A Parallel Probabilistic Load Flow Method Considering Nodal Correlations
With the introduction of more and more random factors in power systems, probabilistic load flow (PLF) has become one of the most important tasks for power system planning and operation. Cumulants-based PLF is an effective algorithm to calculate PLF in an analytical way, however, the correlations amo...
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| Format: | Article |
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MDPI AG
2016-12-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/9/12/1041 |
| _version_ | 1811186093471563776 |
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| author | Jun Liu Xudong Hao Peifen Cheng Wanliang Fang Shuanbao Niu |
| author_facet | Jun Liu Xudong Hao Peifen Cheng Wanliang Fang Shuanbao Niu |
| author_sort | Jun Liu |
| collection | DOAJ |
| description | With the introduction of more and more random factors in power systems, probabilistic load flow (PLF) has become one of the most important tasks for power system planning and operation. Cumulants-based PLF is an effective algorithm to calculate PLF in an analytical way, however, the correlations among the nodal injections to the system level have rarely been studied. A novel parallel cumulants-based PLF method considering nodal correlations is proposed in this paper, which is able to deal with the correlations among all system nodes, and avoid the Jacobian matrix inversion in the traditional cumulants-based PLF as well. In addition, parallel computing is introduced to improve the efficiency of the numerical calculations. The accuracy of the proposed method is validated by numerical tests on the standard IEEE-14 system, comparing with the results from Correlation Latin hypercube sampling Monte Carlo Simulation (CLMCS) method. And the efficiency and parallel performance is proven by the tests on the modified IEEE-300, C703, N1047 systems with distributed generation (DG). Numerical simulations show that the proposed parallel cumulants-based PLF method considering nodal correlations is able to get more accurate results using less computational time and physical memory, and have higher efficiency and better parallel performance than the traditional one. |
| first_indexed | 2024-04-11T13:39:53Z |
| format | Article |
| id | doaj.art-78b22b89d4724fa08bf2678c790d2af5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T13:39:53Z |
| publishDate | 2016-12-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj.art-78b22b89d4724fa08bf2678c790d2af52022-12-22T04:21:17ZengMDPI AGEnergies1996-10732016-12-01912104110.3390/en9121041en9121041A Parallel Probabilistic Load Flow Method Considering Nodal CorrelationsJun Liu0Xudong Hao1Peifen Cheng2Wanliang Fang3Shuanbao Niu4Shaanxi Key Laboratory of Smart Grid, State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaShaanxi Key Laboratory of Smart Grid, State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaShaanxi Key Laboratory of Smart Grid, State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaShaanxi Key Laboratory of Smart Grid, State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaNorthwest Subsection of State Grid Corporation of China, Xi’an 710048, ChinaWith the introduction of more and more random factors in power systems, probabilistic load flow (PLF) has become one of the most important tasks for power system planning and operation. Cumulants-based PLF is an effective algorithm to calculate PLF in an analytical way, however, the correlations among the nodal injections to the system level have rarely been studied. A novel parallel cumulants-based PLF method considering nodal correlations is proposed in this paper, which is able to deal with the correlations among all system nodes, and avoid the Jacobian matrix inversion in the traditional cumulants-based PLF as well. In addition, parallel computing is introduced to improve the efficiency of the numerical calculations. The accuracy of the proposed method is validated by numerical tests on the standard IEEE-14 system, comparing with the results from Correlation Latin hypercube sampling Monte Carlo Simulation (CLMCS) method. And the efficiency and parallel performance is proven by the tests on the modified IEEE-300, C703, N1047 systems with distributed generation (DG). Numerical simulations show that the proposed parallel cumulants-based PLF method considering nodal correlations is able to get more accurate results using less computational time and physical memory, and have higher efficiency and better parallel performance than the traditional one.http://www.mdpi.com/1996-1073/9/12/1041correlation matrixCorrelation Latin hypercube sampling Monte Carlo Simulation (CLMCS)cumulantsdistributed generation (DG)parallel computingprobabilistic load flow (PLF) |
| spellingShingle | Jun Liu Xudong Hao Peifen Cheng Wanliang Fang Shuanbao Niu A Parallel Probabilistic Load Flow Method Considering Nodal Correlations Energies correlation matrix Correlation Latin hypercube sampling Monte Carlo Simulation (CLMCS) cumulants distributed generation (DG) parallel computing probabilistic load flow (PLF) |
| title | A Parallel Probabilistic Load Flow Method Considering Nodal Correlations |
| title_full | A Parallel Probabilistic Load Flow Method Considering Nodal Correlations |
| title_fullStr | A Parallel Probabilistic Load Flow Method Considering Nodal Correlations |
| title_full_unstemmed | A Parallel Probabilistic Load Flow Method Considering Nodal Correlations |
| title_short | A Parallel Probabilistic Load Flow Method Considering Nodal Correlations |
| title_sort | parallel probabilistic load flow method considering nodal correlations |
| topic | correlation matrix Correlation Latin hypercube sampling Monte Carlo Simulation (CLMCS) cumulants distributed generation (DG) parallel computing probabilistic load flow (PLF) |
| url | http://www.mdpi.com/1996-1073/9/12/1041 |
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