The iron core in the circuit breaker of oil dashpot is mainly affected by the electromagnetic force, spring force and oil damping force, which can play the function of anti-time protection when overload current is excited. At low temperature, the viscosity of the methyl silicone oil damping fluid will change, and the parameters of spring and iron core will also change with temperature. The change of this parameters will lead to the change of the resultant force on the iron core, which will affect the operation time. To analyze this problem, a correlation model is established. Through the measurement of parameters at low temperature, numerical calculation, electromagnetic analysis, simulation analysis and compared with the experimental results. The study found that the viscosity of the damping fluid increases, the iron core is deformed, and the spring stiffness increases at low temperature. The influence of a single factor on the operation characteristics of the tripper is not enough to reflect its overall characteristics, and the comprehensive effect of each factor needs to be considered. After comprehensively considering all factors, the results are closer to the experimental results. The results provide a theoretical basis for the optimization design of the circuit breaker.

Energy routers based on the electronic power transformer are suitable for the AC-DC hybrid grid with multiple voltage levels, but their structures are complex. This paper proposes a novel energy router based on the multi-winding line frequency transformer. By a combination of a multi-winding line frequency transformer and power electronic devices, the proposed energy router can take advantage of the high reliability of the multi-winding line frequency transformer and the high controllability of power electronic devices. The proposed energy router is suitable for the AC-DC hybrid grid with multiple voltage levels and has the characteristic of a simple structure. The simulations and experimental results demonstrate the effectiveness of the proposed energy router.

Integration of Distributed Energy Resources (DERs) in power systems exacerbates the existing information problems between power utilities and regulators. DER policies oblivious to the trilemma of information asymmetry between power utilities, DER aggregators, and regulators result in distorted price signals to DER investors, and socially inefficient DER roll-out. Therefore, in this paper, we propose a game-theoretic approach for modeling information asymmetry in distribution network information and consumer data between the DER aggregators and the power utilities. The proposed framework is based on Single Leader Single Follower (SLSF) games, reformulated as Mathematical Programs with Equilibrium Constraints (MPECs), and solved using the Scholtes’s relaxation technique. Our results, based on the 7-bus Manhattan power network, show that unless the DER aggregators have complete information about the distribution network characteristics, the welfare along with the realized installed capacity of DERs in the system decreases. Moreover, progressively decreasing DER investment costs alleviate the effects of information asymmetry, suggesting that early adopters face disproportionately high welfare losses attributed to incomplete information between the DER stakeholders. Hence, policy interventions to alleviate the rampant information problems are imperative to ensure an optimal DER roll-out.

This paper proposes a recurrent neural network (RNN) based model to segment and classify multiple combined multiple power quality disturbances (PQDs) from the PQD voltage signal. A modified bi-directional long short-term memory (BI-LSTM) model with two different types of attention mechanism is developed. Firstly, an attention gate is added to the basic LSTM cell to reduce the training time and focus the memory on important PQD signal part. Secondly, attention layer is added to the BI-LSTM to obtain the more important part of the voltage signal by assigning weightage to the output of the BI-LSTM model. Finally a SoftMax classifier is applied to classify the combined PQD signal in 96 different combinations. The performance of proposed BI-LSTM model with attention gate and attention layer mechanism is compared with the performance of baseline models based on recurrent neural network (RNN) and convolution neural network (CNN). With this model, the PQD signal is easily segmented from the voltage signal which makes the process of PQD classification more accurate with less computation complexity and in less time.

Recently, power interruptions with high impact effects are increased by occurring natural disasters. Furthermore, the uncertain nature of data creates the considerable challenges for enhancing the resiliency of power distribution systems after occurring events. Regarding this, this paper presents a robust optimization approach for resiliency improvement in power distribution system. The robust approach uses the crew teams for switching action as restoration process, demand response programs and Mobile Generators (MGs) simultaneously for improving the resiliency with considering the uncertainty of electrical load and electrical price. The objective function is tri-level consists of minimum, maximum and minimum function. The first level is minimum function that is for minimizing cost of commitment of CHPs with considering location of MGs and reconfiguration structure in power distribution systems. The second level is maximum function that is for finding the worst-case scenario of the uncertainty variables. The third level is minimizing total operation cost in the condition with worst scenario of stochastic data by using demand response programs. The aforementioned algorithm is implemented on an IEEE 33-bus test distribution system with four different cases. Furthermore, several cases and sensitivity analysis are accomplished in order to show the efficiency of the proposed model.

