Reliability of Active Distribution Network Considering Uncertainty of Distribution Generation and Load
Abstract
:1. Introduction
- (1)
- The uncertainty model of the DGs and the load is established in detail. Both the power and the device state are considered simultaneously. Further, the network reliability is comprehensively calculated.
- (2)
- The combination of the roulette wheel selection algorithm and the sequential Monte Carlo algorithm is utilized to analyze the impact of the uncertainty on the active distribution network.
2. Materials and Methods
2.1. Reliability Model Considering Uncertainty Based on Probability Distribution
2.1.1. Uncertainty Model of PV
2.1.2. Uncertainty Model of WG
2.1.3. Uncertainty Model of Load
2.2. Reliability Model Solving Algorithm
2.2.1. Roulette Wheel Selection (RWS) Algorithm
2.2.2. Sequential Monte Carlo (SMC) Method
- Initialize all component states. It is generally assumed that each component is in the initially running state;
- Determine the number of simulation years, and set the initial time as 0;
- A random number between 0 and 1 is generated for the components in the system. The working time and the time which is used to resume running are fixed according to the random number;
- Other load points affected by the faulty component are found. Judge whether these load points are within the island. If the affected load point is inside the island, perform step 5, otherwise jump to step 6;
- Judge whether the renewable energy output power P can meet the total load L in the island. If P is greater than L, there will be no power outage at the load point. Otherwise, the load is optimized until that P can meet L. Then, calculate the uptime and the failure time of the load points;
- If the load point cannot meet its power supply demand, calculate the uptime and the failure time of the load points;
- Determine whether the set time is simulated. If so, go to step 8. Otherwise, skip to step 2;
- Calculating reliability index.
2.2.3. Reliability Index
- 1.
- Load point average failure rate λ;
- 2.
- The annual average outage time of the load point U.
- 3.
- Energy not supplied (ENS);
- 4.
- Average service availability index (ASAI);
- 5.
- System average interruption frequency index (SAIFI);
- 6.
- System average interruption duration index (SAIDI).
2.3. Constrains of the Reliability Model
2.3.1. Power Flow Constraints
2.3.2. Node Voltage Constraints
2.3.3. Transmission Line Load Ratio
2.3.4. DG Power Output Constraints
2.3.5. Energy Storage Device Constraints
3. Results and Discussion
3.1. Case Introduction
3.2. Case Discussion
3.2.1. Scenario Generation
3.2.2. Analysis of Reliability Indicators
3.2.3. Comparing Network Reliability Calculation among Three Algorithms
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Li, P.; Wu, Q.; Yang, M.; Li, Z.; Hatziargyriou, N.D. Distributed Distributionally Robust Dispatch for Integrated Transmission-Distribution Systems. IEEE Trans. Power Syst. 2020, 36, 1193–1205. [Google Scholar] [CrossRef]
- Telukunta, V.; Central Power Research Institute; Pradhan, J.; Agrawal, A.; Singh, M.; Srivani, S.G.; R V College of Engineering. Protection challenges under bulk penetration of renewable energy resources in power systems: A review. CSEE J. Power Energy Syst. 2017, 3, 365–379. [Google Scholar] [CrossRef]
