In order to maximize the promotion effect of renewable energy policies, this paper proposes a capacity allocation optimization method of wind power generation, solar power and energy storage in power grid planning under different policy objectives. Firstly, based on the policy quantification, grey relation analysis is used to calculate the correlation degree of the policy indicators on the planning capacity of renewable energy. Further, a multi-objective capacity estimation model is comprehensively presented. Some highly correlated policy indicators are transformed into the special constraints. And the economy and the stability of the power grid are integrated as the objective function. Meanwhile, the carbon trading and punishment for wind power and solar power abandonment is considered. Finally, the proposed model is solved by NSGA-Ⅱ-PSO algorithm. The novelty of the algorithm is that the crossover operation of NSGA-Ⅱ is replaced by the position updating of particle swarm. The calculation result of the case study can effectively evaluate the optimal planning capacity of renewable energy under different policies, while ensure the economic and the stability of the power system. The paper can provide the reasonable basis and the valid analytical method for the policy formulation and the renewable energy development.

Power system unreliability tracing model allocates the system’s reliability index to individual components, identifying potential weaknesses. This study expands its scope by considering the impact of storage resources. Unreliable factors leading to load shedding are categorized into two groups: objective factors inherent to the component and insufficient storage resources. The latter requires a retrospective analysis of other components that caused unreliability previously. When allocating responsibility for load shedding at a certain time, it begins by allocating it among components based on differences between fixed expected output and actual supply. Expected output insufficiency is considered the unreliable factor. This insufficiency due to insufficient storage resources is then decomposed into segments, each caused by excessive output in earlier instances of the same component. The expected output excess is attributed to the expected output insufficiency of other components in previous times, for which responsibility has been allocated to each component. Consequently, the expected output insufficiency at a particular time can be traced back based on a temporal recursive model, with the load shedding further allocated to components before that time. Case studies based on several systems demonstrate that the proposed model’s allocation results are reasonable and more accurate than the traditional model.

As renewable power generation increases in distribution networks, the real-time power balance is becoming a tough challenge. Unlike simple peak-load shedding or demand turn-down scenarios, generation following requires persistent and precise control due to the temporal response performance of controlled resources. This motivates a comprehensive control design considering the temporal response limitations and execution performance of ACCs when providing such services. Accordingly, this paper proposes a self-constraint MPC that properly allocates the generation following task among different ACCs, consisting of three main parts: response rehearsal, distributed consistency-based power allocation, and real-time task execution. Specifically, the rehearsal knowledge of ACCs is evaluated by introducing model predictive control to track power signals with different values and thus obtain prior factors, including the upward/downward limits and control cost function. On this basis, the coherence of the incremental response costs of different clusters is achieved by containing the prior factors to model the constraints and cost functions. Once the optimised following signals are obtained, a real-time model predictive controller for generation following task execution is employed. Simulations are conducted to verify the feasibility and effectiveness of the proposed method.

Low-carbon development of integrated energy systems is achieved via the sharing of multiple energy interactions by park-level integrated energy systems (PIESs). However, coordinating profit distribution conflict between complex interactive stakeholders under stochastic scenarios is challenging. Accordingly, this study proposes a novel tri-layer framework that aggregates different game mechanisms to investigate the interactions between PIESs and coupled energy markets. First, a linkage trading mechanism is proposed by integrating carbon emissions trading and green certificate trading , which establishes a coupled electricity-carbon-green certificate market. Consequently, a park aggregation operator acts as an intermediary between PIESs and the coupled market, setting purchase and sale prices to guide unit generation in each PIES using the master-slave game theory. Then, the Nash game theory is applied to realize a cooperative bargaining among PIESs for fair revenue distribution. Further, the impact of uncertain environments has been considered using stochastic scenario methods and the conditional value-at-risk theory. Furthermore, to protect the privacy of each participating agent while improving convergence speed, a differential evolutionary method is combined with analysis target cascading to solve the framework. Finally, the proposed scheduling method is verified using a typical case to optimize conflicting PIES interests in multiple scenarios and realize decarbonization transformation.

