Flexible Regulation and Synergy Analysis of Multiple Loads of Buildings in a Hybrid Renewable Integrated Energy System
Abstract
:1. Introduction
- (1)
- A flexible regulation model of heat load is built according to the dynamic heat characteristics and heat comfort elastic interval of the buildings. Flexible regulation of the electrical load is modeled according to its transferability, resectability, and rigidity.
- (2)
- An operation optimization model, which incorporates flexible regulation of multiple loads and the variable load of devices, is then developed to improve the operational performance and reduce the renewable energy curtailment of HRIES.
- (3)
- Comparatively analyze the operation performance of minimum total operating cost and renewable curtailment rate with various flexible loads. Present the flexible regulation and synergy mechanism of multiple types of flexible loads in reducing the operating cost and renewable energy consumption of HRIES.
2. HRIES with Flexible Buildings
3. Flexible Load Model of Buildings
3.1. Flexible Heat Load Model
3.2. Flexible Electrical Load Model
4. Optimization Model for Introducing Flexible Load
4.1. Optimization Objectives
4.2. Model Constraints
4.2.1. Device Model Constraints
- (1)
- Wind power
- (2)
- Gas-fired CHP units
- (3)
- Gas-fired boiler
- (4)
- Energy storage
4.2.2. Energy Balance Constraints
4.2.3. Constraints of the Power Grid
4.3. Model Solution
5. Case Study
5.1. Model Solution
5.2. Optimized Results
5.3. Discussion of Results
6. Conclusions
- (1)
- Flexible electrical load increases the compatibility between load and renewable energy output by regulating the actual load curve of HRIES, thereby increasing the consumption of renewable energy. In addition, flexible electrical load regulation mainly reduces the total operating cost of HRIES by increasing renewable energy consumption.
- (2)
- The flexible heat load reduces the total operating cost of the system by coordinating the renewable energy consumption with the increase in the average electrical efficiency of the gas-fired CHP unit. Blindly pursuing renewable energy consumption will reduce the average power efficiency, which in turn worsens the total operating cost.
- (3)
- Flexible electrical load and heat load regulation have a saturation effect in improving the consumption of renewable energy during HRIES operation and a synergistic effect in reducing the total cost of the system, which can reduce the total cost by 0.73%.
- (4)
- If the regulation of flexible electrical and heat loads is considered in the operation optimization of HRIES, the total economic cost of the system will decrease by 15.13%, and the renewable energy curtailment rate will decrease by 12.08%.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Du, Y.; Liu, H.; Huang, H.; Li, X. The carbon emission reduction effect of agricultural policy—Evidence from China. J. Clean. Prod. 2023, 406, 137005. [Google Scholar] [CrossRef]
- Wen, D.; Aziz, M. Design and analysis of biomass-to-ammonia-to-power as an energy storage method in a renewable multi-generation system. Energy Convers. Manag. 2022, 261, 115611. [Google Scholar] [CrossRef]
- Su, Y.; Cheng, H.; Wang, Z.; Yan, J.; Miao, Z.; Gong, A. Analysis and prediction of carbon emission in the large green commercial building: A case study in Dalian, China. J. Build. Eng. 2023, 68, 106147. [Google Scholar] [CrossRef]
