(22g) Decision Making Under Uncertainty in Integrated Planning of Generation and Transmission Capacities in Interconnected Power Systems

Power systems play an important role in the energy sector, supplying a significant share of the total energy demand for the industrial and residential sectors. However, the power sector also accounts for about 90% and 42% of energy related water withdrawal and CO₂emissions, respectively [1, 2]. Therefore, the optimal design and planning of power systems have become one of the most relevant issues in the energy sector. Besides the innate complexity of the system, the handling of the uncertainties in aspects such as electricity demand, fossil fuel cost, and electricity generation rates from renewable sources, have been recognized as one of the biggest challenges of decision making processes in the power sector.

This work presents a methodology for addressing the problem of planning under uncertainty of the integrated generation and transmission expansion of power systems. First, based on the framework developed by Guerra et al. [3], a global sensitivity analysis is carried out in order to rank the sources of uncertainty in the power system. A Monte Carlo sampling method is coupled with Sobolâ??s sensitivity indices [4, 5] to quantify the effect of uncertain parameters on the net present value of the total cost, which is the performance criteria used in the aforementioned framework. Then, two-stage stochastic formulations are derived based on the deterministic Mixed Integer Linear Programming (MILP) optimization model developed by Guerra et al. [3]. First-stage decisions are associated with investment in new power generation and transmission assets, while second-stage decisions are associated with power allocation, non-spinning and spinning reserve allocation, and power transmission flows. Two metrics, i.e. expected value of perfect information (EVP) and value of stochastic solution(VSS) [6], are used to assess the impact of modeling uncertainty and using the stochastic programming model in power system planning problems. Additionally, scenario reduction strategies are explored to overcome computational challenges. The features of the proposed methodology are illustrated through a real world case study.

References

[1] IEA, â??Water for Energy: Is energy becoming a thirstier resource?â?, World Energy Outlook, pp. 1â??33, 2012.

[2] IEA, â??World Energy Outlook Special Report 2015: Energy and Climate Change,â? 2015.

[3] O. J. Guerra, D. A. Tejada, and G. V. Reklaitis, â??An optimization framework for the integrated planning of generation and transmission expansion in interconnected power systems,â? Applied Energy, vol. 170, pp. 1â??21, May 2016.

[4] I. M. Sobol, â??Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates,â? Mathematics and Computers in Simulation, vol. 55, pp. 271â??280, 2001.

[5] A. Saltelli, S. Tarantola, and K. P.-S. Chan, â??A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output,â? Technometrics, vol. 41, no. May, pp. 39â??56, 1999.

[6] J. Birge and F. Louveaux, Introduction to stochastic programming. 2011.