- Li, Y.L.; Sun, W.; Yin, W.; Lei, S.; Hou, Y. Restoration Strategy for Active Distribution Systems Considering Endogenous Uncertainty in Cold Load Pickup. IEEE Trans. Smart Grid 2021, 13, 2690–2702. [Google Scholar] [CrossRef]
- Verma, R.; Padhy, N.P. Optimal Power Flow Based DR in Active Distribution Network With Reactive Power Control. IEEE Syst. J. 2021, 16, 3522–3530. [Google Scholar] [CrossRef]
- Rullo, P.G.; Braccia, L.; Feroldi, D.; Zumoffen, D. Multivariable Control Structure Design for Voltage Regulation in Active Distribution Networks. IEEE Lat. Am. Trans. 2022, 20, 839–84747. [Google Scholar] [CrossRef]
- Kou, P.; Liang, D.; Gao, R.; Liu, Y.; Gao, L. Decentralized Model Predictive Control of Hybrid Distribution Transformers for Voltage Regulation in Active Distribution Networks. IEEE Trans. Sustain. Energy 2019, 11, 2189–2200. [Google Scholar] [CrossRef]
- Yu, P.; Wan, C.; Sun, M.; Zhou, Y.; Song, Y. Distributed Voltage Control of Active Distribution Networks With Global Sensitivity. IEEE Trans. Power Syst. 2022, 37, 4214–4228. [Google Scholar] [CrossRef]
- Baviskar, A.U.; Das, K.; Koivisto, M.J.; Hansen, A.D. Multi-Voltage Level Active Distribution Network With Large Share of Weather-Dependent Generation. IEEE Trans. Power Syst. 2022, 37, 4874–4884. [Google Scholar] [CrossRef]
- Leon, J.P.A.; Rico-Novella, F.J.; Llopis, L.J.D.L.C. Predictive Traffic Control and Differentiation on Smart Grid Neighborhood Area Networks. IEEE Access 2020, 8, 216805–216821. [Google Scholar] [CrossRef]
- Hsieh, S.-Y.; Lai, C.-C. A Novel Scheme for Improving the Reliability in Smart Grid Neighborhood Area Networks. IEEE Access 2019, 7, 129942–129954. [Google Scholar] [CrossRef]
- Suhaimy, N.; Radzi, N.A.M.; Ahmad, W.S.H.M.W.; Azmi, K.H.M.; Hannan, M.A. Current and Future Communication Solutions for Smart Grids: A Review. IEEE Access 2022, 10, 43639–43668. [Google Scholar] [CrossRef]
- Azmi, K.H.M.; Radzi, N.A.M.; Azhar, N.A.; Samidi, F.S.; Zulkifli, I.T.; Zainal, A.M. Active Electric Distribution Network: Applications, Challenges, and Opportunities. IEEE Access 2022, 10, 134655–134689. [Google Scholar] [CrossRef]
- Amjady, N.; Attarha, A.; Dehghan, S.; Conejo, A.J. Adaptive Robust Expansion Planning for a Distribution Network With DERs. IEEE Trans. Power Syst. 2017, 33, 1698–1715. [Google Scholar] [CrossRef]
- Ahmadigorji, M.; Amjady, N.; Dehghan, S. A Robust Model for Multiyear Distribution Network Reinforcement Planning Based on Information-Gap Decision Theory. IEEE Trans. Power Syst. 2017, 33, 1339–1351. [Google Scholar] [CrossRef]
- Homaee, O.; Najafi, A.; Jasinski, M.; Tsaousoglou, G.; Leonowicz, Z. Coordination of Neighboring Active Distribution Networks Under Electricity Price Uncertainty Using Distributed Robust Bi-Level Programming. IEEE Trans. Sustain. Energy 2022, 14, 325–338. [Google Scholar] [CrossRef]
- Li, X.; Han, B.; Li, G.; Luo, L.; Wang, K.; Jiang, X. Dynamic Topology Awareness in Active Distribution Networks Under DG Uncertainties Using GMM-PSEs and KL Divergence. IEEE Trans. Sustain. Energy 2021, 12, 2086–2096. [Google Scholar] [CrossRef]
- Huang, C.; Wang, C.; Xie, N.; Wang, Y. Robust Coordination Expansion Planning for Active Distribution Network in Deregulated Retail Power Market. IEEE Trans. Smart Grid 2019, 11, 1476–1488. [Google Scholar] [CrossRef]
