User Preference-based Multi-Objective Optimization: Method,Theory And Its Application In Power Systems

With the development of smart grid and increasing penetration of renewable energy sources(RESs),power systems operation involves multiple incompatible objectives,such as economy,reliability and so on.Hence multi-objective optimization(MOO)model is becoming more and more important.Since the high dimension and strong nonlinearity of MOO problems in power systems,the operators’ preference information can significantly improve the solving efficiency,and determines the final solution.To this end,this thesis proposes a user-preference based multi-objective problem formulation,and analyzes the MOO problem in 4 aspects: theoretical analysis,solution methodology,computational algorithms and applications in power systems.The main contributions of the thesis is as follows:(1)Theoretical analysis: a user-preference based multi-objective optimization model is built to achieve different degree of optimality for different objectives.Based on the theoty of nonlinear dynamical systems,a theoretical relationship between the multi-objective optimization problems and a class of non-hyperbolic dynamical systems is built,which leads to the theoretical characterization of the user-preferred feasible region.Based on the theoretical results,a user preference enabling method is proposed to calculate the user preferred-feasible solution of MOO problems.(2)Solution methodology: a three-stage optimization method is proposed based on the consensus-based intelligent optimization algorithms,which improves the search efficiency for Pareto front.Regarding the highly nonlinearility of the power system problem,a UPE guided intelligent optimization method is proposed.This hybrid method improves the search capability of the existing methods.Moreover,in order to meet the requirement of online control and optimization,an iterative UPE method is proposed to calculate an accurate user-preferred Pareto-optimal solution in limited time period.(3)Computational algorithms: highly-efficient computational algorithms are designed combining the three-stage methodology with domain knowledgy of specific applications,which can fast calculate the Pareto-optimal solutions satisfying user preference.(4)Applications: the proposed multi-objective optimization model and methodology are applied into transmission and distribution system operation.Detailed computation algorithms are degined according to the problem features.Three MOO problems in power systems are studied,including:1)Service restoration problem in distribution network: a multi-objective model considering the restored energy amount and switch operation number is proposed.The three-stage method is evaluated on both the IEEE 30-bus test system and a practical unbalanced three-phase distribution system.The case study shows that the proposed method can find diversified restoration schemes with different user preferences.2)Spinning reserve optimization problem with renewable uncertainties: the multiobjective optimization model is established to determine the optimal reserve amount,which minimizes generation cost,reserve cost and maximizes reliability level.The three-stage methodology shows its advantange in generating multiple reserve allocation schemes with different reliability level.3)Look-ahead power dispatch problem with renewable uncertainties: a multiobjective look-ahead power dispatch model is established considering generation cost,emission amount and wind power utilization ration as objectives.The obtained dispatch scheme satisfies network transfer capability and user’s preference over different objectives.It can also accommodate the uncertain set of renewables and maintain a feasible and optimal operation point.