Generating a substantial set of long-term operational scenarios for wind power, photovoltaic (PV) power, and load sequences is the data foundation for planning high-penetration renewable energy power systems. The existing scenario generation methods (SGMs) have some defects, such as neural network-based approaches requiring a large amount of historical data and lack of preservation of the characteristics of extreme scenarios. In response to the above challenge, this paper proposes an SGM for wind/PV power outputs and load sequences, which is able to preserve the characteristics of extreme scenarios. Specifically, the method extracts extreme scenarios via an iterative procedure and generates conventional scenarios using a double-layer Markov chain model. By combining the iterative extraction process with the double-layer model, the proposed framework effectively incorporates extreme scenario characteristics into the scenario generation process. The results of the case study from a northern province of China demonstrate that the proposed method effectively preserves the statistical characteristics of the original data and extracts representative extreme scenarios, providing diverse scenarios for evaluating high-penetration renewable energy power systems.

In the context of energy trading in distribution systems, rural prosumers exhibit distinct agricultural behavioral tendencies. Meanwhile, driven by multidimensional factors, prosumers show heterogeneous preferences for electricity generated from different sources. However, traditional energy trading models generally fail to establish detailed modeling of agricultural production behaviors and neglect the impacts of multidimensional driving factors on prosumers’ electricity preferences. To address this problem, an innovative peer-to-peer (P2P) energy trading model for rural prosumers is proposed, which systematically incorporates both agricultural behavioral tendencies and electricity use preferences. Specifically, agricultural loads are modeled in detail, and agricultural behavior tendencies are incorporated. Moreover, a novel energy preference model is established. Electricity is classified according to its generation source to capture prosumers’ differentiated preferences for various energy types. Since prosumers’ preferences for different types of electricity are affected by multidimensional factors, fuzzy logic is employed to quantify these preferences. A dynamic pricing mechanism is developed to match the categorized energy, and the alternating direction method of multipliers (ADMM) is adopted to solve the model for protecting privacy. Finally, case studies show that the proposed model facilitates the enhancement of social welfare and the improvement of rural prosumers’ energy structure.

The increasing penetration of renewable energy has intensified power-balance challenges and increased the need for coordinated electricity market mechanisms. This paper proposes a joint bidding strategy for a wind–pumped-hydro–battery hybrid energy system (WPB-HES) participating in intra-month medium- and long-term (MLT), spot, and frequency regulation ancillary service markets. First, a weekly bidding optimization model is developed to exploit the cross-day regulation capability of pumped hydro energy storage and coordinate revenues from multiple markets. Based on the weekly trading results, a bi-level optimization model is further established for intra-month daily MLT trading. The upper-level model maximizes the total revenue of the WPB-HES, while the lower-level models represent the clearing processes of the intra-month integrated trading market and the joint energy–frequency regulation market. To improve computational tractability, the bi-level problem is reformulated using Karush–Kuhn–Tucker conditions and duality theory. Case studies show that the proposed strategy effectively coordinates pumped hydro energy storage and battery energy storage, enhances the flexibility of the hybrid system, and improves multi-market revenue compared with conventional bidding strategies. The results demonstrate that the proposed method provides a practical decision-making framework for WPB-HES participation in coordinated electricity markets.

Under the background of large-scale renewable energy development and slowing load growth, power systems are gradually entering a load-saturation stage. The challenge of renewable energy accommodation is shifting from insufficient total capacity to spatiotemporal mismatch and limited flexibility. To address this issue, this study focuses on typical accommodation paradigms, including generation-grid-load-storage integration and integrated multi-energy renewable bases with bundled transmission. A unified mathematical optimization model is constructed to analyze the operational characteristics, accommodation capability, and evolution of key constraints under high, medium, and low renewable penetration levels. From the perspectives of resource endowment, system flexibility, transmission capacity, load characteristics, and generation mix, the study identifies the key factors affecting renewable energy utilization. Based on scenario data and optimization results for the milestone year 2035, the impacts of these factors on renewable utilization under load-saturation conditions are quantitatively evaluated, and their relative importance is ranked accordingly.

