Improving energy storage efficiency for cheaper renewable energy
Due to the increased use of renewable energy sources, the generation of electricity is becoming increasingly dependent on weather conditions. Energy storage plants can benefit from this development by storing power when it’s plentiful and selling it when supply is limited and the price goes up. The better supply and demand are synchronized, the lower the price of electricity. Uncertain weather conditions create a dilemma for storage plants, as they are in danger of missing just the right moment. Nils Löhndorf from WU’s Institute for Production Management has spent years developing a decision-making model to help solve this problem. This model allows energy suppliers to optimize the management of complex storage systems at times when prices and energy supply are uncertain. Austrian energy suppliers already keep an eye on Löhndorf’s research.
The European Union wants to cover 20% of its gross final energy consumption using renewable energy sources by 2020. In Austria, hydropower plants have been in extensive use for many decades. Unlike solar and wind power, hydropower can be stored in reservoirs until needed. The varying supply of power from renewable sources is a major challenge for the Austrian power storage industry. Today, energy is traded on energy exchanges throughout Europe. When wind energy is plentiful, prices drop on the energy exchange; when supplies are low, prices rise. Energy storage providers try to profit from these fluctuations by filling their storage plants when the price is down and releasing the energy for sale when prices are higher. Austria’s reservoirs act as huge batteries for renewable energy from all over Europe. “Power companies rely on forecasts to decide on the optimal time to buy and sell energy. Inaccurate forecasts can lead to losses for energy providers,” explains WU Assistant Professor Nils Löhndorf. He decided to try and solve this problem. After years of work, he has developed a decision-making model based on scenarios rather than forecasts. This model allows power companies to make better decisions when trading their energy.
More efficiency = lower price
Löhndorf’s model allows users to take into account a wide variety of factors in the decision-making process, including connected reservoirs, natural inflow, and variable prices. “The difficult part is finding an optimal solution for all possible scenarios, as the number of scenarios becomes astronomically huge the further into the future you try to plan. The method I developed reduces the problem size by efficiently recombining scenarios to compute an optimal solution,” says the researcher. This method has been integrated into the optimization software QUASAR, already in use by Austrian energy providers to calculate when and how much energy they should buy and sell on the energy exchange to be able to manage their reservoirs most efficiently. Other countries with a large storage capacity, for example Switzerland, Norway, or Canada, could also benefit from the use of this software. “Energy storage is expensive. The better we manage our energy storage systems, the more affordable electricity from sun, wind, and water will become,” says Löhndorf. “With existing storage systems, a 5-10% increase in efficiency is realistic, while high-speed storage systems like batteries, whose output is traded on a ‘day-ahead’ basis, can expect to profit considerably more.”
Better storage management also benefits consumers, as lower costs for producers also result in lower energy prices for end users.
About the researcher
Nils Löhndorf, born in Germany, is an assistant professor at WU’s Institute for Production Management. Löhndorf’s work at WU focuses on stochastic optimization and its application to models and methods for efficient energy storage management and operation. Löhndorf received his doctorate from the University of Vienna in 2011 for his dissertation on the optimization of stochastic-dynamic decision-making processes, and was presented with the 2014 WU Best Paper Award for his published research on optimizing trading decisions for hydro storage systems.
Nils Löhndorf (c) WU
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