Energy management for user’s thermal and power needs: A survey <s> Multi-objective problem formulation <s> A survey of current continuous nonlinear multi-objective optimization (MOO) concepts and methods is presented. It consolidates and relates seemingly different terminology and methods. The methods are divided into three major categories: methods with a priori articulation of preferences, methods with a posteriori articulation of preferences, and methods with no articulation of preferences. Genetic algorithms are surveyed as well. Commentary is provided on three fronts, concerning the advantages and pitfalls of individual methods, the different classes of methods, and the field of MOO as a whole. The Characteristics of the most significant methods are summarized. Conclusions are drawn that reflect often-neglected ideas and applicability to engineering problems. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user’s preferences, the solution requirements, and the availability of software. <s> BIB001 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-objective problem formulation <s> According to operation characteristics of a microgrid with fluctuant energy, such as solar energy, wind energy and alike, a multi-objective economic dispatching model for microgrid is proposed. These objectives are to minimize the production cost, customer outage cost and emission pollution cost. Taking into account characteristics of the proposed model, the maximum fuzzy satisfaction degree method is used for fuzzy multi-objective optimization problem to transform the multi-objective optimization problem into a non-linear single-objective optimum problem, which is solved by using particle swarm algorithm at the hour-level commitment of DGs and the output power distributions of DGs. Simulation results show that multi-objective model is better than single-objective model in reflecting the actual operation characteristics of microgrid more precisely and reaching a higher reliability and environmental benefit at as low operation cost as possible. <s> BIB002 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-objective problem formulation <s> Abstract Energy crisis along with environmental concerns are some principal motivations for introducing “energy hubs” by integrating energy production, conversion and storage technologies such as combined cooling, heating and power systems (CCHPs), renewable energy resources (RESs), batteries and thermal energy storages (TESs). In this paper, a residential energy hub model is proposed which receives electricity, natural gas and solar radiation at its input port to supply required electrical, heating and cooling demands at the output port. Augmenting the operational flexibility of the proposed hub in supplying the required demands, an inclusive demand response (DR) program including load shifting, load curtailing and flexible thermal load modeling is employed. A thermal and electrical energy management is developed to optimally schedule major household appliances, production and storage components (i.e. CCHP unit, PHEV and TES). For this purpose, an optimization problem has been formulated and solved for three different case studies with objective function of minimizing total energy cost while considering customer preferences in terms of desired hot water and air temperature. Additionally, a multi-objective optimization is conducted to consider consumer's contribution to CO2, NOx and SOx emissions. The results indicate the impact of incorporating DR program, smart PHEV management and TES on energy cost reduction of proposed energy hub model. <s> BIB003 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-objective problem formulation <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> BIB004 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-objective problem formulation <s> A smart house generally has a Photovoltaic panel (PV), a Heat Pump (HP), a Solar Collector (SC) and a fixed battery. Since the fixed battery can buy and store inexpensive electricity during the night, the electricity bill can be reduced. However, a large capacity fixed battery is very expensive. Therefore, there is a need to determine the economic capacity of fixed battery. Furthermore, surplus electric power can be sold using a buyback program. By this program, PV can be effectively utilized and contribute to the reduction of the electricity bill. With this in mind, this research proposes a multi-objective optimization, the purpose of which is electric demand control and reduction of the electricity bill in the smart house. In this optimal problem, the Pareto optimal solutions are searched depending on the fixed battery capacity. Additionally, it is shown that consumers can choose what suits them by comparing the Pareto optimal solutions. <s> BIB005 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-objective problem formulation <s> The optimization of operating times and operation modes of devices and systems that consume or generate electricity in buildings by building energy management systems promises to alleviate problems arising in today’s electricity grids. Conflicting objectives may have to be pursued in this context, giving rise to a multi-objective optimization problem. This paper presents the optimization of appliances as well as heating and air-conditioning devices in two distinct settings of smart buildings, a residential and a commercial building, with respect to the minimization of energy costs, CO2 emissions, discomfort, and technical wearout. We propose new encodings for appliances that are based on a combined categorization of devices respecting both, the optimization of operating times as well as operation modes, e.g., of hybrid devices. To identify an evolutionary algorithm that promises to lead to good optimization results of the devices in our real-world lab environments, we compare four state-of-the-art algorithms in realistic simulations: ESPEA, NSGA-II, NSGA-III, and SPEA2. The results show that ESPEA and NSGA-II significantly outperform the other two algorithms in our scenario. <s> BIB006 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-objective problem formulation <s> Abstract Optimal operation of hybrid energy systems is a big challenge in power systems. Nowadays, in addition to the optimum performance of energy systems, their pollution issue has been a hot topic between researchers. In this paper, a multi-objective model is proposed for economic and environmental operation of a battery/fuel cell/photovoltaic (PV) hybrid energy system in the presence of demand response program (DRP). In the proposed paper, the first objective function is minimization of total cost of hybrid energy system. The second objective function is minimization of total CO 2 emission which is in conflict with the first objective function. So, a multi-objective optimization model is presented to model the hybrid system’s optimal and environmental performance problem with considering DRP. The proposed multi-objective model is solved by e-constraint method and then fuzzy satisfying technique is employed to select the best possible solution. Also, positive effects of DRP on the economic