Explained: The Intermittency Issue

A common complaint about renewable energy is that technologies like wind and solar only produce energy when the wind is blowing or the sun is shining. Some argue that renewable energy cannot effectively be utilized until appropriate energy storage technology is developed. Perhaps more importantly, power grids were designed based on the idea of large, controllable electric generators, making integration more difficult. With a low storage capacity, the grid constantly needs to balance supply and demand to avoid blackouts. 

Intermittent renewables disrupt the conventional methods for planning the daily operation of the electric grid. The power fluctuations over multiple time horizons forces the grid operator to adjust its operating procedures. For instance, solar energy is inherently only available during daylight hours, so the grid operator must adjust the day-ahead plan to include generators that can quickly adjust their power output to compensate for the rise and fall in solar generation.

Sunrise and sunset aside, clouds can cause sudden changes in solar panel output. Variability caused by clouds can make it more difficult for the grid operator to predict how much additional electric generation will be required during the next hour of the day, so it becomes difficult to calculate exactly what the output of each generator should be to accomplish the load-following phase shown in the graphic below. These fluctuations also affect second-to second balance: did you know that grid operators send a signal to power plants every four seconds to insure grid supply equal power demand? Wind and solar increase the reserve power requirement for the grid operator to readily and swiftly respond an maintain gird balance.

Fortunately, renewable energy becomes more predictable as the number of renewable generators connected to the grid increases thanks to the effect of geographic diversity and the Law of Large Numbers. The Law of Large Numbers is a probability theorem, which states that the aggregate result of a large number of uncertain processes becomes more predictable as the total number of processes increases. Applied to renewable energy, the Law of Large Numbers dictates that the combined output of every wind turbine and solar panel connected to the grid is far less volatile than the output of an individual generator. Experience has shown that aggregate renewable power available can effectively be modeled and predicted, and both wind and solar depend on natural systems that can similarly be modeled and forecasted with reasonable accuracy.

However, predicting how much renewable energy will be available a day ahead of time is significantly more difficult. Mixing sources helps balance this out: continental wind energy tends to peak at night, coastal wind energy tends to peak during the day, and solar can peak at various times over the day, depending on its orientation. The electricity market will have to incentivize this mix: prices should vary over the day and over a region depending on the local level of electricity supply and demand. Tying renewable energy to these prices should help develop a mix of renewable sources that produces just the right amount of energy when we need it, and reduces the need for costly energy storage.

While integrating intermittent renewable energy sources into the grid is undoubtedly challenging, it hardly compares to the difficulty of initially constructing the current grid (imagine the effort of stringing all those wires to connect freshly built power plants and grids!). Cost-based incentives have an will undoubtedly be a crucial part of achieving this objective. That is, until we do away with centralized energy-distribution models all together.