Studies On Distributed Stochastic Programming For Coordinated Transmission And Distribution Systems

In modern power grids,the shares of distributed energy resources(DERs)especially renewable energies have increased exponentially.Further,the integration of DERs in distribution systems(DS)has altered their roles from "passive" to "active" ones.Various reports from California Independent System Operator(CAISO),European Network of Transmission System Operators for Electricity(ENTSO-E),etc.,and other latest publications have depicted that separate management mode of TS and DS is incapable to control:a)The two-way power flow at boundaries for optimal dispatch to reduce the cost of the overall systemb)The mitigation of the line congestions and the indication of the accurate post-contingency state of the overall system.Therefore,the separate energy management system for transmission system(TS)and DS has failed to provide an optimally secure and economical operation.To tackle such issues in power system operation,it is compulsory to study a coordinated energy management method for transmission and distribution systems(TSDS),which should provide minimal information exchange.This thesis investigates the complexity of the deterministically decentralized coordination model,the stochastically coordinated model with high penetration of renewable energy,and the contingency risk-based deterministically coordinated model for the hierarchical framework of TSDS.A concise summary is presented below:Firstly,a coordinator-based hierarchically coordinated TSDS model is presented in this thesis.The coordinated security-constrained unit commitment(C-SCUC)problem is solved in coupled TSDS.In the traditional TSDS coordination model,the objective function of the transmission system operator(TSO)is quite complex and highly scaled due to the penalty functions for each connected distribution system,which requires high computational resources.Therefore,a coordinator in the upper-level hierarchy is added in the proposed model,whose objective function contains the penalty functions for TSO and distribution system operator(DSO).Consequently,the penalty functions in the objective function of TSO are shifted to the objective function of the coordinator,which reduces the computational burden on TSO.This reduces the complexity of the objective function of TSO,which depicts the efficacy of the proposed algorithm.The convergence and optimality of the proposed method are proved theoretically in the case studies.Secondly,this thesis proposes a probabilistic coordination algorithm to solve the stochastically coordinated stochastic security-constrained unit commitment(SC-SSCUC)problem in coupled TSDS.The current deterministically decentralized approaches face two challenges in optimizing the TS and DS operation due to the high penetration of renewable energy resources:a)the high number of scenarios are required to achieve accurate solution at the expense of high computational burden;b)the low number of scenarios reduces the computational cost along with degradation of accuracy.To resolve these issues,the mean and standard deviation are presented as shared variables in a stochastically coordinated TSDS model,which creates a single coordination problem.This leads to fast convergence and less consumption of computational resources,which is depicted in the case studies.Lastly,this thesis has presented a risk-aware distributed optimal power flow(RA-DOPF)model for coupled TSDS.The coordinated cost optimization in TSDS can lead to insecure power system operations without considering the unexpected outages,which complicates the optimal coordination of TSDS.Therefore,the study of mutual effects on both systems due to outages is of great necessity.The proposed method utilizes the reserves of the transmission system and distributed energy resources of the distribution system to mitigate the risk.The RA-DOPF problem is divided into i)the coordination problem;ii)risk-aware optimal power flow problems for TS,and risk-based security-constraint optimal power flow in DS.Case studies demonstrate the convergence and effectiveness of the proposed method.