and environmental performance of hybrid system are analyzed. A mixed-integer linear program is used to simulate the proposed model and the obtained results are compared with weighted sum approach to show the effectiveness of proposed method. <s> BIB007 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-objective problem formulation <s> Renewable energy resources are often known as cost-effective and lucrative resources and have been widely developed due to environmental-economic issues. Renewable energy utilization even in small scale (e.g., microgrid networks) has attracted significant attention. Energy management in microgrid can be carried out based on the generating side management or demand side management. In this paper, portable renewable energy resource are modeled and included in microgrid energy management as a demand response option. Utilizing such resources could supply the load when microgrid cannot serve the demand. This paper addresses energy management and scheduling in microgrid including thermal and electrical loads, renewable energy sources (solar and wind), CHP, conventional energy sources (boiler and micro turbine), energy storage systems (thermal and electrical ones), and portable renewable energy resource (PRER). Operational cost of microgrid and air pollution are considered as objective functions. Uncertainties related to the parameters are incorporated to make a stochastic programming. The proposed problem is expressed as a constrained, multi-objective, linear, and mixed-integer programing. Augmented Epsilon-constraint method is used to solve the problem. Final results and calculations are achieved using GAMS24.1.3/CPLEX12.5.1. Simulation results demonstrate the viability and effectiveness of the proposed method in microgrid energy management. <s> BIB008 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-objective problem formulation <s> Abstract The ongoing decentralization of the electrical power system encompasses the installation of residential electrical loads and generators, such as electro-thermal heating devices. Clusters of these devices in combination with thermal storage can play a significant role in future power system control as they introduce load and generation flexibility into the system. Hence, control concepts which are attractive for both power system level and end user side are required. This work proposes a novel decentralized scheduling approach for electro-thermal heating devices based on a multi-agent system architecture. As a major novelty, the approach addresses both of the aforementioned appeals on two levels without central control. First, local planning with individual objectives is performed. Second, the heating devices cooperate to find the combination of local plans serving best a given energy system objective. This approach is fully decentralized and has a strong end user involvement. The performance of the proposed approach related to end user and system level objectives is compared to that of centralized approaches. Simulation results show that the new method can successfully satisfy end user objectives and provide energy fluctuation balancing. Finally, drawbacks compared to centralized scheduling are analyzed. <s> BIB009 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-objective problem formulation <s> Abstract Based on the kind of fuel consumed by generation unit, each generation system has different generation costs and emits various types of greenhouse gases like CO 2 , SO 2 and NO 2 to the atmosphere. So, nowadays in the power system scheduling, emission issue has been turned to be an important factor. In this paper, in addition to economic performance, emission problem of energy hub system has been also investigated. Therefore, a multi-objective optimization model has been proposed for cost-environmental operation of energy hub system in the presence of demand response program (DRP). Weighted sum approach has been employed to solve the proposed multi-objective model and fuzzy satisfying technique has been implemented to select the best compromise solution. Implementation of load management programs presented by DRP shifts some percentage of load from peak periods to off-peak periods to flatten load curve which leads to reduction of total cost and emission of energy hub system. A mixed-integer linear programming has been used to model the cost-environmental performance problem of energy hub system and then, GAMS optimization software has been utilized to solve it. A sample energy hub system has been studied and the obtained results have been compared to validate the effectiveness of proposed techniques. <s> BIB010
A general formulation of a MO optimization problem is posed as follows BIB001 : where F(x) is the objective function over decision variables x, m is the number of inequality constraints, and e is the number of equality constraints. The objective functions can have varying degrees of importance, reflecting the preferences of the decision-maker. Let now consider some of the most common approaches proposed. The weighted sum method defines a weight for each objective function, given that the sum of all weights is one. In this way, objectives with higher weights will have a greater impact on determining the final solution. By adding these factors, the MO problem is reduced to a SO one, that is the weighted sum of all objective functions. The bounded objective methods minimize one objective while all others are translated into additional constraints by limiting their values within a given range. If only the upper bound of this range is considered, then it is called ϵconstraint method. By varying the upper bound of each objectives, a set of Pareto optimal solutions is provided BIB001 . For example, the ϵ-constraint method is used BIB003 , BIB007 and BIB008 to minimize both energy cost and emissions. A similar approach is also taken in BIB009 to compare the multi-agent decentralized approach with a centralized benchmark. Local objectives are solved and added as constraints within the global objective, so that the minimization of switching events of each local agent are included in the goal of flattening the energy demand of the cluster agent. The Pareto front is a trade-off curve on which all optimal solutions of a MO problems lie. It helps a decision-maker selecting the best trade-off, after seeing a portfolio of optimal solutions BIB004 . This approach is used in BIB005 to compare and select the best trade-off between electricity costs and regulation efforts, while in BIB006 an approximation of the Pareto front is used to compare the performances of different algorithms given a MO problems with four objective functions. In BIB010 , BIB007 and  , a Pareto front is obtained by tuning the weighting factors and the best trade-off solution is found by means of max-min fuzzy technique. Function transformation methods refer to a group of approaches that modify the original objective functions, for example by scalarizing them given the maximum objective function value BIB001 ). An approach based on membership degree of fuzzy set theory is adopted in BIB002 , and the MO original problem is translated into a non-linear SO one.