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.
. [J]. Frontiers in Energy, 2015, 9(3): 282-296.
Y. HASHEMI, H. SHAYEGHI, B. HASHEMI. Attuned design of demand response program and M-FACTS for relieving congestion in a restructured market environment. Front. Energy, 2015, 9(3): 282-296.
Price offered by demand response i to decrease its demand
ui
Demand response commitment variable
FACTScost
Cost of FACTS devices
Vector of voltage magnitude
Vector of phase angle
E and H
Sets of equality and inequality constraints
x, u, p
State vector, control parameters and fixed parameters
CUPFC, CM-FACTS
Total investment cost related to UPFC and M-FACTS
SUPFC, SM-FACTS
Size (in MVAR) related to UPFC and M-FACTS
UPFCcost
Cost of UPFC
np
Exponent
Real nonnegative weighting coefficient
Total number of lines in the network
PI sensitivity with respect to Vseij and ?seij
PI sensitivity with respect to Vseik and ?seik
PI sensitivity with respect to Ish
Number of particles in a population
Number of current iteration
Position of ith particle at iteration k
Local best of ith particle at iteration k
Global best of all particles
Velocity of ith particle at iteration k
Cognitive parameter (acceleration coefficient)
Social parameter (acceleration coefficient)
Random numbers between 0 and 1
Entropy value
Attribute jth
Dj
Degree of divergence
OWij
Objective weighted normalized value
Positive ideal solution
Negative ideal solution
RCj
Relative closeness to the ideal solution of alternative Xj with respect to A+
Tab.7
1
A Mazer. Electric Power Planning for Regulated and Deregulated Markets. Wiley-IEEE Press, 2007
2
K Singh, V K Yadav, N P Padhy, J Sharma. Congestion management considering optimal placement of distributed generator in deregulated power system networks. Electric Power Components and Systems, 2014, 42(1): 13–22 https://doi.org/10.1080/15325008.2013.843218
3
A Kumar, C Sekhar. Comparison of sen transformer and UPFC for congestion management in hybrid electricity markets. International Journal of Electrical Power & Energy Systems, 2013, 47(10): 295–304 https://doi.org/10.1016/j.ijepes.2012.10.057
4
A Molina-García, M Kessler, J A Fuentes, E Gómez-Lázaro. Probabilistic characterization of thermostatically controlled loads to model the impact of demand response programs. IEEE Transactions on power systems, 2011, 26(1): 241–251
5
E Ghahremani, I Kamwa. Optimal placement of multiple-type FACTS devices to maximize power system loadability using a generic graphical user interface. IEEE Transactions on Power Systems, 2012, 22(99): 764–778
6
A Berizzi, M Delfanti, P Marannino, M S Pasquadibisceglie, A Silvestri. Enhanced security-constrained OPF with FACTS devices. IEEE Transactions on Power Systems, 2005, 20(3): 1597–1605 https://doi.org/10.1109/TPWRS.2005.852125
7
E Shayesteh, M P Moghaddam, A Yousefi, M R Haghifam, M K Sheik-El-Eslami. A demand side approach for congestion management in competitive environment. European Transactions on Electrical Power, 2010, 20(4): 470–490
8
D T Nguyen, M Negnevitsky, M de Groot. Walrasian market clearing for demand response exchange. IEEE Transactions on Power Systems, 2012, 27(1): 535–544 https://doi.org/10.1109/TPWRS.2011.2161497
9
Z Zhou, F Zhao, J Wang. Agent-based electricity market simulation with demand response from commercial buildings. IEEE Transactions on Smart Grid, 2011, 2(4): 580–588 https://doi.org/10.1109/TSG.2011.2168244
10
P T Baboli, M P Moghaddam. Allocation of network-driven load-management measures using multiattribute decision making. IEEE Transactions on Power Delivery, 2010, 25(3): 1839–1845 https://doi.org/10.1109/TPWRD.2010.2045517
11
P T Baboli, M P Moghaddam, M Eghbal. Present status and future trends in enabling demand response programs. In: Proceedings of 2011 IEEE Power and Energy Society General Meeting. San Diego, USA, 2011, 1–6
12
M P Moghaddam, A Abdollahi, M Rashidinejad. Flexible demand response programs modeling in competitive electricity markets. Applied Energy, 2011, 88(9): 3257–3269 https://doi.org/10.1016/j.apenergy.2011.02.039
13
H Aalami, M P Moghaddam, G Yousefi. Demand response modeling considering interruptible/curtailable loads and capacity market programs. Applied Energy, 2010, 87(1): 243–250 https://doi.org/10.1016/j.apenergy.2009.05.041
14
