A Survey on Home Energy Management <s> 1) MULTI-OBJECTIVE TRANSFORMATION <s> Recently, using multiobjective optimization concepts to solve the constrained optimization problems (COPs) has attracted much attention. In this paper, a novel multiobjective differential evolution algorithm, which combines several features of previous evolutionary algorithms (EAs) in a unique manner, is proposed to COPs. Our approach uses the orthogonal design method to generate the initial population; also the crossover operator based on the orthogonal design method is employed to enhance the local search ability. In order to handle the constraints, a novel constraint-handling method based on Pareto dominance concept is proposed. An archive is adopted to store the nondominated solutions and a relaxed form of Pareto dominance, called e-dominance, is used to update the archive. Moreover, to utilize the archive solution to guide the search, a hybrid selection mechanism is proposed. Experiments have been conducted on 13 benchmark COPs. And the results prove the efficiency of our approach. Compared with five state-of- the-art EAs, our approach provides very good results, which are highly competitive with those generated by the compared EAs in constrained evolutionary optimization. Furthermore, the computational cost of our approach is relatively low. <s> BIB001 </s> A Survey on Home Energy Management <s> 1) MULTI-OBJECTIVE TRANSFORMATION <s> Abstract During a demand response program, cost saving in households is achieved by optimal scheduling of appliances. This paper introduces inconvenience to the general problem of appliance scheduling. It measures the disparity between the baseline and optimal schedule and the consumer seeks to minimize this objective in addition to cost minimization. A nonlinear integer optimization problem is formulated taking into account appliance coordination constraints and is solved with genetic algorithm. The results show that by optimally scheduling appliances taking into account coordination, a cost saving is achieved through load shifting and curtailment whereby an incentive is earned. By use of the weighting factor, a consumer can choose how they favour inconvenience over the cost. <s> BIB002 </s> A Survey on Home Energy Management <s> 1) MULTI-OBJECTIVE TRANSFORMATION <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> BIB003 </s> A Survey on Home Energy Management <s> 1) MULTI-OBJECTIVE TRANSFORMATION <s> Graphical abstractDisplay Omitted HighlightsDomain knowledge related to energy systems is incorporated into the algorithms.The proposed technique provides better starting points for the optimization.The adopted state-of-the-art stopping criterion increases computational efficiency.The combined approach produces better energy systems in shorter time. Energy plays a key factor in the advancement of humanity. As energy demands are mostly met by fossil fuels, the world-wide consciousness grows about their negative impact on the environment. Therefore, it becomes necessary to design sustainable energy systems by introducing renewable energies. Because of the intermittent availability of different renewable resources, the designing of a sustainable energy system should find an optimal mix of different resources. However, the optimization of this combination has to deal with a number of possibly contradictory objectives.Multi-objective evolutionary algorithms (MOEA) are widely used to solve this kind of problems. As optimizing an energy system by using a MOEA is computationally costly, it is necessary to solve the problem efficiently. For this purpose, we propose the incorporation of domain knowledge related to energy systems into different phases (i.e., initialization and mutation) of a MOEA run. The proposed approaches are implemented for two widely used MOEAs and evaluated on the Danish Aalborg test problem. The experimental results show that each approach individually achieves significant improvements of the energy systems, which is expressed in better trade-off sets. Moreover, a state-of-the-art stopping criterion is adapted to detect the convergence in order to save computational resources. Finally, all proposed techniques are merged within two MOEAs with the result that our combined approaches yield significantly better results in less time than generic approaches. <s> BIB004 </s> A Survey on Home Energy Management <s> 1) MULTI-OBJECTIVE TRANSFORMATION <s> Abstract In this paper combined demand side management strategy for residential consumers is studied for five households in South Africa. This study is twofold; the first part proposes an energy management system that combines demand side management strategies with a view of minimizing the consumer’s cost and reducing the power consumption from the grid. Appliance scheduling with a dedicated photovoltaic and storage system under time-of-use tariff shows that customers can realize cost savings and the power demanded from the grid is reduced by optimal scheduling of power sources. In the second part of this study, a model is developed to investigate the joint influence of price and CO 2 emissions. It is found that CO 2 emissions could give customers an environmental motivation to shift loads during peak hours, as it would enable co-optimization of electricity consumption costs and carbon emissions reductions. It is also demonstrated that the consumer’s preferences on the cost sub-functions of energy, inconvenience and carbon emissions affects the consumption pattern. These results are important for both the consumer and the electricity suppliers, as they illustrate the optimal decisions considered in the presence of trade-offs between multiple objectives. A further study crucial to the consumer on economic analysis of PV and battery system showed that the consumer could recoup their initial investment within 5 years of their investment. <s> BIB005 </s> A Survey on Home Energy Management <s> 1) MULTI-OBJECTIVE TRANSFORMATION <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> BIB006
Multi-objective transformation addresses constrained optimisation problems by regarding constraints as additional objectives, leading to an unconstrained multi-objective problem (MOP). MOPs can be solved by computing a representative approximation of the set of pareto optimal solutions, using commonly heuristic and metaheuristic-based techniques, mostly in the form of EAs BIB001 - BIB004 . However, a more common approach transforms the MOP into a singleobjective problem (SOP), usually by means of a weighted sum of the underlying objectives BIB006 , BIB002 - BIB005 . Bounded objective and physical programming methods are common alternatives to single-objective problem transformation via weighted sum BIB003 . As for bounded objective methods, they consider all but one objective as constraints, within an acceptable range. This implies that the chosen objective is more relevant to the problem, otherwise it would had been taken as a constraint. Concerning physical programming methods, a deeper knowledge of the underlying problem is required, as they rely on explicitly functions to model tradeoffs between objectives. A HEMS can, thus, come up with an optimal scheduling programme by combining its operational objectives and constraints into a single objective function, associating a scalar weight to each objective and constraint. An alternative approach explicitly considers all constraints, as described in Section V-C.2.