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Bi-level Programming Method for Regional energy Expansion Optimization

Authors: ZHU LI; WANG RONGLIANG; WU SHIKAI; WEI WEI; LUO FENGZHANG;

Bi-level Programming Method for Regional energy Expansion Optimization

Abstract

When facing the worldwide energy and environmental issues, the non-sustainability of traditional power planning method which only depends on increasing the power station investment in order to meet the rapid growth of electricity demand has emerged onto the surface. Based on the integrated resource strategic planning (IRSP), this paper established a bi-level programming model for regional energy expansion optimization of active distribution networks, considering the photovoltaic power generation (PV) and interruptible load (IL) as the generalized power source from the perspective of the whole society. The upper level programming model optimized the power installation capacity of the generalized power sources by minimizing the total cost of power supply construction and maintenance, power generation and pollutants treatment. The lower level programming model, which took the minimum network loss as the purpose, located and sized the generalized power sources. Finally, this paper employed the simplex method and improved PSO algorithm to solve this bi-level programming problem. Comparing with the traditional planning, the proposed model and the solution method has an obvious effect of social benefit in energy-saving, emission-reduction and resource utilization rate improvement, which is verified by the simulation analysis of IEEE33 bus system.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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