Change search
ReferencesLink to record
Permanent link

Direct link
Modeling the optimal energy mix in 2030: Impact of the integration of renewable energy sources
KTH, School of Electrical Engineering (EES).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The European Council has recently set objectives in the matter of energy and climate

policies and thus the interest in renewable energies is more than ever at stake. However,

the introduction of renewable energies in an energy mix is also accelerated and altered

by political targets. The two most widespread renewable technologies, photovoltaic

and wind farms, have specific characteristics - decentralized, intermittency, uncertain

production forecast up until a few hours ahead - that oblige to adapt the network and

the current conventional generator control.

By using optimization techniques, it is possible to characterize the optimal energy mix

(i.e. the optimal share of every power technology in all the countries considered). In

this paper, the optimization function is defined as the sum of the yearly fixed cost of

deploying a certain amount of installed capacity with the cost of electricity generation

over the while year. Then the aim of the model is to evaluate the energy mix of least


One can imagine multiple applications for this model, depending on which issue is to

solve. Two case studies are developed in this report as examples. Renewable technologies

are modifying the organization of the electricity market because of their specific

characteristics. The first case study aims at quantifying the additional cost due to the

integration of renewable energies. The second is targeted to characterize the impact of

the integration of green energy sources on the deployment of Demand Response.

Place, publisher, year, edition, pages
2016. , 89 p.
EES Examensarbete / Master Thesis, TRITA-EE 2016:029
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-187670OAI: diva2:931014
Available from: 2016-05-26 Created: 2016-05-26 Last updated: 2016-05-26Bibliographically approved

Open Access in DiVA

fulltext(5028 kB)41 downloads
File information
File name FULLTEXT01.pdfFile size 5028 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
School of Electrical Engineering (EES)
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 41 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 81 hits
ReferencesLink to record
Permanent link

Direct link