Thermal power units remain important primary frequency regulation (PFR) resources in power systems with high renewable-energy penetration. Standard turbine models used in power-system studies usually represent the turbine body by steam-volume time constants and power fractions; therefore, they cannot explicitly describe regenerative-system thermal storage or PFR technologies that act through regenerative heaters, such as condensate throttling (CT) and high-pressure heater feedwater bypass regulation (HFBR). To address this gap, this paper develops a dynamic PFR model by coupling a thermodynamic turbine-body model with dynamic heat-transfer models of the regenerative system. The proposed model represents the relationship between pressure and flow of turbine stage groups, extraction-steam piping, surface regenerative heaters, and the deaerator, and its initial state and operating-condition-dependent parameters are identified from plant DCS data and design information. Experimental tests on actual 600-MW units under control valve pre-throttle (CVPT), HFBR, and CT conditions are used for validation. For CVPT, the minimum dynamic power-output accuracy of the proposed model is 92.86%, improving the corresponding accuracy by 55.86% and 48.41% over the constant-shell-side-pressure model and the standard turbine model, respectively. For HFBR and CT, the maximum absolute power errors are 1.64 MW and 2.00 MW, corresponding to error reductions of 59.00% and 24.53% compared with the simplified comparison models. The results show that coupling between the turbine body and regenerative system is essential for evaluating short-term PFR capability under both conventional and regenerative-system-based PFR technologies.

The rapid advancement of global healthcare systems has significantly accelerated the generation of medical waste. To address this, the current study proposes a novel hybrid system designed to ensure the safe and clean disposal of medical waste while promoting its resource-efficient utilization. In this system, medical waste is first safely treated in a plasma gasifier with the syngas produced transformed into flue gas sequentially via a solid oxide fuel cell and a combustion chamber. The flue gas is finally directed to a carbon capture unit, ensuring the system is low-carbon and clean. Additionally, the system integrates an organic Rankine cycle, a supercritical CO2 cycle, a gas turbine, and desalination, enabling the cascade utilization of waste heat from both syngas and flue gas, as well as electricity-water cogeneration. Thermo-economic analysis shows that the electrical and exergy efficiencies of the system reach 55.81% and 53.07%, respectively. The primary source of exergy loss is the solid oxide fuel cell, contributing 10.83%. The system achieves payback within 5.83 years, yielding a net profit of 113,785.89 k$. Thus, this study offers a compelling solution for the sustainable and efficient management of medical waste and meets the growing demand for water and electricity resources.

Accurate simulation of primary frequency regulation (PFR) response of thermal power units is important for frequency safety of power systems. Meanwhile, the PFR capability of the units has shown significant differences in different working conditions. At present, the PFR model of thermal power units commonly used in power system simulation neither includes the boiler model nor considers the variation of working conditions, and the simulation results differ significantly from actual responses in large frequency deviation disturbances. This paper first builds a detailed thermal model of a coal-fired unit that includes boiler dynamics. Then, by simplifying the boiler physics or/and control dynamics, three simplified models are obtained. The effectiveness of these models is studied using actual operating and test data. Comparing the simulation results of different models in frequency disturbance, the trend of model accuracy changing with disturbance size and unit load rate is demonstrated. Finally, the conclusion is that the dynamic control of the boiler has a relatively small impact on the PFR response, but the influence of boiler physical dynamics increases with the increase frequency disturbance size, and the boiler model is the key to reflecting the PFR capability variation with working conditions. The conclusion of this paper clearly inditcates the necessity of including boiler models in PFR studies and recommends appropriate model simplification, which is helpful for the selection of thermal power unit models in frequency safety assessment of power system.

