Because of the complex and fragile structures of urban energy systems, which are easily affected by extreme events, constructing highly resilient systems has become a new area of focus. First, we analyze the four major problems of“structural vulnerability, environmental degradation, inadequate coordination, and complex recovery”facing the construction of highly resilient urban energy systems; clarify the research boundary of such energy systems; analyze the threats of natural disasters, environmental degradation, and human intervention faced by“resilient cities”; and summarize the risks associated with energy structure optimization and the complex coordination of multi-stream grids. Secondly, we propose nine key technologies for building highly resilient urban energy systems at three levels of“physical-information-application”and discuss the development status and main features of each technology. Finally, future research and development paths for highly resilient urban energy systems are proposed in the physical, information, and application layers, and suggestions are made to gradually establish intelligent energy networks, intelligent control systems, and security supply and demand systems and to improve urban energy risk emergency control measures, strengthen urban energy supply assurance, enhance important energy facilities, and strengthen energy network security protection.
The use of power electronics in distribution networks has significantly increased owing to the increase in the use of distributed clean energy and flexible loads. Moreover, the use of dynamic processes in distribution networks has become more frequent and complex, and the state observability and coordinated control of the system has become increasingly important. Therefore, high-frequency and accurate data on distribution networks should be obtained using synchronous phase measurement technology. In this paper, the development status and application of synchronous phase measurement technology in distribution networks is reviewed, and the features of synchronous phase measurement technology in distribution networks are concluded considering the device function and system architecture. Furthermore, the typical applications of synchronous phasor measurement technology in state estimation, fault diagnosis and location, and coordinated control in China are summarized. Finally, the development trend and standardization requirements of synchronous phasor measurement technology for distribution networks are discussed.
Access to large-scale electric vehicles poses new challenges for the optimal dispatching of a regional integrated energy system (RIES). This study proposes a bi-level day-ahead scheduling strategy for a RIES while considering the integration of electric vehicles. First, the Monte Carlo method was used to simulate the disordered charging of electric vehicles. On this basis, the time-of-use price was used to guide the orderly charging of electric vehicles on the load side. Next, based on the electricity and carbon trading markets and fully considering the uncertainty factors in the process of wind power output on the source side and the demand-side response on the load side, the system was optimized and scheduled to minimize the daily economic and carbon trading costs. An improved particle swarm optimization algorithm was used to solve the problem. Finally, through the analysis of simulated examples, it was concluded that the bi-level optimal scheduling strategy proposed in this study can effectively reduce the peak-valley difference of the system load, improve the electricity consumption satisfaction of users, reduce carbon emissions, and increase the economic benefits of the system, while realizing the orderly charging of electric vehicles.
Aiming at the current problems of the rough calculation of distribution network available capacity, reliance on personal experience, and failure to give full play to the decision-making of data empowerment, this study combines available capacity with the analysis of business expansion and proposes an available capacity analysis and optimization decision method of business expansion for distribution networks based on depth-first traversal. First, considering the seasonal and daily load characteristics in the substation area, the available capacities of the feeder and public transformer were calculated based on the depth-first traversal algorithm. Second, considering the multi-objective matching of the available capacity with the load curve, load balancing, and three-phase balance in the substation area, an optimization decision model of business expansion was established, and a refined optimal load access scheme from feeder zoning to the substation area was obtained. Finally, the simulation examples using IEEE standard distribution systems and an actual distribution network were used to verify the efficiency of the proposed available capacity calculation method. It was observed that the proposed method can effectively apply the available capacity analysis results to obtain a refined load access scheme considering multiple objectives.
Traditional reporting tools cannot recommend relevant business information to users independently. This has created many challenges in the operation and management of electric power enterprises. To address the above problems, this study proposes a graph convolutional network (GCN) report recommendation algorithm based on user information. First, the overall structure of the reporting tool based on the data center platform is introduced. Considering the marketing business as an example, the correlation between users and indicators is analyzed and a correlation feature extraction model based on a graph convolutional network is proposed. Simultaneously, the general preference characteristics of users and indicators are integrated into the model, and the depth of the graph aggregation information is further improved. Subsequently, the index score is accurately predicted, and a recommendation result is provided. Finally, the models are compared for a public dataset and marketing business dataset, and the average precision rate, recall rate, and normalized discounted cumulative gain are selected as evaluation indicators to verify the accuracy of the proposed algorithm. The results show that compared with existing algorithms, the proposed algorithm significantly improves the recommendation effect, which can empower the operation and management of electric power enterprises and help realize digital transformation.
