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: A Survey on Optimal Control and Operation of Integrated Energy Systems 
Chapter Title:  Solution of Optimal Power Flow of Multienergy. 
1. Abstract of Cited Paper (BIB001):  Integrated energy systems (IESs) are under development for a variety of benefits. District electricity and heating systems (DEHSs) deliver electricity and heat, the most common energy demands, to end-users. This paper studies the interactions in a DEHS considering the time-scale characteristics. Interaction mechanisms of a DEHS are analyzed. A disturbance in one system influences another system through coupling components, depending on the disturbance, operating characteristics, and control strategies. A model of the main components in DEHSs is presented. The time scale characteristics are studied based on a dynamic comparison of the different components. Then the interaction process is divided into four stages; each is a quasi-steady state. A quasi-steady multi-energy flow model is proposed and calculated, with a heating network node type transformation technique developed. A case study with detailed results and discussion of 3 types of disturbance is presented to verify the methods. The results present the interactions between the electricity and the system. It is suggested that attention should be paid both on the fast hydraulic process and slow thermal process for system security and economic operation. 
2. Abstract of Cited Paper (BIB002):  The integration of electrical and heating systems has great potential to enhance the flexibility of power systems to accommodate more renewable power such as the wind and solar. This study was to investigate an optimal way to integrate the energy of both systems in urban areas. The amount of energy conversion between the electrical system and heating system was optimally decided so that the demand within both systems could be met at the least operational cost. Besides, the best node to join with the electrical system and heating system was chosen by consideration of the energy transmission loss. The mathematical formulation of the optimization problem was detailed as a large-scale non-linear program (LSNLP) in this paper. A decomposition–coordination algorithm was proposed to solve this LSNLP. At last, a 6-bus electrical power system with 31-node heating transmission system was studied to demonstrate the effectiveness of the proposed solution. The results showed that coordinated optimization of the energy distribution have significant benefits for reducing wind curtailment, operation cost, and energy losses. The proposed model and methodology could help system operators with decision support in the emerging integrated energy systems. 
3. Abstract of Cited Paper (BIB003):  The residential sector is gaining a more active role in the management of energy resources with the deployment of microgeneration based on renewables, the development of bidirectional communication infrastructures, and the new technologies of the power grid systems including smart metering. The consumer becomes an active agent and should be able to make decisions about the usage of his loads, while keeping the quality of the energy services provided. We propose an integrated energy management approach using Markovian processes that, based on weather conditions, local storage of renewable energy, time based tariffs, and grid/technical constraints, optimally allocates the available resources to fulfill the energy requirements. The goal is to reduce the energy bill, while maintaining the consumer’s satisfaction. The results show that a significant bill reduction can be obtained depending on the tariff structure and technology used. A comparison with a genetic algorithm used for the same purpose is also provided. 
4. Abstract of Cited Paper (BIB004):  Smart districts can provide flexibility from emerging distributed multi-energy technologies, thus bringing benefits to the district and the wider energy system. However, due to nonlinearity and modeling complexity, constraints associated with the internal energy network (e.g., electricity, heat, and gas) and operational uncertainties (for example, in energy demand) are often overlooked. For this purpose, a robust operational optimization framework for smart districts with multi-energy devices and integrated energy networks is proposed. The framework is based on two-stage iterative modeling that involves mixed integer linear programming (MILP) and linear approximations of the nonlinear network equations. In the MILP optimization stage, the time-ahead set points of all controllable devices (e.g., electrical and thermal storage) are optimized considering uncertainty and a linear approximation of the integrated electricity, heat, and gas networks. The accuracy of the linear model is then improved at a second stage by using a detailed nonlinear integrated network model, and through iterations between the models in the two stages. To efficiently model uncertainty and improve computational efficiency, multi-dimensional linked lists are also used. The proposed approach is illustrated with a real U.K. district; the results demonstrate the model’s ability to capture network limits and uncertainty, which is critical to assess flexibility under stressed conditions.

Literature Review:
At present, Newton-Raphson method is mainly used to solve the problem, which can be divided into uni ed solution method and decomposition solution method BIB001 BIB002 BIB003 . Based on the power ow calculation of power system, the uni ed solution method takes the variables of natural gas system and thermal system as extended variables, and solves the problem in a uni ed way. e iterative process of solving the uni ed power ow model by Newton-Raphson method is expressed as follows: where Δ is the deviation value of power ow equations; is the Jacobian matrix; is the number of iterations; ( ) and Δ ( ) are the state variables of the system and its deviation value in th iteration, respectively; is expressed as follows: where the diagonal elements ee , gg , and ehh represent the relationship between the energy ow and the state variables of individual electrical, gas, and thermal systems, and nondiagonal elements represent the coupling relationship between di erent systems. e decomposition method, also known as sequential solution method, solves the power ow equation separately from the natural gas system. In the current power ow program, the coupling nodes of natural gas system and thermal system are represented by PV nodes or PQ nodes. When solving natural gas system or thermodynamic system, the nodes coupled with other systems are equivalent to source nodes or load nodes. To solve the hybrid power ow problem of multienergy ow in IES, the corresponding coupling model is rst established, and di erent solving methods are adopted according to di erent application scenarios. In  , a collaborative optimization method of adaptive robust day-ahead energy reserve for urban energy systems is proposed, which proves the economic bene ts of the proposed adaptive robust framework. Cesena and Mancarella BIB005 introduces an optimal operation model of intelligent multienergy region constrained by internal energy network, and considers the relevant uncertainties. e results show that the physical constraints of the integrated network and the uncertainty of speci c energy sources (such as PV) reduce the exibility of the region. Nistor and Antunes BIB004 proposes an integrated energy management method using Markov process to reduce energy costs while maintaining , need to be further studied. According to the actual situation, the way to adjust the interest relationship between integrated energy supply and integrated energy services, and to establish the trading structure and operation model of the future energy market remain to be further explored.