This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure o...This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model.展开更多
A new algorithm namely the interval sampling method, applicable to the analysisof steady-state simulation output is proposed. This algorithm uses the time series analysisto carry out conrrelation analysis of the stead...A new algorithm namely the interval sampling method, applicable to the analysisof steady-state simulation output is proposed. This algorithm uses the time series analysisto carry out conrrelation analysis of the steady-state simulation output so as to obtain theobservation data which are actually uncorrelated in nature. On the basis of theseuncorrelated data gathered, some satisfactory deductions cam be made on the data under re search. A comparison between batch means method and the interval sampling method hasbeen performed by taking the M/M/l queuing system as an example. The results attestedthat the interval sampling method is mere accurate than the batch means method.展开更多
A novel approach for generative time series simulation of electricity price scenarios is presented.A"Time Series Simulation Conditional Generative Adversarial Network"(TSS-CGAN)generates short-term electrici...A novel approach for generative time series simulation of electricity price scenarios is presented.A"Time Series Simulation Conditional Generative Adversarial Network"(TSS-CGAN)generates short-term electricity price scenarios.In particular,the network is capable of generating a 24-dimensional output vector that corresponds to the expected behavior of electricity markets.The model can replace typical approaches from financial mathematics like statistical factor models to model the price distribution around a given forecast.The data cover a 3-year period from 2020 to 2023.Our empirical study is conducted on the EPEX SPOT market in Europe.An electricity price scenario includes the prices of the hourly contracts of a day-ahead auction at the EPEX SPOT power exchange.The model uses multivariate time series as input factors,consisting of point forecasts of electricity prices and fundamental data on generation and load profiles.The architecture of a CGAN TSS-is based on the idea of Conditional Generative Adversarial Networks combined with 1D Convolutional Neural Networks and Bidirectional Long Short-Term Memory.The model is evaluated using qualitative and quantitative criteria.For the evaluation,10,000 simulations of a test period are carried out.Qualitative criteria are whether the model follows certain electricity market-specific regularities and depicts them adequately.The quantitative analysis includes common error metric,compared to benchmark models,like DeepAR,Prophet and Temporal Fusion Transformer,the examination of the quantile ranges,the error distribution and a sensitivity analysis.The results show that the TSS-CGAN outperforms benchmark models such as DeepAR by reducing the continuous ranked probability score by 50%and considers market-specific circumstances such as the production of fluctuating energies and reacts correctly to changes in the corresponding variables.展开更多
基金supported by the State Grid Science and Technology Project (No.52999821N004)。
文摘This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model.
文摘A new algorithm namely the interval sampling method, applicable to the analysisof steady-state simulation output is proposed. This algorithm uses the time series analysisto carry out conrrelation analysis of the steady-state simulation output so as to obtain theobservation data which are actually uncorrelated in nature. On the basis of theseuncorrelated data gathered, some satisfactory deductions cam be made on the data under re search. A comparison between batch means method and the interval sampling method hasbeen performed by taking the M/M/l queuing system as an example. The results attestedthat the interval sampling method is mere accurate than the batch means method.
基金supported by the German Federal Ministry of Education and Research[grant 13FH587KX1](FederatedF orecasts).
文摘A novel approach for generative time series simulation of electricity price scenarios is presented.A"Time Series Simulation Conditional Generative Adversarial Network"(TSS-CGAN)generates short-term electricity price scenarios.In particular,the network is capable of generating a 24-dimensional output vector that corresponds to the expected behavior of electricity markets.The model can replace typical approaches from financial mathematics like statistical factor models to model the price distribution around a given forecast.The data cover a 3-year period from 2020 to 2023.Our empirical study is conducted on the EPEX SPOT market in Europe.An electricity price scenario includes the prices of the hourly contracts of a day-ahead auction at the EPEX SPOT power exchange.The model uses multivariate time series as input factors,consisting of point forecasts of electricity prices and fundamental data on generation and load profiles.The architecture of a CGAN TSS-is based on the idea of Conditional Generative Adversarial Networks combined with 1D Convolutional Neural Networks and Bidirectional Long Short-Term Memory.The model is evaluated using qualitative and quantitative criteria.For the evaluation,10,000 simulations of a test period are carried out.Qualitative criteria are whether the model follows certain electricity market-specific regularities and depicts them adequately.The quantitative analysis includes common error metric,compared to benchmark models,like DeepAR,Prophet and Temporal Fusion Transformer,the examination of the quantile ranges,the error distribution and a sensitivity analysis.The results show that the TSS-CGAN outperforms benchmark models such as DeepAR by reducing the continuous ranked probability score by 50%and considers market-specific circumstances such as the production of fluctuating energies and reacts correctly to changes in the corresponding variables.