Both wind farms and reverse osmosis (RO) desalination plants exhibit a degree of controllability, and their coordinated participation in power market has the potential to reduce costs or enhance profits. However, due to their historical independence, the coordinated operation of these facilities in the energy and frequency regulation (FR) markets presents significant challenges. To reduce coordinated operating costs and optimize resource allocation between wind farms and RO desalination plants, a coordinated operation model is proposed for their joint participation in multi-type power markets. Additionally, a new profit distribution approach based on distribution coefficient according to the contributions of both participants is proposed. The wind farm is considered as a partially controllable participant capable of participating in power market. It jointly engages in both energy and FR markets alongside a RO desalination plant. The two are treated as an integrated system that fully accounts for the energy exchange between them. Through a case study, the results convincingly demonstrate the feasibility of their joint market participation, effectively utilizing wind energy to engage in power market while enhancing overall profits. When both participate jointly in the energy market and the frequency regulation ancillary service market, overall revenue can be increased by 41.6%, and curtailed wind power generation can be reduced by 80.14%.

To reduce the hydrogen utilization cost of the system and account for the impact of carbon emissions and source-load uncertainty on costs， an optimal scheduling model for hydrogen-integrated energy systems （HIES） is established， which considers source-load uncertainty and diversified hydrogen production and utilization. Firstly， gas-to-hydrogen units and diversified hydrogen utilization equipment are introduced into traditional HIES comprising power-to-gas and carbon capture systems to construct an HIES model featuring diversified hydrogen production and utilization. Secondly， a deterministic system scheduling model is formulated with the objective of minimizing the total cost. Subsequently， to address the uncertainties associated with wind/photovoltaic power and load， the entropy weight method （EWM） is employed to determine the weighting coefficients for the deviation of uncertain factors in the information gap decision theory （IGDT）， leading to the development of an IGDT-based scheduling model. Case study results demonstrate that the proposed model can effectively reduce both the system's carbon emissions and total operating cost， providing a reference for decision-makers in handling uncertainties within HIES.

This paper investigates a multi-stage stochastic economic dispatch problem for power systems with carbon capture systems (MCCSED). The focus is on effectively managing operational risks through a risk-averse approach. To efficiently solve this challenging problem, a cut-selection-enhanced risk-averse stochastic dual dynamic programming (SDDP) algorithm is developed, incorporating an enhanced test of usefulness (ETOU) strategy to remedy the premature discarding of valid cuts inherent in the conventional level 1 (L1) method. Case studies on IEEE 39-bus, 118-bus, and 300-bus systems yield these findings. First, higher carbon capture installation rates enhance risk management through increased storage flexibility. Second, the risk-averse approach reduces tail-risk costs and enables more intuitive parameter tuning than distributionally robust optimization, revealing a consistent two-phase pattern across risk management approaches and underscoring the importance of coordinating risk management and flexibility. Third, the proposed ETOU-L1 method offers competitive convergence by recovering valid cuts usually discarded by the conventional L1 method, while demonstrating scalability for large-scale problems.

The deep integration of hydrogen systems into power systems poses a significant computational challenge for optimal energy flow (OEF) analysis of electricity-hydrogen energy system (EHS), particularly under numerous renewable scenarios. While pure numerical solvers guarantee feasibility and optimality at high computational cost, data-driven methods often sacrifice solution explainability and feasibility. To address this problem, this paper proposes a two-stage framework for OEF to balance efficiency, feasibility and explainability. In the upper stage, an explainable distance-based graph neural network (EX-DGNN) rapidly generates initial solutions. The EX-DGNN uses the distance measurements between different graph nodes to capture the dependencies from distant nodes. Critically, a sample-based explanation mechanism is integrated. Different from explanation method based on importance values or decision reasons, the sample-based explanation method provides human operators with analogous evidences to justify the prediction solutions. In the lower stage, a restoration module, guided by the initial solutions and explanations, fixes discrete variables and rectifies constraint violation. The resulting continuous non-linear OEF model is then efficiently solved using a GPU-accelerated interior point method (IPM) solver. Case studies show that the proposed two-stage method achieves high feasibility and solving efficiency. On the modified IEEE-30 system and 20-node hydrogen system, compared to commercial solvers based on numerical method, the proposed method reduces the solving time by over two orders of magnitude, while maintaining 99.97% satisfied constraints. Traditional graph neural network can only satisfy 3% constraints. The proposed framework necessitates trade-offs among the computational time, feasibility and optimality of solutions. The sample-based explanations offer actionable insights, effectively bridging the gap between data-driven speed and operational transparency. The proposed method is also tested on the modified IEEE 118bus system and 90-node hydrogen system, verifying the performance on the larger cases.

