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Mutual benefit analysis of price-responsive demand response program for demand-side load management through heuristic algorithm by scheduling of multi-classifier residential unit under TOU tariff regulation

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Abstract

Under the umbrella of a smart grid environment, demand response (DR) is a comprehensive way to make the best use of household energy consumption. DR refers to the rescheduling of household energy consumption in response to the pricing signal received from the utility. Interoperability enables the DR loads to communicate with the utility through a smart meter to plan the energy consumption according to the pricing signal received. This paper presents an exclusive tri-objective model that considers real-time data of different parameters like peak, off-peak demand, and time of use pricing signal and performs optimal scheduling to reduce the overall energy expenses, peak-to-average power ratio (PAPR), and improve the load factor (LF) of the system over the entire horizon. Proposed planning considers the least, and highest priority loads for an optimal implementation of proposed planning. The proposed planning is implemented for a time horizon of 30 days. A price-responsive DR model is effectively implemented based on time of use tariff regulation. Based on predefined parameters, a proposed algorithm shifts the least priority load from peak hours to off-peak hours without disrupting the highest priority. Proposed planning is a Heuristic problem tackled as Mixed integer linear programming, mathematically solved in MATLAB. The results demonstrate that; the proposed planning effectively reduces the cumulative energy expenses by 13.36% for the end customer, improves the LF of the system by 5.11%, and reduces PAPR by 15.22% to improve the stability margin of the system. Finally, sensitivity analysis has been performed based on varying peak tariffs which shows the effectiveness of the proposed approach. The results clarify the effectiveness of the proposed model for both customer and utility ends.

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Abbreviations

DF:

Diversity factor

DISCOS:

Distribution companies

DLC:

Direct load control

DLc:

Distributed load control

DOS:

Daily operation slots

DR:

Demand response

DSM:

Demand-side management

DAG:

Direct acyclic graph

ECS:

Energy consumption scheduler

EPRI:

Electric power research institute

HPL:

Highest priority load

HV:

High voltage

IBR:

Inclining block rates

ILM:

Incentive load management

LF:

Load factor

LLC:

Local load control

LP:

Linear programming

M.D:

Maximum demand

MUPEM:

Multi-utility power exchange model

MILP:

Mixed integer linear programming

NOA:

Number of appliances (i.e., 10)

PAPR:

Peak-to-average power ratio

PLC:

Power line communication

PEM:

Point estimate method

RES:

Renewable energy sources

RTP:

Real-time pricing

TND:

Total number of days

TOD:

Time of day pricing

TOU:

Time of use pricing

WDOA:

Wind-driven optimization algorithm

\(E_{{{\text{kWh}}}}^{{{\text{FOD}}}}\) :

Total energy consumed by all appliances in whole days (i.e., 30)

\({\text{en}}_{{\text{a}}}^{{\text{h}}}\) :

Hourly energy consumed by appliance ‘\(a\)’

\({\text{en}}_{{\text{a}}}^{{\text{T}}}\) :

Energy consumed by appliance ‘\(a\)’ in ‘\(T{\text{th}}\)’ hour

\({\text{en}}_{{\text{a}}}^{{\text{t}}}\) :

Energy consumed by appliance ‘\(a\)’ in current time slot ‘\(t\)’

\({\text{En}}_{{{\text{kWh}}}}^{{{\text{a}}, {\text{T}}}}\) :

Energy consumed by appliance ‘\(a\)’ in daily ‘\(T\)’ slots

\(L_{{\text{a}}}\) :

A total load of appliance ‘\(a\)’

\(l_{{\text{a}}}^{{\text{T}}}\) :

Load of appliance ‘\(a\)’ at ‘\(T{\text{th}}\).’

