Optimal policy identification: Insights from the German electricity market

Abstract

The diffusion of renewable electricity generating technologies is widely considered as crucial for establishing a sustainable energy system in the future. However, the required transition is unlikely to be achieved by market forces alone. For this reason, many countries implement various policy instruments to support this process, also by re-distributing related costs among all electricity consumers. This paper presents a novel history-friendly agent-based study aiming to explore the efficiency of different mixes of policy instruments by means of a Differential Evolution algorithm. Special emphasis of the model is devoted to the possibility of small scale renewable electricity generation, but also to the storage of this electricity using small scale facilities being actively developed over the last decade. Both combined pose an important instrument for electricity consumers to achieve partial or full autarky from the electricity grid, particularly after accounting for decreasing costs and increasing efficiency of both due to continuous innovation. Among other things, we find that the historical policy mix of Germany introduced too strong and inflexible demand-side instruments (like feed-in tariff) too early, thereby creating strong path-dependency for future policy makers and reducing their ability to react to technological but also economic shocks without further increases of the budget.

A review of agent‐based modeling of climate‐energy policy

“…Scenarios with different behavioral assumptions are then compared with observed market shares of different car types. A study by Herrmann and Savin (2017) of the electricity market of Germany during 1990–2010 uses parameter values from the literature for learning rates for photovoltaic and wind energy, uses statistical data to define the income distribution of households, and calibrates other parameters to reproduce empirically observed learning and diffusion curves of renewable energy technology.…”

“…The main advantage of the macro models is that they address interactions between multiple markets. For instance, the studies by Ponta et al (2016) and Herrmann and Savin (2017) both address the role of a feed‐in tariff, but with very different model scopes. The trick of simplifying models comes down to making the right choices for the policy problem at hand.…”

“…Another common reason is the possibility to describe bounded rationality, such as through nonoptimal decision rules or adaptive learning. This argument pertains especially to studies on product/technology diffusion, where agents are not necessarily perfectly informed about available options or may have reasons other than price to adopt a certain technology (Bleda & Valente, 2009; Herrmann & Savin, 2017; Safarzynska & van den Bergh, 2017a).…”

“…This applies in particular to specific consumption goods. Associated studies are often focused on product/technology diffusion and household adoption behavior (e.g., Herrmann & Savin, 2017; Rai & Robinson, 2015; Sopha et al, 2011; Wang et al, 2018a). Studies tend to use a generic consumption good in combination with multiple markets.…”

Looking for the Optimal Location of an Eco-District within a Metropolitan Area: The Case of Tricity Metropolitan Area

“…There have been many recent developments around buildings' energy performance and energy efficiency, creating new perspectives for suburban eco-districts by developing their energy independency; to mention a few, renewable energy technologies [26], decentralized energy systems [27,28] and occupant-based smart technologies [29,30] enabling the effective usage and transmission of energy independent from the centralized system.…”