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This study presents a methodology for generation expansion planning (GEP) under the presence of uncertainty of multiple renewable energy sources (RES). Both long‐ and short‐term uncertainties are represented and incorporated within the proposed GEP model. The long‐term RES uncertainty is simulated by the annual variation of the capacity credit. The short‐term uncertainty is modelled by means of the net power based on the hourly variation of RES output power and load curve. The proposed GEP model is solved through three steps. In the first step, the proposed robust GEP model is solved considering the long‐term uncertainty of the multiple RES. Short‐term uncertainty is considered when solving the GEP model in the second step. In the last step, the robustness of the obtained robust GEP model results is verified by checking the reliability criteria. A new correlated polyhedral uncertainty set is introduced considering the correlation between the different RES uncertain coefficients through its correlation matrix. Different GEP results are presented for different uncertainty scenarios. The results demonstrate that considering the correlation among uncertain coefficients provides insight for effects of correlation on investment strategies. Reserve margin is adapted to cope with uncertainty impact. Reliability criteria are verified by DIgSILENT generation adequacy tool.

This article presents a Generation Expansion Planning (GEP) methodology considering the impact of unit commitment constraints under uncertainties of both Renewable Energy Sources (RES) and forecasted load. Spatial and temporal data-driven robust optimization under the correlation of RES uncertainty is analyzed. As the intermittency nature of RES complicates dynamic characteristics of the net load profile and increases the need for operational flexibility, a robust GEP model is proposed considering the unit commitment constraints and data-driven robust optimization in addition to the correlation among different RES uncertainties. Long-and short-term uncertainty is represented and incorporated into the proposed GEP model. The GEP is solved through three stages. In the first stage, the GEP model focuses on the RES generation planning considering the long-term uncertainties. The impact of unit commitment constraints under short-term uncertainty is considered in the second stage. An appropriate Energy Storage System (ESS) is studied in the third stage. The results have demonstrated that: (a) considering the data-driven robust optimization under correlation of RES uncertainty reduces the conservativeness and

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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 analyzed 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.