Corrective Evaluation of Response Capabilities of Flexible Demand-Side Resources Considering Communication Delay in Smart Grids
Abstract
:1. Introduction
1.1. Background
1.2. Literature Review
1.3. Innovations and Contributions
- In the context of large-scale flexible-resource integration scenarios, a method for efficient communication-network delay measurement based on the MQTT protocol is proposed to address potential challenges faced by traditional communication architectures. The MQTT protocol, known for its lightweight nature, low bandwidth consumption, and Quality of Service (QoS) guarantee mechanism, is suitable for reliable communication in resource-constrained environments and unstable network conditions. It provides a solid foundation for precise delay measurement in demand-side resource integration.
- By collecting a large quantity of network latency data through practical deployment and testing, then further analyzing and transforming it, the data are quantified as a high-latency rate indicator to accurately reflect the impact of network conditions on the transmission of resource scheduling commands. Building upon this foundation, the article delves into how to incorporate latency factors into the evaluation model of flexible resources, especially air conditioning response capabilities. By introducing latency constraints, an innovative method for assessing resource response capabilities considering network latency is proposed. This method can more accurately predict and evaluate the actual adjustment capabilities of flexible resources such as air conditioning when facing rapidly changing energy demands.
1.4. Structure
2. A Method for Measuring Bidirectional Channel Delay Based on MQTT
- Measurement Probes: These are primarily responsible for capturing data on key network links. They are deployed on various critical links within the network and typically function as complete network measurement devices with measurement and communication capabilities. Each probe is equipped with a local database for temporary storage of captured data packets and for timely uploading based on the settings of the master measurement station. It also ensures that test data are not lost in case of network congestion.
- Master Measurement Station: This serves as the core component of the measurement system and has two main functions: node management and data analysis. It typically employs server clusters and large databases. The master measurement station manages all the collection nodes and controls the invocation, configuration, status monitoring, and upgrades of the measurement probes. It can issue user commands and measurement parameter requirements to the measurement probes.
3. Evaluation Method for the Tunable Potential of Split Air Conditioning Groups Considering Delay
3.1. Response Reliability Evaluation
Algorithm 1 Response reliability evaluation |
Input: , , , |
Initialization: , I, T, |
Procedure: |
Calculate of I devices with and according to (1) |
Determine equivalent load curve with and according to (3) |
Access reduction in responsiveness with according to (4) |
Calculate total responsive capacity according to (5) |
Adjust actual responsive capacity value with and according to (6) |
Fixed the degree of trusted response with , and according to (7) |
Results: |
3.2. Adjustment of Air Conditioning Response Capability
Algorithm 2 Split air conditioning power |
Input: |
Initialization: |
Procedure: |
Calculate the air conditioning actual power according to (8) |
Results: |
Algorithm 3 Responsiveness correction |
