Search Results (484)

The previously proposed cascaded inverse fast Fourier transform/fast Fourier transform (IFFT/FFT)-based point-to-multipoint (P2MP) flexible optical transceivers have the potential to equip future intensity modulation and direct detection (IMDD) optical access networks with excellent flexibility, adaptability, scalability and upgradability. However, due to their cascaded IFFT-based multi-channel aggregations, P2MP flexible transceivers suffer high peak-to-average power ratios (PAPRs). To address the technical challenge, this paper proposes a novel P2MP flexible optical transceiver, which uses a cascaded discrete Fourier transformation-spread (DFT-Spread) IFFT/FFT-based multi-channel aggregation/de-aggregation and standard signal clipping to jointly reduce its PAPRs. The upstream performances of the proposed transceivers are numerically explored in a 20 km IMDD upstream passive optical network (PON). The results indicate that the proposed transceiver’s PAPRs are mainly dominated by the size of the last IFFT operation of the multi-channel aggregation, and are almost independent of modulation format and channel count. Compared to conventional cascaded IFFT/FFT-based P2MP transceivers with and without clipping operations, the proposed DFT-Spread P2MP transceivers can reduce PAPRs by 2.6 dB and 3.5 dB, respectively, for a final IFFT operation size of 1024. More significant PAPR reductions are achievable when the last IFFT operation size is increased further. As a direct result, compared to conventional P2MP transceivers adopting clipping operations only, the proposed transceiver can improve upstream receiver sensitivities by >1.9 dB and the aggregated upstream transmission capacities by >14.1%. Such aggregated upstream transmission capacity enhancements are independent of channel count and become more pronounced for longer transmission distances. Full article

With the advancement of intelligent power generation and consumption technologies, an increasing number of renewable energy sources (RESs), smart loads, and electric vehicles (EVs) are being integrated into smart grids. This paper proposes a coordinated frequency control strategy for hybrid power systems with RESs, smart loads, EVs, and a thermal power unit (TPU), in which EVs and the TPU participate in short-term frequency regulation (FR) jointly. All EVs provide FR auxiliary services as controllable loads; specifically, the EV aggregations operate in charging mode when participating in FR. The proposed coordinated frequency control strategy is implemented by an improved recurrent neural network (IRNN), which combines a recurrent neural network with a functional-link layer. The weights and biases of the IRNN are trained by an improved backpropagation through time (BPTT) algorithm, in which a chaotic competitive swarm optimizer (CCSO) is proposed to optimize the learning rates. Finally, the simulation results verify the superiority of the coordinated frequency control strategy. Full article

This article focuses on the analysis of a direct air-cooled rotor winding of a wound field synchronous machine, the innovation of which lies in the increase in the internal cooling surface, the cooling of the winding compared to the conventional inter-pole cooling, and the development of a CHT evaluation model accordingly. Conjugate heat transfer (CHT) analysis is used to explore the cooling efficacy of a parallel-cooled hollow-conductor winding of a salient-pole rotor and to identify a cooling performance map. The use of high current densities of 15–20 Arms/mm² in directly cooled windings requires high cooling intensity, which in the case of air cooling results not only in flow velocities above 15 m/s to ensure permissible operating temperatures, but also the need for coolant distribution and heat transfer studies. The experiments and calculations are based on a non-rotating machine and a wind tunnel using the same rotor coil(s). CHT-based thermal calculations provide not only reliable results compared to experimental work and lumped parameter thermal circuits with adjusted aggregate parameters, but also insight related to pressure and cooling flow distribution, thermal loads, and cooling integration issues that are necessary for the development of high power density and reliable electrical machines. The results of the air-cooling integration show that the desired high current density is achievable at the expense of high cooling intensity, where the air velocity ranges from 15 to 30 m/s and 30 to 55 m/s, distinguishing the air velocity of the hollow conductor and bypass channel, compared to the same coil in an electric machine and a wind tunnel at the similar thermal load and limit. Since the hot spot location depends on cooling integration and cooling intensity, modeling and estimating the cooling flow is essential in the development of wound-field synchronous machines. Full article

