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:  Optimal Operation of IES considering Electro-Gas 
1. Abstract of Cited Paper (BIB001):  The restructuring of energy markets has increased the concern about the existing interdependency between the primary energy supply and electricity networks, which are analyzed traditionally as independent systems. The aim of this paper is focused on an integrated formulation for the steady-state analysis of electricity and natural gas coupled systems considering the effect of temperature in the natural gas system operation and a distributed slack node technique in the electricity network. A general approach is described to execute a single gas and power flow analysis in a unified framework based on the Newton-Raphson formulation. The applicability of the proposed approach is demonstrated by analyzing the Belgian gas network combined with the IEEE-14 test system and a 15-node natural gas network integrated with the IEEE-118 test system. 
2. Abstract of Cited Paper (BIB002):  The significant growth in gas-fired units worldwide has increased the grade of interdependency between power and natural gas networks. Since these units are usually required to ramp up during the peak and backup intermittent renewable generation and contingencies, the power system tends to demand more flexibility and reliability from the gas system. This paper contributes with a novel mixed-integer linear programing (MILP) formulation that couples power and gas networks taking into account the gas traveling velocity and compressibility. As a result, the model accounts for the gas adequacy needed to assure the power system reliability in the short term. The robustness of the MILP formulation allows guaranteeing global optimality within predefined tolerances. Case studies integrate the IEEE 24-bus system and Belgian high-calorific gas network for validating the formulation. 
3. Abstract of Cited Paper (BIB003):  In power systems with more and more variable renewable sources, gas generation is playing an increasingly prominent role in providing short-term flexibility to meet net-load requirements. The flexibility provided by the gas turbines in turn relies on the flexibility of the gas network. While there are several discussions on the ability of the gas network in providing this operational flexibility, this has not been clearly modeled or quantified. In addition, the gas network may also be responsible for supplying heating technologies, and low-carbon scenarios see a tighter interaction between the electricity, heating and gas sectors, which calls for a holistic multi-energy system assessment. On these premises, this paper presents an original methodology to quantify the flexibility the gas network can provide to the power system, as well as the constraints it may impose on it, with also consideration of different heating scenarios. This is achieved by a novel multi-stage integrated gas and electrical transmission network model, which uses electrical DC OPF and both steady-state and transient gas analyses. A novel metric that makes use of the concept of zonal linepack is also introduced to assess the integrated gas and electrical flexibility, which is then used to impose gas-related inter-network inter-temporal constraints on the electrical OPF. Case studies are performed for the Great Britain transmission system for different renewables and heating scenarios to demonstrate the proposed integrated flexibility assessment methodology, provide insights into the effects of changes to the heating sector on the multi-energy system’s combined flexibility requirements and capability, and assess how the electrical network can experience local generation and reserve constraints related to the gas network’s lack of flexibility. 
4. Abstract of Cited Paper (BIB004):  Significant growth in gas-fired generator use has strengthened the interdependence between the electrical power systems and the natural gas systems. It is therefore imperative to analyze both systems jointly as an electricity-gas integrated energy system (IES). However, peak electricity and gas demands, intermittent renewable - resource power generation, and severe contingencies can result in an unsolvable optimal energy flow (OEF). Unsolvable OEF poses a serious threat to IES because a secure operating condition is unavailable without corrective controls. Thereby, this paper proposes three models for identifying OEF solvability. Model I minimizes the square of the energy flow mismatches, and Model II maximizes the loadability. Both Models I and II add slack variables to equality constraints, while Model III relaxes inequality constraints. Meanwhile, bidirectional gas flow models are employed. Based on Models I–III, the OEF feasibility margin and the infeasibility degree are defined. Case studies integrate an IEEE 39-node system and a 48-node gas network for validating the proposed models. 
