Search Results (972)

High renewable penetration, distributed energy resources, and fast-varying electric loads are shifting smart-grid planning from energy-balance simulation toward quality-aware operational assessment. Full-unit benchmark models (FULL) preserve unit commitment, ramping memory, and reserve feasibility but are expensive for repeated annual studies, whereas conventional equivalent aggregation (EQ) can overstate the realizable flexibility of heterogeneous units. This paper proposes quality-aware flexibility-envelope aggregation (QFEA), which separates units by inherited boundary state, ranks them by renewable-following flexibility, constructs conservative cluster envelopes, and couples reduced optimization with feasible disaggregation and state write-back. The model coordinates renewable curtailment, reserve sufficiency, tie-line ramping, and a normalized quality-stress proxy without claiming to replace detailed power-flow, harmonic, or electromagnetic studies. In the nominal single-region case, QFEA reduces the number of commitment objects by 46.2% and computation time by 63.7%, while limiting total-cost deviation to 1.1% and renewable-curtailment deviation to 0.2 percentage points. In 20 matched 24-h stress scenarios, its mean quality-stress index is 2.56%, compared with 2.58% for FULL and 6.57% for EQ. A separate 13–104-unit simplified scaling test keeps inverse-mapping closure error below 5.2 × 10⁻⁹ MWh and disaggregation below 1.3% of measured end-to-end time. The results identify QFEA as a traceable intermediate model for renewable-integration screening when annual computational efficiency and implementable unit trajectories are both required. Full article

High-quality renewable energy (RE) scenario generation is essential for secure, reliable, and economic power system operation under high RE penetration. To address the limitations of existing methods in preserving scenario fidelity, diversity, spatiotemporal dependence, and engineering consistency, this paper proposes a three-stage framework that combines a variational autoencoder (VAE) with a conditional latent diffusion model (CLDM), hereafter referred to as VAE-CLDM, for day-ahead renewable energy scenario generation. First, multi-source features are constructed by integrating renewable power outputs, meteorological variables, temporal lag information, and spatial correlation characteristics among wind farms and photovoltaic stations. Then, VAE compresses the high-dimensional features into a compact latent space while retaining key statistical and spatiotemporal information. Based on this latent representation, a CLDM generates realistic scenarios by progressively denoising random noise under meteorological conditions. A spatiotemporal feature modeling strategy is incorporated to better represent temporal fluctuations and inter-site correlations, while a diversity regulation factor is selected on the validation set to balance scenario fidelity and tail-event coverage. Finally, the generated scenarios are reconstructed into the physical space and checked using output-bound and ramp-consistency correction to improve their practical usability. Results on the open dataset released by the Chinese State Grid Renewable Energy Generation Forecasting Competition show that the proposed framework achieves the lowest root mean square error (RMSE), mean absolute error (MAE), and maximum mean discrepancy (MMD) among the tested models on the training-statistics-standardized renewable-power benchmark, with RMSE of $0.3013\pm 0.0022$, MAE of $0.3636\pm 0.0010$, and MMD of $0.04269\pm 0.00040$. After post-correction, lower- and upper-bound violations are reduced to 0.00%, and the ramp-violation rate is reduced to 0.08%, indicating that the proposed VAE-CLDM can provide useful scenario inputs for day-ahead dispatch and risk assessment in renewable-dominated power systems. Full article

China’s power sector is accelerating its transition to spot-market clearing with increasing offshore wind penetration. This transition poses compounded operational and economic challenges, as the interaction between generation variability and price volatility affects both producer revenues and real-time system balancing costs. This study utilizes full-year hourly generation and spot price data from an offshore wind farm in eastern Guangdong, which represents the largest offshore wind industry cluster and a premier high-wind-resource area along China’s near-sea coasts. This empirical dataset provides significant value for characterizing real-world market behaviors under Guangdong’s dual-settlement framework. By employing a settlement-consistent Monte Carlo framework to quantify the joint effects of forecast errors, our results reveal that while downside risk is primarily driven by generation volume errors under normal conditions, the negative correlation between wind output and prices intensifies revenue volatility. Furthermore, under high-stress scenarios characterized by extreme market volatility and large deviations, price uncertainty emerges as the dominant driver of tail risk. Ultimately, these findings demonstrate that probabilistic forecasting for both prices and generation is essential not only for producer risk management but also for supporting dispatchable decision-making and reliable operation of power systems with high shares of renewable energy. Full article

