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.

An online updated data-driven linear power flow (LPF) model based on regression learning is proposed in this paper. We obtain a quadratic power flow model through regression learning first, and then derive the normal and incremental forms of LPF models by Taylor expansion. The parameters of LPF model are updated online, which improves the generalization ability. After only one initial regression learning, the proposed data-driven LPF model avoids model retraining when updated. The new parameter of the proposed model is simply calculated according to the real-time measurement data. Therefore, the LPF model we proposed is accurate, generalizable, and greatly minimizes the data consumption and running time. Performance analysis verifies the superiority of the proposed method.

Emerging forecasting-aided state estimation (FASE) frequently encounters complicated parameter analysis and observation calculation tasks, especially when confronted with intricate and uncertain scenarios. To this end, a concise FASE estimator is developed by combining the precise depiction of dynamic state change and linear power flow approximation. Designing the dynamic system state as a voltage perturbation vector around the nominal value, the forecasted state is firstly derived from the linear approximation of power injection equation solutions. The state forecasting model relies solely on nodal impedance information as the state transition matrix, eliminating the onerous parameter tuning effort. After that, the optimal filtered state is efficiently obtained utilizing line power flow measurements, with branch admittance information to construct the approximate observation matrix. Numerical simulation comparisons on a symmetric balanced 56-node distribution system verify the performance of the proposed estimator in terms of accuracy and robustness.

The multi-energy complementary power generation system with “wind-wind fire storage” as a typical representative is an important path to promote the symbiotic and complementary collaborative development of thermal power and new energy. Under the background of the construction of new power system and the integration of wind-wind, fire and storage, the scientific and rational configuration optimization of multi-energy complementary power generation system has become an important prerequisite for its sustainable development. In this paper, a two-layer optimization model for multi-energy complementary power generation system configuration based on particle swarm optimization is proposed. The outer layer optimization aims to maximize the net income of the wind-wind storage power generation system, and the inner layer optimization aims to minimize the carbon emission of thermal power units, so as to realize the balance between environmental benefits and economic benefits of the multi-energy complementary power generation system. The research results provide a reference for the planning and configuration of wind-wind-fire storage complementary power generation system.

Reliability is a fundamental requirement of wide-area backup protection (WABP) systems, and it is one of the most crucial performance indicators for such a WABP system. Two definitions are introduced: the reliability of WABP systems and the undetectable probability of a power line. They are determined by the specific placement of communication links (CLs) and phasor measurement units (PMUs). A simultaneous optimization model is formulated to minimize the construction cost of WABP systems by optimizing the partitioning and placement of PMUs, protection centers (PCs), and CLs. The model takes into account several constraints, including latency, regional balance, and system reliability. To reduce computational complexity, a cluster-based genetic algorithm is developed to determine the optimal solution. Finally, numerical simulations are conducted using IEEE test cases. Results demonstrate that the proposed method can minimize the construction costs of WABP systems while increasing their reliability.

Due to influences of a control strategy reliant on photovoltaic(PV) inverters, weak feedback of PV power plants and controlled characteristics of current phases make the measured impedance of PV-side distance protection of outgoing transmission lines unable to reflect the location of faults. In this paper, the solution equations of the line short-circuit impedance are accurately derived according to the fault component sequence network diagram of the transmission line. Based on the superconducting magnetic energy storage (SMES) connected to the DC bus of the photovoltaic station, the traditional low-voltage-ride-through(LVRT) control strategy is changed. The unknowns in the equations are eliminated by the control and protection coordination, and then the short-circuit impedance is solved. Compared with the existing methods, there is no approximate calculation in this method, and the accuracy of the calculation is greatly improved. In addition, while reasonably solving the unreliable action of distance protection, it also takes into account the reactive power support of the photovoltaic station for the power grid during the fault period. Instead of being weakened, the LVRT capability is even enhanced.

In interconnected microgrids, the control method for Soft Open Point (SOP) dynamically switches from PQ to Uf control after fault incidents to preserve system stability. However, this mode switching induces frequency and voltage fluctuations, jeopardizing the operational stability of distributed generations (DGs). This paper presents a seamless mode switching control strategy specifically tailored for SOP-based interconnected microgrids incorporating Electric Vehicle (EV) clusters. We investigate the fluctuations and transient impacts associated with emergency control switching and propose a decentralized Virtual Synchronous Generator (VSG) control strategy for energy storage systems. This strategy factors in the State of Charge (SOC) of batteries to mitigate imbalances between source and load during switching delays. Additionally, we introduce a seamless switching method encompassing collaborative phase angle compensation, control mode transition, and pre-synchronization techniques to minimize transient disturbances. Validation through PSCAD/EMTDC simulations and RT-LAB experiments shows effective suppression of fluctuations and transient impacts, thus enabling seamless mode switching in interconnected microgrids.

