Critical Energy Generation in Remote and Austere Locations: Advanced Monitoring, Modeling, and Control for Improved Reliability
Abstract
This basic research entails the development and experimental deployment of new fundamental technologies for monitoring, modeling, and controlling distributed critical infrastructure, namely solar based electrification with on-site battery storage, in remote and austere locations. While testing will be limited to solar based electrification, the technologies outlined for development are generating-source agnostic and the efforts are made for easy abstraction for use on other power common generation sources (such as fuel cells, wind turbines, diesel generators, etc.). This basic research looks to understand and characterize how predictive modeling of energy generation and consumption may be employed to deliver energy more reliably to critical loads for soldiers in remote and austere locations, and to subsequently develop models and predictive analytics, allowing for more efficient asset deployment models (Òright-sizingÓ) and operations, i.e. how logistical planning can be accomplished more favorably by better understanding the generation and use characteristics in remote and austere locations. Through generation and consumption modeling for a specified or networked site, using historical real-world operating condition(s) data, collected remotely through cellular connected hardware and relayed for further analysis, a feedback loop can be completed between consumption, system, and user to maintain power reliability through output throttling and stakeholder communications. The resulting technology will provide superior reliability to the overall installation due to the inherently difficult operating conditions and infeasibility of deploying continual maintenance teams. Power will be available when expected and data collection will be reliable. To affect these goals, this research will develop and test a model for determining the confidence of allowing power to be output to both critical and non-critical (or lower order) loads. To accomplish these, a network of models must be developed and employed including power generation modeling, consumption modeling, battery state modeling, load switching control, and power availability confidence model. The system will include effective communication with stakeholders - energy consumers, maintenance teams, administrators - to manage power consumption expectations and needs. Moreover, to gather data more effectively, signal strength should be increased with a high gain antenna, and hardware will be able to collect the required data for the models described above to a reasonable degree of fidelity and store such data during times of network inactivity. The solution will also be easily deployable and maintainable and will be able to withstand the harsh environmental conditions of deployment in remote and austere conditions, in this case South Africa. The proposed basic research presents a first of its kind solution for integrating a system of predictive models to quantify data relating to a power enablement decision, simultaneously taking into account the changing needs and communications necessary between stakeholders to better affect the probability of reliable power output to critical loads in remote and austere locations. Prior work has been completed on separate predictive models including state of charge, considering generation modeling and consumption modeling, but never in a remote and austere location on critical generation infrastructure and never on an off-grid system with myriad stakeholder communications. Similarly, much prior research has been focused on generation designs, rather than energy availability optimization. University of PretoriaÕs location provides easy access to remote and austere field testing locations. The conditions of the area necessitate solutions that work in Òremote and austereÓ locations due to temperature, humidity, distance from potential support, communications abilities (and lack thereof), possibility for particulate and dust ingress
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 09, 2021
- Source ID
- W911NF2110365
Entities
People
- Hermanus Myburgh
Organizations
- Army Contracting Command
- United States Army
- University of Pretoria