An exciting insight today! I’ve been having difficulty in handling the data recently which has delayed some analysis. The program I have been utilizing which provides the performance output for an average solar power system in NYS, outputs data based on statistical averages for the type of system and geographical location for every hour of every day of the year. The Location Based Marginal Pricing (LBMP) data provided by NYISO however, provides data with a time stamp mostly every 5 minutes throughout the year. When you consider the fact that means it provides you with 105,120 data points, manual translation into hourly data points is not exactly a trivial task. Whilst I work to do that in a more efficient software based fashion, my eagerness to get a glimpse of results prompted me to manually translate the data for the first effective week in July of 2014 (7/1-7/7).
The results a tremendously enticing. The hypothesis in layman’s terms more or less states from a visual standpoint that the peaks and valleys should align. While this is not immediately obvious from the graph, further analysis of the data proves this to be the case, at least for this small section of data.
Utilizing the following formula, one can infer the effective “Solar Value”. Solar value in this context would mean the value of the electricity being produced by the solar panel system.
Where SV(t) refers to solar value, P(t) refers to price, and EG(t) refers to the energy generated. Load Profile pricing was taken from the US EIA data averages. Spot Pricing is generated using Location Base Marginal Pricing (LBMP), which is in essence today’s spot price for wholesale electric markets. To convert to projected retail prices, LBMP was multiplied by (AVG(Load Profile) / AVG(LBMP), thus making up for the retail price margins.
The results are that the solar value for the State of New York over this time period under load profile pricing equaled $613,542,392.38 . Likewise under spot pricing, solar value increased to $731,326,131.29. Why is this an intriguing find you may ask?? Because what this suggests is that with zero change in gross margin to electric prices, no additional solar incentives or tariffs put in place, if a smart grid utility system were realized and our market design shifted from load profile pricing to spot pricing, this would results in a 19% better return for solar panel system owners!
Stay tuned as I continue to build on these findings by analyzing the remaining data from 2014, and look to see how this price change could affect adoption rates.