Air-Conditioning Resource Management and Control Method based on Cloud Platform for Wind Power Consumption
Abstract
:1. Introduction
2. Foundation of Cloud Control Implementation
3. AC Management and Control Model Based on Cloud Platform
3.1. Model of Single AC
3.2. Grouping of AC Resources and Task Assignment for AC Groups
- (1)
- Antigen recognition: It refers to the analysis process of the problem to be solved, and according to the analysis results, the appropriate objective function is constructed.
- (2)
- Generation of initial antibody population: The feasible solution of the problem needs to be represented as an antibody in the solution space by coding. In general, the initial antibody population is randomly generated in the solution space, and each antibody is a real vector. Supposing that there are a total of M groups of aggregated ACs, n groups are selected to participate in the accommodation. The antibody is the number combination of the n AC groups.
- (3)
- Update individuals: The individuals are updated by executing immune operation, which contains the clone, crossover, and mutation.
- (4)
- Evaluation of individuals: Individuals are evaluated through calculating affinity, concentration, and reproductive rate. Individual affinity contains antibody-antigen affinity and antibody-antibody affinity. The affinity calculation equations are shown as follows:
- (5)
- Parent individuals: The antibody was screened by immune balance operation and immune selection operation, and the parental antibody was formed.
- (6)
- Record optimal individuals.
- (7)
- Judging whether the iteration meets the end condition, if it meets the end condition, the recorded optimal individuals is the optimal solution of the objective function, if not, the antibody population should be updated by crossover and mutation and the above process should be repeated until the optimal solution is obtained.
- (8)
- Judge whether the maximum iteration N has been achieved. If the end request is met, then output the optimum individual or go back to Equation (3).
3.3. Modeling of AC Group and Collaborative Control
3.4. The Feasibility of Data Acquisition and the Influence of Communication Delay on the System
4. Simulation and Analysis
4.1. Grouping of AC Resources
4.2. Consumption Task Assignment for AC Groups
4.3. Algorithm Performance Analysis
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Callaway, D.S. Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy. Energy Conv. Manag. 2009, 50, 1400. [Google Scholar] [CrossRef]
- Luo, F.; Dong, Z.Y.; Meng, K.; Wen, J.; Wang, H.; Zhao, J. An Operational Planning Framework for Large-Scale Thermostatically Controlled Load Dispatch. IEEE Trans. Ind. Inform. 2017, 13, 217–227. [Google Scholar] [CrossRef]
- Ghanavati, M.; Chakravarthy, A. Demand-Side Energy Management by Use of a Design-Then-Approximate Controller for Aggregated Thermostatic Loads. IEEE Trans. Control Syst. Technol. 2018, 26, 1439–1448. [Google Scholar] [CrossRef]
- Xu, Z.; Diao, R.; Lu, S.; Lian, J.; Zhang, Y. Modeling of Electric Water Heaters for Demand Response: A Baseline PDE Model. IEEE Trans. Smart Grid 2014, 5, 2203–2210. [Google Scholar] [CrossRef]
- Pourmousavi, S.A.; Patrick, S.N.; Nehrir, M.H. Real-Time Demand Response Through Aggregate Electric Water Heaters for Load Shifting and Balancing Wind Generation. IEEE Trans. Smart Grid 2014, 5, 769–778. [Google Scholar] [CrossRef]
- Kondoh, J.; Lu, N.; Hammerstrom, D.J. An Evaluation of the Water Heater Load Potential for Providing Regulation Service. IEEE Trans. Power Syst. 2011, 26, 1309–1316. [Google Scholar] [CrossRef]
