[Objective] To address the issues of insufficient wind power integration and the high energy consumption and pollution associated with coal-fired captive power plants， conducting generation rights trading between wind power and captive power plants represents a feasible solution. Although extensive research exists on generation rights trading involving wind power and captive power plants， studies on decision-making behavior that involves refined modeling of uncertainties across multiple time scales remain limited. To bridge this gap， this paper proposes a generation rights trading method for wind farms and captive power plants based on multi-time-scale coupling. [Methods] First， the costs and benefits of each market entity before and after participating in generation rights trading are analyzed to calculate the profit margin of such trading. Second， based on multi-time-scale wind curtailment forecasts， the Copula function is used to compute the conditional probability density distribution model of actual wind curtailment. This enables an analysis of the decision-making behavior of wind farms under multi-time-scale conditions and facilitates the implementation of generation rights trading. Finally， operational data from a provincial power system are used in a case study to validate the rationality of the proposed method. [Results] Compared with the traditional generation rights trading model， the multi-time-scale coupled generation rights trading model increases wind power integration by 3.7% and improves economic benefits by 3.3%. [Conclusions] The proposed multi-time-scale coupling model promotes wind power integration more effectively than traditional generation rights trading， while further exploring potential profit margins and maximizing the overall social benefits of generation rights trading.

[Objective] To address the practical challenges confronting China’s power and green finance sectors， which are characterized by prominent data barriers， irregular information disclosure， inconsistent standards and data collection constraints， and to resolve the resulting obstacles to low-carbon energy transition， this study proposes a cross-sectoral data sharing and information disclosure collaborative solution that is both theoretically grounded and practically applicable. [Methods] Based on institutional economics and technological innovation theory， and by integrating case studies regarding integrated development of power and green finance sectors from Chongqing and other regions， this study systematically analyzes the current status， core bottlenecks and underlying contradictions of cross-sectoral data sharing and information disclosure from the perspectives of incomplete data collection scenarios and inefficient information utilization， while clarifying the rights and responsibilities of participating entities. A trinity collaborative mechanism framework integrating "organizational structure， operational rules and technical support" is established. Specifically， it proposes a “top-level coordination + tiered implementation” cross-sectoral data sharing system and a “mandatory disclosure + voluntary supplementation” information disclosure model. Furthermore， the operational pathways for cost allocation and benefit distribution are elaborated in detail， the compatibility verification of multi-scenario technical solutions is implemented， and a comprehensive set of safeguarding measures is provided to ensure the mechanism’s operability. [Results] The proposed solution effectively improves the efficiency of data sharing and the quality of information disclosure. [Conclusions] The proposed collaborative mechanism and operational pathway effectively unblock the data transmission chain connecting electricity， carbon and finance. This provides a supporting pathway with both theoretical value and practical feasibility for promoting the low-carbon energy transition， and holds significant importance for addressing industry development challenges and improving the governance system in related fields.

[Objective] In the practical operation of battery energy storage stations （BESS）， both planned outages caused by periodic maintenance and unplanned outages resulting from sudden equipment failures can lead to available capacity losses， significantly affecting the economic viability of the station. Existing optimization methods for battery energy storage capacity configuration have not adequately considered the impacts of these two types of outage events. To address this gap， this paper proposes an optimization method for battery energy storage capacity configuration that explicitly incorporates planned and unplanned outages. [Methods] First， addressing the periodic maintenance requirements of battery energy storage stations， a life-cycle-based dynamic revenue evaluation model is established for planned outages associated with periodic maintenance. Second， failures of primary and secondary equipment in battery energy storage stations are modeled to assess revenues under unplanned outage scenarios. On this basis， a multi-objective optimization model for energy storage capacity configuration is formulated to maximize aggregate revenues affected by both types of outage events while minimizing the total investment cost. To adaptively balance the two objective functions of the optimization model， an iterative solution algorithm based on marginal improvement rates is designed. Finally， case studies using the IEEE 30-bus system and a provincial regional power grid are conducted to verify the validity of the proposed method. [Results] Compared with traditional allocation methods， the proportion of high-availability days for energy storage is increased by approximately 51.5%， validating the rationality and effectiveness of the proposed capacity allocation optimization method. [Conclusions] By simultaneously considering the impacts of both types of outage events and employing multi-objective optimization， the proposed method can mitigate the effects of cabin-level maintenance and failures during simulated operation， thereby facilitating operational scheduling and transaction fulfillment.

