Accurate power prediction of spatially distributed PV systems using localized irradiance measurements

Abstract

In this paper, a method for estimating power injected into an electrical distribution system from spatially distributed residential PV systems using data from ground-based weather stations is described. This method was developed as part of the High Penetration PV Deployment Project at the Arizona Public Service (APS). Verification of this predictive method is also described in this paper. Finally, correlation statistics and power production ramp rates are calculated for irradiance based on measured power within the APS study area using two different sets of weather station measurements. The method has potential applications to real-time or forecasted power estimation on distribution circuits in addition to the development of broader guidelines on the optimal number and location of irradiance sensors for power estimation in distribution circuits with high penetrations of PV.

Cost Benefit and Alternatives Analysis of Distribution Systems with Energy Storage Systems

“…for accurate cost calculations unlike other works in this area. The evolution of the process-chain proposed in this paper can be sparsely related to the authors work described in papers presented in the references [1][2][3][4][5][6]. This paper summarizes the comprehensive process-chain and focuses on the further design, functional capabilities, and exercises the CBAAT .…”

Resilient and Sustainable Tie-Line Bias Control for a Power System in Uncertain Environments

“…Therefore, obtaining small positive CF 1 min or larger negative CF 1 min indicates better performance of the system. CPS2 related metrics are given in (14)- (16) CP S2 10 min = periods without violations total periods over the month × 100% (14) periods without violations = non-violated ACE (15) non-violated ACE = 1 , if ACE 10 min < L 10 0 , otherwise (16) where ACE 10 min is the average ACE within 10 minutes and L 10 is decided based on the balancing authority size [2], in this paper L 10 = 30.4667, calculated based on the Table IV in [2] assuming the balancing authority size of each Area is 1800 MW. Equation 14is modified to consider " total periods over the simulation", instead of "total periods over the month" due to the availability of test data.…”

“…Very short-term PV power prediction, in the order of seconds to minutes, is a challenging task under dynamic and uncertain weather conditions. PV power prediction is a big data analytics problem and has been predicted using statistical and numerical techniques [14], [15] and ANNs including echo state networks (ESNs) [13], [16]. Similarly, numerous prediction techniques have been used for power system frequency prediction.…”

Estimating Behind-the-meter Solar Generation with Existing Measurement Infrastructure

“…The current state-of-the-art for modeling PV generation are (i) model-based approaches based on PV array geometry and irradiance measurements [4], and (ii) direct measurement of the real-time generation. Both methods have drawbacks.…”