Energy management for user’s thermal and power needs: A survey <s> Limitations of this study <s> The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization. <s> BIB001 </s> Energy management for user’s thermal and power needs: A survey <s> Limitations of this study <s> This paper reviews the advances of mixed-integer linear programming (MILP) based approaches for the scheduling of chemical processing systems. We focus on the short-term scheduling of general network represented processes. First, the various mathematical models that have been proposed in the literature are classified mainly based on the time representation. Discrete-time and continuous-time models are presented along with their strengths and limitations. Several classes of approaches for improving the computational efficiency in the solution of MILP problems are discussed. Furthermore, a summary of computational experiences and applications is provided. The paper concludes with perspectives on future research directions for MILP based process scheduling technologies. <s> BIB002 </s> Energy management for user’s thermal and power needs: A survey <s> Limitations of this study <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> Energy management for user’s thermal and power needs: A survey <s> Limitations of this study <s> Microgrid equipped with heterogenous energy resources and a bank of energy storage devices presents the idea of small scale distributed energy management (DEM). DEM facilitates the minimization of the transmission and operation costs, peak load and environmental pollution. Microgrid also enables active customer participation by giving them the access to the real time information and control. The capability of fast restoration against physical/cyber attack, integration of renewable energy resources and information and communication technologies (ICT) make microgrid as an ideal candidate for distributed power systems. The energy management system of microgrid can perform real time energy forecasting of renewable resources, energy storage elements and controllable loads in making proper short term scheduling to minimize total operating costs. Cost benefit analysis of microgrid reveals that cooperation among different microgrids can play an important role in the reduction of import energy cost from the utility grid. Cooperation among microgrids in smart microgrid network (SMN) brings the energy sharing and management issues. In this paper we present a review of existing optimization objectives, constraints, solution approaches and tools used in microgrid energy management. This review paper can provide a foundation to embark on an in depth study in the area of energy management for smart microgrid network. <s> BIB004
Although we have followed the steps presented in Section 2 in a rigorous way, our work may exhibit the following limitations. The preliminary search was conducted on Google Scholar, based on a list of keywords and subject to a restriction on the publishing date, i.e., 2010 or after. This implies that we may have missed some relevant studies which use different keywords or in a different combination, or have been published before 2010. Yet, given that the initial number of retrieved documents amounted to around 3,790 publications, we are likely to have retrieved the majority of the most recent and significant studies. Another limitation may come from including only papers that address the supply of both power and thermal demand. A large share of the available literature on energy management systems focuses mainly or exclusively on power demand, therefore, we may have missed some interesting methods and techniques that could be applied to MESs as well. Yet, we believe in the importance of having a complete overview of the energy consumptions, that means, considering systematically the thermal load next to the power load. In fact, space heating and cooling, and hot water demand are often the biggest contributors to energy consumption. As for the optimization methods, this study offers an overview of the most common techniques, discussing the main concepts and the most significant differences. However, problem complexity and required computational capacity are not addressed, although they may be a key aspect while developing EMS. The interested is referred to, e.g.,  , BIB002 , BIB003 and BIB001 for more in-depth treatments. Moreover, unlike the present work, the studies could have been classified according to the tools and softwares used to solve the energy management problem, as done in other studies, e.g., Ahmad BIB004 ,. In summarizing the potential economic and environmental achievements (see Section 6), this study considers the methods and the results of all reviewed papers technically valid, although some approaches may be more realistic and/or rigorous than others. In particular, a cost-benefit evaluation of the proposed approaches, e.g., costs due to the integration of storage systems and/or multiple DERs vs. economic savings, could help understanding the feasibility of such potential results. Yet, given the large variety of systems, both in size and complexity, such an evaluation would require the definition of a rigorous economic model and method, that is beyond the scope of the present work.