\(l_{{\text{a}}}^{{\text{t}}}\) :

A load of appliance ‘\(a\)’ in current time slot ‘\(t\)’

\(L_{{{\text{AA}}}}\) :

A load of all appliances (i.e., 10)

\({\text{M}}.{\text{D}}_{{\text{i}}}\) :

Individual maxim demand

\({\text{M}}_{{{\text{cost}}}}^{{\text{t}}}\) :

Total minimized cost in time slot ‘\(a\)’

\(P_{f}^{{\text{t}}}\) :

Energy price rates at time slot ‘\(t\)’

\(P_{\left( \$ \right)}^{{{\text{kWh}}}}\) :

Pricing parameter in dollars

\({\text{PAPR}}_{{{\text{min}}}}\) :

Minimized peak-to-average power ratio

\(S_{{\text{a}}}^{{\text{t}}}\) :

State of appliance ‘\(a\)’ in current time slot ‘\(t\)’

\(T\) :

Daily time slots (i.e., 24)

\(w_{{\text{a}}}^{{\text{T}}}\) :

Power consumption of appliance ‘\(a\)’ in \(T{\text{th}}\) time slot

\(W_{{\text{a}}}^{{\text{T}}}\) :

Total daily power consumption of appliance ‘\(a\)’

\(w_{{\text{a}}}^{{\text{t}}}\) :

Power consumption of individual appliance ‘\(a\)’ in current time slot ‘\(t\)’

References

  1. Kühnlenz F, Nardelli PHJ, Karhinen S, Svento R (2018) Implementing flexible demand: real-time price vs. market integration. Energy 149:550–565. https://doi.org/10.1016/J.ENERGY.2018.02.024

    Article  Google Scholar 

  2. Eid C, Koliou E, Valles M et al (2016) Time-based pricing and electricity demand response: existing barriers and next steps. Util Policy 40:15–25. https://doi.org/10.1016/J.JUP.2016.04.001

    Article  Google Scholar 

  3. Mohsenian-Rad AH, Leon-Garcia A (2010) Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans Smart Grid 1:120–133. https://doi.org/10.1109/TSG.2010.2055903

    Article  Google Scholar 

  4. Pardalos PM, Rebennack S, Pereira MVF, Iliadis NA (2010) Handbook of Power Systems I. https://doi.org/10.1007/978-3-642-02493-1

  5. Thorsnes P, Williams J, Lawson R (2012) Consumer responses to time varying prices for electricity. Energy Policy 49:552–561. https://doi.org/10.1016/J.ENPOL.2012.06.062

    Article  Google Scholar 

  6. Adnan M, Tariq M, Zhou Z, Poor HV (2019) Load flow balancing and transient stability analysis in renewable integrated power grids. Int J Electr Power Energy Syst 104:744–771. https://doi.org/10.1016/J.IJEPES.2018.06.037

    Article  Google Scholar 

  7. Hamilton RI, Papadopoulos PN, Bell K (2020) An investigation into spatial and temporal aspects of transient stability in power systems with increasing renewable generation. Int J Electr Power Energy Syst 115:105486. https://doi.org/10.1016/J.IJEPES.2019.105486

    Article  Google Scholar 

  8. Mehrjerdi H, Bornapour M, Hemmati R, Ghiasi SMS (2019) Unified energy management and load control in building equipped with wind-solar-battery incorporating electric and hydrogen vehicles under both connected to the grid and islanding modes. Energy 168:919–930. https://doi.org/10.1016/J.ENERGY.2018.11.131

    Article  Google Scholar 

  9. Khan I (2019) Energy-saving behaviour as a demand-side management strategy in the developing world: the case of Bangladesh. Int J Energy Environ Eng 10:493–510. https://doi.org/10.1007/S40095-019-0302-3

    Article  Google Scholar 

  10. Javor D, Janjic A (2016) Communications in dependability and quality management. An International Journal Application of Demand Side Management Techniques in Successive Optimization Procedures

  11. Kwac J, Flora J, Rajagopal R (2014) Household energy consumption segmentation using hourly data. IEEE Trans Smart Grid 5:420–430. https://doi.org/10.1109/TSG.2013.2278477

    Article  Google Scholar 

  12. Monyei CG, Adewumi AO (2018) Integration of demand side and supply side energy management resources for optimal scheduling of demand response loads – South Africa in focus. Electr Power Syst Res 158:92–104. https://doi.org/10.1016/J.EPSR.2017.12.033

    Article  Google Scholar 

  13. Samadi P, Mohsenian-Rad H, Wong VWS, Schober R (2013) Tackling the load uncertainty challenges for energy consumption scheduling in smart grid. IEEE Trans Smart Grid 4:1007–1016. https://doi.org/10.1109/TSG.2012.2234769

