scholarly journals The Sensitivity of Power System Expansion Models on Climate Scenarios

Author(s):  
Bruno Schyska ◽  
Alexander Kies ◽  
Markus Schlott ◽  
Lueder von Bremen ◽  
Wided Medjroubi

<p>Power system expansion models are a widely used tool for planning power systems, especially considering the integration of renewable resources. Studies using these models form the basis for far-reaching political decisions. The backbone of power system models is an optimization problem, which depends on a number of economic and technical parameters. Although these parameters contain significant uncertainties, a consistent way to quantify the sensitivity to these uncertainties does not yet exist. Here, we analyze and quantify the sensitivity of a power system expansion model to the meteorological parameter time series based on a novel misallocation metric. We find that the sensitivity to the weather data is in the same order of magnitude as the sensitivity to the definition of cost. By comparing different climatic periods both from a meteorological perspective and with respect to the impacts on the optimal power system design we can, additionally, identify representative weather years and periods which should rather not be used for expansion planning.</p>

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1392
Author(s):  
Mladen Zeljko ◽  
Marko Aunedi ◽  
Goran Slipac ◽  
Dražen Jakšić

This paper presents several applications of Wien Automatic System Planning (WASP) tool to address specific modeling challenges encountered in power system expansion planning problems. Although WASP has been used by power system planners around the world for many decades, its standard formulation does not allow the user to explicitly model many situations that can occur in realistic power systems. Examples of such situations include dual-fuel plants, options for electricity exports, energy exchange agreements with neighboring systems, and considering large generating units as candidates in relatively small-size systems. A number of alternative modeling solutions are proposed in the paper based on the authors’ long-term experience in carrying out generation expansion studies for electricity systems of various types and sizes. These solutions demonstrate the flexibility of using WASP to model atypical features of power systems.


2021 ◽  
Vol 145 ◽  
pp. 111056
Author(s):  
Andrey Churkin ◽  
Janusz Bialek ◽  
David Pozo ◽  
Enzo Sauma ◽  
Nikolay Korgin

2021 ◽  
Vol 13 (12) ◽  
pp. 6708
Author(s):  
Hamza Mubarak ◽  
Nurulafiqah Nadzirah Mansor ◽  
Hazlie Mokhlis ◽  
Mahazani Mohamad ◽  
Hasmaini Mohamad ◽  
...  

Demand for continuous and reliable power supply has significantly increased, especially in this Industrial Revolution 4.0 era. In this regard, adequate planning of electrical power systems considering persistent load growth, increased integration of distributed generators (DGs), optimal system operation during N-1 contingencies, and compliance to the existing system constraints are paramount. However, these issues need to be parallelly addressed for optimum distribution system planning. Consequently, the planning optimization problem would become more complex due to the various technical and operational constraints as well as the enormous search space. To address these considerations, this paper proposes a strategy to obtain one optimal solution for the distribution system expansion planning by considering N-1 system contingencies for all branches and DG optimal sizing and placement as well as fluctuations in the load profiles. In this work, a hybrid firefly algorithm and particle swarm optimization (FA-PSO) was proposed to determine the optimal solution for the expansion planning problem. The validity of the proposed method was tested on IEEE 33- and 69-bus systems. The results show that incorporating DGs with optimal sizing and location minimizes the investment and power loss cost for the 33-bus system by 42.18% and 14.63%, respectively, and for the 69-system by 31.53% and 12%, respectively. In addition, comparative studies were done with a different model from the literature to verify the robustness of the proposed method.


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