Zhao Luo

This paper addresses the challenge of frequency stability in power systems with high penetration of renewable energy by proposing a regulation strategy for a hybrid energy storage system (HESS) comprising electrochemical and flywheel storage. The strategy integrates virtual inertia and virtual droop control, with an adaptive fuzzy logic controller dynamically adjusting the weighting coefficients between them. This coordination enables the HESS to provide rapid power support and sustained energy supply. Simulation results under load step disturbances demonstrate that the proposed control strategy effectively suppresses frequency fluctuations and enhances dynamic response, outperforming methods using single control loops or single-type energy storage.

With the increasing integration of high-proportion renewable energy, power systems are exhibiting low-inertia and low-damping characteristics, posing severe challenges to frequency stability. This paper proposes a coordinated supplementary frequency regulation strategy utilizing electrolytic aluminum (EA) loads and a hybrid energy storage system (HESS). Firstly, a system frequency response model is established, incorporating EA, electrochemical energy storage, pumped hydro storage, and conventional generation units. Secondly, an improved variable filter time constant controller is designed, supplemented by fuzzy logic, to achieve adaptive power allocation under different disturbance magnitudes. Concurrently, regulation intervals are defined based on the area control error (ACE), enabling a tiered response from source-grid-load resources. Simulation results demonstrate that under a severe disturbance of 0.05 p.u., the proposed strategy reduces the maximum frequency deviation from 0.198 Hz to 0.054 Hz, achieving a 72.7% performance improvement, and shortens the system settling time by 59.5%. Furthermore, the state of charge (SOC) of the electrochemical storage is successfully maintained within the range of [0.482, 0.505], effectively balancing frequency regulation performance and device lifespan. The findings demonstrate the effectiveness of the proposed strategy in enhancing the frequency resilience of low-inertia power grids.

To address the challenges of insufficient frequency regulation resources and diverse response capabilities in the Yunnan power grid caused by large-scale integration of renewable energy, this paper proposes a cooperative frequency regulation strategy for a hybrid energy storage system incorporating electrolytic aluminum load. First, the frequency regulation model is established for the integrated system comprising electrolytic aluminum load, abandoned mine pumped storage power station, and electrochemical energy storage. A frequency regulation method for electrochemical energy storage is designed, considering control mode weighting factors and state-of-charge (SOC) recovery characteristics. Subsequently, an improved filter with variable filtering time constants is developed based on the area control error (ACE). The high-frequency and low-frequency signals output by the filter are compensated by electrochemical energy storage and abandoned mine pumped storage, respectively. Furthermore, a frequency regulation strategy accounting for frequency regulation zone division is designed. Finally, simulation results under typical scenarios demonstrate that the proposed strategy effectively improves the SOC characteristics of electrochemical energy storage and enhances the frequency regulation performance of the hybrid energy storage system (HESS), while preventing overcharging and over discharging to extend the lifespan of energy storage devices.

As an energy-intensive heavy industry, the coal mining industry plays a key role in achieving energy conservation and emission reduction. This study presents an energy-carbon efficiency improving strategy aimed at reducing carbon emissions and energy consumption in mining areas by integrating gravity energy storage (GES) with carbon capture and power-to-gas (P2G) technologies. Utilizing the natural topography, abandoned mines can be converted into gravity energy storage systems to stabilize the fluctuating outputs of renewable sources, such as wind power (WP) and Photovoltaic (PV). Concurrently, carbon capture, utilization, and storage (CCUS) technology capture CO₂ in mining areas, which is then transformed into methane using P2G systems. This methane is mixed with low-concentration coalbed methane to create a high-concentration combustible gas, facilitating carbon recycling and a collaborative multi-energy supply. The efficacy of the model was demonstrated through comparative simulations, which showed the utilization rate of renewable energy has increased, leading to a significant reduction in the total cost of the system and carbon emissions. This study confirms that optimizing gravity energy storage with CCUS-P2G effectively resolves the energy supply demand imbalance in mining areas, achieving economic and low-carbon advancements and offering theoretical support for the sector's low-carbon transition.