The distributed and privacy-preserving characteristics of fine-grained smart grid data hinder data sharing, making federated learning an attractive approach for collaborative training among data owners with similar load patterns. However, malicious models can interfere with training in the federated learning aggregation process, making it difficult to ensure the accuracy and safety of the central model in load forecasting. Therefore, we propose a secure aggregation federated learning method for distributed load forecasting based on similarity and distance (Fed-SAD), which effectively eliminates the interference of malicious models by securely aggregating models, thereby ensuring accurate and safe distributed scenario prediction. Experimental results demonstrate that Fed-SAD maintains high accuracy and robustness in both the presence and absence of malicious models, while maintaining data and model security.

A document by Yiwen Huang. Click on the document to view its contents.

This paper proposes an approach to evaluate the loading limits of distribution networks due to increasing EV connections. It focuses on two parameters: after-diversity maximum demand (ADMD) and maximum daily energy demand (MDED). Using actual EV charging data from the UK, Monte Carlo simulations generate daily charging profiles, identifying ADMD, MDED, and seasonal variations. ADMD, MDED, and per-hour maximum EV charging demands are combined with UK residential load profiles before EV connection. Their maximum demands are assessed against thermal rating limits, establishing the network’s hosting capacity (HC) for uncontrolled EV charging. To determine the maximum safe number of connected EVs, different scheduling methods for controlled EV charging are compared, considering per-hour maximum demand values, thermal limits, and MDED. This defines the network’s HC for fully controlled EV charging. The approach is demonstrated on the IEEE 33-bus test network. Pre-EV residential demands are obtained from a UK MV substation, and ambient data is collected from a UK Met Office weather station. Results provide a range of network HC values for uncontrolled and controlled EV charging, representing lower and upper limits. These limits correlate with firm and non-firm network HC concepts and guide optimal network upgrades for exceeding these limits.

In this study, a new short-term wind power prediction model based on a temporal convolutional network (TCN) and the Informer model is proposed to solve the problem of low prediction accuracy caused by large wind speed fluctuations in short-term prediction. First, an input feature selection method based on the maximum information coefficient is proposed after considering the problem of information interference caused by excessively large input features. A dynamic time planning method is used to select the optimal input step of historical power. Then, the combined forecasting model composed of TCN and the Informer is constructed in accordance with the numerical weather forecast and historical power data. Lastly, the pinball loss function is used to expand the prediction model into a quantile regression model, measure the effect of volatility, quantify the volatility range of prediction, and finally, obtain a deterministic prediction result. The actual measured data of wind farms in the Bohai Sea area are selected for analysis and calculation. Results show that the prediction model proposed in this study achieves better accuracy in deterministic prediction and interval prediction than the traditional model.

In time-of-use tariff period partition, clustering algorithms are commonly used. However, as load demands become more diverse in this big data era, large amount of non-linear data makes conventional clustering algorithms methods no longer be applicable in this field alone. Facing high-time-resolution daily load data with strong non-linearity, we propose a new method to partition periods. It consists of an improved fuzzy c-means clustering algorithm and a correction method for abnormal periods. Firstly, we propose modified fuzzy membership functions to improve the initialization of clustering for operation efficiency. Secondly, the method for calculating the fuzzy parameters based on the loss function is given. Thirdly, the initial period partition is obtained by the improved clustering. Next, the recognition model and fuzzy subsethood-based correction model for abnormal periods are structed, then the corrected period partition is confirmed. Finally, the effectiveness of the proposed methods is verified by two daily load data with a time resolution of 5 minutes.

