Most of the models referenced before are not developed for the energy markets of North African countries. So far, studies and models analyzing the energy and electricity markets of North Africa are limited, especially those which cover more than one country. One important reason for low modeling efforts of regional energy markets is the limited interconnection of the national electricity markets which reduces the need to analyze more than one country in the past. Likewise the electricity consumption of the countries (2010: 213 TWh, sum of all countries) is relatively low compared to industrialized countries. Additionally, the operation and planning responsibility is mainly carried out by national authorities, national energy utilities and grid operators. Regarding the future development of the system, each country has a national mid-term investment plan for the electricity sector over the next five to ten years. In the last years, targets for market development of renewable energy also have been set by all of the countries (see Brand and Zingerle (2011) and Hawila et al. (2012)). Public information concerning methodology of the planning process and use of electricity market models is difficult to find. Thus, the investment plans are rarely supported, neither qualitatively or quantitatively, by modeling and publications. Following studies focus on the long-term development of the electricity market in North Africa.
The World Energy Outlook 2010 of the IEA proposes different renewable energy scenarios for the Middle East and North Africa region. Solar technologies will play a key role for the future development of renewable energy in the region due to the abundant availability of locations with excellent solar resources (IEA, 2010). Based on different policy paths for the market introduction of renewable energy technologies, the IEA develops three different scenarios which last to the year 2035. Under the assumption that current policy efforts will be stable in the future, 17% of the electricity (representing 85 TWh) will be produced by renewable energy sources in 2035 in the Current Policy scenario. Higher shares of RES-E with 26% (120 TWh) and 58% (226 TWh) coming mainly from solar or wind technologies can only be reached in scenarios which assume an active commitment of national governments to reduce CO2
emissions and to support a fast market introduction of renewable energy technologies. The scenarios are calculated by the World Energy Model which is used by the IEA in their projections. This model reduces the electricity market of North Africa to one single region. Consequently, detailed national results could not be provided and are missing in the report. Three scientific studies ((Scholz, 2012), (Pfluger and Wietschel, 2012) and (Brand et al., 2012)) recently modeled the electricity system of more than one of the North African countries by using optimization methods to obtain results regarding the future development of electricity markets in the region. Scenario studies like the report MED-CSP (Trieb et al., 2005) are not discussed here due to their different approaches which do not use mathematical models based on an hourly electricity dispatch and economic investment modeling. However, these scenario studies have to be considered as important expert views of potential scenario options of the electricity markets. In section 2.4, the methodology and model approach of Scholz (2012) and Pfluger and Wietschel (2012) are described and classified. Scholz (2012) provides results for the North African region, but Pfluger and Wietschel (2012) only give the methodology which is finally used in the report Desert Power 2050 by Zickfeld et al. (2012) and (Zickfeld et al., 2013) which are a collaborative works between Dii GmbH (an industry association which promotes the Desertec vision) and Fraunhofer Institute for Systems and Innovation Research ISI. The results of all studies are presented below.
32 CHAPTER 2. Modeling fundamentals for electricity systems with renewable energy sources
Brand et al. (2012) analyze the value of CSP plants in the electricity systems in the Morocco and Algeria. This paper uses also a modeling approach of both electricity systems but the markets are not linked because there is currently a low electricity exchange between both countries. With a linear optimization program for a cost minimizing dispatch and an optimal investment path the electricity markets of both countries are optimized for 32 typical days under the constraint to include a certain share of electricity generated from renewable energy sources in different scenarios by the year 2030. The results show that in Morocco and Algeria, PV is the preferred solar technology by the optimization model compared to CSP which increases its value if the PV capacities reach a certain share in the electricity markets. In Algeria large capacities of flexible gas power plants support the market entrance of PV. In Morocco, a smaller share from solar electricity was assumed which could be more easily fulfilled by the “low- hanging renewable fruits” of PV (Brand et al., 2012).
Scholz (2012) has the objective to find a low cost solution for a sustainable power supply (near 100% of renewable energy) for North Africa and Europe in an integrated model for all countries in the year 2050. A specific focus is set on a high geographical resolution for the use renewable energy resources (in the total area covered by the model). The decision criterion of the model is to minimize the overall system costs of the North African and European electricity system. System costs vary between -20% and +30% in all sensitivity analysis. A change of technology costs compared to the base cost assumptions gives a strong technology shift by a multiple as reported in the study. The model strongly reacts with an increase of PV capacities under the assumption of lower costs. In case of higher specific technology costs, the optimization can switch to other technologies by eliminating one technology completely. Therefore, sensitivity of the model is high regarding small cost changes of the selected technologies, but overall system costs are reported to be relatively stable. The study revealed that availability of sufficient transmission capacities between regions is highly necessary for an increasing share of renewable energy. But risks and barriers for these infrastructures are estimated as high due to the relevance of local and international acceptance of the projects. Storage capacities have to be sized up to 7.2% of the total generated electricity. Results for the North African region are presented in both, a connected and island-scenario (transmission between countries possible or not). In the connected scenario Algeria, Libya and Tunisia are declared as strong exporter of electricity due to their geographical location and their excellent solar resources. To a large extend wind power and PV are not selected by the model. CSP is the dominating technology in North African electricity systems due to the assumptions in the model regarding cost, lifetime and output of the power plants. CSP costs are assumed to approximately 3000 EUR/kW for a power plant with 12-hour storage in 2050. That is less than a third of 2010-costs which requires enormous progress for the CSP technology.
