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| Multi-Energy Flow Management and Control Strategy for Highway Traffic Energy System Considering Conditional-Value-at-Risk |
| Su Su1, Wei Cunhao2, Nie Xiaobo1, Li Yujing1, Wang Lei3 |
1. School of Electrical Engineering Beijing Jiaotong University Beijing 100044 China;
2. School of Electrical Engineering Southeast University Nanjing 210096 China;
3. CCCC Mechanical & Electrical Engineering Co. Ltd Beijing 101318 China |
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Abstract To steadily promote carbon reduction policies, the development of electric vehicles (EVs) is currently experiencing a period of rapid growth. As a result, incorporating EV battery swapping stations into the highway traffic infrastructure has become one of the means to alleviate range anxiety and meet the fast charge-replenishment needs of EV users during long-distance driving. However, the introduction of battery swapping stations has also brought new challenges to the energy management and economic operation of highway traffic energy systems (HTES). Considering the unique application scenario of highway traffic, there is still a lack of comprehensive exploration regarding the complex relationship between HTES and battery swapping stations, as well as the joint operation and interaction mode between them. To address these gaps, this paper proposes a multi-energy flow management and control strategy for HTES considering conditional value-at-risk (CVaR). By taking into account the benefits of both HTES and battery swapping stations, as well as the uncertainty of renewable energy, this strategy aims to provide energy management and optimized pricing solutions.
Firstly, a Stackelberg game bilevel optimization model is established. In the upper level of the established model, the reasonable control of multi-energy flows and optimal pricing are carried out to minimize the comprehensive cost of HTES. In the lower level, the battery swapping station flexibly schedules the charging and discharging power of each battery according to the price set by HTES, and then minimizes the cost of energy interaction with HTES on the premise of meeting the power demand. Next, based on the multi-scenario method, CVaR is introduced to measure the risk cost caused by the uncertainty of renewable energy output. Then, using the Karush-Kuhn-Tucker condition and strong duality theorem, the original Stackelberg game bilevel model is transformed into a single-layer mixed integer linear programming problem to solve the Nash equilibrium solution, and hence the optimal operation and pricing scheme is obtained.
Simulation results on a highway service area show that the cost of HTES will increase without participation in the electrolytic tank, fuel cell, hydrogen storage tank and energy storage system, but it will have little impact on the charging and discharging costs of the battery swapping station. Due to wider access ways in electricity compared to hydrogen, power-hydrogen conversion equipment tends to respond more to changes in hydrogen prices. Scenarios without an electrolytic tank have higher costs than those without a fuel cell. Moreover, expanding the pricing range of HTES can improve its pricing flexibility, allowing it to gain more benefits from the charging and discharging behavior of the battery swapping station. This can result in a decrease in the HTES cost and an increase in the battery swapping station cost. If the flexible charging and discharging duration of batteries is increased from 5 hours to 9 hours in the typical day scenario, it can reduce the charging and discharging cost of the battery swapping station by 17.3% and the HTES comprehensive cost by 1.1%. Regarding the analysis of conservation, increasing the risk aversion coefficient from 0.1 to 0.9 will result in a 0.39% decrease in CVaR under the typical day scenario, while the operating cost of HTES will increase by 0.24%. In the holiday scenario, CVaR will correspondingly decrease by 0.26%, while the operating cost of HTES will increase by 0.30%.
The simulation analysis yields the following conclusions: (1) The participation of power-hydrogen conversion equipment, hydrogen storage tank, and energy storage system can effectively improve the energy supply flexibility of HTES, thereby reducing the economic cost of HTES. (2) Expanding the allowed range of charging and discharging prices set by HTES can to some extent reduce the cost of HTES, but it will correspondingly increase the cost of the battery swapping station. If the flexible charging and discharging duration of batteries in the battery swapping station is extended, it can simultaneously reduce the economic costs of HTES and the battery swapping station. However, the duration will be limited by realistic factors such as passenger flow. (3) By adjusting the risk aversion coefficient, strategies with different conservative levels can be formulated to adapt to different operating styles. In summary, the proposed strategy can achieve a win-win situation for different entities and balance the operation cost and risk cost for HTES.
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Received: 05 September 2023
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2024, Vol. 39 
