Wind Farms-Green Hydrogen Energy Storage System Capacity Sizing Method Based on Corrected-Conditional Generative Adversarial Network
Zhu Ling1,2, Li Wei1,2, Wang Qian3, Zhang Xueguang3
1. State Key Laboratory of Smart Grid Protection and Control Nanjing 211106 China; 2. Nari Group Corporation (State Grid Electric Power Research Institute) Nanjing 211106 China; 3. Department of Electrical Engineering Harbin Institute of Technology Harbin 150001 China
Abstract The wind power penetration rate in China is increasing. In order to realize the efficient consumption of wind power, the green hydrogen energy storage system (ESS) is becoming an essential carrier of energy storage. However, the intermittence, randomness, and volatility of wind intraday power make it difficult to produce and store green hydrogen. Providing scenario data for green hydrogen ESS planning and displaying the intraday output fluctuation of wind power is a critical challenge. To address these issues, this paper designs a green hydrogen ESS capacity planning framework based on wind power scenario generation. The corrected-conditional generative adversarial network (CCGAN) is constructed to generate scenario data. Firstly, a corrector based on the improved revolving door algorithm is designed in the CCGAN to identify and clean the prediction data under the prediction misaim and wind power ramp events to ensure the high reference of conditional information. Secondly, the dilated convolution is embedded in the generator and discriminator to overcome the problem of the insufficient receptive field. Thirdly, by imposing constraints on the production and storage of green hydrogen, a multi-objective optimization model for green hydrogen ESS sizing is developed. The definition of knee region under Chebyshev distance is introduced. The non-dominated sorting genetic algorithm based on knee region (kr-NSGA-III) is designed to solve the capacity sizing model. Finally, the simulation based on the New England 39-bus system verifies the generated scenario’s accuracy and the planning model’s effectiveness. Simulation results on the actual wind farm data show that, when predictions are accurate, both conditional generative adversarial network (CGAN) and CCGAN have higher coverage for measured data. At the same confidence level, the power fluctuation range given by CCGAN is smaller, which reflects that the method has higher accuracy for random fluctuation. Under the prediction misaim event, the coverage of CCGAN is much higher than that of CGAN, and the fluctuation range is reduced by 12.4 MW at a 100 % confidence level. Under the wind power ramp event, the mean absolute percentage error of the scenario data generated by CCGAN is kept within 30 %. At a 90 % confidence level, CCGAN can still have a coverage of more than 80 %, reflecting the ability to characterize the short-term fluctuation of wind power. In the four generative scenarios, the sizing results of CCGAN are compared with the measured values, CGAN, robust optimization, and markov chain monte carlo (MCMC). The sizing results of CCGAN are generally smaller than CGAN and robust optimization but larger than the measured values, and the total economic cost is also lower than MCMC. Robust optimization and MCMC are easy to overestimate the uncertainty of wind power because these methods do not use the statistical value of prediction data. The scenario generated by the proposed method reasonably considers wind power fluctuation based on measured data, and the sizing result is a tradeoff between robustness and economy. The following conclusions can be drawn from the simulation analysis: (1) By employing the improved revolving door algorithm, the CCGAN’s corrector can accurately identify the prediction misaim and wind power ramp events and perform targeted correction on the conditional information of the input generator, providing a reliable label for data generation. (2) Compared with the robust optimization, MCMC, and CGAN, the wind power scenario generation based on CCGAN can capture the fluctuation characteristics of wind power, and the generated data can consider both high confidence and narrow fluctuation range, providing a high-quality scenario set for green hydrogen ESS planning. (3) The results of green hydrogen ESS sizing show that under the samples with frequent prediction misaim events, the scenario of CGAN will lead to the deviation of planning results from reality. Compared with robust optimization, the planning results of the proposed method can reduce investment costs.
Zhu Ling,Li Wei,Wang Qian等. Wind Farms-Green Hydrogen Energy Storage System Capacity Sizing Method Based on Corrected-Conditional Generative Adversarial Network[J]. Transactions of China Electrotechnical Society, 0, (): 114-114.
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