[Objective] To address the current tracking challenges in the harmonic suppression of active power filters （APF）， a fast integral terminal sliding mode control （FITSMC） strategy based on a hippocampus-based fuzzy neural network （HBFNN） is proposed. [Methods] The FITSMC is employed to guarantee global robustness and finite-time convergence of the tracking error. To circumvent the dependence on accurate system parameters， the HBFNN is constructed to approximate unknown system dynamics online. By integrating the hippocampus mechanism with fuzzy theory， the HBFNN eliminates redundancy through feature selection and enhances anti-interference performance against time-varying signals via a double recurrent structure. [Results] Simulation and hardware experiments verify that the proposed HBFNN-FITSMC scheme tracks harmonic currents rapidly and accurately. In simulations， the total harmonic distortion （THD） of the grid-side current decreases from 40.30% to 1.25%， while in hardware experiments， it decreases from 32.73% to 2.96%. Compared with traditional methods， the proposed strategy significantly improves dynamic response and steady-state accuracy， while effectively suppressing system chattering. [Conclusion] By virtue of its information screening and double recurrent mechanism， the HBFNN demonstrates superior approximation and anti-interference capabilities， reducing reliance on precise mathematical models. This scheme achieves the complementary advantages of brain-inspired intelligence and sliding mode control， offering significant value for engineering applications.

[Objective] Under the “Dual Carbon”goals， microgrids serve as key carriers for improving energy efficiency and promoting renewable energy consumption. However， they face challenges regarding system stability and economic operation due to significant load fluctuations and difficulties in multi-agent coordination. To address these issues， this paper proposes a load forecasting method integrating a parameter-adaptive long short-term memory （LSTM） network， along with a dynamic energy management method for microgrids based on the whale optimization algorithm （WOA）. [Methods] First， the quantum particle swarm pptimization （QPSO） algorithm is employed to globally optimize the key hyperparameters of the LSTM network. This significantly improves the accuracy of short-term load forecasting and effectively captures the characteristics of load mutations during peak and valley periods. Second， based on the load forecasting results， an economic dispatch model for microgrids containing various distributed generators and energy storage devices is established. With the objective of minimizing daily operating costs and subject to constraints on power balance and equipment output， the WOA is utilized to achieve global optimal dispatch. [Results] The proposed load forecasting method demonstrates high accuracy in short-term predictions and effectively identifies load peak-valley fluctuation characteristics. Based on these forecasting results， the WOA achieves lower operating costs in the economic dispatch model. Furthermore， it outperforms traditional optimization algorithms in improving the utilization rate of local distributed generators and maintaining cost stability. [Conclusions] The synergistic strategy established in this study， combining a high-precision prediction model with a whale global optimization algorithm， provides a reference for the economic operation of microgrids under source-load uncertainty.

[Objective] The soft open point （SOP） is a key solution for addressing challenges such as distributed photovoltaic （PV） integration， as it can connect multiple low-voltage distribution networks and offer functions including flexible power regulation. However， existing SOPs require power data from distribution transformer service areas to formulate their operating strategies， necessitating additional detection devices， energy management systems， and communication infrastructure， which significantly increases system complexity. A pressing problem lies in enabling SOPs to operate autonomously without reliance on external commands. [Methods] This paper proposed a novel neural network-based power control method for SOPs. First， a neural network model is constructed using low-time-resolution historical power sampling data and local voltage sampling data from the SOP. Subsequently， the neural network is deployed within the SOP controller， enabling the device to predict the transformer service area power deficit using real-time voltage sampling data. Finally， the power outputs of the multiple ports are regulated according to the predicted power deficit to achieve real-time power balancing among the connected distribution transformer service areas. [Results] The neural network model is trained using real-world transformer service area data and deployed in both a field-installed SOP and a laboratory test platform. Experimental results demonstrate that the proposed method successfully achieves automatic power distribution among the distribution transformer service areas， with an average deviation of less than 2 kW compared to the ideal equal power distribution values. [Conclusions] The findings indicate that this method enables power balancing using only local voltage sampling， effectively reducing the operation and maintenance costs and complexity of SOPs. This facilitates the broader adoption of SOPs， and promotes the local consumption of distributed PV generation.

