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In this research, we present a new long-term energy planning model that considers endogenous capacity investment, energy dispatch, Power-to-X, and demand response technologies. A thorough literature review of existing energy planning models is also presented, allowing to present the distinctive characteristics of the proposed model. The proposed model considers an energy system with the objective of minimizing the total capacity investment cost, throughout all technologies, and the operational cost faced by the system in satisfying energy demand. The model also considers the links among different demand sectors, including the links between the electricity, industry, heat, transport, and electro-fuels (e.g., Hydrogen) sectors. The proposed model is used to study the decarbonization of the Croatian energy system under distinct policies associated to RES levels and CO2 emissions goals. We demonstrate that Power-to-X technologies can certainly provide the flexibility that is required by new capacity investments in variable renewable energy sources, obtaining systems with lesser levels of critical excess of energy production. Higher usage of battery storage and Power-to-heat technologies are adopted primarily for variable renewable shares and CO2 reductions of close to 80%, while below such levels, the adoption of such technologies is limited. Additionally, Power-to- heat flexibility options become the major technologies when limits on CO2 emissions from the heating sector are imposed and, particularly, when the variable renewable energy shares in the electricity sector gets close to levels of 60%.

Long Term Capacity Planning (LTCP) consists of deciding the type and amount of capacity of production systems for multiple periods in a long term planning horizon. It involves decisions related to strategic planning, such as buying or selling of production technology, outsourcing, and making tactical decisions regarding capacity level and configuration. Making these kinds of decisions correctly is highly important for three reasons. Firstly, they usually involve a high investment; secondly, once a decision like this is taken, it cannot be changed easily (i.e. they are highly irreversible); thirdly, they affect the performance of the entire system and the decisions that will be possible at a tactical level. If capacity is suboptimal, there will be lost demand (in the present and possibly in the future); if the system is oversized, there will be unused resources, which may represent an economical loss. Long term decisions are typically solved with non-formalized procedures, such as generating and comparing solutions, which do not guarantee an optimal solution. In addition, the characteristics of the long term capacity planning problem make the problem very difficult to solve, especially in cases in which products have a short life cycle. One of the most relevant characteristics is the uncertainty inherent to strategic problems. In this case, uncertainty affects parameters such as demand, product life cycle, available production technology and the economic parameters involved (e.g. prices, costs, bank interests, etc.). Selection of production technology depends on the products being offered by the company, along with factors such as costs and productivity. When a product is renewed, the production technology may not be capable of producing it; or, if it can, the productivity and/or the quality may be poor. Furthermore, renewing a product will affect its demand (cannibalization), as well as the demand and value of the old products. Hence, it is very important to accurately decide the correct time for product renewal. This thesis aims to design a model for solving a long term capacity planning problem with the following main characteristics: (1) short-life cycle products and their renewal, with demand interactions (complementary and competitive products) considered; (2) different capacity options (such as acquisition, renewal, updating, outsourcing and reducing); and (3) tactical decisions (including integration strategic and tactical decisions).