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:  Introduction 
1. Abstract of Cited Paper (BIB001):  Energy internet is one of the most promising future energy infrastructures which could enhance energy efficiency and improve operating flexibility from the energy point of view. In analog to the micro-grid, micro energy internet puts more emphasis on the distribution level and demand side. The concept and design principles of the smart micro energy internet are proposed to accommodate micro-grids, distributed poly-generation systems, energy storage facilities, and related energy distribution infrastructures. The dispatch and control system of the smart micro energy internet should be responsible for external disturbance and able to approach a satisfactory operating point which compromises multiple criteria, such as safety, economy, and environment protection. To realize the vision of the smart micro energy internet, engineering game theory based energy management system with self-approaching-optimum capability are investigated. Based on the proposed concepts and design principles, and energy management system, a prototype of the first domestic conceptual solar-based smart micro energy internet is initially established in Qinghai University. 
2. Abstract of Cited Paper (BIB002):  This paper proposes an artificial neural network (ANN)-based reinforcement learning (RL) maximum power point tracking (MPPT) algorithm for permanent-magnet synchronous generator (PMSG)-based variable-speed wind energy conversion systems (WECSs). The proposed MPPT algorithm first learns the optimal relationship between the rotor speed and electrical power of the PMSG through a combination of the ANNs and the Q-learning method. The MPPT algorithm is switched from the online RL to the optimal relation-based online MPPT when the maximum power point is learned. The proposed online learning algorithm enables the WECS to behave like an intelligent agent with memory to learn from its own experience, thus improving the learning efficiency. The online RL process can be reactivated any time when the actual optimal relationship deviates from the learned one due to the aging of the system or a change in the environment. Simulation and experimental results are provided to validate the proposed ANN-based RL MPPT control algorithm for a 5-MW PMSG-based WECS and a small emulated PMSG-based WECS, respectively. 
3. Abstract of Cited Paper (BIB003):  The proliferation of technologies such as combine heat and power systems has accelerated the integration of energy resources in energy hubs. Besides, the advances in smart grid technologies motivate the electricity utility companies toward developing demand response (DR) programs to influence the electricity usage behavior of the customers. In this paper, we modify the conventional DR programs in smart grid to develop an integrated DR (IDR) program for multiple energy carriers fed into an energy hub in smart grid, namely a smart energy (S. E.) hub. In our model, the IDR program is formulated for the electricity and natural gas networks. The interaction among the S. E. hubs is modeled as an ordinal potential game with unique Nash equilibrium. Besides, a distributed algorithm is developed to determine the equilibrium. Simulation results show that in addition to load shifting, the customers in the S. E. hubs can participate in the IDR program by switching the energy resources (e.g., from the electricity to the natural gas) during the peak hours. Moreover, the IDR program can increase the S. E. hubs’ daily payoff and the utility companies’ daily profit. 
4. Abstract of Cited Paper (BIB004):  Microgrid (MG) represents one of the major drives of adopting Internet of Things for smart cities, as it effectively integrates various distributed energy resources. Indeed, MGs can be connected with each other and presented as a system of multimicrogrid (MMG). This paper proposes the optimal operation of MMGs by a cooperative energy and reserve scheduling model, in which energy and reserve can be cooperatively utilized among MMGs. In addition, values of Shapely are introduced to allocate economic benefits of the cooperative operation. Finally, a case study based on a system of MMGs is conducted, and simulation results verify the effectiveness of the proposed cooperative scheduling model. 
5. Abstract of Cited Paper (BIB005):  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. 
6. Abstract of Cited Paper (BIB006):  The drivetrains of many utility-scale wind turbines have a gearbox connected with a doubly fed induction generator (DFIG). Since gearbox failure is a major contributor to the high maintenance cost of wind turbines faced by the wind industry, it is important to perform fault diagnosis for gearboxes. Among different gearbox fault diagnosis methods, those using current signals collected from generator terminals have shown their merits in terms of complexity, cost, and reliability. In this paper, a new method that uses a DFIG stator current signal for the fault diagnosis of wind turbine drivetrain gearbox in nonstationary conditions is proposed. In the proposed method, the dc offset and high frequency noise of the current signal are first eliminated. Then, the envelope of the current signal is obtained by using the Hilbert transform. The current envelope signal only contains nonstationary frequencies that are proportional to the DFIG shaft rotating frequency. Next, a synchronous resampling algorithm is designed to convert the current envelope signal with a constant time interval to a resampled current envelope signal with a constant phase angle interval. Finally, the power spectral density analysis is used to obtain the frequency spectrum of the resampled current envelope signal from which the constant characteristic frequencies can be easily identified for the purpose of fault diagnosis. Laboratory test data collected from a 1/3 hp DFIG wind turbine drivetrain test rig and field data obtained from three 1.6-MW DFIG wind turbines are used to show the superiority of the proposed method. 
