In response to the difficulty of internal regulation and grid peak shaving matching in the multi coupling system of new energy hydrogen production synthetic ammonia, this paper proposes an optimization scheduling method for the participation of source hydrogen ammonia system in grid peak shaving auxiliary services based on power deviation sharing. Firstly, with the goal of maximizing ammonia sales revenue, the economic optimal state of the source hydrogen ammonia system is obtained. Based on the amount of synthesized ammonia in the economic optimal state, a cost model for the source hydrogen ammonia system to participate in grid peak shaving is established using the power grid interaction deviation sharing strategy. Secondly, taking the cost of the hydrogen ammonia system participating in grid peak shaving as a component of the operating cost of the power system, with the goal of minimizing the operating cost and net load fluctuation of the power system, an optimization scheduling model for the hydrogen ammonia system participating in grid peak shaving is constructed. Finally, the calculation results of the source hydrogen ammonia system participating in power grid peak shaving, verifying the effectiveness of the method proposed.

Addressing the challenge of frequency regulation in power systems with high penetration of renewable energy sources, this paper proposes a dual-layer frequency control strategy that combines proportional-derivative (PD) error compensation based on an extended state observer (ESO) with sliding mode control (SMC). Departing from conventional control frameworks, the proposed strategy decouples disturbance compensation from system control. The PD control compensates for power system frequency errors, enhancing the ESO's estimation accuracy and dynamic response to external disturbances. Subsequently, the sliding mode controller generates the power commands required for secondary frequency regulation, which are dispatched to hydro and pumped-storage to achieve robust frequency deviation control. A power system model is developed in MATLAB/Simulink for validation. Simulation results demonstrate that the proposed strategy effectively reduces frequency fluctuation amplitude, shortens recovery time, and exhibits superior dynamic performance and disturbance rejection. This approach provides a robust solution for enhancing secondary frequency regulation capability in modern power systems with high renewable energy penetration.

In the integrated electricity and gas system (IEGS), common failures in the gas system may lead to significant pressure decrease of gas-fired power generators, which causes fluctuations or cascading failures in the power system. In this paper, an applicable early warning and proactive control framework based on the dynamic equivalence of gas transmission networks is proposed to mitigate the impact of such incidents. Firstly, different time scales of the dynamics involved in the gas-electric cascading failures are discussed to get a suitable analysis model of the proposed framework. Secondly, a dynamic equivalent model of the gas network oriented towards the coupling nodes of the IEGS is constructed based on the linearized form of gas transmission equations. Finally, a proactive control method for the power system considering electromechanical transient processes based on the iterated equivalence parameters of the gas network is introduced to minimize the loss of the cascading failure. Case studies demonstrate that the framework proposed in this paper can effectively alleviate the impact of gas failures on the power system and possesses strong scalability.

Under the dual carbon strategy, the high penetration of new energy sources such as wind power has led to prominent fluctuations and intermittency in the power system. Power to gas technology supports multi energy coordination in the integrated energy system through electrical coupling, and is highly compatible with the demand for new energy consumption. This paper proposes an integrated energy system operation optimization method based on the coupling characteristics of power to gas and wind power, taking into account the ability to smooth out power to gas fluctuations. Firstly, propose the synergistic coupling mechanism of electricity-heat-gascarbon and the joint operation mode of power to gas and carbon capture and storage and combined heat and power. Secondly, construct a wind power decomposition model based on adaptive wavelet packet decomposition algorithm. Then, construct an integrated energy system multi-objective operation optimization model considering the ability of power to gas to stabilize fluctuations, and propose a particle swarm optimization algorithm based on hybrid strategy improvement. Finally, develop simulation verification in a typical industrial park in northern China, and the results show that: (1) the complementary utilization efficiency between multiple energy sources is improved. (2) Compared to Case 1 (without power to gas and carbon capture and storage) and Case 2 (without wind power and power to gas coupling strategy), the integrated energy system operation optimization model proposed in this paper, which considers the wind power and power to gas coupling strategy, achieves multidimensional collaborative optimization of system economy environment energy efficiency. The economic costs are reduced by 34.57 % and 25.53 % respectively, carbon emissions are reduced by 79.38 % and 0.48 % respectively, and the efficiency is improved by 17.88 % and 9.97 % respectively. 3) Algorithms have significant advantages in solving core performance indicators such as accuracy, running speed, and convergence stability. The research results have achieved the smoothing of wind power fluctuations and the coordinated optimization of multiple energy sources, forming a scalable modular technology system.

