Project

Building a Cyber-Physical Security Testbed for All-Electric Shipboard Power Systems


Background:
With the global push for sustainable and environment-friendly transportation, all-electric ships have emerged as a promising solution. By running entirely on electricity, these ships can substantially reduce operational costs and decrease pollution, aligning with contemporary needs. However, as technology advances, the threat of sophisticated cyberattacks on such vessels grows, highlighting the urgent need for robust security measures.

Objective:
Under the leadership of Associate Professor Hongyu Wu from Kansas State University, a team has secured a grant exceeding $300,000 from the U.S. Department of Defense's Office of Naval Research. The primary goal of this initiative is to develop a high-fidelity, cyber-physical test bed exclusively for all-electric ships. This initiative is a part of the Defense University Research Instrument Program.

The Team:
Hongyu Wu is steering the project alongside co-principal investigators - Don Gruenbacher, the head of the Department and an Associate Professor of Electrical and Computer Engineering, and Caterina Scoglio, a University Distinguished Professor from the same department.

Significance:
The security challenges of all-electric ships are manifold. Professor Wu articulates that the test bed will primarily focus on multiple facets of security:

Applications:
Beyond research, the test bed promises a multitude of applications. It is poised to serve as an invaluable resource for:

Specifically, the test bed will offer capabilities like decision support, situational awareness, performance studies, protocol evaluations, standards formulation, resiliency assessment, and cyber penetration testing.


Sustainable Engineering Infrastructures and Solutions for Tribal Energy Sovereignty

Overview:

Tribal communities, frequently characterized by rural and spread-out populations, often rely on smaller-scale energy systems that might not be as reliable or resilient, especially considering the impending climatic changes. To tackle these challenges and bolster the research infrastructure in the regions of North Dakota and Kansas, the University of North Dakota, in conjunction with several other notable institutions, has launched a groundbreaking initiative.

This project aims to devise sustainable, efficient, and reliable engineering infrastructures to bolster tribal energy sovereignty. Beyond the technological advancements, the project holds a strong educational component, aspiring to train tribal members. The overarching goal is to enhance tribal energy resilience and independence by tapping into renewable energy sources.


Key Technologies & Strategies:

Scalable Photovoltaic-Thermal Systems: These systems aim to harness both heat and power, offering a versatile energy solution.

On-demand Energy Storage Systems: These systems are tailored for power, heating, and cooling needs, ensuring energy is available precisely when required.

Renewable Fuel Production: By tapping into waste materials, such as plastics and non-food agricultural resources, there's potential for renewable fuel and power generation.

Power Microgrid Technologies: These technologies can operate both on- and off-grid, adapting to demands and surpluses accordingly.


Educational and Societal Impact:

The project isn't merely about technological advancement. It's also about empowerment. With a strong focus on education, the initiative aims to:

Train tribal members, thus fostering a skilled workforce within the tribal nations.

Establish a program in collaboration with tribal colleges to support native students in STEM at traditional research universities.

Focus on workforce development, aiding native STEM students and providing K-12 teachers with professional development opportunities.

Develop and mentor junior and mid-career faculty participants, as well as graduate students, highlighting the project's commitment to holistic growth.

Innovation Analysis Framework for Resilient Futures, with Application to the Central Arkansas River Basin

Overview:

As our planet heads towards a projected population of 9 billion by 2050, balancing essential resources like hydrocarbon, renewable energy sources, agricultural products, and freshwater becomes a critical challenge. This is especially true for Small Town and Rural (STAR) communities in agricultural regions of the U.S., where issues like low agricultural prices, high energy costs, pollution, and depleting water supplies pose significant threats.

The FEWtures project, jointly funded by the INFEWS Initiative and the Established Program to Stimulate Competitive Research (EPSCoR), seeks to address these challenges in the Central Arkansas River Basin (CARB), a region spanning parts of Colorado, Kansas, New Mexico, Oklahoma, and Texas. The project's core mission is to develop a decision-support system driven by engineering, economic research, and stakeholder analysis to evaluate the viability of innovative, renewable-energy-powered solutions for these communities.

Key Objectives & Strategies:

Integrated Renewable-Energy-Powered Solutions: Leverage existing and emerging technologies in water treatment, ammonia synthesis, and electric microgrid planning to increase water resource availability, mitigate agricultural runoff and waste, and promote resilience in agricultural systems.

Decision-Support System: Create a system that aids in evaluating the viability of new solutions, focusing on increasing usable water resources, mitigating pollution, and stimulating STAR-community economies.

Education & Engagement: Educate 5 PhD students and at least 10 undergraduate students. Develop and teach a 3-credit, online, active learning course on food, energy, and water.

