国网四川省电力公司

[Objective]Building and improving the mid-long-term electricity market between provinces is one of the fundamental links in promoting the development of green electricity and implementing the"dual carbon"goals.However,the randomness of green power output affects the physical feasibility of medium-and long-term clearing schemes for inter-provincial electricity transactions,and the participation of large-scale green power increases the burden of solving the clearing model.This paper proposes a fast-clearing method for mid-to long-term inter-provincial electricity transactions that considers the low-carbon value and stochastic characteristics of green electricity to improve the feasibility of clearing schemes and the efficiency of clearing calculations.[Methods]This paper constructs an inter-provincial electricity mid-to long-term transaction-clearing model that considers the low-carbon value and stochastic characteristics of green electricity.The objective function includes the cost of carbon emissions from power generation and the non-executable electricity penalty established based on the distribution characteristics of green electricity output,enabling clean and low-carbon green electricity to gain an advantage in market competition and improve the enforceability of green electricity-winning bids.Subsequently,an acceleration strategy based on fixed transaction variables is proposed.Based on the calculation results of the period decoupling model,transaction variables with a high probability of not closing are fixed,thereby constructing a smaller-scale clearing model and improving the efficiency of the clearing calculation.[Results]Simulation examples based on actual transaction data from China show that the proposed model can balance the feasibility of the green electricity winning bid quantity and winning bid electricity.In addition,the proposed acceleration strategy can improve the computational efficiency by an average of 1.91 times while ensuring feasibility and accuracy(with an average relative error of only 0.4%compared with the optimal solution in multiple examples).[Conclusions]This translation accurately reflects the technical meaning and structure of the original Chinese sentence while being fluent and appropriate for an academic context.

Tree-contact single-phase-to-ground fault(TSF)may ignite trees and cause fire,threatening the life and property of people.However,the fault characteristics of TSF are weak and difficult to identify,so it is urgent to further study the fault mechanism and characteristics.In this paper,TSF is constructed based on 10 kV analog cir-cuit experiment platform.The experimental results show that the development process of TSF can be roughly divided into four stages.Fault point current waveform has different characteristics in different stages.The transition resist-ance and the changing rate of transition resistance of TSF gradually decreases with the development of fault.The length,thickness and species of the tree are all able to affect the magnitude and the changing rate of the transition resistance of TSF.

The topology selection of traditional distribution networks is often based on historical experience， with reliability and economy as the main decision-making criteria. However， this approach is limited by a single evaluation dimension and is not suitable for new-type distribution networks with multiple types of distributed energy. To address this， a new evaluation method for distribution network topology is proposed， which adapts to the integration of “hydropower-PV-charging-storage”. In addition to traditional reliability and economy evaluation indicators， new factors such as topology connection methods， available capacity， and topology flexibility are introduced to establish a multi-attribute evaluation system for new-type distribution network topologies. The available capacity indicator reflects the impact of the time-series volatility of distributed resources on the distribution network structure， while the topology flexibility indicator reflects the network’s ability to respond to load growth and fault recovery. Finally， the entropy weight method and analytic hierarchy process （AHP） are employed to calculate the indicators under various distribution network scenarios. The results can guide the reasonable topology selection of new-type distribution network.

The rapid development of data centers makes it possible for them to participate in power system dispatching as demand response. By dispatching the computing resources in data centers between regions, it is possible to achieve energy saving, emission reduction, and cost saving. However, considering the demand response of data center computing resources in power system dispatching faces the problem of insufficient computing speed. To address this issue, a neural branching power-computation network fast optimization method considering computing demand response is proposed. First, a unit commitment double-layer model considering computing power resource demand response is established. Then, the graph convolutional neural network and the branch and bound method are combined and applied to the double-layer model. Through historical data training, the neural branching power-computation network fast optimization method considering computing demand response has the ability to quickly determine the order of branch and bound variables and minimize the number of iterations, which significantly improves the solution speed and realizes the fast solution of unit combination demand response considering data center computing resources. The performance of the proposed method is verified in the simulation scenario of “east data, west computing” project. The proposed method achieves an average solution time reduction of 39.1% compared to the pseudo-cost branching algorithm, 38.1% compared to the commercial solver CPLEX, and 13.5% compared to the machine learning-based optimization acceleration algorithm Extratrees, the average solution time is reduced by 13.5%. In addition, the system coordinated dispatching frequency is increased from 1 time/h to 4 times/h, and the maximum potential reduction in wind curtailment of the total wind power generation during 24 h accounts for 17.42%.

To improve renewable energy consumption under the background of dual carbon, and solve problem of multi-source coordinated scheduling with DC transmission, a multi-source coordinated scheduling of receiving power system considering voltage stability based on information gap decision theory (IGDT) is proposed. Firstly, the voltage stability indicator is constructed, which indicates distance between the system voltage state and the voltage stability limit state. Then, aim at minimizing the system operation cost and wind curtailment penalty, a multi-source power receiving ability promotion model considering thermal/ cascade hydro/wind power generation, energy storage and DC transmission coordination is proposed. The models of multi-sources in the system are established, and the AC power flow and hydropower conversion constraints are linearized by Taylor series expansion and triangle approximation. On this basis, voltage stability constraints are generated through voltage stability margin threshold, and voltage stability constraints are added to multi-source coordinated optimization problem for the receiving power system through each iteration to achieve the improvement of voltage stability indicator. To consider uncertainty of wind power output and load demand, a multi-source coordinated scheduling of receiving power system considering voltage stability based on IGDT is proposed. Finally, the effectiveness of the proposed model is verified by case studies.