International Journal of Electrical Power & Energy Systems最新文献

Thermal control of semiconductor devices in the submodules (SMs) is a key factor for the safe and reliable operation of modular multilevel converters (MMCs). During normal operation, significant thermal differences can arise among SMs due to the manufacturing tolerance and aging effect of capacitors, which can affect the lifespan of semiconductor devices and even lead to equipment failure. To address the problem, this paper presents a method for achieving thermal balancing among SMs with different capacitances by adjusting their switching principle. Firstly, the relationship between the degree of capacitance degradation and the junction temperature difference among SMs is revealed. Then, by setting data windows to capture the dynamic losses of power devices, which can reflect the trend of junction temperature. The switching principle of thermal balance among SMs is studied based on the trend of junction temperature in power devices. The thermal balancing method has advantages including not requiring high-precision temperature sensors and eliminating the reliance on capacitance monitoring technology. Finally, the simulation and experimentation results demonstrate that the proposed method can effectively achieve balanced junction temperatures of power devices among different SMs.

With the targets of carbon peaking and carbon neutrality, carbon emission reduction measures have become significant issues in new-type power systems. Distribution networks (DNs), which multiple power sources and flexible loads access to, play an essential role in exploiting the potential of reducing carbon emissions on the demand side. To achieve a global carbon emission reduction considering the carbon quota of each customer, shared photovoltaics (PVs) and energy storage systems (ESSs) are allocated with a centralized calculation and optimization conducted by DNs. To solve two key points in demand-side planning of shared PVs and ESSs in distribution networks, i.e., the accuracy of carbon emission flow (CEF) calculation and carbon quota-oriented optimization planning, this paper proposes a low-carbon oriented planning method for shared PVs and ESSs via CEF tracing. Firstly, a novel CEF calculation method is proposed based on the forward current sweep and backward voltage sweep algorithm, which considers the tracing of reactive power and power losses. Next, carbon emission index and economic index of allocating shared PVs and ESSs are defined. Subsequently, a bi-level optimization model is presented, considering the excess carbon emissions of each load based on carbon accounting and carbon quotas. Finally, the superiority of the proposed method is verified through a modified IEEE 33-node distribution network, and the effects of various investment constraints and carbon quotas are discussed.

The rapid integration of renewable energy sources and evolving consumer demands have transformed traditional electrical markets into prosumer-centric markets. In a prosumer-centric economy, prosumers exchange energy with one another on a peer-to-peer (P2P) basis. To emphasize P2P transactions, this study presents a decentralized P2P energy trading framework for direct interaction between market players such as prosumers and retailers in the community. Retailers are profit-based companies that are able to generate energy. They can also trade their energy with prosumers and the grid. The designed model respects the market participant’s preferences and gives them more options for energy transactions. Energy transactions between participants are transported through electrical networks. Hence, network constraints such as power losses and network access charges are considered in the presented model. The primary objective of the proposed model is to maximize the social welfare of the participants integrated with the network constraints. The performance of the developed model is evaluated on an IEEE 14-bus system. The simulation results ensure the efficient and global optimal solution with limited information flow between market participants.

5G base stations have experienced rapid growth, making their demand response capability non-negligible. However, the collaborative optimization of the distribution network and 5G base stations is challenging due to the complex coupling, competing interests, and information asymmetry among different stakeholders. In this paper, a distributed collaborative optimization approach is proposed for power distribution and communication networks with 5G base stations. Firstly, the model of 5G base stations considering communication load demand migration and energy storage dynamic backup is established. Afterward, a collaborative optimal operation model of power distribution and communication networks is designed to fully explore the operation flexibility of 5G base stations, and then an improved distributed algorithm based on the ADMM is developed to achieve the collaborative optimization equilibrium. Finally, the effectiveness of the proposed distributed collaborative optimization model is validated by a modified IEEE 33-bus power distribution and communication networks with 5G base stations.

This paper presents a comprehensive small signal analysis of two types of battery energy storage systems (BESSs), including a voltage-controlled BESS (V-BESS) and a current-controlled BESS (C-BESS). This study also introduces dynamic models for integrating these two BESS configurations within a DC microgrid context. Through small signal analysis and participation factor analysis, this study investigates the interplay between the BESS and the DC microgrid and the internal interactions within the BESS. Subsequently, pivotal modeling parameters are discerned, and their impacts on system dynamics due to variations are unveiled through a sensitivity analysis. These results were verified through real-time software-in-the-loop simulations using an OPAL-RT 5707XH. Furthermore, this paper proposes dynamic models for both V-BESS and C-BESS integrated with a DC microgrid that can capture the dominant behavior of BESSs in a DC microgrid with relatively low computation demands. Potential applications of this study not only include providing a reference for modeling BESSs in a DC microgrid but also providing a guideline during the design and operation stages of a BESS in a DC microgrid. Finally, due to their modularity and scalability, the proposed dynamic models can be easily applied to the design and testing of BESS controllers.

