In modern power systems， the increasing penetration of renewable energy reduces system inertia support capability， and existing market trading mechanisms lack explicit constraints on inertia adequacy. To address these challenges， a trading strategy for power capacity market considering minimum inertia constraint is proposed. First， K-Means clustering combined with the time-series correction method for load growth is used to forecast differentiated seasonal capacity demands for spring， summer， autumn， and winter. Based on these forecasts， a power capacity market model is established with the objective of maximizing the net revenue of capacity providers， incorporating multiple constraints including minimum inertia requirement， capacity adequacy， and unit capacities. The model is solved using the whale optimization algorithm （WOA）， and a simulation platform is built in MATLAB for verification. Results show that the proposed strategy can optimize system’s capacity resource allocation and enhance economic efficiency， while ensuring both capacity and inertia adequacy， providing technical support for inertia security planning in modern power systems.

The energy transition increases wind/solar penetration, raising supply–demand uncertainties. Renewable Energy Sources (RES) intermittency and load variability heighten uncertainties. This study proposes Hierarchical Reinforcement Learning (HRL) with day-ahead scheduling and real-time regulation, enhanced by Generative Adversarial Network (GAN) for uncertainty modeling. A Deep Deterministic Policy Gradient (DDPG)-based reward balances efficiency and stability, while Autoregressive Integrated Moving Average (ARIMA) and Model Predictive Control (MPC) improve temporal matching. Simulation results demonstrate that under the mixed disturbance scenario with 70% renewable penetration, the proposed framework reduces renewable curtailment by 11.2% relative to conventional MPC (from 11.2% to 6.5% in absolute terms), and lowers total operating costs by 9.6% (from $1.487 M to $1.321 M). Under the load mutation scenario, the reductions are 37.1% and 10.8%, respectively. All reported improvements are based on 20 independent runs with averaged results. This offers an adaptive paradigm optimizing generation, grid, load, storage under “dual carbon” goals.

Driven by the global transition toward carbon neutrality, power systems with high penetration of variable renewable energy are confronted with dual challenges: hourly power imbalance induced by short-term renewable fluctuations, and seasonal energy mismatch caused by inter-month and inter-annual climatic variations. This paper systematically reviews the state-of-the-art multi-timescale risk-aware planning methods, and this review systematically identifies three critical research gaps in current studies. On this basis, it develops an integrated analytical framework embedded with Conditional Value at Risk (CVaR) for quantitative comparative verification. This work aims to elucidate the risk hedging effect of dual-timescale coordination, and provide methodological reference for flexible resource allocation in high-renewable power systems. Existing studies have made substantial advances in single-timescale risk measurement and stochastic planning, and the hybrid-resolution modeling scheme that couples hourly operational simulation with multi-scenario monthly energy balance has been widely acknowledged as an effective approach to strike a balance between simulation fidelity and computational tractability. As a coherent risk measure, CVaR has been extensively applied in short-term dispatch and long-term planning respectively, but systematic research on its nested deployment across dual timescales within a unified optimization framework remains absent. This paper embeds CVaR into both hourly operational constraints and monthly energy balance constraints, realizing the coordinated quantification of short-term load-shedding risk and long-term energy shortage risk. Comparative case studies on the Garver-6 and Modified 14-bus TEP benchmark system validate that the proposed dual-timescale risk planning scheme can effectively optimize the trade-off between system economy and tail risk, significantly improve renewable energy consumption rate, and enhance power supply reliability, with a slight and reasonable increase in levelized cost of electricity.