This paper proposes three-phase steady-state models for distributed generators (DGs) with different negative control strategies. An augmented rectangular load flow method is used for three-phase distribution systems. For two types of DGs with power electronic devices: photovoltaic power generators and doubly-fed induction generators, the nodal voltage equations and output power equations are derived. Moreover, under unbalanced conditions, the strategies for mitigating the effects caused by negative sequence components are formulated as functions of the nodal voltage and the injected current. Furthermore, the models are incorporated into an augmented rectangular load flow method with nodal voltage and injected current as state variables in three-phase systems. Case studies are conducted to verify the correctness and accuracy of the proposed models by comparing the results obtained from MATLAB and PSCAD.

Resilience is one of the main features of smart distribution network, and a microgrid access to the distribution network provides an effective way to improve resilience. Microgrid and distribution network belong to different interests, so it is necessary to use price leverage to actively guide microgrids and flexible resources within microgrid to participate in post-disaster restoration of distribution network and enhance the resilience. Firstly, this paper proposes a dynamic restoration electricity price response mechanism for distribution network after extreme disasters and constructs a power response model for loads and electric vehicles within the microgrid. Secondly, the optimal scheduling model of distribution network with multiple-microgrid is proposed to improve the restoration rate of critical loads(RRCL) in the distribution network. Single microgrid achieves the largest microgrid revenue and restoration contribution, and multi-microgrid uses the power headroom index to optimize the dynamic restoration electricity price to achieve the smallest power purchase cost of distribution network. Finally, the optimal scheduling method for resilience enhancement of distribution networks with MMG considering dynamic restoration electricity price response mechanism is validated by dual microgrid access to IEEE 33-node distribution system. The simulation results show that the proposed optimization method effectively improves the RRCL of distribution network.

Offshore wind power is exhibiting an escalated development trend in scaling, clustering, and deployment in deep-sea. Due to the decentralized distribution of offshore wind farms and the large number of onshore optional access points, the selection of a feasible topology solution is challenging and difficult. Therefore, a novel topology optimization method for multiple offshore wind farms integrated with HVDC is proposed. Three indexes are presented in this paper: HVDC submarine cable line investment cost index, HVDC short-circuit ratio equalization index, and HVDC submarine cable network topology robustness index. These indexes represent the economy, safety, and robustness of the system. Secondly, the optimization model for the offshore wind power HVDC transmission network topology is constructed by combining the indicators mentioned above and HVDC operation constraints. Finally, the effectiveness and practicality of the algorithm is demonstrated using the example of an 8-node system containing land and sea switching stations.

With the diversification of electricity price structures, an increasing number of power utilities have incorporated demand charges into their tariff structures. Individual households equipped with photovoltaic (PV) and energy storage systems can adapt to this trend by adopting appropriate energy management strategies. Due to the high uncertainty in photovoltaic and load generation for household users, it is often challenging for them to make the most advantageous choice among the diverse electricity tariffs. This paper proposes a rolling prediction method based on Long Short-Term Memory (LSTM) networks for monthly peak power demand, taking into account historical peak power and applying corrective measures within the same month. Additionally, a profit evaluation method for electricity tariff schemes considering forecast uncertainties is presented. The predictive capabilities of load and PV power are characterized using kernel density estimation, and a large number of scenarios are generated using Monte Carlo simulation. A probabilistic economic evaluation is conducted for different tariff schemes, enabling the optimal selection of electricity tariffs. To validate the effectiveness of the proposed methods, analysis is performed using household data from Arizona, and the results demonstrate that the proposed methods can reduce electricity expenses and help households choose the correct electricity tariff scheme.

Apart from technical factors such as hydrogen production efficiency, the proper geographical distribution of renewable energy bases is essential for promoting large-scale hydrogen production from renewable energy sources on a provincial scale. This paper establishes a hydrogen production capacity planning method that takes into account the distribution of renewable energy bases. Based on the trading pattern of the hydrogen energy market in regions with a high proportion of renewable energy, a non-cooperative game pattern of hydrogen production capacity is constructed, where energy hubs acts as players, hydrogen production capacity acts as a strategy, and the minimum cost of hydrogen is a payment. On this basis, hydrogen production, transportation and consumption models are constructed by combining renewable energy utilization rate, population, and transportation network. Thereby, a non-cooperative game planning method for provincial hydrogen production capacity is proposed, and the gaming problem is transformed into an optimization problem solved iteratively to minimize the global hydrogen production cost. Furthermore, the provincial hydrogen production capacity planning arithmetic is constructed with the data of Qinghai Province. The equilibrium solution of the game with eight energy hub nodes as players is obtained, and the effectiveness of the model is verified. Finally, the impacts of population changes, installed renewable energy production capacity and industrial electricity prices on hydrogen production capacity planning and regional hydrogen costs are quantitatively analyzed.

