City-Scale Decarbonization Experiment & Duck Curve 🦆

Have you ever heard of the term “Duck Curve”? Well, If you have some knowledge in the solar industry, you should be pretty familiar with this word. In this article, I will tell you what is a Duck Curve and how it relates to a CRAZY experiment conducted by Stanford University!

The Duck Curve 🦆

In 2008 when the solar industry is just starting, a group of researchers at the National Renewable Energy Laboratory (NREL) noticed an animal-looking-shaped trend in their modeling. This is what it looks like:

Their objective for this model is to tell what might happen if solar photovoltaic (PV) panels were deployed at scale. They noticed that large-scale solar deployment had an interesting effect on the electricity “load curve”, the shape that electricity demand takes throughout the day. The graph shows the electric energy demand in a region when solar PV is integrated into the grid. The demand spikes in the morning when everyone wakes up, and again when everyone gets home form either work or school. The demand is lowest throughout the night, then it repeats. When solar PV production is incorporated into the graph, it starts to dramatically suppress the net load during midday, when the sun is the brightest ☀️. That makes the graph look like an outline of a duck.

This “ducky” trend is a serious threat to solar and other clean energy communities such as wind. If this problem is not treated seriously, things can get pretty complicated 😖. There are two possible results:

1. Solar plants would have to shut down all their panels in midday, where there is a surplus of energy supply.
2. Utilities could regularly be forced to ramp up their plants for a morning peak, then scale back or even shut down almost all of the plants while the sun is out, and then bring them all back quickly (13,000 MW in just 3 hours) to prepare for the night peak.

Neither of the two results is something we truly wanted. The first result would intentionally decrease the amount of energy produced by renewable power. The second result is hardly viable when the plants would have to generate an estimate of 13,000 MW in just 3 hours from 5 pm–8 pm. That is why when I accidentally ran across this research paper, I thought this could be one of the solutions to this big energy problem!

City-Scale decarbonization experiments with integrated energy systems

“Decarbonization of electricity generation together with electrification of energy-and-carbon-intensive services such as heating and cooling is needed to address ambitious climate goals”

And that is why Stanford University used a model to show that city-scale electrification of heat with large-scale thermal storage can cost-effectively unlocks significant operational benefits for the power sector as a whole. The team led by Professor Jacques A. de Chalendar build a model of fully electrified district heating and cooling networks (operating NOW on Stanford campus) integrated with other electric demands.

The team first used the campus as a testing ground. In their example, electrifying the previously gas-based heating and cooling system has led to a 65% reduction in the overall campus carbon footprint 😦.

This model relates to the Duck Curve in that through least-cost scheduling(city-scale) the load curve’s shape can be flattened, and the annual peak demand for electric power can be reduced by 15%. Energy-and-carbon-intensive services can schedule when they will use energy throughout the day. They can schedule to start using electricity in midday when the electricity supply is high, this can help flatten the body part of the Duck Curve. If this system becomes a reality, the solar plants wouldn’t need to shut down when there is a surplus in energy and the electricity production plants don’t to have to constantly shut down and ramp up the production power of the plants.

Now let’s dive into the experiment!

Storage-Backed Heat Recovery

In 2015, the Stanford University campus district energy system switched form a gas-fired cogeneration-based system with steam distribution to the current electrified, integrated heating and cooling system with hot and cold water, meeting the load using large Heat Recovery Chillers (HRC). The energy system redesign led to an estimated reduction of 65% in the annual carbon emissions from campus energy operations, from 200 to 70 thousand tonnes of CO2 is produced that year.

Created by Energy Resources Engineering department at Stanford University

Although the demand for hot water is dominant in winter and chilled water is dominant in summer, there is a significant daily overlapping of the two demands. The graph below shows the overlap in two years and how up to 51% of cooling and 91% of heating loads could be met by electric Heat Recovery Chillers (HRCs) simultaneously producing heating and cooling.

Created by Energy Resources Engineering department at Stanford University

Optimal Operation Scheduling

An optimization model is built by the team to minimize the campus energy bill over a year. This system solved problems usually faced by the manager of the energy supply companies (in this case CEP).

