In recent years, with the rapid growth of the scale of distributed photovoltaic deployment in cities and towns, the impact of random fluctuation characteristics of its output on urban load is also increasing. The traditional method is difficult to accurately predict the complex load fluctuation after largescale deployment of distributed photovoltaic system, which is not conducive to the safe and stable operation of power grid. To solve these problems, this paper proposes a shortterm load forecasting method considering distributed PV. Since the net load including distributed PV is the difference between the actual consumption load of the user side and the PV output, this paper first adopts the big data mining technology to analyze the characteristics of PV output and the userside load as well as the correlation between the two and their respective influencing factors before constructing input data, and selects the influential factors with high correlation as the input feature set of the net load prediction model. Secondly, the LSTM neural network prediction model integrating selfattention mechanism is constructed to deeply explore the characteristics of load sequence. The grey Wolf algorithm is used to optimize the parameters of the prediction model and determine the model with the best prediction effect. Finally, an example simulation shows that the proposed method can effectively improve the prediction accuracy of net load with distributed PV.

With the increasing penetration rate of new energy in the distribution network, the bearing capacity of new energy in the distribution network is facing challenges; The electric heating load has a certain degree of adjustability and has the potential to participate in the load dispatch of the distribution network. How to improve the new energy bearing capacity of the distribution network through load dispatch has important practical significance. The article proposes a dynamic optimization scheduling strategy for electric heating loads in distribution networks that considers the bearing capacity of new energy. Firstly, a regulation model for the load of thermal storage electric heating was constructed; Then, with the goal of bearing capacity of new energy in the distribution network substation area, and with the constraints of smoothing load fluctuations, stable and safe operation of the distribution network, and user comfort of the heating load, a dynamic optimization scheduling model for the heating load of the distribution network was established, and a solution strategy based on quantum genetic algorithm was proposed. The Latin hypercube sampling method is used to generate typical application scenarios for the applicability analysis of dispatching strategies for the new energy bearing capacity of distribution networks. The calculation results show that the proposed method can fully consider the potential for regulating the electric heating load and improve the application level of new energy in the distribution network.

With the construction and promotion of greener grid, the old electricity marketing process is facing new challenges. Therefore, in order to suit the new requirements of electricity marketing, this paper proposes an electricity pricing method based on the careful consideration of regional carbon emissions. Firstly, consider the economic compensation of carbon emission reduction incentive and controllable load participating in peak regulation response, a load mobilization cost model considering carbon emission and peak regulation cost is established. Then, based on the idea of game theory, the demandside controllable load and energy storage device are used as schedulable resources to construct a 1K Stackelberg masterslave game decision model. Finally, the inverse induction method is attached to solve the model. We can see from the results that the proposed electricity pricing method of new energy power grid considering regional carbon emissions can promote the optimal operation of power system and realize the double improvement of efficiency and benefit of new distribution network.

With the rapid penetration of new energy sources such as photovoltaic power generation, the power system has put forward higher requirements for its participation in primary frequency regulation, requiring it to support the power grid in a more flexible way. In order to achieve more comprehensive frequency regulation, the key indicators for evaluating frequency quality are first determined according to the relevant grid specifications. Then, with the help of the motion equation of synchronous generator rotor, the effects of the constant of inertia and damping gain on the frequency quality are analyzed. Based on these research results, a coordinated control strategy of virtual inertia and frequency damping is designed, aiming to achieve the optimal frequency support of photovoltaic system with a certain power reserve. Finally, the performance of the proposed control strategy is verified by simulation, and the advantages and effectiveness of PV system participating in primary frequency regulation are displayed. By coordinating between virtual inertial control and frequency damping control, photovoltaic systems can support the frequency stability of the grid in an efficient manner.

In order to solve the problem of integrated energy coordinated dispatching, the design of virtual power plant based dispatching platform and the application of integrated energy coordinated dispatching model were proposed. The basic grid structure of virtual power plant is analyzed, and the overall architecture of virtual power plant is designed, which is divided into resource layer, platform layer and application layer. A network architecture and data flow architecture are designed. Based on this architecture, a virtual power plant scheduling application model for comprehensive energy coordination is established. The models for photovoltaic, wind power generation, electrolyzers, fuel cells, hydrogen energy storage, and battery energy storage are built, with reliability indicators as the starting point, an objective function with the lowest cost throughout the whole life cycle is proposed, and numerical examples are analyzed to illustrate the advantages of the proposed model in terms of reliability and cost.

Considering the inconvenient installation and the poor ability to capture wave energy in the traditional directdrive wave energy converter (DDWEC), a series connection floating twobody DDWEC is proposed in this paper. Two rectangular bodies of the WEC float horizontally in the waves bodies and both capture the wave energy. The primary and secondary of the Halbach array permanent magnet linear generator(HPMLG) are respectively installed on the lateral surface between the two floating bodies. Because both floating bodies capture wave energy, the ability of the WEC to capture wave energy is improved, and the structure of the WEC is simple and easy to manufacture and maintain. Then, the prototype of the twobody DDWEC is manufactured and tested in the wave tank in this paper. The experimental results show that in the case with the wave height of 16 cm and the draft of 16 cm, the average generated power of the prototype reaches the maximum value of 6.54 W at the wave period of 1.8 s, when the wave period ranges from 1.2 s to 2.4 s. Finally, the twobody DDWEC prototype is compared with the singlebody DDWEC prototype. The results show that the average power output of the twobody DDWEC prototype is always higher than that of the singlebody DDWEC prototype in a large range of wave period.