WHAT IS THE INTEREST IN ENERGY STORAGE?
Since the installation of the first electrical power plant late in the 19th century, the business of supplying electricity to industry and residences has been “on demand.” When you flip a light switch in your home, you expect the light to go on. For this to happen, electricity has to flow to your light bulb through an elaborate transmission and distribution network (T&D) of copper wires. These wires ultimately connect your light bulb to a generator that sits dozens if not hundreds of miles away from you. When you turn on your light bulb, there is an additional demand in electricity, and that generator “works a little harder” to supply this required electricity. This is what I mean by “on demand.” On a hot summer afternoon, the demand is large, and these generators are working near or at full capacity. At night, the demand is lower, and there is available excess capacity.
This system has worked exceptionally well for over one hundred years. Electrical utilities planned and built a system with high reliability. Now fast forward to the 21st century. Much like many new developments in our modern society, the way we use electricity is changing with new clean energy sources like solar panels and wind farms in diverse geographical locations, plus a general sense by the responsible regulatory bodies to modernize if not liberalize the way electricity is generated and distributed.
Enters the energy storage concept. Imagine an electrical system where the generation of electricity and its consumption are no longer simultaneous. In other words, imagine that the electricity that is flowing through your light bulb was actually generated at an earlier time — in other words, breaking the on demand relationship. That’s precisely what happens in your smartphone. The time you use the electricity in your smartphone is very different from the time the electricity was generated. One can easily notice that the on-demand electricity model fails in a mobile society. But what benefit do we get from breaking this model at a larger scale, e.g., the utility scale?
Let’s imagine the following scenario. You live in a small town in a sunny geography. There is a small power plant outside your town that historically supplied you with electricity, again on demand. You and many of the town residents decide to install solar panels on your rooftops. Your house is now generating all your electricity needs during the daytime. But at night you still rely on your local power plant to supply you with electricity. Let’s sketch what the load on the power plant might look like before and after the installation of the solar panels.
During the night, the load is modest. Most residential lights are off, but appliances such as refrigerators are still on. The load rises in the morning and peaks some time during mid day, depending on the work hours and the need for air conditioning on hot days. The load peaks again in the evening when residents return home from their work day. This is the load that the power plant has to deliver. It is for the most part well characterized and predictable, with two modest peaks near the noon hour and evening. So your local utility sizes the generating plant to match this load demand. Any generating capacity wildly in excess will result in significant upfront capital costs that are not desirable to the rate payers, i.e. you.
Now let’s see what happens to this load curve after many residents in your town install solar panels. As expected, the load during the day time drops, and it drops drastically. This curve, as its shape suggests, is called the “duck curve.” It creates a serious headache for your local utility. These generators that historically supplied your home with electricity are now running at a much reduced capacity during the day. In other words, the utility has idle capacity yet it bore the expense of the generators. Worse yet, it still has to size the generating capacity of the power plant to the maximum needed load which is now in the evening hours when solar is not a factor.
So, let’s take this scenario one step further. Imagine that there is now a big, I mean really big, battery that sits between your home and the power plant. During the peak solar hours in the day time, the power plant continues to produce electricity at or near its maximum capacity but that electricity is now used to charge the battery. At night, the power plant continues to produce electricity at the same rate it did during the day, but the extra demand by the residents is now met by using the electricity from the battery. This has the effect of flattening the load curve and thereby reducing the peak demand on the power plant….resulting in significant reduction of capital costs. This is called “peak shifting” because in effect, this big battery enables us to use the excess capacity we have during the day to cover the excess load we have at night. This is one of several key benefits of energy storage.
In California, the scale of the duck curve is simply overwhelming. California ISO, the state agency responsible for the flow of electricity across its long-distance power lines, estimates that the differential between the peak and trough in the load curve will exceed 14,000 MW in 2020. To put it in perspective, this is equivalent to seven Diablo Canyon nuclear power plants near San Luis Obispo in central California. In essence, it also highlights the scale of the economic opportunity: Build energy storage systems or build expensive power plants. In future posts, I will cover various topics that come out of this discussion, for example, where do we place this “energy storage” system? what are the requirements on such a system? what technologies are most suitable? …etc.
