# Risk-Based Virtual Power Plant Implementation Strategy for Smart Energy Communities
## 1 Introduction  
The power sector, responsible for the generation of electricity and heat, produces the most greenhouse gas emissions at about 30% of total global greenhouse gas emissions [1] and 40% of energy-related CO2 emissions [2]. To reduce this, the use of renewables as generation resources is rapidly increasing. Renewables generated 28% of global power in 2018 with major contributions by wind power, solar power and hydropower [3]. It is expected that renewable power capacity will expand by more than 50% between 2019 and 2024, and renewables will meet 80% of the growth in global electricity demand until 2030 [4]. However, an increase in renewables with intermittent and uncontrollable characteristics makes grid operation difficult. It is reported that the changing resource mix of renewables is the highest threat to grid reliability [5]. The uncertainty of renewables not only reduces their utilization but also acts as a burden on system operation through issues including increases in reserved margins and operational complexity. A virtual power plant (VPP) virtually interconnects various resources through a central control unit, but resources remain physically independent in their ownership and operation [6]. VPPs help establish stable power systems with integration of various types of resources [7]. Types of resources can include roof-top photovoltaic (PV), solar farms, wind farms, combined heat and power units, storage systems and flexible power consumers. The objective of VPPs is to relieve the load on the grid by smartly distributing the power generated by individual units. Many studies have been researched on VPPs, applying technical optimization-based [8, 9, 10], meta-heuristic [11, 12, 13, 14], game-theoretic [15, 16, 17], and deep-learning-based [18, 19] approachesas well as systematically considering irrigation systems [9], buildings [20], electric vehicles [21] and power-to-gas [22]. [PERSON] et al. considered VPP scheduling including day-ahead bidding and real-time operation to maximize economic benefits [8]. It was formulated as a mixed-integer, nonlinear programming problem and was solved effectively through a clustering-based load aggregation method and a disaggregation method for deferrable loads, satisfying consumers' diversified requirements. [PERSON] et al. proposed an optimal technical-economic dispatch model of a VPP that combined large- and small-scale distributed renewable generation [9]. The problem was solved using mixed-integer linear programming. [PERSON] et al. presented a comparative analysis of various scales of virtual power plant models [10]. They formulated a VPP operation problem considering day-ahead and balancing markets and solved it by applying mixed-integer linear programming. [PERSON] et al. formulated multi-objective VPP scheduling to maximize profits and minimize emissions as a non-linear and non-convex problem [11]. The problem was heuristically solved by applying a particle swarm optimization (PSO) algorithm. [PERSON] et al. formulated a bi-objective VPP dispatch problem to optimize economic costs and power quality [12]. A long short-term memory method was applied to increasing the accuracy of wind prediction, and a multi-objective PSO algorithm was utilized as the solving algorithm. [PERSON] et al. introduced an optimal schedule controller to manage renewable energy resources in VPPs [13]. It was also solved using binary PSO. [PERSON] et al. suggested the optimal scheduling of generation in VPPs for a day-ahead market framework using the beetle antenna search algorithm under various scenarios [14]. [PERSON] et al. proposed a two-stage, robust Stackelberg game considering the uncertainties of renewable energy output and market prices for optimal scheduling in a VPP [15]. [PERSON] et al. conducted a feasibility study of urban virtual power plants with the goal of energy self-sufficiency and applied Shapley value-based cooperative game theory, aiming to benefit both the plant and demand sides [16].