Search Results (67)

The high-proportion integration of renewable energy sources, represented by wind power and photovoltaics, into active distribution networks (ADNs) can effectively alleviate the pressure associated with advancing China’s dual-carbon goals. However, the high uncertainty in renewable energy output leads to increased system voltage fluctuations and localized voltage violations, posing safety challenges. Consequently, research on optimal dispatch for ADNs with a high penetration of renewable energy has become a current focal point. This paper provides a comprehensive review of research in this domain over the past decade. Initially, it analyzes the voltage impact patterns and control principles in distribution networks under varying levels of renewable energy penetration. Subsequently, it introduces optimization dispatch models for ADNs that focus on three key objectives: safety, economy, and low carbon emissions. Furthermore, addressing the challenge of solving non-convex and nonlinear models, the paper highlights model reformulation strategies such as semidefinite relaxation, second-order cone relaxation, and convex inner approximation methods, along with summarizing relevant intelligent solution algorithms. Additionally, in response to the high uncertainty of renewable energy output, it reviews stochastic optimization dispatch strategies for ADNs, encompassing single-stage, two-stage, and multi-stage approaches. Meanwhile, given the promising prospects of large-scale deep reinforcement learning models in the power sector, their applications in ADN optimization dispatch are also reviewed. Finally, the paper outlines potential future research directions for ADN optimization dispatch. Full article

The large-scale integration of new energy is an inevitable trend to achieve the low-carbon transformation of power systems. However, the strong randomness of wind power, photovoltaic power, and loads poses severe challenges to the safe and stable operation of systems. Existing studies demonstrate insufficient integration and handling of source-load bilateral uncertainties in wind–solar–fossil fuel storage complementary systems, resulting in difficulties in balancing economy and low-carbon performance in their energy storage configuration. To address this insufficiency, this study proposes an optimal energy storage configuration method considering source-load uncertainties. Firstly, a deterministic bi-level model is constructed: the upper level aims to minimize the comprehensive cost of the system to determine the energy storage capacity and power, and the lower level aims to minimize the system operation cost to solve the optimal scheduling scheme. Then, wind and solar output, as well as loads, are treated as fuzzy variables based on fuzzy chance constraints, and uncertainty constraints are transformed using clear equivalence class processing to establish a bi-level optimization model that considers uncertainties. A differential evolution algorithm and CPLEX are used for solving the upper and lower levels, respectively. Simulation verification in a certain region shows that the proposed method reduces comprehensive cost by 8.9%, operation cost by 10.3%, the curtailment rate of wind and solar energy by 8.92%, and carbon emissions by 3.51%, which significantly improves the economy and low-carbon performance of the system and provides a reference for the future planning and operation of energy systems. Full article

As the global energy system accelerates its transition toward a low-carbon economy, renewable energy sources (RESs), such as wind and photovoltaic power, are rapidly replacing traditional fossil fuels. These RESs are becoming a critical element of deeply decarbonized power systems (DDPSs). However, the inherent non-stationarity, multi-scale volatility, and uncontrollability of RES output significantly increase the risk of source–load imbalance, posing serious challenges to the reliability and economic efficiency of power systems. Scenario generation technology has emerged as a critical tool to quantify uncertainty and support dispatch optimization. Nevertheless, conventional scenario generation methods often fail to produce highly credible wind and solar output scenarios. To address this gap, this paper proposes a novel renewable energy scenario generation method based on a multi-resolution diffusion model. To accurately capture fluctuation characteristics across multiple time scales, we introduce a diffusion model in conjunction with a multi-scale time series decomposition approach, forming a multi-stage diffusion modeling framework capable of representing both long-term trends and short-term fluctuations in RES output. A cascaded conditional diffusion modeling framework is designed, leveraging historical trend information as a conditioning input to enhance the physical consistency of generated scenarios. Furthermore, a forecast-guided fusion strategy is proposed to jointly model long-term and short-term dynamics, thereby improving the generalization capability of long-term scenario generation. Simulation results demonstrate that MDDPM achieves a Wasserstein Distance (WD) of 0.0156 in the wind power scenario, outperforming DDPM (WD = 0.0185) and MC (WD = 0.0305). Additionally, MDDPM improves the Global Coverage Rate (GCR) by 15% compared to MC and other baselines. Full article

