With the great development of distributed generator (DG) and electric vehicle (EV)

it is of great significance to carry out collaborative research of DG and EV accessed to distribution network. Based on the coordination of benefits between distribution companies

DG investors and social communities

this paper comprehensively takes into consideration the operating cost of distribution companies

investment cost of DG investors

and environmental benefit of DG and grid investment saved by vehicle to grid (V2G). A mathematical model for DG optimal planning in distribution network involving EV is developed based on chance constrained programming

with a hybrid coding based improved adaptive genetic algorithm to solve the model. In the calculation process

uncertainties of load forecast

output power of DG and charge or discharge power of EV are fully considered. And the mathematical model of EV influence on the grid maximum power is proposed. The constraints in the model are checked by probabilistic power flow based on semi-invariant and Gram-Charlier expansion theory. Finally

DG planning under different confidence level constraints is respectively computed within an actual distribution network to verify the effectiveness of the mathematical model and corresponding algorithm proposed. It can be concluded from the simulation results that with the reduction of confidence level in the mathematical model

the DG planning capacity in the system has significantly increased

so that the system loss is reduced and environmental benefit is increased. However

the risk of node voltage and branch power constraint violation has also increased

which leads to making higher requirements on real-time control research on the distribution network in the future.