With deregulation of the energy market, the pricing strategy of energy sellers in a regional integrated energy system (RIES) can affect the interests of all participants in the market and the operation of the system. This paper proposes a pricing strategy for integrated energy service providers in RIES based on a deep reinforcement learning (DRL) algorithm considering privacy protection. The transaction process between the integrated energy service provider (IESP) and user aggregators (UAs) in RIES is modeled as a Stackelberg game. IESP serves as the leader in making retail prices, and different UAs serve as followers in optimizing their energy consumption strategies. Considering UAs' strategies are temporally coupled, a Markov decision process (MDP) is designed differently from existing studies. Case studies demonstrate that the proposed method is accurate and stable when solving a Stackelberg equilibrium without privacy leakage. The obtained pricing strategy avoids unreasonable pricing and guarantees the revenue of IESP and the energy demand of UAs.

Medium/long-term time-series production simulation is critical for power balance analysis in high-renewable power systems. This study proposes a yearly 8760-hour simulation model for multi-regional interconnected systems, integrating operational models for conventional thermal, hydropower, pumped/electrochemical storage, wind, and photovoltaic units at the equipment level. System-level constraints, including regional power balance and inter-regional transmission limits, are incorporated to enhance realism. To address computational complexity, a optimizing cycle decomposition technique is introduced, dividing the annual simulation into 52 weekly cycles for sequential optimization, ensuring computational tractability. The model is validated using planning data from a provincial system in Eastern China, achieving a total solving time of ~30 minutes. Results confirm the model's capability to track real-time operational states while balancing accuracy and efficiency. This approach provides a practical framework for power system planning and operations in large-scale renewable-integrated grids

Power systems must address increasingly severe environmental challenges through an efficient low-carbon transition. However, most current studies considered different single technology to achieve this transition. Research on the interactive mechanisms between different carbon reduction measures remains limited. This paper proposes a capacity expansion planning model for a low-carbon power system that integrates multiple technologies, incorporating two complementary types of carbon reduction measures. Specifically, the technologies are categorized into two categories: direct CO2 reduction measures (e.g., carbon capture and storage) and indirect CO2 reduction measures (e.g., flexibility retrofit, energy storage system and wind expansion). Furthermore, a distributionally robust optimization method is developed to address the uncertainty of wind power. The column and constraint generation algorithm is employed to solve the model and derive the optimal planning scheme. Case studies based on the modified IEEE 24-bus system and the IEEE 118-bus system indicate that the proposed method significantly reduces both the system cost and carbon emissions. Additionally, the planning results demonstrate effective synergy between direct and indirect carbon reduction measures.

Planning resilient and elastic distribution networks has become an effective strategy for withstanding extreme weather events. However, conventional research often overlooks the impact of multiple extreme weather conditions and only considers planning for one specific type of extreme weather. This paper proposes a resilienceoriented distributed generation planning approach for distribution networks, which takes into account multiple extreme weather conditions. Firstly, a line fault probability model is established to capture the impact of typhoons, rainstorms, ice and snow weather. Secondly, a fault scenario generation and simplification method based on modified monte carlo simulation and k-means clustering is proposed to ensure the representativeness and computational efficiency of the selected scenarios. Additionally, a two-stage stochastic mixed integer programming model is introduced to enhance system resilience through distributed generation configuration and network topology reconstruction. The first stage focuses on determining the number, location, and size of distributed generation with economic objectives, while the second stage addresses the recovery method after an uncertain extreme event based on the distributed generation configuration obtained in the first stage. The proposed model is applied to modified IEEE 33-bus system and IEEE 123-bus system. Compared with the conventional method, the DG configuration results are consistent and the solution time is reduced by 70-80 %, achieving a balance between accuracy and efficiency. Furthermore, it effectively reduces load shedding by nearly 90 % by optimizing DG utilisation.

With the continuous increase in the installed capacity of renewable energy, the integration and consumption of wind power and other renewable energy sources have become more urgent. In China's “Three North” regions, the power-heat systems not only contain old pure condensing units with poor regulation capabilities but also CHP units operating under the “heat-determined electricity” mode. Retrofitting existing units is an effective method to enhance the system's flexibility. This paper establishes a two-stage robust optimization planning model for power systems considering the uncertainty of wind power, incorporating deep peak shaving of pure condensing units and heat-power decoupling of CHP units. The first stage of the model considers the system's retrofit plan, while the second stage optimizes costs under the wind power uncertainty set, given the decisions made in the first stage, thus obtaining the worst-case scenario. The model is iteratively solved using the inexact column-and-constraint generation (i-C&CG) algorithm. Finally, the improved IEEE-39 bus is used to verify the model's promotion of wind power integration and the acceleration effect of the i-C&CG algorithm on solving the model.

