# Risk-Based Virtual Energy Storage System Service Strategy for Prosumers
###### Abstract
[PERSON] and [PERSON] proposed a method to determine VESS size and service cost to minimize the electricity bill of building units in ToU [18]. [PERSON] et al. considered a VESS investment and pricing decision method for a group of users to minimize their electricity bill [19]. The problem in [17; 18; 19] was formulated as a two-stage optimization problem for the interaction between the SP and the participants, and was solved with an iterative search. [PERSON] et al. formulated an energy storage ownership sharing problem between multiple shared controllers dwelling in a residential community, and suggested an auction-based shared price solution to maximize utility [20]. [PERSON] et al. applied a coalitional game approach to determine how much capacity they could jointly acquire by minimizing the expected daily storage cost to solve an ESS sharping problem [21]. [PERSON] et al. proposed an online energy storage sharing operation algorithm based on the Lyapunov optimization framework with offline parameter selection to minimize the overall cost, including the electricity bill and ESS charging and discharging costs [22]. Some examples have been applied to improve system parameters such as the frequency response [23], voltage regulation [24], and uncertainty management [25]; however, in most studies, the VESS was used for electricity bill minimization. This is because the VESS is an effective way to lower the cost barrier of ESS usage. In fact, the benefits of a VESS come in two forms. For one, due to the aforementioned economies of scale, a VESS can be served at a low cost compared with an individual ESS service. The other is that the logical charging and discharging operations of several participants cancel each other out, so the actual operation is more timely. Existing studies have focused on the first benefit based on economies of scale. The second benefit was only shown experimentally in a few studies such as [18; 19]. This paper proposes a VESS service strategy that not only considers the economic benefit that is presented as a unit ESS cost decrease, but also considers the multi-user benefit of increased service capacity compared with the VESS installation capacity. The work presented here builds on the author's previous work [18; 25]. In [18], the VESS service architecture that consists of physically connected energy and communication infrastructures, and the logical operation of the ESS virtually assigned to each participating unit are presented. Moreover, the conceptual scheme for the VESS service is presented in the architecture. However, the VESS service in [18] is operated on the risk-free condition that the serving capacity is less than the VESS installation capacity in apartment-type factory buildings. However, this paper investigates the VESS service strategy for prosumers considering the risk that more ESS capacity is served than the installed ESS capacity for the operation of the VESS. In [25], the reinforcement-learning based VESS operation strategy is proposed to reduce wind power forecasting uncertainty. The study in [25] focuses on the VESS operation when the VESS installation capacity and service price are fixed. However, this paper proposes the VESS service strategy to determine the VESS installation capacity and service price considering the risk. Prior works such as [16; 17; 18; 19; 20; 21; 22; 23; 24; 25] consider the case that the VESS provide services only within the installed capacity. The novelty of the proposed strategy is to consider the case providing more service capacity than the installed ESS capacity for the operation of the VESS. When a capacity greater than the VESS installation capacity is served, risk compensation for excess capacity occurs. This paper considers the environment in which VESS are serviced for prosumers. Prosumers are users that consume and produce energy [26]. Prosumers achieve an economic benefit by transacting energy as goods and services through peer-to-peer (P2P) energy transactions [27].