HOMEOGRAFÍA Sumario del Capítulo

Balmorel, the "BALtic MOdel of Regional Electricity Liberalized", is an open-source model for analysing the combined power and heat sector, available at [57]. It was originally developed by ELkraft (now Energinet.dk) and the Technical University of Denmark (DTU). The model is coded in GAMS, the General Algebraic Modeling System, which is a high-level modelling system for mathematical programming and optimization [58].

The model has been used both by the Systems Analysis Division at DTU Manage- ment Engineering and EA Energy Analyses for a large number of projects, most of which related to Denmark and the Nordic Countries. In addition, Balmorel projects have been carried in Germany, Mexico, Vietnam, China, Western africa, South Africa and Eastern Africa [59].

Its main characteristics well fit with the analysis requirements, being it a bottom- up, linear and deterministic model, with the possibility for both investment and dispatch optimization. Additionally, the model is a partial-equilibrium tool, since it represent with a good detail only heat and power sector, as opposed to general equilibrium models which include all sectors and have a macro-economic perspective. A more detailed description of the model, in terms of geography, technologies and dynamics, is available in Appendix A, while a presentation of the model setup is shown in Figure 6.1.

Chapter 6. Energy System Modelling

Figure 6.1: Balmorel model setup. Source: [59]

Assumptions and limitations of the model

There are a number of general assumptions which the model is based on, which will be presented below.

First of all, the model assumes that all the power is traded in the day ahead market. Secondly, perfect competition in the market is assumed, meaning among other things no exercise of market power is possible. In reality, power and heat sector are still subject to market failures. Moreover, the model has perfect foresight of the future and optimizes based on the total amount of information available. This can lead to optimal solution which in reality would not practically hold, due to the uncertainty in key parameters such as hydro inflows, wind speeds, irradiation and demand level. On the other hand it gives the advantage that all scenarios are optimal, making them easier to compare.

Beside the assumptions just presented, the model has some further limitations: • Uncertainty of the inputs: due to the very large amount of data in input, many

of which representing projections of costs and performance in the future, the results from the model are largely dependent on the data in input. This limit is not specific to Balmorel, but in general related to modelling simulations. • Short sight in investment optimization: when optimizing investments, the

model takes into account only one year. If the plant pays back the annu- ity of the total investment, the plant is invested in. As a consequence, the condition of the future system are not taken into account.

• No representation of internal grid: each region in the model is considered a copper plate and no detailed representation of the internal grid is present in the model. However, transmission capacities between regions are represented.

• No consideration of power flows: the resultant inter-regional flows from the market clearing have no limit beside the maximum transmission capacity. There is no certainty that the resultant flow would be technically feasible from a grid perspective.

Status of Balmorel model and improvements performed

Thanks to the large amount of projects carried on so far with Balmorel, the setup of the model is quite well defined. A detailed description of Denmark, Germany and the Nordic countries is already present, together with an overall representation of all the other European countries. Data on plants, demands, as well as exogenous parameter for future renewable plans and goals are available and implemented. Beside the implementation of specific data related to the goal of the study and the inclusion in the model of different technologies of wind based on the SP, the effort in the project has been placed in reviewing and improving the methods used to model wind power production.

Specifically, calibration and update of existing parameter has been carried on to- gether with the development of a new way to describe the smoothening of the ag- gregated power curve for the future technologies, which is presented in detail in Chapter 8.

7

Modelling the value of wind in

the power system

7.1

Analysis focus and framework

The present analysis focuses on the evolution of the system value of wind power un- der different turbine design. To assess the system value of wind, both total system costs and market value of wind are analysed under different technology scenarios. The technology scenarios chosen are related to two parameters already introduced: specific power and hub height. Dedicated scenarios are developed for each of the two parameters to assess their contribution separately.

The idea of the study is to have a "policy-maker" approach by simulating the system development in the next 15 years, considering existing policies and targets, as well as plans for RES roll out and transmission expansion. This approach complement what in literature has been defined as medium-term perspective [4], with projections on the expected development based on national and European plans. The distinc- tion between short, medium and long term perspective has been categorized in [4] based on the way the capital stock is treated. In the short term existing capacity is given and only dispatch is simulated. In the long term, a green-field approach is used where no capacity is given and the system is optimized via investment model. With a medium-term approach, existing capacity is included but the system can adapt to evolving conditions and parameters through endogenous investments and disinvestments. The result of using a medium-term perspective and exogenous in- puts is a more realistic representation of the system and its future evolution, based on the information available and country commitments today.

Another thing that is worth mentioning is that existing fleet is modelled and in- cluded as part of the capacity generation stock, together with assumptions of the progressive future decommissioning. On the other hand, all the scenarios related to the technological design of turbines applies to future installations only, which adds on top of the existing fleet. By doing so, the value of wind is affected especially in the short time, since the historical fleet and installations of future technology will 37

have a different production based on their diverse technological characteristics. With respect to this, not taking into account historical capacity and modelling wind as "all advanced" or all "all conventional" could result in overestimating the benefit for onshore wind of switching to advanced design in the coming 15 years.

Timeframe

The timeframe selected for the study is up to 2030. Model simulations are performed for 2015 as a historical base year and consequently for 2020, 2025 and 2030.

Geographical scope

Almost the entire Europe is modelled in Balmorel, with the exclusion of Iberian peninsula and Balkan countries. The model includes a detailed representation of the Nordic countries and Germany, together with an overall representation of the rest of Europe. The list of countries included in the simulations is the following: Nor- way, Finland, Sweden, Denmark, Estonia, Latvia, Lithuania, Germany, UK, France, Belgium, Netherlands, Switzerland, Italy, Austria, Poland, Czech Republic.

While all these countries are modelled, the focus of the study is be placed upon Germany, used as a test case. The rationales behind the choice relates to the level of detail in the modelling, the importance of Germany in the European energy context, but in particular to the ambitious goals for onshore wind development.