Offshore wind power poses great challenges for transmission grid planning as it expands rapidly in China. This paper proposes a transmission expansion planning model that incorporates both economic considerations and environmental externalities under high offshore wind penetration scenarios. The model aims to achieve coordinated optimization of wind power connection points, transmission corridors, and energy storage deployment, and it is formulated as a mixed-integer linear programming problem. Specifically, an improved k-medoids clustering algorithm is employed to generate representative operational scenarios. Additionally, the air pollution is adequately considered and the Intervention Model for Air Pollution (InMAP) is used to monetize its health-related costs. Case studies based on the ISO-NE eight-zone system are conducted, and it validates the practicality of the proposed approach. The results demonstrate that accounting for environmental externalities can effectively reduce the need for onshore transmission expansion, promote a more meshed offshore grid structure, and enhance energy storage capacity in coastal load centers.

To enhance the resilience of power system against typhoon disasters, this paper proposes a bi-level line hardening planning method considering coordination of transmission and distribution systems (T&D). The spatiotemporal evolution of typhoons is modeled using the Batts wind field and empirical path model. A fault uncertainty set is constructed based on line vulnerability and information entropy. The upper level optimizes line hardening decisions to minimize total investment and operational costs, while the lower level simulates system response under disaster scenarios, incorporating coordinated T&D dispatch and distributed generation support. Case studies on a modified IEEE 39-bus transmission system integrated with three IEEE 33bus distribution networks validate the effectiveness of the proposed approach.

With the increasing penetration of renewable energy, power systems face severe wideband-oscillation challenges. Noted that the wideband-oscillation is excited by the interaction between power electronic equipment and power grid, and inappropriate planning may aggravate this interaction. How to take wideband-oscillation stability into account during planning has become a key issue. To cope with the aforementioned challenge, this paper firstly establishes the state-space model within the full frequency range to describe the wideband-oscillation stability of converter-integrated power system. Based on this, stability constraints are formulated by eigenvalue sensitivities and the planning model is established. In addition, a further discussion is conducted to generalize the proposed method against the possible limitations. Simulation results show that the method can effectively foresee and alleviate the potential wideband-oscillation risk at the planning stage, and hence enhance the stability of the planned system.

According to the requirements stipulated in cross-provincial electricity trading contracts for annual electricity volume agreements, monthly plans, and power exchange curves, an optimization model for inter-provincial electricity trading strategies during dry and wet seasons in hydro-rich regions has been developed based on a long-term model. This study extensively explores the long-term optimization model by incorporating future cost functions and employing a stochastic dual dynamic programming (SDDP) solution algorithm. Simulation results reveal that the comprehensive operating costs of the model developed in this study are only 71.8% of those associated with models that do not account for inter-provincial electricity trading strategies. Furthermore, the optimized model not only enhances external electricity transmission and procurement plans for hydro-rich regions but also seeks to reduce purchasing costs for provincial users while maximizing revenue from external electricity transactions. Overall, this research offers theoretical frameworks for devising annual and monthly electricity trading strategies tailored to hydro-rich regions.

Managing the unpredictable renewable energy sources of the grid is one of the primary issues facing power systems. This is particularly important in arid areas where there is a growing need for desalinated water. However, because of their historical independence, little is known about the interactions between power systems and water. This paper incorporates Nash bargaining theory into a two-stage stochastic co-optimization model in order to fill this gap. The objective of this model is to reduce joint operating costs and optimize resource allocation between grid-connected desalination facilities and renewable energy systems. The model takes operational restriction into account, and maximizes the energy exchange and the efficiency of renewable energy usage. It is realized by treating desalination plants as demand-side resources that may be adjusted. Case Studies are conducted and the results of the simulation show that the proposed approach lowers operating expenses, enhances the integration of renewable energy sources, and lessens operational risks.

The wideband oscillations between power electronic equipment and power grid in new power system brings new challenges to the safe and stable operation of power system. In order to study their interaction, this paper analysis the principle of PI structure transmission line model, and establish the admittance model of PI structure transmission line in the synchronous reference frame. On this basis, through numerical analysis, the equivalent small-signal admittance model of the power grid is constructed and the grid can be solved in transient stability calculation together with other parts of the system. Finally, by setting different working conditions, the correctness of the proposed model is verified in Matlab/Simulink environment.