Statistical process control method of demand side management

#Filing Publication Citing Patent date date Applicant Title U.S. Pat. No. 7,110,832 Oct. 23, 2002 Sep. 19, 2006 Ghent Energy Management System for B2 an Appliance U.S. Pat. No. 4,317,049 Sep. 17, 1979 Feb. 23, 1982 Schweppe Frequency Adaptive Power-Energy Re-scheduler U.S. Pat. No. 7,010,363 Jun. 13, 2003 Mar. 7, 2006 Donnelly Electrical Appliance Energy Consumption control methods and electrical energy consumption systems U.S. Pat. No. 7,110,832 Dec. 12, 2005 Oct. 23, 2002 Donnelly Electrical Appliance Energy B2 Consumption control methods anc electrical energy consumption systems U.S. Pat. No. 8,406,937 Mar. 27, 2008 Mar. 26, 2013 Verfuerth System and Method for Reducing B2 et al. Peak and Off-Peak Electricity Demand by Monitoring, controlling and Metering High Intensity Florescent Lighting in a Facility US 2011/0095017 Apr. 28, 2011 Brian M. System for Reduced Peak Power Al Steurer, Consumption by a Cooking Appliance US 2009/0063257 Aug. 29, 2008 Robert Automatic Peak Demand Controller Al Edwin Zak

#In 1982 Schweppe (U.S. Pat. No. 4,317,049) identified rescheduling loads by tracking the AC grid frequency changes. At the time of said prior art, generation and loading was more consistent than today, over a 24-hour period. The majority of the generation and the loads were spinning motors, resulting in a simpler relationship between frequency and load. Since 1982 the load cycles have become much more dynamic, and the frequency response smaller. Today the generation and loads are less likely to be motors. Maximum to minimum summer loads can be almost 2 to 1 day to night. Contributing causes of this load imbalance are higher air-conditioning loads during the day, and the ongoing replacement of incandescent lighting, with LED lighting, which is contributing to considerably less power consumption during the night.

#The frequency change with load has been reduced due to more inverter motor loads and the use of switch mode power supplies, versus older transformer-based supplies. Another change to generation methods are renewables, for example photo voltaic (heretofore referred to as PV) generation. No spinning motors are involved. More significant is the need to switch between PV and traditional fuel generation, every day at sun rise and sun set. California ISO has days nearing 50% PV. Traditional generation needs to quickly ramp up at sunset and then ramp down at sun rise. The effect is increasing yearly and the resting generation curve is known as the “duck curve” (FIG. 19). As more renewables come online said effects will increase. The grid is becoming more chaotic so that simple frequency, or voltage monitoring methods of prior art Demand Side management methods (heretofore referred to as DSM) are no longer effective.

#FIG. 1: Is a flow chart of the daily tracking into bins to be used for predictive scheduling of future loading.

#FIG. 2: Is a flow chart of medium-term tracking to use for real time load decisions as they happen.

#FIG. 3: Is a flow chart of short-term tracking looking of high grid stress events requiring quick response.

#While this invention is susceptible to embodiment in many different forms, there are shown in the drawings and will be described herein in detail, specific embodiments Thereof, with the understanding that the present disclosure is to be considered as an exemplification of the principles of the invention and is not intended to limit the invention to the specific embodiments illustrated.

#The present invention is the DSM method of sensing the status of the grid by monitoring and tracking the voltage and/or frequency of said grid, storing data over time, to utilize SPC techniques to make adaptive future scheduling decisions for loads or generation. The present invention will also use real time monitoring to adjust operational decisions and will also monitor the grid for critical stress events and enable drastic load reduction when deemed necessary. Said invention can be applied to any device with an existing, or added micro controller, or equivalent. Said invention is not dependent on external communication to make operational decisions, instead regards the appliance and the grid as a machine process, using frequency and, or voltage, over time, as the variables to make operational decisions and predictions utilizing SPC methods.

#A plurality of data bins and a data matrix are used to track and decipher patterns as shown in FIG. 1. Said bins can be of any length, but in this exemplification, 32 bins per 24 hours, or 45 minutes for each bin, are used. The line frequency (and/or voltage) average is calculated for each bin and logged. Many methods can be used to determine the average, but in this exemplification the method is to count zero crosses over the entire bin and log the count. Said count will be an inverse of the frequency. Said frequency value and running averages are logged for a given number of days. Said averaging is necessary to account for occasional aberrant values that are not representative of the actual grid operation. Said Averaging can be done by keeping a matrix of the daily counts over many days, for instance, 16 days, in general, longer is better, however too long would not allow algorithm to move between the changing seasonal patterns. The transition of summer to fall and winter and spring grid profiles are different. Presently, the peak load for summer is in the afternoon, while winter has morning and evening peak load periods, but grid conditions are changing rapidly. Presently the grid in the western United States is seeing the effects of an increasing number of solar power farms coming on line, which only generate electricity during daylight hours and then quickly drop off at sunset, requiring traditional power generation to come on line quickly and take over, creating the so-called “duck curve” when looking at the power production demand chart. Solar power is pushing the summer afternoon peak into an evening stress peak. Stress is monitored for the combination of load and generation effects. In choosing bin size the limitation of the particular device and processing power as well as storage must be considered. Since bins are circular in a 24-hour day no real time clock is needed.