With the rapid growth of electric vehicles and the rising share of renewable energy, photovoltaic-storage-charging-swapping stations face complex uncertainties arising from PV output variability, stochastic EV user behaviors, and fluctuating load demands. To enable robust decision-making for capacity sizing and operational strategies under such conditions, this paper first proposes a multi-scale, multi-dimensional uncertainty modeling approach that integrates multi-bandwidth Gamma-Budget polyhedra with alpha-cut fuzzy intervals to accurately characterize uncertainties in PV generation, vehicle arrival patterns, and charging/swapping loads. Building on this, a two-stage robust optimization framework is developed, coupling capacity planning (stage I) with adaptive intra-day dispatch (stage II). By leveraging duality theory and a column-and-constraint generation algorithm, the original complex robust problem is reformulated as a mixed-integer linear program (MILP) amenable to efficient commercial solver implementation. Furthermore, a coordinated robust dispatch strategy is introduced, jointly optimizing charging power, battery-swap power, and dynamic battery inventory flows while explicitly accounting for service-level, inventory-balance, and charging-rate coupling constraints. Based on the case, relative to both conventional deterministic scheduling and overly conservative traditional robust models, the proposed two-stage robust approach achieves a superior balance of robustness and economy, reducing operational costs by up to 43.8%, cuts grid-purchased energy by 43.6%, and lowers daily equivalent full cycles of the storage system by 43.3%, significantly mitigating battery degradation. These results validate the effectiveness of our methods and provide a rigorous, practical toolset for planning and operating photovoltaic-storage-charging-swapping stations under uncertainty.

To address renewable generation and load uncertainty in multi-campus integrated energy systems, this paper proposes a distributionally robust day-ahead–real-time coordinated scheduling model under a cloud-edge collaborative architecture. The studied system consists of photovoltaic, wind power, and combined heat and power campuses, each equipped with energy storage and transferable load resources. The cloud layer determines the day-ahead baseline dispatch plan, while the edge layer performs scenario-dependent real-time corrections. To improve adaptability to adverse operating conditions, bounded forecast-error scenarios are constructed, and a conditional value-at-risk-based distributionally robust objective is formulated. Meanwhile, a soft day-ahead–real-time energy-binding mechanism is introduced to maintain plan-execution consistency while allowing necessary real-time adjustments. Case studies show that, compared with the cases without peer-to-peer energy exchange, demand response, and energy storage, the proposed model reduces the objective value by 5.22%, 10.96%, and 5.05%, respectively. Sensitivity analysis and stress tests verify its feasibility and robustness under increased uncertainty and reduced flexible-resource capacities.

When the multi-station short-circuit ratio (MSCR) of renewable energy stations is excessively low, the system faces the risk of broadband oscillations. Therefore, the planning and allocation of distributed synchronous condensers are required to ensure that the MSCR meets the standard and enhance the stability against broadband oscillations. To address the deficiencies in existing research on the planning and allocation of distributed synchronous condensers, this paper adopts the impedance analysis method to investigate the impedance characteristics of broadband oscillations in a typical renewable energy plant group in a provincial power grid under low MSCR conditions. The differences in synchronous condenser allocation based on two indicators, namely MSCR and impedance phase margin, are analyzed, as well as the frequency range in which synchronous condensers improve the impedance phase margin. Furthermore, a framework for the planning and allocation of distributed synchronous condensers is proposed, which simultaneously considers MSCR improvement and broadband oscillation suppression. This study provides a new direction and decision-making basis for the planning and layout of distributed synchronous condensers.

