# Risk-Based Virtual Power Plant Implementation Strategy for Smart Energy Communities
## 1 Introduction
[PERSON] and [PERSON] presented a VPP coalition-forming model based on auctions considering the competition between VPPs in auctions for attracting distributed energy resources [17]. [PERSON] et al. proposed an optimal bidding strategy for a VPP for participating in electricity markets [18]. A deep-learning-based approach was employed to handle the uncertainties. [PERSON] et al. suggested a deep reinforcement learning algorithm as the optimal online economic dispatch strategy for VPPs and designed an edge computing framework to handle the stochastic and large-state space characteristics of VPPs [19]. Moreover, comprehensive reviews of VPP research were conducted in [6; 23; 24]. [PERSON] et al. reviewed the research on VPPs by dividing it into internal control and external market bidding strategies [6]. Internal control involves resource dispatch, and it is divided into centralized, comprehensive and distributed control by adopting a control agent. The external market bidding strategy is related to the economic effects of the participation of VPPs in electricity markets, and it is mainly formulated as a mixed-integer linear problem solved by optimization or heuristic methods. [PERSON] and [PERSON] also divided the objectives of VPP research into energy management to optimize scheduling resources and bidding strategies [23]. The review concludes that most of the research has been focused on the development of VPP models to achieve optimal control and coordination among their components and thus maximize operating profits. [PERSON] et al. presented a detailed review of extracted documents regarding optimal scheduling techniques, technical and economic limitations and different kinds of uncertainties [24]. This paper focuses on the operation scheduling problem, which plays a significant role in the VPP concept. However, most of the research focused on VPP operation involves how to dispatch resources to maximize benefits or minimize costs. VPPs operate using aggregated resources. This means that in VPP implementation, the resources gathered determine the operation room of the VPP. For example, if a VPP consists of only PV resources, VPP operation cannot be performed at night. A few works touch upon VPP implementation [25; 26]. [PERSON] et al. researched a distributed generation sizing problem considering the price of electricity sales for increasing grid reliability and suggested a heuristic solution applying PSO [25]. [PERSON] et al. proposed a VPP construction model based on decision area division [26]. Theyintroduced a fusion space distance that accounted for geographical information and load density. However, these studies do not sufficiently consider the characteristics of resources, especially temporal characteristics. Renewables, the main resources constituting VPPs, repeat over time. Therefore, it is necessary to consider temporal characteristics when implementing VPPs. This paper focuses on VPP implementation for smart energy communities (SECs). SECs are local energy communities [27; 28]. SECs can be divided into residential SECs centered on households and commercial SECs, consisting of office buildings. SECs have their own energy service providers, so they can choose to supply power using VPPs and the utility grid [29]. A VPP serves the demand of an SEC. However, it is difficult to balance energy in its implementation stage due to uncertainties of demand and resources. Therefore, at the VPP implementation stage, the energy imbalance between the demand and the resources is modeled as the risk factor. Through the risk factor, it is shown that the temporal correlation between demand and resources is the dominant factor in VPP implementation. Based on that, two risk-based VPP implementation strategies are proposed: an optimization-based strategy and a simple strategy. The optimization-based strategy is designed to minimize VPP implementation costs, and the risk factor is relaxed using the relaxation multiplier. It can be optimally solved using linear programming.