Model structure and decision modeling

For each technology, RETMD covers the main components and services of the value chain. As described in section 6.3, interviews with stakeholders from industry, research and government provided qualitative and quantitative input for existing business sectors, specific requirements on production processes and production factories, available technological know-how and the status quo of local manufacturing in recent RE projects in North Africa. Installed capacity per technology and year serves as market demand in the RETMD model (see model structure in Figure 66). For simplicity reasons, only one reference power plant per technology is implemented in RETMD although in reality a variety of technology designs and technology options are possible. Each reference power plant includes a detailed description of the technology design and power plant data regarding components and services for which a breakdown of costs and number of jobs is implemented (see section 6.5.1).

Figure 66: Model structure of RETMD

RE Technologies Market Development Model (RETMD)

Results Decisions Decision Parameters Inputs

Know-how decision per component

Market decision per component Market demand &

Forecast

Installed capacity per technology/component

RETMD output

Annual sales

Annual FTE jobs

Expert interviews

Technology parameter

Cost structure

Cost projection (learning

curve)

Employment rates

Technologies

Reference plant

Layout and capacity

Construction and operation Status quo in projects Know-how (component) Competiveness factors Know-how (countries) RESlion Scenario results Factory specifications Data research Potential of local value creation (PLVC)

Continuous increase Max./Min. potential

Eq46 Model decision Dependencies Eq47 Eq 44 - 45, 48 -51 52-59Eq

CHAPTER 6. Model development for socio-economic impact analysis 153

The model covers all items in the value chain which are necessary to construct and operate power plants: all (large) components, engineering services, financing, power plant erection and installation, operation and maintenance. Employment effects in terms of FTE jobs are derived from dynamic employment rates per component or service. The model is implemented for CSP, PV and wind power and applied to the North African countries (Morocco, Algeria, Tunisia, Libya and Egypt).

First model development (including equations) and model parameters for CSP are published in (Kost et al., 2012g). The model description is now extended, some model dependencies are adjusted and additional calculations are added to obtain a component specific decision model for wind power, CSP and PV.

To quantify the annual (local or international) sales and employment over the total value chain, the model calculates the potential of local value creation (PLVC) per component/service or technology as share of the total value creation as one of its key model outputs. In the following, the model equations and data input of RETMD is explained. In the model, a component/service (n) specific decision regarding PLVC in each country (c) is made based on a know-how based decision and a market based decision for each year (t). In case of t = 0 (respectively in the year 2012), PLVCn,c,t=0 is equal to the status quo of local manufacturing (SQn,c) which is determined

by data research and direct interviews related to real-world projects in North Africa in the years 2010 to 2012.

, , = , ,

(Eq 44)

The future development of PLVC (with t > 0) is subject to two basic requirements which have to be fulfilled annually (decisions in the model). Only if a know-how based decision and a market based decision for PLVC are positive, PLVC can be increased from the status quo. Otherwise PLVCn,c,t is set to SQn,c as the decision for local value creation is negative. The model dynamically

makes these decisions for a time horizon from 2012 to 2030.

In the know-how based decision process, available technological know-how (TKH) in a country has to be higher than the technological know-how which is necessary to be able to run a production line or a single production process of a certain component. This decision provides only a positive or negative answer to the question if local manufacturing is possible (no answer about the height of PLVC).

In the market based decision process, a decision is modelled to define local potential from market perspective. Current and future market demand per component is compared with typical sizes of production facilities and manufacturing condition. Only if a sufficient market demand over the next five years exists, RETMD indicates potential for a local company or for a local subsidy of an international company to set up a new production facility or to update existing production lines. Therefore, mid-term market stability directly influences annual sales and FTE jobs for each component and service. Expectations of future market demand are modeled by perfect knowledge of the market demand over the next five years. Market demand (MDn,c,t)of each component in country (c) and year (t) is equal to power plant installations per

technology in the market scenarios. The average market demand should be higher than the minimum economic throughput (Outputn,min). Additional output of a factory in form of other

products besides the certain component is also implemented (Outputother). Therefore, the

154 CHAPTER 6. Model development for socio-economic impact analysis , , ≥ , ,, , ≥ , ∑ _{, ,} ≥ _, − _, (Know-how decision) (Market decision) (Eq 45) (Eq 46) (Eq 47) Only if both decisions (know-how and market) are positive, PLVC can be calculated for each component or service.

The know-how based decision per component is influenced by the following decision parameters (Kost et al., 2012g):

• Status quo of local manufacturing in year 2012: Existing industry capabilities are included in the model as starting point or benchmark for further development.16

• Know-how (country): To including existing technological know-how and competiveness of the economy in each country, each country is rated according to the expert interviews and the global competiveness index (GCI) (World Economic Forum, 2013). It is assumed that know-how continuously increases due to the exchange of knowledge, knowledge transfer and learning (Table 24).

