David Dauer

A blockchain-based smart grid: towards sustainable local energy markets

The increasing amount of renewable energy sources in the energy system calls for new market approaches to price and distribute the volatile and decentralized generation. Local energy markets, on which consumers and prosumers can trade locally produced renewable generation directly within their community, balance generation and consumption locally in a decentralized approach. We present a comprehensive concept, market design and simulation of a local energy market between 100 residential households. Our approach is based on a distributed information and communication technology, i.e. a private blockchain, which underlines the decentralized nature of local energy markets. Thus, we provide energy prosumers and consumers with a decentralized market platform for trading local energy generation without the need of a central intermediary. Furthermore, we present a preliminary economic evaluation of the market mechanism and a research agenda for the technological evaluation of blockchain technology as the local energy market’s main information and communication technology.

The digital transformation and smart data analytics: An overview of enabling developments and application areas

The digital transformation enables new business models and enhanced business processes by utilizing available data for analytics, prediction, and decision support. We give an overview of the enabling developments for the digital transformation, the areas of application, and concrete use case examples. We summarize our findings in a framework for the digital transformation and discuss the potential for new and adapted business models.

Market-Based EV Charging Coordination

Electric vehicle (EV) charging loads may challenge grid stability due to a combination of high charging power and temporal clustering of charging activity. Hence, EV charging needs to be coordinated appropriately. Prior work addressing this challenge focused on static charging strategies responding to exogenous price vectors. We extend this work in two directions: To achieve an endogenous resource pricing, we substitute exogenous pricing for a local market platform which allocates available charging capacity to demand from EVs. To achieve meaningful interaction with this market, we model the bidding behavior of EVs by means of a Q-learning approach. Using an integrated trip-based state space representation spanned by required battery level and time to departure, we moderate between bidding aggressiveness and mobility requirements. For appropriate learning parameters, bidding behavior converges and the market achieves significant load shifting.