The strong uncertainty of the high proportion of new energy and the gradual decrease in the proportion of thermoelectric units have led to a shortage of system flexibility resources. System-level energy storage can efficiently alleviate the pressure of peak shaving and frequency regulation. Effective aggregation of flexibility resources is a key technical foundation for enhancing economic operation and advanced user-side response strategies of new power systems. However, the decentralization and heterogeneity of flexibility resources across generation, grid, load, and storage sides pose dual challenges of aggregation speed and accuracy. In view of this, this paper proposes a large-scale multi-dimensional flexibility polymerization method based on different response time scales. First, the flexibility resource definitions and response characteristics of generation, grid, load, and storage sides were analyzed and categorized according to their response time scales. Second, flexibility regulation models for resources on each side were established. On this basis, an improved Minkowski aggregation algorithm is proposed to precisely quantify the regulation capabilities of multi-dimensional flexibility resources at different time scales, enabling efficient resource aggregation. Finally, the results of the case analysis show that the proposed method can accurately aggregate the flexibility resource adjustment capabilities at different time scales to respond to the multi-time scale flexibility requirements of the system. Full article

With the transformation of the energy structure, the integration of numerous small-scale, widely distributed renewable energy sources into the power grid has introduced operational safety challenges. To enhance the operational competitiveness, the virtual power plant (VPP) has emerged to aggregate and manage these distributed energy resources (DERs). However, current research on the VPP’s frequency modulation performance and bidding strategy remains insufficient in the joint market of electrical energy and frequency modulation (FM) ancillary services, with inadequate coordination of internally distributed resources. To fully leverage the flexibility of VPPs and incentivize their participation in electricity market operations, this paper investigates game-based bidding strategies and internal distributed resources allocation methods for VPPs in the joint market for electrical energy and frequency ancillary services. Firstly, the regulatory performance indicators of VPPs participating in the joint market and develops the corresponding market-clearing model. Secondly, to address the competition among distributed resources within VPPs, a master-slave game approach is innovatively employed to optimize the VPP’s trading strategies. This method ensures the rational allocation of electricity consumption among distributed energy resources within the VPP and derives the optimized bidding prices and quantities for both the VPP and its internal members. Finally, the case study shows that the proposed trading strategy provides effective bidding strategies for distributed energy resources participating in the joint market for energy and frequency regulation ancillary services. It enhances the regulatory performance of VPPs in the energy-frequency regulation market, ensures the profitability of distributed energy resources, and contributes to the economically stable operation of the market. Full article

Against the backdrop of a “dual-carbon” strategy, the use of photovoltaic storage charging stations (PSCSs), as an effective way to aggregate and manage electric vehicles, new energy sources, and energy storage, will be an important primary component of the electricity market. The operational characteristics of the aggregated resources within a PSCS determine its bidding space, which has an important influence on its bidding strategy. In this paper, a novel bidding space model is constructed for PSCSs, which dynamically integrates electric vehicles, photovoltaic generation, and energy storage. A two-stage bidding strategy for multiple PSCSs is established, with stage I aiming at achieving the lowest cost for the power purchased by a PSCS to optimize the power generation and power plan and stage II aiming at achieving the lowest cost of the grid operator’s power purchase to optimize the system’s power balance. Thirdly, the two-stage model is transformed into a single-layer, mixed-integer linear programming problem using dyadic theory and Karush–Kuhn–Tucker (KKT) conditions, enabling the derivation of the optimal bidding strategy. Finally, the example analysis verifies that the proposed model can achieve a reduction in the PSCS’s day-ahead power purchase cost and flexibly dispatch each resource within the PSCS to maximize revenue, as well as reducing power consumption behavior during peak tariff hours, to enhance the market power of the PSCS in the electricity market. Full article

In the context of a high proportion of new energy grid connections, demand-side resources have become an inevitable choice for constructing new power systems due to their high flexibility and fast response speed. However, the response capability of demand-side resources is decentralized and fluctuating, which makes it difficult for them to effectively participate in power market trading. Therefore, this paper proposes a robust transaction decision model for demand-side resource aggregators considering multi-objective clustering aggregation optimization. First, a demand-side resource aggregation operation model is designed to aggregate dispersed demand-side resources into a coordinated aggregated response entity through an aggregator. Second, the demand-side resource aggregation evaluation indexes are established from three dimensions of response capacity, response reliability, and response flexibility, and the multi-objective aggregation optimization model of demand-side resources is constructed with the objective function of the larger potential market revenue and the smallest risk of deviation penalty. Finally, robust optimization theory is adopted to cope with the uncertainty of demand-side resource responsiveness, the robust transaction decision model of demand-side resource aggregator is constructed, and a community in Henan Province is selected for simulation analysis to verify the validity and applicability of the proposed model. The findings reveal that the proposed cluster aggregation optimization method reduces the bias penalty risk of the demand-side resource aggregators by about 33.12%, improves the comprehensive optimization objective by about 18.10%, and realizes the optimal aggregation of demand-side resources that takes into account both economy and risk. Moreover, the robust trading decision model can increase the expected net revenue by about 3.1% under the ‘worst’ scenario of fluctuating uncertainties, which enhances the resilience of demand-side resource aggregators to risks and effectively fosters the involvement of demand-side resources in the electricity market dynamics. Full article

