Publications

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While the concept of aggregating and controlling renewable distributed energy resources (DERs) to provide grid services is not new, increasing policy support of DER market participation has driven research and development in algorithms to pool DERs for economically viable market participation. Sandia National Laboratories recently undertook a 3 year research programme to create the components of a real-world virtual power plant (VPP) that can simultaneously participate in multiple markets. The authors' research extends current state-of-the-art rolling horizon control through the application of stochastic programming with risk aversion at various time resolutions. Their rolling horizon control consists of day-ahead optimisation to produce an hourly aggregate schedule for the VPP operator and sub-hourly optimisation for the real-time dispatch of each VPP subresource. Both optimisation routines leverage a two-stage stochastic programme with risk aversion and integrate the most up-to-date forecasts to generate probabilistic scenarios in real operating time. Their results demonstrate the benefits to the VPP operator of constructing a stochastic solution regardless of the weather. In more extreme weather, applying risk optimisation strategies can dramatically increase the financial viability of the VPP. The methodologies presented here can be further tailored for optimal control of any VPP asset fleet and its operational requirements.

In this paper, we present a new, light-weight approach for extracting the five single diode parameters (IL, Io, RS, RSH, and nNsVt) for advanced, in-field monitoring of in situ current and voltage (I-V) tracing devices. The proposed procedure uses individual I-V curves, and does not require the irradiance or module temperature measurement to calculate the parameters. It is suitable for operation on a small, single board computer at the point of I-V curve measurement. This allows for analysis to occur in the field, and eliminates the need to transfer large amounts of data to centralized databases. Observers can receive alerts directly from the in-field devices based on the extraction, and analysis of the commonly used single diode equivalent model parameters. This paper defines the approach and evaluates its accuracy by subjecting it to I-V curves with known parameters. Its performance is defined using actual I-V curves generated from an in situ scanning devices installed within an actual photovoltaic production field. The algorithm is able to operate at a high accuracy for multiple module types and performed well on actual curves extracted in the field.

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Literature describes various methods for determining a series resistance for a photovoltaic device from measured IV curves. We investigate use of these techniques to estimate the series resistance parameter for a single diode equivalent circuit model. With simulated IV curves we demonstrate that the series resistance values obtained by these techniques differ systematically from the known series resistance parameter values used to generate the curves, indicating that these methods are not suitable for determining the series resistance parameter for the single diode model equation. We present an alternative method to determine the series resistance parameter jointly with the other parameters for the single diode model equation, and demonstrate the accuracy and reliability of this technique in the presence of measurement errors.

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