Robust Generation Expansion Planning Considering High Penetration Renewable Energies Uncertainty

What is it about?

Abstract The proper optimal generation expansion planning (GEP) should meet the reliability criteria requirements over a planning horizon under the presence of uncertainties. The intermittent nature of renewable energy sources (RES) introduces an enormous uncertainties impact within the planning model. A simulation model for RES uncertainty is developed using the capacity factor (CF) of the RES historical data. The RES simulation model is handled via the probability density function (PDF). The uncertainty parameter of different RES is described as a flexible polyhedral uncertainty set and incorporated within the proposed GEP model. The influence of different uncertainty scenarios for each RES uncertainty on the GEP model can be analysed separately. The RES uncertainty scenarios are predefined and incorporated within the proposed GEP model through a proposed parameter named as a confidence level. The proposed confidence level parameter is beneficial to the power system planner to control the degree of robustness. Different GEP results are presented for various RES uncertainty scenarios. Three methods are proposed as appropriate solutions to deal with the RES uncertainty impact. The most economical method among the three proposed methods is determined by developing an objective function tailored to achieve the optimality of the economic factor.

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Why is it important?

The main contributions of this paper are as follows: 1. An analytical simulation model is developed to handle the estimated future RES uncertainty set accurately. The weight of each renewable uncertainty set can be controlled separately (i.e. the influence of different uncertainty scenarios for each uncertainty source on the GEP model can be analyzed separately). 2. Based on the uncertainty weight, a flexible polyhedral uncertainty set is developed to analyze the impact of uncertainty on the proposed robust GEP model. The degree of robustness is controlled through an adjustable robustness parameter that depends on the weights of each uncertainty set. 3. RES overcapacity, reserve margin, and ESS are introduced as effective methods to cope with the RES uncertainty problem. The optimal method is suggested based on technical and economic aspects.

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