Want to know how Hawai'i can run its grid more efficiently, harvest more wind, and be more reliable all while not actually using many demand resources? Want a low-jargon to learn about what I have been doing for my thesis, with a promise to use zero equations and only one incidence of the word "stochastic?" Now is your chance, my friends. I will be the featured speaker at SDM's Monday 14 November web seminar. Register here for free and don't forget to throw a few difficult questions my way.
I've been waiting for a while to announce this one. Last fall I applied for an Innovative Research Analysis Award Program grant from the National Renewable Energy Lab. It took a while to get the paperwork squared away, but the award is now official. From the NREL website:
Power System Balancing with High Renewable Penetration: the Potential of Demand Response in Hawai'iThe State of Hawai'i has adopted an aggressive renewable portfolio standard of 40% renewable energy by 2030. Most system balance studies in Hawai'i have focused on grid assets such as spinning reserve or energy storage to provide electricity when generation from renewable resources changes unexpectedly. Demand Response (DR) is an alternate strategy in which the grid operator ensures system stability by managing select consumers' loads, such as changing air conditioner set points or turning off non-essential loads within the service area according to a pre-approved prioritization plan. Demand Response may provide a lower-cost solution to balancing intermittent supplies, enabling the State to achieve its goals for reduced energy dependence. This research will use time series data for demand, wind speed, and wind speed forecasts to identify the potential grid-value of Demand Response, as well as DR program design to meet the needs of both the electric utility and electricity customers. A unit commitment model will simulate the relative production of wind, thermal, and demand response resources, then predict the frequency, duration, and scope of curtailment events necessary to maintain a balanced grid. Lessons learned in Hawai'i can be applied in other regions.
Collaborators: Massachusetts Institute of Technology, National Renewable Energy Laboratory
Estimated completion is September, with publications and conferences to follow. This project is the reason why I delayed my graduation from Summer to Fall. It's exciting to see it come together.
Today I signed up 500 MW of demand response customers in Denmark.
Well, simulated customers.
My thesis, you may recall, is on integration of wind resources in Hawaii. Denmark is a pretty good analogue for Hawaii since it has lots of wind and a similar number of thermal generators. There is also a really good stochastic unit commitment model for the nordic countries called WILMAR. I am in the process of modifying WILMAR so that it can simulate Hawaii, but before that I wanted to see how Denmark would behave if it had a lot of responsive demand and 20% wind.
Rather than treating responsive demand as actual curtailment, I model it as another type of generator and leave the demand function unchanged. On the graph above, you see coal/oil/gas powerplants in grey, wind in green, and demand response in red. The x-axis represents about two weeks of operations. To model demand response, I am considering it (to first approximation) to have instantaneous spin-up time, an infinite ramp rate, and no startup/shutdown cost. Its marginal cost is higher than all other generators in the market.
Given these characteristics, I expected DR to be used relatively infrequently when the wind came in less strong than predicted. Not so - the optimization seems to like the flexibility of demand response and dispatches it all the time. I need to look deeper into my assumptions about DR having zero startup cost. (Furthermore, HECO rules promise that DR won't be called for longer than 2 hours at a time. Obviously that still needs to be coded into the constraint set.)
Other interesting observation: that large dark grey band is coal. For all of its world-class wind penetration, the country is still running a lot of dirty coal. You see the cleaner natural gas plants (light grey) kick in mostly when the wind dies down such as on 7/3. Running coal and wind feels to me like eating chocolate cake and diet coke to balance it out. I'd be interested in calculating the carbon-intensity of denmark's dirty/clean hybrid and comparing it to New England's pretty-clean natural gas sector. (Also: 100% wind on the night of 7/12! Go Denmark!)
