| For the uncertainty of the load and fault during the operation of power system,the risk assessment theory is introduced into economic dispatch.Moreover,this paper proposes a novel knowledge transfer bacteria foraging optimization(TBFO)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 reliability test system-79(RTS-79).The simulation demonstrates that the convergence rate of TBFO can be approximately 9 to 20 times faster than that of classical AI algorithm,while the quality of the obtained optimal solution can be guaranteed.In order to ensure that the power system can operate safely and stably under the influence of wind power,PV output and load fluctuation,a new risk-based robust economic dispatch mathematic model is proposed.Non-sequential Monte Carlo simulation is adopted to calculate the expected demand not supplied of generation and load system.Moreover,the fuzzy chance constraint optimization is employed to gurantee that the dispatch plan can meets the requirements of robustness.his paper presents a new algorithm called Pareto tribe evolution to solve risk-based robust economic dispatch.In the proposed algorithm,the Pareto front is divided into many tribes with unequal members whose movement way is various.Exact local search and synergistic search among individuals are achieved according to these distinct members.The simulation result on the 10-generators system verify the effectiveness and practical significance of risk-based robust economic dispatch and Pareto tribe evolution. |
PDF Full Text Request