The community integrated energy system (CIES) can significantly improve energy utilization and promote the consumption of renewable energy through multi-energy coupling complementary and collaborative optimization scheduling. It has become a new energy utilization realization approach for users to meet multi-energy supply and demand. Taking a community in Xiong’an New District, Hebei province as the research object, this paper designed a two-stage optimization approach for the CIES that takes into account the reliability of load supply. The first stage is based on the non-dominated sorting genetic algorithm II(NSGA-II) with elite preservation strategy to optimize the equipment type and capacity of the community energy station. It is a multi-objective planning optimization problem, and its purpose is to achieve the coordinated optimization of economic costs and environmental costs. The second stage is an operation optimization problem. As for the multiple Pareto frontier solutions obtained in the previous planning stage, the mixed integer linear programming (MILP) was used to separately optimize the operation cost and load supply reliability indicators of each planning scheme, and the result is used as an important reference for determining the best planning scheme. Case studies show that the designed planning approach can effectively reduce the system operating cost and guarantee the reliability of load supply, and it is more practical for instructing the CIES planning.

In order to deal with the problem of low economy caused by the uncertainty of renewable energy output and the single energy supply form of traditional microgrid, this paper proposes a two-stage stochastic robust optimization model for multi-energy microgrid. The model considers the grid structure of the power grid and the heating network. The objective function is to minimize the two-stage microgrid cost in the worst wind power output scenario, which includes the start and stop costs of the first stage and the operating costs of the second stage. Because the decision-making and optimization results of the first stage and the second stage influence each other, the two-stage optimization problem is difficult to solve directly. This paper uses a stochastic robust optimization framework for linear decision-making to solve the model. Firstly, the related theories of linear decision-making methods are applied to transform the second stage. Secondly, the cone-shaped fuzzy set is used to describe the uncertainty of renewable energy output. Finally, the“sup-min”problem in the second stage is derived as the“min”problem of cone optimization, and then combined with the“min”problem in the first stage to obtain the single-layer cone optimization problem which can be solved directly, and the optimal solution is obtained by using the solver. The simulation results verify the effectiveness of the proposed model and method.

Active distribution network is gradually replacing traditional distribution network, which has the characteristics of flexible dispatching and control, high user interaction and high energy utilization. It achieves the goal of economic optimization under the premise of safe operation of the system. A hierarchical optimal dispatching method based on ADMM algorithm for active distribution network is proposed. Firstly, the hierarchical optimization dispatching model of active distribution network is established with the goal of minimizing the overall operating cost of distribution network. With the help of ADMM algorithm, the model is decomposed into two layers for solving. The upper layer is solved with the goal of minimizing the overall operating cost of distribution network. The lower layer is to reduce the costs of local energy storage operation and power purchase. The upper and lower layers iterate with each other through limited boundary information exchange until the convergence condition is satisfied and the optimal solution is obtained. Finally, an example is designed to test the effectiveness and feasibility of the proposed scheduling method.

The current trading system for renewable energy is immature. The green certificate system has not performed well in encouraging the integration of renewable energy into the grid and alleviating the pressure of financial subsidies. The current carbon market has no obvious effect on promoting emission reduction. In order to solve the above problems, this paper proposes a joint incentive mechanism for green certificates and carbon trading market applying blockchain. The decentralization, openness and transparency of blockchain are used to combine with the renewable energy trading market to make the renewable energy market more transparent and convenient and safety. In order to motivate market entities who contribute to emission reduction, the consensus algorithm in blockchain is studied, and a PoCT consensus algorithm suitable for consortium blockchain is proposed to encourage market entities to participate in the transaction of renewable energy products, while solving traditional N@S attack problem in the PoS consensus mechanism and the fairness of reward distribution. Simulation results show that PoCT can effectively incentivize renewable energy trading with high efficiency.