A method for identifying vulnerable nodes in distribution networks is proposed, which is based on complex networks and optimized TOPSIS. This method aims to address the issues of one-sided evaluation indicators and inaccurate indicator weights that are present in existing methods for identifying vulnerable nodes in distribution networks. Based on the theory of complex networks, a comprehensive set of vulnerability indicators for distribution network nodes is constructed by considering both the topology structure and system operation status of the distribution network. The TOPSIS comprehensive evaluation model for optimization is proposed to enhance the selection process of optimal and worst indicator values. The advantages and disadvantages of each indicator are characterized using Mahalanobis distance. The calculation of proximity is optimized by establishing a virtual negative ideal solution, which makes the identification of vulnerable nodes more reasonable. The simulation results demonstrate that this method is more effective in identifying vulnerable nodes in the power grid compared to traditional methods, and has significant practical applications.

Wind power is an exceptionally clean source of energy, its rational utilization can fundamentally alleviate the energy, environment, and development problems, especially under the goals of “carbon peak” and “carbon neutrality”. A combined short-term wind power prediction based on LSTM artificial neural network has been studied aiming at the nonlinearity and volatility of wind energy. Due to the large amount of historical data required to predict the wind power precisely, the ambient temperature and wind speed, direction, and power are selected as model input. The CEEMDAN has been introduced as data preprocessing to decomposes wind power data and reduce the noise. And the PSO is conducted to optimize the LSTM network parameters. The combined prediction model with high accuracy for different sampling intervals has been verified by the wind farm data of Chongli Demonstration Project in Hebei Province. The results illustrate that the algorithm can effectively overcome the abnormal data influence and wind power volatility, thereby provide a theoretical reference for precise short-term wind power prediction.

The decarbonization of the power generation and transport sector encourage the analysis of connection of , such as , to the electrical system, as well as the evaluation of their impact on smart cities. A better understanding of the negative impacts on the power systems will lead to propose mitigation measures and eventually revolutionize the way distributed generation works. This paper aims at modeling and evaluating the impact of EVs on a real distribution network. The energy system chosen operates at 60 Hz, 34,5 kV (Medium Voltage) and 0,208 kV (Low Voltage) and it is simulated using PSCAD/EMTDC. To reproduce realistic user consumption profiles, dynamic load profiles based on EV owners behaviour have been simulated. The technology is modeled to mitigate the impacts of high penetration of EVs by supporting the network from undervoltage. The results show the importance of active management in modern power systems, especially considering the increase in DER penetration expected for the coming years. This work shows the benefits of implementing V2G technology while highlighting the challenges involved in a real case.

In the era of cyber-physical-social systems, research lacks a comprehensive framework to optimize dynamic coordination strategies for electric vehicles (EVs) to enhance modern power distribution networks (MPDNs) resilience. Public studies have not comprehensively addressed the impact of intelligent transportation systems (ITS) and smart charging systems (SCS) on MPDN resilience strategies after rare events. To bridge this gap, a two-stage EVs coordination framework is proposed to consider MPDN smart restoration. The first stage involves a novel proactive EV prepositioning model to optimize EV prepositioning plans before a rare event in order to enhance MPDN survivability during the immediate aftermath of the event. The second stage involves an advanced spatial-temporal EV dispatching model to maximize the number of available EVs for discharge, thereby improving MPDN recovery after a rare event. The proposed framework includes an information system facilitating dynamic data exchange between EVs and ITS/SCS automated systems and introduces a novel geographic graph to optimize EV routes between charging points. Effectiveness of the framework is assessed on a modified IEEE 123 node test feeder incorporating real-world transportation and charging infrastructure. The results suggest that significant enhancement in MPDN resilience is possible with smart restoration strategies. Sensitivity analysis specifically demonstrates that integrating recently invented technologies of the smart charging systems and automated systems potentially provides significant benefits if dynamic coordination strategies are employed in MPDNs.

The HVDC transmission based on modular multilevel converter shows the weak feed characteristic of limited fault current amplitude when connected to the traditional AC power grid, which leads to the risk of sensitivity reduction or even failure of the traditional pilot protection. In this paper, a MMC-HVDC AC side transmission line protection scheme based on positive sequence fault component current ratio is proposed. The positive sequence fault component current of the MMC-HVDC side is calculated by using the positive sequence fault component current of the power grid side, and the fault is identified through the significant difference between the ratio of the positive sequence fault component current of the MMC-HVDC side and the actual positive sequence fault component current of the MMC-HVDC side in the case of internal and external faults. This method has the advantages of simple principle, low requirement for synchronous data, not affected by capacitive current and strong resistance to high resistance. The simulation results show that the algorithm can correctly partition internal and external faults, and is not affected by fault location, fault type and operation mode of converter station, and has high sensitivity and reliability.

