Search Results (1,829)

Under high renewable energy penetration, nuclear power units face significant challenges in peak regulation and market clearing due to constraints on minimum technical output and ramping capability. To address this issue, a long-term power system of Guangdong Province in 2035 is taken as the study case, and an energy–reserve co-clearing simulation framework based on Security-Constrained Unit Commitment (SCUC) and Security-Constrained Economic Dispatch (SCED) is established to systematically evaluate the clearing performance of nuclear power and the formation mechanism of residual electricity under multiple market scenarios. On this basis, a nuclear power-coupled Alkaline Electrolysis (AEL) hydrogen production pathway is proposed as a peak-shaving utilization option, and an economic assessment model for nuclear-based hydrogen production is developed to quantify the investment performance under different hydrogen production capacities and operating modes. The results indicate that the integration of an AEL hydrogen production system can effectively alleviate the rigidity of nuclear power output. Under the “12-3-48-3” flexible peak-shaving mode, the residual electricity available for hydrogen production increases by approximately 30% compared with a typical peak-shaving strategy. Under scenarios with low electricity prices and green hydrogen prices, when the hydrogen production capacity is configured at 50–100 MW, the investment payback period is approximately six years, and the project exhibits strong economic robustness against variations in the discount rate. These findings demonstrate that nuclear-based hydrogen production is economically feasible in future power systems with high renewable penetration, providing quantitative support for nuclear flexibility enhancement and the coordinated development of low-carbon energy systems. Full article

Under the “dual carbon” goals, the large-scale integration of distributed photovoltaics (DPVs) presents a challenge for the flexibility supply–demand mismatch in distribution systems. To address the issue of accurately matching flexibility supply and demand in the process of DPV consumption, this paper proposes a coordinated optimization method for the distribution network (DN)- multi-microgrid (MMG) system, based on flexibility margin assessment. First, the mechanism of flexibility supply–demand imbalance under high penetration of DPV is analyzed, and a flexibility margin index considering network constraints is developed to quantify the flexibility surplus or deficit at different levels and periods. Next, within the framework of energy interaction between the DN-MMG systems, a centralized collaborative optimization model is established, aiming to enhance global flexibility margins. This model coordinates power exchange between nodes and the inter-temporal dispatch of energy storage, achieving the collaborative utilization of various flexibility resources. Finally, a case study based on a 10 kV distribution system with MMGs in a northern region is presented. The results show that the proposed method can effectively improve the spatiotemporal matching of system flexibility, reduce the risks of solar power curtailment and load shedding, while enhancing the economic performance of the system and the capacity for DPV integration. Full article

Addressing the challenges of insufficient reserve capacity allocation and wind power uncertainty-induced security and economic concerns under high wind power penetration, this paper develops an integrated energy–reserve market clearing model for regional electricity markets. Firstly, a comprehensive day-ahead market clearing mechanism is designed, encompassing market participant bidding, security-constrained unit commitment (SCUC), security-constrained economic dispatch (SCED), nodal marginal price calculation, and market settlement. Secondly, a SCUC model targeting the minimization of total system operating costs and a SCED model targeting the minimization of energy and reserve procurement costs are established, comprehensively incorporating constraints, such as power balance, unit output and ramping limits, reserve requirements, and network power flows, with nodal marginal prices calculated using the Lagrangian multiplier method. Finally, simulation verification is conducted using a modified IEEE 30-bus system as a case study. Results demonstrate that the proposed model effectively coordinates wind power integration with system reserve requirements, achieving economically optimal dispatch while ensuring grid security and stability. Thermal units obtain substantial market revenues by providing reserve ancillary services, while wind units achieve high revenues through zero marginal cost advantages, fully validating the model’s effectiveness and economic efficiency under high wind power penetration conditions. The research findings provide theoretical foundations and practical guidance for constructing electricity spot market mechanisms adapted to large-scale renewable energy integration. Full article

To address the challenges of real-time control in power systems with high renewable penetration, identifying historical transmission sections similar to future scenarios enables efficient reuse of mature control strategies. However, existing data-driven identification methods exhibit two primary limitations: they typically rely on static Total Transfer Capacity (TTC), ignoring the rapid regulation capability of Energy Storage Systems (ESS) in alleviating congestion; and they employ fixed weights for similarity measurement, failing to distinguish the varying importance of different features (e.g., critical line flows vs. ordinary voltages). To overcome these issues, this paper proposes a similarity identification method for transmission sections considering ESS regulation capabilities and adaptive feature weights. First, a hierarchical decision model is utilized to screen basic grid features. An optimization model incorporating ESS charge/discharge constraints and emergency power support potential is established to calculate the Dynamic TTC, constructing a multi-scale feature set that reflects the real-time safety margin of the grid. Second, a Dispersion-Weighted Fuzzy C-Means (DW-FCM) clustering algorithm is proposed. By introducing a dispersion-weighting mechanism, the algorithm utilizes data distribution characteristics to automatically learn and assign higher weights to key features with high distinguishability during the iteration process, overcoming the subjectivity of manual weighting. Furthermore, fuzzy validity indices (XB, PC, FS) are introduced to adaptively determine the optimal number of clusters. Finally, case studies on the IEEE 39-bus system verify that the proposed method significantly improves identification accuracy compared to traditional methods and provides more reliable references for dispatching decisions. Full article

