### Literature Review
To achieve a proper balance between reliability guarantee and conservativeness, distributionally robust optimization is introduced in the energy hub operation problem [22]; the stochastic output of solar output is modeled via a family of inexact probability distributions; the total operation cost in the worst-case distribution is optimized, and the problem finally boils down to a semidefinite program. When there are multiple hubs, data privacy may be a concern, and distributed operation is desired. The interactive dispatch of multiple energy hubs is modeled as a potential game in [23], a distributed algorithm is developed to locate the Nash equilibrium based on the theory of potential game. In a standard Nash game, the strategy set of each player is decoupled. To incorporate coupled constraints which may impose an upper bound on the total import energy, a generalized Nash game model iscast for the operation of networked energy hubs in [24], and a distributed algorithm is proposed for computing the generalized Nash equilibrium based on variational inequality theory and an improved Tikhonov regularization technique. A double-consensus distributed algorithm is developed in [25] to solve the optimal operation problem with multiple energy hubs; the method can cope with coupled variables in the objective function and constraint limits. The operation of energy hubs in the market environment is attracting increasing attention recently. The bidding strategy of an energy hub in the day-ahead and real-time markets is studied in [26], and a mixed integer linear program is given to determine the optimal bidding strategy; uncertainty is modeled by a scenario tree. The strategic behavior of an energy hub in electricity and heat markets is discussed in [27] via a bilevel optimization model; the market-clearing process which captures the market power of the energy hub is explicitly modeled in the lower level. The similar problem for a cogeneration plan is considered in [28]; the alternating current power flow model is used for distribution market clearing, and the energy trading between the power distribution system and the district heating network is taken into account. The retail market led by an energy hub is considered in [29]; the energy hub purchases electricity and heat from upstream markets and resells energy to clients; the decision-making problem gives rise to a stochastic bilevel program, and is converted to a mixed-integer linear program. Such a framework is further studied in [30; 31] by considering the competition among multiple energy hubs through a bilevel equilibrium program. Energy sharing among networked energy hubs is modeled in [32]; it is found that the sharing scheme can achieve almost the same efficiency as a centralized market. From the planning perspective, the capacity expansion planning of energy hubs in multi-energy systems is firstly considered in [33]; the optimal sizes of gas furnaces and CHP units in the energy hubs, and capacities of generation units and transmission lines in the system, are determined from a mixed integer linear program. A mixed integer programming model is proposed in [34] to optimize the configuration and capacities of components based on graph theory. The method is applied to a multi-energy system in Beijing's new subsidiary administrative center [35]. An energy hub for a cement factory with electricity, heat and water demands is designed in [36]; exergy is taken into account to make full use of waste heat; economic and environmental impact is also analyzed. A reliability assessment method is proposed in [37], and a two-layer approach is developed for design of an energy hub; the upper layer aims to minimize investment cost, and the lower layer minimizes operation cost subject to reliability requirements in different scenarios. A bi-objective optimization method is presented in [38] to size the components in the energy hub and determine the maintenance cycles under different demand scenarios.