Real-time system resolves power grid outages with comprehensive, cost-efficient solutions and quickly updates restoration plans in response to changing conditions
UCF researchers have developed an online self-healing power grid tool that responds to power outages faster and more efficiently than current restoration methods. The new Adaptive Restoration Decision Support System (ARDSS) uses novel computational methods to holistically assess and respond to power outages, even as conditions affecting the grid change.
Power outages can jeopardize lives and are very costly, especially those triggered by natural disasters or cyberattacks. According to the Electric Power Research Institute, the United States economy loses more than $150 billion annually from power outages. Yet, the methods for restoring power grids are typically specified in manuals that are based on a set of power outage scenarios. Thus, the restoration plans lack the ability to dynamically adapt to changing conditions. As a result, recovery can be slow, long and expensive. The methods also only solve either the planning stage or action stages of a restoration.
As a solution to such limited restoration strategies, the ARDSS works in real-time, considering both the steady state and transient behavior of a power grid to reduce the recovery time from a power outage. By adapting restoration plans and actions to accommodate varying conditions, the new online tool offers a more comprehensive, integrated response that can save time, lives and money. It also ensures a power system’s security.
The invention encompasses a system and methods for enabling self-healing power grids after a power outage. When integrated with a company’s energy management system, the unique ARDSS combines the many stages of power restoration into one holistic problem, and then decomposes it into a two-stage mixed-integer linear programming (MILP) problem. Once the first-stage problem is solved, the ARDSS addresses the second-stage problem using an integer L-shaped algorithm, auxiliary variables and optimality cuts to ensure the restoration feasibility.
To run the necessary computations, the system employs two types of functions: optimal planning (OP) and optimal real-time (OR) functions; each is executed at specific time periods. The OP function is executed at the early stages of restoration, and the OR function is run periodically at each restoration time step. Moreover, the OP function solves the two-stage problem as a whole, while the OR function only solves the second-stage problem with the first-stage decisions fixed. Both functions may be implemented via hardware or software.
In one example use, the ARDSS generates solutions using both static and dynamic input data. At the start of the restoration period, the ARDSS resolves the first-stage problem, determining the generators, start-up times and transmission line energization sequences. Based on the first-stage decisions, the system solves the second-stage problem, including the optimal load pickup amount and location, dynamic reserve allocation, voltage profile, frequency behavior, and real and reactive power losses.
- Fast, efficient and reliable
- Adapts to changing operating conditions in power restoration
- Performs better than existing methods when the restoration time is less
- Electric utility companies
- Independent system operators