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Integration of storage systems in distribution networks through multiobjective optimization

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Abstract

The use of storage systems in distribution networks allows smoothing the load diagram. In fact, the cost of energy is different along the day and companies can be encouraged to use these systems, since the extra energy required to charge the storage system can be obtained in periods where the cost of the energy is lower and used in periods when the energy cost is higher. Storage systems also allow reducing losses of the lines and improving voltage profile. However, in distribution networks there are benefits in using distributed storage instead of centralized storage. Under this context, this paper proposes a multiobjective optimization approach for the location and sizing of storage systems. In this problem, the objective functions are in conflict. Increasing the number of storage systems leads to a reduction in the peak power and losses, but also will increase the investment cost. This approach allows obtaining solutions of different trade-offs with respect to the two objectives. An IEEE 69 buses and a real 94 buses test feeders are used to demonstrate the effectiveness of the proposed approach.

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Abbreviations

m :

Indicates the first (or preceding) bus of the radial branch

\(B_m\) :

Bus m

\(t, t+1, t+2,{\ldots }, t+{n}\) :

Indicates the following buses, considering n buses connected to the first bus of the radial branch

\(\overline{I}_{{(t}+{i)}}\) :

Current flowing from the bus t+I, t+i, with i=1,..., n, and n the number of branches fed from bus m

\(\overline{V}_{{(t}+{i)}}\) :

Voltage at bus \(t+{I}\), \(t+i\), with \(i=1,{\ldots }, n\), and n the number of branches fed from bus m

\(\overline{I}_m \) :

Current flowing from the bus m (A)

\(\overline{S}_{{(t}+{i)}}\) :

Apparent power delivered from bus \(t+I\) (VA), with \(i=1,{\ldots }, n\), and n the number of branches fed from bus m

\(\overline{S}_m\) :

Apparent power delivered from bus m (VA)

\(\overline{V}_m\) :

Voltage at bus m (V)

\(\overline{Z}_m\) :

Falta

\(\overline{V}_{m(t+i)}\) :

Difference between voltages at buses m and \({t}+{i}\), with \({i}=1,{\ldots }, n\), and n the number of branches fed from bus m

\(\overline{S}_{\mathrm{Load}}\) :

Apparent power of the load directly connected to the bus m

\(\overline{S}_{\mathrm{Storage}}\) :

Apparent power of the storage system connected to bus m

\({P}_{{S}_{m}} \) :

The active power that the storage system will inject to the grid or in charge condition

\(V_{\mathrm{bus}k}^i\) :

Voltage at bus k for interaction i of the power flow algorithm

\(V_i^{\max }\) :

Voltage upper limit at interaction i

\(V_i^{\min }\) :

Voltage lower limit at interaction i

\(a_m^k\) :

Binary decision variable denoting whether or not a storage system of type j is installed in bus m

\(c_{j}\) :

Storage (\({P}_{{S j}} )\) cost where \({j} = 1, {\ldots }, {Y}\) represents the storage type

\(b_m \) :

Variable related to the technical feasibility of installing storage systems at bus m

d :

The mode of operation of the storage system (charge or discharge)

References

  1. Alarcon-Rodriguez A, Ault G, Galloway S (2010) Multi-objective planning of distributed energy resources: a review of the state-of-the-art. Renew Sustain Energy Rev 14(5):1353–1336

    Article  Google Scholar 

  2. Kahourzade S, Mahmoudi A, Mokhlis HB (2015) A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm. Electr Eng 97(1):1–12

    Article  Google Scholar 

  3. Kanwar N, Gupta N, Niazi KR, Swarnkar A (2015) Simultaneous allocation of distributed resources using improved teaching learning based optimization. Energy Convers Manag 103:387–400

    Article  Google Scholar 

  4. Ali ES, Abd Elazim SM, Abdelaziz AY (2016) Optimal allocation and sizing of renewable distributed generation using ant lion optimization algorithm. Electr Eng. https://doi.org/10.1007/s00202-016-0477-z

    Google Scholar 

  5. Vatani M, Alkaran DS, Sanjari MJ, Gharehpetian GB (2016) Multiple distributed generation units allocation in distribution network for loss reduction based on a combination of analytical and genetic algorithm methods. IET Gener Transm Distrib 10(1):66–72

    Article  Google Scholar 

  6. Antunes CH, Lima P, Oliveira E, Pires DF (2011) A multi-objective simulated annealing approach to reactive power compensation. Eng Optim 43(10):1063–1077

    Article  MathSciNet  Google Scholar 

  7. Abd Elazim SM, Ali ES (2016) Optimal locations and sizing of capacitors in radial distribution systems using mine blast algorithm. Electr Eng. https://doi.org/10.1007/s00202-016-0475-1

    Google Scholar 

  8. Tonkoski R, Lopes LAC (2008) Voltage regulation and radial distribution feeders with high penetration of photovoltaic. Energy 2030 Conference, pp 1–7

  9. Liu X, Aichhorn A, Liu L, Li H (2012) Coordinated control of distributed energy storage system with tap changer transformers for voltage rise mitigation under high photovoltaic penetration. IEEE Trans Smart Grid 3(2):897–906