[Objective] With the rapid development of microgrids， ensuring the safety of system frequency and voltage has become a critical issue that urgently needs to be addressed. However， traditional frequency and voltage control strategies fail to consider the coupling relationship between voltage control and frequency control， making it impossible to achieve optimal real-time coordinated control of system frequency and voltage. Therefore， a data-driven frequency and voltage control method for isolated microgrids is proposed. [Methods] Firstly， the microgrid system model is recursively approximated online in real-time using the weighted least squares （WLS） method. Secondly， based on the identified system model and combined with a feedback-based approximate gradient algorithm， the frequency-voltage coupling relationship is utilized. By adjusting the power of voltage-sensitive loads through voltage regulation， active power balance in the system is achieved， enabling optimal real-time coordinated control of microgrid frequency and voltage. [Results] A microgrid system is built on Matlab/Simulink to simulate and verify the proposed method， and the results show that， compared with traditional frequency and voltage control strategies， the proposed method can fully exploit the regulation potential of voltage-sensitive loads， reduce microgrid frequency fluctuations， and achieve a maximum system frequency deviation of only 0.75%. [Conclusions] The proposed frequency and voltage control method for isolated microgrid clusters fully takes into account the coupling relationship between frequency and voltage. By utilizing voltage-sensitive loads， it achieves real-time optimal control of system frequency and voltage through voltage regulation.

[Objective] The significant anti-peak regulation characteristics of renewable energy sources like wind and solar power lead to large-scale curtailment during low-load periods， which is detrimental to the economic and low-carbon operation of integrated energy systems （IES）. Hydrogen and ammonia， with their zero-carbon and high-energy-density features， hold great significance for promoting the energy transition when integrated into IES. To fully leverage their advantages in reducing emissions and enhancing economic performance， this study aims to develop an optimal scheduling model. [Methods] This paper proposes an optimal scheduling model for an IES that incorporates dynamic co-firing of hydrogen and ammonia， and couples power-to-gas （P2G） with carbon capture and storage （CCS）. Equipment models， including P2G， power-to-ammonia （P2A）， electric boilers， and energy storage systems， are constructed. The hydrogen produced by P2G serves as an energy link， enabling gas turbines to co-fire hydrogen and coal-fired units to co-fire ammonia. Furthermore， a tiered carbon trading mechanism is introduced to enhance the flexibility of carbon emission reduction. Targeting at minimizing the total operating cost， an analysis is made on the impact of different hydrogen/ammonia co-firing ratios on the system's economy and carbon emissions. [Results] Simulation results indicate that the P2G-CCS coupling combined with a fixed 20% hydrogen/ammonia co-firing ratio minimizes the total operating cost and carbon emissions. Adopting a dynamic co-firing ratio further reduces the total cost by 11.65% and carbon emissions by 33.63 tons. [Conclusions] The tiered carbon trading mechanism combined with a dynamic hydrogen/ammonia co-firing strategy can effectively enhance both the economic and low-carbon performance of the IES， providing a viable solution for its optimal scheduling.

