Putting into Practice the Dream of a Self-healing Smart Grid
- Written by Chen-Ching Liu, Wei Sun, Yunhe Hou, and Shanshan Liu
A team at the Electric Power Research Institute (EPRI) has developed and tested advanced decision support tools for power system restoration based on adoption of a pre-formulated strategy with defined "milestones." System operators adopt a an approach for system restoration and identify markers along a path so that goals can be achieved efficiently.
The benefits of a self-healing grid for energy consumers and suppliers are well recognized. The Electric Power Research Institute (EPRI) has estimated that "across all business sectors, the U.S. economy is losing between $104 billion and $164 billion a year to outages." By minimizing or eliminating interruptions, a self-healing grid could dramatically reduce this cost.
At present power system operators perform system recovery or restoration following a major outage manually, based on guidelines from a restoration plan that is prepared off-line. However, this may or may not reflect the scenario that is actually taking place. It is a time-consuming, complicated and highly stressful task.
Working under the auspices of EPRI, the authors have developed and tested advanced decision support tools for power system restoration based on adoption of a pre-formulated strategy with defined “milestones.” These milestones are generic optimization modules that can be tailored to implement a strategy taken by system operators. That is, the system operators determine a specific strategy for system restoration and identify the milestones that ensure efficient achievement of the strategy’s goals.
What we call Generic Restoration Milestones (GRMs) is substantiated for implementation by the system restoration strategy. This provides the fundamentals to develop a systematic restoration strategy that can be adaptively implemented and communicated among system engineers and various stakeholders. We believe this strategy represents a significant breakthrough. Lessons learned from the last 20 years show that developing intelligent system tools for system restoration reveals that decision support tools must be highly flexible and adaptive; this allows seamless collaboration between human operators and computer-based optimization tools that allow ever-changing power grid conditions to be properly reflected.
Power system restoration involves a large number of generation, transmission and distribution and load facilities and constraints. After analyzing system conditions and characteristics of an outage, system restoration planners, or operators, select a sequence of GRMs, such as "Form Black_Start_Non_Black_Start Building Block," "Establish Transmission Grid," "Form Electrical Island," "Synchronize Electrical Islands," "Serve Load in Area," and "Connect with Neighboring System." Each GRM module is developed utilizing state-of-the-art optimization techniques and incorporating practical considerations, such as generator start-up sequencing, transmission path search, Optimal Power Flow (OPF) with stability considerations, and the times to take restorative actions. A restoration strategy is established by a combination of GRMs, and constraints can be considered during implementation of each GRM. Thus, an interactive system restoration decision support tool is established. Based on the proposed concept of GRM, a prototype software tool entitled "System Restoration Navigator" has been developed with the support of EPRI. EPRI funders have direct access to the tool, and then it is available to the public.
System restoration methodology has been tested with scenarios from Hawaiian Electric Company (HECO) and Long Island Power Authority. Based on the characteristics of HECO system, some GRMs are selected and implemented. The simulation on the benchmark strategy shows that the GRM-based restoration strategy can reduce the total time for generating units’ restoration. Although the case study does not model all practical aspects in HECO’s restoration plan, the results indicate that the decision support tool has the potential to assist restoration planners in developing alternative restoration paths to energize power plants and substations and optimizing the sequence of cranking non-blackstart units and picking up critical loads. As a step toward on-line application in the control room, the tool will be added as a module in the Operator Training Simulator.
The GRM-based tools help system planners assess the blackstart capability and prepare the system restoration plan. Typical blackstart generating units are diesel, hydro or combustion turbine units, which are expensive and often used to serve the peak load. By installing more blackstart generators, these units can serve the peak load and enhance power system resilience. The GRM-based algorithm is now available to provide the optimal installation strategies of additional blackstart units. After installing new blackstart generators, GRMs will help update the restoration plan, and the updated total restoration time will be obtained. In this algorithm, the level of benefit from additional blackstart generators is quantified in terms of reduced restoration time, increased generation capability and the amount of investment. The information is valuable for power systems to compare and determine their best installation strategies in their own perspective.
In the future, with higher penetration of renewable resources and demand-response systems, greater variability and more uncertainty will widely affect power system operational and recovery technologies. Effective system restoration is an important step toward a self-healing smart gird. The proposed decision support tool provides an adaptive and optimized strategy to idenitify power system restoration decisions that will reduce restoration time while simultaneously maintaining system integrity. With the implementation of such decision support tools, power grids will be better prepared and equipped for handling extreme events and streamlining the communication with stakeholders.