Leipzig
Smart and Sustainable Communities
SPARCS
New research shows potential for residential demand response and virtual power plants to increase renewable energy usage and reduce costs
The dissemination of research results in SPARCs related to the virtual energy community took place in Leipzig in November 2022. The goals of the event included knowledge transfer between the Leipzig University and Leipziger Stadtwerke (LSW), evaluating whether model results can be incorporated into product development, and discussing formats for the dissemination of project results.
The study, led by Dr. Hendrik Kondziella, showed the benefits for both customers and utilities, including reduced electricity bills and optimised purchasing strategies. Sensitivity analyses were also conducted, highlighting the importance of dynamic electricity tariffs and investment in smart metering equipment.
The research goals of the virtual energy community are to enable customers to actively participate in the energy market and increase the share of renewable energy sources for their energy consumption. The study assessed the technical, economic, and environmental potential of residential demand response in combination with a virtual power plant. The model framework included electricity generation, storage, residential demand, the virtual power plant as a layer of control, and interconnections to the market. The model data was defined for two customer groups, and four different electricity tariffs were applied, ranging from very static to highly dynamic ones.
The model results showed changing energy flows due to demand response compared to the reference scenario, annual load shift per household according to different variable electricity tariffs, and annual cost savings when applying dynamic electricity tariffs compared to a fixed tariff. The economic effects were separated for customers and the utility, with customers able to reduce their electricity bill depending on the individual load shift potential, and the net effect for the utility composed of revenue losses due to the behavioural change of the customer groups and gains from an optimization of the purchasing strategy. Sensitivity analysis was also conducted, showing changing market interaction for different scenario settings.
The study's findings will be disseminated through various formats, and the team will continue to use data-driven strategies to develop new business models based on flexibility and integrate corporate strategy for utilities and industry.