As the share of renewable energy sources increases, thermal power units remain crucial for maintaining system flexibility through fast-ramping capability, especially where hydropower is limited. Existing coal consumption models of thermal power units based on steady-state operations fail to capture the additional coal use induced by fast-ramping, potentially leading to inaccuracies in scheduling and cost assessment. This study develops a field-test-based, heat balance-informed bivariate coal consumption modeling framework for flexible thermal power unit scheduling. First, a multi-point field measurement design is employed to collect high-resolution steam-side heat parameters from a retrofitted high-performance unit, enabling coal consumption quantification at minute-level intervals under ramp-up, ramp-down, and steady-state conditions. Second, a scheduling-oriented bivariate model is developed, incorporating both current output and output changes between adjacent dispatch intervals to capture ramping-induced coal deviations. Third, numerical and probabilistic simulations are conducted to assess potential coal consumption shifts under high renewable penetration with frequent ramping. Results show that fast-ramping operations can cause deviations exceeding 10% from steady-state coal use, with asymmetries of around 4% between ramp-up and ramp-down operating conditions.

With the increasing penetration of renewable energy, coal-fired thermal power units are required to provide reliable primary frequency regulation (PFR) under ultra-low load conditions (below 40% turbine heat acceptance (THA)), where conventional grid-oriented models using a fixed boiler heat storage coefficient may lead to large simulation errors. To address this problem, this study develops a dynamic PFR model for subcritical drum-boiler units that explicitly describes the boiler's transient heat transfer, phase change, and pressure/flow coupling within the PFR time scale. To make the model applicable under variable operating conditions, a steady-state distributed control system (DCS)-based initialization and parameter-identification method is further proposed, so that key boiler parameters can be updated without disturbance tests. A 600 MW subcritical thermal power unit is used for validation under 30% and 40% turbine heat acceptance. The results show that the proposed model improves the average accuracy of power output simulation from 77.91% to 91.70% and the average accuracy of main steam pressure simulation from 90.58% to 96.93%, compared with the standard model. The model also reproduces the rapid steam-flow increase, subsequent pressure drop, and corresponding power response during PFR more realistically. Furthermore, the mechanism analysis shows that the unit exhibits stronger PFR capability at 40% THA than at 30% THA, although the difference gradually narrows as the control valve opening increases. The proposed model provides a physically interpretable and computationally efficient tool for assessing the realistic PFR capability of subcritical thermal power units under ultra-low load conditions.

Medium- and long-term dispatching of renewable energy bases is an important method for ensuring large-scale transmission and consumption. However, most existing medium- and long-term dispatching methods ignore the uncertainties of wind and photovoltaic power output, resulting in excessive maintenance-window margins and insufficient regulation reserves. However, relevant studies that consider such uncertainties are mostly limited to short-term scheduling and are therefore inadequate for medium- and long-term dispatching needs. To this end, a two-stage robust optimal dispatch method for renewable energy bases that considers the impacts of wind and photovoltaic output uncertainties and unit maintenance is proposed. Firstly, the first stage decision variables consist of the on/off and maintenance statuses of thermal power units. Next, the output of each power source is taken as the conventional decision variables in the second stage, while the curtailed wind/photovoltaic power and load shedding are taken as the unconventional decision variables when the balance cannot be achieved by adjusting the power source output under the given wind and solar power output scenarios. In the end, a polyhedron set based on an uncertainty budget was adopted to describe the fluctuations in wind and photovoltaic output, and the minimum scheduling cost in the worst scenarios was solved using the column and constraint algorithm. A renewable energy base in Northwest China was selected as a case to validate the proposed model’s effectiveness. The results show that the proposed model significantly reduces the operating cost in actual operation compared to deterministic optimization and pre-maintenance robust optimization.