- Wang, X.; Sheng, X.; Qiu, W.; He, W.; Xu, J.; Xin, Y.; Jv, J. Fault Reconfiguration Strategies of Active Distribution Network With Uncertain Factors for Maximum Supply Capacity Enhancement. IEEE Access 2021, 10, 72373–72380. [Google Scholar] [CrossRef]
- Gao, H.; Lyu, X.; He, S.; Wang, L.; Wang, C.; Liu, J. Integrated Planning of Cyber-Physical Active Distribution System Considering Multidimensional Uncertainties. IEEE Trans. Smart Grid 2022, 13, 3145–3159. [Google Scholar] [CrossRef]
- Xiong, Z.; Huang, Y.; Wang, W.; Zhang, Y.; Xu, X.; Sun, X. A Day-Ahead Chance Constrained Volt/Var Control Scheme With Renewable Energy Sources by Novel Scenario Generation Method in Active Distribution Networks. IEEE Access 2021, 9, 64033–64042. [Google Scholar] [CrossRef]
- Kabirifar, M.; Fotuhi-Firuzabad, M.; Moeini-Aghtaie, M.; Pourghaderi, N.; Shahidehpour, M. Reliability-Based Expansion Planning Studies of Active Distribution Networks with Multiagents. IEEE Trans. Smart Grid 2022, 13, 4610–4623. [Google Scholar] [CrossRef]
- Shang, L.; Hu, R.; Wei, T.; Ci, H.; Zhang, W.; Chen, H. Multiobjective optimization for hybrid AC/DC distribution network structure considering reliability. In Proceedings of the 2021 IEEE Sustainable Power and Energy Conference (iSPEC), Nanjing, China, 23–25 December 2021; pp. 3034–3041. [Google Scholar] [CrossRef]
- Liu, W.; Gong, Q.; Han, H.; Wang, Z.; Wang, L. Reliability Modeling and Evaluation of Active Cyber Physical Distribution System. IEEE Trans. Power Syst. 2018, 33, 7096–7108. [Google Scholar] [CrossRef]
- Karimi, H.; Niknam, T.; Dehghani, M.; Ghiasi, M.; Ghasemigarpachi, M.; Padmanaban, S.; Tabatabaee, S.; Aliev, H. Automated Distribution Networks Reliability Optimization in the Presence of DG Units Considering Probability Customer Interruption: A Practical Case Study. IEEE Access 2021, 9, 98490–98505. [Google Scholar] [CrossRef]
- Liu, W.; Lin, Z.; Wang, L.; Wang, Z.; Wang, H.; Gong, Q. Analytical Reliability Evaluation of Active Distribution Systems Considering Information Link Failures. IEEE Trans. Power Syst. 2020, 35, 4167–4179. [Google Scholar] [CrossRef]
- Heidari, A.; Agelidis, V.G.; Pou, J.; Aghaei, J.; Ghias, A.M.Y.M. Reliability Worth Analysis of Distribution Systems Using Cascade Correlation Neural Networks. IEEE Trans. Power Syst. 2017, 33, 412–420. [Google Scholar] [CrossRef]
- Jose, J.; Kowli, A. Optimal Augmentation of Distribution Networks for Improved Reliability. IEEE Syst. J. 2021, 16, 1965–1973. [Google Scholar] [CrossRef]
- Park, J.; Liang, W.; Choi, J.; El-Keib, A.A.; Shahidehpour, M.; Billinton, R. A probabilistic reliability evaluation of a power system including Solar/Photovoltaic cell generator. In Proceedings of the 2009 IEEE Power & Energy Society General Meeting, Calgary, AB, Canada, 26–30 July 2009; pp. 1–6. [Google Scholar]
- Niknam, T.; Zare, M.; Aghaei, J. Scenario-Based Multiobjective Volt/Var Control in Distribution Networks Including Renewable Energy Sources. IEEE Trans. Power Deliv. 2012, 27, 2004–2019. [Google Scholar] [CrossRef]
- Vallée, F.; Brunieau, G.; Pirlot, M.; Deblecker, O.; Lobry, J. Optimal Wind Clustering Methodology for Adequacy Evaluation in System Generation Studies Using Nonsequential Monte Carlo Simulation. IEEE Trans. Power Syst. 2011, 26, 2173–2184. [Google Scholar] [CrossRef]
- Heydt, G.T.; Graf, T.J. Distribution System Reliability Evaluation Using Enhanced Samples in a Monte Carlo Approach. IEEE Trans. Power Syst. 2010, 25, 2006–2008. [Google Scholar] [CrossRef]
