Robustness Assessment of Wind Power Generation Considering Rigorous Security Constraints for Power System: A Hybrid RLO-IGDT Approach

Fossil fuel depletion and environmental pollution problems promote development of renewable energy(RE)glob-ally.With increasing penetration of RE,operation security and economy of power systems(PS)are greatly impacted by fluctuation and intermittence of renewable power.In this paper,information gap decision theory(IGDT)is adapted to handle uncertainty of wind power generation.Based on conventional IGDT method,linear regulation strategy(LRS)and robust linear optimization(RLO)method are integrated to reformulate the model for rigorously considering security constraints.Then a robustness assessment method based on hybrid RLO-IGDT approach is proposed for analyzing robustness and economic performance of PS.Moreover,a risk-averse linearization method is adapted to convert the proposed assessment model into a mixed integer linear programming(MILP)problem for convenient optimization without robustness loss.Finally,results of case studies validate superiority of proposed method in guaranteeing operation security rigorously and effectiveness in assessment of RSR for PS without overestimation.Index Terms-Hybrid RLO-IGDT approach,information gap decision theory(IGDT),operation security,robustness assessment,robustness security region(RSR).

Multi-objective robust secure beamforming for cognitive satellite and UAV networks

A multi-objective optimization based robust beamforming(BF)scheme is proposed to realize secure transmission in a cognitive satellite and unmanned aerial vehicle(UAV)network.Since the satellite network coexists with the UAV network,we first consider both achievable secrecy rate maximization and total transmit power minimization,and formulate a multi-objective optimization problem(MOOP)using the weighted Tchebycheff approach.Then,by supposing that only imperfect channel state information based on the angular information is available,we propose a method combining angular discretization with Taylor approximation to transform the non-convex objective function and constraints to the convex ones.Next,we adopt semi-definite programming together with randomization technology to solve the original MOOP and obtain the BF weight vector.Finally,simulation results illustrate that the Pareto optimal trade-off can be achieved,and the superiority of our proposed scheme is confirmed by comparing with the existing BF schemes.