Building-integrated photovoltaics (BIPV) requires tools that improve and facilitate simulating and predicting the system’s output energy. The efficiency of a photovoltaic (PV) system can be determined by the performance ratio (PR), which relates the actual system’s output energy to the theoretical output according to the installed power and the solar irradiation, thus accounting for the power losses the PV system undergoes. Among the different parameters affecting PR, module temperature and the angle of incidence of irradiance are the most dependent on the BIPV application due to the varied module positioning. This paper assesses the suitability of several BIPV temperature models and determines the angular losses for any possible module positioning. The proposed methodology is easy to replicate and results in polar heatmap graphs to estimate PR at the desired location as a function of the tilt and azimuth angles of the modules. The calculations require irradiance, ambient temperature, and wind speed data, which can easily be obtained worldwide. Dynamic sky conditions are addressed through filters that smooth out quickly changing input data to avoid high and low peaks. The developed graphs are helpful in the decision-making process for BIPV designs by allowing the designer to estimate the expected PR of the BIPV system for any possible position of the modules on the building envelope, reducing the effect of uncertainties and resulting in more accurate and better predictions of the system’s output energy. The method applied to a BIPV façade in Madrid showed a deviation of less than 3% between the estimated and monitored PRs; the PR values ranged between 0.74 and 0.82, depending on the BIPV application and module position. Full article

Under the “dual-carbon” strategic framework, the installed capacity of renewable energy sources has continuously increased, while that of conventional generation units has progressively decreased. This structural shift significantly diminishes the operational flexibility of power generation systems and intensifies grid imbalances caused by renewable energy volatility. To address these challenges, this study proposes a carbon-aware multi-timescale virtual power plant (VPP) scheduling framework with coordinated multi-energy integration, which operates through two sequential phases: day-ahead scheduling and intraday rolling optimization. In the day-ahead phase, demand response mechanisms are implemented to activate load-side regulation capabilities, coupled with information gap decision theory (IGDT) to quantify renewable energy uncertainties, thereby establishing optimal baseline schedules. During the intraday phase, rolling horizon optimization is executed based on updated short-term forecasts of renewable energy generation and load demand to determine final dispatch decisions. Numerical simulations demonstrate that the proposed framework achieves a 3.76% reduction in photovoltaic output fluctuations and 3.91% mitigation of wind power variability while maintaining economically viable scheduling costs. Specifically, the intraday optimization phase yields a 1.70% carbon emission reduction and a 7.72% decrease in power exchange costs, albeit with a 3.09% increase in operational costs attributable to power deviation penalties. Full article

In renewable energy generation, uncertainties mainly refer to power output fluctuations caused by the intermittency, variability, and forecasting errors of wind and photovoltaic power. These uncertainties have adverse effects on the secure operation of the power systems. Probabilistic load supply capability (LSC) serves as an effective perspective for evaluating power system security under uncertainties. Therefore, this paper studies the influence of renewable energy generation on probabilistic LSC to quantify the impact of these uncertainties on the secure operation of the power systems. Global sensitivity analysis (GSA) is introduced for the first time into probabilistic LSC evaluation. It can quantify the impact of renewable energy generation on the system’s LSC and rank the importance of renewable energy power stations based on GSA indices. GSA necessitates multiple rounds of probabilistic LSC evaluation, which is computationally intensive. To address it, this paper introduces a novel probabilistic repeated power flow (PRPF) algorithm, which employs a basis-adaptive sparse polynomial chaos expansion (BASPCE) model as a surrogate model for the original repeated power flow model, thereby accelerating the probabilistic LSC evaluation. Finally, the effectiveness of the proposed methods is verified through case studies on the IEEE 39-bus system. This study provides a practical approach for analyzing the impact of renewable generation uncertainties on power system security, contributing to more informed planning and operational decisions. Full article