Accurate assessment of source-load complementarity and system regulation capacity is critical for secure dispatch and planning in high-penetration renewable power systems. Addressing limitations of existing methods—which rely heavily on static metrics, struggle to capture temporal and tail dependence characteristics, and provide insufficient support for dispatch decisions—this paper proposes a multi-level integrated evaluation framework. First, from a source—load matching perspective, we develop a novel complementarity metric, integrating real-time rate of change, temporal consistency, and tail dependency. An improved adaptive noise-complete set empirical mode decomposition combined with a hybrid Copula model is employed to isolate noise and to precisely quantify dynamic dependency structures. Second, we introduce the Minkowski measure and construct a net load fluctuation domain accounting for extreme fluctuations and coupling relationships. Subsequently, combining the Analytic Hierarchy Process (AHP) with probabilistic convolution enables multi-level comparative quantification of resource capacity and fluctuation domain requirements under varying confidence levels. Simulation results demonstrate that the proposed framework not only provides a more robust assessment of source-load complementarity but also quantitatively outputs the adequacy and risk level of system regulation capacity. This delivers hierarchical, actionable decision support for dispatch planning, significantly enhancing the engineering applicability of evaluation outcomes.

With the continuous advancement of smart grid construction, the coupling degree between distribution information system and physical system is increasing, and the degree of distribution automation will inevitably become higher and higher. This paper establishes a two-layer model for the optimal configuration of intelligent terminals in distribution networks considering reliability and economy: the upper model optimizes the investment cost of intelligent terminals in distribution networks and the system operation cost, and the lower model analyzes the loss of accidental power outages and the cost of operating network losses. A Benders decomposition algorithm based on a multi-cut strategy is proposed to achieve efficient solution, and the effectiveness of the planning method is verified by an IEEE 33-node example. Compared with the scheme without intelligent terminals, the optimized configuration scheme reduces the overall system cost by 37.9%, which is close to the performance of the full "three remote" configuration scheme, and significantly reduces the investment cost by 20.1%. This method provides a practical decision-making framework for distribution system planners to balance reliability improvement and economic constraints.

Accurate probabilistic forecasting of the multi-energy loads can provide essential uncertainty information about future loads for the management of integrated energy systems. The selection of appropriate features lays a critical foundation to achieve accurate forecasting, but such an issue is not thoroughly studied for probabilistic load forecasting, especially for multi-energy loads. In this article, we propose an adaptive feature selection framework for probabilistic multi-energy load forecasting by considering different operation patterns to select pattern-specific features. Specifically, we develop a ProbLassoNet model by integrating the multi-quantile regression model with the residual-connecting-Lasso operation to capture both linearity and nonlinearity for effective feature selection. We conduct experiments on an open dataset and validate that the proposed method can significantly improve probabilistic multi-energy load forecasting by distinguishing important features from redundant features. We also provide a comprehensive analysis of important features and multi-energy relationships in different periods, which can serve as a reference for further research on multi-energy load forecasting.

The increasing penetration of distributed renewable energy and the emergence of large-scale, flexible loads such as data centers pose significant challenges to the economic and secure operation of distribution systems. Traditional static pricing mechanisms are often inadequate, leading to inefficient resource dispatch and curtailment of renewable generation. To address these issues, this paper proposes a hierarchical pricing and dispatch framework modeled as a tri-level Stackelberg game that coordinates interactions among an upstream grid, a distribution system operator (DSO), and multiple virtual power plants (VPPs). At the upper level, the DSO acts as the leader, formulating dynamic time-varying purchase and sale prices to maximize its revenue based on upstream grid conditions. In response, at the lower level, each VPP acts as a follower, optimally scheduling its portfolio of distributed energy resources—including microturbines, energy storage, and interruptible loads—to minimize its operating costs under the announced tariffs. A key innovation is the integration of a schedulable data center within one VPP, which responds to a specially designed wind-linked incentive tariff by shifting computational workloads to periods of high renewable availability. The resulting high-dimensional bilevel optimization problem is solved using a Kriging-based surrogate methodology to ensure computational tractability. Simulation results verify that, compared to a static-pricing baseline, the proposed strategy increases DSO revenue by 18.9% and reduces total VPP operating costs by over 28%, demonstrating a robust framework for enhancing system-wide economic and operational efficiency.

