A Survey on Home Energy Management <s> C. SCHEDULING CONSTRAINTS <s> We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations. <s> BIB001 </s> A Survey on Home Energy Management <s> C. SCHEDULING CONSTRAINTS <s> This paper considers the minimum electricity cost scheduling problem of smart home appliances. Operation characteristics, such as expected duration and peak power consumption of the smart appliances, can be adjusted through a power profile signal. The optimal power profile signal minimizes cost, while satisfying technical operation constraints and consumer preferences. Constraints such as enforcing uninterruptible and sequential operations are modeled in the proposed framework using mixed integer linear programming (MILP). Several realistic scenarios based on actual spot price are considered, and the numerical results provide insight into tariff design. Computational issues and extensions of the proposed scheduling framework are also discussed. <s> BIB002 </s> A Survey on Home Energy Management <s> C. SCHEDULING CONSTRAINTS <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> C. SCHEDULING CONSTRAINTS <s> This thesis investigates the optimal scheduling of smart home applianceswith respect to economic benefits (electricity bill) and reducing environmentalimpacts (CO2emissions) for the Stockholm Royal Seaport project. Theaim of this project is to develop a new urban district developing in the easternStockholm which will house 10,000 new apartments and 30,000 new officespaces where modern living is combined with environmental thinking to createsustainable living. In a previous work the scheduling objective was to minimizeelectricity bill, subject to various constraints such as sequential processingand consumer preferences. In this work the optimization framework will beextended to consider the trade-off between electricity bill and CO2emissionminimization. This is a main concern in the Royal Seaport project. The studyof this thesis shows that a well balanced result between minimizing the electricitycost and reducing the CO2emissions for an unusual cold day in Sweden(2010-01-05) with three typical home appliances showed that for formulationsuggested in a previous work one could save up to 35.9% of electricity costsas well as reducing the CO2emissions with up to 16.5 %. This saving is withrespect to the worst case scheduling for that specific day. The trade-off analysisis based on multi-objective Pareto frontier exploration, which requires solvingmultiple schedules instead of one as in the previous single objective case. Inaddition, for practical implementation the smart home control devices that willbe used in the Stockholm Royal Sea project apartments will have a CPU andmemory similar to those of a smart phone. Therefore there exists a need forfaster implementation. The studies in this thesis indicate that the proposedsimplified formulation can lead to a almost sixfold speed up in solve time, whileproviding schedules similar to those by the previous approach. The solve timeof the proposed formulation decreases when the number of breakpoints in thepiecewise linear objective decreases. A variant of the Ramer-Douglas-Peuckeralgorithm is applied to reduce the number of breakpoints while guaranteeingthe objective function error is within a pre-specified bound. Finally, extensionsof the current framework are discussed. <s> BIB004 </s> A Survey on Home Energy Management <s> C. SCHEDULING CONSTRAINTS <s> Abstract Microgrid works as a local energy provider for domestic buildings to reduce energy expenses and gas emissions by utilising distributed energy resources (DERs). The rapid advances in computing and communication capabilities enable the concept smart buildings become possible. Most energy-consuming household tasks do not need to be performed at specific times but rather within a preferred time. If these types of tasks can be coordinated among multiple homes so that they do not all occur at the same time yet still satisfy customers’ requirement, the energy cost and power peak demand could be reduced. In this paper, the optimal scheduling of smart homes’ energy consumption is studied using a mixed integer linear programming (MILP) approach. In order to minimise a 1-day forecasted energy consumption cost, DER operation and electricity-consumption household tasks are scheduled based on real-time electricity pricing, electricity task time window and forecasted renewable energy output. Peak demand charge scheme is also adopted to reduce the peak demand from grid. Two numerical examples on smart buildings of 30 homes and 90 homes with their own microgrid indicate the possibility of cost savings and electricity consumption scheduling peak reduction through the energy consumption and better management of DER operation. <s> BIB005 </s> A Survey on Home Energy Management <s> C. SCHEDULING CONSTRAINTS <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> C. SCHEDULING CONSTRAINTS <s> Author(s): Sardahi, Yousef Hafedh | Advisor(s): Sun, Jian-Qiao | Abstract: Feedback controls are usually designed to achieve multiple and often conflicting performance goals. These incommensurable objectives can be found in both time and frequency domains. For instance, one may want to design a control system such that the closed-loop system response to a step input has a minimum percentage overshoot , peak time, rise time, settling time, tracking error, and control effort. Another designer may want the controlled system to have a maximum crossover frequency, maximum phase margin and minimum steady-state error . However, Most of these objectives cannot be achieved concurrently. Therefore, trade-offs have to be made when the design objective space includes two or more conflicting objectives. These compromise solutions can be found by techniques called multi-objective optimization algorithms. Unlike the single optimization methods which return only a single solution, the multi-objective optimization algorithms return a set of solutions called the Pareto set and a set of the corresponding objective function values called the Pareto front.In this thesis, we present a multi-objective optimal (MOO) design of linear and nonlinear control systems using two algorithms: the non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective optimization algorithm based on the simple cell mapping. The NSGA-II is one of the most popular methods in solving multi-objective optimization problems (MOPs). The