In the context of the clean and low-carbon transformation of power systems, addressing the challenge of day-ahead electricity market price prediction issues triggered by the strong stochastic volatility of power supply output due to high-penetration renewable energy integration, as well as problems such as limited dataset scales and short market cycles in test sets associated with existing electricity price prediction methods, this paper introduced an innovative prediction approach based on a multi-modal feature fusion and BiGRUSA-ResSE-KAN deep learning model. In the data preprocessing stage, maximum–minimum normalization techniques are employed to process raw electricity price data and exogenous variable data; the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods are utilized for multi-modal decomposition of electricity price data to construct a multi-scale electricity price component matrix; and a sliding window mechanism is applied to segment time-series data, forming a three-dimensional input structure for the model. In the feature extraction and prediction stage, the BiGRUSA-ResSE-KAN multi-branch integrated network leverages the synergistic effects of gated recurrent units combined with residual structures and attention mechanisms to achieve deep feature fusion of multi-source heterogeneous data and model complex nonlinear relationships, while further exploring complex coupling patterns in electricity price fluctuations through the knowledge-adaptive network (KAN) module, ultimately outputting 24 h day-ahead electricity price predictions. Finally, verification experiments conducted using test sets spanning two years from five major electricity markets demonstrate that the introduced method effectively enhances the accuracy of day-ahead electricity price prediction, exhibits good applicability across different national electricity markets, and provides robust support for electricity market decision making.

Reactive power optimization (RPO) is a key task in the operation and control of active distribution networks (ADNs). The nonlinear power flow constraints and the integers introduced by voltage regulator (VR) constraints make the nonconvex RPO model difficult to solve. In this paper, a RPO model is proposed considering the nonuniform tap ratios of VRs. The three phases in VRs are independently controlled and the phase coupling effects are strictly considered. Moreover, to deal with the bilinear relationship among continuous complex voltage phasors and discrete tap ratios, the voltage phasor matrix is decoupled into real and imaginary parts, and a status variable method is proposed to exactly linearize the bilinear terms. Further, the nonlinear power flow constraints are relaxed to a set of second-order cone constraints without neglecting the coupling effect among the three phases and are proved to be equivalent to the semi-definite constraints, which does not require the rank-1 verification and is more efficient and scalable. Thus, the original nonconvex RPO for three-phase unbalanced ADNs is simplified to a mixed-integer second-order cone programming (MISOCP) model. Case studies validate the model’s effectiveness, and the computational efficiency meets practical requirements. Note to Practitioners —This paper simplifies the complex problem of reactive power optimization for active distribution networks with unbalanced three-phase power flows. We offer a novel RPO model improving the typically nonconvex and nonlinear optimal power flow problem, and addressing the particular challenges imposed by voltage regulators optimization with nonuniform tap ratios. Independent control of the VR phases and accurate incorporation of phase coupling effects have been realized, greatly simplifying the optimization process. The transformation of nonlinear power flow constraints into a computationally efficient set of second-order cone constraints eliminates the need for cumbersome rank-1 verification. Our mixed-integer second-order cone programming model integrates smoothly with standard optimization procedures, improving voltage regulation and network stability. The effectiveness and efficiency of the approach have been validated in case studies, offering a viable tool for industry application.

In engineering applications, the accuracy of on-load tap changer (OLTC) mechanical fault identification methods based on vibration signals is constrained by the quantity and quality of the samples. Therefore, a novel small-sample-size OLTC mechanical fault identification method incorporating short-time Fourier transform (STFT), synchrosqueezed wavelet transform (SWT), a dual-stream convolutional neural network (DSCNN), and support vector machine (SVM) is proposed. Firstly, the one-dimensional time-series vibration signals are transformed using STFT and SWT to obtain time–frequency graphs. STFT time–frequency graphs capture the global features of the OLTC vibration signals, while SWT time–frequency graphs capture the local features of the OLTC vibration signals. Secondly, these time–frequency graphs are input into the CNN to extract key features. In the fusion layer, the feature vectors from the STFT and SWT graphs are combined to form a fusion vector that encompasses both global and local time–frequency features. Finally, the softmax classifier of the traditional CNN is replaced with an SVM classifier, and the fusion vector is input into this classifier. Compared to the traditional fault identification methods, the proposed method demonstrates higher identification accuracy and stronger generalization ability under the conditions of small sample sizes and noise interference.

Electrical energy substitution is an important way to achieve the optimization of the energy consumption structure as well as to alleviate environmental problems, and it is also an important source of benefits of the integrated energy system. However, there are few works that study the effects of electrical energy substitution on the construction of the integrated energy system (IES) and electrical energy substitution work without incentives carried out in the IES. To this end, this paper proposed a G1 method with constructing consistency matrix to determine the evaluation index weights for the IES with electrical energy substitution. Specifically, we firstly construct the evaluation index system for the IES including electrical energy substitution indicators, low-carbon indicators, technical indicators and economic indicators as well as their secondary indicators. Then, a tri-level evaluation index system of target-criteria-indicator benefits is established based on pertinent standards and norms, taking the practical operability into account. Finally, a G1-method-constructed consistency judgment matrix is proposed. Compared with the G1 method, it is simple and practical, and the weight calculation results are more in line with the reality, which effectively solves the consistency problem of the judgment matrix. The rationality and feasibility of the proposed weight calculation method are verified by the calculation and analysis of an example.