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This research investigates the feasibility of using mobile hybrid microgrids to increase energy resilience in DoD Installations. The primary question examined is whether a standardized mobile microgrid, constrained within an International Standards Organization (ISO) Triple Container (TriCon) and not to exceed 10,000 lbs (approximately 4535 kg), can provide the necessary power for small critical sites with an average 10 kW load on DoD installations with similar resilience to a customized single load microgrid or emergency backup generator. Key assumptions for this research are that power outages may be accompanied by a fuel constrained environment (e.g., natural disaster that restricts fuel transport), an existing installation microgrid is in place, and the risk of outages does not warrant the development of redundant customized single load microgrids for each critical load. The feasibility of a mobile hybrid microgrid is investigated by constructing an architectural design that attempts to find a satisfactory combination of commercial off-the-shelf components for battery energy storage, photovoltaic power, and generator power within the constraints of an 8 ft × 6 ft 5 in × 8 ft (approximately 2.4 m × 2 m × 2.4 m) shipping container. The proposed design is modeled and simulated over a two-week period using Global Horizontal Index solar irradiance data, and a randomized average 10 kW load. Results of the model are used to analyze the feasibility of the system to meet the load while reducing dependency on fuel resources. Trade-offs between a customized single load microgrid and standardized mobile microgrid are discussed. The result of this research indicates that a standardized mobile microgrid holds significant promise for DoD and other potential users (public safety, private industry, etc.) in having a rapidly deployable solution to bring critical loads back online during an emergency situation that reduces generator usage.

Fully electrified ships, which is known as the 'all-electric ships (AESs)', have the potentials to bring great economic /environmental benefits. To further improve the energy efficiency of AESs, PV generations are gradually integrated, which introduces uncertainties to the AES operation. However, current researches mostly focus on sizing problem whereas rarely concern the operation. In this perspective, a data-driven robust coordination of generation and demand-side is proposed to properly address the onboard PV generation uncertainties as well as reducing the fuel cost of AESs, which consists of an extreme learning machine (ELM) based PV uncertainty forecasting method and a two-stage operating framework, where the first stage for the worst PV generation case and the second stage targets at the uncertainty realization. A 4-DG AES is implemented into the case study and the simulation results show that the ELM-based method can well characterize the PV uncertainties, and the two-stage operating framework can well accommodate the onboard PV uncertainties. Further analysis also demonstrates the proposed method has enough flexibility when facing working condition variations. Ministry of Education (MOE) Nanyang Technological University National Research Foundation (NRF) This work was supported in part by the Ministry of Education, Republic of Singapore, under Grant AcRF TIER 1 2019- T1-001-069 (RG75/19), and in part by National Research Foundation (NRF) of Singapore under Project NRF2018-SR2001-018. The work of Y. Xu was supported by Nanyang Assistant Professorship from Nanyang Technological University, Singapore. The work of S. Fang was supported by the Open Funding of Key Laboratory of Maritime Intelligent Equipment and System, Ministry of Education, Shanghai Jiao Tong University. Paper no. TPWRS-01765-2018.

This paper proposes a mixed generation portfolio model of hybrid energy generating station (HEGS) for standby emergency power supply (EPS). The HEGS functions as a mobile and transportable reserve power source for critical loads in case of electricity outages. In the proposed model, various renewable and non-renewable energy sources with different mobility, energy density, and power density characteristics are integrated to form a sufficient and reliable hybrid energy EPS system, and the uncertainties of demand load, wind speed, solar radiation intensity, as well as solar cell temperature are modeled as interval numbers to formulate the optimal sizing problem of HEGS under uncertainty into a deterministic combinatorial optimization model. Furthermore, the multiobjective generation portfolio model of EPS configuration is also designed to generate Pareto frontier between implementation cost reduction and mobility improvement. Four generic evaluation metrics are formulated to evaluate the resulting generation-mix schemes of HEGS with case study demonstrated the effectiveness and superiority of the proposed model.

An all-electric ship (AES) uses diesel generators and energy storage system (ESS) to meet both propulsion and service loads. Thus, it can be viewed as a mobile microgrid. During the operation of an AES, significant uncertainties such as water wave and wind introduce considerable speed loss, which may lead to severe voyage delays. To fully address this issue, a new robust energy management model is proposed to coordinately schedule an AES's power generation and voyage considering the uncertain wave and wind. Two objectives are minimized simultaneously: the fuel consumption (FC) and energy efficiency operational indicator (EEOI). The problem is formulated as a bi-level robust optimization model after certain constraint decomposition. Normal boundary intersection method is utilized to solve this multi-objective programming. Compared with existing joint scheduling methods, the proposed method can fully guarantee the on-time rates of AES in various uncertain scenarios and providing high-quality Pareto solutions. Nanyang Technological University National Research Foundation (NRF) The work in this paper was supported in part by National Research Foundation (NRF) of Singapore under project NRF2018-SR2001-018. Y. Xu’s work is supported by Nanyang Assistant Professorship from Nanyang Technological University, Singapore.