Under the situation of open electricity market

the cooperation and interest games among regional grids coexist

and the issues of information security among regional grids become more and more important. The current Pareto optimized power flow algorithms belong to centralized algorithms

and need to get the global information about the whole power system in the optimization process

thus it is hard to meet the high privacy and the high reliability requirements. In this context

it is particularly important to seek a decentralized distributed optimization method to secure the information security of the regional grids within the power system. To solve these issues

a Pareto optimized algorithm for distributed multi-objective power flow based on multi-area parallel cooperation is proposed. In the proposed algorithm

the normal boundary intersection is used as basis

and the multi-objective power flow optimization problem of the whole power system is decomposed into the sub-optimal problems corresponding to the sub regions. To continuosly approach the Pareto optimal solution set of the original problem

each sub region uses independent optimizer to optimize sub problems

and only the boundary variables between the interconnecting regions and the virtual objective coefficients are exchanged for the global regulation. The simulation results of the IEEE 118-bus power system verify that the proposed algorithm can effectively realize the distributed parallel solving of multi-objective Pareto optimal power flow

and simultaneously enhance the solution precision enhancement and reduce the calculation storage

which is suitable for the operation mode of the coexistence of the regional cooperation and interest games in the current background of open electricity market.