Thanks to my co-authors, the journal and Vijay Mookerjee for handling the article, as well as the reviewers and all discussants who have commented in the past on various occasions!

We show how effective choices of notification intervals depend on the forecasting capabilities of the DR program operator, customers’ expectations, and the participating loads.

Third, we provide novel insights regarding the length of the notification interval, i.e. the timespan for which future prices must be set in advance. Load operators decide upon dispatch based on current & upcoming prices. Such a decision may lock in demand for the upcoming hours.

Second, we address pricing under unknown, time-interdependent, and discontinuous demand. Flexible loads can be of different elementary load types – such as elastic loads, storage, interruptible, and noninterruptible loads – and respond differently to DR prices.

The performance remains robust under a variety of system characteristics such as different load type combinations, wholesale price variability, and forecasting quality. The DR prices can be identified quickly and robustly.

We address the social welfare maximization problem of a local utility. Using Deep RL, we identify effective electricity prices even if the properties of electricity demand are unknown and wholesale market prices are highly variable.

In the article, we demonstrate the effectiveness of Deep Reinforcement Learning in identifying social welfare increasing electricity prices for Demand Response programs in local electricity systems where load flexibility is unknown.

Our paper „Online Demand Response Programs for Constrained Local Electricity Systems“ has been accepted for publication in Production and Operations Management. The paper has been co-authored with Prof. Dr. Gunther Gust from @uni-wuerzburg.de and Prof. Dr. Dirk Neumann from @uni-freiburg.de .

Job advertisement: University of Bayreuth PhD researcher (m/f/d)

You can find more information on the position here: www.uni-bayreuth.de/job-vacancy-... For any questions, pls do not hesitate to contact me (arlt@uni-bayreuth.de )!

Our team „Information Systems Research, in particular on Connected Energy Storage “ is based at the Dep of Law, Business, & Economics and the University of Bayreuth’s Bavarian Center for Battery Technology (BayBatt). For more information on our group: www.isrenergy.uni-bayreuth.de/en/index.html

🚨Job alert 🚨

I am looking for a PhD student to work on the economics of renewable energy systems and, in particular, the role of batteries and electric vehicles at the @unibayreuth.bsky.social . Application date is May 25, with approximate start date in fall 2025.

In der Tat… Dann kanns ja vielleicht nicht ganz falsch sein 😉

Mit @lionhirth.bsky.social, @maurerchr.christophmaurer.de, @grimmveronika.bsky.social, Anke Weidlich, Klaus Müller, @kerstinandreae.bsky.social, Mathias Mier und anderen.

… ist es, alle Einsparpotentiale zu nutzen (zB Flexibilisierung der Nachfrage), und 2) & 3) zu senken. Insbesondere der Netzausbau hat Potentiale zur Kostensenkung, wenn Strompreise geographisch differenziert werden und flexible Verbraucher bei der Planung berücksichtigt werden.

Strompreise für Verbraucher bestehen aus 1) Beschaffungskosten, 2) Netzentgelten und 3) Steuern, Abgaben und Umlagen. Trotz Umstieg auf Sonne und Wind werden wir 1) nur bedingt senken können, umso wichtiger…

ifo Schnelldienst 03/2025: Strommarkt Getrieben von Klimaschutz und technologischem Fortschritt befinden sich die Strommärkte in Deutschland und Europa in einer Phase umfassender Transformation. Auch das heutige Strommarktdesign wird ange...

Danke für die Gelegenheit, meine Perspektive zur Zukunft von Strommarkt und Strompreisen beim neuen ifo-Schnelldienst! einzubringen! www.ifo.de/publikatione...

@apeichl.bsky.social