District heating networks (DHNs), due to their thermal storage capacity, can provide flexibility for integrated electricity and heat energy systems and improve wind power accommodation. However, the coordinated optimization of large-scale DHNs and integrated energy systems faces great challenges due to the large size and complexity of the DHN dynamic model. The combination of a single pipe's dynamic equations and topological equations between pipes is so complex that the networks cannot be optimized globally; thus, only local optimization can be achieved. To improve the accuracy of coordinated optimization, this paper contributes to developing a lightweight dynamic model of a large-scale DHN based on the thermal-electrical analogy circuit model. First, a heat-transportation contribution matrix for each single pipe is proposed. Second, the matrix-form heat balance equations between pipes are established. Third, the temperature response between the DHN's last outlet and the first inlet can be reflected by a coefficient matrix, which is obtained by multiplying all the heat-transportation contribution-matrix and heat balance matrices. Finally, a lightweight dynamic model of a large-scale DHN is established without any intermediate pipe variables. It is validated by the experiments, and the errors between the experimental and theoretical results are less than 5%.

The rapid integration of distributed renewable energy and flexible loads significantly intensifies supply and demand uncertainty in active distribution networks (ADNs), threatening economic and secure grid operations. Existing deep reinforcement learning (DRL) dispatch methods fail to extract spatial features properly, leading to a local optimal solution. To address these limitations, this paper proposes a state-adaptive topology-aware continuous-dispatch framework via multi-head graph attention network and deep deterministic policy gradient (GAT-DDPG). A multi-head graph attention network is embedded within a centralized Actor–Critic training paradigm to adaptively update spatial message-passing weights based on operational states. Extensive simulations on a modified IEEE 33-bus ADN over a 125-day unseen test set demonstrate that the proposed framework achieves lower comprehensive operating costs and fewer voltage violations compared with representative DRL-based dispatch baselines. Visualizations of state-dependent attention shifts confirm the model’s capability to track dynamically shifting network vulnerabilities, providing physical interpretability. Based on this, and combined with the optimized dispatch method of balancing responsibility, the ability of different flexible resources to support safe and stable operation and the ideal dispatch results under the temporary reduction in new energy output are further simulated.

Thermal power units are the most important primary frequency regulation (PFR) resource for the power system with a high proportion of renewable energy. In order to provide higher PFR capacity during the dynamic process, thermal power units need to reserve more valve opening margin or set higher main-steam pressure under steady-state. However, higher PFR capacity leads to lower energy efficiency, which leads to a lack of sufficient quantity results. This study investigates the energy efficiency loss under different PFR operation strategies for a supercritical thermal power unit. New steady-state valve control strategies are designed to improve the PFR capacity based on the dynamic model during the PFR process. A coupled steady-state valve-turbine model is solved to quantify how different reserved control valve openings affect throttling loss, effective enthalpy drop, required steam flow, fuel demand, and heat rate. The control valve route has been modeled in detail, while the boiler side is treated as a fixed upstream boundary. Energy efficiency loss is obtained under different strategies by taking a 300 MW supercritical thermal power unit as a case. Results show that the steady valve opening from 0.95 to 0.70 lowers the valve-downstream pressure from 16.124 to 15.659 MPa, raise the required steam flow increases from 273.319 to 274.131 kg/s. Under the same 300.102 MW load condition, increasing the reserved control valve opening margin from 10% to 30%, i.e., a 20% absolute increase, reduces the steady-state operating efficiency by approximately 0.26%, with fuel flow and specific fuel consumption increasing by 0.059 kg/s and 0.714 g/kWh, respectively.

High spatial resolution wind speed data is very important for wind farm planning, design, operation and maintenance. But due to cost, site and other factors, it is impossible to build a large number of anemometer towers to obtain high spatial resolution measured data. Therefore, this paper proposes a method for generating wind speed data in renewable energy bases based on physics-informed neural networks, which incorporates fluid mechanics control equations such as the Navier-Stokes equation as physical constraints into the model training process. The model's input includes the wind speed data and the wind direction data of the anemometer towers as input, as well as the geographical difference data between the input anemometer towers and the output point, enabling to learn the mapping relationship between geographical differences and wind speed differences at different locations, achieving the goal of generating high spatial resolution wind speed data. Using normalized root mean absolute error (NMAE) to measure the model error, the average wind speed error and the average wind direction error of the proposed wind speed data generation method on different test sets are 8.28% and 10.50%, which is lower than that of BP neural network and graph convolutional neural network, and can provide more refined data support for wind turbine layout planning and wind farm power prediction of renewable energy bases.

