Due to the energy savings and environmental protection they provide, plug-in electric vehicles (PEVs) are increasing in number quickly. Rapid development of PEVs brings new opportunities and challenges to the electricity distribution network’s dispatching. A high number of uncoordinated charging PEVs has significant negative impacts on the secure and economic operation of a distribution network. In this paper, a bi-level programming approach that coordinates PEVs’ charging with the network load and electricity price of the open market is presented. The major objective of the upper level model is to minimize the total network costs and the deviation of electric vehicle aggregators’ charging power and the equivalent power. The subsequent objective of the lower level model after the upper level decision is to minimize the dispatching deviation of the sum of PEVs’ charging power and their optimization charging power under the upper level model. An improved particle swarm optimization algorithm is used to solve the bi-level programming. Numerical studies using a modified IEEE 69-bus distribution test system including six electric vehicle aggregators verify the efficiency of the proposed model.

Hydrogen storage plays a key role in mitigating the long-term fluctuations in integrated energy systems (IESs), such as the multi-day low-output events (MLEs) that last for several days. However, most studies do not consider the impacts of the MLEs on the IES planning. Therefore, this paper proposes an optimal planning for IESs considering the MLEs of wind power and the efficiency degradation of electrolyzer (EZ). First, a comprehensive evaluation index characterizing the severity of the MLE is proposed. On this basis, a novel representative chronological time period clustering (RCTPC) method is proposed to preserve the MLEs during the clustering, where the MLEs and regular intraday fluctuations are clustered separately. Moreover, an improved IES planning model considering the MLEs of wind power and hydrogen storage is further proposed to address the long-term fluctuations, where an EZ model considering the efficiency degradation is developed with different load rates and continuous operation time. Finally, the methodology is validated on an IES comprising an IEEE 33-node power network and 6-node heat network. Numerical results show that the proposed RCTPC can better retain MLE in the clustering process, and considering the efficiency attenuation of EZ can reduce the total annual cost by 4.13%. This method provides guidance for IES planning under extremely renewable intermittent conditions.

In micro grid (MG), active/reactive power sharing for all dis-patchable units is an important issue. To meet fluctuating loads’ active and reactive power demands, the units generally adopt primary P-f and Q-U droop control methods. However, at different state of charge (SOC) values, the capability of Lead Acid Battery Bank (LABB) based units to take loads varies in a large range; active power should not be shared according to the units P capacities in a constant ratio. Besides, influenced by the output and line impedance between units, reactive power is not able to be shared in proportion to the units Q capacities. Another problem, after MG power balance requirement is satisfied, frequency and voltage are deviating from their rated values thus power quality is reduced. This paper presents a new smart MG which is based on the multi agent system. To solve the problems mentioned above, P-f and Q-U droop curves are adjusted dynamically and autonomously in local agents. To improve the power quality, secondary restoration function is realized in a decentralized way, the computation tasks are assigned to local, the computation capability and communication reliability requirements for central PC are low, and operation reliability is high. Simulation results back the proposed methods.