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To balance the objective of all stakeholders’ interests in the electric vehicle (EV) charging station planning, a multi-objective optimization method based on an improved Cuckoo search algorithm is proposed in this paper. Firstly, the temporal and spatial charging demand model based on trip chain theory for a single EV and the nodal EV charging wait model for the evaluation of charging congestion are established, hence the total charging demand of EVs is achieved with the Monte Carlo simulation. Then, considering the interests of the EV users, the charging station investor and the grid operator, a multi-objective optimization model of an EV charging station is proposed. Finally, the Cuckoo search algorithm improved by the dynamic search step is used to solve the proposed optimization model. The case studies show that the proposed optimization method has well balanced the interests of all stakeholders, short waiting time for EV users, full use of charging facilities for the station investor, and stable static voltage for the power grid.

Testing complex micro-networks requires the availability of specialized laboratories and interconnected devices. This paper describes the development of a controller-hardware-in-the-loop simulation in the context of the microgrid. This approach provides genuine testing and debugging environment for converter controllers in microgrid under various test conditions by means of real-time simulation. It is also viable to test microgrid dispatching strategies. The platform structure and real-time simulation issues including modeling, circuit partition, and multi-rate design are studied, demonstrating the rationality, and transferability of the design scheme. In the interest of studying the integration of distributed energy resources, a novel low-level control method, which enables the power converters to function as both grid-supplying and grid-supporting, is tested. Some practical implementation issues of the theoretical control algorithm are exposed and alleviated, which shows the value of hardware-in-the-loop simulation, and at the same time contributes evidence to modifying theoretical algorithms in industrial applications.

The uncertainty is a pivotal problem in Multi-Energy Carrier (MEC) systems, which leads to the strong demand of reasonable tools to evaluate uncertainties. When both possibilistic and probabilistic uncertainties exist in the real MEC systems, traditional possibilistic or probabilistic methods are no more suitable to be applied. Therefore, this paper proposes a hybrid possibilistic-probabilistic energy flow assessment method to evaluate these uncertainties. Firstly, to build a more precise uncertain model, the probabilistic and possibilistic uncertainties are respectively modeled by considering different uncertainties of sources, networks and loads of MEC systems, and the correlations among wind generation and energy loads. Then, the product t-norms of the extension principle plus $\alpha $ -cut method is firstly implemented in processing fuzzy energy flow, which can reduce overestimation compared with the sole $\alpha $ -cut method. Next, on the basis of Dempster-Shafer evidence theory, the hybrid possibilistic-probabilistic energy flow assessment approach is presented. Finally, two cases are carried out to verify the effectiveness and practicability of the proposed method.