In recent years, with the deepening of power system architecture adjustment and market-oriented reform, smart microgrid has become the main development direction of power grid architecture. In order to meet the higher requirements of decentralization, autonomy, intelligence and marketization in the process of power grid reform, digital technologies such as artificial intelligence and block chain should be introduced as the key support. In view of the consistency between the technical characteristics of block chain and the development demand of power grid, a power trading system model based on alliance chain is proposed. In order to realize the power balance and maximize the benefits of the microgrid within the power grid, a trading strategy optimization scheme based on MADDPG algorithm is proposed. The experimental results show that the algorithm can help microgrids to formulate the trading strategy which is most in line with the overall benefits of the grid and maximize the total revenue of the system. The performance of this algorithm is better than DDPG algorithm and random trading method.

Based on the general background of the transformation from energy consumption dual control to carbon dual control, this paper takes S City, a southern coastal city, as an example, analyzes its energy consumption, economic development and energy transformation route, and sets up two different development scenarios for energy consumption in S City during 2022–2030 by combining the current policy guidance. The elastic coefficient method combined with multiple linear regression is used to predict the future energy consumption and its change trend. The forecast results show that the current energy transformation development route is relatively in line with the requirements of low-carbon development.

Aiming at the problem of insufficient adaptability to the new elements of the new power system in the current distribution network investment method, this paper innovatively proposes a distribution network investment method based on the new power system. By constructing a source-grid-load-storage-side investment calculation model, the investment in the new power system can be accurately calculated. First, the distributed power investment is calculated from the two aspects of new construction and renovation. Secondly, construct the grid investment demand and grid investment capacity measurement model, and obtain the grid side investment model by weighted summation. Then, a model for calculating the scale of investment that can be saved due to demand-side response is constructed, and the cost of demand response is subtracted to obtain a model for calculating the scale of investment that can be saved on the load side. Finally, the energy storage side investment calculation model is constructed from the power supply side, grid side, user-side energy storage investment, and energy storage investment benefit. The research results are applied to the empirical area, and scientific guidance is provided to realize the precise investment in the area.

This project intends to study a new type of compound cascade solar photovoltaic inverter based on supercapacitors to solve the problems of power loss and system instability. In this paper, the working mechanism of this new type of DC inverter power supply is studied, and a method of compensating the power fluctuation of photovoltaic power generation is proposed. A method combining PI and repeated adjustment is proposed. A distributed maximum power point regulation method based on working cycle correction is proposed to realize the maximum power point regulation of solar cells. The power adjustment of the inverter is realized by adjusting the power of the inverter. The simulation and test of the designed system are carried out, and the results show that the designed system is feasible.

A control method is proposed in this article for grid-connected inverters, and the main feature of the method is that it contains a modified odd-harmonic repetition controller (MORC), and the other parts of the controller also contain proportional-integral controller (PI) and capacitor current feedforward. The MORC is originated from the repetitive controller and has the characteristics of greater gain and wider bandwidth at odd-harmonic frequencies. Due to this feature, the proposed control scheme has high control accuracy, fast transient response and stronger adaptability to grid frequency fluctuations. The control parameters are designed based on the stability analysis of the system under the proposed control method. Simulation results verify the effectiveness of the control method.

In order to address the issue of missing actual measurement data and improve the redundancy of measurement data in practical distribution network systems, this paper proposes a new approach for distribution network state estimation based on Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) pseudo-measurement modeling. According to the different feature extraction capabilities of CNN and LSTM, a two-level information extraction multi-information fusion CNN-LSTM prediction framework is proposed by using LSTM to learn the short-term load characteristics and CNN to capture the long-term load characteristics of users. Input the historical data of node load at different time scales into CNN-LSTM, fuse and further extract the information of multiple time scales at the high-level stage, and input the test data after the training to obtain the pseudo-measurement with high accuracy; After that, weighted least squares (WLS) method is used to perform three-phase state estimation of the distribution network. Through simulations, we demonstrate the efficacy of our proposed approach.

