(577b) Automated Electricity Bidding Solution Under Price Uncertainty

This talk will discuss aspects of energy transition that will directly impact the process industries. Along the growing number of renewable energy source (RES) units, the volatility of power supply is increasing, and this can have unwanted effects both in reliability, as well as in electricity pricing. Especially the latter is critical to the profitability of many production processes. To guarantee grid stability, there must be sufficient resources to balance these RES-fluctuations ensuring that the demand and supply are always balanced. Demand-side management (Paulus and Borggrefe, 2011) has been the major vehicle of engaging industrial consumer in supporting the power grid stability (Zhang and Grossmann, 2016).

In addition to this, some industries are also investing in battery energy storage systems (BESS) to ensure the stability of electricity supply and thus avoid unwanted disturbances that might cause process instabilities or significant losses. Nonetheless, investing in storage systems is expensive and this also adds to the operational costs. To reduce these costs there is the option to use the power storage for offering ancillary services and to participate in energy trading. This can be done for the surplus capacity and can optimally add to the profit of the system owner, also justifying the investment costs. However, participating in trading is untrivial due to the fluctuations in energy prices, which – if not well considered – may result in lost opportunities or even financial losses. Having perfect energy price forecasts would make it trivial to optimize the capacity use by a deterministic optimization approach.

Unfortunately, in real life there is no perfect energy price forecast and the best we can do is to try to rely on, for instance, quantile price forecasts or define probabilistic scenarios expressing the uncertainty range of the expected price fluctuations. There are many options to deal with optimization under uncertainty (Sahinidis, 2004) such as stochastic and robust optimization (Grossmann et al., 2016). While a rigorous multi-stage stochastic optimization problem would result in combinatorial explosion, we have applied a simplified stochastic optimization approach to deal with the uncertainty space. This can be deployed for automated bidding across different markets and products and will be compared to a deterministic approach using an average forecast scenario. The comparison between these two approaches will be done using real-life price data and showing use cases of different complexities. The focus of this work lies in both the solution robustness (not violating the process or battery limits), and profit (ensuring better return-on-investment for BESS), which are both analyzed and discussed.

References

Paulus M., Borggrefe F. (2011). The potential of demand-side management in energy-intensive industries for electricity markets in Germany. Applied Energy, 88 (2), pp. 432 - 441. DOI: 10.1016/j.apenergy.2010.03.017

Zhang Q., Grossmann I.E. (2016). Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives. Chemical Engineering Research and Design, 116, pp. 114-131. DOI: 10.1016/j.cherd.2016.10.006

Sahinidis N.V. (2004). Optimization under uncertainty: State-of-the-art and opportunities. Computers and Chemical Engineering, 28 (6-7), pp. 971-983. DOI: 10.1016/j.compchemeng.2003.09.017

Grossmann I.E., Apap R.M., Calfa B.A., García-Herreros P., Zhang Q. (2016). Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty. Computers and Chemical Engineering, 91, pp. 3-14. DOI: 10.1016/j.compchemeng.2016.03.002