Input: , Z |
Procedure: |
Update responsiveness with according to (13) |
with according to (14) |
Computes trusted response capability with according to (15) |
Confidence interval of adjusted ability [,] with Z according to (16) and (17) |
Results: |
4. Example Analysis
4.1. Fixed Responsiveness of Resident Users
4.2. Discussions
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Zhang, S.; Chen, Y.; Shao, X.; Zhang, J.; Chen, X.; Tang, W. Multi-objective Day-ahead Optimization Dispatch of Distribution Network Side Flexible Resources Based on Fine Evaluation of Residential User Regulation Ability. Electr. Power Supply Util. 2022, 39, 42–50. [Google Scholar]
- Xue, C.; Ren, J.; Ma, X.; Cui, W.; Liu, Y.; Wang, X. Mechanism and Practice of Northwest Peak-shifting Ancillary Service Market Oriented to High Proportion of New Energy Integration. China Electr. Power 2021, 54, 19–28. [Google Scholar]
- Hong, H.; Leng, A.; He, J.; He, X.; Liang, C.; Luo, D. Research on Network Segment Delay Measurement Technology of Smart Low-voltage Power Grid. Mod. Electron. Tech. 2020, 43, 179–182+186. [Google Scholar]
- Wang, G.; Song, Y.; Guan, L.; Zhang, H.; Gao, K.; Ding, H.; Wang, Z.; Zheng, Q.; Zhao, J. Architecture and Evolution Path of Secure Interactive Communication Network for Distributed Source-Load-Storage Resources. Electr. Power Inf. Commun. Technol. 2023, 21, 10–18. [Google Scholar]
- Cao, C.; Xie, J.; Yue, D.; Huang, C.; Wang, J.; Xu, S.; Chen, X. Distributed Economic Dispatch of Virtual Power Plant under a Non-Ideal Communication Network. Energies 2017, 10, 235. [Google Scholar] [CrossRef]
- Zhang, H.; Li, J.; Wu, Q.; Wu, S.; Zhu, S.; Zhang, T. Communication Network System Architecture and Communication Mode Adaptation Method for Virtual Power Plant. Electr. Power Inf. Commun. Technol. 2022, 20, 47–54. [Google Scholar]
- Wang, G.; Su, J.; Pan, J.; Zhang, H.; Gao, K.; Liu, C. Prospects for Research on Communication Network Architecture and Key Technologies of Virtual Power Plant. Autom. Electr. Power Syst. 2022, 46, 15–25. [Google Scholar]
- Yin, S.; Jin, M.; Chen, X.; Guo, X.; Feng, J. Modeling and simulation of optimal configuration of virtual power plant oriented to power Internet of Things. In Proceedings of the IEEE 4th International Conference on Automation, Electronics and Electrical Engineering, Shenyang, China, 19–21 November 2021; pp. 751–754. [Google Scholar]
- Chen, Q.; Wang, W.; Wang, H. Coordinated Optimization of Active Distribution Network with Multiple Microgrids Considering Demand Response and Hybrid Game. Autom. Electr. Power Syst. 2023, 47, 99–109. [Google Scholar]
- Sheng, H.; Wang, C.; Li, B.; Liang, J.; Yang, M.; Dong, Y. Multi-timescale active distribution network scheduling considering demand response and user comprehensive satisfaction. IEEE Trans. Ind. Appl. 2021, 57, 1995–2005. [Google Scholar] [CrossRef]
- An, X.; Wang, L.; Hu, X.; Zhang, Y.; Wang, Z. Collaborative regulation control optimization on demand side of microgrid based on multiagent. In Proceedings of the 2019 4th International Conference on Intelligent Green Building and Smart Grid (IGBSG), Yichang, China, 6–9 September 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 493–496. [Google Scholar]
- Yu, Y.; Sun, H.; Fang, Z.; Wang, W.; Ding, J.J.; Gao, B. Research on optimization of 5G communication slice access with time-delay requirement constraints for protection communication in distribution network. Electr. Power Supply Util. 2021, 38, 29–34. [Google Scholar] [CrossRef]