Distributed energy resources provide local power as a supplement on the customer side. Recent rapid development of the distributed energy source enhances the clear energy production at the terminal of the power system. Whereas the small capacity of a single distributed energy source and the scaling of numbers pose difficulties for market design and clearance. In addition, the stochastic and quickly varying output power of the amount of (distributed) renewable energy sources increases the necessity for flexible regulation capacities. In response to the above issues, this paper develops a modified Leiden algorithm to aggregate distributed energy sources with similar regulation properties and connectives, avoiding complex power allocation strategies within the intra-aggregator and ensuring ordered power flow among inter-aggregators. Then, a bi-level market mechanism is proposed to highlight the regulation contributions of both distributed aggregators and conventional energy sources. The upper-level model optimizes the price of combined frequency regulation and electricity markets. The lower-level model regulates the output power of the aggregators and conventional energy sources. Furthermore, the modification of the bi-level model is proposed via the Karush–Kuhn–Tucker condition to ensure its solvability. The proposed market mechanism and the aggregating method are verified using a modified IEEE 30-bus system with IEEE 123-node test feeders and terminal-side energy resources. The results reflect the incentive impacts of the designed market mechanism and the effectiveness of the aggregating algorithm.

The inherent intermittency and strong meteorological dependence of photovoltaic (PV) power generation pose severe challenges to the secure and stable operation of power systems with high penetration of renewable energy. To enhance the accuracy of short-term PV power forecasting, this paper proposes a hybrid neural network forecasting model optimized by the Artificial Lemming Algorithm (ALA), namely ALA-BiTCN-BiGRU. The model first employs Pearson correlation analysis for feature selection, effectively reducing input dimensionality by constructing an optimal subset of meteorological features most relevant to PV output. The core of the model is composed of a cascaded Bidirectional Temporal Convolutional Network (BiTCN) and a Bidirectional Gated Recurrent Unit (BiGRU), designed to deeply capture both local temporal patterns and long-range dependencies within the PV power sequence. Furthermore, the Artificial Lemming Algorithm (ALA) is introduced to perform global optimization of the hybrid network's hyperparameters, significantly enhancing the model's feature learning and generalization capabilities. Case studies based on measured data demonstrate that, compared with baseline models such as TCN and BiGRU, the proposed ALA-BiTCN-BiGRU model exhibits superior forecasting accuracy and robustness across various evaluation metrics. This research provides effective decision support for the precise dispatch of power systems containing high shares of PV generation.

The advent of large-scale renewable energy and High voltage direct current (HVDC) transmission has resulted inAstability problems inAfrequency and voltage. Grid-forming (GFM) strategies are characterized by excellent voltage and frequency support properties. Nevertheless, it is not feasible to precisely delineate the precise mathematical relations between GFM converter capacity and the associated stability margin. This paper addresses the optimal configuration of GFM converters in a large receiving-end power grid scenario with a significant proportion of renewable energy and HVDC feed-in. It presents a GFM converter evaluation method constrained by static voltage and frequency security. Firstly, the GFM converter technology based on virtual synchronous machine control is introduced, with a detailed explanation of its control frame. Secondly, the principles of how GFM control improves the static stability limit and frequency dynamic response characteristics are analyzed in depth, and a comprehensive assessment of GFM converter requirements is conducted by combining static stability margin constraints and frequency security constraints. Finally, the effectiveness of the proposed method is verified through a modified IEEE 39-bus model based on the Matlab/Simulink platform.

The rising integration of renewable energy sources has resulted in a diminished capacity for voltage support within the system, which is characterized by low inertia and a reduced short circuit ratio (SCR). In order to improve grid strength and enhance the capacity for renewable energy integration, an initial analysis was conducted on the grid support capabilities of grid-forming (GFM) stations, followed by an investigation into how grid strength influences the dominant operational modes of GFM converters. Subsequently, leveraging the definition of the multi-infeed short circuit ratio, a calculation method for the SCR, applicable to new energy base stations featuring GFM substations, is developed. Additionally, a strategic approach to optimal location selection and sizing of these substations aimed at enhancing the SCR within new energy grids is proposed, with the model being solved through genetic algorithms. Finally, the effectiveness of the proposed method is verified based on the IEEE39-node system and a real new energy station. The results show that the system strength is greatly improved after the optimized configuration of the GFM equipment, and the maximum tolerable space of 90% new energy stations reaches 95% of the theoretical maximum tolerable space of each new energy station.

