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Darwin in Smart Power Grids - Evolutionary Game Theory for Analyzing Self-Organization in Demand-Side Aggregation

Maintaining a balance between power consumption and production is paramount for the safe and efficient usage of our power infrastructure. With the increased adoption of less predictable producers such as windmills and photovoltaics, maintaining this balance has become even more challenging. Demand-side aggregators are relatively new actors in smart power systems, offering balancing services. Aggregators maintain a portfolio of flexible consumers (e.g. Production plants, electric vehicles or shift able household appliances) that they can use for such balancing purposes. Typically, these balancing actions involve some form of financial compensation. Together these actors form a complex, interacting, self-organizing system, where an individual's behavior fluctuates as a result of maximizing their own objectives. In this work, we analyze the influence of competing aggregator's pricing strategies on the flexibility providers by using evolutionary game theory with replicator dynamics. Applying this analysis approach to concrete multi-aggregator scenarios allows for predictions on stable system states under varying conditions. Concrete results show that pricing strategies favoring reservation payments over activation payments increases the aggregator's market share size and its associated balancing potential in stable equilibrium.