Against the backdrop of the “dual-carbon” goals, the penetration of distributed photovoltaics (PV) in distribution networks continues to increase. Under the combined effects of stochastic PV generation and load variability, assessing PV hosting capacity in distribution networks under fault conditions has become increasingly challenging. To address the limited consideration of uncertainty and fault conditions in existing studies, this paper proposes a stochastic optimization framework for evaluating PV hosting capacity under fault conditions that considers load uncertainty and mobile shared energy storage (MSES). First, historical data are used to generate representative uncertainty scenarios through Latin hypercube sampling. An optimization model is then formulated subject to secure distribution network operation constraints to determine the optimal capacities and locations of distributed PV installations. Meanwhile, MSES is incorporated to further enhance PV hosting capacity through flexible spatial and temporal energy transfer. Finally, simulations on the modified IEEE 33-bus system demonstrate that MSES can effectively increase PV hosting capacity and renewable energy accommodation. However, under fault conditions, changes in voltage profiles and the emergence of reverse power flows further reduce PV hosting capacity. Although incorporating load uncertainty increases the system cost, the resulting PV hosting capacity is more representative of practical operating conditions, thereby providing quantitative decision-making support for the planning and operation of distribution networks with high PV penetration.

Coastal integrated energy systems increasingly employ renewable generation and desalination to reduce carbon emissions and improve operational performance. This study develops a day-ahead coordinated electricity-heating-water dispatch model for an integrated energy system comprising condensing units, combined heat and power (CHP) units, a wind farm, a reverse-osmosis seawater desalination plant, freshwater storage, a district-heating network, and building thermal inertia. At the system level, electricity, heat, and water are coordinated through integrated dispatch, while the desalination plant provides electricity-to-water flexibility. The objective is to minimize the variable operating cost, including coal consumption cost, wind curtailment penalty cost, and carbon trading cost. Two desalination operating schemes are compared, namely wind-following operation during high-wind periods and TOU (time of use) −guided off-peak operation. The results show that, compared with the TOU-guided off-peak scheme, the wind-following desalination scheme reduces the total variable operating cost from 102.74 × 104 USD to 96.30 × 104 USD and lowers the wind curtailment rate from 18.24% to 12.72%, while maintaining the indoor temperature within 23.3–23.9 °C. A sensitivity analysis on carbon trading price further indicates that the relative cost advantage of Scheme 1 increases from 6.57% to 7.27% as the carbon price rises from 0 to 100 USD/t. The results demonstrate that coordinated use of desalination load, freshwater storage, district-heating flexibility, and building thermal inertia can effectively improve wind-power accommodation and system economy in coastal integrated energy systems.

