With the rapid development of new power systems, distributed new energy, energy storage, electric vehicles, and other servers will be involved in power distribution networks. The use of appropriate communication technology is important to ensure the safety and reliability of power distribution networks. In this paper, the transmission demand of distribution network services against the background of a new power system and the relevant performance of available communication technologies of power distribution networks are first summarized, and a multidimensional evaluation index system that considers access, coverage, economy, reliability, and safety is proposed. Second, a subjective and objective weighting-based comprehensive matching model is proposed based on the Bayesian best-worst method (BBWM) and measurement alternatives and ranking according to compromise solution (MARCOS). Finally, a matching analysis of medium- and low-voltage power distribution network services and communication technologies is conducted, and a comprehensive matching of the entire service of the power distribution network and communication technology is proposed.
Accurate short-term load forecasting can provide guidance for the dispatching operation of power grids by predicting the required power loads. However, the power load is not only related to the user’s electricity consumption habits but is also easily affected by meteorological factors such as temperature and humidity, Therefore, based on existing historical load data, this paper incorporates meteorological data that affect regional power loads, considers the overfitting problem of high-dimensional meteorological parameter data to the prediction algorithm, and proposes a dimensionality reduction method for high-dimensional meteorological data based on sparse kernel principal component analysis (SKPCA). Subsequently, taking the historical load power and principal components reconstructed by SKPCA as the input, we construct a hybrid deep learning prediction model based on a convolutional neural network (CNN) and a long short-term memory (LSTM) neural network. The CNN-LSTM model can extract the spatial and temporal correlation characteristics of the load power and meteorological data simultaneously to fully utilize the temporal-spatial correlation characteristics of the data and improve the short-term prediction accuracy of the load power. Compared with common methods of data dimension reduction and load forecasting, the data dimensions of this method decrease by 71.43%, and the prediction accuracy reaches 98.92%. The results show that the proposed algorithm can significantly improve the accuracy of regional power short-term load forecasting by fusing meteorological data after dimensionality reduction using SKPCA.
In the island microgrid system dominated by grid-forming(GFM) and grid-following(GFL) inverters, the active participation of GFL inverter in the secondary reactive power sharing can make the reactive power regulation and load capacity performance of microgrid enhanced. However, reactive power sharing is difficult to be achieved between inverters because of the different capacities, control methods of inverters and the mismatched impedance of transmission lines. Therefore, the freq/watt and volt/var droop control is applied to GFL inverter to make its power characteristic similar as GFM inverter, and the principle of reactive power sharing between GFM and GFL inverters is analyzed. On this basis, an adaptive virtual impedance strategy driven by reactive power sharing deviation information of adjacent inverters is proposed, which can achieve the adaptive reactive power sharing of GFM and GFL inverters when transmission line impedance and inverter capacity are different, and the selection method of related control parameters is also given. Finally, the applicability of the proposed strategy under the cases of line impedance variation, load switching, inverter capacity inequality and plug-and-play are tested in MATLAB/Simulink, so the correctness of the theoretical analysis is verified.
With high penetration renewable energy and a new type of accessed load, traditional distribution networks have been gradually transformed into multiconnected networks. AC/DC hybrid distribution networks with multi-voltage level interaction have become the development trend of distribution networks. Based on this trend, a two-layer optimization method for an AC/DC distribution network is proposed, considering multi-connected and different voltage level interactions. The objective function of the low-voltage network problem is to minimize the schedule cost and achieve dispatching of energy storage and EV. The objective function of the mid-voltage problem is to minimize the schedule cost and voltage deviation. Next, the exchange power of the voltage-source converter is obtained. Two issues are iterated and optimized until they reach convergence. Finally, a case study was conducted based on the IEEE33 system. The results show that the proposed method can minimize power loss and voltage fluctuation.
In the modern power system, the large-scale access of distributed energy enlarges system exposed surface, and making backdoors of intelligent terminal easy to be exploited by attackers. Therefore, the paper proposes an intelligent terminal backdoor detection method for modern power system. Firstly, the abnormal behavior and its code characteristics are analyzed and summarized, and then backdoors static detection method for intelligent terminal is proposed from two aspects: character string and function call sequence. Secondly, according to characteristic of intelligent terminal function behavior fixed, a dynamic detection method based on system running state is proposed from three aspects: file state, network state and hidden behavior. Detection accuracy can be further improved by dynamically detecting malicious behaviors of backdoors. The experimental results show that the backdoor detection method proposed can effectively discover backdoor code and behavior of power intelligent terminal, detection accuracy is 98.5%, false positive rate is 0.8%.
