Storing energy? Not without “herds” and “ducks”!

Is the emerging market for energy storage a standalone ‘new tech’ development, or part of a bigger movement where different interconnecting technologies, business models, and regulations are revolutionizing the way we generate, transport, sell, buy, and consume energy?

I guess we all would say the latter, but the million dollar questions is: how? In order to find some pieces of that puzzle, the Energy & Utility team at Capgemini Netherlands composed a research topic in cooperation with Erasmus University Rotterdam around ‘Emerging Business Models in homes with Energy Storage.’ The university assigned the topic to Michael Zigo, a graduation student at Erasmus University, and our Energy & Utility team hosted and guided this assignment.

The shift towards the energy generated from renewable sources such as solar, wind, and hydro power imposes a number of challenges on the energy market. Compared to the traditional fossil-based energy sources, renewables have three main inherent properties:

  1. Energy generation from renewable energy sources is volatile;
  2. Renewable energy sources are difficult to forecast because of their inherent dependency on natural cycles and weather;
  3. Power generation from renewables is location specific, greenhouse emissions are free, and involve very low operating expenses.

The grid has to accommodate for such energy production variability by extending its flexibility. This grid flexibility can be extended by utilizing two main techniques. Electricity can either be stored using energy storage or the electricity demand can be influenced by actively managing the demand side. One of the main use cases for  home energy storage and influencer of utilities’ business model innovation is real-time electricity bill management. In other words, purchasing and charging during the low real-time electricity price and discharging the home energy storage when the electricity prices are high. Recently, we see how energy retailers are increasingly introducing real-time variable pricing models. Next in line are the grid companies, which plan to value the flexibility and reward households when they shift their energy usage based on price incentives, thereby consequently assisting in grid management.

The aim of our research was to provide a power storage simulation under a variable time-of-use pricing scenario. The outcome of this simulation offers an overall picture of typical household production and consumption with and without utilizing power storage. With this overall picture, we were also able to calculate household cost benefits when using power storage. For the research, an optimization function was developed to simulate the utilization of energy storage in a household. Real data from 300 households located in a microgrid community in The Netherlands with a 15-minute real-time pricing scheme was used as the basis for the analysis. The energy generation capability of the microgrid featured a number of wind turbines and half of the households with solar panels.

The utilization of home energy storage capacity in a microgrid community mitigated the “duck curve” effect; a graph of power production over the day, showing the timing imbalance between declining renewable power production combined with peak demand after sunset. Despite the fact that the “duck curve effect” is mitigated in the mid-afternoon hours, steep ramps and drops were observed. Such steep fluctuations are caused by a “herd effect” when all households in a microgrid trigger discharging or charging their energy storage unit at the same time.

The typical household from the community with an energy bill of 50€ per month was able to achieve a cost benefit of 14€, a 28% decrease from the original monthly bill. We simulated this case with a 7KW storage unit. However, if a more expensive 10KW solution is implemented, the energy bill could be reduced by up to 18€. The calculation is based on the fundamental principle of net metering, where the same price of energy is used in the simulation when consumed from and supplied to the grid. The variables such as charging speed, capacity of storage, and discharge speed to a grid, directly influence the behavior of an energy storage solution. Optimal values should be developed per each use case.

Duck curve

Utility companies – both retail and grid – will be facing a growing number of digital consumers who operate in the digital energy environment. If utility companies want to include these consumers as an instrument for balancing the grid, this can be induced via an efficient variable pricing strategy in combination with automated energy storage. In this perspective, energy storage is not limited to storage of electrical energy alone, but can also be storage of heat, mobility, or any other source of energy. Thus, each household would like to consume energy at the cheapest price, thereby inducing a higher overall consumption of the grid. On the other hand, in times of undersupply, the utility company would be able to increase energy price to directly reduce the consumption. In case the battery storage is programmed to trigger utilization automatically, utility companies can directly benefit from customers’ home power storage solutions in real time.

Possibilities for disruptive business model innovations in the digital energy segment will follow the development of energy storage. For instance, if consumers are able to link their households’ storage systems, they will be able to exchange energy among each other, thus creating a major stepping stone for the utility company 2.0. New business models will emerge, such as pooling of batteries that would link small home power storage solutions to earn on energy price arbitrage and at the same time contribute grid balancing. It is extremely difficult to predict what the future holds, however, we can say with certainty that both digital energy and digital consumers will be major keystones in a future that is founded upon the distributed digital and sustainable energy world!

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