    Article  Google Scholar 

  14. Rauf S, Rasool S, Rizwan M et al (2016) domestic electrical load management using smart grid. Energy Procedia 100:253–260. https://doi.org/10.1016/J.EGYPRO.2016.10.174

    Article  Google Scholar 

  15. Chen C, Wang J, Kishore S (2014) A distributed direct load control approach for large-scale residential demand response. IEEE Trans Power Syst 29:2219–2228. https://doi.org/10.1109/TPWRS.2014.2307474

    Article  Google Scholar 

  16. Chai Y, Xiang Y, Liu J et al (2019) Incentive-based demand response model for maximizing benefits of electricity retailers. J Mod Power Syst Clean Energy 7:1644–1650. https://doi.org/10.1007/S40565-019-0504-Y

    Article  Google Scholar 

  17. Singh PP, Khosla PK, Mittal M (2019) Energy conservation in IoT-based smart home and its automation. Stud Syst Decis Control 206:155–177. https://doi.org/10.1007/978-981-13-7399-2_7/COVER

    Article  Google Scholar 

  18. Osório GJ, Shafie-Khah M, Carvalho GCR, Catalão JPS (2019) Analysis application of controllable load appliances management in a smart home. Energies 12:3710. https://doi.org/10.3390/EN12193710

    Article  Google Scholar 

  19. Naz M, Iqbal Z, Javaid N et al (2018) Efficient power scheduling in smart homes using hybrid grey wolf differential evolution optimization technique with real time and critical peak pricing schemes. Energies 11:384. https://doi.org/10.3390/EN11020384

    Article  Google Scholar 

  20. Siano P (2014) Demand response and smart grids—a survey. Renew Sustain Energy Rev 30:461–478. https://doi.org/10.1016/J.RSER.2013.10.022

    Article  Google Scholar 

  21. Luo T, Ault G, Galloway S (2010) Demand side management in a highly decentralized energy future

  22. Wang Z, Paranjape R, Chen Z, Zeng K (2019) Multi-agent optimization for residential demand response under real-time pricing. Energies. https://doi.org/10.3390/en12152867

    Article  Google Scholar 

  23. Hu M, Xiao F (2018) Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm. Appl Energy 219:151–164. https://doi.org/10.1016/j.apenergy.2018.03.036

    Article  Google Scholar 

  24. Nan S, Zhou M, Li G (2018) Optimal residential community demand response scheduling in smart grid. Appl Energy 210:1280–1289. https://doi.org/10.1016/j.apenergy.2017.06.066

    Article  Google Scholar 

  25. Alsokhiry F, Siano P, Annuk A, Mohamed MA (2022) A novel time-of-use pricing based energy management system for smart home appliances: cost-effective method. Sustainability. https://doi.org/10.3390/su142114556

    Article  Google Scholar 

  26. Duan Q, Quynh NV, Abdullah HM et al (2020) Optimal scheduling and management of a smart city within the safe framework. IEEE Access 8:161847–161861. https://doi.org/10.1109/ACCESS.2020.3021196

    Article  Google Scholar 

  27. Yin F, Hajjiah A, Jermsittiparsert K et al (2021) A secured social-economic framework based on PEM-blockchain for optimal scheduling of reconfigurable interconnected microgrids. IEEE Access 9:40797–40810. https://doi.org/10.1109/ACCESS.2021.3065400

    Article  Google Scholar 

  28. Alsokhiry F, Annuk A, Mohamed MA, Marinho M (2023) An innovative cloud-fog-based smart grid scheme for efficient resource utilization. Sensors. https://doi.org/10.3390/s23041752

    Article  Google Scholar 

  29. Kakran S, Chanana S (2019) Operation management of a renewable microgrid supplying to a residential community under the effect of incentive-based demand response program. Int J Energy Environ Eng 10:121–135. https://doi.org/10.1007/s40095-018-0286-4

    Article  Google Scholar 

  30. Pandey Y, Hasan N, Husain MA et al (2022) An Environment friendly energy-saving dispatch using mixed integer linear programming relaxation in the smart grid with renewable energy sources. Distribu Gener Altern Energy J. https://doi.org/10.13052/DGAEJ2156-3306.37414