Short-term wind power prediction is pivotal for maintaining the stability of power grids characterized by high renewable energy penetration. However, wind power time series exhibit complex characteristics, including local turbulence-induced fluctuations and long-term temporal dependencies, which challenge traditional forecasting models. Furthermore, the performance of hybrid deep learning models is often compromised by the difficulty of tuning hyperparameters over non-convex optimization surfaces. To address these challenges, this study proposes a novel framework: CPO—BiTCN—BiGRU—Attention. Adopting a physically motivated “Filter–Memorize–Focus” strategy, the model first employs a Bidirectional Temporal Convolutional Network (BiTCN) with dilated causal convolutions to extract multi-scale local features and denoise raw data. Subsequently, a Bidirectional Gated Recurrent Unit (BiGRU) captures global temporal evolution, while an attention mechanism dynamically weights critical time steps corresponding to ramp events. To mitigate hyperparameter uncertainty, the Crowned Porcupine Optimization (CPO) algorithm is introduced to adaptively tune the network structure, balancing global exploration and local exploitation more effectively than traditional swarm algorithms. Experimental results obtained from real-world wind farm data in Xinjiang, China, demonstrate that the proposed model consistently outperforms State-of-the-Art benchmark models. Compared with the best competing methods, the proposed framework reduces MAE and MAPE by approximately 30–45%, while maintaining competitive RMSE performance, indicating improved average forecasting accuracy and robustness under varying operating conditions. The results confirm that the proposed architecture effectively decouples local noise from global trends, providing a robust and practical solution for short-term wind power forecasting in grid dispatching applications. Full article

With the rapid development of power systems rich in renewable energy, inertia shortages pose significant challenges to frequency security. There is an urgent need for appropriate market pricing mechanisms to quantify the economic value of inertia and incentivize inertia resources to participate in system frequency regulation. Existing market pricing mechanisms struggle to address non-convex generation scheduling problems involving inertia constraints, often resulting in substantial uplift payments that undermine market efficiency and reduce market transparency. To address this issue, this paper proposes a novel convex hull pricing framework specifically designed for the integrated energy–inertia market. The core innovation lies in combining Dantzig–Wolfe decomposition with column generation algorithms to efficiently solve non-convex optimization problems by dynamically constructing the convex hull of feasible dispatch schemes. Based on transient frequency security metrics, the method derives the minimum inertia requirement constraint for the system and calculates the economic value of inertia in non-convex markets using convex hull pricing. Simulation studies on a modified IEEE 39-node system demonstrate two major breakthroughs: the method accurately assesses the economic value of synchronous inertia, with prices reflecting scarcity as wind penetration increases and significantly reduces total system uplift payments compared to integer relaxation pricing schemes. Consequently, this research provides a transparent, incentive-compatible, and cost-effective tool for designing and operating future inertia ancillary service markets. Full article

The increasing penetration of renewable energy and the frequent occurrence of extreme weather events have significantly heightened the uncertainty in power system operations. Simultaneously, the scarcity of renewable energy output samples under extreme meteorological conditions constrains the accurate assessment of extreme risks in system planning and dispatch. To bridge this gap, this work aims to propose a method for generating extreme wind power output scenarios that possess both diversity and statistical accuracy under limited sample conditions. To address this, this paper proposes a method for generating scenarios of extreme wind power output guided and constrained by statistical features. First, multidimensional statistical features are extracted from historical wind power output scenarios and combined, and a quantile threshold method is applied to screen out extreme wind power output scenarios. Subsequently, based on differentiated application requirements of the power system, extreme scenarios undergo preliminary classification followed by category-specific clustering analysis, achieving refined classification of the scenario set. Building on this, an improved generative adversarial network model is constructed, and the Wasserstein distance and gradient penalty mechanism are introduced to enhance training stability. Additionally, a statistical feature self-attention mechanism and feature loss function are designed to effectively constrain the consistency between generated scenarios and real scenarios in key statistical features. Results demonstrate that the proposed method can generate a set of extreme wind power output scenarios with both diversity and statistical accuracy under limited sample conditions, providing effective data support for system safety operation and risk prevention and control. Full article

