Mobile Emergency Generator Pre-Positioning and Real-Time Allocation for Resilient Response to Natural Disasters

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Enhancing Distribution Resilience with Mobile Energy Storage: A Progressive Hedging Approach

Electrochemical energy storage (ES) units (e.g. batteries) have been field-validated as an efficient back-up resource that enhance resilience of the distribution system in case of natural disasters. However, using these units for resilience is not sufficient to economically justify their installation and, therefore, these units are often installed in locations where they incur the greatest economic value during normal operations. Motivated by the recent progress in transportable ES technologies, i.e. ES units can be moved using public transportation routes, this paper proposes to use this spatial flexibility to bridge the gap between the economically optimal locations during normal operations and disaster-specific locations where extra back-up capacity is necessary. We propose a two-stage optimization model that optimizes investments in mobile ES units in the first stage and can re-route the installed mobile ES units in the second stage to avoid the expected load shedding caused by disaster forecasts. Since the proposed model cannot be solved efficiently with off-the-shelf solvers, even for relatively small instances, we apply a progressive hedging algorithm. The proposed model and progressive hedging algorithm are tested through two illustrative examples on a 15-bus radial distribution test system.Comment: Accepted for publication in the Proc. of the 2018 IEEE General Meeting in Portland, Orego

Stochastic Resource Allocation for Electricity Distribution Network Resilience

In recent years, it has become crucial to improve the resilience of electricity distribution networks (DNs) against storm-induced failures. Microgrids enabled by Distributed Energy Resources (DERs) can significantly help speed up re-energization of loads, particularly in the complete absence of bulk power supply. We describe an integrated approach which considers a pre-storm DER allocation problem under the uncertainty of failure scenarios as well as a post-storm dispatch problem in microgrids during the multi-period repair of the failed components. This problem is computationally challenging because the number of scenarios (resp. binary variables) increases exponentially (resp. quadratically) in the network size. Our overall solution approach for solving the resulting two-stage mixed-integer linear program (MILP) involves implementing the sample average approximation (SAA) method and Benders Decomposition. Additionally, we implement a greedy approach to reduce the computational time requirements of the post-storm repair scheduling and dispatch problem. The optimality of the resulting solution is evaluated on a modified IEEE 36-node network.Comment: 6 pages, 5 figures, accepted to 2020 American Control Conferenc

Wind-hydrogen storage in distribution network expansion planning considering investment deferral and uncertainty

With respect to the recent developments of hydrogen storage system (HSS), it is relevant to model these storage units in the network expansion planning. Also, most of the available expansion planning tools consider constant locations and sizing for renewable resources and only study the impacts of renewables on the model. It seems that considering variable location and capacity for renewable energies and finding their optimal levels may result in more flexible model. With regard to these issues, this paper presents distribution network expansion planning incorporating wind power and hydrogen storage. The optimal site and size of wind and hydrogen systems are denoted. The stochastic optimization programming is addressed to minimize the plan budgets. The purpose is to defer the investment and operating budgets. The uncertainty modeling is developed to handle the load-wind errors. The achievements demonstrate that the model finds optimal location, sizing, operation pattern, and setting for wind turbines and HSSs while the planning cost is deferred and minimized. 2020 Elsevier LtdScopus2-s2.0-8508270440

Coordinated operation of mobile emergency generators and local flexible resources for distribution network resilience enhancement

The increasing electrification of energy demand and connection of distributed energy resources pose a high burden on electrical power systems. Future power distribution networks are increasingly vulnerable to disruptions and extreme events with less redundancy of network capacity. This paper proposes a novel coordinated operation scheme to improve power distribution network resilience, assessing the value of operating mobile emergency generators (MEG) in coordination with other flexible resources. Three forms of flexibilities are considered in this research: flexibility from networks, local distributed energy resources, and mobile emergency generators. An optimization model is formulated and demonstrated on a European representative distribution network. Results show the value of mobile emergency generators to provide emergency services through coordinating with existing energy networks and distributed energy resources, thereby contributing significantly to power distribution network resilience