The article presents the results of simulations based on a linear optimization model of a storage system that calculates the economic benefits of distributed storage devices at the end consumer level by determining the cost optimal chargedischarge-schedule. The primary objective of the storage application is arbitrage accommodation.Particularly, parameters for a li-ion-based and a lead-acidbased storage system are simulated. All parameters of the model are varied and analyzed regarding their impact on the economic benefits. The simulation results quantify these impacts and show that the costs per storage capacity unit (EUR/kWh) and the efficiency degrees of the storage system have the highest impact. Additionally, the price spreads as well as the distribution of the market price curve in relation with the distribution of the consumer's load curve (demand) influence the achievable benefits significantly. Overall, the model reveals a saving potential of 17% on total cost for the reference case.Index Terms--Batteries, demand-side management, economic sensitivity analysis, energy storage
This article analyzes the economic impact of price forecast errors on the optimal operation schedules of distributed (battery) storage systems. The presented simulation model extends a linear optimization model that achieves up to 17% annual savings for a storage system in an environment with dynamically changing electricity prices and under the assumptions of ex-ante known load and price data. The main contribution of this paper is to replace the deterministic load and price curves by imperfect forecasts of which the effect of price forecast errors is systematically analyzed. All results are benchmarked against the optimal result of the basic model.The main finding is that the underlying storage optimization model performs with a high robustness against price forecast errors. E.g., up to 10% Mean Absolute Percentage Error (MAPE) for day-ahead price forecasts lead to less than 10% deviation from the optimal result. I.e., the storage model yields up to 15% annual savings vs. 17% in the optimal case.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.