We focus on the influence of learning by doing and policy but will not take the environmental externality into account

Arnaud

Description

We focus on the influence of learning by doing and policy, but will not take the environmental externality into account Description of the model We consider two periods 1 and 2, in which economic agents can install photovoltaic (PV) systems. The size and lifetime of the PV systems are standard. The cost of the installation is composed of the cost of PV modules which is the same for every system installed since PV panels are commodities, but which is different in period 1 and 2 due to learning effect, and additional costs (BOS, installation, etc.) which is responsible for PV systems heterogeneity. The actualisation rate r is positive. A total quantity Qmax of PV systems can be installed over the two periods. We consider three situations: Without policy (Business as usual), Optimal (were the quantity of solar panels installed in periods 1 and 2 are optimal), and Policy, through the implementation of a feed-in tariff (FIT). Model parameters: Table 1 shows the parameters used in the model which are explained below. Period 1 Period 2 Installed quantity Q1 Q2 Q1 Electricity cost E E PV modules cost C1 C1 Q1 Additional cost Q Q Feed-in-tariff The quantities of PV modules installed are Q1 in the first period, and Q2 over the two periods, so Q2 Q1 during the second period.

• blind agents install


• installed quantities


• pv systems


• than lim


• standard system


• qmax

Informations

• Publié par: pefav
• Nombre de lectures: 5
• Langue: English

Extrait

We focus on the influence of learning by doing and policy, butwill not take the environmental externality into account

Description of the model We consider two periods 1 and 2, in which economic agents can install photovoltaic (PV) systems. The size and lifetime of the PV systems are standard. The cost of the installation is composed of the cost of PV modules which is the same for every system installed since PV panels are commodities, but which is different in period 1 and 2 due to learning effect, and additional costs (BOS, installation, etc.) which is responsible for PV systems heterogeneity. The actualisation rate r is positive. A total quantityQmaxof PV systems can be installed over the two periods. We consider three situations: Without policy (Business as usual), Optimal (were the quantity of solar panels installed in periods 1 and 2 are optimal), and Policy, through the implementationof a feed-in tariff (FIT).

Model parameters: Table 1 shows the parameters used in the model which are explained below.

Installed quantity Electricity cost PV modules cost Additional cost Feed-in-tariff

Period 1 Q 1 E C 1 Q 

Period 2 Q 2 Q 1 E C 1 Q 1 Q 

The quantities of PV modules installed areQ 1 in the first period, andQ 2 over the two

periods, soQ 2 Q 1 during the second period.

The cost of electricity is the same for both periods,E. It corresponds to the quantity of electricity produced by a standard system (size and lifetime). Therefore ifSis the size of  rt a project in kW/year, andElecthe electricity cost in $/kW:ES  Elec.e.dt 0 The cost of PV panels isC 1 in period 1, which is exogenous. The cost in period 2 is defined endogenously according to the learning curve theory suggesting that Cum 2  CC 1  whereCum j is the cumulative installed PV systems at the beginning 2 Cum 1 of period j, andthe learning parameter. For small quantities of installed projectsQ 1 in period 1 (compared toCum 1 ), we can write:

Q 1 CC 1 1C 1 Q 1 2 Cum 1

 C 1 with Cum 1