With the proposal of the carbon peak and carbon neutrality goal, a significant influx of new energy sources with random and fluctuating characteristics is being integrated into the power grid. Consequently, the adapta...
详细信息
ISBN:
(数字)9798350353204
ISBN:
(纸本)9798350353211
With the proposal of the carbon peak and carbon neutrality goal, a significant influx of new energy sources with random and fluctuating characteristics is being integrated into the power grid. Consequently, the adaptability of the traditional four-parameter model, as well as new energy models, to the actual power grid is becoming increasingly prominent. Conversely, simulations often rely solely on the accuracy of models and algorithms and are based on predefined conditions. To address the aforementioned challenges, this paper utilizes PMU-measured data to identify error-dominant regions. Initially, it employs a hybrid dynamic simulation method to partition the power grid simulation system into blocks. Concurrently, it establishes a simulation credibility evaluation system and employs comprehensive simulation credibility to identify error-dominant regions. Subsequently, it iteratively decouples sub-regions to systematically identify error elements. Subsequently, the perturbation method was employed to analyze the trajectory sensitivity of each parameter of the element. Parameters with low sensitivity were assigned typical values, while those with high trajectory sensitivity were chosen for identification. The genetic algorithm was then utilized for online parameter verification and optimization. Finally, the proposed method was validated using RTDS real-time simulation software. Experimental results demonstrate that the calibrated model simulation results exhibit better alignment with measured data, highlighting the accuracy and reliability of the proposed approach.
The interaction between electric vehicle charging stations(EVCS) and PV, energy storage, and other equipment progressively increases as the penetration rate of electric vehicles and distributed new energy increases. S...
详细信息
ISBN:
(数字)9798331528096
ISBN:
(纸本)9798331528102
The interaction between electric vehicle charging stations(EVCS) and PV, energy storage, and other equipment progressively increases as the penetration rate of electric vehicles and distributed new energy increases. Simultaneously, the safe operation of urban distributionnetwork will be significantly impacted by the volatility and randomness introduced by new energy access. Consequently, the issue of examining the multisource cooperative operation strategy of urban distributionnetwork in light of uncertainty has emerged as a pressing requirement for urgent research and resolution. To address the uncertainty of PV prediction, the data-driven algorithm of the Wasserstein generative adversarial network is employed to generate PV output scenarios. PV typical scenarios are obtained through K-mediods clustering cuts, and the probabilistic confidence intervals of the distribution of the typical scenarios are constrained by the 1 -paradigm and $\infty$-paradigm numbers. Subsequently, the typical scenario data of PV is employed to develop a data-driven distributionally robust urban distribution grid multi-source cooperative operation model that takes into account electric vehicle charging stations, energy storage, and PV. The first stage of this model ascertains the power procured from the primary grid, whereas the second stage identifies the PV power relinquished, the storage charging and discharging capacity, and the equivalent load of the EVCS. Finally, the suggested model’s validity is confirmed using data from an urban distributionnetwork.
The uncertainty and suddenness of wind power output can significantly change the system tide distribution, making it difficult to adjust the reactive power regulation equipment to the optimal state, and therefore, the...
详细信息
The uncertainty and suddenness of wind power output can significantly change the system tide distribution, making it difficult to adjust the reactive power regulation equipment to the optimal state, and therefore, the...
详细信息
With the large-scale integration of distributed energy resources and the rapid development of electric vehicles (EVs), microgrids operating in islanded mode face significant challenges related to frequency imbalances ...
详细信息
With the large-scale integration of distributed energy resources and the rapid development of electric vehicles (EVs), microgrids operating in islanded mode face significant challenges related to frequency imbalances and increased control costs caused by severe stochastic disturbances from distributed generators and loads. The introduction of Vehicle-to-Grid (V2G) technology enables EVs to participate in load frequency control within microgrids. However, the V2G process can also compromise the charging needs of EV users. To address these issues, this paper proposes an intelligent multi-microgrid control strategy based on V2G technology, which coordinates frequency (power) balance, operating costs, and user demands. First, a multi-microgrid control structure is designed, incorporating EV charging stations, distributed generators, and micro gas turbine (MT) units. The coupling between multi-microgrids is achieved through multi-agent collaboration. The control model also accounts for the power generation characteristics of wind turbines under different operating conditions, making it more applicable to real-world scenarios. Second, a multi-objective coordination framework is developed to ensure system frequency stability while minimizing EV discharge time and the output cost of MT units. Additionally, the proximal policy optimization (PPO) algorithm is improved to effectively handle multi-objective control tasks with deceptive rewards. Simulation results demonstrate that the proposed controller significantly outperforms other control methods, such as PID, fuzzy logic, and traditional Deep Deterministic Policy Gradient and PPO algorithms, in terms of control performance. It adapts to varying operating states of wind turbines, achieves high-quality frequency regulation, reduces the regulation costs of MT units, and prevents unnecessary discharging of EVs. This ensures system stability while meeting user demands and optimizing cost-effectiveness.
暂无评论