5. Abstract of Cited Paper (BIB005):  Combined cooling, heating, and power (CCHP) systems have been widely applied in various kinds of buildings. Most operation strategies for CCHP microgrids are designed based on day-ahead profiles. However, prediction error for renewable energy resources (RES) and load leads to suboptimal operation in dispatch scheduling. In this paper, we propose an online optimal operation approach for CCHP microgrids based on model predictive control with feedback correction to compensate for prediction error. This approach includes two hierarchies: 1) rolling optimization; and 2) feedback correction. In the rolling part, a hybrid algorithm based on integrating time series analysis and Kalman filters is used to forecast the power for RES and load. A rolling optimization model is established to schedule operation according to the latest forecast information. The rolling dispatch scheduling is then adjusted based on ultrashort-term error prediction. The feedback correction model is applied to minimize the adjustments and to compensate for prediction error. A case study demonstrates the effectiveness of the proposed approach with better matching between demand and supply. 
6. Abstract of Cited Paper (BIB006):  Exponential penetration of renewable generation has led to the new issues in power distribution systems, such as reverse power flow in the feeders. In the case of sizable distributed generation during lower demand, the power tends to travel in the reverse direction. This, in turn, could cause serious operational issues such as voltage rise, extra heat in the transformers, and maloperation of protective devices. Newly emerging technologies for power-to-gas (PtG) conversion are now offering effective solutions to the problem. The proliferation of PtG facilities can lead to the deployment of integrated natural gas and power systems in the near future. PtG and gas-fired, i.e. gas-to-power (GtP), units can be utilised to address several issues in an integrated power and gas network. This study unveils the application of bi-directional energy converters within an integrated gas and power system for distribution system reverse power management (DSRPM). To that end, a new real-time algorithm is proposed for optimal joint scheduling of PtG and GtP units for DSRPM. A new index is also developed that would quantify the contribution of the PtG-GtP system to DSRPM. Numerical results using real-world data on a test system substantiate the effectiveness of the proposed algorithm.

Literature Review:
Coupling. e coupling relationship between power system and natural gas system is mainly caused by P2G equipment and gas turbine. For power system, P2G equipment is a load, and for natural gas system, it can be used as a gas source for transmission. e coupling between power system and natural gas system can make these two systems stand by each other and improve the exibility of the system BIB001 . One-way transmission of energy between natural gas network and power grid is carried out by gas turbine. P2G device makes the power grid and natural gas network interconnected, and energy ow becomes bidirectional. As for the coupling relationship between power grid and natural gas system, the following literatures make positive exploration. Clegg and Mancarella BIB004 evaluates multienergy systems considering the short-term exibility provided by natural gas power generation. Chen et al. BIB005 considers the problem of optimal energy ow, gives the method of solving the optimal power ow, measuring the feasibility margin and the infeasibility of energy ow. It also proposes three models to solve the optimal energy ow, studies the identi cation method of solvability of optimal multienergy ow, and veri es the e ectiveness of the proposed models through case studies. Khani and EI-Taweel BIB007 considers the problems of reverse power ow of feeder, additional heating of transformer 9 Complexity where and are the active power and reactive power of the node, respectively; is the current injected into node ; and are the voltage of node and node , respectively; is the number of nodes; " * " is the conjugation of complex numbers; is admittance matrix of the node and is voltage vector of the node. ermal Network. ermal network is composed of hydraulic model and thermal model. Generalized Kirchho 's law should be satis ed at each node, i.e. injected ow should be equal to outgoing ow BIB002 BIB003 BIB006 BIB002 , that is where ℎ is the node-branch correlation matrix of heating network; is ow of each pipeline; is outgoing ow of each node, i.e., consumption ow of heat load node; ℎ is the loopbranch correlation matrix of heating network; ℎ is loss vector of pressure head; is the resistance coe cient vector of pipeline. e thermodynamic model is used to calculate the heating and regeneration temperature of heating network nodes. Heating equipment converts energy into heat energy through natural gas and other fuels, and transmits energy through heat network. For each heat load node, the thermal power and node temperature are expressed as follows: where is the thermal power of the thermal network node; start and end are the temperature vectors at the beginning and end of the pipeline; is the length of the pipeline; shc is the speci c heat capacity of water; is the environmental temperature; pipe is the heat conductivity of the pipeline; out and in are the out ow and in ow of hot water, respectively; out and in are the temperature of out ow and in ow of hot water.