[Objective]The excellent electrochemical stability,high safety,and long service life of lithium iron phosphate(LiFePO4)batteries have led to their widespread use in energy storage systems.However,in practical applications,large-capacity energy storage batteries often suffer from significant inhomogeneity of the internal current.This uneven current distribution can lead to localized temperature variations,alter the shape of the battery's terminal voltage curve,and accelerate degradation processes such as the formation of lithium dendrites and thermal stress.Existing models fail to comprehensively account for the coupled thermal-electrical-aging characteristics when modeling LiFePO4 batteries,rendering them incapable of accurately reflecting the variations in the voltage curve caused by inconsistencies in the internal current distribution.Furthermore,such models lack credible and systematic validation across multiple operating conditions.[Methods]Thus,in this study,a 280.000 Ah LiFePO4 battery was selected as the research target for investigating a thermal-electrical-aging coupling model for large-capacity LiFePO4 batteries that considers changes in the shape of the voltage curve.By modeling the battery as a parallel combination of multiple sub cells with varying electrical characteristics,the internal inhomogeneity of the battery and the resulting influence on deformation of the voltage curve are effectively simulated,where the voltage of the parallel battery pack serves as the terminal voltage output by the model.A temperature estimation module is integrated to simulate the processes of heat generation and transfer,while an aging module is introduced to capture the evolution of capacity degradation.Together,these modules form a comprehensive thermal-electrical-aging coupling model.By constructing a comprehensive thermal-electrical-aging coupling model,the terminal voltage,temperature,and capacity of the battery can be obtained directly from the input current.The model is then validated under constant current discharge conditions with variations in temperature,dynamic operating conditions,and multiple charge-discharge cycle conditions.[Results]Experimental validation using LiFePO4 batteries demonstrated that the proposed model could accurately predict the voltage,temperature,and aging state with relatively low computational complexity.Specifically,during constant current discharging of LiFePO4 batteries,the root mean square error(RMSE)of the temperature estimation remained below 1.0 ℃ across different temperatures and discharge rates,except under the highest rate condition.The increased RMSE at the highest discharge rate was attributed to a larger internal-external temperature gradient,which reduced the accuracy of the estimation.Additionally,faster heat dissipation at lower temperatures further reduced the precision of the temperature prediction.Because the thermal and electrical models were coupled,their respective errors were compounded.Across the four dynamic conditions,the absolute error in the voltage simulated by the model was below 20.0 mV for all intervals,except at the current-switching points,and the absolute error for the temperature estimation was below 1.0℃.The RMSEs for capacity degradation estimated through simulation using the coupled model were 0.223,1.640,1.320,and 2.700 Ah for cycling aging at 35.0 ℃/0.5 C,35.0 ℃/1.0 C,45.0 ℃/0.5 C,and 45.0 ℃/1.0 C,respectively—all below 1％of the total capacity—demonstrating the strong ability of the model to accurately simulate the battery capacity after aging.[Conclusions]The proposed thermal-electrical-aging coupling model effectively addresses the limitations of traditional equivalent circuit models,which often lack the capability to account for inhomogeneity in the internal current,temperature variations,and aging effects.This model thus provides a solid theoretical foundation and practical methodology for estimating the state of the battery in real-time,diagnosing faults,and predicting the lifetime of energy storage systems.

The accuracy of new energy power forecasting is vital for new energy power generation enterprise (NEPGE) in both avoiding the forecasting deviation penalty and improving the formulation of market trading strategy. Due to the different geographical and climatic environments in different stations that equipped with different NEPFSs, the resource complexity of station which affects the performance of NEPFS is also different. It leads to NEPGE's inability to directly evaluate the performance of different NEPFSs through the accuracy of forecasting results, because it is not clear whether the lower accuracy is caused by the poor performance of NEPFS or the high complexity of station resources. However, current research does not involve how to quantify the complexity of resources. The performance of the forecasting system is mostly directly quantified by the accuracy evaluation index such as root mean square error. To address these issues, this paper first introduces a dynamic framework for assessing resource complexity, featuring seven indicators spanning holistic and local perspectives, to facilitate scientifically quantified complexity assessments of new stations. Second, considering the real-time dynamic change of resource complexity, this paper puts forward an evaluation method of NEPFS performance, called complexity-adjusted performance assessment indicator (CAFA). Case studies based on actual operational data demonstrate that the proposed framework effectively quantifies the complexity of power station resources and accurately evaluates the performance, which can provide valuable guidance for NEPGE to choose the most outstanding NEPFS for new energy power stations.

To address the issue that transient voltage cannot be stabilised solely by the system's own reactive power regulation capability after faults occur at some weak nodes in high-penetration wind-photovoltaic (PV) hybrid grid-connected system, this paper proposes a dynamic reactive power planning method that accounts for transient voltage stability. Based on the system's own reactive power regulation capability, this method constructs a reactive power planning benchmark scenario using the typical output of wind power (WP) and PV power and determines the installation locations and compensation capacities of the dynamic reactive power compensation devices through multiple power flow calculations. First, the key fault nodes are identified using the transient voltage stability recovery index (TVRI) to preliminarily determine the system's weak nodes. On this basis, the sensitivity index (SI) is used to determine the installation nodes of dynamic reactive power compensation devices. Subsequently, an optimisation model for dynamic reactive power planning is established with the objective of enhancing system transient voltage stability while minimising the investment cost of dynamic reactive power compensation devices. The compensation capacities at reactive power compensation nodes are optimised using a particle swarm optimisation algorithm based on differential evolution (DE-PSO). Finally, the effectiveness and superiority of the proposed method in improving the transient voltage stability and economic performance of the system are verified by the improved IEEE 39-bus system.