- Qian, C.; Xu, X.; Mei, J.; Yu, J.; Zheng, J.Y.; Wang, Y.; Ji, W.L.; Zhang, M. Clock synchronization compensation algorithm for terminal queue time delay asymmetric communication path with IEEE 1588 applied in distribution network. Power Syst. Technol. 2015, 39, 3622–3626. [Google Scholar]
- Rubino, L.; Rubino, G.; Esempio, R. Linear Programming-Based Power Management for a Multi-Feeder Ultra-Fast DC Charging Station. Energies 2023, 16, 1213. [Google Scholar] [CrossRef]
- Luckie, M. Scamper: A scalable and extensible packet prober for active measurement of the internet. In Proceedings of the ACM Sigcomm Conference on Internet Measurement, Melbourne Australia, 1–30 November 2010; Association for Computing Machinery: New York, NY, USA, 2010; pp. 239–245. [Google Scholar]
- Wang, X. Research on Link Delay Measurement and Flow Table Management Methods for SDN Data Center Network. Doctoral Dissertation, University of Electronic Science and Technology of China, Chengdu, China, 2018. [Google Scholar]
- Afaq, M.; Song, W.C. sFlow-based Resource Utilization Monitoring in Clouds. In Proceedings of the 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), Kanazawa, Japan, 5–7 October 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 1–3. [Google Scholar]
- Liu, X.; Tang, Z.; Yang, B. Predicting Network Attacks with CNN by Constructing Images from NetFlow Data. In Proceedings of the 2019 IEEE 5th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High-Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), Washington, DC, USA, 27–29 May 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 61–66. [Google Scholar]
- Li, Z.; Hou, J.; Wang, H.; Wang, C.; Kang, C.; Fu, P. Ethereum Behavior Analysis with NetFlow Data. In Proceedings of the 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), Matsue, Japan, 18–20 September 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1–6. [Google Scholar]
- Ren, Z.J. Research and Tool Implementation of Network Performance Measurement Method Based on Optimized Probe. Doctoral Dissertation, Harbin Institute of Technology, Harbin, China, 2020. [Google Scholar]
- Xiong, W.X. Accurate Measurement Method for Substation Network Delay using PTP Technology. Inf. Commun. 2016, 30, 62–63. [Google Scholar]
- Chao, I.C.; Lee, K.B.; Candell, R. Software-defined Radio based Measurement Platform for Wireless Networks. In Proceedings of the 2015 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control, and Communication (ISPCS), Beijing, China, 11–16 October 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 7–12. [Google Scholar]
- Watabe, K.; Hirakawa, S.; Nakagawa, K. Accurate Delay Measurement for Parallel Monitoring of Probe Flows. In Proceedings of the 2017 13th International Conference on Network and Service Management (CNSM), Tokyo, Japan, 26–30 November 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1–9. [Google Scholar]
- Mao, S.; Ma, S.; Wei, B.; He, X.; Leng, A. Estimation of Power Line Communication Network Delay Based on Kernel Density. Electron. Des. Eng. 2023, 31, 133–137+142. [Google Scholar]
- Zhang, L.; Zhan, Z.; Wei, L.; Shi, B.; Xie, X. Segmented Delay Measurement and Analysis of Wide Area Measurement System. Autom. Electr. Power Syst. 2016, 40, 101–106. [Google Scholar]
- Li, B.; Hao, Y.; Qi, B.; Sun, Y.; Chen, S.S. Research Prospect of Key Technologies Supporting Interaction of Virtual Power Plants. Power Syst. Technol. 2022, 46, 1761–1770. [Google Scholar]
- Xia, F.; Bao, L.; Wang, J.; Zhuang, L.; Li, H.C. Introduction of Communication Network Scheme for Source-Grid-Load Friendly Interaction System. Jiangsu Electr. Eng. 2016, 35, 65–69. [Google Scholar]