1. Decide how much power and gas to buy from the grid at each hour.
2. Set the hourly schedule for the machines in the company to meet demand from the campus buildings for electricity, heating and cooling.

Power and energy scheduling

Both in the summer and winter, the heating and cooling loads on the campus are met by the Heat Recovery Chillers. The hot and cold thermal storage tanks are used with the chillers to create hot and chilled water buffers and shift the electrical energy consumption of the CEP throughout the day.

Under the optimal condition of energy scheduling, the HRCs produce most heavily at night and when both the electricity price and the campus electricity load are low. During peak price periods, they are typically turned off. This can save a significant amount of money spent on electricity bills for heating and cooling. This can also flatten the electric load curve to reduce the appearance of Duck Curves.

Created by Energy Resources Engineering department at Stanford University

These figures illustrate how systems that have heating and cooling streams can fit into a range of operating conditions. There is a repetitive daily pattern both in summer and winter(more complex), the scheduling can be precisely calculated using AI to read the patterns and respond.

In 2016, 50% of cooling and 89% of heating loads are met by the HRCs, within two percent of the estimated values in the graph. The remainder is met by the chillers and the heaters, so electricity is the main energy input to the system, and yearly gas consumption can be kept super low.

Carbon-Aware Scheduli-ng

Carbon-aware scheduling will have values in grids where the carbon emission level varies over the course of the day depending on the mix of production methods (in the case of Duck Curve).

Below is a heat map for hourly Average Emissions Factors (AEFs): 2016 actuals (top) and 2025 estimated with 3 times of the solar production compared to 2016 (bottom). Each row in the images corresponds to an hour of the day, and each column to a day of the year.

Created by Energy Resources Engineering department at Stanford University

The graphs below aggregate Stanford campus electricity imports for a Business-as-Usual (BAU) schedule(top) to those for a carbon-optimal schedule where the grid carbon intensity guides are 3 times the usual operation (bottom). The graphs illustrate how operations are shifted from a usual mode which goal is to minimize peak load and avoid the high prices that will occur in the afternoon to one that increases the load in the middle of the day and avoids nighttime emissions.

Created by Energy Resources Engineering department at Stanford University

The two graphs above can fit together perfectly to create a nice linear electric energy load curve. That is exactly the purpose of carbon-aware scheduling.

In the case of optimal carbon-aware scheduling, the HRCs are used at full capacity during the daylight hours to fill the water storage tanks, regardless of energy costs and demand charges. This will increase the electricity cost but will help flatten the load curve and regulate carbon-emissions throughout the day to be in a linear trend.

Conclusion

The Duck Curve is a curve in the energy demand load when solar and other renewables are incorporated. The overproduction of energy around midday posses the risk of not utilizing renewables to its maximum and possible shut down of traditional energy plants, then reopen to maximum production power in just 3 hours to meet a peak in electricity demands in the afternoon. Both are big problems faced by many workers in the energy sector. If renewables are not used at its maximum capacity, then the meaning of using renewables in the first place will be lost.

The experiment of city-scale decarbonization with integrated energy systems performed by Stanford University professors and students could be a possible solution to flatten the energy load trend and avoid the appearance of duckies. 😆

The study conducted by Stanford University demonstrates the operations and values of an electrified heating and cooling system. This system consists of large-scale thermal storages, power and energy-aware scheduling, and carbon-aware scheduling. Benefits from electrification are provided in three ways:

1. shifting electrical loads to reduce electricity bill
2. decreasing carbon emissions now and in the future when the carbon intensity of the electrical grid decreases
3. will become a cost-effective alternative to battery storage for providing operational flexibility and a decrease in price

This system could be the solution to cope with the ambitious climate goals facing the current climate problems. This experiment can also reduce the appearance of Duck Curves by using energy scheduling and carbon-aware scheduling. If this becomes a reality, renewable energy plants can finally operate at its maximum capacity and the wild ambition of carbon-free energy production can become a reality.

Credit to https://pubs.rsc.org/en/content/articlelanding/2019/ee/c8ee03706j#!divAbstract