In order to promote the transformation to a new power system in the context of China's energy reform, a large number of distributed energy sources are integrated into the grid in the form of virtual power plants as aggregators, which poses a considerable challenge to the grid entities. In this paper, we design a “power-carbon-green” joint trading market framework and propose a Stackelberg game based on the optimal scheduling method of virtual power plant, with virtual power plants as the leader and load aggregators as the followers. The Nash equilibrium solution is obtained by iterative genetic algorithm. Finally, the algorithm is verified on the MATLAB platform by calling the CPLEX solver, the experimental results prove the effectiveness of the model, and both the virtual power plant and the load aggregator can obtain more benefits through this model.

Optimal Scheduling of Virtual Power Plant Based on Stackelberg Game in the Context of “Electricity-Carbon-Green” Market

The traditional single form of energy supply can no longer meet the needs of development. The integrated energy system has significant advantages in improving the energy utilization rate, but the participation of multiple subjects brings some difficulties to the operation optimization of the system. In this paper, a park-level integrated energy system composed of integrated energy operators and users is proposed and their benefit models are constructed, and the users' model is linearized. Since they influence each other when pursuing the maximum of their own interests, the master-slave game idea is adopted to analyze the interaction mechanism of them, and the particle swarm optimization algorithm and linear programming are used to solve the problem. Finally, the feasibility of the proposed method is verified by simulation.

Study on Master-Slave Game Strategy of Integrated Energy System Considering Users Demand Response and Energy Balance

Optimizing the transmission curve of DC power transmitted to Henan Province is of great significance for serving the new energy consumption of Henan Province and improving the overall operational efficiency of the power system. Based on the analysis of the net load characteristics of the power system in Henan Province, a two-step optimization method for the transmission curve of DC power transmitted to Henan Province is proposed. On the premise of meeting the full amount of transmission energy in the power transmission agreement, reasonably determine the transmission power, duration, and period distribution in the peak and valley sections of the power transmission curve. Through the simulation of production, the effect of optimizing the power transmission curve in matching the power demand of the provincial grid, reducing the abandonment of new energy, reducing the cost of ancillary services, and electricity purchases are evaluated quantitatively. This method has reference significance for provincial power companies to organize curve transactions and coordinate system peak shaving.

Optimization of UHVDC power transmission curve for auxiliary peak shaving

In recent years, the construction level of electric vehicle (EV) charging infrastructure in China has been improved continuously. EV participating in the power market has been studied and the trading and energy scheduling mechanism of EV charging combined with storage has been proposed. The integrated PV-Storage-Charging (PSC) system proposed in this paper integrates the charging of EV and the energy scheduling of storage and PV output. At the same time, a two-stage market bidding and scheduling mechanism framework is designed in this paper to price EV charging at PSC station. EV charging is priced based on locational marginal pricing (LMP) and VCG method in day-ahead and real-time market, respectively. Cases studies are conducted to evaluate the energy scheduling performance of PSC station and proposed pricing method.

Pricing Strategy of PV-Storage-Charging Station Considering Two-Stage Market Bidding

The accuracy of electricity demand forecasting is closely related to the correctness of decision-making in the power system, ensuring stable energy supply. Stable energy supply is a necessary guarantee for socioeconomic development and normal human life. Accurate electricity demand forecasting can provide reliable guidance for electricity production and supply dispatch, improve the power system's supply quality, and ultimately enhance the security and cost-effectiveness of power grid operation, which is crucial for boosting economic and social benefits. Currently, research on electricity demand forecasting mainly focuses on the single-factor relationship between power consumption and economic growth, industrial development, etc., while neglecting the study of multiple influencing factors and considering different time dependencies. To address this challenge, we propose a transformer-based forecasting model that utilizes transformer networks and fully connected neural networks (FC) for electricity demand forecasting in different industries within a city. The model employs the encoder part of the transformer to capture the dependencies between different influencing factors and uses FC to capture time dependencies. We evaluate our approach on electricity demand forecasting datasets from multiple cities and industries using various metrics. The experimental results demonstrate that our proposed method outperforms state-of-the-art methods in terms of accuracy and robustness. Overall, we provide a valuable framework in the field of electricity demand forecasting, which holds practical significance for stable power system operations. 

Transformer-Based Multi-industry Electricity Demand Forecasting

Henan Province has a large energy consumption and a fossil fuel-based energy structure, and the realization of the “dual-carbon” goal is of great significance for the whole country. Based on the IPCC emission coefficient method, the carbon emissions of Henan Province from 2000 to 2020 are estimated, the relationship between carbon emissions and economic development is explored in depth, and the PSO-BP optimization algorithm is used to simulate and predict the carbon emissions of Henan Province. The results show that the carbon emissions of Henan Province in 2000–2020 show an increasing trend and the intensity of carbon emissions shows a decreasing trend, and the combination of PSO and BP can effectively improve the prediction accuracy of neural network. The prediction results show that under the baseline scenario, low carbon scenario and enhanced low carbon scenario, the results of carbon peaking are predicted, and relevant policy recommendations are put forward.

Research on Provincial Energy Carbon Emission Prediction Based on PSO-BP Optimization Algorithm

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