Rapid advances in renewable energy technologies offer significant opportunities for the global energy transition and environmental protection. However, due to the fluctuating and intermittent nature of their power generation, which leads to the phenomenon of power abandonment, it has become a key challenge to efficiently consume renewable energy sources and guarantee the reliable operation of the power system. In order to address the above problems, this paper proposes an electric vehicle aggregator (EVA) scheduling strategy based on a two-layer game by constructing a two-layer game model between renewable energy generators (REG) and EVA, where the REG formulates time-sharing tariff strategies in the upper layer to guide the charging and discharging behaviors of electric vehicles, and the EVA respond to the price signals in the lower layer to optimize the large-scale electric vehicle scheduling. For the complexity of large-scale scheduling, this paper introduces the A2C (Advantage Actor-Critic) reinforcement learning algorithm, which combines the value network and the strategy network synergistically to optimize the real-time scheduling process. Based on the case study of wind power, photovoltaic, and wind–solar complementary data in Jilin Province, the results show that the strategy significantly improves the rate of renewable energy consumption (up to 97.88%) and reduces the cost of power purchase by EVA (an average saving of RMB 0.04/kWh), realizing a win–win situation for all parties. The study provides theoretical support for the synergistic optimization of the power system and renewable energy and is of great practical significance for the large-scale application of electric vehicles and new energy consumption. Full article

Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings. So, the scene knowledge will include global context information, fitting fine-grained visual information and scene structure information. Then, a scene filter module is designed to learn the global context information and fitting fine-grained visual information, and a scene structure module is designed to learn the scene structure information. Finally, the scene semantic features are used as the carrier to integrate three categories of information into the relative scene features, which can assist in the recognition of the occluded fittings and the tiny-scale fittings after feature mining and feature integration. The experiments show that the proposed network can effectively improve the performance of the multi-fitting detection task compared with the Faster R-CNN and other state-of-the-art models. In particular, the detection performances of the occluded and tiny-scale fittings are significantly improved. Full article

Aiming at the load fluctuation problem caused by a high proportion of new energy grid connections, a reactive power optimization method based on deep reinforcement learning (DRL) considering topological characteristics is proposed. The proposed method transforms the reactive power optimization problem into a Markov decision process and models and solves it through the deep reinforcement learning framework. The Dueling Double Deep Q-Network (D3QN) algorithm is adopted to improve the accuracy and efficiency of calculation. Aiming at the problem that deep reinforcement learning algorithms are difficult to simulate the topological characteristics of power flow, the Graph Convolutional Dueling Double Deep Q-Network (GCD3QN) algorithm is proposed. The graph convolutional neural network (GCN) is integrated into the D3QN model, and the information aggregation of topological nodes is realized through the graph convolution operator, which solves the calculation problem of deep learning algorithms in non-European space and improves the accuracy of reactive power optimization. The IEEE standard node system is used for simulation analysis, and the effectiveness of the proposed method is verified. Full article

The integration of electrolyzers and fuel cells can cause voltage fluctuations within microgrids if not properly scheduled. Therefore, controlling voltage and reactive power becomes crucial to mitigate the impact of fluctuating voltage levels, ensuring system stability and preventing damage to equipment. This paper, therefore, seeks to enhance voltage and reactive power control within reconfigurable microgrids in the presence of innovative power-to-hydrogen technologies via electrolyzers and hydrogen-to-power through fuel cells. Specifically, it focuses on the simultaneous coordination of an electrolyzer, hydrogen storage, and a fuel cell alongside on-load tap changers, smart photovoltaic inverters, renewable energy sources, diesel generators, and electric vehicle aggregation within the microgrid system. Additionally, dynamic network reconfiguration is employed to enhance microgrid flexibility and improve the overall system adaptability. Given the inherent unpredictability linked to resources, the unscented transformation method is employed to account for these uncertainties in the proposed voltage and reactive power management. Finally, the model is formulated as a convex optimization problem and is solved through GUROBI version 11, which leads to having a time-efficient model with high accuracy. To assess the effectiveness of the model, it is eventually examined on a modified 33-bus microgrid in several cases. Through the results of the under-study microgrid, the developed model is a great remedy for the simultaneous operation of diverse resources in reconfigurable microgrids with a flatter voltage profile across the microgrid. Full article

