Search Results (163)

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article

Angle crack defects significantly affect compressor blade radial deformation characteristics, posing critical challenges for reliability assessment under operational uncertainties. This study proposes a novel osprey optimization algorithm (OOA)-optimized Kriging and radial basis function (RBF) method (OOA-KR) for the efficient reliability evaluation of blade radial clearance with angle crack defects. The approach integrates Kriging’s uncertainty quantification capabilities with RBF neural networks’ nonlinear mapping strengths through an adaptive weighting scheme optimized by OOA. Multiple uncertainty sources including crack geometry, operational temperature, and loading conditions are systematically considered. A comprehensive finite element model incorporating crack size variations and multi-physics coupling effects generates training data for surrogate model construction. Comparative studies demonstrate superior prediction accuracy with RMSE = 0.568 and R² = 0.8842, significantly outperforming conventional methods while maintaining computational efficiency. Reliability assessment achieves 97.6% precision through Monte Carlo simulation. Sensitivity analysis reveals rotational speed as the most influential factor (S = 0.42), followed by temperature and loading parameters. The proposed OOA-KR method provides an effective tool for blade design optimization and reliability-based maintenance strategies. Full article

Battery energy storage systems (BESSs) have become integral components of grid modernization because of their ability to provide system stabilization in the presence of high levels of renewable generation. Specifically, the dynamic response capabilities of BESSs can be a valuable tool in ensuring reliability and security of the grid during contingencies. This paper explores the utilization of BESSs in improving the contingency response of the SRP power system by providing selection criteria that enable a viable and cost-effective solution from a planning perspective. In particular, this study focuses on optimal BESS selection from a list of actual queued projects to enhance system stability by maintaining voltage and mitigating fault impacts. Additionally, the work involves generating both normal and abnormal operational scenarios for varying loads and renewable generation profiles of the system to capture diverse sources of uncertainty. A comprehensive reliability planning approach is adopted to identify the worst-case scenarios and ensure network robustness by optimizing BESS operations under these conditions. The results obtained by applying the proposed methodology to a 2500+-bus real-world system of SRP indicates that with as few as four strategically selected BESS units, the system is able to effectively mitigate more than 90% of under-voltage violations and approximately 75% of over-voltage violations. Full article

Continuous decrease in inertia and sensitivity to load/generation fluctuation are significant challenges for present-day power networks. The primary reason for these issues is the increased penetration capabilities of renewable energy sources. An imbalanced load with significant power output has a substantial impact on the frequency and voltage characteristics of electrical networks. Various load frequency control (LFC) technologies are widely used to address these issues. Existing LFC approaches in the literature are inadequate in addressing system uncertainty, parameter fluctuation, structural changes, and disturbance rejection. As a result, the purpose of this work is to suggest a better LFC approach that makes use of a combination of a one plus tilt fractional filtered derivative (1+TDF^λ) cascaded controller and a fractional order proportional–integral–derivative (PI^λD^μ) controller, which is referred to as the recommended 1+TDF^λ/PI^λD^μ controller. Drawing inspiration from the dynamics of religious societies, including the roles of followers, missionaries, and leaders, and the organization into religious and political schools, this paper proposes a new application of the efficient divine religions algorithm (DRA) to improve the design of the 1+TDF^λ/PI^λD^μ controller. A triple-area test system is constructed to analyze a realistic power system, taking into account certain physical restrictions such as nonlinearities as well as the impact of PV and wind energy integration. The effectiveness of the presented 1+TDF^λ/PI^λD^μ controller is evaluated by comparing their frequency responses to those of other current controllers like PID, FOPID, 2DOF-PID, and 2DOF-TID^μ. The integral time absolute error (ITAE) criterion was employed as the objective function in the optimization process. Comparative simulation studies were conducted using the proposed controller, which was fine-tuned by three recent metaheuristic algorithms: the divine religions algorithm (DRA), the artificial rabbits optimizer (ARO), and the wild horse optimizer (WHO). Among these, the DRA demonstrated superior performance, yielding an ITAE value nearly twice as optimal as those obtained by the ARO and WHO. Notably, the implementation of the advanced 1+TDF^λ/PI^λD^μ controller, optimized via the DRA, significantly minimized the objective function to $0.4704\times {10}^{-4}$. This reflects an approximate enhancement of $99.5\%$ over conventional PID, FOPID, and 2DOF-TID^μ controllers, and a $99\%$ improvement relative to the 2DOF-PID controller. The suggested case study takes into account performance comparisons, system modifications, parameter uncertainties, and variations in load/generation profiles. Through the combination of the suggested 1+TDF^λ/PI^λD^μ controller and DRA optimization capabilities, outcomes demonstrated that frequency stability has been significantly improved. Full article

