The different wind and solar output and load levels of island microgrids in different seasons and the lack of cross-seasonal long-term energy storage methods have led to the current large-scale abandonment of wind and solar energy and high carbon emissions in island microgrids. In order to improve the utilization rate of wind and solar energy and reduce carbon emissions, an island integrated energy microgrid including hydrogen energy storage system and seawater source heat pump is established. The objective function is to minimize the annualized cost, the abandoned wind rate and the carbon emissions. Taking the output of distributed power generation and power balance as constraints, the NSGA-III algorithm is used to optimize the capacity of each distributed generation source with multiple objectives, and the fuzzy membership function is used to select the solution with the highest comprehensive satisfaction. The superiority of the integrated energy microgrid is verified by the example and it provides a reference for the planning and construction of island microgrids in the future.

Aiming at the problems of photovoltaic power reverse and transformer overload operation caused by the difference of source and load characteristics in distribution station areas under independent operation mode, a flexible interconnection planning method for distribution station area of regional distribution network is proposed. Firstly, the VSC-based flexible interconnection topology and operation mode are analyzed, and its power flow model is established. Secondly, according to the operation characteristics of distribution station area, the interconnection demand index is put forward, and on this basis, the distribution station area connectivity analysis method of regional distribution network is proposed. Then, taking the whole regional distribution network as research object, a bilevel planning model of the flexible interconnection scheme and interconnection device configuration is established. The upper level model solves the optimal planning scheme, and the lower level model realizes the economic system operation. The model is solved by a hybrid optimization algorithm based on simulated annealing and cone programming. Finally, the proposed planning model is verified on a 40-node example. The comparative analysis of distribution system cost, photovoltaic absorption rate and transformer load rate before and after planning verifies the effectiveness of this model in improving the economy and operation efficiency of regional distribution network.

In view of the interaction between distribution network structure and distributed power generation planning stage and power grid operation stage, a coordinated planning operation model under the Stackelberg game framework and its solution algorithm between power grid company and consumers is proposed. Firstly, a bi-level coordinated planning and operation optimization framework is proposed, aiming at the optimization of the benefit of the distribution network company and utility of consumers. Secondly, aiming at the maximum economy and reliability in distribution network planning, the distribution network grid structure and distributed power access nodes are optimized. A multi-objective planning model for distribution network under reliability constraints is built, and a solution method for the multi-objective distribution network planning model is proposed based on the NSGA-II. Then, a distributed generation planning model considering the grid operation stage and consumers’ operating strategies is established, and consumers’ load and energy storage equipment operation strategies are proposed. Thirdly, based on the bi-level planning problem, an Stackelberg game model is proposed which considers the interactions between the power grid company and consumers. Finally, through the simulation analysis of practical example, the results show that the proposed cooperative optimization method of distribution network planning and operation strategies under the Stackelberg game model can effectively improve the benefits of both power grid company and consumers, and further improve the reliability of power grid operation.

To meet the challenge of global climate change, the low-carbon transformation of China's industrial parks is imperative. Driven by the carbon peaking and carbon neutrality goals, distributed photovoltaic, wind power, and other renewable energies are connected to the industrial park on a large scale. The intermittent, fluctuating, and uncertain outputs have induced the surging demand for energy storage in the low-carbon park. The industrial park faces the dilemma between expansive investment in energy storage and the limitation of carbon emission. Therefore, this paper introduces the super-quantile to measure the carbon emission risk of industrial parks with uncertainties, proposes the carbon emission risk index, transforms its integral form into the linear constraint form that can be embedded in the energy storage planning model, and then establishes the energy storage capacity planning model of low-carbon parks considering the carbon emission risk constraint. Through the analysis of an industrial park, the effectiveness of the proposed energy storage capacity planning method is verified, and the carbon emission risk of the industrial park is reduced under the condition of a slight increase in the annual comprehensive cost. Finally, the sensitivity of risk threshold and confidence level is analyzed.

Strong grid structure can significantly reduce the scope and time if power outage of distribution system, improve the reliability of power supply. The main measures to improve the grid structure mainly include the retrofitting and optimization planning of the automation switches. This paper proposes a method for evaluating weak lines in distribution network, which provides a theoretical basis for searching of weak lines of power grid structure. The reliability improvement index is proposed to evaluate the power grid structure from the perspective of power supply reliability. In order to calculate the reliability improvement, an improved harmonic search swarm algorithm is proposed to obtain the switch distribution scenario with the greatest reliability improvement. This paper proposes a comprehensive evaluation method based on entropy-fuzzy-VIKOR to comprehensively evaluate the reliability of the medium voltage grid structure. 30 lines are selected in a certain area for example analysis, the results show that the proposed method can effectively screen out the weak lines of grid structure and realize the accurate positioning of the switch optimization planning project.

