Desarrollo de la aplicación de la metodología emergética al

The transformation from an energy system based on conventional power plants to a system with high shares of renewable energy currently implies a realization of new models or extension of existing energy models to cover the new requirements adequately. The main targets are to find solutions how high shares of RES-E can be integrated in electricity systems and to analyze the effects on the system. Golling (2011) summarizes how different model approaches integrate renewable energy sources into electricity models:

• Modeling of electricity generation or deployment paths of RE technology is separated from other market players (such as conventional power plants). Focus is often set on RE deployment in connection with policy instruments.

• Modeling of power markets includes exogenous model results for renewable energy generation.

• Iterative modeling approach links generation model for conventional power plants with a separate model of renewable energy sources (e.g. residual loads after feed-in of renewable energy are iteratively linked to a conventional dispatch model).

• Integrative modeling implements all technologies in one single model approach. Allocation and operations of all infrastructure projects (power plants, transmission lines, storages, etc.) are equally reflected by using a single model.

An integrative model approach has the advantage of considering all different technologies and their relations within model. However, dividing a problem into different models with focus on

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specific parts of the electricity system (e.g. optimal RES expansion without parallel expansion planning for conventional power plants) can help to detail the results of a specific problem. Furthermore, this approach can facilitate solving time.

A key issue for the selection of the modeling approach covering renewable energy sources is the large amount of variables which is mainly caused by many small-sized and decentralized RE generation capacities. In contrast to large centralized conventional power plants, RE power plants produce electricity at many different locations with non-flexible generation profiles depending strongly on weather conditions. According to different site conditions and project costs, many RE power plants at different sites have to be implemented in the model to obtain a representative coverage. Three modeling solutions from the literature are presented here. They simplify the investment planning and reflect a large number of variables or increasing computing time:

a) Investment planning on a green field site with hourly time steps of one year with reduced coverage of some technologies or electrical grid (e.g. (Scholz, 2012))

b) Investment planning based on the use of (synthetic) typical days of different years while considering shut-down of old power plants (e.g. (Golling, 2011))

c) Investment planning based on full-load hours and peak-demand analysis

In case (b), the problem of choosing a few type days makes the solution very. For instance, Traber and Kemfert (2012) use only a selection of 48 hours to define the long-term technology portfolio with specific capacities per technology. In scenarios with high shares of renewable energy sources, this approach is not proven to sufficiently reflect the different weather conditions and thus electricity generation throughout the year. Therefore, the approach of using type days to simplify the expansion planning on a few representative days during one year (weekday, weekend, winter, summer) has to be extended and adapted to RES.

The following four models from literature present different solutions how large expansion problems with RES are addressed in literature. Iterative and integrative approaches are discussed in terms of their advantages and limitations.

Scholz (2012) optimizes the composition of the electricity system of one target year (e.g. 2050) for Europe and North Africa by using also a linear optimization model. Hourly generation profiles for RE technologies ae pre-processed by a C-code and input to the optimization model. A large resource assessment of renewable energy sources is based on a GIS (geographical information system) analysis to obtain the maximum of installable capacities and power generation potentials per 10km x 10km raster in Europe and North Africa. Conventional power plants are implemented by only one technology due to constraints of the computing resources and to simplify the model. Therefore a gas turbine is implemented to provide a flexible technology for the final dispatch in a scenario with high shares of renewable energy. Although the model optimizes the composition of the electricity system by using the data of one year (8760 hours), the model has to use different approaches for the reduction of model variables to be solvable within a reasonable time period. Therefore the amount of time steps of one model run was reduced to 876 hours (5 runs with only every second hour on each fifth day) and/or the number of regions was reduced from 36 to 9. To be able to use hourly data for renewable energy sources with a parallel approach of operation and investment analysis, this model the conventional power sector is almost completely reduced in the model as explained. In contrast to this, the deployment of renewable energy sources and the resource assessment including an in-depth cost potential analysis are modeled very carefully.

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Golling (2011) presents a sequential approach to find cost-efficient scenarios for the EU. Firstly, the model calculates optimal RE deployment for a certain policy target which is set exogenously. Those targets can be defined according to the National Renewable Energy Action Plans by 2020 or by other long-term target. Then the model approach optimizes the conventional power plant capacities and their generation dispatch by using a linear optimization. The developed approach always selects the cheapest renewable energy sources based on levelized cost of energy in the first step of the model approach. Consequently, market value of renewable energy is not covered as interactions with the conventional power plants are not modeled in the investment decision for RE power plants. This approach is valid due to the current energy policy which helps renewable energy to enter the market under feed-in tariffs. Market value of renewable energy is only partly represented under such investment conditions. Technologies which generate electricity during hours of high demand or which provide dispatchable generation should be underestimated in this approach.

A similar approach with a separate modeling of the RE deployment is proposed by Pfluger and Wietschel (2012). In the model PowerACE-Europe, generation profiles of renewable energy sources are generated by using annual data from the PowerACE-ResInvest model described by Held (2010). In a second step, the model can optimize the European power sector including conventional storages and interconnector capacities with a least-cost approach of a linear optimization problem. The use of satellite data for PV and meteorological stations for wind helps to include the correlation between weather conditions at different sites. Therefore, the model is able to analyze the interactions of renewable energy sources in a large deployment scenario based on resource data with highly geographical resolution. Regarding their model approach of having two models, one for the investment decision of renewable energy sources and one for the final dispatch, Pfluger and Wietschel (2012) come to the conclusion that an detailed integration of renewable energy diffusion seems to be interesting. Development of conventional and renewable energy sources should be simulated simultaneously, if computing resources are available.

A combination of an investment and dispatch model is presented by Nicolosi (2011) by using Benders decomposition algorithm. In his work, the investment decision (master problem) considering different years was enriched by additional constraints obtained by Benders cuts which provide upper bounds for the investment decision of installed capacities by solving the dispatch problem (sub problem) for each month of the observation period. With an iterative approach the capacity mix is improved by each step and then the current capacity mix is used in the dispatch problem. In the analysis, it is necessary to include exogenous inputs from RES. In addition a fixed annual peak demand and a fixed annual demand are necessary in order to generate sufficient results, because the iterative approach requires several steps (13).

As shown, all four model approaches demonstrate solutions to solve the problem of large data connected with renewable energy sources by choosing methods to simplify the overall problem. Especially, the interactions between renewable energy projects and the conventional power system require simplifications which often are implemented by using iterative or reduced investment decisions. The models also limit the number of model nodes by defining a certain number of regions for Germany, Europe or North Africa (country as one region). The transmission problem is only partly covered in the models to reduce model variables (copperplate approach or one model node per country). Further transmission problems (local distribution, line losses, etc.) are excluded from the analysis to reduce the complexity of the models.

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