# A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform
## 1 Introduction  
There have been concerns throughout many countries and regions about the rational consumption of electricity, which is increasing every day, along with concern for the environment. This has sparked a quest for new ways to improve energy efficiency, and has been a motivation for the development of more efficient and cleaner technologies.  
This increasing energy demand, along with higher penetration of variable renewable energy, is posing many challenges for the electricity sector due to the need to balance generation and load in real-time and meet energy-efficiency and sustainability goals. In addition, the number of inverter-based distribution generation (DG) and other distributed-energy resources (DERs) has been increasing continuously, and has created additional complexities for power-system operation, thus highlighting the need for innovative methods to model various uncertainties and reduce overall operational complexity.  
Distributed-Energy Resources (DERs) are smaller energy sources that can be aggregated to provide the amount of energy needed to meet demand. However, their small installed capacities, intermittences, and uncertain generation make it difficult for these plants to enter and directly participate in the electricity market.  
Innovative technologies must be harnessed to make the most of DERs. Because of this, a paradigm for the operation of modern power-distribution and transmission systems is being presented through a Virtual Power Plant (VPP) [1]. Important solutions for the reliable supply of electricity in a power system are coming to light, establishing an efficientand effective mechanism [2]. The literature and studies regarding this technology already indicate the direction of this new tool as a promising solution [3; 4].  
VPPs demonstrate their relevance as an integral part of predictability research on power-generation variability and demand. However, various uncertainties are present due to the intermittent nature of renewable production units, market prices, and power demand. Therefore, it is necessary to consider and know the degree of uncertainty during the operation of a VPP, since this information is crucial for efficient operational planning [5]. In this paper, the Unscented Transform (UT) method is used to improve the daily predictability of a VPP system with minimal aggregation error [6].  
Typically, this problem could be solved by electrical engineering using the Monte Carlo (MC) model, as it is one of the most widespread techniques for modeling uncertainty in a short-term forecast. However, the need for real-time usage makes the MC model impractical [7]; the method uses millions of iterations to obtain a satisfactory result in terms of confidence intervals. This makes its use computationally impracticable in several cases [8].  
The UT method, however, presents a new approach for solving this problem, which requires less computational effort when compared to the Monte Carlo method [9]. In the next chapter, we will present the UT method for individual forecasting of each VPP user in order for the aggregator to have prior knowledge of the data and be able to make relevant decisions.