To address the conflicts between high-penetration distributed photovoltaics (PV) integration causing voltage limit violations, reverse power flow issues, and the grid connection needs of industrial and commercial users, this paper proposes an optimal capacity planning method for distributed PV considering the user’s grid connection locations. This method effectively increases the acceptance capacity of the distribution transformer network for distributed PV while ensuring the safe and stable operation of the distribution network. First, the source–load uncertainty is considered, and the k-means clustering algorithm is used to select multiple typical daily probability scenarios. Then, the PV optimal connection node range is obtained through a PV site selection and sizing model. For the planning of nodes within the optimal range, an optimal capacity planning model focusing on the economic benefits of users is established. This model aims to optimize the improvement of wheeling cost and maximize the economic benefits of grid-connected users by determining the optimal PV access capacity for each node. Finally, for PV users outside this range, after determining the maximum allowable capacity for each node, the capacity margin and static voltage stability are comprehensively considered to evaluate the network access scheme. Simulation examples are used to verify the effectiveness of the proposed method, and the simulation results show that the proposed method can effectively increase the acceptance capacity of the distribution network for photovoltaic systems. By fully considering the wheeling cost collection strategy, the distributed PV acceptance capacity is increased by 20.14%, while both user benefits and the operational safety and economic performance of the distribution network are significantly improved, ultimately resulting in a 27.77% increase in total revenue. Full article

The uncertainties arising from high renewable energy penetration on both the generation and demand sides pose significant challenges to distribution network security. Smart microgrids are considered an effective way to solve this problem. Existing studies exhibit limitations in prediction accuracy, Alternating Current (AC) power flow modeling, and integration with optimization frameworks. This paper proposes a closed-loop technical framework combining high-confidence interval prediction, second-order cone convex relaxation, and robust optimization to facilitate renewable energy integration in distribution networks via smart microgrid technology. First, a hybrid prediction model integrating Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and Quantile Regression (QR) is designed to extract multi-frequency characteristics of time-series data, generating adaptive prediction intervals that accommodate individualized decision-making preferences. Second, a second-order cone relaxation method transforms the AC power flow optimization problem into a mixed-integer second-order cone programming (MISOCP) model. Finally, a robust optimization method considering source–load uncertainties is developed. Case studies demonstrate that the proposed approach reduces prediction errors by 21.15%, decreases node voltage fluctuations by 16.71%, and reduces voltage deviation at maximum offset nodes by 17.36%. This framework significantly mitigates voltage violation risks in distribution networks with large-scale grid-connected photovoltaic systems. Full article

With the increasing uncertainty introduced by the large-scale integration of renewable energy sources, traditional power dispatching methods face significant challenges, including severe frequency fluctuations, substantial forecasting deviations, and the difficulty of balancing economic efficiency with system stability. To address these issues, a deep learning-based dispatching framework is proposed, which integrates spatiotemporal feature extraction with a stability-aware mechanism. A joint forecasting model is constructed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to handle multi-source inputs, while a reinforcement learning-based stability-aware scheduler is developed to manage dynamic system responses. In addition, an uncertainty modeling mechanism combining Dropout and Bayesian networks is incorporated to enhance dispatch robustness. Experiments conducted on real-world power grid and renewable generation datasets demonstrate that the proposed forecasting module achieves approximately a 2.1% improvement in accuracy compared with Autoformer and reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 18.1% and 14.1%, respectively, compared with traditional LSTM models. The achieved Mean Absolute Percentage Error (MAPE) of 5.82% outperforms all baseline models. In terms of scheduling performance, the proposed method reduces the total operating cost by 5.8% relative to Autoformer, decreases the frequency deviation from 0.158 Hz to 0.129 Hz, and increases the Critical Clearing Time (CCT) to 2.74 s, significantly enhancing dynamic system stability. Ablation studies reveal that removing the uncertainty modeling module increases the frequency deviation to 0.153 Hz and raises operational costs by approximately 6.9%, confirming the critical role of this module in maintaining robustness. Furthermore, under diverse load profiles and meteorological disturbances, the proposed method maintains stable forecasting accuracy and scheduling policy outputs, demonstrating strong generalization capabilities. Overall, the proposed approach achieves a well-balanced performance in terms of forecasting precision, system stability, and economic efficiency in power grids with high renewable energy penetration, indicating substantial potential for practical deployment and further research. Full article