7. Abstract of Cited Paper (BIB007):  Accurate state-of-power (SOP) estimation is critical for building battery systems with optimized performance and longer life in electric vehicles and hybrid electric vehicles. This paper proposes a novel parameter identification method and its implementation on SOP prediction for lithium-ion batteries. The extremum seeking algorithm is developed for identifying the parameters of batteries on the basis of an electrical circuit model incorporating hysteresis effect. A rigorous convergence proof of the estimation algorithm is provided. In addition, based on the electrical circuit model with the identified parameters, a battery SOP prediction algorithm is derived, which considers both the voltage and current limitations of the battery. Simulation results for lithium-ion batteries based on real test data from urban dynamometer driving schedule (UDDS) are provided to validate the proposed parameter identification and SOP prediction methods. The proposed method is suitable for real operation of embedded battery management system due to its low complexity and numerical stability.

Literature Review:
e environmental pollution in the process of energy conversion and transmission and the requirement of sustainable development of energy urge human beings to seek diverse supply and e cient use of green energy. It is di cult to maintain the long-term development of human society due to the exponential growth of energy consumption and the insucient reserves of nonrenewable energy. e development of renewable energy, such as solar energy, wind energy, hydropower, geothermal energy, and biomass energy, makes it possible for sustainable use of energy BIB002 . Although the renewable energy-related technology has developed rapidly, the most important issue for power system is to ensure the reliable and secure operation. Due to the inherent intermittence and uctuation of renewable energy, direct connection to power system will impact the voltage and frequency of the power grid BIB003 . Most of the renewable energy is connected to the power grid through power electronics devices. However, power electronics device itself is one of the main sources of harmonics in the power grid during the treatment of the power grid pollution. Although energy storage technology can directly solve the problem of renewable energy absorption, large-scale commercial application of energy storage in power grid cannot be carried out for the time being due to the high cost, low e ciency of energy storage technology, and special requirements for engineering application sites  . e renewable energy is also facing the problem of e ective connection with external power grid, the economic problems in long-distance transmission such as high cost of infrastructure construction and wheeling cost of power ow, and the lack of market competition, which is not conducive to the development and promotion of renewable energy technology. In order to solve these problems, the concept of integrated energy system (IES) has been proposed. IES is a highly abstract classi cation of energy supply and energy demand, which integrates multifunctional collaborative technologies such as P2G (power to gas) and CCHP (combined cooling, heating and power supply) in the process of planning, construction, and operation of energy systems, thus achieving multienergy conversion, scheduling, and storage. As an important component of the energy internet and the main carrier of social energy, IES integrates power, natural gas, hot and cold system, instead of a single energy system in traditional energy supply form, which is an e ective way to solve the problem of low energy e ciency and the coupling of various types of energy BIB006 . As a multisupply system, IES can alleviate or eliminate the ine ciency of energy equipment through the coordination of subsystems BIB005 . It can also reduce the cost of system maintenance, realize the peak-valley interleaving of multienergy system, and fully absorb renewable energy through P2G technology BIB001 . IES can make full use of potential synergistic bene ts, fully tap the potential of interconnected data and information networks, and achieve coordination, optimal scheduling, and synergistic utilization among di erent energy supply systems BIB004 BIB007 . e electricity, gas, heat, and other energy sources are complementary in IES to achieve time-sharing utilization of energy and improve the scope of renewable energy consumption, improve the utilization of social infrastructure and the exibility of energy supply BIB008 . In the aspect of comprehensive use of energy, IES is regarded as one of the important development directions of the energy industry by all countries because of its advantages such as high energy e ciency and economy. IES can solve the shortcomings of traditional power system that cannot meet the energy demand under the new situation because of the one-way transmission of power load, and achieve the optimal allocation of resources through multienergy collaboration. Many detailed researches on IES have been carried out in di erent countries, including modeling and planning for various scenarios, optimal operation, security assessment, and steady-state analysis of IES. is paper mainly reviews the related technologies of optimal operation and control of IES. Section 2 brie y introduces the main forms and development status of IES. Section 3 reviews the commonly used modeling methods of IES. Section 4 discusses the optimal operation and control methods of IES, Section 5 concludes the whole paper and summarizes the key scienti c issues in IES research, and proposes future challenges and development trends of IES.