Distributed multi-energy storage is critical for enhancing the operational flexibility of power systems with high renewable energy penetration. However, the heterogeneity between electric and thermal energy storage in dynamic response, multi-time-scale features and coupling interactions causes three key limitations of existing aggregation methods: conservative feasible-region characterization, excessive computational complexity, and inaccurate dispatch capability evaluation. To address these issues, this paper proposes a distributed multi-energy storage aggregation method based on support functions and the Minkowski sum. First, a unified convex polyhedral feasible-region model incorporating electro-thermal coupling constraints is established. Then, a distributed parallel aggregation strategy with constraint relaxation–reconstruction and adaptive optimal direction selection is developed to achieve high-precision approximation of the aggregated feasible region. Case studies on a park-level integrated energy system with 10 electric and 5 thermal storage units show an average support function error of 1.35%, a volume similarity ratio of 0.953, and a dispatch feasibility rate of 98.70%. Electro-thermal coupling reduces feasible-region volume by 32.50% with only 5.20% overestimation. Under fluctuating commands, the tracking deviation is 2.80% with no constraint violations. Furthermore, measured engineering data verify its linear scalability for large clusters and 1.27% cost deviation in day-ahead dispatch. The proposed method accurately characterizes the joint regulation capability of multi-energy storage clusters while ensuring favorable computational scalability. Full article

Smart grids with high renewable penetration are increasingly evaluated through long-horizon production simulation, but conventional decomposed simulation mainly reports energy balance and unit feasibility, while power-quality stress remains weakly quantified in the storage correction layer. This paper presents a power-quality-proxy-guided state-replay framework for renewable-rich smart grids. Instead of claiming feeder-level electromagnetic simulation, the method defines planning-level proxy indicators that can be exported by production-simulation software: a voltage-deviation proxy obtained from net-power sensitivity, a net-load ramp proxy, an inverter/charger harmonic-risk proxy, and a renewable-curtailment exposure proxy. These normalized indicators are combined into a composite score $S_{PQ}$, which is then used to distinguish two storage values: charge retention during renewable-surplus voltage-rise intervals and discharge support during voltage-dip, ramp-stress, or inverter-stress intervals. A base decomposed production-simulation schedule is first obtained. The proposed layer then constructs storage accounting cycles independent of monthly and rolling-window boundaries, attaches the proxy ledger to each interval, backtracks terminal residual storage energy to low-value charging actions, and reallocates physically feasible discharge to high-$S_{PQ}$ intervals. The corrected storage path is projected onto power and energy limits and replayed before storage and conventional-unit states are inherited by the next monthly solve; cycles outside the replay validity envelope are escalated to full redispatch rather than counted as successful corrections. An eight-interval case reports explicit $S_{PQ}$ values and shows that a trajectory ending 45 MWh above the 30 MWh reference can be corrected by trimming 35 MWh of low-proxy-value charging and adding 10 MWh of discharge in two high-score intervals. A 96-interval experiment further shows that the full method reduces explicitly discarded residual energy from 214 MWh to 31 MWh, provides 128 MWh of proxy-guided support, and lowers weighted PQ-proxy exposure by 46.3%. The framework links smart-grid data analysis, renewable integration, and power-quality improvement within a traceable production-simulation workflow. Full article

The transition toward a sustainable society requires the large-scale integration of renewable energy resources into modern power systems. Among the available technologies, photovoltaic distributed generation (PDG) plays a key role in achieving these goals. This paper proposes an optimization model for the sizing of PDG in rural distribution systems (DSs). The active power contribution of PDG reduces the loading of the DS, while reactive power injection through Volt–VAR control improves the voltage profile. The optimization problem incorporates probabilistic constraints associated with voltage regulation, line ampacity, and reverse power flow at the substation. The proposed methodology, based on the enhanced snow geese algorithm (ESGA), was validated using two rural DSs with 95 and 170 buses. For the 95-bus system, the results demonstrated a significant improvement in the voltage profile and a 22.9% reduction in the annual energy supplied by the substation. For the 170-bus system, ESGA achieved a high-quality solution with an objective function value only 1.4% higher than that obtained by PSO. The resulting PV penetration levels reached 27.3% and 30.8%, respectively. These results demonstrate the capability of ESGA to provide solutions comparable to those obtained with well-established optimization techniques. Full article