Urban power grids have high load densities and are subjected to many critical loads. Improving the extreme survival capacity of an urban power grid under extreme conditions and events help to ensure uninterrupted power supply for important users, improve the anti-vulnerability capacity of the power grid, and reduce the impact and losses caused by extreme events. In this study, an anti-fragile planning method for a large-scale urban distribution network is developed to improve the extreme survival capacity. First, a two-step decision-making framework for urban distribution network resilience planning based on the stochastic programming theory is proposed. The first step of the framework is to determine the set of candidate line reinforcement/upgrading schemes, and the second step is to determine the optimal deployment scheme of distributed power sources in the line-planning scheme based on the stochastic programming theory. The final resilience planning scheme is determined by considering factors, such as investment economy and extreme survival capacity improvement effect. Among different candidate line reinforcement/upgrading schemes, an extreme scene generation and representative scene-screening method based on Monte Carlo simulation and K-means clustering is proposed, considering the possible typhoon extreme disasters. Next, the planning problem of distributed generation is constructed as a two-stage random mixed-integer programming to optimize investment economy and maximize extreme survival capacity, and the random programming problem is transformed into a deterministic mixed-integer linear-programming problem based on the above extreme scenarios. The IEEE 33-node and 123-node distribution systems are used to verify the effectiveness of the proposed method.
Extreme disasters such as earthquakes can cause severe damage to active distribution networks (ADN) and transportation networks. These disasters can lead to road damage, congestion, and large-scale power outage, which makes it difficult to restore power supply to the power grid. In this study, an ADN dynamic collaborative recovery strategy is proposed based on the characteristics of the traffic network and ADN after an earthquake. The proposed strategy integrates finite source and load resources, network reconstruction, and rush repair scheduling. First, a joint disaster damage model of roads and lines of the transportation network was constructed based on the relationship between the transportation network and ADN that is difficult to separate and map to each other after the earthquake. A model of the traffic rush repair travel time of the transportation network was established considering the comprehensive influence of the traffic capacity and traffic flow, and the ADN resilience index combining the time demand and load importance. Second, an ADN hierarchical dynamic cooperative restoration optimization model reflecting the post-earthquake traffic network conditions was established. The outer layer was targeted at determining the maximum resilience index and minimum total emergency repair time, whereas the inner layer was targeted at minimizing economic losses and weighted switch operation times. Third, the improved grey wolf optimization algorithm was used to solve the proposed model, and resources such as multi-type power generation, emergency demand response load, network reconstruction and emergency repair team were coordinated and optimized to improve the resilience of ADNs after earthquakes. Finally, the feasibility and effectiveness of the proposed strategy are verified using an example analysis of the earthquake damage scenario.
In this study, a method for evaluating the distribution network resilience that reflects switchgear and feeder automation modes was developed to quantitatively analyze the effects of the switchgear and different feeder automation modes on the resilience level of a distribution network. First, the block-circuit breaker correlation matrix was constructed to determine the action state of the switchgear in the case of failure. The action logic process of three feeder automation modes, that is, the centralized feeder automation, volt-time local recombination feeder automation, and quick-acting intelligent distributed feeder automation, was analyzed, and the formula for calculating load outage duration in the non-fault area in different modes was derived. Second, an elastic evaluation index system was established based on two levels of macro-results and micro-processes, and the influence of switchgear and different feeder automation modes on the resilient level of distribution network was evaluated in multiple dimensions. Finally, a case study was conducted based on the improved IEEE-33 node distribution network to verify the effectiveness of the proposed evaluation method.
This paper proposes a reactive power optimization model for offshore wind farms based on second-order cone convex relaxation for reducing active network loss. The model considers the grid stability requirements and safety margin involved in the optimal power flow process and optimizes the reactive power output of each turbine to maximize the active power output, reduce the network loss, and improve the overall power output of a wind farm. Through mathematical optimization of the second-order cone convex relaxation, the power flow is improved, the line active power loss is reduced, and the power-flow optimization problem under various constraints is solved. Finally, with the Zhoushan Putuo wind farm as an example, the reactive power output of each wind turbine is optimized and compared with the results of heuristic particle swarm optimization and the classical interior-point optimization algorithm. The comparison shows that the proposed method can significantly reduce active power loss and improve the overall active power output of the wind farm.
This paper proposes a low-carbon optimization methodology that considers carbon capture and rotating standby capacity allocation to reduce the carbon emissions of power systems, promote grid connection and consumption of large-scale wind power, and study the impact of wind power uncertainty on system operation. First, the operating mechanism and standby principle of the integrated flexible operation of a carbon capture plant are analyzed. Second, the system operation risk caused by wind power and load forecast error is considered, and the risk during the optimization process is measured using the conditional value-at-risk (CVaR), and a low-carbon optimal dispatch model of the proposed methodology is established to optimize the operation cost of the system. Finally, the stochastic problem in this paper is determined using Latin hypercubic sampling and scenario reduction. The IEEE 39-node system is analyzed as an example, which verifies that the carbon capture plant can reduce CO2 emissions and provide rotating standby capacity for the system. Additionally, it provides more options for the scheduling decision-makers to improve the low-carbon optimization, robustness, and economy of the system.
