
Demand response refers to changes in electricity use from normal consumption patterns exhibited by demand-side resources, as a result of changes in the price of electricity or induced by incentive payments, aiming at lowering electricity use when wholesale market prices are high, just to name out a few BIB010 . Moreover, the ability to change appliance schedules in real-time allows the accommodation of unexpected grid demand peaks, while contributing to demand flexibility. This is particularly important in addressing the variability of energy production from renewable sources, both at demand and supply sides. From a practical point of view, DR has been shown to cut peak demands, helping with the integration of renewable sources and supporting short-term balancing of the grid BIB009 . As for DR-based techniques, there is also no consensual categorisation. One of the proposals BIB001 considers splitting DR into market and physical techniques. Market DR techniques are directly focused on energy billing by means of load shifting, taking into account static or dynamic billing methods or financial incentives. On the other hand, physical DR is centred around the smart grid and underlying infrastructure, consisting of signals sent out by the utility to reduce or remove demands due to maintenance or failure events. Another more exhaustive categorisation is proposed in BIB003 . Here, the authors split DR techniques into the following six categories ( Figure 2 ): (i) frequency-based, (ii) direct control over utility equipment, (iii) direct control over end-use equipment, (iv) price-based, (v) market-based, and (vi) model-based. Frequency-based techniques use frequencybased mechanisms to control devices on the demand-side, switching them on or off, performing load shedding and restoration. Direct control over either utility or end-use equipment aims to control grid assets, such as transformers and feeders, or demand-side appliances. For utility equipment, the adoption of voltage reduction and protection relays is reported, while protection fuses and clock-based controllers can be considered at demand-side. In price-based techniques, indirect load control is carried out via tariffs such as TOU, real-time pricing (RTP) or critical-peak pricing (CPP), while in market-based demand-side resources are explicitly incorporated into electricity markets. Finally, model-based techniques focus on coordinating devices and resources at both demand and supply sides, in order to optimise energy use and billing. The authors focus their discussion on modelbased predictive control (MPC), but alternative methodologies, such as machine learning-based techniques, can also be considered. Recently, two additional categories were considered in BIB010 and BIB006 , namely price-based and incentive-based techniques. Both categories, in essence, are focused on scheduling devices to minimise energy bill. Price-based techniques adjust loads to dynamic grid prices, while incentive-based techniques rely on deterministic and time-invariant policies, such as direct load control and interruptible loads, to promote load reduction. Customers of incentive-based programs should agree to reduce operative loads during specific periods. If they comply with this requirement a financial incentive is awarded, otherwise a penalty is applied. In recent years, renewable energy and storage systems, both at demand and supply-side, have been increasingly integrated on power grids. On the other hand, AMI, dynamic billing policies, as well as automated DSM and DR also need to be properly incorporated. In addition, demand-side load adjustment should not be considered just to improve just a single criterion. The utility, smart grid and customers often have distinct and conflicting objectives. This has motivated extensive research on multi-objective optimal resource management, notably MPC BIB007 , BIB008 , linear programming (LP) and non-linear programming (NLP) BIB011 , BIB002 , BIB004 as well as evolutionary algorithms (EAs) BIB005 -  .