This paper proposes the solution to an optimal scheduling problem of a thermal-wind power system. Here, the power output from a wind energy generator (WEG) is assumed to be schedulable, therefore the wind power penetration limits can be determined by the system operator (SO). The intermittent nature of wind power and speed is modeled using the Weibull density function. Here, 3 objective functions i.e., total operating cost, voltage stability enhancement index and system losses are selected. The total generation cost minimization objective has the cost of power generated from thermal and WEGs, under and over estimation costs of wind power. In the present paper, a multi-objective optimal power flow (MO-OPF) problems are framed by considering different objective functions simultaneously, and they are solved using the multi-objective Glowworm Swarm Optimization (MO-GSO) technique. The proposed optimization problem is solved on a modified IEEE 30 bus test system with two wind farms situated at two different buses in the system. The obtained simulation results show the suitability of proposed MO-OPF method for large scale power systems.
MomohJ.A.,Outreach program in electrical engineering: Pre-college for engineering systems (PCES), IEEE Trans Power Systems29 (4) (2014), 1880–1887.
2.
Sailaja KumariM.,and MaheswarapuS., Enhanced genetic algorithm based computation technique for multi-objective optimal power flow solution, International Journal of Electrical Power and Energy Systems32 (6) (2010), 736–742.
3.
BurchettR.C., HappH.H. and WirgauK.A., Large-scale optimal power flow, IEEE Trans Power Apparatus and Systems101 (1982), 3722–3732.
4.
MomohJ.A.,Electric power system applications of optimization, Markel Dekker, 2001.
5.
ShoultsR.R. and SunD.T., Optimal power flow based on P-Q decomposition, IEEE Trans Power Apparatus and Systems101 (1982), 397–405.
6.
LiuX. and XuW., Economic load dispatch constrained by wind power availability: A here-and-now approach, IEEE Trans Sustainable Energy1 (1) (2010), 2–9.
7.
SaberA.Y. and VenayagamoorthyG.K., Resource scheduling under uncertainty in a smart grid with renewables and plug-in vehicles, IEEE Systems Journal6 (1) (2012), 103–109.
8.
DicoratoM., ForteG., PisaniM. and TrovatoM., Planning and operating combined wind-storage system in electricity market, IEEE Trans. Sustainable Energy3 (2) (2012), 209–217.
9.
LubinM., DvorkinY. and BackhausS., A robust approach to chance constrained optimal power flow with renewable generation, IEEE Trans Power Systems31 (5) (2016), 3840–3849.
10.
AmjadyN., DehghanS., AttarhaA. and ConejoA.J., Adaptive robust network-constrained AC unit commitment, IEEE Trans Power Systems32 (1) (2017), 672–683.
11.
XuT. and ZhangN., Coordinated operation of concentrated solar power and wind resources for the provision of energy and reserve services, IEEE Trans Power Systems32 (2) (2017), 1260–1271.
12.
SaftaC., ChenR.L.Y., NajmH.N., PinarA. and WatsonJ.P., Efficient uncertainty quantification in stochastic economic dispatch, IEEE Trans Power Systems32 (4) (2017), 2535–2546.
13.
Dall’AneseE., BakerK. and SummersT., Chance-constrained AC optimal power flow for distribution systems with renewables, IEEE Trans Power Systems32 (5) (2017), 3427–3438.
14.
ZhaoS., WangY., XuY., YinJ.Optimal scheduling of energy storage system in wind power integrated system based on bi-level programming, IEEE Power & Energy Society General Meeting, Denver, CO, 2015, pp. 1–5.
15.
YangG., ZhouM., LinB. and DuW., Optimal scheduling the wind-solar-storage hybrid generation system considering wind-solar correlation, IEEE PES Asia-Pacific Power and Energy Engineering Conference, Kowloon, 2013, pp. 1–6.
16.
LiuJ., HuangC. and LiP., Optimal scheduling of wind farm with storage and forecasting based on improved genetic algorithms, The 26th Chinese Control and Decision Conference, Changsha2014, pp. 80–85.
17.
PandaA. and TripathyM., Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm, Energy93 (1) (2015), 816–827.
18.
BanerjeeS., DasguptaK. and ChandaC.K., Short term hydro–wind–thermal scheduling based on particle swarm optimization technique, Energy Systems81 (2016), 275–288.
19.
ChangY.C., LeeT.Y., ChenC.L. and JanR.M., Optimal power flow of a wind-thermal generation system, Energy Systems55 (2014), 312–320.
20.
DolatabadiA. and IvatlooB.M., Stochastic risk-constrained scheduling of smart energy hub in the presence of wind power and demand response, Applied Thermal Engineering123 (2017), 40–49.
21.
PandaA. and TripathyM., Solution of wind integrated thermal generation system for environmental optimal power flow using hybrid algorithm, Journal of Electrical Systems and Information Technology3 (2) (2016), 151–160.
22.
ElDesoukyA.A.,Security constrained generation scheduling for grids incorporating wind, photovoltaic and thermal power, Electric Power Systems Research116 (2014), 284–292.
23.
ReddyS.S., BijweP.R. and AbhyankarA.R., Joint energy and spinning reserve market clearing incorporating wind power and load forecast uncertainties, IEEE Systems Journal9 (1) (2015), 152–164.
24.
HetzerJ., YuD.C. and BhattaraiK., An economic dispatch model incorporating wind power, IEEE Trans Energy Conversion23 (2) (2008), 603–611.
25.
MishraS., MishraY. and VigneshS., Security constrained economic dispatch considering wind energy conversion systems, IEEE Power and Energy Society General Meeting, San Diego, CA, 2011, pp. 1–8.
26.
Surender ReddyS., BijweP.R. and AbhyankarA.R., Faster evolutionary algorithm based optimal power flow using incremental variables, Energy Systems54 (2014), 198–210.
27.
BansilalD.T. and ParthasarathyK., Optimal Reactive power dispatch algorithm for voltage stability improvement, Energy Systems18 (7) (1996), 461–468.
28.
Surender ReddyS., AbhyankarA.R. and BijweP.R., Reactive power price clearing using multi-objective optimization, Energy36 (5) (2011), 3579–3589.
29.
KothariD.P. and DhillonJ.S., Power System Optimization, Second Edition, PHI learning Private limited, 2011.
30.
NiknamT., MojarradH.D. and MeymandH.Z., A novel hybrid particle swarm optimization for economic dispatch with valvepoint loading effects, Energy Conversion Management52 (4) (2011), 1800–1809.
31.
KrishnandK.N. and GhoseD., Glowworm swarm optimisation for simultaneous Capture of multiple local optima of multimodal functions, Swarm Intelligence3 (2009), 87–124.
32.
Surender ReddyS. and RathnamC.S., Optimal power flow using glowworm swarm optimization, Energy Systems80 (2016), 128–139.
