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AIMS Energy, 2017, 5(6): 887-911. doi: 10.3934/energy.2017.6.887. Research article Topical Section

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A virtual power plant model for time-driven power flow calculations

Gerardo Guerra, Juan A. Martinez Velasco

Departament d’Enginyeria Electrica, Universitat Politecnica de Catalunya, Av. Diagonal 647, 08028 Barcelona, Spain

Received: , Accepted: , Published:

Topical Section: Smart Grids and Networks

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This paper presents the implementation of a custom-made virtual power plant model in OpenDSS. The goal is to develop a model adequate for time-driven power flow calculations in distribution systems. The virtual power plant is modeled as the aggregation of renewable generation and energy storage connected to the distribution system through an inverter. The implemented operation mode allows the virtual power plant to act as a single dispatchable generation unit. The case studies presented in the paper demonstrate that the model behaves according to the specified control algorithm and show how it can be incorporated into the solution scheme of a general parallel genetic algorithm in order to obtain the optimal day-ahead dispatch. Simulation results exhibit a clear benefit from the deployment of a virtual power plant when compared to distributed generation based only on renewable intermittent generation.

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Keywords: distribution system; energy resource; energy storage system; OpenDSS; photovoltaic generation; power flow; virtual power plant; wind generation

Citation: Gerardo Guerra, Juan A. Martinez Velasco. A virtual power plant model for time-driven power flow calculations. AIMS Energy, 2017, 5(6): 887-911. doi: 10.3934/energy.2017.6.887

References:

1. Willis HL, Scott WG (2000) Distributed power generation. Planning and evaluation. CRC Press.
2. Ackermann T, Andersson G, Söder L (2001) Distributed generation: a definition. Electr Pow Syst Res 57: 195–204.
3. Mahmoud PHA, Huy PD, Ramachandaramurthy VK (2017) A review of the optimal allocation of distributed generation: objectives, constraints, methods, and algorithms. Renew Sust Energ Rev 75: 293–312.
4. Sandia National Laboratories and NRECA (2015) DOE/EPRI Electricity Storage Handbook.
5. Li X, Hui D, Lai X (2013) Battery energy storage station (BESS)-based smoothing control of photovoltaic (PV) and wind power generation fluctuations. IEEE T Sustain Energ 4: 464–473.
6. Grillo S, Marinelli M, Massucco S, et al. (2012) Optimal management strategy of a battery-based storage system to improve renewable energy integration in distribution networks. IEEE T Smart Grid 3: 950–958.
7. Katsanevakis M, Stewart RA, Lu J (2017) Aggregated applications and benefits of energy storage systems with application-specific control methods: a review. Renew Sust Energ Rev 75: 719–741.
8. Etherden N, Vyatkin V, Bollen MHJ (2016) Virtual power plant for grid services using IEC 61850. IEEE T Ind Inform 12: 437–447.
9. International Electrotechnical Commission (2011) Electrical Energy Storage. Available from: https://www.mendeley.com/research-papers/electrical-energy-storage-white-paper-3/.
10. Dugan RC (2016) Reference Guide. The Open Distribution System Simulator (OpenDSS). EPRI.
11. Pudjianto D, Ramsay C, Strbac G (2007) Virtual power plant and system integration of distributed energy resources. Iet Renew Power Gen 1: 10–16.
12. Ghavidel S, Li L, Aghaei J, et al. (2016) A review on the virtual power plant: Components and operation systems. IEEE International Conference on Power System Technology. IEEE, 1–6.
13. Kolenc M, Nemček P, Gutschi C, et al. (2017) Performance evaluation of a virtual power plant communication system providing ancillary services. Electr Pow Syst Res 149: 46–54.
14. Martínez-Velasco JA, Guerra G (2015) Analysis of large distribution networks with distributed energy resources. Ingeniare 23: 594–608.
15. Dugan RC, Taylor JA, Montenegro D (2017) Energy storage modeling for distribution planning. IEEE T Ind Appl 53: 954–962.
16. Chassin DP (2013) Electrical load modeling and simulation. In: Khaitan SK, Gupta A, editors, High Performance Computing in Power and Energy Systems, Berlin: Springer.
17. Guerra G, Martinez-Velasco JA (2017) A solid state transformer model for power flow calculations. Int J Elec Power 89: 40–51.
18. Farzin H, Fotuhi-Firuzabad M, Moeini-Aghtaie M (2017) A stochastic multi-objective framework for optimal scheduling of energy storage systems in microgrids. IEEE T Smart Grid 8: 117–127.
19. Levron Y, Shmilovitz D (2012) Power systems' optimal peak-shaving applying secondary storage. Electr Pow Syst Res 89: 80–84.
20. Jayasekara N, Wolfs P, Masoum MAS (2014) An optimal management strategy for distributed storages in distribution networks with high penetrations of PV. Electr Pow Syst Res 116: 147–157.
21. Meirinhos JL, Rua DE, Carvalho LM, et al. (2017) Multi-temporal optimal power flow for voltage control in MV networks using distributed energy resources. Electr Pow Syst Res 146: 25–32.
22. Hejazi H, Mohsenian-Rad H (2016) Energy storage planning in active distribution grids: a chance-constrained optimization with non-parametric probability functions. IEEE T Smart Grid: 1–13.
23. Silvestre MLD, Graditi G, Ippolito MG, et al. (2011) Robust multi-objective optimal dispatch of distributed energy resources in micro-grids. Power Tech, 2011 IEEE Trondheim. IEEE, 1–5.
24. Agamah SU, Ekonomou L (2016) Peak demand shaving and load-levelling using a combination of bin packing and subset sum algorithms for electrical energy storage system scheduling. Iet Sci Meas Technol 10: 477–484.
25. Qin J, Sevlian R, Varodayan D, et al. (2012) Optimal electric energy storage operation. 2012 IEEE Power and Energy Society General Meeting, 1–6.
26. Pandzic H, Kuzle I (2015) Energy storage operation in the day-ahead electricity market. European Energy Market. IEEE, 1–6.
27. Lazaroiu GC, Dumbrava V, Balaban G, et al. (2016) Stochastic optimization of microgrids with renewable and storage energy systems. IEEE, International Conference on Environment and Electrical Engineering.
28. Yeh EC, Venkata SS, Sumic Z (1996) Improved distribution system planning using computational evolution. IEEE T Power Syst 11: 668–674.
29. Konfrst Z (2004) Parallel genetic algorithms: advances, computing trends, applications and perspectives. Parallel and Distributed Processing Symposium. Proceedings. International. IEEE, 162.
30. Buehren M. MATLAB Library for Parallel Processing on Multiple Cores. Available from http://www.mathworks.com.
31. Kieny C, Berseneff B, Hadjsaid N, et al. (2009) On the concept and the interest of Virtual Power plant: Some results from the European project FENIX. Power & Energy Society General Meeting, IEEE, 1–6.

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Copyright Info: 2017, Juan A. Martinez Velasco, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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