The operation of the integrated energy system will be affected by uncertainties, leading to sub-optimal design decisions. Accurate and effective modeling of these uncertainties is essential to ensure the optimal integration of renewable energy in the integrated energy system. This article first gives the basic optimal operation model of the integrated energy system, and analyzes various uncertain factors in the optimal operation of the integrated energy system. The source side mainly considers the intermittent and volatility of renewable energy output. The load side mainly considers the uncertainty of load forecast errors. The network side mainly considers the characteristics of equipment under variable conditions, equipment failures, and the uncertainty of the coupling of multi-energy flow systems. The energy storage side mainly considers the uncertainty of mobile energy storage grid connection and virtual energy storage participation in demand response. The multi-energy trading market mainly considers the uncertainty of market trading methods and market operation mechanisms. Then, various integrated energy system uncertainty modeling techniques and solving methods are summarized. Finally, it looks forward to the future research directions and challenges faced by the optimal operation of the integrated energy system under the influence of uncertainty.

Gas generation and heat storage are playing a prominent role to multi-energy systems (MES), and coordinated planning of integrated electricity, heat and gas infrastructures can highly benefit MES. In this context, a coordinated planning model of MES is proposed to determine the optimal expansion of conventional generators, transmission lines, gas boilers, combined heat and power units, and gas pipelines. In the model, the one-off investment cost of MES devices in the planning phase plus the operation cost and energy not served cost in the operation phase are considered in the objective, while the energy supply reliability, coupled operational security of multiple energy carriers, as well as the electricity, gas, and heat demand balance are comprehensively taken into account as the constraints. Afterwards, the Benders Decomposition method is adopted to solve the proposed expansion planning model in a divide and conquer manner. Finally three case studies on a 14-bus MES are conducted to demonstrate the effectiveness of the proposed expansion planning model. Simulation results indicate that the total cost of the proposed model in case 3 is saved by 9% and 2.8% compared with the separately planning scheme in case 1 and the coordinated planning scheme without candidate gas pipelines in case 2. Therefore, the proposed coordinated expansion model is very effective for MES Infrastructure planning.

This paper investigates the stochastic optimal operation of microgrid considering the influence of energy storage system (ESS). The uncertain factors related to renewable energies are also fully considered. Monte Carlo simulation (MCS) and scenario reduction technique are used to capture the randomness of renewable energy output. The objective is to minimize the operation cost and pollutant treatment cost of the microgrid system. Then, the multi-objective optimization problem is transformed to a single-objective problem by a line weighted sum method. A nonlinear programming tool—General Algebraic Modeling System (GAMS)—is adopted to solve the single-objective problem. Simulation results indicate that the total cost of the microgrid system can be reduced effectively when considering the effect of ESS and verify the correctness of the proposed strategy.

Low-carbon communities integrate various energy producers and consumers and have been forming an important architecture for future multi-energy system to tackle challenges in improving energy utilization efficiency, alleviating environmental pollution, and achieving reliability of energy supply systems. This paper proposes a multi-building network model containing hydrogen energy storage systems, and verifies the applicability of the multi-agent deep deterministic policy gradient (MADDPG) algorithm in solving the multi-energy coordinated dispatch of a multi-building network by comparing various energy dispatch methods. First, the model of hydrogen storage system based on liquid organic hydrogen carrier (LOHC) is completed by unifying the process of electrolysis, storage and release of energy from hydrogen storage system, and an energy dispatch method is further constructed by utilizing the hydrogen storage system, heat storage system, and renewable energy generation equipment in the building for coordinated energy supply to the load. Secondly, the observation space and action space of grid-interactive building multi-energy dispatch in the network are respectively established by considering the ability of MADDPG algorithm to learn deterministic policies for continuous actions. Finally, the advantage of grid-interactive buildings containing hydrogen storage systems to reduce operating costs is validated in a numerical simulation based on three dispatch scenarios for six buildings. Four deep reinforcement learning (DRL) algorithms are used to compare the operating costs with model predictive control (MPC) algorithms in a network model containing six grid-interactive buildings, and validate the advantages of MADDPG in optimizing the cost of co-dispatching multi-energy sources in networked grid-interactive buildings.

As an important part of the electricity trading market, the power sales company is an important part of the electricity trading market. It can not only be used as the main body to buy electricity from the market transaction, but also as the main body to transmit electricity to the market and users, which changes the power flow distribution of the distribution network and has a certain impact on the power supply capacity of the distribution network. Based on the dual roles of power sales companies, this paper first analyzes the interaction between market transactions and power supply capacity of distribution networks. Secondly, considering the internal controllable distributed generation and controllable load participating demand-side response, a double-layer model of distribution network power supply capacity considering the participation of power sales companies in spot trading is established. According to the characteristics of the model, this paper uses the improved repetitive power flow algorithm and the original dual interior point method to solve the model. Finally, the improved IEEE33 node is verified. The example shows that the power sales company participates in the power transaction by flexibly adjusting the power purchase and sale strategy, which not only ensures the profit, but also improves the power supply capacity of the distribution network.