In this study, a single-ended fault diagnosis method for flexible DC power grids based on Swin Transformer is proposed to address the problems of low precision, susceptibility to transition resistance, and requiring manual input to set the threshold in the existing fault detection methods of flexible DC power grids. First, we collect the transient voltage time-domain data at fault and convert it into a two-dimensional gramian angular field(GAF) image with a better recognition effect after data processing, which is used for offline training of the Swin Transformer; Second fault features are extracted using the moving window of the Swin Transformer, and different fault diagnoses are realized according to the training results. This method does not require manual setting of the threshold. Finally, after many simulations, it is proven that the method proposed in this study satisfies the quick action requirement, can accurately diagnose faults, and has strong transition resistance and anti-noise ability.
Nonintrusive load monitoring is an effective method for comprehensively perceiving load data and optimizing energy efficiency. At present, the main observation object of nonintrusive load monitoring algorithms is the load with a regulation potential; however, the identification accuracy is poor for electrical appliances with small power and similar load curves. Moreover, the algorithm is highly dependent on prior data. Therefore, an SOM-K-means non-intrusive load identification algorithm based on multi-feature joint sparse expression is proposed in this study. The algorithm uses load features to train the optimal dictionary. The objective function is constructed by combining the optimal dictionary and multi-feature joint sparse representation, and the multi-feature joint sparse matrix is solved, which overcomes the problem of identifying load types limited by single-type load characteristics. Considering the multi-feature joint sparse matrix as the input, combined with the K-means algorithm optimized by a self-organizing map (SOM) neural network and the mean absolute error, the load was quickly identified. Finally, experimental verification using the PLAID dataset shows that the identification accuracy of the proposed algorithm can reach 90% with only 120 iterations, improving the convergence speed of the algorithm and proving that the method can realize load identification accurately and efficiently.
With the increasing penetration of clean energy into existing power systems, the collaborative optimization of multi-microgrid systems has become an effective way to promote the utilization of clean energy. A reasonable distributed demand response mechanism has research significance for improving the capacity of clean energy consumption. In this study, a multi-microgrid collaborative optimization mechanism and an autonomous bidding strategy of demand response resources that consider the distributed demand response are proposed. First, a microgrid autonomous optimal dispatch model is established to formulate power supply-demand plans and DR strategies by considering the incentive characteristics of demand response contracts. Second, referring to historical transaction information, each microgrid proposes IDR bidding strategies to maximize prospective benefits. Third, to implement an economic and efficient distributed demand response, a two-way auction mechanism is introduced in the day-ahead stage to develop a peer-to-peer matching framework for demand response resources. Finally, numerical example results verify that the proposed mechanism can realize the distributed transactions of demand response resources, improve the economy of multi-microgrid systems, and effectively promote clean energy consumption.
Research on high-precision forecasting for distributed small wind power with strong random fluctuations is of great significance for enhancing the stability of small wind turbines and providing reliable support for distribution. Therefore, a distributed wind power forecasting method based on frequency domain decomposition and a precision-weighted ensemble was proposed. First, the original wind power signal was decomposed into different frequency bands through complete ensemble empirical mode decomposition with adaptive noise to capture the local fluctuation characteristics of wind power. Then, a precision-weighted Stacking model with two-layer heterogeneous learners was constructed to fully utilize the performance advantages of different learners and increase the generalization ability to deal with strong fluctuations. Finally, the model was verified on actual dataset from four wind turbines. It was observed that the proposed method is superior to several advanced prediction methods currently used in large wind farms, wind farm clusters, and single wind turbines, which proves the validity and generalization of the proposed method for distributed wind power forecasting.
Wind turbines are vulnerable to the negative sequence current injection. Therefore, a three-phase reclosing scheme is generally utilized in the outgoing lines when there is more than one outgoing line for the wind farm(s). When the shunt reactors are not installed, it is difficult to identify the transient fault from the permanent one because of the absence of the coupling loop after a three-phase trip of the outgoing line. To solve the abovementioned problems, a novel adaptive reclosing scheme for wind power outgoing line is proposed in this study. Specifically, a single-terminal partial tripping method is proposed for multiple types of outgoing line faults; simultaneously, phase-to-phase coupling loops are established by the tripping. Based on the capacitor coupling characteristics, the transient fault is rapidly and accurately identified from the permanent one by the voltage magnitude criterion. The theoretical derivation and simulation experiments based on the PSCAD/EMTDC platform demonstrate that the proposed adaptive reclosing scheme can avoid the injection of negative sequence currents into wind turbines, while significantly improving the reclosing success rate of wind power outgoing lines and ensuring the power transmission continuity of the wind farm(s).
