A local energy market for high grid efficiency and fair cost distribution: “OrtsNetz”

Research Highlights

- We develop a real-world prototype of a local market for energy trading, incorporating distributed sources and storage as well as automated flexible loads

- Market and tariff design are optimized for household participation and grid efficiency simultaneously

- Field deployment as a pilot- and demonstration-project in a Swiss community with 500 participating households

- Evaluation of technical, market-related, and behavioral aspect

MOTIVATION

The desire to render the energy system more sustainable is leading to a restructuring of the electrical power grid. Distributed resources (generation, storage, flexible loads) play an important role and are increasingly shaping the lower voltage levels of the power grid infrastructure. Peer-to-peer platforms are currently in use in some countries to enable local energy trading. This can bring cost advantages for some participating households, but for grid operation the potential of local coordination of resources remains mostly unrealized. Likewise, fair allocation of grid usage costs is an open challenge.

CHALLENGE

- The objective incorporates a bi-level optimization, accounting for customer participation and grid efficiency simultaneously

- Cost-attractiveness for customers is essential to reach a significant number of participating households

- Accordance with local regulation needs to be guaranteed and incorporated into the system design

- The viability of the approach will be demonstrated with today’s technology but is designed for the energy system of the future

- Smart energy meter devices and intelligent control systems need to be made available to leverage the potential for load shifting in an automated way

Approach

The approach aims to steer the customer behavior via dynamic tariffs, which influence the grid load, increase the attractiveness of intelligent control mechanisms to effectively promote a socially desirable behavior of the grid participants. Central to the implementation of the objective is the understanding of the interplay between tariff setting and customer reactivity. For this purpose, market mechanisms as well as static and dynamic tariff models are defined. The basis for modeling and implementation are tools based on optimization and learning algorithms for automated energy management. By optimizing and automating the interactions, the foundation is laid for reliable and cost-efficient grid operation. The results will provide important data and insights for the Swiss electricity system of the future and the future design of the regulatory framework.