On most modern energy markets, electricity is traded in advance and a power producer has to commit to deliver a certain amount of electricity some time before the actual delivery. This is especially difficult for power producers with renewable energy sources that are stochastic (like wind and solar). Thus, short term electricity storages like batteries are used to increase flexibility.
By contrast, long term storages allow to exploit price fluctuations over time, but have a comparably bad efficiency over short periods of time.
In this paper, we consider the decision problem of a power producer who sells electricity from wind turbines on the continuous intraday market and possesses two storage devices: a battery and a hydrogen based storage system. The problem is solved with a backwards approximate dynamic programming algorithm with optimal computing budget allocation. Numerical results show the algorithm’s high solution quality. Furthermore, tests on real-world data demonstrate the value of using both storage types and investigate the effect of the storage parameters on profit.

Most modern energy markets trade electricity in advance for technical reasons. Thus, market participants must commit to delivering or consuming a certain amount of energy before the actual delivery. In Germany, two markets with daily auctions coexist. In the day-ahead auction market, the energy is traded in 60-minute time slots, which are further partitioned into 15-minute time slots for the intraday auction market. Because of the slow ramp-ups of nuclear and fossil power plants, these price-makers trade mostly in the day-ahead market. Only the residual energy is traded in the intraday market, where the market prices fluctuate substantially more. These fluctuations as well as the expected price difference between these markets can be exploited by fast ramping energy storage systems. We address the decision problem of an owner of an energy storage who trades on both markets, taking ramping times into account. Because the state variable of our dynamic programming formulation includes all features of our high-dimensional electricity price forecast, this problem cannot be solved to optimality. Instead, we use approximate dynamic programming. In a numerical study based on real-world data, we benchmark the algorithm against an adapted state-of-the-art approach from literature and an expectation model with a receding horizon. Furthermore, we investigate the influence of the price forecast on expected profit and demonstrate that it is essential for the dynamic program to capture the high dimensionality of the price forecast to compete with the expectation model, which does not suffer from the curses of dimensionality.

Product-related and market-related uncertainties often cause users to defer from switching to new IT devices. There is a value of waiting (VoW) for users because waiting allows them to collect more information. At the same time, many IT switching decisions are increasingly complex due to increased connectivity and the resulting interdependencies between jointly used devices. Therefore, switching decisions for connected devices not only need to consider the new device in isolation, but must also account for the potential benefits from internally or externally connecting the device with other devices. Although crucial for users and providers alike, existing models cannot explain whether and when users switch in such connected

environments.

We focus on connected Smart Home Devices (SHDs) and simulate users’ actual switching timing based on a real options model which combines switching and deferral concepts in a context-specific setting. We examine how Smart Home Network (SHN) density influences switching and how providers can use incentives to accelerate switching to foster product diffusion. The findings show an accelerating effect of connectivity and a deferring effect of uncertainty on actual switching timing. We also learn that SHD providers should focus more on immediate than on delayed incentives to promote product diffusion, since the latter can also have undesired effects. Interestingly, external connectivity has almost no influence on decision timing in scenarios with highly dense SHNs, leading to further key implications for SHD providers.

In deregulated markets, electricity is usually traded in advance, and the advance commitments have a time lag of several periods. For example, in the German intraday market, the seller commits to providing electricity 45 min before the 15-min interval in which delivery has to be made. We consider the problem of a producer that generates energy from stochastic, renewable sources, such as solar or wind and uses a storage device with conversion losses. We model the problem as a Markov Decision Process and consider lagged commitments for the first time in the literature. The problem is solved using an innovative approximate dynamic programming approach. Its key elements are the analytical derivation of the optimal action based on the value function approximation and a new combination of approximate policy iteration with classical backward induction. The new approach is quite general with regard to the stochastic processes describing the energy production and price evolution. We demonstrate the application of our approach by considering a wind farm/storage combination. A numerical study using real-world data shows the applicability and performance of the new approach and investigates how the storage device’s parameters influence profit.