This paper presents a modeling framework for large-capacity lithium-ion battery energy storage systems (BESSs), developed within the Modelica LIBSystems library and focused on system-level integration. The framework builds on a combined analysis of the electrical, thermal and degradation behavior at the cell level to model the BESS interconnection to the electrical grid. A semi-empirical aging model was incorporated following its validation at the cell level against capacity loss experimental measurements. Two case studies were conducted for a 10.5 MW/15 MWh BESS installed in the isolated power system of Terceira Island. The first analyzed the short-term response to a 5% load step decrease under 60% and 80% renewable penetration scenarios, yielding a frequency nadir improvement of 3 mHz and 21 mHz, respectively. The second projected long-term degradation under two dispatch strategies: one derived from historical time series, and another synthetically constructed to induce more frequent and deeper cycling. After 1000 days of operation, the state of health declined to 95.2% in the historical-based case and to 93.5% under the aggressive profile. The proposed framework establishes a unified, cross-domain modeling workbench for Li-ion BESS applications, enabling evaluation of the system design, control strategies, operation conditions, and system-level performance across both dynamic and long-term horizons. Full article

The increasing penetration of distributed renewable energy resources and the emergence of prosumers are reshaping the operational landscape of distribution grids. This work proposes a comprehensive prosumer-centric control and coordination framework integrated into the IEEE 33-bus radial distribution feeder. Selected buses are modeled as aggregated prosumer nodes equipped with photovoltaic (PV) generation, wind turbines, oncentrated solar power (CSP), a hybrid energy storage system (HESS) including redox flow batteries (RFBs), superconducting magnetic energy storage (SMES), and fuel cells (FCs), as well as electric vehicle (EV) fleets. A hierarchical power management strategy is developed, combining a decentralized fuzzy logic controller for real-time dispatch with a Particle Swarm Optimization (PSO) layer that tunes membership functions and rule weights to enhance system stability and renewable utilization. Time-series simulations are conducted to evaluate the impact of prosumer integration on network performance. The results show a significant improvement in the voltage profile across all buses, particularly at downstream nodes, highlighting the effectiveness of distributed renewable injections and coordinated storage management. The proposed framework illustrates the potential of clustered prosumers to support voltage stability, improve grid operation and enable high-renewable penetration in distribution networks. Full article

This paper presents an adaptive virtual oscillator control in coordination with an adaptive filter to mitigate subsynchronous oscillations in grid-forming converters caused by series compensation. Although series compensation enhances power transfer capability and transient stability margins, it can introduce subsynchronous resonance, leading to subsynchronous oscillations. Virtual oscillator control fed with set points is made dispatchable for grid-forming control to ensure the power-sharing, fast-synchronization, and subsynchronous oscillation damping capability of inverters. In this paper, taking advantage of power reserves in grid-forming operation, virtual oscillator control law is modified to dynamically change the set power point during low-resonance conditions to mitigate subsynchronous oscillations. Moreover, to overcome the limited damping capability of adaptive VOC during severe-resonance conditions, a coordinated adaptive adjustment of the grid-side filter inductance based on the modified power set point is designed. The IEEE’s first benchmark model is altered by integration with a 1000 MW grid-forming inverter in a MATLAB R2024b/Simulink environment. The previously proposed dispatchable virtual oscillator control and electronic-based FACT device, i.e., thyristor-controlled series capacitor, are implemented and analyzed under the same test system for the sake of comparison with the designed coordinated strategy. The simulation results are investigated in the time domain and frequency domain, and by calculating performance indices to verify the effectiveness of the proposed scheme. The overall analysis justifies the mitigated, low transient overshoot and high power quality of subsynchronous oscillations by using the designed strategy with varying compensation levels. Full article

To address the inherent mismatch between the fluctuating power output of renewable energy and the continuous production requirements of ammonia in off-grid wind–solar–hydrogen–ammonia systems, this paper proposes a “day-ahead–intraday–real-time” multi-time-scale coordinated optimization scheduling strategy. In the day-ahead layer, Wasserstein Distributionally Robust Optimization (WDRO) is employed to determine a conservative and stable baseline plan for ammonia load under high uncertainty of wind and solar output. The intraday layer utilizes Model Predictive Control (MPC) with a 2-h prediction horizon and 15-min rolling steps to correct short-term forecast deviations. The real-time layer achieves minute-level power balancing through priority dispatch and deadband control. Furthermore, hydrogen storage tanks serve as a material buffer between hydrogen production and ammonia synthesis, with their state variables transmitting across layers to achieve flexible multi-time-scale coupling. Simulation results demonstrate that, although this strategy slightly reduces the theoretical maximum ammonia yield, it completely avoids load-shedding risks. Compared with the deterministic scheduling (Scheme 1), which suffers a net loss due to severe penalty costs, the proposed strategy achieves a positive daily profit of CNY 277,700, representing an absolute increase of CNY 429,300. Furthermore, it provides an additional daily profit of CNY 65,800 compared to the stochastic optimization approach (Scheme 2), demonstrating superior economic robustness in off-grid environments. Full article

