Electricity Distribution Network Planning Considering Distributed Generation
2014 (English)Licentiate thesis, monograph (Other academic)
One of EU’s actions against climate change is to meet 20% of our energy needs from renewable resources. Given that the renewable resources are becoming more economical to extract electricity from, this will result in that more and more distributed generation (DG) will be connected to power distribution. The increasing share of DG in the electricity networks implies both increased costs and benefits for distribution system operators (DSOs), customers and DG producers. How the costs and benefits will be allocated among the actors will depend on the established regulation.
Distribution networks are traditionally not designed to accommodate generation. Hence, increasing DG penetration is causing profound changes for DSOs in planning, operation and maintenance of distribution networks. Due to the unbundling between DSOs and electricity production, DSOs can not determine either the location or the size of DG. This new power distribution environment brings new challenges for the DSOs and the electric power system regulator. The DSOs are obliged to enable connection of DG meanwhile fulfilling requirements on power quality and adequate reliability. Moreover, regulatory implications can make potential DG less attractive. Therefore regulation should be able to send out incentives for the DSOs to efficiently plan the network to accommodate the increasing levels of DG. To analyze the effects of regulatory polices on network investments, risk analysis methods for integrating the DG considering uncertainties are therefore needed.
In this work, regulation impact on network planning methods and network tariff designs in unbundled electricity network is firstly analyzed in order to formulate a realistic long-term network planning model considering DG. Photovoltaic (PV) power and wind power plants are used to demonstrate DG. Secondly, this work develops a deterministic model for low-voltage (LV) networks mainly considering PV connections which is based on the worst-case scenario. Dimension the network using worst-case scenario is the convention in the long-term electricity distribution network planning for the reliability and security reason. This model is then further developed into a probabilistic model in order to consider the uncertainties from DG production and load. Therefore more realistic operation conditions are considered and probabilistic constrains on voltage variation can be applied. Thirdly, this work develops a distribution medium-voltage (MV) network planning model considering wind power plant connections. The model obtains the optimal network expansion and reinforcement plan of the target network considering the uncertainties from DG production and load. The model is flexible to modify the constraints. The technical constraints are respected in any scenario and violated in few scenarios are implemented into the model separately.
In LV networks only PV connections are demonstrated and in MV networks only wind power connections are demonstrated. The planning model for LV networks is proposed as a practical guideline for PV connections. It has been shown that it is simple to be implemented and flexible to adjust the planning constraints. The proposed planning model for MV networks takes reinforcement on existing lines, new connection lines to DG, alternatives for conductor sizes and substation upgrade into account, and considers non-linear power flow constraints as an iterative linear optimization process. The planning model applies conservative limits and probabilistic limits for increasing utilization of the network, and the different results are compared in case studies. The model’s efficiency, flexibility and accuracy in long-term distribution network planning problems are shown in the case studies.
Place, publisher, year, edition, pages
Stockcholm: KTH Royal Institute of Technology, 2014. , xi, 99 p.
TRITA-EE, ISSN 1653-5146
Electricity distribution network, network planning, distributed generation
Engineering and Technology
Research subject Electrical Engineering
IdentifiersURN: urn:nbn:se:kth:diva-141482ISBN: 978-91-7595-030-3OAI: oai:DiVA.org:kth-141482DiVA: diva2:697178
2014-03-07, Seminarierum H21, Teknikringen 33, KTH, Stockholm, 10:00 (English)
Chen, Peiyuan, Assistant Professor
Söder, Lennart, ProfessorAlvehag, Karin, Docent
ProjectsElforsk Risknanlys II
QC 201402172014-02-172014-02-172014-02-17Bibliographically approved