Electric Vehicles (EV) have the potential to completely transform the transportation industry, slash carbon emissions significantly, and pave the road for significant climate progress. According to surveys, worries regarding range and charging accessibility are a significant barrier to EV customer adoption. The electricity grid will be significantly impacted by EV charging. A coordinated charging approach that facilitates EV charging while avoiding the power grid from overloading is required to manage EV charging. In this work, the EVs are classified based on their charging capacity. This classification includes Car, truck and bus. With this a chargingdischarging scheme has been established using Real Time Pricing (RTP) for each hour for a 20 min block. Augmenting this, in the present work the power loss is analyzed when Distributed Generation (DG) units are installed after the EVs are located in the system for charging and discharging. The DG units are installed considering the Voltage Stability Index (VSI) and the power losses incurred due to the presence of EVs. The Monetary benefit of such an investment is analyzed for different topologies of IEEE-33 and 69 bus systems and compared with the existing methods where DGs are installed before the EVs are located in the system for charging and discharging.

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.

The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data-driven models currently used assume that the input data is complete, which has obvious limitations in real-world applications. This paper suggests an IVS-GAN model to assess power system stability using incomplete PMU measurement data with random loss. The proposed method combines the super-resolution perception technology based on Generative Adversarial Network (GAN) with a time-series signal classification model. The generator adopts a one-dimensional U-Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new GRU-Attention architecture proposed in this paper to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10-machine 39-bus system.

With the rapid development of active distribution networks, the “petal”-type distribution network has become the mainstream power supply structure. To ensure the safe and reliable power supply of a distribution system, power control methods for active distribution networks should be further studied. An electromagnetic rotary power flow controller (RPFC) is a feasible solution for active distribution network power control. However, when testing the effectiveness of the PQ decoupled control method for RPFC based on instantaneous reactive power theory, difficulties were encountered with the synchronous control of the rotor position angle of two rotating-phase transformers, and the accuracy of power control was unsatisfactory. Given this condition, PQ control is improved in three ways. First, the system periodic oscillation problem is solved via variable speed control. Second, the servo motor–rotary phase-shifting transformer synchronous rotation scheme is designed, reducing power control error and improving stability. The overshoot phenomenon in power control is improved using the variable domain fuzzy proportional–integral adaptive method. Experimental results show that the proposed advanced control scheme exhibits good dynamic and static performance in power control scenarios and achieves effective improvement of RPFC.

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 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.

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.

The indirect benefits of battery energy storage system (BESS) on the generation side participating in auxiliary service are hardly quantified in prior works. Nevertheless, the configuration of BESS could be affected by its indirect benefits. In this paper, we purpose a quantitative economic evaluation method of BESS considering the indirect benefits from the reduction in unit loss and the delay in investment. First, we complete further the cost model of BESS for frequency and peak regulation based on the whole life cycle theory. Second, we quantify the indirect benefits of BESS in thermal power plants based on the theory of rotor fatigue life loss and establish a benefits model that considers the unit loss reduction during frequency regulation and the delay in investment during peak regulation. Finally, we propose a set of indexes for economic evaluation of the thermal power plant with BESS. The simulation results show that the total benefits of BESS can be improved effectively by considering the indirect benefits from unit loss reduction and the delay in investment, proving the effectiveness of the proposed approach which can be meaningful for the future investment in BESS on the generation side.

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.

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.

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.

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.

Decision-dependent (endogenous) uncertainties (DDUs), as a new type of uncertainties revealed recently, couple dispatch decisions with uncertainty parameters and thus render power system dispatch more challenging. However, most previous works handled various DDUs via stochastic programming (SP) or robust optimization (RO) in a two-stage framework, which undoubtedly introduces the drawbacks of SP and RO, and cannot meet the nonanticipativity requirements in power scheduling. In this paper, we propose a multistage distributionally robust optimization (DRO) method for generation dispatch with demand response (DR) considering the DDUs of deferrable loads and the decision-independent (exogenous) uncertainties (DIUs) of wind power and regular loads. By analyzing the structure of decision-dependency parameters, a novel data-driven decision-dependent ambiguity set is proposed, which provides a generic framework for formulating DDUs and DIUs simultaneously. Then a multistage DRO model with nested max-min structure is developed to integrate the merits of DRO and nonanticipativity into generation dispatch. The proposed model is solved by tailored reformulation method and improved stochastic dual dynamic integer programming (SDDiP). Case studies illustrate the effectiveness of the proposed approach by comparing with the multistage SP, RO, and decision-independent DRO methods.