As the share of renewable energy in modern power systems continues to grow, its inherent uncertainty and variability pose severe challenges to grid stability and the accuracy of traditional thermal power dispatch. To address this issue, this study fully exploits the fast response and flexible operation of variable-speed pumped storage (VS-PS) by developing a day-ahead scheduling model for a wind–photovoltaic–thermal–VS-PS system. The optimization model aims to minimize system operating costs, carbon emissions, and thermal power output fluctuations, while maximizing the regulation flexibility of the VS-PS plant. It is assessed using the improved multi-objective jellyfish search (IMOJS) algorithm, and its effectiveness is demonstrated through comparison with a fixed-speed pumped storage (FS-PS) system. Simulation results show that the proposed model significantly outperforms the traditional FS-PS system: it increases renewable energy accommodation capacity by an average of 68.51%, reduces total operating costs by 14.13%, and lowers carbon emissions by 3.63%. Full article

The intervention of distributed loads, propelled by the swift advancement of distributed energy sources and the escalating demand for diverse load types encompassing electricity and cooling within virtual power plants (VPPs), has exerted an influence on the symmetry of the grid. Consequently, a quantitative assessment of the annual peak-shaving capability of a VPP is instrumental in mitigating the peak-to-valley difference in the grid, enhancing the operational safety of the grid, and reducing grid asymmetry. This paper presents a peak-shaving optimization method for VPPs, which takes into account renewable energy uncertainty and flexible load demand response. Firstly, wind power (WP), photovoltaic (PV) generation, and demand-side response (DR) are integrated into the VPP framework. Uncertainties related to WP and PV generation are incorporated through the scenario method within deterministic constraints. Secondly, a stochastic programming (SP) model is established for the VPP, with the objective of maximizing the peak-regulation effect and minimizing electricity loss for demand-side users. The case study results indicate that the proposed model effectively tackles peak-regulation optimization across diverse new energy output scenarios and accurately assesses the peak-regulation potential of the power system. Specifically, the proportion of load decrease during peak hours is 18.61%, while the proportion of load increase during off-peak hours is 17.92%. The electricity loss degrees for users are merely 0.209 in summer and 0.167 in winter, respectively. Full article

The quantity of wind and photovoltaic power-based distributed generators (DGs) is continually rising within the distribution network, presenting obstacles to its safe, steady, and cost-effective functioning. Active distribution network dynamic reconfiguration (ADNDR) improves the consumption rate of renewable energy, reduces line losses, and optimizes voltage quality by optimizing the distribution network structure. Despite being formulated as a highly dimensional and combinatorial nonconvex stochastic programming task, conventional model-based solvers often suffer from computational inefficiency and approximation errors, whereas population-based search methods frequently exhibit premature convergence to suboptimal solutions. Moreover, when dealing with high-dimensional ADNDR problems, these algorithms often face modeling difficulties due to their large scale. Deep reinforcement learning algorithms can effectively solve the problems above. Therefore, by combining the graph attention network (GAT) with the deep deterministic policy gradient (DDPG) algorithm, a method based on the graph attention network deep deterministic policy gradient (GATDDPG) algorithm is proposed to online solve the ADNDR problem with the uncertain outputs of DGs and loads. Firstly, considering the uncertainty in distributed power generation outputs and loads, a nonlinear stochastic optimization mathematical model for ADNDR is constructed. Secondly, to mitigate the dimensionality of the decision space in ADNDR, a cyclic topology encoding mechanism is implemented, which leverages graph-theoretic principles to reformulate the grid infrastructure as an adaptive structural mapping characterized by time-varying node–edge interactions Furthermore, the GATDDPG method proposed in this paper is used to solve the ADNDR problem. The GAT is employed to extract characteristics pertaining to the distribution network state, while the DDPG serves the purpose of enhancing the process of reconfiguration decision-making. This collaboration aims to ensure the safe, stable, and cost-effective operation of the distribution network. Finally, we verified the effectiveness of our method using an enhanced IEEE 33-bus power system model. The outcomes of the simulations demonstrate its capacity to significantly enhance the economic performance and stability of the distribution network, thereby affirming the proposed method’s effectiveness in this study. Full article