- Huo, T.; Du, Q.; Xu, L.; Shi, Q.; Cong, X.; Cai, W. Timetable and roadmap for achieving carbon peak and carbon neutrality of China’s building sector. Energy 2023, 274, 127330. [Google Scholar] [CrossRef]
- Ali, K.A.; Ahmad, M.I.; Yusup, Y. Issues, Impacts, and Mitigations of Carbon Dioxide Emissions in the Building Sector. Sustainability 2020, 12, 7427. [Google Scholar] [CrossRef]
- Ke, Y.Y.; Zhou, L.; Zhu, M.L.; Yang, Y.; Fan, R.; Ma, X.R. Scenario Prediction of Carbon Emission Peak of Urban Residential Buildings in China’s Coastal Region: A Case of Fujian Province. Sustainability 2023, 15, 2456. [Google Scholar] [CrossRef]
- Zeng, P.L.; Xu, J.; Zhu, M.C. Demand Response Strategy Based on the Multi-Agent System and Multiple-Load Participation. Sustainability 2024, 16, 902. [Google Scholar] [CrossRef]
- Al-Rawashdeh, H.; Al-Khashman, O.A.; Al Bdour, J.T.; Gomaa, M.R.; Rezk, H.; Marashli, A.; Arrfou, L.M.; Louzazni, M. Performance Analysis of a Hybrid Renewable-Energy System for Green Buildings to Improve Efficiency and Reduce GHG Emissions with Multiple Scenarios. Sustainability 2023, 15, 7529. [Google Scholar] [CrossRef]
- Reddy, V.J.; Hariram, N.P.; Ghazali, M.F.; Kumarasamy, S. Pathway to Sustainability: An Overview of Renewable Energy Integration in Building Systems. Sustainability 2024, 16, 638. [Google Scholar] [CrossRef]
- Shi, S.H.; Zhu, N. Challenges and Optimization of Building-Integrated Photovoltaics (BIPV) Windows: A Review. Sustainability 2023, 15, 15876. [Google Scholar] [CrossRef]
- Liu, M.; Wang, S.; Yan, J. Operation scheduling of a coal-fired CHP station integrated with power-to-heat devices with detail CHP unit models by particle swarm optimization algorithm. Energy 2021, 214, 119022. [Google Scholar] [CrossRef]
- Turk, A.; Wu, Q.; Zhang, M.; Ostergaard, J. Day-ahead stochastic scheduling of integrated multi-energy system for flexibility synergy and uncertainty balancing. Energy 2020, 196, 117130. [Google Scholar] [CrossRef]
- Zeng, Q.; Zhang, B.; Fang, J.; Chen, Z. A bi-level programming for multistage co-expansion planning of the integrated gas and electricity system. Appl. Energy 2017, 200, 192–203. [Google Scholar] [CrossRef]
- Wang, H.; Zhang, C.; Li, K.; Ma, X. Game theory-based multi-agent capacity optimization for integrated energy systems with compressed air energy storage. Energy 2021, 221, 119777. [Google Scholar] [CrossRef]
- Zhou, Y.; Zhao, P.; Xu, F.; Cui, D.; Ge, W.; Chen, X.; Gu, B. Optimal Dispatch Strategy for a Flexible Integrated Energy Storage System for Wind Power Accommodation. Energies 2020, 13, 1073. [Google Scholar] [CrossRef]
- Ji, L.; Liang, X.; Xie, Y.; Huang, G.; Wang, B. Optimal design and sensitivity analysis of the stand-alone hybrid energy system with PV and biomass-CHP for remote villages. Energy 2021, 225, 120323. [Google Scholar] [CrossRef]
- Li, H.; Wei, Z.; Miao, Q.; Zhao, L.; Sun, B.; Zhang, C. Multi-energy flow cooperative dispatch for generation, storage, and demand in integrated energy systems with dynamic correction. Sustain. Cities Soc. 2022, 76, 103494. [Google Scholar] [CrossRef]
- Zeng, R.; Guo, B.; Zhang, X.; Li, H.; Zhang, G. Study on thermodynamic performance of SOFC-CCHP system integrating ORC and double-effect ARC. Energy Convers. Manag. 2021, 242, 114326. [Google Scholar] [CrossRef]