Scientific Challenges & Collaborative Research:

This project tackles four key scientific challenges:

Water Treatment: Reducing salinization and managing nitrates to make poor quality sources like saline and produced waters usable.

Small-Scale Ammonia Production: Evaluating viability thresholds adapted to intermittent energy availability.

Electric Microgrid Planning: Developing a planning process suitable for renewably powered local-scale water treatment and ammonia production.

Integration and Resilience Evaluation: Integrating advances with decision and exogenous variables to evaluate effects on local and global systems' resilience.


Robust Matrix Completion State Estimation in Low-Observability Distribution Systems under False Data Injection Attacks

Overview:

The evolution of electric distribution grids is witnessing a paradigm shift. The emergence of distributed renewable energy sources and the mounting emphasis on enhancing cybersecurity have initiated the need for innovative strategies to boost situational awareness in the distribution grid. Under the patronage of an EPSCoR Research Fellowship, the Principal Investigator (PI), along with a Ph.D. student, will undergo training on groundbreaking techniques at the National Renewable Energy Laboratory (NREL). The training will concentrate on enhancing state estimation in low-observability distribution grids under cyber data attacks, offering invaluable experience and skill acquisition for the participants.

Key Objectives & Approaches:

Enhancing Distribution Grids: The aim is to improve situational awareness in legacy distribution systems that traditionally have very low observability due to limited sensors.

Advanced Training: The PI and a Ph.D. student will receive in-depth training on state-of-the-art techniques, including a unique state estimation approach and a next-gen cyber-physical system simulation platform at NREL.

Collaboration with NREL: The fellowship promotes close collaboration with NREL researchers. The focus lies in leveraging advanced techniques to achieve superior state estimation in distribution grids under cyberattacks.

Research Integration: The acquired techniques will be incorporated into Kansas State University's curriculum, benefiting other researchers in related domains.

Research Focus:

Cybersecurity and Distribution Grids: Address the susceptibility of information in distribution systems to cyber data attacks, especially given the potential of advanced metering infrastructure and phasor measurement units to improve grid observability.

Matrix Completion State Estimation: Develop a bad data detection approach grounded on matrix completion state estimation.

Scalable Solutions: Design a fully distributed solution ensuring scalability.

Cyber Data Attack Modeling: Construct models for false data injection attacks in distribution networks.

Simulation Platform Training: Receive training on the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS) platform.

Proof-of-Concept Validation: After development, the solutions will undergo validation to ascertain their feasibility.

AMPS: Deep-Learning-Enabled Distributed Optimization Algorithms for Stochastic Security Constrained Unit Commitment

Overview:

The electric power systems domain is on the cusp of a radical transformation. Key drivers include the integration of renewable energy sources, a push for a greener energy ecosystem, and the pressing need to address climatic challenges. This initiative aims to establish the mathematical foundation that is imperative for maximizing the benefits of deep machine learning techniques to augment power system operations. This is especially pertinent for incorporating renewable energy forms, such as wind and solar. The project's objective is to conceive a novel set of distributed optimization instruments that will capacitate extensive power system operations to tackle uncertainties while effectively assimilating renewable energy resources.

Key Objectives & Approaches:

Harnessing Machine Learning: The project aspires to leverage deep machine learning methodologies to bolster power system operations, especially in the realm of renewable energy sources.

Distributed Optimization Tools: The research is poised to unfold a new gamut of distributed optimization tools tailored to bolster large-scale power system operations in managing uncertainties and assimilating renewable energy.

Deep Neural Network-Based Approach: Design a comprehensive, tri-stage deep neural network-based machine learning mechanism.

Hybrid Distributed Parameter System Control: Adopt solution strategies rooted in hybrid distributed parameter system control theory.

Algorithm Validation: Engage in extensive validations of the conceptualized algorithms utilizing large-scale, real-world power system datasets.

Expected Impact:

The successful fruition of this endeavor can revolutionize operational paradigms within the power system. By offering stakeholders, market participants, policymakers, and regulators a clearer understanding and public awareness, the project holds the potential to influence a broader demographic. The introduction of state-of-the-art algorithms will enable power system operators to refine their operational practices, especially when integrating renewable generation. In addition, the project endeavors to foster educational and outreach avenues, particularly targeting students from both collaborating institutions and placing an emphasis on those from underrepresented groups within the STEM spectrum.

CONTACT INFORMATION

Department of Electrical and Computer Engineering

Kansas State University

3093 Engineering Hall

1701D Platt St., Manhattan, KS 66506


📞 Phone: (785) 532-4588

📠 Fax: (785) 532-1188

✉️ E-mail: HongyuWu@ksu.edu