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Route game is recognized as an effective method to alleviate Braess’ paradox, which generates a new traffic congestion since numerous vehicles obey the same guidance from the selfish route guidance (such as Google Maps). The conventional route games have symmetry since vehicles’ payoffs depend only on the selected route distribution but not who chose, which leads to the precise Nash equilibrium being able to be solved by constructing a special potential function. However, with the arrival of smart cities, the real-time of route schemes is more of a concerned of engineers than the absolute optimality in real traffic. It is not an easy task to re-construct the new potential functions of the route games due to the dynamic traffic conditions. In this paper, compared with the hard-solvable potential function-based precise method, a matched Q-learning algorithm is designed to generate the approximate Nash equilibrium of the classic route game for real-time traffic. An experimental study shows that the Nash equilibrium coefficients generated by the Q-learning-based approximate solving algorithm all converge to 1.00, and still have the required convergence in the different traffic parameters.

AbstractDriven by the demand for carbon emission reduction and environmental protection, battery swapping stations (BSS) with battery energy storage stations (BESS) and distributed generation (DG) have become one of the key technologies to achieve the goal of emission peaking and carbon neutrality. Therefore, this paper proposes a strategy to optimize the operation of BSS with photovoltaics (PV) and BESS supplied by transformer spare capacity. Firstly, it introduces the operation mechanism of BSS and uses the spare capacity of building special transformers and the roof PV to supply power to BSS to avoid the investment of transformers. Secondly, this paper establishes the load model of BSS and proposes the charging rules of battery swapping. Thirdly, a segmented pricing mechanism for the rental price of special transformers is formulated to guide BSS operators to preferentially rent spare capacity during low load rate periods. Aiming at the maximum daily profit of BSS, an optimization model is established to optimize the number of batteries to be charged and the charging status of BESS in each period; on this basis, the demand response model is further proposed. Simulation results show that the proposed strategy can improve the daily profit of BSS through shifting load. And the configuration of BESS can improve the battery swapping capacity and peak‐shaving ability. Moreover, the exponential segmented pricing mechanism can greatly reduce the number of high load periods and reduce the burden on the power supply.

Efficient parking tends to be challenging in most large cities in China. Drivers often spend substantial amounts of time looking for parking lots while driving at low speeds, thereby resulting in interference with road traffic. This paper focuses on efficiently allocating parking spaces to the demanders. A double-objective model is proposed that considers both the utilizing rate and the walking distance. First, managers want to utilize parking resources fully. Therefore, they tend to prioritize the efficient distribution of parking spaces in response to parking demands. However, demanders typically choose parking spaces according to convenience. The second objective is the acceptable walking distance from the parking space to the destination. The particle swarm optimization (PSO) algorithm is used to solve this model. We collected parking demand and supply data in a central business district (CBD) of Harbin in China and evaluated the feasibility of the model. The results demonstrate that the proposed model increases the occupying rates of parking lots in residential zones while decreasing the walking distance. The shared use of parking spaces maximizes the utility and alleviates the shortage of parking spaces in downtown.

In light of the increasing number of electric vehicles (EV), disorderly charging in mountainous cities has implications for the stability and efficient utilization of the power grid. It is a roadblock to lowering carbon emissions. EV aggregators are a bridge between EV users and the grid, a platform to achieve energy and information interoperability, and a study of the orderly charging of EVs to reach carbon emission targets. As for the objective function, the EV aggregator considers the probability of EV charging access in mountainous cities, the SOC expectation of EV users, the transformer capacity constraint, the charging start time, and other constraints to maximize revenue. Considering the access probability of charging for users in mountainous cities, the optimized Lagrange relaxation method is used to solve the objective function. The disorderly charging, centralized optimized charging, and decentralized optimized charging modes are investigated using simulation calculations. Their load profiles, economic benefits, and computational efficiency are compared in three ways. Decentralized optimal charging using the Lagrange relaxation method is shown to be 50% more effective and to converge 279% faster than centralized optimal charging.

The European Commission (EC) has set ambitious CO2 emission reduction objectives for the transport sector by 2050. In this context, most decarbonisation scenarios for transport foresee large market penetration of electric vehicles in 2030 and 2050. The emergence of electrified car mobility is, however, uncertain due to various barriers such as battery costs, range anxiety and dependence on battery recharging networks. Those barriers need to be addressed in the 2020–2030 decade, as this is key to achieving electrification at a large scale in the longer term. The paper explores the uncertainties prevailing in the first decade and the mix of policies to overcome the barriers by quantifying a series of sensitivity analysis scenarios of the evolution of the car markets in the EU Member States and the impacts of each barrier individually. The model used is PRIMES-TREMOVE, which has been developed by E3MLab and constitutes a detailed energy-economic model for the transport sector. Based on model results, the paper assesses the market, energy, emission and cost impacts of various CO2 car standards, infrastructure development plans with different geographic coverage and a range of battery cost reductions driven by learning and mass industrial production. The assessment draws on the comparison of 29 sensitivity scenarios for the EU, which show that removing the barriers in the decade 2020–2030 is important for electrification emergence. The results show that difficult policy dilemmas exist between adopting stringent standards and infrastructure of wide coverage to push technology and market development and adverse effects on costs, in case the high cost of batteries persists. However, if the pace of battery cost reductions is fast, a weak policy for standards and infrastructure is not cost-effective and sub-optimal. These policies are shown to have impacts on the competition between pure electric and plug-in hybrid vehicles. Drivers that facilitate electrification also favour the uptake of the former technology, the latter being a reasonable choice only in case the barriers persist and obstruct electrification.

The diffusion of electric vehicles (EVs) can be sustained by the presence of integrated solutions offering parking and clean power supply. The recourse to DC systems allows better integration of EV bidirectional energy exchange, photovoltaic panels, and energy storage. In this paper, a methodology for optimal techno-economic sizing of a DC-microgrid for covering EV mobility needs is carried out. It is based on the definition of different scenarios of operation, according to typical EV usage outlooks and environmental conditions. In each scenario, optimal operation is carried out by means of a specific approach for EV commitment on different stations. The sizing procedure is able to handle the modular structure of microgrid devices. The proposed approach is applied to a case study of an envisaged EV service fleet for the Bari port authority.

To take advantage of the high tidal current velocity near the free surface, the horizontal axis turbine is installed, which inevitably causes hydrodynamic characteristics to effect the turbine by the waves. In this article, we established a numerical calculation method for the hydrodynamic load of a horizontal axis turbine under wave-current conditions. Based on the numerical calculation results, the hydrodynamic loads were decomposed and the influence rules of wave parameters and blade tip immersion depth on the hydrodynamic load were obtained. The study found the following: (1) the multi-frequency fluctuations based on the rotation frequency and incident wave frequency occurred in instantaneous values of the axial load coefficients and energy utilization ratios, and the fluctuation amplitude decreased with the increase of the blade tip immersion depth; (2) the fluctuation amplitude, according to rotation frequency, changed less with the increase of wave period and wave height, and was smaller according to wave frequency; (3) the fluctuation amplitude based on wave frequency increased linearly with the increase of wave height and wave period. The research results can provide the basis and reference for the design and engineering application of tidal current power station.