Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes

Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes

Abstract

:

1. Introduction

2. Literature Review

• Optimal scheduling of the proposed system considering the various types of demand response.
• EV-based charging and discharging modeling is considered for the real-time analysis of smart buildings in developing countries.
• The effect of different demand responses on the operational cost of the building is analyzed.
• In the next section, the system description is presented considering the main objective of the proposed model.

3. System Description

3.1. Demand Response Type

3.1.1. Time of Use (ToU) Pricing

3.1.2. Critical Peak Pricing (CPP)

3.1.3. Real-Time Pricing (RTP)

3.2. Photovoltaic System

3.3. Energy Storage System (ESS)

3.4. Electric Vehicles (EVs)

4. Mathematical Modeling

4.1. PV Modeling

4.2. Energy Storage Modeling

4.3. EV Constraints

4.4. Grid Connection

4.4.1. Objective Function

4.4.2. Solution Methodology

5. Results and Discussion

5.1. Case 01, Analysis Based on Real-Time Pricing

i.

Without Scheduling

ii.

With Proposed Scheduling

5.2. Case 02, Analysis Based on Critical Peak Pricing

i.

Without Scheduling

ii.

With Proposed Scheduling

5.3. Case 03, Analysis Based on Time of Use Pricing

i.

Without Scheduling

ii.

With Proposed Scheduling

6. Conclusions

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Acknowledgments

Conflicts of Interest

References

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Scenarios	Grid Availability	Solar PV	BESS and EV
Case (01) RTP Case (02) CPP Case (03) ToU	✓	–	–
✓	✓	–
✓	✓	✓
Proposed Scheduling (Incentivized 10%)	✓	✓	✓

Scenarios	Cost Grid Availability (USD)	Cost Solar PV (USD)	BESS and EV	Saving (%)
Case (01) RTP	i.	797.31	–	–	–
ii.	394	–	50
iii.	354	55
Proposed Scheduling (Incentivized 10%)	717	355	301	58

Scenarios	Cost Grid Availability (USD)	Cost Solar PV (USD)	BESS and EV	Saving (%)
Case (02) CPP	i.	1002	–	–	–
ii.	489	–	51
iii.	480	52
Proposed Scheduling (Incentivized 10%)	902	440	451	55

Scenarios	Cost Grid Availability (USD)	Cost Solar PV (USD)	BESS and EV	Saving (%)
Case (03) ToU	i.	918.54	–	–	–
ii.	565	–	38
iii.	526	42
Proposed Scheduling (Incentivized 10%)	826	508	498	45

Ref.	Year	Application	Technique	Remarks	Savings
[74]	2018	Campus µG	MILP	Peak demand	5.32%
[75]	2018	Residential Level	MILP	Frequency-based regulation	7%
[76]	2019	Residential µG	LP	Grid for the mode of outage	16%
[77]	2020	Campus µG	MILP	Peak mitigation	23%
[78]	2021	Campus µG	MILP	ESS degradation cost, peak demand	5.27%
Proposed Model	2022	Campus µG	LP	ESS, Demand response, EV	58%

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Fei, L.; Shahzad, M.; Abbas, F.; Muqeet, H.A.; Hussain, M.M.; Bin, L. Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes. Sensors 2022, 22, 7448. https://doi.org/10.3390/s22197448

Fei L, Shahzad M, Abbas F, Muqeet HA, Hussain MM, Bin L. Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes. Sensors. 2022; 22(19):7448. https://doi.org/10.3390/s22197448

Chicago/Turabian Style

Fei, Liu, Muhammad Shahzad, Fazal Abbas, Hafiz Abdul Muqeet, Muhammad Majid Hussain, and Li Bin. 2022. “Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes” Sensors 22, no. 19: 7448. https://doi.org/10.3390/s22197448