Attuned design of demand response program and M-FACTS for relieving congestion in a restructured market environment

doi:10.1007/s11708-015-0366-6

Front. Energy

2015, Vol. 9

Issue (3) : 282-296 https://doi.org/10.1007/s11708-015-0366-6

RESEARCH ARTICLE

Attuned design of demand response program and M-FACTS for relieving congestion in a restructured market environment

Y. HASHEMI¹, H. SHAYEGHI¹(

), B. HASHEMI²

¹. Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran
². Network Studies Group, Bakhtar Regional Electric Company, Arak 3818385354, Iran

Download: PDF(1466 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

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.

Keywords multi-converter flexible alternative current transmission systems (M-FACTS) demand response fuzzy decision making multi-objective particle swarm optimization-nonlinear time-varying evolution (MOPSO-NTVE)

Corresponding Author(s): H. SHAYEGHI

Just Accepted Date: 15 May 2015 Issue Date: 11 September 2015

Cite this article:

Y. HASHEMI,H. SHAYEGHI,B. HASHEMI. Attuned design of demand response program and M-FACTS for relieving congestion in a restructured market environment[J]. Front. Energy, 2015, 9(3): 282-296.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-015-0366-6
https://academic.hep.com.cn/fie/EN/Y2015/V9/I3/282

Fig.1 Operational principle of M-FACTS with a shunt converter and a multi series converter

Fig.2 Equivalent circuit of M-FACTS with a shunt converter and a multi series converter

Fig.3 Linear membership function.

Fig.4 Azarbaijan test system

Fig.5 Flowchart of proposed method

Line No.	From Bus	To Bus	$c 2 k$	$c 4 k$
1	1	2	?5.7021	?6.356
2	1	2	?6.893	?6.1621
3	1	5	?2.6452	?6.7946
4	3	9	?4.3413	?8.396
5	3	8	?7.8139	?7.6868
6	2	3	?9.2335	?0.5464
7	2	3	?3.9404	?0.8199
8	4	5	?3.9002	?5.4302
9	4	15	3.9999	?3.2212
10	5	11	?9.5465	?2.4438
11	5	11	?9.1858	?0.2233
12	5	15	?3.684	?1.7063
13	5	16	?5.629	?9.5095
14	5	16	?8.0039	?3.3838
15	5	6	?8.9832	?6.6123
16	5	6	?9.2429	?3.702
17	6	18	?4.1309	1.4828
18	6	16	?2.0326	?4.6569
19	6	7	?9.8956	?8.9273
20	6	7	?0.0276	?2.5955
21	7	8	?9.8355	?3.8029
22	7	8	?9.2047	?1.3095
23	13	21	?7.7612	?0.4621
24	8	9	?7.9433	?6.782
25	8	10	?5.5165	?2.9832
26	11	12	?7.1142	?1.0534
27	11	13	?0.6261	?4.6752
28	12	13	?0.6165	?6.8229
29	14	17	?6.0638	?7.7998
30	14	22	?2.4681	?8.2763
31	15	22	?0.0184	?4.2807
32	15	22	?7.6619	?5.0431
33	15	18	?4.2543	?6.3422
34	16	19	?0.6244	?7.267
35	16	18	?0.7025	?4.1296
36	17	22	?1.2451	?3.5632
37	18	22	?9.6841	?3.2155
38	19	20	?8.7722	?4.6244
39	20	21	?2.4037	?6.6405
40	22	27	?5.1592	?6.46
41	23	24	?0.7877	?3.4389
42	23	25	?1.9710	?4.3204
43	23	27	?4.879	?0.7146
44	23	27	?2.5524	?0.4784
45	23	25	?1.9673	?1.7386
46	23	27	?2.7763	?0.3638
47	23	26	?4.8772	?2.7222
48	25	27	?2.2394	?4.8270

Tab.1 Sensitivity of PI with respect to M-FACTS parameter

Line No.	From Bus	To Bus	$c 2 k$
1	1	2	?3.6962
2	1	2	?3.1744
3	1	5	?8.1032
4	3	9	?1.0262
5	3	8	?2.1276
6	2	3	?4.4642
7	2	3	?3.8655
8	4	5	?7.7465
9	4	15	?4.7423
10	5	11	?7.9679
11	5	11	?0.2486
12	5	15	?0.9914
13	5	16	?2.8305
14	5	16	?7.6782
15	5	6	?0.3506
16	5	6	?8.7036
17	6	18	?7.4294
18	6	16	?1.0929
19	6	7	?8.6662
20	6	7	?8.2153
21	7	8	?0.0379
22	7	8	?6.6026
23	13	21	?6.3236
24	8	9	?2.1048
25	8	10	?4.3495
26	11	12	?9.3311
27	11	13	?4.5728
28	12	13	?1.1003
29	14	17	?6.7989
30	14	22	?9.7173
31	15	22	?4.3538
32	15	22	?2.3796
33	15	18	?8.8742
34	16	19	?9.5277
35	16	18	?9.9914
36	17	22	?7.6333
37	18	22	?1.0005
38	19	20	?2.2386
39	20	21	?1.0248
40	22	27	?0.2142
41	23	24	?5.1693
42	23	25	?2.5869
43	23	27	?0.339
44	23	27	?7.4968
45	23	25	?5.4171
46	23	27	?8.8195
47	23	26	?5.6139
48	25	27	?9.6869