• Know-how (component): Each component and service of the value chain is technically evaluated in terms of required production processes and technological know-how for manufacturing or providing the service.16

Table 24: Technological know-how in North African countries related to RE technologies based on expert interviews and competiveness indices (own rating).

Wind CSP PV Annual increase of _know-how Comments

Morocco 4.0 4.0 4.0 2.50%

- Few activities in all three technologies

- High annual decrease due to economic strategy on manufacturing in the field of RE

Algeria 4.5 4.2 4.0 1.50%

- Activities to set up a production line for PV modules

- Low annual decrease due to economic framework conditions

Tunisia 4.0 4.0 4.0 2.50%

- Few activities in all three technologies - High annual decrease due to active business

relations with EU

Libya 4.5 4.5 4.5 1.50%

- Limited activities in the field of RE

- Low annual decrease due to economic and political framework conditions

Egypt 3.5 3.5 4.0 2.00% - Existing companies in the field of wind and CSP - High capacities of construction sector and

manufacturing sector (Scale for know-how: 1.0 to 5.0; 1.0 represents highest know-how)

(Annual increase = annual increase of know-how)

CHAPTER 6. Model development for socio-economic impact analysis 155

The market decision requires the following decision parameters (Kost et al., 2012g):

• Factory specification: Production of a component in a production factory is only possible if a sufficient factory output or throughput is fulfilled by the market demand. The demand for components and services is given by the amount of power plant capacity which is constructed according to the market demand scenarios.16

• Market demand and forecast: Creation of local production factories is based on the long-term expectations about future market demand in a country or region (Lewis and Wiser, 2007; Kinkel, 2009).

The temporal development of PLVCn also depends on the potential in the time period before

and the specific market demand in year t. If PLVCn is larger than zero in the year before, PLVCn

is increased by the factor (incn). But PLVCn is restricted to a range of a minimum and maximum

potential (Min.PLVCn, Max.PLVCn). In case of PLVCn,c,t-1 = 0 and a positive decision in year t,

PLVCn is set to the minimum potential (Min.PLVCn).

, , = + , , , , , > 0 , , = . , , , = 0 , , > 0 . ≤ _{, ,} ≤ . , _{, ,} > 0 (Eq 48) (Eq 49) (Eq 50) The limitations of PLVCn are based on the following assumptions:

• Minimum and maximum potential per component: Some components obtain a reasonable minimum and maximum potential which can be localized, as international suppliers certainly remain in the market regardless of ongoing local production.16

• Continuous increase of local production: The potential of local companies increases due to constant learning or better sales channels. Annual increase is assumed with 2% to 4% per year depending on already realized potential.

After calculating PLVCn of each component (n), it is possible to sum up the PLVC of each

technology per country (c) and year (t).

, = , , (Eq 51)

By using the component specific results, potential sales and employment impact for each technology are calculated. Results on annual sales and annual FTE jobs are separated in values for the local and international market. Operation and maintenance of power plants are assumed to be carried out by local companies and local workforce. Only for replacement of specific components and major power plant revision, international experts or technology providers are considered. Therefore potential of local value creation during operation and maintenance PLVC.O&Mc,t can be deducted from the calculation of PLVCn,c,t.

Sales and employment effects created by the construction of new power plants are calculated on country level by the use of component specific costs (Cn,t) and employment rates (ERc,n,t).

Both values depend on learning rates (LRn,t).

156 CHAPTER 6. Model development for socio-economic impact analysis

. , = , , ( , ) ∗ , , (Eq 53)

Similar to construction effects, sales (O&M.salesc,t) and jobs (O&M.jobsc,t) of operation and

maintenance are calculated for all years (T).

& . _, = _{& , ,} ∗ _, (Eq 54)

& . _, = _{& , ,} ∗ _, (Eq 55)

Finally, annual total values of sales and employment caused by construction and operation of RE power plants in each country can be calculated for each country (or for the total region, if the sum of all countries is calculated).

. _, = . _, + & . _, (Eq 56)

. , = . , + & . , (Eq 57)

After defining PLVCc,t and PLVCO&Mc,t for all components/services, countries and time steps, as

well as calculating sales and employment effects of each market scenario, potential economic impact in terms of sales of local companies and potential employment effects in terms of local jobs are determined by the model.

. . _, = _, ∗ . _, + . & _, ∗ & . _, (Eq 58)

. . , = , ∗ . , + . & , ∗ & . , (Eq 59)

The volumes of sales and amounts of jobs per scenario are then compared regarding economic and socio-economic effects. In addition to the calculation of economic and socio-economic effects, the model can display the specific year of breakthrough for component manufacturing by a local company.