Smart meter. Smart meter. Smart meter. Smart meter.
Smart grid. Smart grid. Smart grid. Smart grid.
So what? What are customers, utilities, rate payers, and tax payers getting for their money?
At an AESP conference several years ago, I sat in place of a colleague for a Pricing and Demand Response Committee meeting. I’ve been in/on the committee ever since. Within the last year, I took a survey from the committee, and I asked questions that went something like this: What does demand response in the US look like? How much of it is interruptible rates? How much is direct load control? How much is voluntary and how much is automatic? How much is commercial, residential, and industrial? … and so on. Believe it or not, that spawned a big study that somebody will be doing. You’re welcome.
My inquiry was about this: I hear all the chatter. What is actually being done? Some methods seem quite successful, but what does the big picture look like, and what about the ballyhooed smart meter?
I’m a simpleton engineer, but there is more than enough technology to turn residential customers loose with DR in an engaging way. According to Utility Dive, only 5% of households in the US participate in DR. Let’s play ball.
Use smart meters, home area networks, and smart phones and tablets to establish day-ahead or even same day demand response auctions – like buying power but instead buying DR.
Homeowners are a pretty homogeneous bunch, and/or they are predictable when it comes to energy use. With a smart meter, the utility has interval data. Interval data can be read automatically like an EKG. When do people get up in the morning? Do they eat breakfast at home? Do they have electric appliances or natural gas appliances? Do they set their thermostat back for heating or cooling? What gets blasted at 100% capacity when they get home from work/school? For how long? When do they get home? Do they even leave home on an average day?
The smart meter can develop a predictive model for each home owner. Predictive models are already available for much more complex commercial buildings. Software is also available to disaggregate the residential interval data to determine the major things that are running at any given time – air conditioners, electric water heaters and clothes dryers, refrigerators, freezers. We (Michaels) can see a lot of similar energy demand by end use just by looking at interval data for a commercial building. We can see when people come and go en masse, when the lights come on and off, and when the heating and cooling starts and stops.
Conventional auto DR with radio/pager/cellular network signals cycle AC units in homes every 15 minutes. Some utilities offer several options and customers willing to leave their AC off more get a greater incentive. Like interruptible programs for large commercial and industrial, these customers like it until there is a DR event which triggers an acute onset of Alzheimer’s and they forget their commitment.
Moreover, the problem with these relatively crude direct load control programs is they are one-way and the utility doesn’t know for any given customer what they will get for demand reduction with the program. For instance, if the AC unit is twice as big as it needs to be and it is locked out half the time; demand savings = 0.0 kW.
Opower says customers will curtail simply by asking. Certainly, this would be true for some customers. However, in total, it would be inconsistent with the findings of this recent 10 year study for the DC metro area. The report says customers “front-load” by pre-cooling, pushing the grid to the brink in the morning and early afternoon. I have lived in several metro areas of the country and each one has its quirks. I lived in the DC metro area for several years. Their quirk: when there is a pending weather event – hoard! If the forecast calls for two inches of snow, get to the store and grab all the water, bread, toilet paper, and milk that will fit in a shopping cart. I joke not. The bottled water and toilet paper shelves are stripped bare. This would explain the hoarding of electricity for fear the grid might crash for a month.
So, engage customers. Explain on a high level how power markets work. Anyone smart enough to own a home, I would hope, understands supply and demand. That’s all it is. Then you say, “Here are some major home appliances and what they typically consume for power.”
Now the utility, with its big data, knows what every customer will be using tomorrow evening at 5:30 when it’s 102F in the valley. The customer has his iPad app with probable kW for major appliances. The utility auctions DR resulting in credits for consumers. The consumer gets credit for precisely what they save versus baseline, and they can see what their credit is the next day, or even at the instant the DR event ends. Now they’re saying, “Wow, I just saved $10 for locking out my freezer,” or “I saved $20 for locking out my AC.” The technology is all there to control stuff by Wi-Fi, and it’s cheap!
Customers now have what they apparently want – control over their energy cost. No fake games – just real ones – the kind I like to play.
It’s like anything else. Customers will learn what it takes to curtail 2 kW for a few hours. Utilities will learn the difference between what customers think they will save versus what they actually save.