- Shad, M.; Momeni, A.; Errouissi, R.; Diduch, C.P.; Kaye, M.E.; Chang, L. Identification and Estimation for Electric Water Heaters in Direct Load Control Programs. IEEE Trans. Smart Grid 2017, 8, 947–955. [Google Scholar] [CrossRef]
- 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]
- Lu, N.; Chassin, D.P. A state-queueing model of thermostatically controlled appliances. IEEE Trans. Power Syst. 2004, 19, 1666–1673. [Google Scholar] [CrossRef]
- Bashash, S.; Fathy, H.K. Modeling and Control of Aggregate Air Conditioning Loads for Robust Renewable Power Management. IEEE Trans. Control Syst. Technol. 2013, 21, 1318–1327. [Google Scholar] [CrossRef]
- Hu, J.Q.; Cao, J.; Chen, M.Z.Q.; Yu, J.; Yao, J.G.; Yang, S.C.; Yong, T.Y. Load Following of Multiple Heterogeneous TCL Aggregators by Centralized Control. IEEE Trans. Power Syst. 2017, 32, 3157–3167. [Google Scholar] [CrossRef]
- Mahdavi, N.; Braslavsky, J.H.; Seron, M.M.; West, S.R. Model predictive control of distributed air-conditioning loads to compensate fluctuations in solar power. IEEE Trans. Smart Grid 2017, 8, 3055–3065. [Google Scholar] [CrossRef]
- Elghitani, F.; Zhuang, W. Aggregating a Large Number of Residential Appliances for Demand Response Applications. IEEE Trans. Smart Grid 2017, 9, 5092–5100. [Google Scholar] [CrossRef]
- Voice, T. Stochastic Thermal Load Management. IEEE Trans. Autom. Control 2018, 63, 931–946. [Google Scholar] [CrossRef]
- Iacovella, S.; Ruelens, F.; Vingerhoets, P.; Claessens, B.; Deconinck, G. Cluster Control of Heterogeneous Thermostatically Controlled Loads Using Tracer Devices. IEEE Trans. Smart Grid 2017, 8, 528–536. [Google Scholar] [CrossRef]
- Incremona, G.P.; Rubagotti, M.; Ferrara, A. Sliding Mode Control of Constrained Nonlinear Systems. IEEE Trans. Autom. Control 2017, 62, 2965–2972. [Google Scholar] [CrossRef]
- Chang, Y. Adaptive Sliding Mode Control of Multi-Input Nonlinear Systems with Perturbations to Achieve Asymptotical Stability. IEEE Trans. Autom. Control 2009, 54, 2863–2869. [Google Scholar] [CrossRef]
Parameter | Value |
---|---|
P kW | 14; 11.2; 8.4; 5.6; 2.8 |
R °C/kW | 2; 2.5; 3.33; 5; 10 |
C kWh/°C | 10; 8; 6; 4; 2 |
°C | 20; 21; 22; 23; 24; 25; 26 |
No. | R °C/kW | C kWh/°C | P kW | °C | NL |
---|---|---|---|---|---|
A3 | 2.5 | 8 | 11.2 | 24 | 82 |
A6 | 5 | 4 | 5.6 | 24 | 53 |
B3 | 2 | 10 | 14 | 20 | 60 |
B6 | 3.33 | 6 | 8.4 | 20 | 115 |
C11 | 5 | 4 | 5.6 | 20 | 85 |
C12 | 5 | 4 | 5.6 | 26 | 50 |
D13 | 2 | 10 | 14 | 26 | 60 |
D14 | 10 | 2 | 2.8 | 23 | 66 |
E4 | 2 | 10 | 14 | 23 | 85 |
E7 | 2 | 10 | 14 | 26 | 44 |
© 2019 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 (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liang, K.; Yu, J.; Wu, X. Air-Conditioning Resource Management and Control Method based on Cloud Platform for Wind Power Consumption. Processes 2019, 7, 467. https://doi.org/10.3390/pr7070467
Liang K, Yu J, Wu X. Air-Conditioning Resource Management and Control Method based on Cloud Platform for Wind Power Consumption. Processes. 2019; 7(7):467. https://doi.org/10.3390/pr7070467
Chicago/Turabian StyleLiang, Kaixin, Jinying Yu, and Xin Wu. 2019. "Air-Conditioning Resource Management and Control Method based on Cloud Platform for Wind Power Consumption" Processes 7, no. 7: 467. https://doi.org/10.3390/pr7070467
APA StyleLiang, K., Yu, J., & Wu, X. (2019). Air-Conditioning Resource Management and Control Method based on Cloud Platform for Wind Power Consumption. Processes, 7(7), 467. https://doi.org/10.3390/pr7070467