R Blundell, M Browning, I Crawford. Best nonparametric bounds on demand responses. Econometrica, 2008, 76(6): 1227–1262 https://doi.org/10.3982/ECTA6069
15
H Chao. Demand response in wholesale electricity markets: the choice of customer baseline. Journal of Regulatory Economics, 2011, 39(1): 68–88 https://doi.org/10.1007/s11149-010-9135-y
16
A Molina-García, M Kessler, J A Fuentes, E Gómez-Lázaro. Probabilistic characterization of thermostatically controlled loads to model the impact of demand response programs. IEEE Transactions on Power Systems, 2011, 26(1): 241–251
17
A J Conejo, J M Morales, L Baringo. Real-time demand response model. IEEE Transactions on Smart Grid, 2010, 1(3): 236–242 https://doi.org/10.1109/TSG.2010.2078843
18
N Yu, J L Yu. Optimal TOU decision considering demand response model. In: Proceedings of PowerCon 2006. International Conference on Power System Technology. Chongqing, China, 2006, 1–5
19
B Fardanesh. Optimal utilization, sizing, and steady-state performance comparison of multiconverter VSC-based FACTS controllers. IEEE Transactions on Power Delivery, 2004, 19(3): 1321–1327 https://doi.org/10.1109/TPWRD.2004.829154
20
M Saravanan, S M R Slochanal, P Venkatesh, J P S Abraham. Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability. Electric Power Systems Research, 2007, 77(3−4): 276–283 https://doi.org/10.1016/j.epsr.2006.03.006
21
A J Wood, B F Wollenberg. Power Generation, Operation, and Control. Beijing: Tsinghua University Press, 2003
22
Federal Energy Regulatory Commission. Assessment of Demand Response & Advanced Metering Staff Report (Docket AD-06-2-000). 2006-08
23
A Yousefi, T Nguyen, H Zareipour, O Malik. Congestion management using demand response and FACTS devices. International Journal of Electrical Power & Energy Systems, 2012, 37(1): 78–85 https://doi.org/10.1016/j.ijepes.2011.12.008
24
B Fardanesh. Optimal utilization, sizing, and steady-state performance comparison of multiconverter VSC-based FACTS controllers. IEEE Transactions on Power Delivery, 2004, 19(3): 1321–1327 https://doi.org/10.1109/TPWRD.2004.829154
25
C Ko, Y Chang, C Wu. A PSO method with nonlinear time-varying evolution for optimal design of harmonic filters. IEEE Transactions on Power Systems, 2009, 24(1): 437–444 https://doi.org/10.1109/TPWRS.2008.2004845
26
K Y Chan, T S Dillon, C K Kwong. Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm. Information Sciences, 2011, 181(9): 1623–1640 https://doi.org/10.1016/j.ins.2011.01.006
27
Y Leung, Y Wang. An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Transactions on Evolutionary Computation, 2001, 5(1): 41–53 https://doi.org/10.1109/4235.910464
28
Z Yue. TOPSIS-based group decision-making methodology in intuitionistic fuzzy setting. Information Sciences, 2014, 277: 141–153 https://doi.org/10.1016/j.ins.2014.02.013
29
A Lashkar Ara, A Kazemi, S Nabavi Niaki. Multiobjective optimal location of FACTS shunt-series controllers for power system operation planning. IEEE Transactions on Power Delivery, 2012, 27(2): 481–490 https://doi.org/10.1109/TPWRD.2011.2176559
30
J van der Lee, W Svrcek, B Young. A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making. ISA Transactions, 2008, 47(1): 53–59 https://doi.org/10.1016/j.isatra.2007.06.003
31
R Kazemzadeh, M Moazen, R Ajabi-Farshbaf, M Vatanpour. STATCOM optimal allocation in transmission grids considering contingency analysis in OPF using BF-PSO algorithm. Journal of Operation and Automation in Power Engineering, 2013, 1(1): 1–11
32
Y J Wang. A fuzzy multi-criteria decision-making model by associating technique for order preference by similarity to ideal solution with relative preference relation. Information Sciences, 2014, 268: 169–184 https://doi.org/10.1016/j.ins.2014.01.029
33
H Shayeghi, Y Hashemi. Technical−economic analysis of including wind farms and HFC to solve hybrid TNEM−RPM problem in the deregulated environment. Energy Conversion and Management, 2014, 80: 477–490 https://doi.org/10.1016/j.enconman.2014.01.011
34
W K G Assunção, T E Colanzi, S R Vergilio, A Pozo. A multi-objective optimization approach for the integration and test order problem. Information Sciences, 2014, 267: 119–139 https://doi.org/10.1016/j.ins.2013.12.040
35
K Deb. Multi objective optimization using evolutionary algorithms. Singapore: John Wiley and Sons, 2001