Aiming at the distribution network with microgrid, a distribution network planning method considering the flexibility resource attribute of microgrids is proposed. The flexibility resource attribute of microgrids is considered from substation planning and grid planning. Firstly, the mathematical models of flexible resources in microgrids are established and then the external equivalent model of microgrids is formed. Then, the flexibility resources are considered by updating the constant volume formula and adding constraints in network planning. Finally, a distribution network programming model with minimum investment and operating cost as the objective function is established and it is solved by improved genetic algorithm. Case study is designed according to the area to be planned with three microgrids. The results show that the planning method proposed in the paper can improve the system transmission flexibility with a small increase in cost, so when the distributed power supply fluctuates or the load forecast is inaccurate during the operation of the distribution network in the future, the line will have enough flexibility for the main network or the microgrid to transmit power, which proves the rationality and effectiveness of the proposed model.

With the increasing penetration of clean energy into existing power systems, the collaborative optimization of multi-microgrid systems has become an effective way to promote the utilization of clean energy. A reasonable distributed demand response mechanism has research significance for improving the capacity of clean energy consumption. In this study, a multi-microgrid collaborative optimization mechanism and an autonomous bidding strategy of demand response resources that consider the distributed demand response are proposed. First, a microgrid autonomous optimal dispatch model is established to formulate power supply-demand plans and DR strategies by considering the incentive characteristics of demand response contracts. Second, referring to historical transaction information, each microgrid proposes IDR bidding strategies to maximize prospective benefits. Third, to implement an economic and efficient distributed demand response, a two-way auction mechanism is introduced in the day-ahead stage to develop a peer-to-peer matching framework for demand response resources. Finally, numerical example results verify that the proposed mechanism can realize the distributed transactions of demand response resources, improve the economy of multi-microgrid systems, and effectively promote clean energy consumption.

The new power system with new energy as the major energy resources has become the development direction of the future power grid. In the distribution system, it is manifested in the high penetration of distributed generation (DG), e.g., photovoltaic, wind power and electric vehicles (EVs). The carrying capacity of the distribution system for DG and EV is of great significance to the planning of the system. This paper firstly analyzes and summarizes the theoretical basis for evaluating the carrying capacity of distribution systems for DG and EV from four perspectives: the modeling method for DG and EV uncertainty, the site selection strategy of DG and EV charging pile/station, the evaluation index of distribution system’s carrying capacity, and the carrying capacity evaluation method. Then the paper analyzes the key technologies for improving the carrying capacity of the distribution system from the four aspects of source, network, load and storage. Finally, combined with the development trend and characteristics of China's new power system, the research on the carrying capacity of distribution systems is prospected. This review provides a useful reference for the planning of large-scale distributed new energy and new loads in distribution systems.

Power grids in the future will evolve from a single mode driven by a load to a dual-driven mode of a power source and a load. Diversified and flexible resources are urgently required for multiple interactive developments to realize a scenario with a high proportion of renewable energy. The effective identification of evolution-driven paths and the comprehensive optimization of evolution paths have an important guiding significance for clarifying the development direction of future power grids and constructing specific implementation paths. This study analyzed the uncertainty faced by power grid evolution from the aspects of technological maturity, potential, and energy cost and proposed a method for generating massive evolution paths. Subsequently, a data-driven evolution path analysis method was proposed, including path dimensionality reduction and visualization, driving factor identification based on time-varying patterns, and optimal path proposal generation based on the Pareto frontier. Finally, the evolution path of a high-proportion renewable energy system was analyzed using North China as an example. The analysis results indicated that photovoltaics in North China will gradually surpass wind power to become the most important power generation resource in the future and that carbon emissions in 2060 will be 81% lower than those in 2030. The relative importance of each factor differed marginally. At the economic and environmental levels, the most important factor was the price of coal, while the maximum investable capacity of battery energy storage was the main factor at the technical level. Efforts should be made to reduce unit investment in renewable and battery energy storage and coal prices and increase the upper limit of battery energy storage allocation to achieve an evolutionary path that considers both low cost and low carbon emissions.

In the context of low-carbon distribution network, this paper proposes a mixed-integer second-order cone programming model for active distribution networks considering carbon emissions and flexible loads, with the investment strategy with minimum total cost. Considering the uncertainty of renewable energy, load and energy price, a scenario clustering method based on K-means is proposed. The decision variables of the model are the replacement of overloaded lines, the construction of new energy and energy storage devices, and the construction of voltage control equipment such as voltage regulators and capacitor banks. The polynomial voltage-dependent flexible loads, network reconfiguration and carbon emission constraints are considered. Aiming at the non-convex nonlinear characteristics of the planning model, the virtual demand method is used to model the network reconstruction as a mixed integer linear programming form, and an improved second-order cone relaxation method based on Taylor expansion is proposed to solve the problem of traditional second-order cone relaxation caused by flexible load model. The model is tested with a 69-node system, and the results show that the proposed model not only has a lower overall planning cost, but also helps reduce carbon emissions.