Page 52 - Energize May 2022

TECHNICAL

Therefore, location III is eliminated because it is not a stable Location I Location II Location III Location IV Units
solution. As shown in Table 3, location II has an overall better SAIDI 17.1247 5.8517 Unstable 5.9258 min
performance than the other locations. It has less time duration of low SAIFI 20.41 1.7647 Unstable 1.94117 —
and high voltage violations and shows a higher SCE and SDE for a 24- CAIDI 0.838963 0.301568 Unstable 0.32756 min
hour interval, probing the effectiveness of location II versus the other Table 4: Comparison of power quality indexes
locations. In addition, the power quality indexes, namely SAIDI, SAIFI,
and CAIDI, are calculated.
These parameters are widely used by utility companies to Acknowledgment
evaluate power quality and reliability. SAIDI is the average outage This material is based upon work supported by the US Army Corps of
duration for each customer served. SAIDI is measured in units of time, Engineers (ERDC/CERL) under Contract No. W9132T-11-C-0022. Any
often minutes. It is usually measured over the course of a year, and opinions, findings, conclusions, or recommendations expressed in
the median value for North American utilities is around 1,50 hours. this material are those of the author(s) and do not necessarily reflect
the views of the US Army Corps of Engineers.
This article was originally produced as a White Paper by Eaton.
It was authored by LF Montoya, Q Fu, A Nasiri, and V Bhavaraju
It is described as follows: SAIFI is the average number of under the title “Novel methodology to determine the optimal energy
interruptions that a customer would experience. SAIFI is measured in storage location in a microgrid and address power quality and
units of interruptions per customer. It is usually measured over the stability”.
course of a year, and the median value for North American utilities is
approximately 1,10 interruptions per customer. References
SAIFI is described as: 1. JM Gantz and MM Amin: “Optimal mix and placement of energy storage systems
in power distribution networks for reduced outage costs”, in Proc. IEEE ECCE
2012, Raleigh, NC.
2. G Celli, S Mocci, F Pilo and M Loddo: “Optimal integration of energy storage in
distribution networks”, in Proc. IEEE PowerTech 2009, Bucharest, Romania.
CAIDI is the Customer Average Interruption Duration Index and is 3. G Carpinelli, G Celli, S Mocci, F Mottola, F Pilo and D Proto: “Optimal integration
described as: of distributed energy storage devices in smart grids”, IEEE Transactions on Smart
Grid, vol. PP, issue 99, pp: 1-11, 2013.
4. X Huang, G Zhang and L Xiao: “Optimal location of SMEs for improving power
system voltage stability”, IEEE Transactions on Applied Superconductivity, vol.
20, pp 1316-1319, 2010.
The results obtained for the 24-hour simulation are given in Table 5. AK Barnes, JC Balda, A Escobar-Mejia and SO Geurin: “Placement of energy
4. Coherently to Table 3, location II is the best solution that exhibits storage coordinated with smart PV inverters”, in Proc. IEEE ISGT 2012,
fewer customer interruptions than other locations. Washington D.C.
6. J Zou, K Zhou, K Lei, Z Zhang and X Xin: “A method to optimize the placement of
Conclusions PV-wind-storage hybrid system”, in Proc. IEEE APPEEC 2012, Shanghai, China.
This article presents voltage sensitivity indexes derived from the 7. Q Fu, LF Montoya, A Solanki, A Nasiri, V Bhavaraju, T Abdallah and DC Yu:
“Generation capacity design for a microgrid for measurable power quality
inverse of Jacobian matrix from Newton-Raphson power flow indexes,” in Proc. IEEE ISGT 2012 Conf., Washington, D.C.
analysis. The indexes imply the sensitivity of nodal voltages (both 8. A Solanki, Q Fu, LF Montoya, A Nasiri, V Bhavaraju, T Abdallah and DC Yu:
angles and magnitudes) in terms of four components: ΔV/ΔP, ΔV/ “Managing intermittent renewables in a microgrid,” in Proc. IEEE ISGT 2012
ΔQ, Δδ/ΔP, and Δδ/ΔQ. By calculating these indexes, the candidate Conf., Washington, D.C.
buses for installing the energy storage devices are found. To test and 9. Q Fu, LF Montoya, A Solanki, A Nasiri, V Bhavaraju, T Abdallah and DC Yu:
“Microgrid generation capacity design with renewable and energy storage
demonstrate the methodology, three cases have been studied. The addressing power quality and surety,” IEEE Transactions on Smart Grid, vol. 3,
results from the sensitivity analysis and case studies indicate the same pp. 2019-2027, 2012.
conclusion, that locations II, III, and IV are better than the original 10. RC Duga and WH Kersting: “Induction machine test case for the 34-bus test
selection in previous studies [7 to 9]. After applying a 24-hour time- feeder – description,” IEEE Power Engineering Society General Meeting, 2006,
7-9
sequence simulation, location II is found to be the best solution. Montreal, Quebec.
11. JJ Grainger and W Stevenson: “Power system analysis”, McGraw-Hill Science.
12. C Hill and D Chen: “Development of a real-time testing environment for battery
Location I Location II Location III Location IV Units energy storage systems in renewable energy applications”, in Proc. IEEE PESGM
2011, Detroit, MI.
Frequency of HVV 328 13 Unstable 17 —
13. Q Fu, D Yu and J Ghorai: “Probabilistic load flow analysis for power systems with
Frequency of LVV 19 17 Unstable 16 — multi-correlated wind sources”, in Proc. IEEE PESGM 2011, Detroit, MI.
Time duration of HVV 195.84 25.92 Unstable 27.12 min 14. A Keane and M O’Malley: “Optimal allocation of embedded generation on
Time duration of LVV 95.28 73.56 Unstable 73.62 min distribution networks”, IEEE Transactions on Power Systems, vol. 20, pp. 1640-1646.
15. V Bhavaraju, A Nasiri and Q Fu: “Multi-inverter controls and management of
SCE 0.02466 0.621118 Unstable 0.568181 MW energy storage for microgrid islanding”, The Electricity Journal, vol. 25, pp. 36-44.
SCE 0.510204 0.56497 Unstable 0.543478 MW
Table 3: Comparison of five indexes for 24-hour simulation Send your comments to rogerl@nowmedia.co.za

energize | May 2022 | 50