The energy controlled by virtual inertia of wind turbine mainly comes from rotor kinetic energy. The inertia level of wind power is difficult to estimate due to the uncertainty of wind speed and the state of the unit itself. In order to solve this problem, an effective inertia estimation method considering the spatio-temporal distribution of wind speed and the operating state of the unit is proposed. First, a wind farm wind speed distribution probability model is established, and the advantages of mixed Copula function in correlation fitting are used to analyze wind speed of adjacent units in combination with wake effect. Secondly, the response process of the fan virtual inertia under different operating conditions and different control parameters is analyzed. Finally, an effective inertia estimation method for wind farm is proposed considering the spatio-temporal distribution of wind speed and the difference of unit operating state. Based on the actual data of a wind field of the State Grid, a simulation model of inertia response of the wind farm is constructed, which verifies that the wind speed correlation model proposed in this paper has high computational efficiency and accuracy. The evaluated effective inertia response ability can reflect the actual response process of the fan.
With the increase of PV permeability, the demand of dispatching PV becomes more and more urgent on the behalf of power grid economy and stability. However, traditional grid dispatching is based on fixed-capacity power generation equipment, for the PV dispatching needs to consider the impact of available capacity uncertainty. The current dispatching algorithms quantify the uncertainty of PV available capacity into probability function or scalable variable processing, or use the trained model to adapt to PV fluctuations. However, the accurate response to the PV power deviation can not be realized. Therefore, a multi-time-scale PV dispatching algorithm is proposed in this paper. On the long-time scale, the centralized algorithm is utilized to solve the PV dispatching strategy with the minimum network loss and the minimum average voltage according to the optimal power flow model. In the short time scale, the distributed consistent averaging algorithm is adopted to achieve the average sharing of power deviation among all PV generations, and the standby power is used to make up the power deviation. The power flow model of the centralized algorithm and its convex optimization model based on the second-order cone relaxation are given in this paper. Moreover, the implementation principle and pseudo-code of the distributed algorithm is illustrated. Finally, the algorithm is verified on the IEEE-5 node and IEEE-14 node grids.
The rational allocation of transmission and distribution costs for power grids with high renewables is an important problem to promote the construction of new-type power system. In this paper, a multi-stakeholder allocation model oriented to connection cost and reinforcement cost is proposed for the problem of transmission and distribution costs allocation bought by high renewables integration. For the connection cost, considering the difference in the cost-bearing capacity of renewables, renewable energy enterprise clusters are obtained based on heterogeneity, and hence connection cost is allocated based on cost responsibility measurement in the enterprise clusters. For the reinforcement cost, it’s divided between renewables and grid based on the cost-benefit, and the risks of renewable grid-connection are analyzed. Then, due to the shared attributes of reinforcement cost, a dual-layer cost allocation framework for renewable energy enterprise aggregation-alliance aggregation is constructed. Furthermore, the risk factors are introduced to quantify the impact of grid-connection risks on the reinforcement cost, and a nonseparable cost is regarded as a measure of renewable energy grid-connection risk. Consequently, a reinforcement cost allocation model based on EANS-Owen value is proposed. Finally, examples are given to verify that the proposed model can reasonably divert the transmission and provide theoretical support for power grid investment planning for new power systems.
As the deep decarbonization process continues to advance, the co-existence of wind abandonment and power shortage caused by a high proportion of new energy connected to the grid needs to be solved urgently. To this end, this paper proposes a peer-to-peer trading mechanism for distribution network users to enhance the ability of power preservation, in order to guarantee the power supply during the low valley of wind power output. Firstly, the distribution market peer-to-peer trading mechanism for power preservation is introduced, and the basic strategy of guiding large power users to participate in power preservation is proposed; secondly, the profit maximization model of each participating subject is constructed; then, the adaptive alternating direction multiplier method is used to solve the model to obtain the market clearing results and energy management strategies; finally, the effectiveness of the proposed model and the correctness of the computational algorithm are verified through the arithmetic example. Finally, the validity of the proposed model and the correctness of the computational algorithm are verified through examples. The simulation results show that the proposed method can guide the large power users to guarantee the power supply of residential users when there is a shortage of wind power, and the strategy can also reduce the power purchase cost of the large power users by 4% and improve the operational efficiency by 5%.
A wind-power balance cost allocation method was developed in response to China’s goal of increasing wind-power consumption capacity and achieving sustainable development, combined with the need for electricity spot market construction. The proposed method considers the spatiotemporal characteristics of wind power and the impact of relative load power fluctuations on system operation. The “equal electricity quantity following load” method was used to construct a zero-wind power balance cost equivalent scenario to optimize and dispatch system operation in advance. By comparing the predicted scenario with the equivalent scenario of the optimized dispatch, the total operating cost of wind power balance was solved for the dispatch cycle. Based on the spatial distribution and capacity of each wind power plant, a balance cost allocation model between wind power plants was established. An indicator, the relative fluctuation rate of each time period, was proposed to quantitatively express the temporal evolution characteristics of wind-power relative load fluctuations, and the corresponding model was established. Based on the relative fluctuation rate of each time period, the local fluctuation impact factor was obtained to allocate the wind-power balance cost of the dispatch cycle by time period, resulting in wind power balance costs for each time period. Using the grid structure of a region with a high proportion of wind power as an example, the balance costs of different wind-power grid capacities and fluctuation levels were calculated to verify the effectiveness of the proposed calculation method and model.