With the increasing presence of large-scale new energy sources, such as wind and photovoltaic (PV) systems, integrating traditional hydropower with wind and PV power into a hydro–wind–PV complementary system in economic dispatch can effectively mitigate wind and PV fluctuations. In this study, Markov chains and the Copula joint distribution function were adopted to quantize the spatiotemporal relationships among hydro, wind and PV, whereby runoff, wind, and PV output scenarios were generated to simulate their uncertainties. A dual-objective optimization model is proposed for the long-term hydro–wind–PV co-scheduling (LHWP-CS) problem. To solve the model, a well-tailored evolutionary multi-objective optimization method was developed, which combines multiple recombination operators and two different dominance rules for basic and elite populations. The proposed model and algorithm were tested on three annual reservoirs with large wind and PV farms in the Hongshui River Basin. The proposed algorithm demonstrates superior performance, with average improvements of 2.90% and 2.63% in total power generation, and 1.23% and 0.96% in minimum output expectation compared to BORG and NSGA-II, respectively. The results also infer that the number of scenarios is a key parameter in achieving a tradeoff between economics and risk. Full article

To enhance low-carbon economies within Park Integrated Energy Systems (PIES) while addressing the variability of wind power generation, an innovative optimization scheduling strategy is proposed, incorporating a reward-and-punishment ladder carbon trading mechanism. This method effectively mitigates the unpredictability of wind power output and integrates Power-to-Gas (P2G), Carbon Capture and Storage (CCS), and Combined Heat and Power (CHP) systems. This study develops a CHP model that combines P2G and CCS, focusing on electric-heat coupling characteristics and establishing constraints on P2G capacity, thereby significantly enhancing electric energy flexibility and reducing carbon emissions. The carbon allowance trading strategy is refined through the integration of reward and punishment coefficients, yielding a more effective trading model. To accurately capture wind power uncertainty, the research employs kernel density estimation and Copula theory to create a representative sequence of daily wind and photovoltaic power scenarios. The Dung Beetle Optimization (DBO) algorithm, augmented by Non-Dominated Sorting (NSDBO), is utilized to solve the resulting multi-objective model. Simulation results indicate that the proposed strategy increases the utilization rates of renewable energy in PIES by 28.86% and 19.85%, while achieving a reduction in total carbon emissions by 77.65% and a decrease in overall costs by 36.91%. Full article

The energy transition towards achieving carbon neutrality is marked by the decarbonization of the power system and a high degree of electrification in end-use sectors. The decarbonization of the power system primarily relies on large-scale renewable energy, nuclear power, and fossil fuel-based power with carbon capture technologies. This structure of power supply introduces significant uncertainty in electricity supply. Due to the technological progress in end-use sectors and spatial reallocation of industries in China, the load curve and power supply curve is very different today. However, most studies’ analyses of future electricity systems are based on today’s load curve, which could be misleading when seeking to understand future electricity systems. Therefore, it is essential to thoroughly analyze changes in end-use load curves to better align electricity demand with supply. This paper analyzes the characteristics of electricity demand load under China’s future energy transition and economic transformation pathways using the Integrated Energy and Environment Policy Assessment model of China (IPAC). It examines the electricity and energy usage characteristics of various sectors in six typical regions, provides 24-h load curves for two representative days, and evaluates the effectiveness of demand-side response in selected provinces in 2050. The study reveals that, with the transition of the energy system and the industrial relocation during economic transformation, the load curves in China’s major regions by 2050 will differ notably from those of today, with distinct characteristics emerging across different regions. With the costs of solar photovoltaic (PV) and wind power declining in the future, the resulting electricity price will also differ significantly from today. Daytime electricity prices will be notably lower than those during the evening peak, as the decrease in solar PV and wind power output leads to a significant increase in electricity costs. This pricing structure is expected to drive a strong demand-side response. Demand-side response can significantly improve the alignment between load curves and power supply. Full article

