SmartControl - Standardized and smart control of municipal energy systems

Starting point / motivation

Renewable and decentralized energy must be increasingly relied upon in order to achieve the goal of a clean and secure energy transition (#mission2030). Local energy communities show a high potential for the efficient use of decentralized energy technologies, including volatile energy generation from renewable resources. Costs and CO2 emissions can be reduced by up to 17.6% and 37.2%^[1][2], respectively.

Therefore, community energy systems are also pushed by policy makers (EU Winter Package 2017, Erneuerbare Ausbaugesetz-EAG). However, until now there are only limited possibilities to implement local energy communities in practice because of several limitation factors.

In addition to the lack of regulatory framework conditions, uniform, standardized procedures for the collection of load and generation data (e.g. smart meters, PV, storage systems, etc.) in municipalities, communities or neighborhoods are missing.

Furthermore, real-time data collection, if at all, rarely takes place. Moreover, an intelligent higher-level control system communicating with individual technologies is essential for an optimal, resilient operation of energy communities. Currently, technology manufacturers use different communication interfaces. This increases the effort required to develop a universally applicable higher-level control algorithm that can be installed without great effort and cost.

[1] Stadler Michael, Groissböck Markus, Cardoso Gonçalo, Müller Andreas, Lai Judy. 2013. Encouraging Combined Heat and Power in California Buildings. California Energy Commission, PIER Program; 2019

[2] A. Cosic, M. Stadler, M. Mansoor, M. Zellinger. MILP-based optimization strategies for renewable energy communities. Energy

Contents and goals

The SmartControl project addresses all these challenges and aims to develop a standardized, holistic concept for monitoring, control and operation of municipal energy systems. Adaptive, self-learning methods such as machine learning (ML) will be used for the ongoing operation, primarily to optimize forecasting procedures for load and generation data and to be able to transfer them to other municipalities, communities or neighborhoods without calibration efforts.

In combination with higher-level control algorithms, optimal energy demand coverage by renewable and distributed energy is achieved (e.g.: self-consumption optimization), which in turn leads to CO2 and cost savings in the operation. At the same time, this will relieve and stabilize local grids, since occurring power generation peaks are smoothed and compensated accordingly with the optimized control given by the SmartControl concept.

Methods

In the project, open communication protocols and standards for data transmission, such as TCP/IP, will be applied. Moreover, open standards and open source solutions (e.g. openHAB) will be used and built upon for the establishment of interfaces. Over the entire project duration, two communities, an energy supplier and a network operator will be included in the process to address their challenges and technical requirements.

In order to test the implementation potential of all approaches in the project, municipal energy systems in the communities of Wieselburg and Yspertal will be put into operation and evaluated on a laboratory scale (integration of real data and evaluation in an open loop test).

Expected results

The research activities planned in this project will form the basis for subsequent experimental development of higher-level control systems for local energy communities, municipalities and neighborhoods.

Project management

BEST - Bioenergy and Sustainable Technologies GmbH

Project or cooperation partners

• municipality Yspertal
• municipality Wieselburg
• Wüsterstrom E-Werk GmbH

Supporting Partners

• University of California - San Diego, Prof. Jan Kleissl, Director of UCSD Center for Energy Research
• Innovationslabor act4.energy

BEST - Bioenergy and Sustainable Technologies GmbH
Stefan Aigenbauer
Gewerbepark Haag 3
A-3250 Wieselburg-Land
Tel.: +43 (5) 02378-9447
E-Mail: stefan.aigenbauer@best-research.eu
Web: www.best-research.eu