    Article  Google Scholar 

  31. Hasan N, Ibraheem, Pandey Y (2014) Transmission loss allocation using fairness index in deregulated market. In: Proceedings of the international conference on innovative applications of computational intelligence on power, energy and controls with their impact on humanity, CIPECH 2014. Institute of Electrical and Electronics Engineers Inc., pp 211–215

  32. Hasan N, Nasiruddin I, Pandey Y (2019) A novel technique for transmission loss allocation in restructured power system. J Electr Eng Technol. https://doi.org/10.1007/s42835-019-00163-4

    Article  Google Scholar 

  33. Khalid A, Javaid N, Guizani M et al (2018) Towards dynamic coordination among home appliances using multi-objective energy optimization for demand side management in smart buildings. IEEE Access 6:19509–19529. https://doi.org/10.1109/ACCESS.2018.2791546

    Article  Google Scholar 

  34. Li P, Wang Z, Wang N et al (2021) Stochastic robust optimal operation of community integrated energy system based on integrated demand response. Int J Electr Power Energy Syst. https://doi.org/10.1016/j.ijepes.2020.106735

    Article  Google Scholar 

  35. Wang P, Ma Z, Shao M et al (2022) Data-driven energy management in residential areas leveraging demand response. Energy Build. https://doi.org/10.1016/j.enbuild.2022.112235

    Article  Google Scholar 

  36. Masihabadi D, Kalantar M, Majd Z, Saravi SVS (2023) A novel information gap decision theory-based demand response scheduling for a smart residential community considering deep uncertainties. IET Gener Transm Distrib. https://doi.org/10.1049/gtd2.12744

    Article  Google Scholar 

  37. Hussain SS, Decision on the authority in the matter of requests filed by xwdiscos regarding periodic adjustments in tariff for 3rd and 4th quarters of fy 2018–2019. National Electric Power Regulatory Authority Islamic Republic of Pakistan NEPRA/RIADG(Tariff)/TRF-100/XWDISCOs/1080-1082

  38. Hussain SS, Decision on the authority in the matter of requests filed by xwdiscos regarding periodic adjustments in tariff for 1rd and 2th quarters of fy 2018–2019. National Electric Power Regulatory Authority nnnr Islamic Republic of Pakistan NEPRA/RIADG(Tariff)/TRF-100/XWDISCOs/1080-1082

  39. Javaid N, Hafeez G, Iqbal S et al (2018) Energy efficient integration of renewable energy sources in the smart grid for demand side management. IEEE Access 6:77077–77096. https://doi.org/10.1109/ACCESS.2018.2866461

    Article  Google Scholar 

  40. Kinhekar N, Padhy NP, Gupta HO (2014) Multiobjective demand side management solutions for utilities with peak demand deficit. Int J Electr Power Energy Syst 55:612–619. https://doi.org/10.1016/J.IJEPES.2013.10.011

    Article  Google Scholar 

  41. Costanzo GT, Zhu G, Anjos MF, Savard G (2012) A system architecture for autonomous demand side load management in smart buildings. IEEE Trans Smart Grid 3:2157–2165. https://doi.org/10.1109/TSG.2012.2217358

    Article  Google Scholar 

  42. Seyyedeh-Barhagh S, Majidi M, Nojavan S, Zare K (2019) Optimal scheduling of hydrogen storage under economic and environmental priorities in the presence of renewable units and demand response. Sustain Cities Soc 46:101406. https://doi.org/10.1016/J.SCS.2018.12.034

    Article  Google Scholar 

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Acknowledgements

Author (s) likes to acknowledge the special support of the National University of Sciences and Technology (NUST), Islamabad, Pakistan, especially for granting access to the smart grid laboratory. Uncommon gratitude to the management of the “U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E)” and “Smart grid and power system” laboratory.

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  1. U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), H-12, Islamabad, 44000, Pakistan

    Shahid Nawaz Khan

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Shahid Nawaz Khan contributed to conceptualization, investigation, methodology, software, validation, visualization, roles/Writing—original draft, and reviewing and formatting

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Khan, S.N. Mutual benefit analysis of price-responsive demand response program for demand-side load management through heuristic algorithm by scheduling of multi-classifier residential unit under TOU tariff regulation. Electr Eng 105, 2825–2844 (2023). https://doi.org/10.1007/s00202-023-01860-0

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