Comprehensive Solutions for Integration of Solar Resources into Grid Operations

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This project primarily looks at the benefits from more cost-effective unit commitment and dispatch, and reduction in balancing reserves due to reducing uncertainty in solar forecasting. This project will improve the Pacific Northwest National Laboratory’s ramp and uncertainty prediction tool by incorporating accurate forecasting of solar generation, and then integrate the tool with the Siemens market applications software currently used by the California Independent System Operator (CAISO) to perform unit commitment and dispatch. This project will utilize probabilistic forecast algorithms for solar energy production of large-scale PV plants and rooftop PV installations, to enable CAISO to incorporate solar generation forecasts directly into their tools that perform power system operations, thus reducing the uncertainty and hence the costs of system integration of solar generation into the bulk power system.

APPROACH
INNOVATIONS
OUTCOMES

This project addresses the uncertainty problem comprehensively by including all types of uncertainties (such as load, variable generation, etc.) and all aspects of uncertainty including the ramping requirements. The main objective is to provide rapid (every 5 min) look-ahead (up to 5 to 8 hours ahead) assessment of the resulting uncertainty ranges for the balancing effort in terms of the required capacity, ramping capability, and ramp duration. The uncertainty range is called a “performance envelope” in this work. A methodology for self-validation of the predicted performance envelope has been developed.

This project will capture the uncertainty and variability present in power forecasting, load, and other variables that challenge the flexibility of grid balancing:

Forecasting algorithms aiming to reduce the uncertainty range rather than just the standard deviation or root mean square value of the forecast error. The difference between the old and the new objectives becomes evident when dealing with nonparametric distributions and non-stationary distributions. The size of uncertainty interval corresponding to a certain level of confidence directly influences the balancing requirements.
Forecast geographically distributed PV generation mixed with local loads and other local generation resources.
Provide for concurrent consideration of all sources of uncertainty and variability (solar generation, system load, uninstructed deviations of conventional units, and forced outages).The concurrent consideration will help to reduce the overall uncertainty and, consequently, the resulting balancing effort required from the grid operators.
Apply geographically and temporally distributed forecast models help to quantify the collective impacts of all sources of renewable generation uncertainty in their interaction on the transmission system. Proactive minimization of uncertainty intervals by separating more predictable, slower quasi-deterministic components from less predictable, faster components in the forecast errors.
Quantify uncertainty and variability on the transmission system based on the risks of transmission problems (overloads, voltage problems) in various contingencies.
First-ever industrial implementation of close loop real-time uncertainty-based unit commitment and dispatch procedures helping to exercise preventive (rather than corrective) control and avoid accidents, when the system balancing capacity and ramping capability is not sufficient to address random deviations of the resulting system load.
Quantify of the system balancing requirements based on the new NERC control performance standard (BAAL) and new scheduling requirements recently introduced into the grid control practices.
Incorporate ramping information provided by forecasts in the unit commitment and dispatch processes. Develop multi-dimensional uncertainty quantification procedures including a concurrent consideration of the capacity, ramp rate, and ramp duration requirements to the balancing generators.

This project aims a practical deployment of its system, which will have far-reaching national and international impacts. The system operators will be able to determine the potential future reliability impacts of solar resources, other renewables and sources of uncertainty on the system reliability, their probability and timing, as well as the corrective actions needed to minimize the risk (e.g. in terms of balancing capacity, ramping capability, and transmission system limits). Moreover, the uncertainty and variability information will be fed directly into the unit commitment and dispatch procedures, so that the system will be automatically positioned to address potential system imbalances and transmission violations. Although the project is located in California, it will have a profound impact on the rest of the country. The toolbox developed and perfected in this project will be made available for utilities and system operators across the country, and the California experience and know-how will be widely disseminated. Ultimately, this project will significantly contribute to the EERE SunShot objective “to support the development of innovative, cost-effective solutions to boost the amount of solar energy that utilities can integrate seamlessly with the national power grid”. This effort will advance previous research efforts at PNNL and test its integration capabilities with operational tools in use at the California ISO.

The collective consideration of all sources intermittency distributed over a wide area unified with the comprehensive evaluation of various elements of balancing process, i.e. capacity, ramping, and energy requirements, will help system operators more robustly and effectively balance generation against load and interchange to ultimately provide for more solar and other renewable resources on the grid, without compromising reliability and control performance. The need for the proactive integration of uncertainty information into system operations and probabilistic close-loop controls is widely recognized, but rarely implemented in real systems. This project leads toward a practical deployment of such systems in California. The project will help system operators clearly identify the potential future reliability impacts of solar resources and other renewables as well as all other concurrent sources of uncertainty on system reliability, including an assessment of potential adverse impacts, their probability and timing, and the recommended corrective actions needed to minimize risk by adding more flexible balancing capacity, ramping capability, and/or adjusting transmission limits.