In an energy-only market, private investors play a crucial role in realizing the security of supply. However, several specific characteristics inherent to his market design cause concern about whether this market design is suitable for system adequacy. Moreover, due to policy goals aimed at working towards low-carbon energy systems, these concerns are increased. In this light, many countries have implemented a capacity remuneration mechanism to increase the stability of the security-of-supply in their energy system. While earlier research has been conducted on this topic, we observe two knowledge gaps: 1) from a theoretical perspective, there is debate on the effectiveness of capacity remuneration mechanisms, especially on the role of seasonal storage, 2) from a methodological perspective, we find that there are no models that are equipped to consider myopic investments while having a high enough detailed operational model also to consider investments in (seasonal) storage and the impact of extreme weather scenarios. This research aims to fill these knowledge gaps by presenting a proof-of-concept of a myopic investment detailed operational (MIDO) model. In our thesis, we focus on system adequacy. However, by developing the MIDO model, we hope to enable researchers to analyze any problem related to long-term energy system development with a multi-time scale nature. At the center of the MIDO model are three sub-models: the investment decision model (ID), the future price (FP) model, and the present price model (PP). The first step in this loop is the present-price model, which generates highly accurate information on the operation of a market. After running a year in the PP model, information on the performance of all assets in a market is sent to the ID model. With this data, the ID model performs two tasks: invest based on limited information in an iterative greedy process and dismantle assets losing money. The ID model gets the information on the performance of investment assets under consideration from the FP model. The FP model generates less reliable information than the PP, but it does so very fast. After the investment decisions are made, this information is transferred to the PP model, and the loop starts from the beginning. In this way, a market is simulated with investment cycles, delayed responses, uncertainty, and risk upon investment decisions. This process enables the comparison of different forms of capacity remuneration mechanisms and the effect on the security-of-supply in an isolated energy system. We explore two distinctly different futures within our thesis to see if there is any added value in a capacity market in any of these futures. Moreover, within these futures, we analyze how a capacity market can help withstand an extreme-weather scenario. From this analysis, we find that a capacity market has added value in all scenarios, from the point of system adequacy but also a total consumer cost perspective. However, because we only have explored two futures, we do not argue that a capacity market will always positively impact the system adequacy within an energy system. Instead, we argue against the notion that seasonal storage on its own is always enough to reduce all shortages created in an energy-only market and disagree with the idea that seasonal storage makes capacity markets redundant. We identify two avenues for future research. First, researchers interested in generating more and better results should utilize the MIDO model to explore more future scenarios and compare different forms of capacity mechanisms. Second, research aimed at improving the MIDO model should focus on limiting the computational time generated by the investment loop and seek to expand on the model’s functionalities.