电动汽车充换储一体站是集充电站、换电站和梯级储能电站于一体的电动汽车（electric vehicle ，EV）集中型综合供储能电站，可以发挥充电站和换电站的可调度潜力，并能够减小功率波动。提出了一种新型电动汽车充换储一体站模型，该模型综合考虑了电动汽车快充站、换电站和梯级储能电站。首先，基于快充用户的行驶行为特点，建立了电动汽车快充站模型；其次，根据城市道路速度流量实用模型，建立了电动汽车换电站模型；最后，结合梯级储能模型，构建了集成的电动汽车充换储一体站模型。以某地区公交线路实际道路情况为仿真算例，验证了提出的一体站模型能够满足电动汽车充电负荷的需求，且具有降低运维成本和抑制功率波动的优势。

The electric vehicle （EV） charging-swapping-storage integrated station （CSSIS） is an electric vehicle centralized integrated energy storage and power station that integrates charging stations， power conversion stations and cascade energy storage power stations， which can reduce system power fluctuations. This paper proposes a model of electric vehicle CSSIS considering fast charging station， battery swapping station and cascade energy storage station. Firstly， according to the behavior characteristics of fast-charging users， a model of EV fast charging station is established. Secondly， on the basis of speed-flow practical model of city roads， a model of EV battery swapping station is established. Finally， combined with the cascade energy storage model， an integrated electric vehicle CSSIS model is constructed. Taking the actual road conditions of bus lines in a city into account， a case verifies that the integrated station model proposed in this paper can meet the demand of electric vehicle charging load， and has the advantages of reducing the daily total operation cost and suppressing power fluctuation.

电动汽车(electric vehicles, EV)的大规模接入,给综合能源系统调度带来了机遇和挑战。文章考虑电-热-气混合潮流和电动汽车的调度灵活性,建立了含电动汽车的综合能源系统两层嵌套调度模型,对电动汽车进行分群分层调度,合理制定每辆汽车的充放电策略。调度计划层以调度方案成本最小、能量波动最小和环保性最优为目标函数,采用改进的多目标粒子群优化(multi-objective particle swarm optimization, MOPSO)算法求解日前调度计划。EV调度层以用户满意度为目标,采用粒子群(particle swarm optimization, PSO)算法制定出各集群的充放电计划,集群内根据动态优先级制定每辆EV的充放电策略。算例分析表明,所建立的调度模型可有效求解含电动汽车的综合能源系统调度问题,且计算维度小、速度快,具有实用性。

Large-scale electric vehicles (EV) accessing to the system brings opportunities and challenges to the scheduling of integrated energy system. With the power-heat-gas multi-energy flow and the scheduling flexibility of electric vehicles taken into consideration, this paper establishes a two-level scheduling model for integrated energy system with electric vehicles, performing grouped and hierarchical scheduling to make a rational charge-discharge strategy for each EV. Minimum scheduling plan cost, the smallest energy fluctuation and the best environmental protection are taken as objective functions in the scheduling plan level. Improved multi-objective particle swarm optimization (MOPSO) algorithm is adopted to solve the day-ahead scheduling plan. The EV scheduling level uses particle swarm optimization (PSO) algorithm to make the charge-discharge plan of each EV cluster with the goal of user satisfaction. According to the dynamic priority, the charge-discharge strategy of each EV in the cluster is solved. The analysis of case shows that the established scheduling model can effectively solve the scheduling problem of integrated energy system with electric vehicles, which has less calculation dimension, shorter simulation time and better practicality.

In this paper, a novel population-based, nature-inspired optimization paradigm is proposed, which is called Harris Hawks Optimizer (HHO). The main inspiration of HHO is the cooperative behavior and chasing style of Harris' hawks in nature called surprise pounce. In this intelligent strategy, several hawks cooperatively pounce a prey from different directions in an attempt to surprise it. Harris hawks can reveal a variety of chasing patterns based on the dynamic nature of scenarios and escaping patterns of the prey. This work mathematically mimics such dynamic patterns and behaviors to develop an optimization algorithm. The effectiveness of the proposed HHO optimizer is checked, through a comparison with other nature-inspired techniques, on 29 benchmark problems and several real-world engineering problems. The statistical results and comparisons show that the HHO algorithm provides very promising and occasionally competitive results compared to well-established metaheuristic techniques. Source codes of HHO are publicly available at http://www.alimirjalili.com/HHO.html and http://www.evo-ml.com/2019/03/02/hho. (C) 2019 Elsevier B.V.

针对启发式算法在机器人路径规划过程中存在路径长度不稳定和易陷入局部极小点的问题，提出一种基于自适应调整哈里斯鹰优化（AAHHO）算法。首先，利用收敛因子调整策略，调节全局搜索阶段和局部搜索阶段的平衡，同时利用自然常数为底数，提高搜索效率和收敛精度；其次，在全局搜索阶段，采用精英合作引导搜索策略，通过3个精英哈里斯鹰合作引导其他个体更新位置以提高搜索性能，通过3个最优位置加强种群间的信息交流；最后，通过模拟种内竞争策略增强哈里斯鹰跳出局部最优的能力。函数测试和机器人路径规划对比实验结果表明，所提算法无论是函数测试还是机器人路径规划都优于IHHO（Improve Harris Hawk Optimization）和CHHO（Chaotic Harris Hawk Optimization）等对比算法，对于求解机器人的路径规划具有较好的有效性、可行性和稳定性。

Aiming at the problem that the heuristic algorithms have unstable path lengths and are easy to fall into local minimum in the process of robot path planning， an Adaptively Adjusted Harris Hawk Optimization （AAHHO） algorithm was proposed. Firstly， the convergence factor adjustment strategy was used to adjust the balance between the global search stage and the local search stage， and the natural constant was used as the base to improve the search efficiency and convergence accuracy. Then， in the global search phase， the elite cooperation guided search strategy was adopted， by three elite Harris hawks cooperatively guiding other individuals to update the positions， so that the search performance was enhanced， and the information exchange among the populations was enhanced through the three optimal positions. Finally， by simulating the intraspecific competition strategy， the ability of the Harris hawks to jump out of the local optimum was improved. The comparative experimental results of function testing and robot path planning show that the proposed algorithm is superior to comparison algorithms such as IHHO（Improve Harris Hawk Optimization） and CHHO（Chaotic Harris Hawk Optimization）， in both function testing and path planning， and it has better effectiveness， feasibility and stability in robot path planning.