*2.1. Modeling for Renewable Energy Support*

Renewable energy support is meant to foster investments in renewable energy. In order to understand what investment incentives a policy creates, one needs to take the investor's perspective and to analyze the investment profitability and how the policy affects it. Traditionally, such an investment analysis is conducted with a cost–benefit approach and in particular real options framework [21]. The real options framework, apart from plain profitability, recognizes uncertainty connected to the project implementation and possible flexibilities that allow the benefits to be captured or the shortfalls of unfolding uncertainties to be avoided [22]. Therefore, the real options framework becomes especially useful in understanding the effects of policies since a policy aims to reduce uncertainty for investors that otherwise hinders technology diffusion.

A considerable share of renewable energy valuation studies specifically focused on the analysis of policy effects [23]. The majority of such studies recognize uncertainty coming from volatile electricity market prices, and the main type of flexibility is to postpone investment. Such a study design allows for addressing the question of whether one or another policy sufficiently shields investors from uncertainty to incentivize investments sooner rather than later. Especially beneficial for policymaking are comparative studies, where the performance of different types of support instruments is analyzed [24,25]. Methodologically, real options research encompasses both analytical and numerical methods, including standard methods such as dynamic programming, Monte Carlo simulation, and various trees and lattices [23,26]. However, the majority of studies take an individual investor's perspective.

System-level energy models rarely come down to the policy details [27]. One prominent exception is the Green-X model [28], which intentionally recognizes different types of support for renewable energy and analyzes their performance and costs on the system level. However, GREEN-X lacks modeling of realistic investment behavior. The decisions to invest are based on a plain cost–benefit analysis and investors, for example, are not given a right to postpone their investments.

Meanwhile, in the real world, professional investors and utilities behave in accordance with the real options logic [29], even if they do not use real options models for decisionmaking [30]. To the best of our knowledge, the only model, so far, that integrates real options logic into the energy system level is the one by Rios et al. [31]. However, it does not focus on renewable energy sources or their support policies. Instead, the aim of this model is to capture the fluctuations in investments in new power generation after electricity market liberalization. The cyclic behavior of these new capacity additions makes it possible to simulate the flexibility in postponing an investment in the model. Thus, this finer detail of investment behavior—flexibility under uncertainty—is a must-have in system-level energy models if the aim is to estimate policy effects on investments.
