Establishing a competitive market to achieve 100% clean energy

Today’s primary approach to decarbonizing the electric grids is to add renewable capacity to offset thermal generation. For instance, many corporations have signed power purchase agreements (PPAs) with renewable producers to offset partially or totally their annual electricity use with renewable energy.

However, the efficiency of renewable energy linked to the reduction of CO2 decreases considerably as soon as we exceed a certain threshold of installed capacity. Indeed, four problems impact their effectiveness:

In regions with a critical share of thermal generation, these four issues force an extended use of flexible but inefficient thermal power plants, resulting in marginal carbon intensity increases. Germany is an excellent example where the strong seasonality of solar and the high variance of onshore wind require the use of some coal-fired power plants for the foreseeable future. We need to address all these issues to decarbonize the electric grids adequately. That’s why organizations like Google, Microsoft, and the US federal government advocate a 24/7 carbon-free approach that consists of sourcing clean energy for every location and every hour of operation.

A cash market to cost-effectively decarbonize the electric grids

We want a market mechanism that rewards clean energy, flexibility, and location to reduce carbon emissions more effectively while ensuring price competitiveness. For example, we should modulate our consumption according to, among other things, the current carbon intensity of the system and be rewarded accordingly, and not just for reducing our energy bill. It will be critical when electric vehicles represent a significant portion of energy consumption.

The principle is to tag each unit of energy produced by power plants with its associated CO2 emissions and to follow the energy path to the end-user. The related carbon accounting should include the embedded emissions for construction and operation and the upstream methane emissions to be as accurate as possible concerning the actual carbon reduction. This measure allows making the best technological choice to decarbonize the electric grid and reward the elimination of upstream methane leakage potentially associated with carbon capture and storage (CCS).

We define the following three rules that will allow us to put a price on clean energy, flexibility, and location:

The result is like how we trade gasoline, where specifications, timing, and locations matter. An over-the-counter or organized forward market can straightforwardly emerge from this approach.

We can implement this market mechanism to complement existing energy, capacity, and green certificate markets so that participants can tap them for additional revenue. For example, we ask holders of renewable energy credits or guarantees of origin who have sold energy in the proposed market to withdraw the associated credits or guarantees to avoid double counting. We can coordinate with initiatives like EnergyTag and M-RETS, which work on a granular version of green certificates.

Finally, we can set up a shared ledger that unifies all the participating entities and ensures the consistency of the approach. This represents a good application of blockchain technology.

An illustration using Texas and Great Britain

We have set up a prototype pricing model based on the proposed market mechanism to illustrate how such a market can work in practice. We chose to apply our prototype on Texas (ERCOT) and Great Britain (GB) because they have a significant share of renewable and fossil energy production. We have chosen 2019 as the base year because it symbolizes the last typical year before COVID-19.

Four points are worth mentioning about our implementation.

The cases on which we have run our prototype are as follows:

We have used figures for CAPEX and O&M in line with the industry’s median for the US and Europe/UK.

Key takeaways

Deep dive into some results

We have run our prototype on numerous carbon intensity targets to assess how the procurement cost changes as we lower the target and how a 100% renewable strategy compares to a 24/7 carbon-free approach. Since we use life-cycle carbon accounting, zero-carbon does not exist without negative carbon solutions. Therefore, we assume that reaching a 45 kgCO2e/MWh target is considered clean for the sake of this article because it is about the median of the life-cycle carbon intensity associated with PV.

Finally, we present our results with and without CCS to compare the cost of mitigating renewable intermittency when using EES or clean dispatchable energy.

The following two exhibits illustrate the cost of procuring a 100 MW baseload based on the marginal value of clean energy for different carbon intensity targets.

The CCS case is cheaper and relatively stable as we decrease the target because it provides clean and dispatchable energy that avoids extending batteries to regulate a large volume of renewable energy.

The next graphics show the installed capacity required for each carbon intensity objective — we present in annex the installed storage energy usage associated with batteries.

We can see the impact of considering congestion because, in ERCOT, it is better to build the battery close to the load. We also observe that a 100% renewable strategy is not always the most effective approach in reducing the carbon intensity of the load.

One of the critical points of setting up a clean energy market mechanism is to reward consumption for modulating its profile to lower the carbon intensity of the system. Suppose it takes five hours to charge an electric vehicle. In that case, the following illustration shows that the cheapest hours considering only the energy price, are very different when including carbon intensity.

Therefore, such a mechanism will allow consumers to participate in decarbonizing the system actively. In addition, it also rewards renewable energy better as it receives a premium above the price of energy for its low carbon intensity, as indicated in the annex.

Finally, we said earlier that we trade clean energy like gasoline, i.e., a bundle of organic compounds. This graph compares the distribution of the carbon intensity of the system with the one used to achieve the load target.

As we can see, we realize the goal by mixing a wide variety of carbon intensities, which minimizes the cost of energy while reaching our target. If we had been required to reach our goal on a daily rather than an annual basis, the distribution would have been much smaller but much more expensive to serve because it penalizes renewable intermittency severely.


Carbon intensity assumptions


Installed storage energy usage

Clean energy premium



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