In this project, we aim to develop an Artificial-intelligence engineering system analysis assistant (Aiesaa) for auto-creation of compact, integrated electric power transmission-distribution (T&D) network models.

Through multidisciplinary doctoral education in Cybersecurity for Electric Power Systems (CEPSE), North Carolina State University (NCSU) will increase its commitment to graduate training in two areas designated by the GAANN Program as critical to national need: Cybersecurity and Electrical Engineering. The goal of is to enlarge the pool of U.S. citizens and permanent residents who will pursue teaching and research careers in cybersecurity for electric power systems, thereby promoting workforce development and technological innovation impacting, national security, energy security, and environmental sustainability.

The proliferation of Distributed Energy Resources (DER) on distribution (D) networks, as well as inverter-based transmission (T) connected generation, especially PV solar generation with energy storage, and advancements in information and communication technologies, make it possible to regulate real and reactive power in the power T&D networks. However, they also introduce unprecedented operational challenges such as unexpected voltage fluctuations, overloading distribution lines and transformers, and reverse power flow. In the Southeast, a new Southeast Energy Exchange Market (SEEM) has been proposed. This will introduce operational and market changes that require the development of many real-time operational tools to seamlessly integrate a large amount of DER and other, including behind-the-meter (BTM) DERs (primarily distributed PV) into the Southeast power grid operation. It is crucial that those real-time DER operation tools be fully tested and validated before integrated into the existing SCADA and EMS control center platforms. Thus, in this project, we aim to develop a Grid Observatory, a real-time simulation platform that is compatible to interface with multiple software packages. The Grid Observatory will be used for conducting extensive tests and evaluation of newly developed control and monitoring algorithms (e.g., DER for regulation and load following, hybrid PV energy management systems, and BTM data analysis algorithms) before they can be integrated into the existing Energy Management Systems (EMS) and SCADA systems. We plan to leverage the existing real-time, co-simulation platforms in Clemson and NCSU for developing the Grid Observatory. Testing of cyber security tools for preventing cyber and physical attacks will be also investigated.

In this project, we will develop a Photovoltaic Analysis and Response Support (PARS) platform for improving solar situation awareness and providing resiliency services. The team will focus on developing new operation modes for solar energy systems and a PV+DER situation awareness tool to enable accurate estimation and predication of PV and DER operation conditions in both normal operation conditions and in emergency operation when there is a wide spread outage caused by natural disasters or coordinated cyber attacks. Real-time dynamic studies will be conducted to compute system operation conditions for different operation options. This tool will be run on real-time simulation platform so that optimal restoration plans can be developed in real-time using operation modes enabled by Tasks 1 and parameters derived in Task 2. The team will model transmission, distribution, and all the way down to each DER and inverter units at utility scale PV farms on a multi-core OPAL-RT real-time simulation platform.

Next year is our center?s critical review year. Demonstrating an operational green energy hub is important for the NSF as well as for our research needs. Such a hub will also serve as an important testbed to work with our member companies. The center recently moved into the brand new Keystone Science Center in June 2010. The FREEDM Systems Center occupies 20,000 square feet of office space, laboratory, and meeting room space in the building. The FREEDM Center lab space inside the Keystone Science Center was designed specifically to accommodate the 1 MW Green Energy Hub and will house the distributed generation, storage, and renewable energy technologies that FREEDM is working on. At the heart of the lab is a 12 kV distribution system (Figure 1), generated by the two 1 MVA transformer and the back to back (BTB) converter. The BTB converter acts as a substation solid state transformer (SST) to control the system fault current. The equipment marked in green color are already in place, while those marked in blue color are key project deliverables from various projects in the center in Year 3. Those marked in red color are those equipment that this project need to purchase.

With new technologies such as AMI, utilities now have an abundance of data, however, they are doing very little with this data. Because of this situation, we will collaborate with our sponsor ElectriCities to investigate and develop applications for data analytics for utility operations and customers programs.

The Mobile Electric Generating Appliance (MEGA) is a novel portable solar carport with integrated EV charger. This project will assess the impact on electric utility distribution operations from various levels of MEGA penetration. The project will also provide a high level design review.

Apply for planning grant funding to establish an ERC.

Pacific Northwest National Laboratory (PNNL) has been collaborating with NC state on developing battery dispatch and valuation methodologies for capacity charge reduction. The proposed dispatch method consists of two steps: 1) determine whether a BESS is used on an operating day based on the peak-day probability, and 2) determine the optimal dispatch based on peak-hour probabilities. The peak-day and peak-hour probabilities are determined assuming the dispatch of the BESS will not affect the peak hour of the month, ignoring the potential risk of shifting the DEP monthly peak demand.

The proposed concept entails the research, development, and demonstration of an economical, data-fused grid edge processor (EDGEPRO) that can generate required data to support flexible grid operations by processing raw data from various existing sources (e.g., smart transformers, smart pole-top sensors, distribution automation (DA) controllers, smart inverters). The edge device will process high-speed and high-volume data by leveraging data fusion and machine learning (ML) technologies, making and executing local grid control decisions, and communica-ting certain processed data with other control systems, the cloud, and/or the utility control center. Because of its capabilities, the EDGEPRO will be able to calculate ����������������virtual meter��������������� data that, will eliminateing the need for installing additional grid monitoring sensors and devices at many feeder locations such as a service transformer, to and reduce the overall cost of flexible grid control. The target cost for the advanced edge data processor is $3000 USD per unit.