- Jiang, Y.; Xu, J.; Sun, Y.; Wei, C.; Wang, J.; Liao, S.; Ke, D.; Li, X.; Yang, J.; Peng, X. Coordinated operation of gas-electricity integrated distribution system with multi-CCHP and distributed renewable energy sources. Appl. Energy 2018, 211, 237–248. [Google Scholar] [CrossRef]
- Li, Z.; Wu, W.; Shahidehpour, M.; Wang, J.; Zhang, B. Combined Heat and Power Dispatch Considering Pipeline Energy Storage of District Heating Network. IEEE Trans. Sustain. Energy 2016, 7, 12–22. [Google Scholar] [CrossRef]
- Wang, J.; Huo, S.; Yan, R.; Cui, Z. Leveraging heat accumulation of district heating network to improve performances of integrated energy system under source-load uncertainties. Energy 2022, 252, 124002. [Google Scholar] [CrossRef]
- Xue, Y.; Shahidehpour, M.; Pan, Z.; Wang, B.; Zhou, Q.; Guo, Q.; Sun, H. Reconfiguration of District Heating Network for Operational Flexibility Enhancement in Power System Unit Commitment. IEEE Trans. Sustain. Energy 2021, 12, 1161–1173. [Google Scholar] [CrossRef]
- Qin, M.; Yang, Y.; Chen, S.; Xu, Q. Bi-level optimization model of integrated biogas energy system considering the thermal comfort of heat customers and the price fluctuation of natural gas. Int. J. Electr. Power Energy Syst. 2023, 151, 109168. [Google Scholar] [CrossRef]
- Zhang, J.; Kong, X.; Shen, J.; Sun, L. Day-ahead optimal scheduling of a standalone solar-wind-gas based integrated energy system with and without considering thermal inertia and user comfort. J. Energy Storage 2023, 57, 106187. [Google Scholar] [CrossRef]
- Yang, X.; Chen, Z.; Huang, X.; Li, R.; Xu, S.; Yang, C. Robust capacity optimization methods for integrated energy systems considering demand response and thermal comfort. Energy 2021, 221, 119727. [Google Scholar] [CrossRef]
- Huang, H.; Wang, H.; Hu, Y.-J.; Li, C.; Wang, X. Optimal plan for energy conservation and CO2 emissions reduction of public buildings considering users’ behavior: Case of China. Energy 2022, 261, 125037. [Google Scholar] [CrossRef]
- Wang, D.; Zhi, Y.-Q.; Jia, H.-J.; Hou, K.; Zhang, S.-x.; Du, W.; Wang, X.-d.; Fan, M.-h. Optimal scheduling strategy of district integrated heat and power system with wind power and multiple energy stations considering thermal inertia of buildings under different heating regulation modes. Appl. Energy 2019, 240, 341–358. [Google Scholar] [CrossRef]
- Saberi-Beglar, K.; Zare, K.; Seyedi, H.; Marzband, M.; Nojavan, S. Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads. Appl. Energy 2023, 329, 120265. [Google Scholar] [CrossRef]
- Zhang, Y.; Ge, Z.; Yang, Y.; Hao, J.; Xu, L.; Du, X.; Traeholt, C. Carbon reduction and flexibility enhancement of the CHP-based cascade heating system with integrated electric heat pump. Energy Convers. Manag. 2023, 280, 116801. [Google Scholar] [CrossRef]
- Wang, Y.; Li, Y.; Zhang, Y.; Xu, M.; Li, D. Optimized operation of integrated energy systems accounting for synergistic electricity and heat demand response under heat load flexibility. Appl. Therm. Eng. 2024, 243, 122640. [Google Scholar] [CrossRef]
- Sun, H.; Sun, X.; Kou, L.; Zhang, B.; Zhu, X. Optimal scheduling of park-level integrated energy system considering ladder-type carbon trading mechanism and flexible load. Energy Rep. 2023, 9, 3417–3430. [Google Scholar] [CrossRef]
