Referring to the load and fault uncertainty during power system operation

the risk assessment theory is introduced into economic dispatch. Moreover

a new knowledge transfer bacteria foraging optimization(TBFO)algorithm is proposed for risk based economic dispatch

which is developed by combining bacteria foraging optimization(BFO)and the try-error mechanism of Q-learning. Besides

the knowledge matrix is updated by multiple agents with cooperative collaboration

in which the knowledge extension is adopted to handle the curve of dimension. After obtaining all the optimal knowledge matrices

the convergence of the online learning can be accelerated by knowledge transfer. The performance of TBFO has been fully tested for risk based economic dispatch on the IEEE RTS-79. The simulation demonstrates that the convergence rate of TBFO can be approximately 9 to 20 times faster than that of classical intelligent algorithm

while the quality of the obtained optimal solution can be guaranteed. Hence

it is suitable for fast risk based economic dispatch of large-scale and complex power grid. This work is supported by National Basic Research Program of China(973 Program)(No. 2013CB228205)and National Natural Science Foundation of China(No. 51477055).