DC power flow control and stability analysis methods are two major challenges in VSC-HVDC systems, especially under the fast fluctuation characteristic of offshore wind farm. This paper proposes an optimized power flow control and stability enhancement strategy for multi-terminal direct current (MTDC) systems for large-scale renewable energy integration. Under various contingencies, the proposed method achieves precise power flow redistribution by dynamically adjusting the VSCs’ droop parameters proportionally according to the available headroom. A hierarchical control architecture is therefore implemented, incorporating power flow optimization and MTDC system stability constraints to maintain system security during contingencies. Finally, small-signal stability analysis is enabled to confirm that the control strategy effectively preserves system stability under various contingencies. Dynamic simulations of a five-terminal MTDC grid are carried out using MATLAB/Simulink SimPowerSystems/Specialized Technology to verify that the proposed control method can provide an effective solution for secure and stable operation of MTDC systems with high renewable energy penetration.

The capacity optimization configuration of offshore wind power and hydrogen production systems has traditionally focused on initial investment and cost-benefit analysis during the project construction lifecycle, neglecting considerations of scheduling economy in the system's actual operation. This article presents a novel approach to optimize the allocation mechanism of grid power and hydrogen production power in offshore wind farms with the aim of maximizing net income. The study employs simulations to assess the hydrogen production scenario for a 1000 MW Sheyang offshore wind power facility under various offshore distance scenarios across different seasons. Results indicate that peak shaving through electrolysis is most effective during autumn, yielding substantial benefits. Electrolysis cells can achieve peak shaving operation ranging from 6 MW to 300 MW, constituting 26.5% of the total electricity generated for hydrogen production.

The integration of electric vehicles (EVs) into power systems is crucial for sustainability but poses prediction challenges for dispatchable EV capacity, essential for grid stability. This paper presents a novel capacity prediction model for power systems using an improved long short-term memory (LSTM) algorithm. Our enhanced LSTM model addresses the limitations of traditional models by optimizing architecture and integrating power system-specific features. Through extensive data analysis and model adjustments, our approach outshines existing methods in accuracy and reliability, showcasing significant improvements in EV capacity forecasting. The simulation results verifies the effectiveness of the proposed method.

The advent of novel power systems has given rise to a multitude of safety and stability concerns associated with the integration of emerging energy sources and power electronic equipment. The active support of the grid-forming control strategy represents an effective solution to the voltage and frequency stability issues associated with the weak damping and low inertia inherent to high-ratio new energy systems. Firstly, a static voltage stability index based on critical impedance is proposed for assessment of the static stability margin of a new energy grid-connected system, based on the static voltage stability theory of a traditional single-unit single-load system. Secondly, an analysis is conducted of the control principle of the grid-forming control converter and its impedance characteristics. In conclusion, a method for enhancing the static voltage stability margin of grid-connected new energy stations through parameter control of grid-forming converters is presented. The simulation verification of the single-feed and multi-feed systems demonstrates the efficacy and accuracy of the methodology presented in this study.

In low-inertia grids, distributed energy storage systems can provide fast frequency support to improve the frequency dynamics. However, the pre-determination of locational demands for distributed energy storage systems is difficult because the classical frequency dynamic equivalent response cannot capture the dynamic characteristics of the entire system. Additionally, optimal allocation of the distributed energy storage systems required for the different buses is challenging because of nonlinear constraints that account for these locational effects. This paper develops a method to evaluate locational frequency security. By simulating the worst-case G-1 contingency, the distribution network buses that violate the limit of the maximum rate of change of frequency are determined as the installation locations of distributed energy storage systems. We then propose a feasible region-based linearization method to replace the complex nonlinear constraints. Finally, we present a two-stage robust allocation model for distributed energy storage systems that is intended to maintain locational frequency security. The validity of the proposed method is verified through case studies performed on a modified IEEE RTS-24 bus system and a modified IEEE 118 bus system. The results show that the optimally allocated distributed energy storage systems effectively reduce the frequency safety risk that originally existed due to oscillation at the distribution network buses.

