Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distribut-ed generation(DG).However,the DG capacity of a distribution system is often underestimated due to either overly conservative electrical demand and DG output uncertainty modelling or ne-glecting the recourse capability of the available components.To improve the accuracy of DG capacity assessment,this paper proposes a distributionally adjustable robust chance-con-strained approach that utilises uncertainty information to re-duce the conservativeness of conventional robust approaches.The proposed approach also enables fast-acting devices such as inverters to adjust to the real-time realisation of uncertainty us-ing the adjustable robust counterpart methodology.To achieve a tractable formulation,we first define uncertain chance con-straints through distributionally robust conditional value-at-risk(CVaR),which is then reformulated into convex quadratic con-straints.We subsequently solve the resulting large-scale,yet con-vex,model in a distributed fashion using the alternating direc-tion method of multipliers(ADMM).Through numerical simula-tions,we demonstrate that the proposed approach outperforms the adjustable robust and conventional distributionally robust approaches by up to 15％and 40％,respectively,in terms of to-tal installed DG capacity.
Distributed generation(DG)capacity assess-mentdistributionally robust optimisationchance-constrained optimisationdistribution system
Masoume Mahmoodi、Seyyed Mahdi Noori Rahim Abadi、Ahmad Attarha、Paul Scott、Lachlan Blackhall
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College of Engineering and Computer Sci-ence,The Australian National University,Canberra,Australia
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