**4. Discussion and Conclusions**

Some models integrate hourly fluctuations in the demand and supply of electricity into long-term generation technology mix planning. These models show that renewable energy sources possess a degree of complementarity that, if captured, can reduce the needed backup capacity and can ease the requirements on system flexibility. However, a complementary renewable energy power plant might be suboptimal in terms of profitability from an investor's perspective. Thus, in order to steer renewable energy investments in favor of energy system reliability, different investment incentives need to be introduced. Such incentives need to capture the value of complementarity of a power plant to the existing power system. Numerous design choices are required to create such an incentive mechanism.

This paper introduces a conceptual model that can analyze the effects of different designs of support for reliability-based renewable energy on power system operations and development. In its simplest deterministic form, the model is applied to a stylized case, and the potential benefits in terms of power system reliability, overall technology, and policy costs and the environmental footprint are demonstrated. In contrast, currently, policymakers rely on models that are wired to calculate a fixed amount of backup capacity for every unit of newly built renewable energy source [20], hindering the very possibility to design a policy for a more efficient power system.

The hybrid model introduced in this paper allows us to redesign the support for renewable energy and to analyze whether a reliability-conditioned instrument makes sense for a particular system. The same model can be used to quantify the effects of different types of storage and demand response. With this model, one would be able to model the effects of different capacity mechanisms with or without separate support for renewable energy and to optimize the overall policy mix for the power system. The model will also be able to show the optimal limit of renewable energy adoption in a particular region. After such a limit, any more renewable energy of any type in any location would not provide any marginal contribution to the power capacity of the system. Pushing for the growth of renewable energy sources beyond this limit will become a futile attempt at decarbonization since more stable power output plants will be needed to offset the variability of renewable energy sources, which in turn would increase fossil fuel usage and jeopardize decarbonization. Instead, other sources of flexibility should be promoted in these system, such as storage, hydrogen and power-to-X solutions, and demand-response programs.

The results of such a modeling exercise would heavily depend on region-specific characteristics. They include the technology mix currently in place; the electricity demand profile; its variability and projections; the transmission capabilities in a system and its connections to neighboring areas; the system flexibility, in particular the development and deployment levels of storage and demand response solutions; the availability of renewable energy resources; and their possible complementarity. Political, economic, and social factors clearly play their roles as well; however, their effects would depend on whether they are wired to the model.

The complementarity of renewable energy sources has been shown in multiple cases, such as the temporal and spatial heterogeneity of wind power among power used on the European continent [19] and the uncaptured value of southwest-oriented solar panels in California compared with commonly built south-oriented solar panels [47]. Some studies suggest that one way to discover the complementarity of renewable energy resources is

to consider them over larger geographic areas. For instance, Grams et al. [54] suggested considering continent-scale wind patterns to implement pan-European collaborations for the development of renewable energies. Of course, capturing that complementarity value requires massive network investments, of which the economic viability can be thoroughly investigated using the proposed hybrid model.

One can argue that replacing the fossil-based flexible generation with renewable energy sources is not needed since synthetic fuels will soon replace fossil fuels. However, even according to very optimistic estimations, the adoption of power-to-X technologies and the corresponding massive production of synthetic fuels as well as massive installations of storage technologies (batteries) are expected to start worldwide not earlier than in the 2030s [55]. In this light, the introduction of policies, which aim to replace the currently used fossil-based flexible generation with the optimal mix of renewable energy technologies remains relevant.

Departing from modeling-related matters, actual policy implementation has numerous issues to consider as well. The transition from support for classical renewable energy to a reliability-based instrument might not be easy due to the associated paperwork, design, and arrangement burden. Although in the recent years, a trend has switched to more market-oriented mechanisms in supporting renewable energy, that is, from fixed feed-in tariffs to premiums, auctions and certificate trading [1], they still do not have a sufficient foundation for such a change since a power system perspective and procedures for calculating reliability are missing. However, some countries have introduced capacity markets, where calculations for the contribution of renewable energy to system reliability are already a routine procedure [7]. In these cases, the transition to reliability-based support for renewable energy sources would be much smoother. Countries that have capacity mechanisms in place and, most importantly, some procedures for calculating the contribution of renewable energy sources to reliability, are displayed in Figure 4.