The report Desert Power 2050 by Zickfeld et al. (2012) also examines the power supply in year 2050 while the path to 2050 is not considered by the model. This electricity system is mainly based on electricity from renewable energy sources and targets of CO2 emission reduction in
Europe by 95% and in North Africa by 50% of today’s values. North Africa and Europe are analyzed in different scenarios by adding Middle East and Turkey to the modeled regions compared to Scholz (2012). High cost savings (33 bn EUR per year) can be reached by connecting all electricity markets and the use of the best resources of renewable energy in the whole area. Exports from MENA are calculated to be beneficial up to 63 bn EUR per year. This South to North electricity trade leads to European imports of 20% of its electricity. In the connected scenario 833 TWh are generated in North Africa mainly from wind (approx. 70%), PV (approx. 15%) and CSP (approx. 15%). Transmission capacities are calculated at 189 GW in
CHAPTER 2. Modeling fundamentals for electricity systems with renewable energy sources 33
the connected scenario between North Africa and Europe. The average system costs are calculated to 61 EUR/MWh for all regions; whereas in North Africa each additional generated MWh has a cost of 57 EUR/MWh. Morocco and Libya are identified as the largest exporter of the North African countries due to their enormous wind potential assumed in the model.
Both reports highlight the large benefits of a HVDC supergrid to exchange large volumes of electricity from renewable energy sources over long distance. The fluctuating generation from renewable energy sources is very well balanced in the total system as curtailment is relatively low. The substantial integration of renewable energy sources is shown in both models that cover a large area, but cannot give a detailed national perspective of each country as the implementation of each country as one node does not allow a detailed assessment. Whereas both studies show cost assumptions for the RE technologies in a similar range, the large difference in the technology selection, especially the different results for wind and CSP, cannot be explained without an assessment of all data and used assumptions. A detailed model description of Zickfeld et al. (2012) is missing and makes it difficult to evaluate the study results comprehensively. Both electricity system of the year 2050 are planned without taking existing infrastructure into account (e.g. from the year 2049). Dynamic developments from today to 2050 have a strong influence on the electricity system in the year 2050 and might change the outcomes. Both studies provide an insight how a new electricity system in 2050 might look like if it would be built on a green field. However, both studies do not answer the question whether the results for the electricity system are the best solutions if a continuous long-term path would be considered and optimized, e.g. a time period form today to 2060. In 2013, the report Desert Power – Getting started (Zickfeld et al., 2013) was published to provide a first analysis on the path from today to 2050 (from the similar group of authors such as Desert Power 2050). By combining a RE diffusion model (GreenX from Technical University of Vienna) with a generation dispatch model (PowerACE), the CO2 emission targets of the
EUMENA is reached on a continuous path. In terms of power generation, the North African countries are completely based on renewable energy sources without any conventional power plants in 2050 according to the model results. Wind power remains the most important resource with about 70% of the total electricity generation, 25% comes from CSP, about 4% from PV and about 1% from hydro power. Compared to Desert Power 2050, the share of PV has significantly decreased. As costs of CSP might become competitive later in time than PV, this discrepancy of the results between both reports cannot be explained as PV deployment has to start earlier compared to CSP, but this is not projected in the results. At the same time a cost projection of 2000 EUR/kW for a CSP plant with eight hours is used which does seem to be reached as the conventional part of this technology (including the thermal storage system) exceeds the price of 2000 EUR/kW today. The results per country in terms of technology share look quite similar to the overall situation whereas PV obtains the highest share in Egypt and CSP in Algeria. In total, a capacity of 624 GW of renewable energy sources is installed in the year 2050 of the connected scenario with export. Between North Africa and South of Europe a transmission capacity of 134 GW will be installed for an electricity export from North Africa of 745 TWh in 2050. When analyzing the results of the Desert Power studies, Schubert and Möst (2014) indicate some critical problems of very large electricity export to Europe: The model results might be an outcome of using the green-field approach, disregard of decentralized PV and important model assumptions such as cost for grid expansion or demand forecast.
34 CHAPTER 2. Modeling fundamentals for electricity systems with renewable energy sources