The integration of high-penetration renewable energy sources (RESs) into transmission networks introduces profound uncertainty that challenges traditional infrastructure planning approaches. Existing transmission expansion planning (TEP) models either rely on static scenario sets or over-conservative worst-case assumptions, failing to capture the operational stress triggered by rare but structurally impactful renewable behaviors. This paper proposes a novel bi-level optimization framework for transmission planning under adversarial uncertainty, coupling a distributionally robust upper-level investment model with a lower-level operational response embedded with physics and market constraints. The uncertainty space was not exogenously fixed, but instead dynamically generated through a physics-informed spatiotemporal generative adversarial network (PI-ST-GAN), which synthesizes high-risk renewable and load scenarios designed to maximally challenge the system’s resilience. The generator was co-trained using a composite stress index—combining expected energy not served, loss-of-load probability, and marginal congestion cost—ensuring that each scenario reflects both physical plausibility and operational extremity. The resulting bi-level model was reformulated using strong duality, and it was decomposed into a tractable mixed-integer structure with embedded adversarial learning loops. The proposed framework was validated on a modified IEEE 118-bus system with high wind and solar penetration. Results demonstrate that the GAN-enhanced planner consistently outperforms deterministic and stochastic baselines, reducing renewable curtailment by up to 48.7% and load shedding by 62.4% under worst-case realization. Moreover, the stress investment frontier exhibits clear convexity, enabling planners to identify cost-efficient resilience strategies. Spatial congestion maps and scenario risk-density plots further illustrate the ability of adversarial learning to reveal latent structural bottlenecks not captured by conventional methods. This work offers a new methodological paradigm, in which optimization and generative AI co-evolve to produce robust, data-aware, and stress-responsive transmission infrastructure designs. Full article

In the context of building a new type of power system, the optimal operation of high-proportion new-energy distribution networks (HNEDNs) is a current hot topic. In this paper, a stochastic distribution robust optimization method for HNEDNs that considers energy-storage refinement modeling is proposed. First, an energy-storage lifetime loss model based on the rainfall-counting method is constructed, and then an optimal operation model of an HNEDN considering energy storage refinement modeling is constructed, aiming to minimize the total operation cost while taking into account the energy cost and the penalty cost of abandoning wind and solar power. Then, a source-load uncertainty model of HNEDN is constructed based on the Wasserstein distance and conditional value at risk (CvaR) theory, and the HNEDN optimization model is reconstructed based on the stochastic distribution robust optimization method; based on this, the multiple linearization technique is introduced to approximate the reconstructed model, which aims to both reduce the difficulty in solving the model and ensure the quality of the solution. Finally, the modified IEEE 33-bus power distribution system is used as an example for case analysis, and the simulation results show that the method presented in this paper, through reducing the loss of life in the battery storage device, can reduce the average daily energy storage depreciation cost compared to an HNEDN optimization method that does not take the energy storage life loss into account; this, in turn, reduces the total operating cost of the system. In addition, the stochastic distribution robust optimization method used in this paper can adaptively adjust the economy and robustness of the HNEDN operation strategy according to the confidence level and the available historical sample data on new energy-output prediction errors to obtain the optimal HNEDN operation strategy when compared with other uncertainty treatment methods. Full article

Under the guidance of carbon peaking and carbon neutrality goals, the power industry is transitioning toward environmentally friendly practices. With the increasing integration of intermittent renewable energy sources (RES) and the enhanced self-regulation capabilities of grids, traditional distribution networks (DNs) are transitioning into active distribution networks (ADNs). To fully exploit the synergistic optimization potential of the “source-grid-load-storage” system in electricity-carbon coupling scenarios, leverage user-side flexibility resources, and facilitate low-carbon DN development, this paper proposes a low-carbon optimal scheduling strategy for ADN incorporating demand response (DR) priority. Building upon a bi-directional feedback mechanism between carbon potential and load, a two-layer distributed robust scheduling model for DN is introduced, which is solved through hierarchical iteration using column and constraint generation (C&CG) algorithm. Case study demonstrates that the model proposed in this paper can effectively measure the priority of demand response for different loads. Under the proposed strategy, the photovoltaic (PV) consumption rate reaches 99.76%. Demand response costs were reduced by 6.57%, and system carbon emissions were further reduced by 8.93%. While accounting for PV uncertainty, it balances the economic efficiency and robustness of DN, thereby effectively improving system operational safety and reliability, and promoting the smooth evolution of DN toward a low-carbon and efficient operational mode. Full article

Aiming to solve optimization scheduling difficulties caused by the double uncertainty of source-side photovoltaic (PV) output and load-side demand response in active distribution networks, this paper proposes a two-stage distribution robust optimization method. First, the first-stage model with the objective of minimizing power purchase cost and the second-stage model with the co-optimization of active loss, distributed power generation cost, PV abandonment penalty, and load compensation cost under the worst probability distribution are constructed, and multiple constraints such as distribution network currents, node voltages, equipment outputs, and demand responses are comprehensively considered. Secondly, the second-order cone relaxation and linearization technique is adopted to deal with the nonlinear constraints, and the inexact column and constraint generation (iCCG) algorithm is designed to accelerate the solution process. The solution efficiency and accuracy are balanced by dynamically adjusting the convergence gap of the main problem. The simulation results based on the improved IEEE33 bus system show that the proposed method reduces the operation cost by 5.7% compared with the traditional robust optimization, and the cut-load capacity is significantly reduced at a confidence level of 0.95. The iCCG algorithm improves the computational efficiency by 35.2% compared with the traditional CCG algorithm, which verifies the effectiveness of the model in coping with the uncertainties and improving the economy and robustness. Full article

