Optimal Power Control Strategy of a Hybrid Energy System Considering Demand Response Strategy and Customer Interruption Cost

Author(s): O. Dzobo* , Y. Sun .

Journal Name: Recent Advances in Electrical & Electronic Engineering

Volume 12 , Issue 1 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: The integration of distributed renewable energy sources into the conventional power system network has created opportunities for electricity customers to reduce their electricity cost. This paper investigates the optimal power scheduling of a hybrid energy system connected to the grid in the presence of demand response strategy and inconvenience cost.

Methods: A new proposed method of calculating the inconvenience cost which is dependent on total home appliance load, Customer Interruption Cost (CIC) and delay time operation of home appliances is proposed. The hybrid energy system consists of solar photovoltaic (PV) module and battery bank storage system. The home appliance scheduling is formulated as a non-convex mixed integer programming with a binary decision variable to switch ON/OFF the home appliances. The optimization objective is to minimize both the total daily electricity cost and inconvenience cost of a residential customer with different time-shiftable, power shiftable home appliances and customer time preference constraints.

Results: The results show that it is important to schedule home appliances and include their inconvenience cost so that home appliances are not only shifted to the lower electricity tariff periods but can also start at their customer preferred operation times.

Conclusion: The results also show that the hybrid energy system is able to cater for all the energy requirements of home appliances during the day, reducing power demand from the grid by a significant percentage and thus, relieve the power system network and afford electricity consumers significant monetary savings.

Keywords: Battery bank storage, customer interruption cost, demand response, electricity cost/bill, home appliance scheduling, solar PV module system.

