This paper addresses the attuned use of multi- converter flexible alternative current transmission systems （M-FACTS） devices and demand response （DR） to perform congestion management （CM） in the deregulated envi...This paper addresses the attuned use of multi- converter flexible alternative current transmission systems （M-FACTS） devices and demand response （DR） to perform congestion management （CM） in the deregulated environment. The strong control capability of the M- FACTS offers a great potential in solving many of the problems facing electric utilities. Besides, DR is a novel procedure that can be an effective tool for reduction of congestion. A market clearing procedure is conducted based on maximizing social welfare （SW） and congestion as network constraint is paid by using concurrently the DR and M-FACTS. A multi-objective problem （MOP） based on the sum of the payments received by the generators for changing their output, the total payment received by DR participants to reduce their load and M-FACTS cost is systematized. For the solution of this problem a nonlinear time-varying evolution （NTVE） based multi-objective particle swarm optimization （MOPSO） style is formed. Fuzzy decision-making （FDM） and technique for order preference by similarity to ideal solution （TOPSIS） approaches are employed for finding the best compromise solution from the set of Pareto-solutions obtained through multi-objective particle swarm optimization-nonlinear time-varying evolution （MOPSO-NTVE）. In a real power system, Azarbaijan regional power system of Iran, comparative analysis of the results obtained from the application of the DR ＆ unified power flow controller （UPFC） and the DR ＆ M-FACTS are presented.展开更多
文摘This paper addresses the attuned use of multi- converter flexible alternative current transmission systems （M-FACTS） devices and demand response （DR） to perform congestion management （CM） in the deregulated environment. The strong control capability of the M- FACTS offers a great potential in solving many of the problems facing electric utilities. Besides, DR is a novel procedure that can be an effective tool for reduction of congestion. A market clearing procedure is conducted based on maximizing social welfare （SW） and congestion as network constraint is paid by using concurrently the DR and M-FACTS. A multi-objective problem （MOP） based on the sum of the payments received by the generators for changing their output, the total payment received by DR participants to reduce their load and M-FACTS cost is systematized. For the solution of this problem a nonlinear time-varying evolution （NTVE） based multi-objective particle swarm optimization （MOPSO） style is formed. Fuzzy decision-making （FDM） and technique for order preference by similarity to ideal solution （TOPSIS） approaches are employed for finding the best compromise solution from the set of Pareto-solutions obtained through multi-objective particle swarm optimization-nonlinear time-varying evolution （MOPSO-NTVE）. In a real power system, Azarbaijan regional power system of Iran, comparative analysis of the results obtained from the application of the DR ＆ unified power flow controller （UPFC） and the DR ＆ M-FACTS are presented.
