Transportable energy storage systems (TESSs) have great potential to enhance resilience of distribution systems (DSs) against large area blackouts. A joint post-disaster restoration scheme for TESS and generation scheduling in microgrids (MGs) and network reconfigurations is proposed to minimize the total system cost, including customer interruption cost, generation cost, and TESS related costs. A temporal-spatial TESS model which is related to both transportation networks and DSs is proposed to represent the difference between TESS and ESS in terms of flexibility and cost reduction of ESS sharing among MGs. The proposed restoration problem is formulated as a mixed-integer linear programming with considering various network and TESS constraints. The proposed model and scheme are tested in a modified 33-bus test system with three MGs and four TESSs. The results verify that a distribution system with TESS is more resilient compared with conventional ESS because of the benefit from total cost reduction.

Mobile energy storage systems (MESSs) provide promising solutions to enhance distribution system resilience in terms of mobility and flexibility. This paper proposes a rolling integrated service restoration strategy to minimize the total system cost by coordinating the scheduling of MESS fleets, resource dispatching of microgrids and network reconfiguration of distribution systems. The integrated strategy takes into account damage and repair to both the roads in transportation networks and the branches in distribution systems. The uncertainties in load consumption and the status of roads and branches are modeled as scenario trees using Monte Carlo simulation method. The operation strategy of MESSs is modeled by a stochastic multi-layer time-space network technique. A rolling optimization framework is adopted to dynamically update system damage, and the coordinated scheduling at each time interval over the prediction horizon is formulated as a two-stage stochastic mixed-integer linear program with temporal-spatial and operation constraints. The proposed model is verified on two integrated test systems, one is with Sioux Falls transportation network and four 33-bus distribution systems, and the other is the Singapore transportation network-based test system connecting six 33-bus distribution systems. The results demonstrate the effectiveness of MESS mobility to enhance distribution system resilience due to the coordination of mobile and stationary resources.

Renewable energy generations have been attracting sustained attentions in academic and industry. Current-fed isolated bidirectional DC/DC converters (CF-IBDCs) are widely adopted in low voltage high current applications such as solar photovoltaic, fuel cell and energy storage. This paper gives an overview and a comprehensive comparative evaluation of CF-IBDCs. The active clamped, dual half-bridge, L-L type dual active bridge, resonant type, naturally clamped and other type topologies of CF-IBDCs are investigated, analyzed and compared regarding their circuit topological structures, characteristics, modulation methods and soft-switching technologies. In addition, component cost models and loss models of converters are deduced and presented. On this basis, quantitative and comprehensive performance comparison of six typical CF-IBDC topologies selected from each type are conducted in terms of costs, losses, weight, volume and power density for the given system specifications and design constraints.

The all-electric ship (AES) usually employs battery energy storage systems (ESSs) in the shipboard microgrid. However, the battery-only storage usually experiences frequent deep discharging or charging to meet the sudden load variations in a voyage, which may lead to significant degradation of battery lifetime. This paper, hybridizes two types of ESSs and proposes a two-step multi-objective optimization method for hybrid ESS (HESS) management. The first step regulates the HESS with the onboard diesel generators to simultaneously optimize both the economic and environmental objectives, and the second step is to split the active power of HESS into two individual ESSs for minimizing the battery cycle degradation. The first step is formulated as a bi-level optimization model through constraint decomposition. Then, a normal boundary intersection method combining with the column-and-constraint generation algorithm is developed to solve the proposed model. Extensive simulations demonstrate that the HESS can effectively resolve the power-density shortage of the battery-only system, and its integration into AES is able to extend the battery lifetime and improve both the economic and environmental indices.

Microgrid (MG) represents one of major drives of adopting Internet of Things for smart cities, as it effectively integrates various distributed energy resources. Indeed, MGs can be connected with each other and presented as a system of multimicrogrid (MMG). This paper proposes the optimal operation of MMGs by a cooperative energy and reserve scheduling model, in which energy and reserve can be cooperatively utilized among MMGs. In addition, values of Shapely are introduced to allocate economic benefits of the cooperative operation. Finally, a case study based on a system of MMGs is conducted, and simulation results verify the effectiveness of the proposed cooperative scheduling model.

Fast and stochastic power fluctuations caused by renewable energy sources and flexible loads have significantly deteriorated the frequency performance of modern power systems. Power system frequency control aims to achieve real-time power balance between generations and loads. In practice, it is much more difficult to exactly acquire the values of unbalance power in both transmission and distribution systems, especially when there is a high penetration level of renewable energies. This paper explores a deep learning approach to identify active power fluctuations in real-time, which is based on a long short-term memory recurrent neural network. The developed method provides a more accurate and faster estimation of the value of power fluctuations from the real-time measured frequency signal. The identified power fluctuations can serve as control reference so that the system frequency can be better maintained by automatic generation control, as well as emerging frequency control elements, such as energy storage system. A detailed model of Singapore power system integrated with distributed energy storage systems is used to verify the proposed method and to compare with various classical methods. The simulation results clearly demonstrate the necessity for power fluctuation identification, and the advantages of the proposed method.