By the end of 2023, the installed capacity of renewable energy (RE) in China accounted for 36.0% of the total installed capacity, while the installed capacity of conventional thermal power units had decreased from 64.0% in 2016 to 47.6% in 2023. RE accounts for a higher proportion, leading to a shortage of peak shaving resources (PSR). As a result, the current RE quota system becomes unsustainable. In this paper, RE stations can classify their electricity into high-quality and low-quality parts based on the accuracy of their power curves. High-quality RE will be fully purchased and under stricter assessment, while low-quality RE will compete for clearing in the intraday spot market. Firstly, the article estimates the degree of PSR shortages in different periods based on technical parameters of thermal units, RE forecast information, and load forecast information. Consequently, the PSR supply capacity (PSR supply capacity) can be divided into PSR shortage status and sufficient status. Secondly, a clearing method for low-quality RE in the spot market is designed. The grid can set different PSR supply capacitys for different time periods according to the degree of PSR shortage. The assessment indicator values vary under different PSR supply capacitys. Lastly, based on the classification sales channels, this paper proposes a power curve optimization strategy that considers self-owned battery energy storage (BES). Case studies show that the proposed classification sales channels can effectively decrease the degree of PSR shortage while taking into account the benefits of RE station. The designed PSR supply capacity dividing method can accurately reflect the degree of PSR shortage in real-time, and the proposed power curve optimization strategy can further enhance the economic benefits of RE station.

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In the dynamics of grid-connected operation of brushless doubly fed generators (BDFG), when the grid voltage dips, it is equivalent to adding a reverse voltage source at the parallel node suddenly. By deriving the expressions of the transient current of power winding (PW), control winding (CW), and rotor winding (RW) of BDFG in the complex frequency domain under natural state, it is concluded that the overshoot and oscillation time are affected by the generator parameters, the drop degree and phase of the grid voltage, and the rotor speed. Then, a state feedback control strategy is proposed, and a state model with PW, CW, and RW current as state variables is constructed. State variables are added to the input through the feedback matrix, the parameters of system matrix of the generator are changed, that is, the equivalent parameters of the generator are changed. The pole distribution diagram shows that under the state feedback strategy, the poles shift significantly to the left compared with the poles with traditional control, so it could make the system accelerate convergence, reduce the amplitude. Finally, the feasibility of the state feedback algorithm was verified using tests on an experimental platform.

Modern large-scale active distribution networks have complex and dynamic operating circumstances, which pose major difficulties to state estimation (SE) technology. This study suggests a novel forecasting aided state estimate (FASE) framework based on an enhanced pseudo measurement modeling approach to overcome these issues and boost management and control decisions. Specifically, the suggested framework employs an advanced kind of pseudo measurement modeling that builds a model that is consistent with the distribution network’s real operation by using a support vector machine (SVM) with an upgraded kernel function. Furthermore, we introduce a numerical stability enhanced FASE algorithm that enhances the accuracy and efficiency of the estimation process. Through the application of measurement transformation and trustworthy pseudo measurement data as input, the FASE algorithm attains high-precision operational parameter awareness of the distribution network. Ultimately, the case study illustrates the benefits of the suggested framework over existing methods in terms of estimation accuracy, efficiency, and numerical stability compared to existing methods.

The cooperation between multiple microgrids (MGs) take a substantial role in enabling the process of information and energy exchange, which can increase the reliability of multi-microgrid (MMG) systems. Since MGs tend to be selfish in real-world operations, it is vital to address the way to motivate MGs to cooperate efficiently in MMG systems. In addition, considering the participated connected electric vehicles (CEVs) in CMT systems, the synergy between the MG-transportation coupled networks shall be accounted. In this paper, we propose a Stackelberg game theoretic approach for the non-cooperative coupled microgrid-transportation (CMT) system by exploiting connected electric vehicles (CEVs). This formulated game can jointly trigger the MG energy trading and vehicle-to-grid coordination schemes in the system. To solve the CMT game with imperfect information, a Markov Decision Process (MDP) and a deep deterministic policy gradient-based methods are developed. The devised algorithm can learn a deterministic optimal response from the opponents. The game’s Nash equilibrium can be approximately converged when the game is with imperfect information. Simulation results show that the proposed model can guarantee the effectiveness of the energy trading process in the CMT system. In addition, through the efficient CEV coordination strategies, the V2G regulation service is provided efficiently while it can further alleviate the grid power fluctuations. Furthermore, large economic benefits can be achieved under the CMT system.

Accurate and efficient power flow (PF) analysis is crucial in modern electrical networks’ operation and planning. Therefore, there is a need for scalable algorithms that can provide accurate and fast solutions for both small and large scale power networks. As the power network can be interpreted as a graph, Graph Neural Networks (GNNs) have emerged as a promising approach for improving the accuracy and speed of PF approximations by exploiting information sharing via the underlying graph structure. In this study, we introduce PowerFlowNet, a novel GNN architecture for PF approximation that showcases similar performance with the traditional Newton–Raphson method but achieves it 4 times faster in the IEEE 14-bus system and 48 times faster in the realistic case of the French high voltage network (6470rte). Meanwhile, it significantly outperforms other traditional approximation methods, such as the DC power flow, in terms of performance and execution time; therefore, making PowerFlowNet a highly promising solution for real-world PF analysis. Furthermore, we verify the efficacy of our approach by conducting an in-depth experimental evaluation, thoroughly examining the performance, scalability, interpretability, and architectural dependability of PowerFlowNet. The evaluation provides insights into the behavior and potential applications of GNNs in power system analysis.