Electric power companies complain about wind power because it’s intermittent: if suddenly the wind stops, they have to bring in other sources of power.
This is no big deal if we only use a little wind. Across the US, wind now supplies 4% of electric power; even in Germany it’s just 8%. The problem starts if we use a lot of wind. If we’re not careful, we’ll need big fossil-fuel-powered electric plants when the wind stops. And these need to be turned on, ready to pick up the slack at a moment’s notice!
So, a few years ago Xcel Energy, which supplies much of Colorado’s power, ran ads opposing a proposal that it use renewable sources for 10% of its power.
But now things have changed. Now Xcel gets about 15% of their power from wind, on average. And sometimes this spikes to much more!
What made the difference?
Every few seconds, hundreds of turbines measure the wind speed. Every 5 minutes, they send this data to high-performance computers 100 miles away at the National Center for Atmospheric Research in Boulder. NCAR crunches these numbers along with data from weather satellites, weather stations, and other wind farms – and creates highly accurate wind power forecasts.
With better prediction, Xcel can do a better job of shutting down idling backup plants on days when they’re not needed. Last year was a breakthrough year – better forecasts saved Xcel nearly as much money as they had in the three previous years combined.
It’s all part of the emerging smart grid—an intelligent network that someday will include appliances and electric cars. With a good smart grid, we could set our washing machine to run when power is cheap. Maybe electric cars could store solar power in the day, use it to power neighborhoods when electricity demand peaks in the evening – then recharge their batteries using wind power in the early morning hours. And so on.
I would love if it the Network Theory project could ever grow to the point of helping design the smart grid. So far we are doing much more ‘foundational’ work on control theory, along with a more applied project on predicting El Niños. I’ll talk about both of these soon! But I have big hopes and dreams, so I want to keep learning more about power grids and the like.
Here are two nice references:
• Keith Parks, Yih-Huei Wan, Gerry Wiener and Yubao Liu, Wind energy forecasting: a collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy.
The first is fun and easy to read. The second has more technical details. It describes the software used (the picture on top of this article shows a bit of this), and also some of the underlying math and physics. Let me quote a bit:
High-resolution Mesoscale Ensemble Prediction Model (EPM)
It is known that atmospheric processes are chaotic in nature. This implies that even small errors in the model initial conditions combined with the imperfections inherent in the NWP model formulations, such as truncation errors and approximations in model dynamics and physics, can lead to a wind forecast with large errors for certain weather regimes. Thus, probabilistic wind prediction approaches are necessary for guiding wind power applications. Ensemble prediction is at present a practical approach for producing such probabilistic predictions. An innovative mesoscale Ensemble Real-Time Four Dimensional Data Assimilation (E-RTFDDA) and forecasting system that was developed at NCAR was used as the basis for incorporating this ensemble prediction capability into the Xcel forecasting system.
Ensemble prediction means that instead of a single weather forecast, we generate a probability distribution on the set of weather forecasts. The paper has references explaining this in more detail.
We had a nice discussion of wind power and the smart grid over on G+. Among other things, John Despujols mentioned the role of ‘smart inverters’ in enhancing grid stability:
• Smart solar inverters smooth out voltage fluctuations for grid stability, DigiKey article library.
A solar inverter converts the variable direct current output of a photovoltaic solar panel into alternating current usable by the electric grid. There’s a lot of math involved here—click the link for a Wikipedia summary. But solar inverters are getting smarter.
While the solar inverter has long been the essential link between the photovoltaic panel and the electricity distribution network and converting DC to AC, its role is expanding due to the massive growth in solar energy generation. Utility companies and grid operators have become increasingly concerned about managing what can potentially be wildly fluctuating levels of energy produced by the huge (and still growing) number of grid-connected solar systems, whether they are rooftop systems or utility-scale solar farms. Intermittent production due to cloud cover or temporary faults has the potential to destabilize the grid. In addition, grid operators are struggling to plan ahead due to lack of accurate data on production from these systems as well as on true energy consumption.
In large-scale facilities, virtually all output is fed to the national grid or micro-grid, and is typically well monitored. At the rooftop level, although individually small, collectively the amount of energy produced has a significant potential. California estimated it has more than 150,000 residential rooftop grid-connected solar systems with a potential to generate 2.7 MW.
However, while in some systems all the solar energy generated is fed to the grid and not accessible to the producer, others allow energy generated to be used immediately by the producer, with only the excess fed to the grid. In the latter case, smart meters may only measure the net output for billing purposes. In many cases, information on production and consumption, supplied by smart meters to utility companies, may not be available to the grid operators.
The solution according to industry experts is the smart inverter. Every inverter, whether at panel level or megawatt-scale, has a role to play in grid stability. Traditional inverters have, for safety reasons, become controllable, so that they can be disconnected from the grid at any sign of grid instability. It has been reported that sudden, widespread disconnects can exacerbate grid instability rather than help settle it.
Smart inverters, however, provide a greater degree of control and have been designed to help maintain grid stability. One trend in this area is to use synchrophasor measurements to detect and identify a grid instability event, rather than conventional ‘perturb-and-observe’ methods. The aim is to distinguish between a true island condition and a voltage or frequency disturbance which may benefit from additional power generation by the inverter rather than a disconnect.
Smart inverters can change the power factor. They can input or receive reactive power to manage voltage and power fluctuations, driving voltage up or down depending on immediate requirements. Adaptive volts-amps reactive (VAR) compensation techniques could enable ‘self-healing’ on the grid.
Two-way communications between smart inverter and smart grid not only allow fundamental data on production to be transmitted to the grid operator on a timely basis, but upstream data on voltage and current can help the smart inverter adjust its operation to improve power quality, regulate voltage, and improve grid stability without compromising safety. There are considerable challenges still to overcome in terms of agreeing and evolving national and international technical standards, but this topic is not covered here.
The benefits of the smart inverter over traditional devices have been recognized in Germany, Europe’s largest solar energy producer, where an initiative is underway to convert all solar energy producers’ inverters to smart inverters. Although the cost of smart inverters is slightly higher than traditional systems, the advantages gained in grid balancing and accurate data for planning purposes are considered worthwhile. Key features of smart inverters required by German national standards include power ramping and volt/VAR control, which directly influence improved grid stability.