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Resiliently designed and constructed integrated gas‐electric distribution networks (GEDNs) against natural disasters are crucial to social welfare. In this study, a two‐stage robust optimisation‐based co‐expansion planning model is proposed to attain an integrated GEDN with a given resilience level, by optimising the investment strategies of hardening and selective expansion of power distribution feeders and natural gas pipelines, as well as the location and capacity of natural‐gas‐fired distributed generation. In the first stage, the overall annual investment and operation cost is minimised under normal operation conditions while in the second stage, the feasibility of the investment decisions under the identified worst‐case natural disaster scenario is checked with an adjustable load shedding cost criterion. The proposed model is formulated as a mixed integer second‐order cone programming problem with the column and constraint generation algorithm employed to seek the optimal solution. Case studies on two integrated GEDNs demonstrate the performance of the proposed methodology. [ABSTRACT FROM AUTHOR]

Efficient and well‐timed investment in distributed generation (DG) in distribution networks in the electricity market environment presents a big challenge for distribution companies. In this study, a real option valuation framework is proposed to determine the optimal investment strategies for DG including the investment location, size, and timing. Within the proposed framework, the profit from investing in DG is modelled, where the benefits include the operation cost savings and capacity update deferral benefit compared with a no‐DG‐investment scenario over the study period. Future power demands and electricity prices are modelled as stochastic variables. The candidate DG investment plans are considered as multiple mutually exclusive options, and the corresponding managerial flexibility for seizing opportunities and mitigating risk of loss upon an unfavourable unfolding of future uncertainties is assessed with real option analysis using the extended least square Monte Carlo method. The distribution of future investment strategies and optimal initial investment threshold levels under various scenarios are analysed in a case study to demonstrate the characteristics of the proposed framework and methodology. [ABSTRACT FROM AUTHOR]