- Li, P.; Wang, H.; Wang, Y.; Han, Y.; Li, W. Improvement of Peak Load Regulation Capacity of Combined Heat and Power Units Considering Dynamic Thermal Performance of Buildings and District Heating Pipelines Network. Autom. Electr. Power Syst. 2017, 41, 26–33. [Google Scholar]
- Lu, N. An Evaluation of the HVAC Load Potential for Providing Load Balancing Service. IEEE Trans. Smart Grid 2012, 3, 1263–1270. [Google Scholar] [CrossRef]
- Liu, W.; Tian, X.; Yang, D.; Deng, Y. Evaluation of individual thermal sensation at raised indoor temperatures based on skin temperature. Build. Environ. 2021, 188, 107486. [Google Scholar] [CrossRef]
- Lin, L.; Gu, J.; Wang, L. Optimal Dispatching of Combined Heat-power System Considering Characteristics of Thermal Network and Thermal Comfort Elasticity for Wind Power Accommodation. Power Syst. Technol. 2019, 43, 3648–3655. [Google Scholar]
- Yang, H.; Xiong, T.; Qiu, J.; Qiu, D.; Dong, Z.Y. Optimal operation of DES/CCHP based regional multi-energy prosumer with demand response. Appl. Energy 2016, 167, 353–365. [Google Scholar] [CrossRef]
- Li, H. Evaluation of a Distributed Energy System Combined with Heating, Cooling and Power Generation Through Multi-Criteria Optimization. In Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Washington, DC, USA, 15–21 November 2003; pp. 277–284. [Google Scholar]
Devices | Unit Maintenance Cost (Yuan/MWh) | Technical Parameters |
---|---|---|
GB | 20 | RPGB = 12 MW; URGB = DRGB = 6 MW/h |
EES | 83 | ; ; ; |
HES | 20 | ; ; ; |
CHP | 20 | RPCHP = 35 MW; URCHP = DRCHP = 12.25 MW/h |
WT | 68 | RPWT = 50 MW |
Optimized Results | Curtailment Rate (%) | TOC (Thousand Yuan) | MC (Thousand Yuan) | EC (Thousand Yuan) | CEC (Thousand Yuan) | REP (Thousand Yuan) |
---|---|---|---|---|---|---|
Case 1 | 27.24 | 413.8 | 48.6 | 310.5 | 12.8 | 45.6 |
Case 2 | 19.06 | 385.6 | 47.6 | 290.3 | 14.1 | 33.6 |
Case 3 | 22.68 | 382.4 | 45.5 | 282.2 | 15.8 | 38.8 |
Case 4 | 15.04 | 351.2 | 50.0 | 263.1 | 12.4 | 25.7 |
Optimized Results | Curtailment Rate (%) | TOC (Thousand Yuan) | MC (Thousand Yuan) | EC (Thousand Yuan) | CEC (Thousand Yuan) | REP (Thousand Yuan) |
---|---|---|---|---|---|---|
Case 1 | 26.73 | 417.6 | 48.6 | 310.5 | 12.8 | 45.7 |
Case 2 | 19.06 | 385.6 | 47.6 | 290.3 | 14.1 | 33.6 |
Case 3 | 22.05 | 386.0 | 50.7 | 282.3 | 15.8 | 37.7 |
Case 4 | 14.65 | 354.2 | 53.7 | 263.1 | 12.4 | 25 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wu, M.; Fan, J.; Yan, R.; Hu, X.; Zhang, J.; He, Y.; Cao, G.; Zhao, W.; Song, D. Flexible Regulation and Synergy Analysis of Multiple Loads of Buildings in a Hybrid Renewable Integrated Energy System. Sustainability 2024, 16, 2969. https://doi.org/10.3390/su16072969
Wu M, Fan J, Yan R, Hu X, Zhang J, He Y, Cao G, Zhao W, Song D. Flexible Regulation and Synergy Analysis of Multiple Loads of Buildings in a Hybrid Renewable Integrated Energy System. Sustainability. 2024; 16(7):2969. https://doi.org/10.3390/su16072969
Chicago/Turabian StyleWu, Mou, Junqiu Fan, Rujing Yan, Xiangxie Hu, Jing Zhang, Yu He, Guoqiang Cao, Weixing Zhao, and Da Song. 2024. "Flexible Regulation and Synergy Analysis of Multiple Loads of Buildings in a Hybrid Renewable Integrated Energy System" Sustainability 16, no. 7: 2969. https://doi.org/10.3390/su16072969