# Flexibility Based Day-Ahead Generation-Reserve Bilevel Decision Model
## 1 Introduction  
Renewable energy (RE) represented by wind power and solar power has become the main player of the energy industry revolution in China. Due to the uncertainty and fluctuation nature of RE, the integration of large-scale RE has brought about great risks to the safe and reliable operation of the power system, leading to the curtailment of RE [1], which therefore requires the power system to equip itself with a more reasonable reserve capacity [2; 3; 4]. Therefore, it remains an important issue how to reasonably quantify the reserve capacity needed under the background of integrating large-scale renewable energy into the power system [5]. The reserve capacity of the power system mainly comes from the generation side. Seen from the system level, the reserve capacity at the generation side in dispatch issues refers to the generator operating reserve dealing with net load uncertainty and fluctuation. With the reform of the power industry in China, reserve service will become a major product [6; 7]. It can be seen that reasonable decision of system operating reserve capacity has become the key to both the economy and security of the power system. Hence, it is essential to reasonably evaluate the system operation state, develop operating reserve decision method that considers both security and efficiency and research on the generation-reserve optimization method for power system integrated with large-scale RE. Traditionally, the operating reserve in the power system is served by conventional units, and the formulation of operating reserve is closely related to the operation scheme of conventional units, so the operating reserve is usually coordinative optimized with the generation dispatch [8; 9; 10; 11]. Reference [12]proposed an energy and reserve bilevel co-optimization model based on the two compensation mechanisms of loss probability and uplift payment under deregulated market environment, realizing the maximization of market efficiency. [PERSON] et al. [13] presented a multi-objective energy and reserve joint optimization model with the objective of minimum total expected cost and conditional risk value, and meanwhile proposed an improved variant of the epsilon-constraint algorithm. These methods add economic factors like expected outage cost in the objective function, thereby realizing the coordinated optimization of generation and reserve. However, they fail to establish the quantitative relationship between the reserve and system operating state. Flexibility of the power system is defined as the ability to meet power fluctuations by system resources [14; 15; 16], and it has become one of the most important performance indices of the power system in addition to security, reliability and economy [17]. There is already some research on quantitative evaluation of flexibility. The flexibility envelope method was developed to describe the flexibility potential dynamics of a power system and its individual resources in the operational planning time-frame [18]. The International Energy Agency proposed a flexibility evaluation method with an expert scoring system that has comprehensively considered power grid strength, power balance scale, schedulable flexible resource ratio and so on [19]. [PERSON] et al. [17] presented a flexibility evaluation method for power system planning issue that has comprehensively considered system resources regulation features and renewable energy sources fluctuation based on a conditional probability convolution algorithm. Based on a Monte Carlo simulation, [PERSON] et al. [20] proposes a power system flexibility assessment system considering wind power correlation. A novel flexibility index (FLEXIN) is proposed by [PERSON] et al. [21]. The FLEXIN takes generation reserve, transmission reserve, renewables, storage, demand and market into consideration, provides several opportunities for analysis and improving the operation.