With the increasing penetration of renewable energy, frequency stability issues in new-type power systems have become increasingly prominent due to reduced system inertia and weakened primary frequency regulation capability. To address the insufficient frequency response capability of power systems in high-renewable regions, this paper proposes a coordinated multi-type load frequency control strategy based on controllable load damping factors. First, an improved system frequency response model considering renewable penetration is established to analyze the impacts of renewable penetration on maximum frequency deviation, rate of change of frequency (RoCoF), and quasi-steady-state frequency deviation. Subsequently, coordinated frequency control strategies are designed for feeder voltage-sensitive loads, distributed constant power loads, and energy-intensive industrial loads. Finally, an electromagnetic transient simulation model based on an actual Yunnan power grid is established to verify the effectiveness of the proposed method. The results show that increasing renewable penetration deteriorates frequency response by reducing the frequency nadir, increasing RoCoF, and enlarging quasi-steady-state frequency deviation. Under a −0.1 p.u. active power disturbance, the studied grid triggers under-frequency load shedding when renewable penetration exceeds approximately 44% without load-side frequency regulation, whereas the proposed strategy enables the system to satisfy the 49.2 Hz UFLS constraint at 70% renewable penetration, increasing the allowable renewable accommodation level by about 26 percentage points. Economic analysis further indicates that the proposed load-side control has lower regulation cost than renewable curtailment-based frequency support. Full article

High penetration of renewable energy sources has intensified voltage fluctuations and violations in modern distribution networks. Although distributed control offers a scalable solution, existing methodologies frequently require the exchange of sensitive operational data. This critical limitation, which is intrinsic to distributed algorithms, raises significant privacy concerns. This paper proposes a novel privacy-preserving distributed voltage regulation scheme based on the framework of state-based potential games. Specifically, we first formulate a potential function that aligns the local objectives of individual buses with the voltage profile improvement goal. To reduce the risk of sensitive-data disclosure, we introduce a decoupling mechanism where buses only exchange information regarding coupling constraint violations rather than bus voltage or power injection data. Furthermore, a parallel update law is established, allowing buses to optimize their strategies independently. A key strength of the proposed scheme is its resilience to stochastic communication outages (SCOs). Numerical tests demonstrate that, in comparison to existing distributed voltage control methods, the proposed scheme not only reduces the risk of sensitive-data disclosure but also maintains highly consistent performance across diverse SCO scenarios. Finally, the effectiveness and convergence performance of the proposed voltage regulation scheme are validated by the case studies. Full article

Short-term electricity demand forecasting is a critical enabler of the secure and efficient operation of modern power systems, particularly amid increasing renewable energy integration, smart grid expansion, and the broader energy transition. This paper presents a rigorous comparative analysis of electricity demand forecasting models, encompassing statistical methods, Machine Learning (ML), Deep Learning (DL), and hybrid architectures. A structured taxonomy is proposed to classify models according to their methodological family, application horizon, and data requirements, thereby providing a unified reference framework for researchers and energy-sector practitioners. Models are evaluated using a multi-criteria framework comprising accuracy, robustness, scalability, interpretability, computational cost, and the capacity to handle exogenous variables. The analysis identifies critical research gaps, including the limited integration of probabilistic forecasting into operational contexts and the absence of standardized evaluation protocols under real-world conditions. Future research directions are outlined, with particular emphasis on uncertainty quantification, adaptive learning strategies, and hierarchical forecast coherence in systems with high penetration of distributed energy resources. Full article

