Abstract
The rapid development of renewable energy power has improved global energy and environmental problems. However, with the high volatility of renewable energy, it is an important challenge to guarantee the consumption of renewable energy and the reliable operation of high percentage renewable energy power systems. To solve this problem, this paper proposes a tracking absorption strategy for renewable energy based on the interaction between the supply side and the demand side, which adjusts the charging process of electric vehicles (EVs) through electric vehicle aggregator (EVA) to realize the tracking absorption of renewable energy abandoned electricity. In view of this process, we analyze the interaction among power grid, EVA and renewable energy generation (REG) as well as their market characteristics. The master-slave game model of EVA and REG was constructed considering the charging behavior characteristics of EVs and the output characteristics of REGs. Then the model solving strategy based on soft actor-critic (SAC) algorithm is proposed, and the REG pricing strategy and EVA scheduling strategy are calculated to optimize the mutual benefits. The case analysis shows that, under the same scale of electric vehicles, the proposed method can promote about 93.89% of the power abandonment consumption of wind power system, 96.00% of the photovoltaic system, and 97.41% of the wind-solar system. This strategy reduces the electricity purchase cost of EVA, promotes the interaction among renewable energy, vehicles and power grid, and improves the utilization efficiency of renewable energy.
Highlights
China's renewable energy is in a period of rapid development, by the end of 2020, the total installed capacity of renewable energy generation in China reached 930 million kW, accounting for 42.4% of the total installed capacity, and the National Energy Administration expects that by the 14th Five-Year Plan period, the proportion of clean energy in the incremental energy consumption will reach 80%
The first part of this paper is the introduction, which discusses the development of electric vehicles (EVs) and renewable energy; the other parts of this paper are structured as follows: section 2 dissects the scenario of electric vehicle aggregator (EVA) tracking renewable energy for abandoned power consumption; section 3 constructs the behavioral model and market game model of EVA tracking renewable energy generation (REG) absorbing abandoned power ; section 4 establishes the model solving algorithm based on soft actor-critic (SAC) deep reinforcement learning; section 5 constructs a case study of EVA tracking for renewable energy consumption which verifies the effectiveness of this paper's model; section 6 concludes and discusses the whole paper
RGE sends the price signal to EVA; EVA schedules the charging and discharging process of EVs in real time according to its own power demand and power purchase cost; it completes the adjustment of load distribution and consumes renewable energy power
Summary
China's renewable energy is in a period of rapid development, by the end of 2020, the total installed capacity of renewable energy generation in China reached 930 million kW, accounting for 42.4% of the total installed capacity, and the National Energy Administration expects that by the 14th Five-Year Plan period, the proportion of clean energy in the incremental energy consumption will reach 80%. This paper studies the method of consuming renewable energy on the demand side, and proposes a strategy of reasonably scheduling EVS charging process to absorb renewable energy power. This paper proposes a market model for EVA to track and consume abandoned electricity; for the market game process of EVA and REG, it proposes a master-slave game based REG pricing model and EVA dispatching model, which is helpful to achieve a winwin situation for multiple subjects; and it proposes a model solving method based on SAC deep reinforcement learning, which realizes real-time optimal dispatching of large-scale electric vehicles; the interaction between wind power, photovoltaic, wind-solar systems and EVA is analyzed, and the energy exchange efficiency between different energy systems and EVA is studied. The first part of this paper is the introduction, which discusses the development of EV and renewable energy; the other parts of this paper are structured as follows: section 2 dissects the scenario of EVA tracking renewable energy for abandoned power consumption; section 3 constructs the behavioral model and market game model of EVA tracking REG absorbing abandoned power ; section 4 establishes the model solving algorithm based on SAC deep reinforcement learning; section 5 constructs a case study of EVA tracking for renewable energy consumption which verifies the effectiveness of this paper's model; section 6 concludes and discusses the whole paper
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