- Xu, G.X.; Deng, H.; Fang, L.; Gong, K.; Wang, X.; Jiang, C.W. Review of Domestic and International Power Auxiliary Service Market Mechanisms Considering Demand-Side Flexible Resources. Zhejiang Electr. Power 2022, 41, 3–13. [Google Scholar]
- Wang, B.; Ye, B.; Zhu, L.Z.; Hao, J.; Ye, B.; Cheng, Q.J.; Gao, C.W. Practice and Exploration of Power Demand Response in China’s Market Environment. Demand Side Manag. Power 2021, 23, 91–95. [Google Scholar]
- Guo, M.X.; Lü, R.; Lan, L.; Wang, S.; Chen, T. Demand-Side Management Strategy Considering Customer Satisfaction. Smart Power 2023, 51, 73–78. [Google Scholar]
- Shi, J.X.; Tai, N.L.; Li, K.; Tang, Y.Z. Energy Optimization Strategy for Heat Pump-Storage Type Microgrid Considering Demand-Side Management. Electr. Power Autom. Equip. 2017, 37, 146–151. [Google Scholar]
- Cheng, J.; Xie, X.Y.; Yang, X.Y.; Ma, F.M.; Cui, X.; Ying, L.M. Bi-Level Optimization Model for Demand-Side Resource Aggregation Participation in Peak Shaving. J. Wuhan Univ. (Eng. Ed.) 2024, 57, 338–347. [Google Scholar]
- Qu, Y.; Xiao, Y.P.; Zhang, C.; Wang, X.L. Clearing Model and Pricing Method for Capacity Market Considering Flexible Adjustment Demand. Autom. Electr. Power Syst. 2024, 48, 64–76. [Google Scholar]
- Yan, H.; Hou, H.; Deng, M.; Si, L.; Wang, X.; Hu, E.; Zhou, R. Stackelberg game theory based model to guide users’ energy use behavior, with the consideration of flexible resources and consumer psychology, for an integrated energy system. Energy 2024, 288, 129806. [Google Scholar] [CrossRef]
- Li, W.; Liang, R.; Luo, F.; Feng, S.; Yang, B.; Liu, Z.; Li, Z.; Zhang, W.; Kong, K.; Lu, S. Response potential assessment of user-side flexible resources of regional power distribution networks based on sequential simulation of optimal operation. Front. Energy Res. 2022, 10, 1096046. [Google Scholar] [CrossRef]
- Ling, X.; Tian, Y.; Chen, H.; Liu, Y.; Zhao, X.; Liu, B. Optimal scheduling of flexible resources on the source and load sides considering comprehensive user satisfaction. Energy Rep. 2022, 8, 285–294. [Google Scholar] [CrossRef]
- Zhu, Y. Evaluation of Electric Heating Load Response Capability and Research on Cluster Control Strategy. Doctoral Dissertation, Northeast Electric Power University, Jilin, China, 2019. [Google Scholar]
- Chen, C.; Du, W.; Bai, K.; Sun, B.B.; Sun, L.; Fu, X.Y.; Wu, J.Y. Evaluation of Air Conditioning Temperature Control Load Cluster’s Participation in Photovoltaic Integration and Interactive Framework. Mod. Electr. Power 2024, 41, 479–489. [Google Scholar]
- Zhang, T. Assessment of Adjustable Capacity of Electric Vehicle Load Aggregation Considering Economic and Physical Constraints. Doctoral Dissertation, North China Electric Power University, Beijing, China, 2022. [Google Scholar]
- Fan, Y.; Jiang, T.; Huang, Q.; Ju, P. Assessment of Demand Response Capability in Industrial Parks Based on Portraits. Autom. Electr. Power Syst. 2024, 48, 41–49. [Google Scholar]
- Nie, S.; Chen, L.; Min, Y.; Zhang, J.F.; Yin, F.; Wu, J.P.; Cui, D. Analysis of Industrial Load Participation in Primary Frequency Regulation Capability and Characteristics. Power Syst. Technol. 2023, 47, 3994–4005. [Google Scholar]
- Jiang, Z.; Zhang, F.; Hu, F.; Sun, Y.; Jiang, W.; Deng, Y. Aggregated Response Capability Evaluation Method for Distributed Resources in Virtual Power Plants. Electr. Power Eng. Technol. 2022, 41, 39–49. [Google Scholar]