In order to minimize the power generation cost while satisfying the active power balance, the unit output, voltage level and line safety constraints should also be met. This paper introduces the application status and development trend of genetic algorithm in short-term economic dispatch of power system from two aspects: global convergence of generalized genetic algorithm and high efficiency of cell exclusion genetic algorithm. At the same time, the optimization of power system economic dispatch is also of great significance to environmental protection, energy conservation and cost control. Due to the restriction of valve point effect and embargo zone, the unit economic dispatch problem is non-convex, nonlinear, discontinuous and so on, which makes the optimal solution of this problem very difficult. By analyzing the application of traditional genetic algorithm in active load distribution, it is found that there are some shortcomings in dealing with constraint conditions, scaling fitness function and local search. Aiming at these shortcomings, an improved processing method-direct comparison-proportion method is proposed, which is combined with hill climbing algorithm to form an improved genetic algorithm for solving active load distribution problem, and then experimental calculation is carried out. The calculation results show that using the improved genetic algorithm to solve the active load distribution problem can overcome the shortcomings of the traditional genetic algorithm in constraint treatment, fitness function calibration, local search, etc. to some extent, so as to obtain a high-quality solution.

With the growth and penetration of distributed renewable energies, the limitations of the distribution network with traditional topology are gradually manifested. The uncertainties from renewable energies and the diverse needs of electricity consumers bring additional challenges to the distribution network and its operators. A novel honeycomb distribution network (HDN) topology, together with its key device smart power/information exchange station (SPIES), is introduced in this paper to improve the traditional distribution network in terms of the integration of renewable energies. The features and advantages of HDN are firstly introduced followed by the mathematical models of main devices. Then a multi-objective two-level optimization dispatching model of HDN considering system reliability is proposed and then solved. Through a numerical simulation, the feasibility of proposed optimization dispatching model is verified and the functions of SPIES are presented. The results of this paper also reveal some advantages of HDN for the future distribution network with renewable energies.

5G base stations (BSs), which are the essential parts of the 5G network, are important user-side flexible resources in demand response (DR) for electric power system. However, a 5G BS has little and difference dispatchable potential, how to make massive 5G BSs participate in DR conveniently is an urgent problem to be solved. Clustering is an effective solution. Aiming at the special requirements of big data analysis and dispatching difficulties brought by the massive and ultra-dense distribution of 5G BSs, this paper proposes a double-layer K-means++ clustering method for regional 5G BSs considering the main characteristics of 5G BSs. The proposed clustering method considers both the geographical location and power consumption characteristics of the regional 5G BSs to partition the 5G BSs into appropriate number of clusters. Besides, the comprehensive charging and discharging dispatchable potential evaluation indices are established based on the clustering results to evaluate the dispatchable potential of each cluster. The effectiveness of the proposed method and the dispatchable potential evaluation indices are verified through a case of 5000 5G BSs in Jiaxing, China. The simulation findings reveal that the performance of the proposed clustering method is better than classical clustering algorithm and the dispatchable potential of the 5G BSs clusters can be accurately analyzed by the proposed comprehensive indices, which lay the foundation for friendly and fine interaction between 5G BSs and the power grid.

Growing renewable energy access poses serious challenges for traditional distribution network, especially from the point of view of uncertainty and volatility. In this paper, a new type of distribution network structure called honeycomb distribution network (HDN) is introduced, as well as the core device smart power/information exchange station (SPIES), which performs as the agent for power exchange and electricity trading. Firstly, the characteristics and benefits of HDN are described. Then the mathematical physics models of HDN system with the functions of multi-agent system are established. The two-level optimization operating model of HDN with multi-agent system is proposed and then solved and verified through a numerical simulation of simplified HDN system. The optimal results show that HDN with multi-agent system can realize the economic operation of the distribution network and the consumption of renewable energies, which reveal the potential of HDN in the future new power network.

The construction of a strong and smart grid is inseparable from the advancement of the power system, and the effective application of modern communication technologies allows the traditional grid to better transform into the energy Internet. With the advent of 5G, people pay close attention to the application of network slicing, not only as an emerging technology, but also as a new business model. In this article, we consider the delay requirements of certain services in the power grid. First, we analyze the security issues in network slicing and model the 5G core network slicing supply as a mixed integer linear programming problem. On this basis, a heuristic algorithm is proposed. According to the topological properties, resource utilization and delay of the slice nodes, the importance of them is sorted using the VIKOR method. In the slice link configuration stage, the shortest path algorithm is used to obtain the slice link physical path. Considering the delay of the slice link, a strategy for selecting the physical path is proposed. Simulations show that the scheme and algorithm proposed in this paper can achieve a high slice configuration success rate while ensuring the end-to-end delay requirements of the business, and meet the 5G core network slice security requirements.