ABSTRACT With the rapid advancement of electrochemical energy storage power stations and electric vehicles, lithium‐ion batteries have gained widespread adoption due to their high specific energy and superior power performance. However, the increasing frequency of safety incidents in electrochemical energy storage facilities in recent years has raised significant concerns. Effective monitoring of lithium‐ion battery conditions is crucial to ensure the safety of power systems and support the sustainable growth of the electrochemical energy storage industry. Among the key challenges, accurate prediction of the remaining useful life (RUL) of lithium‐ion batteries is essential for maintaining the safe and reliable operation of battery management systems. This paper proposes an advanced RUL prediction model that combines the seagull optimization algorithm (SOA) with the extreme learning machine (ELM) to enhance prediction accuracy. The proposed SOA‐ELM model is validated using the NASA dataset, and the results demonstrate its effectiveness and potential in improving RUL prediction for lithium‐ion batteries. This study contributes to the development of more reliable and efficient battery management systems, paving the way for safer and more sustainable energy storage solutions.

The energy utilization system in husbandry park is a typical integrated energy system of electricity and heat. The coordinated utilization of the adjustable resources is an important means to promote the electrification, intellgence, and low-carbon level of husbandry park. This paper research an optimal scheduling strategy of electric-thermal coupling considering energy load aggregation transactions in husbandry park. Firstly, the scheduling organization process of adjustable resource aggregation transaction is constructed. Secondly, a two-stage optimal scheduling model is constructed. The first stage is the aggregation trans-action optimization of the adjustable resources in the husbandry park, and the second stage is the dispatching optimization based on the power trading results of the adjustable resources. Finally, an integrated energy system based on the improved power distribution system and heating system was constructed. The simulation and analysis results indicate that the proposed the strategy can tap the response potential of husbandry park electricity and heat resources through trading capability, reduce the energy cost and economy of husbandry park, and verify the effectiveness of the proposed method.

In the context of the transition to a power system with a high proportion of new energy sources, the increased uncertainty on both the supply and demand sides has placed higher demands on the flexible operation of the system. This paper addresses the spatiotemporal uncertainty of wind and solar power generation and load demand in multi-energy coupled systems, proposing a refined flexibility quantification and evaluation framework. First, a polyhedral uncertainty set is employed to characterize the fluctuating characteristics of photovoltaic power generation, and a net load directional change model is constructed to distinguish between upward and downward flexibility requirements. Subsequently, a collaborative supply model encompassing thermal power, energy storage, and interruptible loads is established to quantify the regulatory contributions of various resources under ramping rates and capacity constraints. Innovatively, a “global-local” two-layer evaluation metric is proposed: at the global level, system regulation capacity is dynamically reflected through sufficiency, insufficiency, and insufficiency rate; at the local level, the contribution weights of each resource to flexibility supply and demand are analyzed. Based on simulation studies of the IEEE 39-node system, this framework provides a decision-making tool that balances robustness and economic efficiency for flexible scheduling in high-penetration renewable energy systems.

The multi-agent interconnected microgrid at the park level needs to coordinate multiple distributed power sources, flexible loads, and the complex game relationships among multiple agents. Meanwhile, the power prediction difficulty brought about by the high volatility of renewable energy and the uncertainty of loads also makes the power balance control more difficult. To this end, a deep optimization control method for microgrid power balance under the interconnection of multiple agents at the park level is proposed. Considering the interconnection of multiple entities at the park level, the interconnection structures of the transformer area in the “AC-AC” mode and the transformer area in the “AC-DC” mode were analyzed respectively. Based on this, the load demand of the transformer area was analyzed. Based on this, the multi-branch smooth voids convolution method is introduced to complete the prediction of microgrid power. The different operating states of the microgrid are set as the comprehensive surplus state, the energy storage priority surplus state, the internal adjustable defect state and the emergency defect state respectively. A multimodal and multi-objective optimization model is constructed based on the operating states. By dynamically adjusting the optimization objectives and constraint conditions, more efficient deep optimization control of power is achieved. The test results show that under the research method, the power fluctuation amplitude of the micro grid is relatively small, and the power always remains above 800MW. Moreover, under the four operating conditions of isolated island operation mode, grid-connected operation mode, mode switching condition and load sudden change condition, the power can still be stably maintained at 500MW to 700MW. It has been proved that this method has very ideal reliability.