Based on the background of the continuously rising global demand for clean energy, offshore wind power, as an important form of renewable energy utilization, is booming. However, the pile foundations of offshore wind turbines are subject to long-term erosion in the harsh marine environment, and the problem of corrosion damage is prominent, which seriously threatens the safe and stable operation of the wind power system. In view of this, a meshless numerical simulation method based on smoothed particle hydrodynamics (SPH) and a method for generating the concrete meso-structures are developed. Concrete pile foundation models with different aggregate contents, particle sizes, and ion concentration diffusion coefficients are established to simulate the corrosion damage processes under various conditions. The rationality of the numerical algorithm is verified by a typical example. The results show that the increase in the aggregate percentage gradually reduces the diffusion rate of chemical ions, and the early damage development also slows down. However, as time goes, the damage will still accumulate continuously; when the aggregate particle size increases, the ion diffusion becomes more difficult, the damage initiation is delayed, and the early damage is concentrated around the large aggregates. The increase in the ion diffusion coefficient significantly accelerates the ion diffusion process, promotes the earlier and faster development of damage, and significantly deepens the damage degree. The research results contribute to a deeper understanding of the corrosion damage mechanisms of pile foundations and providing important theoretical support for optimizing the durability design of pile foundations. It is of great significance for ensuring the safe operation of offshore wind power facilities, prolonging the service life, reducing maintenance costs, and promoting the sustainable development of offshore wind power. Full article

Spatially and temporally resolved data on electricity production from run-of-river plants are very useful to study various aspects of this renewable energy at both the local and regional scale. In the absence of disaggregated feed-in data from such power plants in Germany, it is necessary to apply numerical simulations to determine their electricity production for a desired region and time period. We show how a simulation model can be created using publicly accessible power plant data and information from transmission system operators as model input. The developed physical model is applied to an ensemble of 7974 run-of-river plants in Germany, including those with and without water storage facilities, to simulate their electricity production for the year 2021. The resulting and spatially aggregated simulation results correlate well with the official total electricity feed-in from run-of-river plants in Germany, as well as on smaller spatial scales such as the city of Hamburg. Such disaggregated time series can be used to assess the renewable hydropower generation at different spatial and temporal levels, as each power plant is simulated with its geographical and technical data. Moreover, this study presents the electricity production landscape of run-of-river power plants in Germany as a highly resolved map and at the federal state level with related energy indicators, which enables a better monitoring of this renewable energy. The obtained results also support the expectation that the existing run-of-river plants will play an important role in the future transformation and decarbonization of the German power sector. Full article

Against the background of the dual challenges of global energy demand growth and environmental protection, this paper focuses on the study of microgrid optimization and scheduling technology and constructs a smart microgrid system integrating energy production, storage, conversion, and distribution. By integrating high-precision load forecasting, dynamic power allocation algorithms, and intelligent control technologies, a microgrid scheduling model is proposed. This model simultaneously considers environmental protection and economic efficiency, aiming to achieve the optimal allocation of energy resources and maintain a dynamic balance between supply and demand. The goose optimization algorithm (GO) is innovatively introduced and improved, enhancing the algorithm’s ability to use global search and local fine search in complex optimization problems by simulating the social aggregation of the goose flock, the adaptive monitoring mechanism, and the improved algorithm, which effectively avoids the problem of the local optimal solution. Meanwhile, the combination of super-Latin stereo sampling and the K-means clustering algorithm improves the data processing efficiency and model accuracy. The results demonstrate that the proposed model and algorithm effectively reduce the operating costs of microgrids and mitigate environmental pollution. Using the improved goose algorithm (IGO), the combined operating and environmental costs are reduced by 16.15%, confirming the model’s effectiveness and superiority. Full article

Although they are primarily installed for specific applications, decentralised energy systems, storage systems, and controllable loads can provide flexibility. However, this varies over time. This study investigates the fundamentals of flexibility provision, including quantification, aggregation, simulation, and impact on energy systems and the power grid. We extended our methods by integrating adjustments to calculate the flexibility potential of heat pumps (HPs) and heat storage (HS) systems, as well as by incorporating variability and uncertainty. The simulations revealed the relevance of energy systems operation to flexibility, e.g., 2 K deviation in HS temperature increased theoretical coverage by 16 percentage points. The results also proved that aggregating multiple systems could obviously enhance their flexibility potential, e.g., six investigated battery storage (BS) systems could have covered up to 20 percentage points more external flexibility requests than any individual unit. The provision of flexibility by decentralised energy systems can lead to energy surpluses or deficits. Such imbalances could have been fully balanced in a system- and grid-oriented manner in 44% of BS simulations and in 32% of HP-HS ones. Overall, the findings highlight the importance of the system- and grid-oriented operation of decentralised energy systems, alongside local optimisation, for a future energy infrastructure. Full article