Grid-forming (GFM) control is a key technology for ensuring the safe and stable operation of renewable power systems dominated by converter-interfaced generation (CIG), including wind power, photovoltaic, and battery energy storage. In this paper, we challenge the traditional approach of emulating a synchronous generator by proposing a frequency-fixed GFM control strategy. The CIG endeavors to regulate itself as a constant voltage source without control dynamics due to its capability limitation, denoted as the frequency-fixed zone. With the proposed strategy, the system frequency is almost always fixed at its rated value, achieving system active power balance independent of frequency, and intentional power flow adjustments are implemented through direct phase angle control. This approach significantly reduces the frequency dynamics and safety issues associated with frequency variations. Furthermore, synchronization dynamics are significantly diminished, and synchronization stability is enhanced. The proposed strategy has the potential to realize a renewable power system with a fixed frequency and robust stability.

The increase in the proportion of renewable energy generation capacity has placed significant pressure on thermal power operations. Constructing energy storage facilities within the metering output of thermal power plants, allowing them to participate in peak load regulation with generating units jointly, is one effective way to alleviate the operational pressure on thermal power. To achieve comprehensive scheduling of thermal power plants with energy storage, this paper first establishes an overall output model for the coupling operation of thermal power and energy storage. Secondly, a staged optimal scheduling method for a multi-source industrial park with wind, solar, thermal, and energy storage is proposed to solve the optimization scheduling problem for the industrial park involving thermal power plants with energy storage participation. Finally, the simulation results based on a specific regional power grid in Inner Mongolia validate the feasibility of the proposed method.

As market bidding and optimal control participants, virtual power plants (VPPs) employ commonly centralized uniform clearing bidding involving price and quantity processes. Existing literature overlooks price bidding risks, focusing only on quantity-related revenue risks. This study addresses this gap by constructing a risk management model-based bidding risk-return function, quantifying price risks via historical clearing price probability matrix with conditional value-at-risk (CVaR). A two-dimensional risk framework merges price and quantity metrics into unified assessment for distribution transformer areas. Methods for market bidding and optimal control in energy and ancillary markets evaluate profit potential and control regulation capability. Cases show VPPs should adapt strategies to market traits and risk preferences. Profit analysis confirms the multi-market coordinated approach enhances profitability: total system revenue rises by 22.1% vs. single-energy market maximum; conservative VPP4’s risk loss ratio drops from 4.50% to 1.83%. The framework improves system coordination efficiency and supports VPPs in balancing risk and return under uncertainty.

This paper proposes a dynamic-simulation-based method for online evaluation of the primary frequency regulation (PFR) capability of supercritical coal-fired units under high renewable-energy penetration. An integrated dynamic model covering the boiler-turbine system, DEH, and CCS is developed, explicitly incorporating key physical characteristics and control constraints such as valve flow properties, boiler heat-transfer inertia, fuel-water coupling, and rate limits. Based on this model, a set of grid-oriented PFR capability indicators is established to enable real-time capability assessment and early warning. Four scenarios with different frequency deviations are simulated, and the results show that the model accurately reproduces the unit's dynamic response, reveals the asymmetry between up- and down-regulation, and identifies how PFR contribution and valve-power sensitivity vary with disturbance magnitude. The proposed method provides an effective tool for monitoring PFR capability, identifying constraint mechanisms, and supporting secure operation of coal-fired units in renewable-rich systems.