With the rapid increase of renewable energy integration into power systems, its inherent volatility and intermittency pose significant challenges to frequency stability. Traditional Automatic Generation Control (AGC) units struggle to meet the frequency regulation demands of power systems with high renewable energy penetration, necessitating the coordinated participation of diverse resources in frequency regulation. This paper proposes a coordinated frequency control strategy for generation-grid-Ioad-storage systems. By leveraging minute-level power prediction and an opportunity-constrained optimization model, the strategy coordinates the output of thermal power, hydropower, energy storage, and adjustable loads to reduce frequency regulation costs while enhancing system stability. Case studies demonstrate that, under typical scenarios such as anti-peak regulation of wind power and load or in-phase fluctuations, the proposed strategy reduces frequency regulation costs by over 15% compared to conventional methods while meeting the CPS performance requirements. The study reveals the impact of energy storage dynamic efficiency on economic dispatch and provides insights for future research on multi-timescale coordinated control and intelligent algorithm optimization. This strategy offers an effective solution to the frequency stability challenges in power systems with high renewable energy penetration.

In order to solve the problems of excessive noise sensitivity, mode mixing, and signal reconstruction distortion during the parameter identification process of sub-synchronous oscillations, this paper improves the Variational Mode Decomposition (VMD) algorithm, which can effectively address the above-mentioned issues. Firstly, the Pearson analysis method is combined with the VMD algorithm to complete the decomposition, denoising, and reconstruction of sub-synchronous oscillation signals containing noise, the signal-to-noise ratio, mean squared error, and normalized correlation cofficient are used as evaluation indices for signal reconstruction;secondly, the Prony algorithm is employed to identify the parameters of the frequency and amplitude of the reconstructed sub-synchronous oscillation signals; finally, simulation examples on the MATLAB/Simulink platform are used to verify the effectiveness of the method proposed in this paper.

With the rapid penetration of renewable energy sources, the source-grid-load-storage (SGLS) system has become a key solution to address the intermittency and volatility of RESs while promoting localized consumption. Existing planning research overlooks critical policy constraints. In this paper, a SGLS integrated planning and optimal dispatch under policy constraint is proposed. A mixed-integer linear programming (MILP) model is proposed to minimize total costs, incorporating operational and policy constraints. A case study for a ${4 8 0 \sim M W}$ load validates the model, the scheme meets all policies and minimizes the total planning and operation costs. Sensitivity analysis shows longer storage scheduling cycles and higher load adjustability reduce storage capacity.

Accurate power system load forecasting is the core prerequisite for guaranteeing the safe and stable operation of power grids and supporting the efficient scheduling of power systems. To improve the accuracy of load forecasting and portray the non-stationarity and multi-scale characteristics of the load sequence, this paper proposes a short-term load forecasting method based on ICEEMDAN decomposition-LSTM feature extraction-hybrid cosine attention mechanism iTransformer. Firstly, the original load sequence is decomposed using the Improved Complete Ensemble Empirical Modal Decomposition (ICEEMDAN) to extract the intrinsic modal function (IMF) and residuals (Res), and the multidimensional input feature set is constructed by combining exogenous variables such as meteorology. Secondly, multi-source features were extracted using the Long Short-Term Memory (LSTM) network to capture the complex nonlinear correlations and long-term dependencies. Finally, the extracted features are input into the iTransformer model that introduces the hybrid cosine attention mechanism. The hidden feature representation is obtained through the encoder layer modeling, and the linear mapping in the output layer generates the load forecast value. The results show that the prediction method proposed in this paper achieves better performance and can effectively improve the accuracy of short-term load prediction, which provides an effective technical support for the short-term scheduling and flexible operation of the power system.