[1]
“Mckinsey on smart grid”, No. 1 Summer 2010, Mckinsey Co. [Available Online:] .http://sedc-coalition.eu/wp-content/uploads/ 2011/06/mckinsey-10-08-05-smart -grid-benefits.pdf 2011.(Accessed Nov 23, 2016)
[2]
H. Aalami, G. Yousefi, and M. Moghadam, Demand response model considering EDRP and TOU In: IEEE/PES Transmission and Distribution Conference and Exposition, Chicago, IL, USA 2008.
[3]
M. Malette, and G. Venkataramanan, Financial incentives to encourage demand response participation by plug-in hybrid electric vehicle owners., IEEE Energy Conversion Congress and Exposition, 2010, pp. 4278-4284.
[4]
D. Setlhaolo, X. Xia, and J. Zhang, "Optimal scheduling of household appliances for demand response", Electr. Power Syst. Res., vol. 116, pp. 24-28, 2014.
[5]
V.A. Evangelopoulos, P.S. Georgilakis, and N.D. Hatziargyriou, "Optimal operation of smart distribution networks: A review of models, methods and future research", Electr. Power Syst. Res., vol. 140, pp. 95-106, 2016.
[6]
M. Erol-Kantarci, and H. Mouftah, "Wireless sensor networks for cost efficient residential energy management in the smart grid", IEEE Transactions on Smart Grid 1, pp. 320-325, 2011..
[7]
"M. Song, K. Alvehag, J. Widen, A. Parisio, Estimating the impacts of demand response by simulating household behaviors under price and CO2 signals", Electr. Power Syst. Res., vol. 111, pp. 103-114, 2014. 2014
[8]
D. Setlhaolo, and X. Xia, "Optimal scheduling of household appliances with a battery storage system and coordination", Energy Build., vol. 94, pp. 61-70, 2015.
[9]
O. Dzobo, and X. Xia, "Optimal operation of smart multi-energy hub systems incorporating energy hub coordination and demand response strategy", J. Renew. Sustain. Energy, vol. 9, p. 045501, 2017.
[10]
O. Dzobo, K. Alvehag, C.T. Gaunt, and R. Herman, "Multi-dimensional customer segmentation model for power system reliability-worth analysis", Int. J. Electr. Power Energy Syst., vol. 62, pp. 532-539, 2014.
[11]
O. Dzobo, C.T. Gaunt, and R. Herman, "Reliability worth assessment of electricity consumers: A South African case study", J. Energy South. Afr., vol. 23, pp. 31-39, 2012.
[12]
O. Dzobo, C.T. Gaunt, R. Herman, and M.J. Saulo, "The effect of backup power supply on calculation of reliability cost and worth indices", In: Proceedings of the third IASTED African conference, vol. Vol. 684 pp. 032-128,, 2010
[13]
O. Dzobo, C.T. Gaunt, and R. Herman, "Investigating the use of probability distribution functions in reliability-worth analysis of electric power systems", Int. J. Electr. Power Energy Syst., vol. 37, pp. 110-116, 2012.
[14]
"K. Alvehag and L. Söder, “An activity-based interruption cost model for households to be used in cost-benefit analysis”, In: Proceed. Power Technol., Lausanne, Switzerland, 2007",
[15]
R. Herman, and C.T. Gaunt, "Direct and indirect measurement of residential and commercial CIC: Preliminary findings from South African surveys In", Proceedings of the 10th International Conference on Probabilistic Methods Applied to Power Systems PMAPS '08., 2008
[16]
F. Fernandes, and H. Morais, "Z. VaZ and C. Ramos, “Dynamic load management in a smart home to participate in demand response events", Energy Build., vol. 82, pp. 592-606, 2014.
[17]
P. Du, and N. Lu, "Appliance commitment for household load scheduling", IEEE Trans. Smart Grid, vol. 2, pp. 411-419, 2011.
[18]
H. Tazvinga, X. Xia, and J. Zhang, "Minimum cost solution of photovoltaic-diesel-battery hybrid power systems for remote consumers", Sol. Energy, vol. 96, pp. 292-299, 2013.
[19]
M.J. Morshed, and A. Asgharpour, "Hybrid imperialist competitive-sequential quadratic programming (hicsqp) algorithm for solving economic load dispatch with incorporating stochastic wind power: A comparative study on heuristic optimization techniques", Energy Convers. Manage., vol. 84, pp. 30-40, 2014.
[20]
G.J. Osorio, J.M. Lujano-Rojas, J.C.O. Matias, and J.P.S. Catal, "A probabilistic approach to solve the economic dispatch problem with intermittent renewable energy sources", Energy, vol. 82, pp. 949-959, 2015.
[21]
V.S. Tabar, M.A. Jirdehi, and R. Hemmati, "Energy management in microgrid based on the multi objective stochastic programming incorporating portable renewable energy resource as demand response option", Energy, vol. 118, pp. 827-839, 2017.
[22]
S.M. Nosratabadi, R.A. Hooshmand, and E. Gholipour, "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems", Renew. Energ. Sustain. Energy Rev., vol. 67, pp. 341-363, 2017.
[23]
N. Good, K.A. Ellis, and P. Mancarella, "Review and classification of barriers and enablers of demand response in the smart grid", Renew. Sustain. Energy Rev., vol. 72, pp. 57-72, 2017.
[24]
J. Wang, H. Zhong, Z. Ma, Q. Xia, and C. Kang, "Review and prospect of integrated demand response in the multi-energy system", Applied. Energy, vol. 202, pp. 772-782, 2017.
[25]
L. Swan, and V. Ugursal, "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques", Renew. Sustain. Energy Rev., vol. 13, pp. 1819-1835, 2009.
[26]
X. Xia, D. Setlhaolo, and J. Zhang, Residential demand response strategies for South Africa.In:IEEE PES Power Africa 2012., Conference and Exhibition: Johannesburg, South Africa, 2012.
[27]
"P. Khajavi, H. Monsef and H. Abniki, “Load profile reformation through demand response programs using smart grid”, In: Proc. of the International Symposium on Modern Electric Power Systems, Wroclaw, Poland, 2010",
[28]
J.H. Yoon, R. Bladick, and A. Novoselac, "Demand response for residential buildings based on dynamic price of electricity", Energy Build., vol. 80, pp. 531-541, 2014.
[29]
K. Alvehag, and L. Soder, "Considering extreme outage events in cost-benefit analysis of distribution systems In", Proceedings of Australasian Universities Power Engineering Conference (AUPEC), 2008


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 12
ISSUE: 1
Year: 2019
Page: [20 - 29]
Pages: 10
DOI: 10.2174/2352096511666180312142859

Article Metrics

PDF: 19
HTML: 2