cell mapping methods were originated by Hsu in 1980s for global analysis of nonlinear dynamical systems that can have multiple steady-state responses including equilibrium states, periodic motions, and chaotic attractors. However, this method can be also used also to solve multi-objective optimization problems by using a direct search method that can steer the search into any pre-selected direction in the objective space.Four case studies of robust multi-objective/many-objective optimal control design are introduced. In the first case, the NSGA-II is used to design the gains of a PID (proportional-integral-derivative) control and an observer simultaneously. The optimal design takes into account the stability robustness of both the control system and the estimator at the same time. Furthermore, the closed-loop system's robustness against external disturbances and measurement noises are included in the objective space.The second case study investigates the MOO design of an active control system applied to an under-actuated bogie system of high speed trains using the NSGA-II. Three conflicting objectives are considered in the design: the controlled system relative stability, disturbance rejection and control energy consumption. The performance of the Pareto optimal controls is tested against the train speed and wheel-rail contact conicity, which have huge impact on the bogie lateral stability.The third case addresses the MOO design of an adaptive sliding mode control for nonlinear dynamic systems. Minimizing the rise time, control energy consumption, and tracking integral absolute error and maximizing the disturbance rejection efficiency are the objectives of the design. The solution of the MOP results in a large number of trade-off solutions. Therefore, we also introduce a post-processing algorithm that can help the decision-maker to choose from the many available options in the Pareto set. Since the PID controls are the most used control algorithm in industry and usually experience time delay, a MOO design of a time-delayed PID control applied to a nonlinear system is presented as the fourth case study. The SCM is used in the solution of this problem. The peak time, overshoot and the tracking error are considered as design objectives and the design parameters are the PID controller gains. <s> BIB007 </s> A Survey on Home Energy Management <s> C. SCHEDULING CONSTRAINTS <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> BIB008 </s> A Survey on Home Energy Management <s> C. SCHEDULING CONSTRAINTS <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> BIB009 </s> A Survey on Home Energy Management <s> C. SCHEDULING CONSTRAINTS <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> BIB010 </s> A Survey on Home Energy Management <s> C. SCHEDULING CONSTRAINTS <s> This work studies the management of electrical and water appliances in a household with the ultimate goal of reducing its energy-related costs. We consider a scenario where the operation of energy and water appliances are scheduled during one day, in which water demands and energy restrictions needs must be satisfied.Operation periods for these appliances was determined by solving an optimisation problem whose objective function reflects the overall energy costs. The Bees optimisation algorithm was employed to solve this problem and schedule appliances usages. Documented results select periods when energy is cheaper to operate appliances: early in the morning (6 am and 8 am) and later at night (0 am). A profit was also achieved by selling excess photovoltaic energy to the grid. <s> BIB011 </s> A Survey on Home Energy Management <s> C. SCHEDULING CONSTRAINTS <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> BIB012
The household energy management problem includes the following two main groups of constraints: appliance constraints and comfort constraints. Appliance constraints are related to how household appliances are modelled. In this context, average non-varying consumption BIB010 , BIB005 - BIB011 , BIB012 , BIB001 is often adopted instead of individual consumption profiles assigned to each operation cycle phase BIB008 , BIB005 , BIB002 , BIB004 , BIB003 . This is mainly due to model simplicity and lack of detailed consumption profile data for controllable appliances. Other constraints include household contracted power, energy generation and availability of external batteries. Evaluation of customer comfort is a very complex task, in part due to the perception and subjectiveness of individual's comfort. Customer comfort can be assessed in terms of BIB006 , BIB009 : (a) Inconvenience due to timing -is related to discomfort perception resulting from scheduling appliances outside their preferred time window. (b) Inconvenience due to appliance use -considers any discomfort stemming from a load that was prematurely stopped, whose intensity was reduced or not even performed at all. Examples include premature stop of a clothes dryer load and lowering indoor reference temperature for HVACs, resulting in a deterioration of indoor thermal comfort. (c) Inconvenience due to appliance priorities -refers to any precedence and priority of certain appliances over others. A common example is related to clothes washing and dryer machines operation, as laundry needs to be previously washed before being dried out. User-defined priorities concerning household appliances are also addressed here. For instance, a customer can specify which clothes washing loads should be given preference over dishwashing. Failure to comply with such priorities can be modelled by a HEMS through discomfort penalties. Constraints are not all equally processed by HEMSs. Some need always to be met at the risk of major household impact, while others may only be partially met. The degree to which a constraint is not satisfied distinguishes hard from soft constraints, being for instance the capacity of external batteries and contracted power common examples of hard constraints. It should be mentioned that the majority of comfort constraints can be incorporated as soft constraints, since a lower comfort lever can be perfectly acceptable if it leads to a meaningful energy bill reduction. Constraints can be addressed in two different ways, either by including them as part of the objective function within an unconstrained multi-objective framework or by explicitly formulating the scheduling problem as a constrained optimisation problem BIB007 .