While the idea of reliability premium is conceptually simple, in reality, it faces multiple design choices.


**Figure 4.** Geographical coverage of reliability schemes and their inclusion of renewable energy technologies, based on *[7]* but modified by the authors (Source: Author). **Figure 4.** Geographical coverage of reliability schemes and their inclusion of renewable energy technologies, based on [7] but modified by the authors (Source: Author).

> While the idea of reliability premium is conceptually simple, in reality, it faces multiple design choices. • Which reliability indicator should be used? The proposed model can compare the difference in effects of various reliability indicators. However, an important factor is the existing procedures for calculating reliability for a country. Different system operators adopt different practices in that respect [17], and implementing perhaps subefficient but already working solutions would create much less administrative burden, better transparency, and a faster transition. The same applies to the other design choices for the calculation of reliability and system modeling. • Should projects be exposed to a dynamically changing premium, or should it be fixed for a project's lifetime once calculated? The former has higher uncertainty and unpredictability for individual investors, computationally heavier systems, more room for administrative disorder, and more room for human mistakes. The latter allows for better order and provides more certainty for investors but might result in a less dynamic and responsive system. The question of which policy mix would potentially be able to steer the mix of renewable energy technologies was briefly discussed in the previous research devoted to international policy review [7]. The modeling exercise performed in this work sheds light onto and brings additional insight into this discussion. Naturally, if renewable energy sources are excluded from a capacity mechanism, the common types of renewable energy support alone would not provide investment incentives favoring system reliability. If participation in a capacity mechanism requires renewable energy sources to forgo the corresponding amount of support, the overall revenue from renewable energy sources stays the same, which again excludes incentives favoring system reliability. If, on the other hand, participation in a capacity mechanism entirely prohibits receiving other types of support, then such incentives come into the scene. The latter two points become clear with the modeling exercise performed in this paper, whereas in the previous qualitative-only analysis [7], these conjectures were made differently. Most importantly, however, is the conclusion that the incentive to steer a mix of renewable energy technologies in favor of energy system reliability can be implemented outside of a capacity mechanism and independently of its very presence.

> • If the reliability premium is fixed, how often should it be recalculated? The recalculation can be carried out for each project, for each auction, or on an annual basis. • If a capacity market is already in place and renewable energy sources can participate in it, how should the reliability-based premium be integrated? The two can co-exist or be merged. The former requires carefully accounting for the economic meaning of As we can see, the introduction of such a conceptual hybrid model with the hypothetical idea of supporting renewable energy sources via a reliability-conditioned instrument leads to a variety of consequent design and implementation choices. However, the authors believe that the direction is worth perusing for the sake of more reliable, cost-efficient, and environmentally friendly energy systems.

> both types of support and prevents over-subsidization. In addition, a close collaboration would need to be established between the departments of system reliability and support for renewable energy sources. The latter creates a risk of distorting capacity prices and jeopardizing the effectiveness of the market by adversely affecting **Author Contributions:** M.K.: conceptualization, data curation, methodology, writing—original draft preparation, writing—review and editing, and visualization; A.L.: conceptualization, data curation, methodology, writing—original draft preparation, and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

> other categories of participants (non-renewable generation, storage, and demand response). The question of which policy mix would potentially be able to steer the mix of re-**Funding:** This research was funded by the Foundation of Economic Education, grant number 200153, and by Kone Foundation, grant number 201710464.

> newable energy technologies was briefly discussed in the previous research devoted to **Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All used data is presented in the manuscript.

**Acknowledgments:** The authors deeply appreciate the extensive critical commentary from Paolo Mastropietro that allowed for improvements to be made to this paper and that inspired future research directions.

**Conflicts of Interest:** The authors declare no conflict of interest.