    Article  Google Scholar 

  10. Zillmann M, Yan R, Saha TK (2011) Regulation of distribution network voltage using dispersed battery storage systems: a case study of a rural network. 2011 Power Energy Society General Meeting, pp 1–8

  11. El-Saadany YMAEF (2010) Optimal allocation of ESS in distribution systems with a high penetration of wind energy. IEEE Trans Power Syst 25(4):1815–1822

    Article  Google Scholar 

  12. Nick M, Cherkaoui R, Paolone M (2014) Optimal allocation of dispersed energy storage systems in active distribution networks for energy balance and grid support. IEEE Trans Power Syst 29(5):2300–2310

    Article  Google Scholar 

  13. Makarov Y, Du P, Kintner-Meyer M, Jin C, Illian H (2012) Sizing energy storage to accommodate high penetration of variable energy resources. IEEE Trans Sustain Energy 3(1):34–40

    Article  Google Scholar 

  14. Sebastián R (2016) Application of a battery energy storage for frequency regulation and peak shaving in a wind diesel power system. IET Gener Transm Distrib 10(3):764–770

    Article  Google Scholar 

  15. Majumder R, Chakrabarti S, Ledwich G, Ghosh A (2013) Advanced battery storage control for an autonomous microgrid. Electr Power Compon Syst 41(2):157–181

    Article  Google Scholar 

  16. Kaldellis JK, Zafirakis D, Kondili E (2010) Optimum sizing of photovoltaic-energy storage systems for autonomous small islands. Int J Electr Power Energy Syst 32:24–36

    Article  Google Scholar 

  17. Fernão Pires V, Romero-Cadaval E, Vinnikov D, Roasto I, Martins JF (2014) Power converter interfaces for electrochemical energy storage systems—a review. Energy Convers Manag 86:453–475

    Article  Google Scholar 

  18. Reddy SS (2016) Optimal power flow with renewable energy resources including storage. Electr Eng. https://doi.org/10.1007/s00202-016-0402-5

    Google Scholar 

  19. Oh E, Son S-Y, Hwang H, Park J-B, Lee KY (2015) Impact of demand and price uncertainties on customer-side energy storage system operation with peak load limitation. Electr Power Compon Syst 43(16):1872–1881

    Article  Google Scholar 

  20. Jung K-H, Kim H, Rho D (1996) Determination of the installation site and optimal capacity of the battery energy storage system for load levelling. IEEE Trans Energy Convers 11(1):162–167

    Article  Google Scholar 

  21. Alt JT, Anderson MD, Jungst RG (1997) Assessment of utility side cost savings from battery energy storage. IEEE Trans Power Syst 13(3):1112–1120

    Article  Google Scholar 

  22. Lo CH, Anderson MD (1999) Economic dispatch and optimal sizing of battery energy storage systems in utility load-levelling operation. IEEE Trans Energy Convers 14(3):824–829

    Article  Google Scholar 

  23. Bahceci S, Dogan A, Yalcinoz T, Daldaban F (2017) Energy storage system location selection for smart grid applications on distribution networks. Electr Eng 99(1):357–366

    Article  Google Scholar 

  24. Munawar W, Chen J-J (2013) Peak power demand analysis by using battery buffers for monotonic controllers. In: 23rd international workshop on power and timing modeling, optimization and simulation (PATMOS), pp 255–258

  25. Oudalov A, Cherkaoui R, Beguin A (2007) Sizing and optimal operation of battery energy storage system for peak shaving application. In: IEEE Power Tech, pp 621–625

  26. Saboori H, Hemmati R, Jirdehi MA (2015) Reliability improvement in radial electrical distribution networks by optimal planning of energy storage systems. Energy 93:2299–2312

    Article  Google Scholar 

  27. Böhm R, Rehtanz C (2016) Inverter-based hybrid compensation systems contributing to grid stabilization in medium voltage distribution networks with decentralized, renewable generation. Electr Eng 98(4):355–362

    Article  Google Scholar 

  28. Fu Q, Montoya LF, Solanki A, Nasiri A, Bhavaraju V, Abdallah T, Yu DC (2012) Microgrid generation capacity design with renewables and energy storage addressing power quality and surety. IEEE Trans Smart Grid 3(4):2019–20171

    Article  Google Scholar 

  29. Pires DF, Antunes CH, Martins AG (2012) NSGA-II with local search for a multi-objective reactive power compensation problem. Int J Electr Power Energy Syst 43(1):313–324

    Article  Google Scholar 

  30. Baran F, Wu F (2012) Optimal capacitor placement on radial distribution systems. Int J Electr Power Energy Syst 43(1):313–324

    Article  Google Scholar 

  31. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  32. Deb K (2001) Multi-objective optimization using evolutionary algorithm. Wiley, Chichester

    MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by national funds through FCT— Fundação para a Ciência e a Tecnologia, under Project UID/CEC/50021/2013.

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Correspondence to V. Fernão Pires.

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Pires, V.F., Lopes, R. & Costa, D. Integration of storage systems in distribution networks through multiobjective optimization. Electr Eng 100, 1939–1948 (2018). https://doi.org/10.1007/s00202-017-0672-6

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