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. Full article

The decarbonization of electricity supply has intensified interest in standalone microgrids capable of achieving high renewable penetration while maintaining strict reliability. This study addresses the research questions of how cost-optimal standalone hybrid microgrids emerge under near-zero unmet-load constraints, how renewable variability and storage dynamics influence system behavior, and how cost-optimal designs compare with emissions-minimizing alternatives. A hybrid photovoltaic–wind–battery microgrid with dispatchable generation supplying a hospital facility in Carbondale, Illinois, USA, is analyzed under islanded operation. Site-specific data are combined with a constrained techno-economic optimization framework implemented in the Hybrid Optimization Model for Electric Renewables (HOMER) to minimize net present cost (NPC) while enforcing hourly power balance and battery state-of-charge constraints. Sensitivity analysis on photovoltaic derating evaluates robustness under performance uncertainty. Results show that the cost-optimal hybrid configuration achieves a renewable fraction of 74.6%, with a renewable utilization index of approximately 0.78 and excess electricity of 22.4%. Limited and intermittent use of dispatchable generation reduces lifecycle cost to approximately $38.2 M. In contrast, a diesel-free configuration nearly doubles net present cost to $71 M under identical reliability constraints. The findings demonstrate that economically viable decarbonization of standalone microgrids is best achieved through diversified hybrid architectures rather than fully renewable extremes. Full article

The global shift toward decarbonized power systems is driving unprecedented penetration of variable renewable energy sources, especially wind and solar PV. Legacy grid architectures, built around centralized, dispatchable synchronous generation, are ill-suited to manage the bidirectional power flows, reduced inertia, and new stability constraints introduced by inverter-based resources. Existing research offers deep but fragmented insights into individual elements of this transition, such as advanced power electronics, microgrids, or market design, but rarely integrates them into a coherent architectural vision for resilient, high-renewable grids. This review closes that gap by synthesizing technical, architectural, and institutional perspectives into a unified framework for resilient grid design toward 2030 and beyond. First, it traces the evolution from traditional hierarchical grids to smart, prosumer-centric, and modular multi-layer architectures, highlighting the implications for reliability and resilience. Second, it critically examines the core technical challenges of high VRES penetration, including stability, power quality, protection, and operational planning in converter-dominated systems. Third, it reviews the enabling roles of advanced power electronics, hierarchical control and wide-area monitoring, microgrids, and hybrid AC/DC networks. Case studies from Germany, China, and Egypt are used to distil context-dependent pathways and common design principles. Building on these insights, the paper proposes a scalable multi-layer framework spanning physical, data, control, and regulatory/market layers. The framework is intended to guide researchers, planners, and policymakers in designing resilient, converter-dominated grids that are not only technically robust but also economically viable and socially sustainable. Full article

Renewable energy systems are increasingly critical for achieving decarbonization and long-term energy security, particularly in rural regions with abundant local resources. While solar and wind technologies have become cost-competitive, their intermittency limits reliability when deployed independently. Biomass, by contrast, offers dispatchable renewable power but faces economic challenges related to feedstock logistics. This study evaluates a biomass-led hybrid renewable energy system (HRES) for Grenada County, Mississippi, integrating biomass, solar photovoltaic (PV), and wind resources to enhance system reliability and reduce environmental impacts. System performance and optimization were assessed using the System Advisor Model (SAM) and the Hybrid Optimization of Multiple Energy Resources (HOMER). The proposed configuration comprises approximately 80% biomass, 10% solar PV, and the remaining share from wind, producing a total annual electricity output of about 423 GWh, sufficient to meet regional demand. The subsystem-level levelized cost of energy (LCOE) was estimated at 12.10 cents/kWh for biomass, 4.07 cents/kWh for solar PV, and 8.62 cents/kWh for wind, with the overall hybrid cost influenced primarily by biomass feedstock transportation and storage. Environmental impact assessment based on U.S. EPA eGRID and IPCC factors indicates that the hybrid system achieves a weighted emission intensity of approximately 28.4 kg CO₂-eq/MWh, representing a reduction of over 94% compared to the regional grid. When scaled to annual generation, this corresponds to roughly 197,000 metric tons of avoided CO₂-equivalent emissions per year, alongside 80–95% reductions in acidification and eutrophication impacts. The results demonstrate that biomass-anchored hybrid systems can provide a reliable, low-carbon pathway for rural energy development, with further cost reductions achievable through targeted policy incentives and financing support. Full article