With the strategic advancement of energy structure transformation and the implementation of carbon peaking and carbon neutrality goals, the Integrated Energy System (IES) has become a core research direction owing to its superior performance in multi-energy complementation, operational efficiency, and low-carbon emission characteristics. Nevertheless, existing studies reveal that the optimal operation of IES still faces significant challenges, including the high complexity of multi-energy coupling, supply–demand imbalance caused by renewable energy penetration, and insufficient exploitation of demand-side flexibility. As a core measure of demand-side management, demand response (DR) provides an effective approach to motivate users to adjust power load via price incentives or direct load control. DR can effectively smooth load profiles, improve resource utilization, and boost the consumption level of renewable energy. To meet the operational demands of modern IES, this paper establishes a security-constrained economic dispatch model embedded with multi-level demand response mechanisms. The proposed framework is divided into four key modules: First, a price-based demand response strategy is developed to dynamically guide users in regulating multi-energy consumption behaviors. Second, electric vehicles (EVs) are considered flexible demand-side resources with unique response characteristics. An aggregated EV charging–discharging model is established to suppress power fluctuations and support high proportions of renewable energy integration. Third, to precisely calculate the overall operating cost of IES, a combined economic evaluation index integrating time-of-use tariff and Levelized Cost of Electricity is adopted. It maintains a balance between amortized long-term generation investment and short-term operational expenditure, and coordinates the economic benefits and operational reliability of the whole system. Finally, numerical simulations are performed on a coupled test system comprising an IEEE 33-bus distribution network and a 20-node natural gas network. Simulation results verify that the proposed co-optimization model can effectively reduce total system operating costs and greatly improve the local assumption of fluctuating renewable energy. Full article

Cost-effective renewable accommodation is essential for sustainable power-system planning. In power systems with high renewable penetration, using a high renewable energy utilization rate as a rigid target may require excessive investment in peak regulation, energy storage, and grid reinforcement to absorb a small amount of marginal renewable generation, which increases the total cost of power supply. This paper proposes a dynamic estimation method for determining a reasonable renewable energy utilization rate by linking planning and operation under the criterion of minimum total system cost. At the planning layer, the optimal renewable capacity expansion and its corresponding utilization rate are determined by minimizing the sum of annualized investment cost and comprehensive operating cost. At the operation layer, year-round chronological production simulation is combined with discrete search over curtailment penalty parameters to identify the utilization level that gives the minimum total cost under given system boundaries. A case study of the Gansu power grid shows that the reasonable planning-layer utilization rates in 2028, 2030, 2035, and 2040 range from 84% to 88%, while the operation-layer utilization rates in 2028 and 2030 are 90.81% and 91.84%, respectively. City-level decomposition further shows that resource-rich areas such as Jiayuguan-Jiuquan generally have lower utilization rates than most load centers. An equal-investment comparison shows that deep peak regulation of thermal power units improves the utilization rate the most but increases the power supply cost, whereas pumped storage gives better cost-reduction performance. The results indicate that reasonable renewable energy utilization rates should be evaluated on a rolling basis to support sustainability-oriented target setting and coordinated source-grid-load-storage planning. Full article

The transition from centralized fossil fuel-based power systems toward decentralized smart grids with a high penetration of renewable energy sources (RES) introduces substantial challenges in monitoring, control, coordination, and management. These challenges are particularly evident at the active power grid periphery, defined in this work as the decentralized edge layer of modern power systems comprising low-voltage distribution networks, distributed energy resources (DERs), prosumers, energy storage systems, electric vehicles (EVs), and localized intelligent control entities operating near the consumer side of the grid. This review systematically examines the role of multi-agent systems (MASs) in addressing these emerging challenges. A total of 160 articles, drawn predominantly from top-tier Q1 journals and published up to March 2026, were systematically analyzed to evaluate recent methodological advances, identify persistent research gaps, and compare existing problem formulations and mathematical techniques. The review covers MAS-based applications including distributed energy management, voltage and frequency regulation, demand-side management, microgrid coordination, EV charging coordination, resilience enhancement, and cyber-physical supervisory control. The findings indicate that although MASs offer enhanced scalability, flexibility, resilience, and decentralized decision-making capabilities, existing approaches continue to face significant limitations associated with communication latency, cybersecurity vulnerabilities, interoperability constraints, heterogeneous agent dynamics, and limited real-time experimental validation. Furthermore, this review proposes six emerging research hypotheses targeting underexplored domains, presents a methodological decision flowchart for MAS implementation and selection, and discusses future research directions involving the integration of digital twins, blockchain technologies, edge intelligence, and advanced communication architectures with MAS frameworks. Full article