- Zhao, W.; Wu, Z.; Zhou, B.; Gao, J. Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives. Sustainability 2024, 16, 3248. [Google Scholar] [CrossRef]
- Yang, L.; Li, R.; Shi, L.; Wu, F.; Zhou, J.; Liu, J.; Lin, K. Adjustable Capability Evaluation of Integrated Energy Systems Considering Demand Response and Economic Constraints. Energies 2023, 16, 8048. [Google Scholar] [CrossRef]
- Rovera, G.D.; Siccardi, M.; Römisch, S.; Abgrall, M. Time delay measurements: Estimation of the error budget. Metrologia 2019, 56, 035004. [Google Scholar] [CrossRef]
- Zhu, Y.; Lu, J.; Xu, Z.; Wang, K. Optimization Method of IEEE 1588 Synchronization Delay in Smart Substations. Autom. Electr. Power Syst. 2018, 42, 148–153. [Google Scholar]
- Zhou, H.; Zheng, Y.; Xu, J.; Li, Y. Reliability Evaluation of Transmission Delay Measurement in Mixed Networking. Autom. Electr. Power Syst. 2019, 43, 162–169. [Google Scholar]
- Chen, K. Research on Flexible Load Aggregation Modeling and Coordinated Control; Southeast University: Nanjing, China, 2021. [Google Scholar]
- DeGroot, M.H.; Schervish, M.J. Probability and Statistics, 4th ed.; Pearson: London, UK, 2012. [Google Scholar]
- Mao, S.; Xie, S.; Pan, C. Probability and Statistics, 5th ed.; Higher Education Press: Beijing, China, 2020. [Google Scholar]
- Ren, H.; Lu, H.; Lu, J.; Jing, Z. Analysis of Demand Response Characteristics of Load Aggregators Considering Cyber-Physical System Coupling and User Response Differences. Power Syst. Technol. 2020, 44, 3927–3936. [Google Scholar]
- Wen, G.; Yu, X.; Liu, Z.; Yu, W. Adaptive Consensus-Based Robust Strategy for Economic Dispatch of Smart Grids Subject to Communication Uncertainties. IEEE Trans. Ind. Inform. 2018, 14, 2484–2494. [Google Scholar] [CrossRef]
- Hosseini, S.M.; Carli, R.; Dotoli, M. Robust Optimal Demand Response of Energy-efficient Commercial Buildings. In Proceedings of the 2022 European Control Conference (ECC), London, UK, 12–15 July 2022; pp. 1604–1610. [Google Scholar]
- Meng, Z.; Lou, X.; Shi, H.; Xie, Y.; Guo, X.; Sun, F. Research Review on Load Resource Regulation Technology for Power System Dispatching Needs. Zhejiang Electr. Power 2022, 41, 31–40. [Google Scholar]
Peculiarity | TCP | UDP | HTTP | MQTT |
---|---|---|---|---|
Lightweightness | × Relatively heavy | ✔ Relatively lightweight | × Relatively heavy | ✔ Extremely lightweight, suitable for devices with limited resources |
Reliability | ✔ Very reliable | × No guarantee, may experience packet loss | ✔ Transport layer reliability, no additional reliability mechanisms at the application layer | ✔ Based on TCP, with QoS mechanism enhancing reliability |
Secure communication | × Does not provide on its own, needs to be combined with Secure Sockets Layer/Transport Layer Security | × Does not provide on its own, needs to be combined with Secure Sockets Layer/Transport Layer Security | ✔ Supports HTTPS (Hypertext Transfer Protocol Secure) | ✔ Supports Secure Sockets Layer/Transport Layer Security |
Bidirectional communication | ✔ Supports bidirectional communication, connection-oriented | ✔ Supports, but session management is required at the application layer | ✔ Supports mechanisms such as HTTP/2 server push | ✔ Supports the publish/subscribe model |
Continuous connection | ✔ Long-lasting connections, continuous until closed | × No connection, does not maintain state | × HTTP/1.1 uses short-lived connections | ✔ Typically uses long-lasting connections and continuous sessions |
Stateful sessions | × Does not maintain application layer state on its own, but has connection state | × No connection state | × Stateless, but can be managed through cookies/sessions | ✔ Supports session state retention and recovery |
