Impact of Distribution Tariff Design on the Profitability of Aggregators of Distributed Energy Storage Systems

Abstract

The distribution tariff design, which is currently being overhauled in multiple European countries, is a significant part of a residential consumer's electricity bill. This paper studies how the tariff design influences the behavior of a strategic aggregator, of residential consumers with photovoltaics (PV) and energy storage systems (ESS), on a wholesale market. The aggregator-wholesale market interaction is formulated as a Stackelberg game. We show that the distribution tariff design impacts the strategic operation of the aggregator's PV and ESS and the accompanying cost savings the aggregator attains compared to a retailer of active consumers.

Impact of advanced electricity tariff structures on the optimal design, operation and profitability of a grid-connected PV system with energy storage

“…Borenstein (2017) analyzed the effect of rebates and tax incentives on the net present value of US residential PV installations, showing that wealthier households get higher profitability from their PV installations than lower-income households, due to their larger system sizes, higher consumption and lower interest rates. As emerging business models arise, such as flexibility providers and aggregators of distributed energy storage system to provide grid services, Govaerts et al (2018) studied the strategic operation of an aggregator of residential PV and energy storage systems under various distribution tariffs. The best profitability of a flexibility aggregator against an energy retailer is achieved under a capacity-based tariff.…”

Implications of bid structures on the offering strategies of merchant energy storage systems

“…Under such a market structure, a ESS owner would bid separately its availability to discharge (as any other generation unit) and its availability to charge (as any other load). Scientific literature adopting this bid structure include, i.a., Nasrolahpour et al (2016); Pandžić and Kuzle (2015); Pandžić et al (2019); Cui et al (2018); Dvorkin et al (2018); Govaerts et al (2018) and Mohsenian-Rad (2016).…”

“…However, the potential influence of large volumes of grid-scale storage and those particular situations in which ESS's behavior could have an impact on electricity prices, such as during peak load hours or when grid constraints are binding, raise questions regarding their participation in markets and the implications of different market designs. Researchers investigating the strategic behavior of a price-making ESS in electricity markets typically adopt one of the following three approaches: (i) the impact on prices is determined by means of a demand function (Schill and Kemfert, 2011;Baillo et al, 2004;De La Torre et al, 2002) or market resilience functions (Brijs et al, 2016); (ii) the strategic behavior is represented through a bilevel optimization model or mathematical problem with equilibrium constraints (MPEC), reflecting a Stackelberg relationship between the ESS owner and the market (operator) (Hartwig and Kockar, 2016;Mohsenian-Rad, 2016;Ye et al, 2016Ye et al, , 2017Nasrolahpour et al, 2017;Schillemans et al, 2018;Cui et al, 2018;Dvorkin et al, 2018;Jia et al, 2018;Chen et al, 2019;Govaerts et al, 2018;Kardakos et al, 2016;Huang et al, 2018) or (iii) multiple strategic agents are considered in an equilibrium problem with equilibrium constraints (EPEC) (Sioshansi, 2014;Shahmohammadi et al, 2018b,a;Pandžić et al, 2019;Zou et al, 2016). This last type of problems can be considered as a set of MPECs with a common set of equilibrium constraints.…”

“…In another paper, Ye et al (2017) study how ESS owners may withhold capacity to maximize their profits in day-ahead electricity markets. Govaerts et al (2018) study the impact of distribution tariffs on the attainable operating profit of a strategic aggregator of distributed battery storage systems. Similarly, Jia et al (2018), Mohsenian-Rad (2016) and Kardakos et al (2016) develop a bilevel optimization model governing the strategic participation of an aggregator in day-ahead electricity and operating reserve markets.…”

“…; Mohsenian-Rad (2016); Ye et al (2016, 2017); Nasrolahpour et al (2017); Schillemans et al (2018); Cui et al (2018); Dvorkin et al (2018); Pandžić et al (2018); Jia et al (2018); Chen et al (2019);Govaerts et al (2018);Kardakos et al (2016) andHuang et al (2018)). …”