Reliability-constrained generation expansion planning (GEP) is crucial to balance economy and reliability for power systems with high renewable penetration. Most of the existing reliability-constrained GEP methods treat reliability indices as exogenous constraints, which yields subjective and empirical results. Endogenous methods are computationally intractable due to the substantial computational burden caused by massive samples and the complexity of transmission networks. This work develops an efficient non-iterative GEP framework with endogenous reliability assessment built upon a novel risk surrogate model. The implicit relationship among high-dimensional uncertainties, installation capacity, and expected energy not served (EENS) is characterized by the proposed risk surrogate model based on polynomial chaos expansion (PCE), which incorporates network constraints. To enhance approximation precision, a multi-cluster PCE strategy is proposed to construct piecewise analytical formulations that accurately represent the underlying non-convex risk function. Relying on this surrogate model, an efficient non-iterative reliability-constrained GEP framework is proposed, which eliminates iterative solving and balances economy and reliability. Embedding the reliability analytical formulations into the GEP model, the two-level optimization model is transformed into a single-level optimization model. The simulation results demonstrate the effectiveness of the proposed method. Full article

The high energy demand driven by industrial development has transformed the power system from a single energy source to multiple energy systems (MESs). These systems, which involve thermal generators, combined heat-and-power (CHP) units, electrolyzers, fuel cells, etc., with realistic forecast uncertainty, are very operationally challenged. This paper proposes a Distributionally Robust Optimization (DRO) based on a Wasserstein-metric ambiguity set, which simultaneously optimizes the annual maintenance schedules and short-term operational dispatch across MESs. The ambiguity set is constructed using joint samples of forecast errors for the three carriers’ demand, allowing for a data-driven worst-case distribution approach that mitigates the excessive conservatism typically associated with conventional robust optimization (CRO). The penalties are explicitly enforced for load and renewable energy curtailments across each of the MESs with source-specific value-of-lost-load coefficients. The Wasserstein radius is improved by sensitivity analysis, obtaining a θ value of 0.20 as the cost reduction radius for a 40% RES penetration. Five RES penetration levels are implemented here on the IEEE 39-bus New England network, with CHP, electrolyzer, fuel cell, thermal storage, and hydrogen storage. The DRO reduces the total annual system cost by 56% compared to CRO, while reducing the unbalanced energy. Full article

Reliable state estimation is essential for the secure and efficient operation of sustainable energy systems, especially under the increasing integration of renewable energy, distributed resources, and heterogeneous sensing devices. However, in practical power systems, SCADA, PMU, and AMI measurements often have different sampling rates, accuracies, communication delays, and availability levels, which makes reliable data completion and multi-source fusion difficult. This paper focuses on the state estimation problem of renewable-integrated distribution networks under multi-source heterogeneous measurement conditions. In such distribution networks, the increasing penetration of distributed renewable energy resources and the joint deployment of multiple measurement devices, including SCADA, PMU, and AMI, may lead to incomplete measurements, asynchronous sampling, differences in measurement accuracy, and reduced system observability. To address these issues, this paper proposes a model-based digital twin reference-guided physics-constrained DDPM framework to improve the quality of missing-measurement completion and the reliability of state estimation in distribution-network scenarios. A four-layer simulation-oriented cyber–physical framework is first constructed to integrate physical sensing, model-based digital twin reference mapping, AI-based measurement completion, and state estimation feedback. Within this framework, a physics-constrained self-supervised denoising diffusion probabilistic model is developed to recover missing measurements by combining observed data, digital twin reference measurements, real-time topology information, and power system operational constraints. The completed pseudo-measurements and physical measurements are then fused through a credibility-aware weighting strategy that considers timeliness, data integrity, measurement accuracy, and virtual–real consistency verification under simulation settings. Simulation results on the IEEE 14-bus system show that the proposed method improves pseudo-measurement completion and supports more reliable voltage magnitude and phase angle estimation under different measurement configurations. Under the tested simulation settings and multi-source measurement configurations, the results indicate that the proposed method can improve pseudo-measurement completion and support more reliable voltage magnitude and phase angle estimation. However, its performance under frequent topology switching, high missing-data ratios, and complex abnormal data conditions remains to be further evaluated. Full article