Large-scale offshore wind power generation has become one of the research hotspots in the development of new energy in the world. However, the planning of far-reaching offshore wind power is faced with many technical difficulties, such as the need to consider the optimization of line transmission capacity caused by the truncation effect in combination with the wind power output probability, and solve the problem that traditional algorithms cannot solve the dynamic programming problem, and the greedy algorithm for solving the dynamic programming problem cannot solve the global optimal solution. Therefore, in order to solve the above problems, based on the full life cycle (FLC) investment calculation of the collecting and transmission system, the topology optimization model of the offshore wind power system is established. In addition, in order to solve the grid-connection problem of offshore wind farms, an improved topology optimization algorithm based on greedy idea is proposed. The optimization results show that the critical values of economic transmission distance of HVAC, FFTS and HVDC are usually 50 km and 170 km respectively. Finally, the collection scheme of the wind farm cluster including three far-reaching offshore wind farms are analyzed, and the results show that the total cost of the optimized point-to-point scheme is reduced by 5.6% and 10.2% respectively compared with the star topology and the circular collection topology, with significant economic benefits.

Power systems are the main source of carbon emissions. Currently, coordinated operation strategies of the source–grid–load–storage model considering carbon emissions is primarily expanded from the generation side. For practical power systems, where multiple types of generating units coexist at a single node, it is difficult to develop unit combination strategies that simultaneously consider carbon emission factors and power flow constraints. Therefore, a new power flow calculation method based on connectivity matrix theory was proposed, aiming to address the issues of existing approaches that are too coarse and unable to accurately represent the operating states of multiple units under each node. Furthermore, a new method for dynamic calculation of regional carbon emission based on connectivity matrix and carbon emissions flow was introduced to improve the accuracy of carbon emission measurements. Firstly, a simulation model for a coordinated optimization operation based on the minimum system cost for the source–grid–load was established and an optimal flow calculation method using a connectivity matrix was introduced. Second, a dynamic carbon emission calculation method, considering electricity sources, was developed by combining the results of the optimal power flow calculation with carbon emission flow theory. Finally, the effectiveness of the approach in this article was verified by the IEEE 14-bus system example and a provincial power grid, ensuring strict adherence to the conservation principle of carbon emissions between the supply and demand sides and satisfying power flow constraints.

With the advantage of integrating distributed energy, storage and DC load with high efficiency, the DC distribution network recently attracted wide attention in the field of nearly zero energy buildings. Considering the large number of buildings and the enormous energy-saving potential, the distribution form and the revenue distribution are key factors affecting energy efficiency and operational economy. Aiming at the characteristics of AC and DC distribution topologies in nearly zero energy buildings, an energy efficiency evaluation method is proposed based on time-sequential power flow simulation, where the piecewise linear efficiency function of converters and dynamic line power loss model are established. Based on a DC distribution demonstration project, an AC power distribution system with the same boundary conditions is designed. The results show that the efficiency of DC distribution topology is about 6% higher than the AC system, while the efficiency advantage in office, hotel, commercial, educational and residential buildings in Beijing, Shenzhen and Shanghai varies from 0.6–4.96%, which shows a high correlation with the proportion of a high-power load, with Pearson correlation coefficient of 0.794. With the increase of DC load ratio and distributed power supply access capacity, the efficiency of the system will be improved accordingly. On the basis of this, a method to equalize DC distribution income in the nearly zero energy buildings power market is proposed. In addition, 15 typical scenarios of different cities were selected to evaluate efficiency and influencing factors. Finally, assessing the multi-stakeholder impact of DC retrofitting to balance the revenue generated by the DC distribution model suggests that transport costs should be raised by 8.2–13.1% to balance revenue with the equipment prices decreased by about 0.1 yuan/W.

Pumped-storage plants are the most significant electrical storage component in new power systems and show great potential for scaling up. In this paper, economic costs and benefits have been investigated. Both the costs and benefits can be divided into transmission and distribution tariffs; however, various factors need to be considered to reduce costs in transmission and distribution tariffs. The cost characterization methodology for pumped-storage power plants has been developed. A mathematical model for dispersal through the medium and long-term electricity market, the electricity spot market, the ancillary services market, and the leasing of capacity to scenic power stations has been proposed. A case study has been discussed for a regional grid company and a provincial grid company; also, the effectiveness of dispersal has been analyzed based on the case study. The results indicate that when the spot market and the capacity leasing of new energy sites play the largest role in the diversion, the transmission and distribution prices in Province A can be controlled within 1 cent/kWh by applying various diversion methods.

Distributed photovoltaic power stations have advantages such as local direct power supply and reduced transmission energy consumption, and whose demands are constantly being developed. Conducting research on medium- and long-term distributed photovoltaic prediction will have significant value for applications such as the electricity trade market, power grid operation, and the planning of new power stations. Due to characteristics such as long time dependence, disperse power stations, and strong randomness, making accurate and stable predictions becomes very difficult. In this research, we propose a multiple time series feature and multiple-model fusion-based ensemble learning model for medium- and long-term distributed photovoltaic power prediction (M2E-DPV). Considering the wave influence and the differences in distributions in different areas of photovoltaic power, multiple feature combinations are designed to increase feature expression ability and adaptability. Based on the boost ensemble learning model, trained on a single model of different time scale features, the optimal scoring strategy is used for multiple model fusion in the rolling prediction process, and finally, time-segmented probabilistic correction is performed. The experiment results show the effectiveness of the M2E-DPV under multiple feature combinations and multi-model fusion strategies. The average MAPE, R², and ACC indicators are 0.15, 0.96, and 0.91, respectively. Compared with other methods, there is a significant improvement, indicating that the prediction ability of the model framework proposed in this paper is advanced and robust.

To address the insufficient flexibility in power systems caused by renewable energy output uncertainty during extreme weather events, a coordinated source–network–load–storage (SNLS) dispatch method that combines a flexibility supply–demand balance approach with a partitioned grid framework is proposed to achieve the effective enhancement of operational resilience. Firstly, a convolution method is employed to aggregate net load forecast error distributions, and expected flexibility demand metrics are introduced to construct a probabilistic model of compound weather impacts, thereby improving flexibility requirement quantification. Secondly, uncertainties arising from extreme meteorological conditions are considered, and an integrated economic dispatch model for the partitioned grid is established based on chance-constrained reserves and regulation capability envelopes, in order to co-optimize generation costs, demand response, and expected flexibility insufficiency penalties. Then, inter-zone power exchange and spatiotemporal unit commitment dynamics are introduced to optimally redistribute spatial generation surpluses and load deficits, so that a system-wide flexibility supply–demand balance is enabled. Finally, simulations are conducted on the real-world Guangdong 500 kV transmission network under typhoon, heatwave, and rainstorm scenarios, and the results demonstrate the effectiveness of the proposed method in eliminating flexibility deficits, reducing total dispatch costs, and capturing distinct weather-adaptive operational patterns.