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Webinar: The Fundamentals of Behind-the-Meter (BTM) PV Forecasting

July 27, 2017 @ 2:00 pm - 4:00 pm EDT

John ‘Skip’ Dise from Clean Power Research joins us for a discussion on the increase of behind-the-meter photovoltaic (PV) systems and the key changes installed PV systems have had on the electrical system. Dise is a Lead Manager for Clean Power Research and works with solar developers, utilities and energy agencies to design and integrate SolarAnywhere software tools into their planning and operating processes. Dise holds an S.B. in Chemical Engineering from the Massachusetts Institute of Technology (MIT), and an M.S. in Mechanical Engineering from the University of California, San Diego.

Registration Cost: Free

Abstract:

The growth of customer-sited, behind-the-meter (BTM) photovoltaic (PV) systems continues to increase as a result of reduced costs to the consumer, innovative business models such as third-party ownership and state-mandated renewable portfolio standards. This expansion of installed PV systems is increasing the amount of on-site energy generation which has led to two key changes in the electrical system: (1) the ways that electric load is predicted during critical parts of the day and (2) the way distribution utilities plan and operate their network. In the load prediction case, the increased penetration of PV impacts the energy forecast and capacity deployment inconsistently across hours of the day or day of the year. This is because PV generation is variable by nature across multiple time domains: the production of a fleet of PV systems may not produce the same amount of energy today as it did yesterday. In the case of distribution grid management, the increase penetration of PV largely targets more localized impacts, such as voltage regulation and line power quality.

In California for example, where nearly 40% [1] of the nationwide solar generation occurs, the California Independent System Operator (CAISO) and state utilities will need to balance the swings in “net load” caused by the variability in solar generation by scheduling reserve resources with increasingly steep ramp rates and increasing associated costs. The net load profile presents an additional ramp-down in the morning and a bigger, steeper ramp-up before peak load in the evening. The magnitude of these additional ramps will only increase as additional BTM PV systems are installed to those already on residential and commercial rooftops, as shown in Figure 1. The absolute accuracy of current load prediction methods will continue to decrease as overall PV capacity grows within a balancing area. This will have a direct impact on the CAISO’s ability to manage and purchase energy and deploy reserve capacity. Separately, on more geographically confined distribution networks like those operated by Pepco Holdings Incorporated (PHI) in Delaware and New Jersey, weather variability has the potential to impact their feeder-level voltage which leads to a feeder-specific PV hosting capacity limit as depicted in Figure 2; above which additional PV will cause regular violations in voltage limits.

Whether looking at system-wide load or more regionalized solar generation, the fundamentals of BTM PV forecasting is similar. Prediction of individual PV site behavior requires very localized irradiance and weather grids. Whereas traditional weather models are not well-suited for high resolution (i.e. sub-1-km) cloud prediction, remote detection through satellite image capture continues to show promise, as newer satellites like Himawari-8 and GOES-R deliver finer digital image grids. This webinar will present aspects of forecasting BTM PV for transmission- and distribution-scale integration challenges, focusing on alignment of the technology requirement to integrate distributed generation to the grid with the evolving set of forecasting tools that are currently being developed.

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