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:  RES incentives and emission costs 
1. Abstract of Cited Paper (BIB001):  This paper addresses a hybrid renewable system that consists of a micro-Combined Cooling Heat and Power (CCHP) unit and a solar energy conversion device. In addition to a traditional PV system, a High Concentrator Photovoltaic (HCPV) device, the design of which is suitable for building integration application, was also modelled and embedded in the hybrid system. The work identifies the optimal management strategies for the hybrid renewable system in an effort to minimise the primary energy usage, the carbon dioxide emissions and the operational costs for variable electricity prices that result from the day-ahead electricity market. An "ad hoc" model describes the performance of the HCPV module, PV and Internal Combustion Engine, whilst the other units were simulated based on their main characteristic parameters. The developed algorithm was applied to three different building typologies. The results indicate that the best configuration is the hybrid renewable system with PV, which can provide a yearly primary energy reduction of between 20% and 30% compared to separate production. The hybrid renewable system with HCPV becomes competitive with the PV technology when the level of solar radiation is high. 
2. Abstract of Cited Paper (BIB002):  The concept of smart grid SG has been introduced to improve the operation of the power systems. In modern structures of power systems, different reasons prompt researchers to suggest integrated analysis of multi-carrier energy systems. Considering synergy effects of the couplings between different energy carriers and utilising intelligent technologies for monitoring and controlling of energy flow may change energy system management in the future. In this paper, we propose a new solution which is entitled ‘smart energy hub’ SEH that models a multi-carrier energy system in a SG. SEH solutions allow homeowners to manage their energy consumption to reduce their electricity and gas bill. We present this concept for a residential customer by an ‘energy management system’ which uses reinforcement learning algorithm and Monte Carlo estimation method for finding a near optimal solution. The simulation results show that by using this concept and then applying the algorithm for a residential customer, running costs are reduced up to 40% while keeping the household owner's desired comfort levels. 
3. Abstract of Cited Paper (BIB003):  This paper presents a probabilistic Unit Commitment (UC) model for optimal scheduling of wind power, load forecasts and controllability of vehicles in a microgrid using a stochastic programming framework. The microgrid is made up of microturbines, wind turbine, boiler, Plug-in Electric Vehicles (PEVs), thermal storage and battery storage. The proposed model will help the power grid operators with optimal day- ahead planning even with variable operating conditions in respect of load forecasts, controllability of vehicles and wind generation. A set of valid scenarios is assigned for the uncertainties of wind sources, load and PEVs and objective function in the form of expected value. The objective function is to maximize the expected total profit of the UC schedule for the set of scenarios from the viewpoint of microgrid management. The probabilistic unit commitment optimizes the objective function using Particle Swarm Optimization (PSO) algorithm. In order to verify the effectiveness of the stochastic modelling and make a comparison with a simple deterministic one, a typical microgrid is used as a case study. The results can be used to evaluate the effect of integration of PEVs on the economic operation of the microgrid. The results also confirm the necessity to consider the key uncertainties of the microgrid; otherwise the results could overly misrepresent the real world operation of the system. 
4. Abstract of Cited Paper (BIB004):  Abstract Today, as a consequence of the growing installation of efficient technologies, such as combined heat and power (CHP) as a co-generation, the integration of electricity through grid supply, Photovoltaic (PV) and energy storage systems as an integrated network is attracting a lot of attention in smart grid applications. To model the interaction among electricity and natural gas, the energy hub framework is adopted to determine a modelling procedure for such multi-carrier energy systems. This paper presents a residential energy hub model for a smart home as a modified framework of conventional energy hubs in a smart grid with consideration of heat pump water heater, coordination of sources and carbon emissions. Therefore, this study is twofold; the first part optimizes the operation of the combined CHP, Photovoltaic and storage system under dynamic pricing. Since residential load management plays a key role in realizing household demand response programs in a smart grid, performing optimal load management in the proposed residential energy hub model is also studied in this paper. To achieve this, the optimization problem is extended by considering modelling of a heat pump water heater. It is also found out that CO 2 signal could give customers an environmental motivation to shift or reduce loads during peak hours, as it would enable co-optimization of electricity consumption costs and carbon emissions reductions. 
5. Abstract of Cited Paper (BIB005):  Due to the 2012 change in the renewable energy act the feed-in tariffs, and therefore the number of newly installed photovoltaic systems decreased dramatically in Germany. Particularly in the residential sector as the biggest market new business ideas for photovoltaic systems were developed. Hence a photovoltaic and combined heat-and-power system, which provides not only electricity but also heat. This complex system requires flexible control strategies. A new combined control algorithm is proposed that in contrast to the standard strategies can operate even under incorrect weather and load forecasts without creating discomfort. Furthermore is it applicable for cloud-based solutions. In this paper a high-level model predictive control based on a mixed integer linear programming problem is combined with an additional low-level, rule-based controller. The low-level control compares the set-points of the optimization with the actual values and corrects the set-points according to each system component until the next optimization takes place. The results show that a hybrid system can be successfully controlled by a combined control approach. In case of a cloud-based application the need for an optimization can be reduced by a factor of four without diminishing comfort or facing much higher operational costs. It has also been shown that the combined control algorithm can be used as an energy management of microgrids.

Literature Review:
Along with electricity and fuel prices, there might be other economic factors influencing the total production and operation costs of a system, such as incentives for renewable production and emission penalties. Possible revenues for the prosumer come not only from the electricity sold to the grid via feed-in tariff schemes, but also from support mechanisms for the power produced by small-scale DER devices BIB002   and from the Tradable White Certificates for supporting CHP production BIB002 . Moreover, according to national schemes for supporting CHP and solar production, in BIB002 and BIB006 part of the fuel costs for the CHP unit are subjected to a tax rebate. Regarding the emission costs, a fix cost e/tonCO 2 is considered in BIB002 ,  , BIB003 and BIB005 , as well as in BIB001 and Moghaddas BIB004 , although the latter studies do not specify which emitted pollutants are included in the model.