Fast and accurate fault direction criteria is crucial for efficient protection in hybrid multi-terminal HVDC transmission systems. The current variation method is commonly used to identify fault direction but is affected by fault resistance and distributed capacitance. This paper proposes a new direction criterion based on the backward fault traveling-wave, which has stronger resistance to interference from transition resistance and operates at a faster speed than other methods. Additionally, this paper provides the time-domain expression of the traveling wave after multiple reflections between the fault point and the converter station port, enabling the calculation of the threshold setting for the criterion. The new direction criterion is tested in PSCAD/EMTDC, and simulation results demonstrate that it can accurately identify fault direction and has a strong ability to withstand transition resistance.

The extreme power output scenarios of renewable energy sources (RES) proposed new challenges to the safe and stable operation of the power system. Transmission expansion planning (TEP) with large-scale RES grid integration needs considering the risk of extreme scenarios. In this paper, an adaptive decision-making approach for the TEP problem based on planning-risk assessment-replanning iterative process is proposed. The method obtains massive temporal and spatial correlated wind-photovoltaic (PV) power output scenarios by generalizing the historical data to describe the uncertainties. A data-driven load loss risk assessment model (RAM) based on the power system’s actual operating state is built, referring to the degree of extreme scenario risks on the balance of supply and demand, and the probability of extreme scenario occurrence. The planning decision is progressively revised according to the risk assessment result. The Garver’s 6-bus system and the IEEE RTS 24-bus system are adopted as simulation cases. The results show that the optimal expansion plans achieve a balance between the economy and robustness, which verifies the effectiveness of the proposed method.

In order to improve the ability of the modular multi-level converter to quickly clear and isolate faults in the multi-terminal flexible DC grid, this paper analyzes the characteristics of current faults in the multi-terminal flexible direct current grid, introduces the comprehensive distance of current fault waveform, and proposes a line protection method based on the similarity of current fault waveform. This protection method firstly uses Euclid DTW (Dynamic Time Warping) distance and entropy weight method to process electrical quantity signals, calculates the comprehensive distance of current fault waveform of two converter stations, and builds the protection criterion based on the similarity of current fault waveform. Then, the fault identification is realized by using the calculated comprehensive distance, and the fault pole identification is realized by using the ratio of comprehensive distance between positive and negative poles. Finally, PSCAD was used to build a simulation model to output the fault data under different fault conditions, and MATLAB was used to process the fault data to verify the protection algorithm. The simulation results show that the proposed method has good resistance to high resistance, and its rapidity and adaptability are further improved. The research results will provide a theoretical basis for the safe and reliable operation of flexible DC power grid.

The objective of this paper is to determine the transient characteristics and sites of the pre-breakdown (pre-BD) in the spark conditioning under power frequency voltage (PFV) in vacuum. A multi-angle optical path system was built to record the arc of pre-BD at two viewing angles by one camera, realizing the determination of the pre-BD sites in spark conditioning. The recording rate of high-speed camera was 580,000 frames/s. The experimental results indicate that, in the spark conditioning under PFV, the pre-BDs continuously occurred at a random moment. Within several half-waves, the number of the pre-BDs could reach dozens of times, which was the key for the treatment of the electrode surface in conditioning. Under the application of PFV, the single pre-BD occurred at a random moment, in which would last for several microseconds and the peak value of the current would reach several amperes. Moreover, the pre-BD sites randomly distributed in the surface of the electrode and almost covered the whole surface of the electrode. The number of the pre-BDs in edge region of the electrode tent to be more. The pre-BDs took place independently in both time and space.