Current Projects

Block-level Approach for Risk Mitigation in Electricity Networks

Berkley Lab is developing an innovative bottom-up risk-controlled methodology for distribution system planning to reduce the uncertainty "seen" from the transmission-grid. This methodology will help utilities and regulators understand how distribution grid investments in DERs can decrease the long-term uncertainty in peak load at the substation and, therefore, decrease the need for transmission capacity. By structuring utility investments into blocks of risk mitigation, bottom-up uncertainty control options are provided while guaranteeing network operational constraints. 

Modeling Decision Dependent Ambiguous Uncertainty in Power Systems

A stable and resilient operation of the power system requires a comprehensive framework to help the industry make decisions under uncertain grid conditions. In real applications, this uncertainty is difficult to characterize (ambiguous) due to lack of data or the uncertainty characteristics depend on decisions from the system operator. This project is developing a foundational mathematical framework to handle these complex aspects of real-life uncertainty in power systems and help utilities and ISOs make better risk-informed decisions regarding planning and operation of the grid. 

Figure 1: Decision-making process under decision dependent ambiguous uncertainty

Advanced ISO Models for Storage and Hybrid Resources Operation

This research addresses key modeling challenges that ISOs/RTOs and the software industry are currently facing in representing energy storage resources in day-ahead and real-time operations that can be easily integrated with existing systems in the next couple years. The modeling challenges and solutions proposed by the project team are informed by industry feedback through a recurrent engagement with ISOs/RTOs and the storage industry. The objective is to produce analyses and models that can support decisions around storage operation in bulk power systems. 

Segment bids for storage dispatch and operation proposed by CAISO

Past Projects

Risk-controlled Expansion and Planning with Distributed Resources (REPAIR)

The Risk-controlled Expansion Planning with Distributed Resources (REPAIR), developed by Lawrence Berkeley National Lab, is an innovative tool to support decisions around utility grid planning to prevent and mitigate the impact of outages caused by routine equipment failures (reliability) or by extreme events (resilience), such as storms, earthquakes or wildfires that long term interruption of service.

More information about REPAIR can be found here.

Scientific Machine Learning for Simulation and Control in Large Scale Power Systems

This project aims to develop new tools at the intersection of scientific machine learning (SciML) and power systems engineering. These tools will accelerate the simulation of power systems with high penetration of power electronic converters (PECs), to ensure that we can simulate these systems in near-real time. This acceleration will be achieved by 1) using SciML to develop accurate models of aggregations of PECs in order to reduce the number of equations we need to solve and 2) using SciML to improve the mathematical techniques we use for solving these equations.

More information can be found here.