Optimal power flow (OPF) calculation methods are important for the power system operation and mainly focus on the deterministic power flow calculation, neglecting the impact of demand response on online security calculation of power systems with renewable energy sources. Therefore, this paper proposes an OPF calculation method that considers the uncertainties of wind power, photovoltaic (PV) power generation and demand-side response. Firstly, the research focuses on the renewable energy grid, considering the uncertainties of wind power and PV power generation, and establishes uncertainty models for wind power and PV output. Secondly, based on cloud model theory, an uncertainty model for demand response is established. According to the established models, an efficient OPF model is constructed with a linearized submodels considering multiple uncertainties. By testing on the IEEE 30-bus system as a typical example, we found the effectiveness and superiority of the proposed OPF calculation method can benefit the power system economic operation and demand side resource utilization.

With the development of science and technology, the energy issue has gradually become a topic of global common exploration. According to the relevant information, this paper designed a distributed new energy system based on the blockchain consensus algorithm. The system took the distributed new energy system as the main body, integrated it with blockchain technology, and finally formed a distributed new energy system based on blockchain consensus algorithm. The feasibility of the system was finally determined after the technical tests of voltage detection, power allocation, information collection, and algorithm security. In order to understand the differences between the performance of this system and the traditional system, this paper has made several performance data comparative analysis tests for this system and the traditional system. The test items included energy conversion utilization rate, energy storage, energy use price, energy cleanliness, energy access rate, energy use, etc. Finally, the relevant data of commercial power consumption and residential power consumption accounting for 30 % and 47 % of the total power consumption were obtained, and the data was used to analyze the source of power access. The proportion of green energy is 10 % and 17 %, respectively. According to a series of operation processes such as design, experiment, and analysis, it was finally determined that the system was feasible and effective.

The volatility and uncertainty associated with the high proportion of wind and PV output in the new power system significantly impact the power and energy balance, making it challenging to accurately assess the risks related to renewable energy abandonment and supply guarantee. Therefore, a probabilistic power and energy balance risk analysis method based on distributed robust optimization is proposed. Firstly, the affine factor and the flexible ramp reserve capacity of thermal power are combined to establish a probabilistic index, which serves to characterize the risk associated with the power and energy balance. Drawing upon the principles of the conditional value at risk theory, the risk indexes of the load shedding power and curtailment power under a certain confidence probability are proposed. Secondly, the probability distribution fuzzy sets of uncertain variables are constructed using the distributionally robust method to measure the Wasserstein distance between different probability distributions. Finally, aiming at minimizing the operation cost of thermal power, the risk cost of power abandonment, and the risk cost of load shedding, a distributed robust optimal scheduling model based on a flexible ramp reserve of thermal power is established.

Renewable energy resources, such as wind and solar energy, have become the primary components of power systems. However, the uncertainty and fluctuations associated with these resources increase the difficulty to follow renewable fluctuations using conventional generators. Energy storage systems are one of the best choices for improving the mechanical performance limitations of conventional units. In this paper, we analyze the dynamic performance of the conventional-storage frequency regulation model and provide parameter and capacity setting rules for storage. Furthermore, we allocate the storage capacity to buses under different operation modes to minimize transmission-line fluctuations. We use a possible planning power systems in Jiangsu Province, China, in 2030 to analyze and verify our proposal. The system has more than 1000 buses, 2000 branches and 700 generators, and 220, 500 and 1000 kV voltage levels. The results show that using a small amount of storage is feasible for improving regulation performances. Additionally, the optimal energy storage placement effectively reduces the fluctuations and optimizes the power flows in the transmission systems.