Scheduling optimization of wind-thermal interconnected low-carbon power system integrated with hydrogen storage

Abstract

To improve the consumption of wind energy and reduce carbon emission, this paper proposes a wind-thermal interconnected low-carbon power system integrated with hydrogen storage. An energy scheduling optimization model aiming at minimizing the daily operation cost of the system is constructed considering environmental operation cost quantification, and Whale Optimization Algorithm is used to optimize multiple variables. Finally, in simulation example, various scenarios are set considering the application way of hydrogen and the scenarios with and without the carbon capture and storage (CCS) are optimized respectively. The horizontal comparison results show that the system with hydrogen production (S2) and the system with hydrogen fuel cell (S3) have higher economic operation cost than that of wind-thermal interconnected power system only (S1), but the environmental cost is reduced. The wind curtailment rate decreases from 11.0% (S1) to 3.8% (S2 and S3) without CCS, and from 9.0% (S1) to 2.1% (S2 and S3) with CCS. The longitudinal comparison shows that the thermal power output is reduced and the wind power consumption is improved with CCS. The total operating cost increases, but the environmental cost decreases significantly. Configuring hydrogen storage system in the wind-thermal interconnected power system can effectively promote the consumption of wind energy and reduce the system operation cost, however, the utilization of CCS is economic unfriendly at present.

Research on Renewable Energy Trading Strategies Based on Evolutionary Game Theory

“…Domestic research has primarily focused on the applicability of policies, initial allocation, auction pricing methods, and other aspects. At present, scholars have used methods such as the Bass diffusion model [8], the Learning Curve Theory [9], system dynamics [10,11], and game theory [12] to solve this problem. Reference [8] used the Bass model to predict the trend of wind power diffusion in coupled energy systems.…”

“…At present, scholars have used methods such as the Bass diffusion model [8], the Learning Curve Theory [9], system dynamics [10,11], and game theory [12] to solve this problem. Reference [8] used the Bass model to predict the trend of wind power diffusion in coupled energy systems. Reference [9] used a learning curve to study the impact of onshore wind and photovoltaic power generation investments on future installed capacity.…”

“…The (1,1,1) strategy to achieve evolutionary stability needs to satisfy conditions ( 6), (8), and (10); i.e., EC's need to pay the appraisal fee for not fulfilling the responsibility of the consumption volume is greater than the cost of EC purchasing green electricity. When TE participates in trading, the revenue gained by NE by quoting a high price is greater than the revenue gained by NE quoting a low price.…”