Publications

This paper is motivated by the need to ensure fast microgrid stability. Modeling for purposes of establishing stability criterion and possible implementations are described. In particular, this paper proposes that highly heterogeneous microgrids comprising both conventional equipment and equipment based on rapidly emerging new technologies can be modeled as purely electric networks in order to provide intuitive insight into the issues of network stability. It is shown that the proposed model is valid for representing fast primary dynamics of diverse components (gensets, loads, PVs), assuming that slower variables are regulated by the higher-level controllers. Based on this modeling approach, an intuitively-appealing criterion is introduced requiring that components or their combined representations must behave as closed-loop passive electrical circuits. Implementing this criterion is illustrated using typical commercial feeder microgrid. Notably, these set the basis for standards which should be required for groups of components (sub grids) to ensure no fast instabilities in complex microgrids. Building the need for incrementally passive and monotonic characteristics into standards for network components may clarify the system level analysis and integration of microgrids.

Small electrical networks (i.e., microgrids) and machine models (synchronous generators, induction motors) can be simulated fairly easily, on sequential processes. However, running a large simulation on a single process becomes infeasible because of complexity and timing issues. Scalability becomes an increasingly important issue for larger simulations, and the platform for running such large simulations, like the MIT Supercloud, becomes more important. The distributed computing network used to simulate an electrical network as the physical system presents new challenges, however. Different simulation models, different time steps, and different computation times for each process in the distributed computing network introduce new challenges not present with typical problems that are addressed with high performance computing techniques. A distributed computing network is established for some example electrical networks, and then adjustments are made in the parallel simulation set-up to alleviate the new kinds of challenges that come with modeling and simulating a physical system as diverse as an electrical network. Also, methods are shown to simulate the same electrical network in hundreds of milliseconds, as opposed to several seconds--a dramatic speedup once the simulation is parallelized.

Each year, thousands of software vulnerabilities are discovered and reported to the public. Unpatched known vulnerabilities are a significant security risk. It is imperative that software vendors quickly provide patches once vulnerabilities are known and users quickly install those patches as soon as they are available. However, most vulnerabilities are never actually exploited. Since writing, testing, and installing software patches can involve considerable resources, it would be desirable to prioritize the remediation of vulnerabilities that are likely to be exploited. Several published research studies have reported moderate success in applying machine learning techniques to the task of predicting whether a vulnerability will be exploited. These approaches typically use features derived from vulnerability databases (such as the summary text describing the vulnerability) or social media posts that mention the vulnerability by name. However, these prior studies share multiple methodological shortcomings that infl ate predictive power of these approaches. We replicate key portions of the prior work, compare their approaches, and show how selection of training and test data critically affect the estimated performance of predictive models. The results of this study point to important methodological considerations that should be taken into account so that results reflect real-world utility.

This report describes the development of a real-time hardware-in-the-loop (HIL) power system simulation platform to evaluate commercial microgrid controllers. The effort resulted in the successful demonstration of HIL simulation technology at a Technical Symposium organized by the Mass Clean Energy Center (CEC) for utility distribution system engineers, project developers, systems integrators, equipment vendors, academia, regulators, City of Boston officials, and Commonwealth officials. Actual microgrid controller hardware was integrated along with actual, commercial genset controller hardware in a particular microgrid configuration, which included dynamic loads, distributed energy resources (DERs), and conventional power sources. The end product provides the ability to quickly and cost-effectively assess the performance of different microgrid controllers as quantified by certain metrics, such as fuel consumption, power flow management precision at the point of common coupling, load-not-served (LNS) while islanded, peak-shaving kWh, and voltage stability. Additional applications include protection system testing and evaluation, distributed generation prime mover controller testing, integration and testing of distribution control systems, behavior testing and studies of DER controls, detailed power systems analysis, communications testing and integration, and implementation and evaluation of smart grid concepts. Microgrids and these additional applications promise to improve the reliability, resiliency, and efficiency of the nation's aging but critical power distribution systems. This achievement was a collaborative effort between MIT Lincoln Laboratory and industry microgrid controller manufacturers. This work was sponsored by the Department of Homeland Security (DHS), Science and Technology Directorate (S&T) and the Department of Energy (DOE) Office of Electricity Delivery and Energy Reliability.