Aiming at the problems of frequent voltage overruns in distribution networks and difficulties in centralized optimal dispatch due to the uncertainties of distributed renewable energy sources and bus loads, this paper proposes a dynamic cluster voltage control method considering power deficit and resource allocation in an active distribution network. First, the modularity index is constructed by considering the ability of the bus electrical coupling, and the voltage regulation resources are allocated by balancing power compensation capacity and physical connectivity. This method competes with cluster partitioning and selects pilot buses. Then, an active and reactive power coordinated control model based on non-dominated sorting genetic algorithm II (NSGA-II) is developed. The model aims to minimize voltage violations, distribution network losses, and power consumption costs. Finally, five representative control scenarios are simulated and compared on an enhanced IEEE 51 bus distribution network. The results show that the proposed strategy effectively mitigates node voltage violations, reduces the losses, and enhances resource efficiency. Full article

The increasing prevalence of extreme weather events poses significant challenges to the stability of distribution networks (DNs). To enhance the resilience of DNs against such events, a typhoon-oriented resilience framework for DNs is proposed that incorporates multiple sources of typhoon uncertainty. First, component failure probability is modeled by tracking time-sequential variations in typhoon landfall parameters, trajectory, and intensity, thereby improving the quantitative estimation of typhoon impacts. Then, the integrated component failure probability and the importance factor of bus load under disaster are combined and hierarchical analysis is performed to achieve the vulnerability identification for DNs. Next, based on the vulnerability identification results, a resilience enhancement model for DNs is constructed through the strategy of coordinating line reinforcement and energy storage configuration, and the resilience optimization scheme that takes into account the system resilience enhancement effect and economy is obtained under the optimal investment cost. Finally, analysis and verification are conducted in the IEEE 33-bus system. The results indicate that the proposed method can reduce the load loss cost of the system by 5.112 million and 0.2459 million, respectively. Full article

Accurate short-term load forecasting is vital for the reliable and efficient operation of smart grids, particularly under the uncertainty introduced by variable renewable energy sources (RESs) such as solar and wind. This study introduces ST-CALNet, a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with an Attentive Long Short-Term Memory (LSTM) network to enhance forecasting performance in renewable-integrated smart grids. The CNN component captures spatial dependencies from multivariate inputs, comprising meteorological variables and generation data, while the LSTM module models temporal correlations in historical load patterns. An embedded attention mechanism dynamically weights input sequences, enabling the model to prioritise the most influential time steps, thereby improving its interpretability and robustness during demand fluctuations. ST-CALNet was trained and evaluated using real-world datasets that include electricity consumption, solar photovoltaic (PV) output, and wind generation. Experimental evaluation demonstrated that the model achieved a mean absolute error (MAE) of 0.0494, root mean squared error (RMSE) of 0.0832, and a coefficient of determination (R²) of 0.4376 for electricity demand forecasting. For PV and wind generation, the model attained MAE values of 0.0134 and 0.0141, respectively. Comparative analysis against baseline models confirmed ST-CALNet’s superior predictive accuracy, particularly in minimising absolute and percentage-based errors. Temporal and regime-based error analysis validated the model’s resilience under high-variability conditions such as peak load periods, while visualisation of attention scores offered insights into the model’s temporal focus. These findings underscore the potential of ST-CALNet for deployment in intelligent energy systems, supporting more adaptive, transparent, and dependable forecasting within smart grid infrastructures. Full article

To address the impacts of source load temporal–spatial uncertainties on distribution network planning considering the global transition towards sustainable energy systems with high-penetration photovoltaic (PV) integration, this paper proposes a source–network–storage coordinated stochastic planning method. A temporal–spatial correlation probability model for PV output and load demand is constructed based on Copula theory. Scenario generation and efficient reduction are achieved through Monte Carlo sampling and K-means clustering, extracting representative daily scenarios that preserve the temporal–spatial characteristics. A coordinated planning model targeting the minimization of comprehensive costs is established to holistically optimize PV deployment, energy storage system (ESS) configuration, and network expansion schemes. Simulations on typical distribution network systems demonstrate that the proposed method, by integrating temporal–spatial correlation modeling and multi-element collaborative decision-making, significantly improves PV accommodation capacity and reduces planning costs while improving the overall economic efficiency of distribution network planning. This study provides a robust technical pathway for developing economically viable and resilient distribution networks capable of integrating large-scale renewable energy, thereby contributing to the decarbonization of the power sector and advancing the goals of sustainable energy development. Full article