## 1 Introduction
In this study, we focus on the two most widely used programs within PBP. The tariffs offered to end customers in the set-up of our natural experiment are Time-Of-Use (TOU) and Real-Time Pricing (RTP) pricing. Both tariff schemes vary according to timing and impact on process quality [10]. TOU pricing is changing the unit price of electricity during specific time periods at a fixed rate. The tariff tries to encourage customers to shift the consumption from peak to off-peak periods, by reflecting the production and investment cost structure with higher rates during high demand periods. The tariff variations are, however, predetermined and fixed at delivery, making it easily interpretable for the customer to optimize his portfolio. Market DR (Demand Response) relies on Real-Time Pricing (RTP) or wholesale market spot prices, where price signals reflect supply conditions. Although this creates a higher level of demand flexibility for the system, it requires a higher involvement from a customer perspective. Therefore, as customer involvement is dependent on the tariff scheme, correct customer targeting can enhance utility portfolio optimization and by extension smart grid management. In RTP schemes, customers are informed about, mostly hourly changing, varying electricity prices on a day-ahead or hour-ahead basis [11]. Given the recent technological developments and an advanced metering infrastructure worldwide, effective communication of prices and detailed load profiles between utility providers and electricity consumers have become possible. Dynamic pricing schemes are usually based on retail prices and reflect real-time system costs, thus encouraging energy consumers to reduce or shift energy consumption during high wholesale price periods. This study contributes to the growing stream of literature discussing the effects of electricity prices on energy consumption behavior. It is argued that the effectiveness of energy information is not generalizable across cultures and demographic groups [12] and large variations in effect sizes and significance of different electricity pricing types can be found. Previous studies suggest that dynamic pricing strategies would encourage the price-responsive demand to balance supply and demand of electricity [13], with various dynamic pricing schemes impacting consumption behavior of residential households in different ways [14]. The first ever hourly Real-Time Pricing (RTP) program for residential customers, however, was only conducted in 2011 in Chicago. The results of the demand estimates of the respective study suggest that dynamic prices only influence the residential electricity demand during peak hours. More specifically, it shows that participating residential households were engaged in peak shaving behavior, but not in load shifting behavior [15]. Earlier work however suggests different results depending on the dynamic pricing scheme, where demand based TOU electricity tariffs have decreased peak demand and shifted electricity demand from peak to off-peak periods [16]. We contribute by analyzing both respective dynamic pricing schemes, comparing TOU pricing performance to Real-Time Pricing schemes in the same natural experiment. Our study extends the current line of literature by providing results from one of the first natural large-scale open sign up experiments involving dynamic electricity prices. In addition, we focus on the individual preferences and beliefs of each individual, crucial for learning of smart home energy management systems, as household attributes influence price sensitivity and the effectiveness of the dynamic pricing schemes [17]. Household attributes have been proven to play an important role in explaining how willing energy users are to conserve energy [18]. [PERSON] et al. [19] have found evidence that the influence of electricity prices is different across household groups. Households with higher income, higher education levels, and higher electricity use are more reactive to energy consumption behavior change than other groups [20; 21].