- Zhi-jian, L.; Gui-hong, W.; Dong-hui, Y.; Qi, S. Reliability Assessment of Distribution Network Considering The Randomness of Distributed Generation. In Proceedings of the 2016 China International Conference on Electricity Distribution (CICED), Xi’an, China, 10–13 August 2016; pp. 1–6. [Google Scholar] [CrossRef]
- Li, D.; Li, X.; Liu, Y.; Ran, L.; Wan, J. Research on Reliability Evaluation Algorithm based on Distribution Network containing Microgrid. In Proceedings of the 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 14–16 December 2018; pp. 1915–1919. [Google Scholar] [CrossRef]
- Li, J.; Wang, Q.; Shao, B.; Ge, W.; Gao, K.; Wang, S.; Hui, Q. Reliability Evaluation of Distribution Network Considering Distributed Intermittent Renewable Energy Access. In Proceedings of the 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS), Hunan, China, 10–11 August 2018; pp. 214–218. [Google Scholar] [CrossRef]
Load Level | Load Point Number | Number of Users |
---|---|---|
First class load | L5 | 132 |
First class load | L7, L23 | 1 |
First class load | L9, L21 | 1 |
Second class load | L1, L6 | 147 |
Second class load | L15, L20 | 1 |
Second class load | L10, L12, L16, L22 | 76 |
Third class load | L2 | 126 |
Third class load | L4, L18 | 1 |
Third class load | L3, L13, L17 | 1 |
Third class load | L8, L11, L14, L19 | 79 |
Length/km | Feeder Number |
---|---|
0.5 | 7,13,30 |
0.75 | 1,4,6,9 |
0.8 | 2,3 |
0.85 | 5,8,10,15,18,20,23,24,28 |
1.0 | 11,12,16,22 |
1.3 | 14,17,19,27 |
1.5 | 21,25,26,29 |
Reliability Indexes | Scenario 0 | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 |
---|---|---|---|---|---|
SAIFI (times/household*year) | 2.2980 | 1.9479 | 2.0927 | 2.0914 | 2.0881 |
SAIDI (h/household*year) | 6.0743 | 5.7969 | 5.7096 | 5.7524 | 5.7367 |
First class load | 1434.96 | 1346.19 | 1355.51 | 1281.65 | 1256.53 |
ENS (MW·h/year) | 0.99930658 | 0.9993825 | 0.999348 | 0.9997612 | 0.999345117 |
Indexes | MP | NMC | Proposed Method |
---|---|---|---|
SAIFI (times/household*year) | 2.5134 | 2.0678 | 1.9479 |
SAIDI(h/household*year) | 6.2348 | 6.0916 | 5.7969 |
ENS (MW·h/year) | 0.9998719 | 0.9994171 | 0.9993825 |
Calculation time (s) | 25.3 | 18.7 | 19.5 |
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. |
© 2023 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
Xu, W.; Zeng, S.; Du, X.; Zhao, J.; He, Y.; Wu, X. Reliability of Active Distribution Network Considering Uncertainty of Distribution Generation and Load. Electronics 2023, 12, 1363. https://doi.org/10.3390/electronics12061363
Xu W, Zeng S, Du X, Zhao J, He Y, Wu X. Reliability of Active Distribution Network Considering Uncertainty of Distribution Generation and Load. Electronics. 2023; 12(6):1363. https://doi.org/10.3390/electronics12061363
Chicago/Turabian StyleXu, Wentao, Siming Zeng, Xiaodong Du, Jianli Zhao, Yuling He, and Xuewei Wu. 2023. "Reliability of Active Distribution Network Considering Uncertainty of Distribution Generation and Load" Electronics 12, no. 6: 1363. https://doi.org/10.3390/electronics12061363
APA StyleXu, W., Zeng, S., Du, X., Zhao, J., He, Y., & Wu, X. (2023). Reliability of Active Distribution Network Considering Uncertainty of Distribution Generation and Load. Electronics, 12(6), 1363. https://doi.org/10.3390/electronics12061363