This paper addresses the frequency stability deterioration in low-inertia receiving-end power systems. An adaptive multi-stage coordinated frequency control strategy considering multiple state indicators is proposed. A frequency model of the low-inertia system is constructed, incorporating synchronous generators, wind farms, battery energy storage system (BESS), and voltage-source-converter-based multi-terminal high-voltage direct current (VSC-MTDC). A dynamic threshold correction mechanism based on equivalent inertia, rate of change of frequency, state of charge (SOC) of BESS, and wind power reserve capacity is introduced to achieve adaptive adjustment of stage switching boundaries. An adaptive BESS control strategy considering SOC constraints and a dynamic wind-storage power allocation strategy are designed. A VSC-MTDC cross-regional support method considering sending-end frequency status and power margins is also constructed. Simulation results show that, compared with the fixed-threshold method, the proposed strategy reduces frequency fluctuations and accelerates frequency recovery, while maintaining sending-end frequency stability within acceptable limits. The strategy exhibits good adaptability under various conditions, including load step disturbances of different magnitudes, random load fluctuations, wind speed sudden changes, and extreme operating conditions, fully leveraging the synergistic support advantages of multiple resources.
The widespread deployment of photovoltaic generation and wind power has introduced complex wideband disturbances, including harmonics, interharmonics, and supraharmonics, which cause conventional electric energy meters to deviate from their calibrated accuracy classes. IEC 62053-21 specifies accuracy requirements for Class 1 active-energy meters; however, the present composite dataset exhibits uncorrected errors of several percent under wideband conditions, highlighting the need for operating-condition-dependent correction. To address the frequency-dependent measurement errors in renewable grid-connected systems, a Mamba-transformer wideband metering error correction network (MTWEC) is proposed. The framework integrates a selective state space branch and a band-biased attention branch to simultaneously capture long-term error evolution and spectral coupling characteristics. A learnable gating mechanism adaptively fuses the two representations, while physics-constrained optimization enforces spectral consistency and energy conservation. Experiments on a composite dataset comprising inverter test-bench measurements, hardware-in-the-loop data, field recordings, and simulation samples demonstrate that MTWEC achieves a mean absolute error of 2.47 × 10−3 p.u. and an R2 value of 0.991, demonstrating improved error-estimation performance under the investigated wideband operating conditions. A three-month field deployment at a 300 MW hybrid station reduces the mean settlement deviation from 1.43% to 0.28%, while inference latency remains 6.8 ms per window, below the 10.24 ms window-update interval. These results demonstrate the effectiveness and practical potential of the proposed method for online wideband electric energy metering error correction in renewable grid-connected systems.
To reduce fossil energy consumption and carbon emissions in oilfield production, a solar-powered steam turbine generation (SSTG) combined heat and power system integrating solar collection and thermal storage is proposed for heavy crude oil exploitation, crude oil dehydration, storage tank temperature maintenance, and pipeline heat tracing. Thermal oil is used as the heat collection and storage medium, while water/steam is adopted as the working fluid for power generation. Based on the first and second laws of thermodynamics, mathematical models of the main components are established, and Aspen HYSYS is used to analyze the effects of thermal oil flow rate, evaporation temperature, evaporation pressure, condensation pressure, solar irradiance, and thermal storage capacity on system performance. The results show that the net power output of the SSTG system increases from 450.1 to 1065 kW as solar irradiance increases, with an increase in 136.61%, while the exergy efficiency decreases due to increased irreversibility. With increasing thermal storage capacity, the net power output decreases, but the exergy efficiency improves. Dynamic simulations indicate that the system has the longest effective operating period on the summer solstice, whereas both output power and operating duration decrease on the winter solstice. Energy and exergy flow analyses show that the system realizes cascade usage of solar energy, while the evaporator, heat exchangers, condenser, and working fluid circulation are the main loss sources. The economic analysis confirms the feasibility of the proposed system for clean and low-carbon oilfield energy supply.
Prediction methods that rely on a single temporal scale struggle to capture both the low-frequency trend information and high-frequency stochastic fluctuations in wind power time series, thereby limiting further improvements in forecasting accuracy. To improve the accuracy of wind power forecasting, this paper proposes a wind power prediction model based on multiscale decomposition and an attention-enhanced deep-learning approach. First, the original wind power sequence is decomposed into several intrinsic mode functions by complete ensemble empirical mode decomposition with adaptive noise. These intrinsic mode functions are classified by using fuzzy entropy, and intrinsic mode functions with similar complexity characteristics are clustered and reconstructed. Next, the high-frequency sequence is further decomposed using variational mode decomposition. Finally, the model uses temporal convolutional network combined with standard-deviation convolutional block attention module to extract local spatiotemporal features from decomposed sequences, followed by bidirectional long short-term memory network captures bidirectional temporal dependencies. The forecasting results of the decomposed sequences are aggregated to obtain the final prediction. Comparative experiments are conducted on wind power datasets from Turkey and France to evaluate the proposed model against other methods. The experimental results demonstrate that the proposed model achieves the best performance on both datasets in terms of error metrics, including mean absolute error, normalized mean absolute error, and root mean square error, while also attaining the highest coefficient of determination. These results indicate that, compared with the benchmark models, the proposed model can deliver more accurate wind power forecasts under complex wind speed fluctuation conditions.
The large-scale integration of wind and photovoltaic generation turns power-system dispatch into a strongly coupled spatiotemporal decision problem. Conventional optimization models become computationally demanding as system scale and uncertainty grow, whereas existing data-driven dispatch models usually rely on fixed topologies and cannot ensure physically admissible decisions. To address these issues, this paper proposes a physics-constrained spatiotemporal graph cooperative dispatch network (PC-STGCD) for multi-node coordinated scheduling of renewable-dominated power systems. First, an adaptive coupling graph fuses impedance-based electrical distance with similarity learned from node embeddings, so that weather-driven correlations among electrically remote stations can be identified. Second, a dual-branch encoder combines multi-head graph attention with multi-scale gated dilated convolution to jointly capture spatial cooperation and renewable fluctuations over different horizons. Third, power balance, ramping, branch-capacity, and storage-energy constraints are embedded into training as differentiable penalties, and a warm-started projection layer maps the network output onto the linearized feasible region during inference. On the modified IEEE 118-bus system, PC-STGCD reduces the operating cost by 6.94% and the renewable curtailment rate by 47.9% relative to the strongest baseline, limits the constraint violation rate to 0.43%, and produces a dispatch decision within 0.36 s; these improvements are statistically significant over five independent runs and persist under N−1 and N−2 contingencies without retraining. Validation on 1 year of measured data from a 236-node provincial grid yields an average cost saving of 5.84% and a 43.3% reduction in curtailment, demonstrating the potential of the proposed method for online cooperative dispatch of renewable-rich networks.