Transformer inrush current occurs due to the saturation of magnetic core. The magnetic characteristic of the core directly affects the inrush current waveform. Second harmonic and higher-order harmonics are present in the inrush harmonic spectrum. A harmonic-based method analyzing the amplitude and phase of different harmonics is presented in this paper. Based on the proposed method, the differences between the harmonic spectrum of new low-loss and previous generation materials are investigated. Then, using the patterns of 1st to 10th harmonic phases, a novel method for inrush current detection is proposed. In the proposed method, possible patterns for the harmonic phases related to the inrush current are specified. The harmonic phases are compared with those specified patterns. In the case of matching, the inrush current condition is detected. To evaluate the theoretical principles, simulations are performed, also, the proposed method is applied to the field data to assess the practical performance, and results are presented.

Hydrogen energy plays an important role in improving the operation efficiency and reliability of power systems with high renewable energy penetration. Hydrogen to power (HtP) system is the key link of hydrogen applications. However, the single HtP equipment is limited in power output range and efficiency. Hybrid HtP system is an important scheme to realize the performance complementary. However, the hybrid HtP system is coupled with hydrogen system and power system. The characteristics of each subsystem and coupling characteristics are complex. In addition, the multiple support potential of hybrid HtP system for power system needs to be fully explored. In this paper, firstly, a power system including traditional units, high penetration renewable energy generation and hybrid HtP system is established. Secondly, a bi-level hybrid HtP system optimal configuration model including planning and operation is constructed. Then, a bi-level optimal configuration method based on improved PSO algorithm is proposed. Finally, the optimization model and its solution method are applied to IEEE one area RTS-96. The discussions based on the optimization results show that the hybrid HtP system has significant contributions in optimizing the output cost of traditional units, improving the utilization of renewable energy, and reducing load shedding losses.

Integrated Demand Response is increasingly recognized as a crucial tool for maintaining balance between supply and demand in integrated energy systems, and for enhancing the absorption and utilization rate of renewable energy. A prerequisite for implementing IDR effectively is the accurate modeling of consumer characteristics. However, existing models have not accounted for the coupling characteristics of inter-energy comfort and inter-period incentives when consumers participate in IDR; they also overlook the Inter-consumer coupling characteristics. This paper presents an inter-energy comfort coupling matrix and a cross-elasticity coupling matrix for incentives, offering effective modeling of the coupling characteristics between different energies and periods. By introducing a dynamic participation rate, a mathematical model of inter-consumer coupling characteristics is established. Simulation results demonstrate that the model considering inter-energy coupling characteristics produces significant reductions in response power deviations and total costs of IESP, while also enhancing consumer motivation to participate in IDR. The model that incorporates inter-period coupling characteristics not only improves consumer benefits and comfort levels in participating in IDR but also further reduces response power deviations and total costs of IESPs. The model considering inter-consumer coupling characteristics effectively mitigates response power deviations resulting from consumers’ bounded rationality by calculating the mathematical expectation of each consumer’s response power.

Affected by inverter fast control and commutation failure, most protection schemes for AC lines connected to LCC-HVDC inverter station(ACLs) have the problems of complex setting, weak anti-noise and high demand for data synchronization. This paper proposes a pilot protection scheme based on high-frequency transient current waveform similarity. Firstly, with analyzing the fault traveling wave propagation process, the obvious differences are found in the transient current waveforms at both ends of the line when internal and external faults occur. Then the similarity of transient current waveforms is measured with the improved longest common similar subsequence (LCSS) method. And the weak high-frequency current is extracted with the improved synchronous squeezed S-transform (SST) method. Finally, a large number of PSCAD simulation results show that the proposed protection method does not require strict data synchronization, has strong anti-noise performance, and is tolerant to the influence of abnormal point fluctuations.

Insulators are one of the key components in high-voltage power systems that prevent transmission lines from grounding. Since they are exposed to different kinds of harsh environments and climates, periodic inspection is indispensable for the safety and high quality of power grid. Nowadays, Unmanned Aerial Vehicle (UAV) inspection is more widely used, facilitating incorporation of CNN-based detectors in the insulator detection task. However, these methods are generally based on the assumption that the image samples are balanced among different categories and possess completely ideal annotations. The problem of sample imbalance or incomplete annotation is rarely investigated in depth for insulator defect detection. In this paper, we focus on insulator defect detection with imbalanced data and incomplete annotations. Our proposed framework, named Pi-Index, introduces Positive Unlabeled (PU) learning to solve the problem of incomplete annotation and designs a novel index the class prior, which is a key parameter in PU learning. Moreover, focal loss is integrated in our framework to alleviate the effect of sample imbalance. Experiment results demonstrate that the proposed framework achieves better performance than the baseline methods in situations of sample imbalance and missing annotation.