[Objective] To address the challenge of accurately evaluating the regulation capability of battery-swapping stations （BSSs） and optimizing charging schedules under uncertain battery swapping demand， this paper proposes a BSS regulation capability assessment and optimal control method that explicitly considers the seasonal number of surplus battery slots. [Methods] Based on measured data from 77 BSSs in Northeast China， the study identifies that stations face significant charging and swapping pressure in winter， while possessing surplus regulation capacity in spring， summer， and autumn. By leveraging the inherent regularity of taxi battery-swapping behavior， we improve the cyclic utilization efficiency of battery slots and minimize the number of slots required for cyclic swapping. The number of seasonally surplus battery slots is subsequently used as a quantitative metric for the time-shiftable regulation capability of BSSs. On this basis， a delayed charging strategy for sealed battery slots is developed using mixed-integer linear programming， incorporating an innovative risk-threshold protection mechanism to maximize the potential regulation capability of surplus battery slots and batteries. [Results] Case studies demonstrate that during each peak-valley electricity price transition period in spring， summer， and autumn， the proposed strategy unlocked regulation potential equivalent to 25% of the total battery capacity of a given swapping station， reducing its electricity procurement and operational costs by approximately 9%. [Conclusions] The proposed strategy can effectively reduce electricity procurement and operational costs for BSSs while ensuring the timeliness of battery swapping services. This approach offers new insights and methodologies for research on the participation of BSSs in power grid demand response and the accommodation of renewable energy， and holds significant theoretical significance and practical value.

[Objective] To address the problems of electrolyzer operation instability， frequent start-stop cycles， and efficiency reduction caused by strong wind power fluctuations in off-grid wind-storage hydrogen production systems， a power smoothing control strategy for multiple scenarios that does not rely on wind power forecasting is proposed. [Methods] Based on the principle of first-order low-pass filtering， a smoothed power command equation is constructed. According to the working and efficiency characteristics of alkaline electrolyzers， the operation process is divided into four typical scenarios： startup， high-efficiency， rated， and overload， with smoothing factors independently configured for each scenario. The energy storage system compensates in real-time for the power difference between wind power and the electrolyzer， and dynamic switching of operating scenarios is achieved using a state machine to build a multi-scenario coordinated control architecture， realizing adaptive smoothing of wind power fluctuations. [Results] Simulation results show that， compared with existing control methods， the proposed strategy can significantly suppress fluctuations in electrolyzer input power and effectively improve system operation stability and hydrogen production efficiency. [Conclusions] The strategy can greatly extend the high-efficiency operating time of the electrolyzer， reduce the number of start-stop cycles， and increase hydrogen production per unit energy consumption without substantially increasing the total system energy consumption， demonstrating good comprehensive performance. This provides a power smoothing solution with a clear structure， strong robustness， and no reliance on forecasting for off-grid wind-storage hydrogen production systems.

[Objective] With the large-scale integration of doubly-fed induction generator （DFIG） based wind farms via series-compensated transmission lines， the issue of sub-synchronous oscillation （SSO） induced by the interaction between units and the grid has become increasingly prominent. Existing sub-synchronous damping controllers （SSDC） face limitations in adaptability and robustness， making it difficult to cope with complex and varying operating conditions. To enhance the suppression of wide-band SSO， this paper proposes a model-data fusion damping control method featuring high reliability and strong adaptability. [Method] A model-data fusion damping control architecture based on energy storage is proposed. This architecture combines a mechanism-based traditional SSDC with a model-free adaptive control （MFAC） based SSDC， injecting damping signals via the same grid-side energy storage system. The traditional SSDC provides fundamental damping support near the fundamental frequency， while the MFAC-based SSDC tracks system dynamics in real-time and adapts to frequency deviations， thereby avoiding multi-device coordination and complex parameter tuning. [Results] Under various operating conditions， including parameter variations and large disturbance faults， the proposed controller can rapidly and smoothly suppress SSO at different frequencies compared to its constituent units. It demonstrates superior bandwidth adaptability and dynamic regulation performance. [Conclusions] The constructed fusion damping control architecture effectively combines the stability of fixed structures with the flexibility of data-driven approaches. It features a concise structure and ease of implementation， showing significant effectiveness in improving system dynamic performance and robustness.