In the traditional distribution network, substations often adjust and control the operation mode by changing the bus segment switch, so it is difficult to realize the real-time and continuous power regulation. In this paper, soft open point (SOP) is used to replace the segment switch of substation main transformer bus, and the related nodes of substation are flexibly interconnected to construct flexible interconnected nodes, so as to enhance the flexible power exchange control ability of power grid and realize power flow optimization. At the same time, in order to minimize the active power loss of the whole network, the scheduling SOP control variables and the switching of reactive power compensation equipment of relevant nodes are optimized. Because the problem involved belongs to mixed integer programming, a hybrid particle swarm optimization algorithm is proposed to solve the problem, and the effectiveness and rationality of the proposed strategy are verified in a high voltage distribution network.

As a component of the new type of distribution system, the DC building distribution and utilization system (DCBDUS) has a great prospect for the development of the new infrastructure construction ecological chain for the “double carbon” objective. At present, the research on the low-voltage DCBDUS is still concentrating on system architecture design. While the study of operation control strategy is relatively less. For this field, a systematic summary of the low-voltage DC building structures with different application scenarios is indicated. In consideration of the “flat and distributed” networking characteristics, together with the demand for “weak centralized” control application requirement, the preferred operation control schemes both for buildings with AC to DC transformation and newly building DCBDUs are discussed to realize the flexible and stable operation of diversified DC equipment integration. Taking the demonstration project of low-carbon city-based future building-“Shenzhen IBR Future Complex” as an example, the daily operation control scheme and operation results are analyzed. Finally, the development route for the market-oriented applications of power electronic conversion controllers and countermeasures of the control strategies in DCBDUS are explored.

The DC transformer is an important equipment to interconnect DC grids with different voltage levels, however, the impedance mismatch between source and load subsystem adversely affects the system stability. For the cascade system based on multi-module series-parallel combined DC transformer, the inverse droop control strategies are proposed for the corresponding subsystems to achieve equal power sharing among the individual modules. In order to analyze the stability issue of cascade system, the impedance and admittance models of the corresponding subsystem are derived by establishing the small signal model, and the derived results are verified by MATLAB. Then, the influences of the variation of system parameters on the stability of the cascade system are analyzed by using the impedance stability criterion. Finally, the application effect of the proposed inverse droop control strategies are verified by simulation.

With the proposed carbon-neutral target, the integration of large-scale distributed photovoltaics (PVs) to distribution network has become an inevitable trend. However, the integration of PVs aggravates the voltage violation, three-phase unbalance and reverse power of low voltage distribution network, which brings great challenges to the stable operation of the distribution network. In view of the accommodation problem of high-proportion PVs, this paper fully taps the power regulation potential of low voltage hybrid AC/DC distribution network and energy storage, and a spatial-temporal coordinated optimization method based on voltage source converter (VSC) and energy storage is proposed. Firstly, the power transfer characteristics of VSC and energy storage at the spatial and temporal levels are analyzed. Secondly, a spatial-temporal coordinated optimization model of low voltage hybrid AC/DC distribution network is established with the objective of minimizing the PV curtailment and power losses, and the power of energy storage and VSC are optimization variables. The proposed model is transformed into a second-order cone programming model to solve. Finally, taking the typical low voltage hybrid AC/DC distribution network as an example, the simulation results show that the proposed method can effectively improve the voltage and reverse power problems caused by PVs, and enhance the PV accommodation capability of low voltage distribution network.

With the rapid increase in the number of electric vehicles, the disordered grid-connected charging of electric vehicles will bring huge uncertainty to the load stability of the power grid. Therefore, it is extremely important to optimize the charging of electric vehicles. In order to solve this problem, a multi-objective electric vehicle charging optimization strategy based on hybrid particle swarm optimization genetic algorithm (HPSOGA) is proposed in this paper. The Monte Carlo method is used to establish a charging load curve based on the travel patterns of car owners. Based on the traditional PSO, the iterative mechanism of GA is introduced, and HPSOGA is formed to solve the multi-objective optimization model established based on the minimum user charging cost and the minimum grid load fluctuation rate. The simulation analysis is carried out in combination with specific cases. The results show that the multi-objective electric vehicle charging optimization strategy based on HPSOGA has a faster optimization speed and a better optimization effect, further decreasing the grid load peak, increasing the grid load valley, effectively reducing the grid load fluctuation rate, and effectively cutting the charging cost of car owners.