Support for large-scale IoT | × Available, but managing a large number of connections is complex | × Suitable for simple transmission, lacks advanced functionality | × Relatively low efficiency | ✔ Specifically designed, efficient, and flexible |
Subject | MQTT Message |
---|---|
/ltd/device/delay/req | { “rid”: “23445678”, “id”: “233534535”, “addr”: “192.168.100.1”, “curSec”: “662256002”, “curUsec”: “122228” } |
Subject | MQTT Message |
---|---|
/ltd/device/delay/resp | { “rid”: “23445678”, “id”: “233534535”, “addr”: “192.168.100.1”, “delaySec”: ”0”, “delayUsec”: ”12222”, “curSec”: ”662256002”, “curUsec”: ”145478”, “status”: 1 } |
Field | Type | Description |
---|---|---|
rid | string | Request number |
id | string | Terminal ID |
addr | string | Terminal IP address |
curSec | string | Time the message is sent, seconds part |
curUsec | string | Time the message is sent, microseconds part |
delaySec | string | Seconds part of the delay value |
delayUsec | string | Microseconds part of the delay value |
status | int | Result of the terminal’s calculation of downstream latency: if the latency is greater than 0, the status is 1; if the latency equals 0, the status is 2; if the latency is less than 0, the status is 3 |
Outdoor temperature (℃) | 28 | 30 | 32 |
Aggregate power (MW) | 1.97 | 3.26 | 4.55 |
Outdoor temperature (°C) | 28 | 30 | 32 |
Set temperature range of air conditioner (°C) | [20, 22] | [22, 24] | [24, 26] |
Aggregate power (MW) | 4.76 | 4.76 | 4.76 |
High Latency | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 |
---|---|---|---|---|---|---|---|---|---|
Degree of trusted response | 0.329 | 0.297 | 0.260 | 0.238 | 0.184 | 0.165 | 0.122 | 0.063 | 0.034 |
Time (h) | Credible Response Potential (kW) | Response Potential Confidence Interval (kW) | Response Degree |
---|---|---|---|
1 | 371.094 | [353.035, 389.142] | 0.083 |
2 | 106.055 | [101.171, 110.938] | 0.027 |
3 | 21.901 | [21.129, 22.694] | 0.006 |
4 | 0 | [0, 0] | 0 |
5 | 0 | [0, 0] | 0 |
6 | 76.068 | [71.275, 80.850] | 0.018 |
7 | 208.241 | [195.041, 221.442] | 0.047 |
8 | 389.880 | [365.024, 414.736] | 0.088 |
9 | 584.678 | [547.278, 622.079] | 0.15 |
10 | 3342.069 | [3127.222, 3556.906] | 0.785 |
11 | 3430.005 | [3044.948, 3815.062] | 0.793 |
12 | 3754.074 | [3270.087, 4238.061] | 0.921 |
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
Liu, Y.; Liu, C.; Tao, J.; Liu, S.; Wang, X.; Zhang, X. Corrective Evaluation of Response Capabilities of Flexible Demand-Side Resources Considering Communication Delay in Smart Grids. Electronics 2024, 13, 2795. https://doi.org/10.3390/electronics13142795
Liu Y, Liu C, Tao J, Liu S, Wang X, Zhang X. Corrective Evaluation of Response Capabilities of Flexible Demand-Side Resources Considering Communication Delay in Smart Grids. Electronics. 2024; 13(14):2795. https://doi.org/10.3390/electronics13142795
Chicago/Turabian StyleLiu, Ying, Chuan Liu, Jing Tao, Shidong Liu, Xiangqun Wang, and Xi Zhang. 2024. "Corrective Evaluation of Response Capabilities of Flexible Demand-Side Resources Considering Communication Delay in Smart Grids" Electronics 13, no. 14: 2795. https://doi.org/10.3390/electronics13142795
APA StyleLiu, Y., Liu, C., Tao, J., Liu, S., Wang, X., & Zhang, X. (2024). Corrective Evaluation of Response Capabilities of Flexible Demand-Side Resources Considering Communication Delay in Smart Grids. Electronics, 13(14), 2795. https://doi.org/10.3390/electronics13142795