This paper reviews scenario generation techniques for modeling uncertainty in wind and photovoltaic (PV) power generation, a critical component as renewable energy integration into power systems grows. Scenario generation enables the simulation of variable power outputs under different weather conditions, serving as essential inputs for robust, stochastic, and distributionally robust optimization in system planning and operation. We categorize scenario generation methods into explicit and implicit approaches. Explicit methods rely on probabilistic assumptions and parameter estimation, which enable the interpretable yet parameterized modeling of power variability. Implicit methods, powered by deep learning models, offer data-driven scenario generation without predefined distributions, capturing complex temporal and spatial patterns in the renewable output. The review also addresses combined wind and PV power scenario generation, highlighting its importance for accurately reflecting correlated fluctuations in multi-site, interconnected systems. Finally, we address the limitations of scenario generation for wind and PV power integration planning and suggest future research directions. Full article

With the widespread adoption of renewable energy sources like wind power and photovoltaic (PV) power, uncertainties in the renewable energy output and the battery-swapping demand for electric heavy-duty trucks make it challenging for battery-swapping stations to optimize battery-charging management centrally. Uncoordinated large-scale charging behavior can increase operation costs for battery-swapping stations and even affect the stability of the power grid. To mitigate this, this paper proposes a multi-timescale battery-charging optimization for electric heavy-duty truck battery-swapping stations, taking into account the source–load–storage uncertainty. First, a model incorporating uncertainties in renewable energy output, time-of-use pricing, and grid load fluctuations is developed for the battery-swapping station. Second, based on day-ahead and intra-day timescales, the optimization problem for battery-charging strategies at battery-swapping stations is decomposed into day-ahead and intra-day optimization problems. We propose a day-ahead charging strategy optimization algorithm based on intra-day optimization feedback information-gap decision theory (IGDT) and an improved grasshopper algorithm for intra-day charging strategy optimization. The key contributions include the following: (1) the development of a battery-charging model for electric heavy-duty truck battery-swapping stations that accounts for the uncertainty in the power output of energy sources, loads, and storage; (2) the proposal of a day-ahead battery-charging optimization algorithm based on intra-day-optimization feedback information-gap decision theory (IGDT), which allows for dynamic adjustment of risk preferences; (3) the proposal of an intra-day battery-charging optimization algorithm based on an improved grasshopper optimization algorithm, which enhances algorithm convergence speed and stability, avoiding local optima. Finally, simulation comparisons confirm the success of the proposed approach. The simulation results demonstrate that the proposed method reduces charging costs by 4.26% and 6.03% compared with the two baseline algorithms, respectively, and improves grid stability, highlighting its effectiveness for managing battery-swapping stations under uncertainty. Full article

The global energy crisis and the pursuit of carbon neutrality have introduced significant challenges to the optimal dispatch of power systems. Despite advancements in optimization techniques, existing methods often struggle to efficiently handle the uncertainties introduced by renewable energy sources and the dynamic behavior of plug-in electric vehicles (PEVs). This study presents a multi-energy collaborative optimization approach based on a dynamic opposite level-based learning optimization swarm algorithm (DO3LSO). The methodology explores the impact of integrating PEVs and renewable energy sources, including photovoltaic and wind power, on unit commitment (UC) problems. By incorporating the bidirectional charging and discharging capabilities of PEVs and addressing the volatility of renewable energy, the proposed method demonstrates the ability to reduce reliance on traditional fossil fuel power generation, decrease carbon emissions, stabilize power output, and achieve a 7.01% reduction in costs. Comparative analysis with other optimization algorithms highlights the effectiveness of DO3LSO in achieving rapid convergence and precise optimization through hierarchical learning and dynamic opposite strategies, showcasing superior adaptability in complex load scenarios. The findings underscore the importance of multi-energy collaborative optimization as a pivotal solution for addressing the energy crisis, facilitating low-carbon transitions, and providing essential support for the development of intelligent and sustainable power systems. Full article