At present, the modeling of integrated energy systems (IES) is more described from the input-output relationship of the system and the relationship between internal state and input-output, and the transfer process of its internal state in different stages is difficult to be presented clearly. After the large increase of the proportion of distributed renewable energy, the operating states and state transition processes of IES at different stages need to be observable and controllable. Therefore, a hybrid automata-based phased state transfer space modeling method for IES is proposed, which can accurately describe the transfer conditions of the internal state of the IES and its transfer process, and is conducive to phased management and optimal control of all energy production units and energy consumption units in IES from start-up, operation to stop. The simulation results show that the proposed model achieves the whole process observation for the state transfer trajectory of the IES, which is conducive to the efficient management and optimal control of the whole process in stages.

Based on the framework of edge computing, this paper proposes a parallel distributed optimization method to solve the complex optimal economic dispatch problem of microgrid containing massive renewable energy, which effectively improves the solution efficiency. In this method, edge nodes only exchange information with neighboring nodes, which reduces the communication complexity. At the same time, each node solves in a parallel manner, breaking the constraints of “combination explosion”, improving the solution efficiency, and obtaining the global optimal solution after multiple iterations. Further, in the microgrid with massive renewable energy, an optimal dispatch model with minimum operating cost is constructed, and the proposed method is used to solve the large-scale optimization model. The simulation results show that, compared with the standard ADMM, the number of iterations of the proposed method is only half or even less than that of the ADMM under the same accuracy. In addition, when the output power of massive renewable energy fluctuates, the incremental cost of distributed power sources tends to be consistent, indicating that after the interactive regulation of the microgrid and the upper-level power grid, the method in this paper can make the microgrid operate stably and minimize the cost at the same time.

Firstly, a probability model is established for the randomness of distributed power sources and loads, and then a voltage stability index of the distribution network is established. Secondly, The probability model, and the voltage stability index is applied to the voltage optimization model of the distribution network, and the probability flow of the distribution network is calculated based on the unscented transformation method. The probabilistic multi-objective voltage optimization model of the distribution network with the objective function, and the improved particle swarm algorithm is used to solve the model. Finally, t The calculation example shows that the proposed method can reduce the network loss of distribution network by configuring shunt capacitors, static var compensators, voltage and reactive power control equipment of on-load tap changer improve the voltage stability, over-limit and fluctuation problems of the distribution network including DG, and improve the ability of the distribution network to cope with source and load uncertainty.

With the construction of new power system, large-scale distributed energy access and high proportion power electronic equipment application, its inherent volatility, randomness and gap may lead to voltage fluctuation and out of limit, system inertia decline, fault handling difficulties, etc. At the same time, the requirements for reliability are becoming higher, so it is urgent to introduce new technologies such as distribution network situation awareness control and protection to solve the corresponding challenges. This paper proposes that the core of distribution network situational awareness control and protection technology is synchronous measurement, including synchronous phasor, synchronous waveform and synchronous traveling wave. The key technologies include situational awareness sensing technology, situational awareness control technology and situational awareness protection technology at three levels: steady, dynamic and transient state. The main technologies include voltage and current broadband multi-level synchronous measurement and situational awareness, multi-level voltage control based on distribution network situation, the distributed resource frequency and inertia active support based on the distribution network situation, and the diagnosis and precise positioning based on the fault situation are proposed. The idea of deploying appropriate equipment in the “substation-line-transformer-house” is proposed. Finally, the research scheme is preliminarily verified.

The traditional wireless sensor network (WSN) is based on IP protocol, and it is difficult to exert the performance of various access networks in s SDGs. Meanwhile, it is also difficult to obtain node IP addresses in dynamic scenarios such as meteorological disasters or inspection drones, and data collection tasks cannot be completed in a timely and effective manner. The emergence of named data networking (NDN) provides a possibility to solve the above problems. The NDN is driven by data requirements, which is in line with the original intention of WSN data collection tasks. The NDN's network architecture is transparent to the underlying network protocol, in this paper, combined with this advantage of NDN, a sensing data collection protocol for SDGs called T-ND-WSN is proposed. Combined with the caching mechanism within network, T-ND-WSN can satisfy the time delay supremum of data communication in SDGs and has obvious advantages in QoS of data communication.

Load forecasting is the basis of energy management and optimal scheduling of integrated energy system (IES), the forecasting accuracy is directly related to the overall operation performance of the system. This paper proposes a short-term load forecasting model for electric-thermal energy based on Transformer network and multi-task learning. Firstly, the basic architecture and theory of Transformer network and multi-task learning structure are introduced. Then, through the feature selection step based on random forest method, the typical factors reflecting the load characteristics and change law are extracted, and the input characteristics of multi-task learning are constructed. Then, the multi-task learning weight sharing layer is constructed based on transformer network, and finally the forecasting value of multi-energy load is output through the full connection layer. Finally, the effectiveness of the proposed method and algorithm is verified by the collected data from the actual micro-energy system. The results show that the proposed model can fully learn the characteristics of electricity-heat coupling and improve the accuracy of load forecasting.