Tab.2 Sensitivity of PI with respect to UPFC parameter

Tab.3 Selected buses for demand response implementation

Tab.4 Results of generator participating in electricity market

Fig.6 Pareto-optimal set with MOPSO-NTVE and INSGA-II in two-dimensional and three-dimensional objective space with M-FACTS application

Fig.7 Pareto-optimal set with MOPSO-NTVE and INSGA-II in two-dimensional and three-dimensional objective space with UPFC application

Reference setting of controller	UPFC		M-FACTS
	$\| V se \| / pu$	$\| θ se \| / (°)$	$\| V se 1 \| / pu$	$\| θ se 1 \| / (°)$	$\| V se2 \| / pu$	$\| θ se2 \| / (°)$
FDM based MOPSO-NTVE	0.5469	68.6805	0.6557	88.1576	0.6787	135.8436
FDM based INSGA-II	0.1576	143.1360	0.8491	116.3363	0.7431	122.3465
TOPSIS based MOPSO-NTVE	0.6557	136.3932	0.2769	70.6009	0.3171	30.8136
TOPSIS based INSGA-II	0.6787	133.7638	0.6948	117.9860	0.4387	127.0883

Tab.5 Reference setting of controller

Tab.6 Results of Azarbaijan test system

Fig.8 Comparison of performances

FACTS	Flexible alternating current transmission system
M-FACTS	Multi-converter FACTS
DR	Demand response
CM	Congestion management
SW	Social welfare
MOP	Multi-objective problem
MOPSO-NTVE	Multi-objective particle swarm optimization
NTVE	Nonlinear time-varying evolution
FDM	Fuzzy decision-making
TOPSIS	Technique for order preference by similarity to ideal solution
ESI	Electricity supply industry
SO	System operator
MP	Market participation
GA	Genetic algorithm
UPFC	Unified power flow controller
TCSC	Thyristor controlled series compensator
TCPS	Thyristor controlled phase shifter
TCPAR	Thyristor controlled phase angle regulator
SSSC	Static synchronous series compensator
CBL	Customer baseline load
IPFC	Interline power flow controller
INSGA-II	Improved non-dominated sorting genetic algorithm-II
DM	Decision maker
SBX	Simulated binary crossover
PM	Polynomial mutation
DCD	Dynamic crowding distance
$Z sh i, Z se i n$	Shunt and series transformer impedances
$V sh i, V se i n$	Controllable injected voltage sources of M-FACTS
$Δ P g, ? j$	Change in the schedule of the jth generator
$P g, j 0$	jth generator schedule in step 1
M-FACTS_cost	Cost of M-FACTS
$P l m, ? P m max$	Real power flow and rated capacity of line m
$PO D, ? i down$	Price offered by demand response i to decrease its demand
u_i	Demand response commitment variable
FACTS_cost	Cost of FACTS devices
$\| V \|$	Vector of voltage magnitude
$θ$	Vector of phase angle
E and H	Sets of equality and inequality constraints
x, u, p	State vector, control parameters and fixed parameters
C_UPFC, C_M-FACTS	Total investment cost related to UPFC and M-FACTS
S_UPFC, S_M-FACTS	Size (in MVAR) related to UPFC and M-FACTS
UPFC_cost	Cost of UPFC
n_p	Exponent
$ω m$	Real nonnegative weighting coefficient
$N l$	Total number of lines in the network
$c 1 k, ? c 2 k$	PI sensitivity with respect to V_se_ij and ?_se_ij
$c 3 k, ? c 4 k$	PI sensitivity with respect to V_se_ik and ?_se_ik
$c 5 k,$	PI sensitivity with respect to I_sh
$m$	Number of particles in a population
$k$	Number of current iteration
$x i ? (k)$	Position of ith particle at iteration k
$x i l ? (k)$	Local best of ith particle at iteration k
$x g$	Global best of all particles
$v i ? (k)$	Velocity of ith particle at iteration k
$c 1$	Cognitive parameter (acceleration coefficient)
$c 2$	Social parameter (acceleration coefficient)
$ϕ 1, ? ϕ 2$	Random numbers between 0 and 1
$EV j$	Entropy value
$AT j$	Attribute jth
D_j	Degree of divergence
OW_ij	Objective weighted normalized value
$AT +$	Positive ideal solution
$AT −$	Negative ideal solution
RC_j	Relative closeness to the ideal solution of alternative X_j with respect to A⁺

Tab.7 Notations

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

[1]	Meng SONG, Wei SUN. Applications of thermostatically controlled loads for demand response with the proliferation of variable renewable energy[J]. Front. Energy, 2022, 16(1): 64-73.
[2]	Kai GONG, Jianlin YANG, Xu WANG, Chuanwen JIANG, Zhan XIONG, Ming ZHANG, Mingxing GUO, Ran LV, Su WANG, Shenxi ZHANG. Comprehensive review of modeling, structure, and integration techniques of smart buildings in the cyber-physical-social system[J]. Front. Energy, 2022, 16(1): 74-94.
[3]	S. L. ARUN, M. P. SELVAN. Smart residential energy management system for demand response in buildings with energy storage devices[J]. Front. Energy, 2019, 13(4): 715-730.

Viewed

Full text

Abstract

Cited

Shared

Discussed