You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Energy management for user’s thermal and power needs: A survey 
Chapter Title:  Multi-objective 
1. Abstract of Cited Paper (BIB001):  In modern electric power grids, microgrids are utilized in order to improve electric power grids' technical, economical, and environmental performance. In other words, both operator and consumers benefit the gained improvements by microgrids. However, operators confront certain ordeals to plan and operate the specific microgrid. In this paper, Combined Heat and Power (CHP) units, renewable energy resources, auxiliary thermal units and Electric Vehicles (EVs) are taken into consideration as a specific microgrid in California in the United States. Also, stochastic price of gas and electricity is taken into account. Moreover, uncertainty of the output power associated with non-dispatchable units like wind power plants as well as stochastic availability of the EVs adds to the complexity of the problem. The proposed approach plans to minimize total cost associated with the utilities as well as to minimize released emissions into the environment. Hence, the mentioned problem is a Multi-objective Linear Programming (MIP) which is solved using GAMS software. Finally, it has been showed that obtained solution is robust against uncertain parameters since there is just insignificant difference between solutions under this condition compared to condition that stochastic parameters are not modeled. 
2. Abstract of Cited Paper (BIB002):  Providing a cost-efficient and reliable energy is one of the main issues in human societies of the 21st century. In response to this demand, new features of micro grid technology have provided huge potentials, specifically by the capability of having an interactive coordination between energy suppliers and consumers. Accordingly, this paper offers an improved model for achieving an optimal Demand Response programing. To solve the proposed multi-objective optimization problem, Artificial Bee Colony algorithm and quasi-static technique are utilized. The considered objectives in this paper are minimizing the overall cost of energy consumption and also improving the technical parameters of micro grid over a time horizon. This optimization is subject to several constraints such as satisfying the energy balance and the operating constraints of each energy supply sources. Manageable load or load as source is another enabling feature existing in smart energy networks, which is considered in this paper and its effect on cost reduction and reliability improvement is studied. Trying to examine the performance of the proposed Demand Response Programing in real conditions, the uncertainties are also analyzed by stochastic methods. The results show significant improvements which are obtained by applying just intelligent programming and management. 
3. Abstract of Cited Paper (BIB003):  Abstract With the emergence of smart grid which has advanced metering infrastructure (AMI) and microgrid running as a local energy provider for dwellings through distributed energy resources (DERs), dwellers have the opportunity to schedule their in-home energy usage by themselves to reduce energy expense. Household tasks along with DER operation are scheduled according to electricity real-time price (RTP) and natural gas fixed price. Various studies have shown that the lack of effective home automation systems and the lack of awareness among users to respond to time-varying prices are two chief obstacles in order to fully exploit the potential benefits of dynamic pricing schemes. This paper drives to handle these problems by proposing an automatic and optimal residential energy consumption scheduling technique which tries to achieve a favorable trade-off between minimizing the energy costs as well as the inconvenience for the operation of both electrical and thermal in a smart home environment. Simulation results show that the proposed scheduling method leads to significant reduction in energy costs for diverse load scenarios with the electricity demand from the grid. Therefore, the deployment of the proposed method is advantageous for both users and utilities. 
4. Abstract of Cited Paper (BIB004):  Abstract Nowadays with the emerging of smart micro-grids(SM-Gs) in residential sectors, a large portion of energy consumption can be saved through optimal scheduling of household devices and management of domestic hybrid energy sources. By the aid of such technologies, residential consumers have the capability to mitigate their energy costs and satisfy their own requirements paying less attention to the configuration of the energy supply system. This paper presents a novel residential energy management system (REMS) to improve the efficiency of energy consumption in a typical SM-G taking into account minimum cost of energy as well as maximum user's comfort level as competitive objectives. The optimization model is also formulated as a mixed integer nonlinear problem (MINLP) and its performance is tested under different operating scenarios with real data. The simulation results show that the proposed model not only reduces energy consumption costs, but also ensures a comfortable lifestyle for occupants. 
5. Abstract of Cited Paper (BIB005):  Pre-packaged cogeneration systems are popular as lead supply in distributed and isolated energy systems aimed at empowering remote off-grid communities and in supporting eco-villages in nature reserves. Energy cooperatives and independent power producers deploy cogeneration systems in community shared microgrid configurations as building blocks in smart localised multi-carrier energy grids. Small-scale renewable energy and distributed energy resources in the energy mix of multi-carrier combined heat and power microgrids call for the development of intelligent control automation solutions. This paper describes a multi-objective optimization solution where hierarchical digital microgrid control is integrated into solar powered micro-cogeneration. The control solution includes an integrated cost-competitive economic and environmental optimization algorithm customized around the needs of small off-grid isolated village settlements. A secondary stage optimization loop functions as a storage capacity decision support system, to alert the community when operating costs can be reduced through an investment into increased storage capacity for the village microgrid. This cost-aware control optimization algorithm is tested in a rural village microgrid by using parametric computer simulation models of a hybrid residential solar cogeneration system. Digital simulation experiments evaluate the operational plans and operational cost performances of energy management for different storage scenarios at an isolated remote rural location in Africa. 
6. Abstract of Cited Paper (BIB006):  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. 
7. Abstract of Cited Paper (BIB007):  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. 
8. Abstract of Cited Paper (BIB008):  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. 
9. Abstract of Cited Paper (BIB009):  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.

Literature Review:
Many studies propose a MO scheduling model, which usually includes two or more objectives, often of different nature. The most common approach is to combine objective functions of economic and environmental nature, for instance minimization of operation costs and pollutant emissions BIB009  BIB002 BIB006 BIB010 , while the authors of BIB001 include primary energy consumption costs as well. Economic and technical goals are combined in BIB003 and BIB004 , where DERs and the partially flexible load of a building are scheduled in order to minimize the energy bills, while minimizing the peak demand, aiming to improve the grid operation. An original problem is proposed in BIB007 , where the objective functions have an economic and a social goal, namely the minimization of the electricity bill and of the consumer's regulation effort. The latter is defined as the reduced cost due to the modification of the initial usage plan of flexible electrical appliances, according to realtime pricing. The aim is to minimize the costs of buying power from the main grid, while limiting the user's discomfort due to rescheduling of shiftable appliances. Similarly, the MO problem proposed in BIB005 aims at minimizing the total operation costs and the user's discomfort, which is due to the deviation from the thermal and electrical comfort zones. In the first case, the inside temperature varies more than 2 • C from the set point; in the latter, residential appliances are scheduled outside the desirable time window. In BIB008 , smart residential buildings are optimized with respect to four objective functions of different nature, namely total energy costs, CO 2 emissions, thermal discomfort, and technical wear out due to switching HVAC devices on and off. The reader interested in the most common approaches to deal with multi-objective problems is referred to Section 5.1.