In the traditional low-carbon dispatch of power systems, it is extremely unfair to the power generation side to treat power plants as the sole bearers responsible for carbon emissions. It is of great significance to track the sources of carbon emissions, distribute carbon emissions fairly to the power generation and user sides, and guide users in using electricity sustainably. To address the problem of negative carbon emissions in existing carbon flow tracking methods due to complex power calculations, this study proposes a carbon flow tracking method based on carbon emission flow, which directly tracks the carbon flow of the system based on carbon flow calculations. Based on the tracking results, a carbon emission responsibility-sharing model jointly borne by the power plants and users was developed. Furthermore, a low-carbon economic dispatching method for the power system source-grid-load based on carbon flow tracking is proposed, which optimizes the unit combination in the first stage with the goal of minimizing the power generation cost and carbon emission cost on the power generation side. The second stage optimizes the load distribution with the minimum demand response and user carbon emission costs. The analysis shows that, compared with existing tracking methods, the carbon flow tracking method proposed in this study can avoid the occurrence of negative carbon emissions. The proposed low-carbon economic dispatch method can consider the economy and environmental protection of the system and guide users to actively respond and reduce the carbon emissions of the system.
The low-carbon transformation of the power system requires a“multi-line attack”and it is increasingly difficult to rely only on the consumption of a high proportion of new energy, primarily because the entire chain can only be optimized by achieving the diversified development of electricity and carbon. On the basis of comprehensively considering the synergistic relationship between carbon quotas and electricity, such as the correlation, matching degree, and relative degree of freedom, the collaborative trading mode of carbon quotas and electricity is studied by all entities in each link of the“source-grid-load”and a two-factor trading scenario model of carbon quotas and electricity is developed. Combining the natural compatibility of blockchain technology with the characteristics of weak centralization, distributed trading, and the development of the electric carbon market, the transaction model of the carbon quota trading chain and electricity trading chain is studied. Considering source-end thermal and clean energy power plants as examples, a trading model framework for carbon quotas and power generation indicators between power plants in the provincial area under a double chain is proposed. Through the smart contract, a multi-objective search optimization algorithm based on the particle swarm algorithm is programmed to execute the script to realize the simulation and verification of the trading model of the power generation index. A feasibility analysis of the carbon quota trading model is performed, which confirms the adaptability and effectiveness of the proposed trading scheme and provides a reference for the development of the electric carbon market.
The development of energy storage is crucial for the construction of China’s new power system and the realization of a dual-carbon energy goal, which is a crucial determinant of participation in the energy storage power market. To address the scenario of joint wind-solar-storage participation in green power trading, this study first constructs a green power-trading model with the objective function of maximizing the benefits of the joint system, followed by a multi-entity benefit allocation model that considers the load contribution, costs, and operational risks of each entity. Finally, an example analysis is conducted using actual data from a park. The results show that the benefit allocation model effectively enhances the benefits of the energy storage system, promotes the joint participation of wind-light storage in green power trading, and facilitates the realization of the green value of electricity.
Distribution automation transformation of existing distribution network equipment can improve the reliability of distribution networks. Seeking the best balance between the reliability improvement of the distribution network and the economy of transformation is a key problem to be solved in the investment of distribution automation. This paper proposes a distribution automation investment strategy based on a time Petri net, establishes a modular time Petri net model of the distribution network, and applies the principle of improving the unit power supply reliability of the distribution network and minimizing the cost to ensure the economy of distribution automation investment. The effectiveness of the proposed model is verified using relevant examples. The proposed method improves the economy of investment by ensuring the improvement of power supply reliability, and provides a reference for distribution network planning and the technical transformation of power supply companies.
At present, China’s electricity market is mainly based on medium- and long-term transactions and is gradually transitioning to a market model of long-term transactions and spot transactions. As a new market entity, integrated power generation and trading companies face the risk of profit loss caused by uncertain factors, such as the unstable output of renewable energy generating units and load fluctuation of end users. In this study, a continuous dynamic adjustment method for risk aversion factors is proposed, and a conditional value risk model of profit and loss for power-generating and power-trading companies is developed. On this basis, the medium- and long-term trading profit under the cooperative game between power generation and power trading companies is consistently higher than the profit under the non-cooperative game as a constraint, and a medium- and long-term trading cooperation game decision-making model of integrated companies considering the risk of profit loss and the optimal shareholding ratio of power generation and power trading is proposed. Numerical simulation results show that the alliance transaction effectively improve the profits of all subsidiaries. The relationship between the value of the dynamic adjustment risk aversion factor and subsidiary trading profits is analyzed. The trading price interval for the cooperative game is provided.
