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Optimal Dump Load Allocations in High RBDG Penetrated Microgrid for Voltage and Frequency Regulation

  • Research Article-Electrical Engineering
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Abstract

The rising renewable penetrations has paved ways for the microgrids to operate independent of the conventional centrally located power plants. At low load hours, the microgrid operation is often disrupted due the excess power generations creating voltage (V) and frequency (f) regulation issues. The use of energy storage devices to absorb such excess generations would be infeasible due to their less usability, high cost and uneconomical trading. The offline planning of distributing dump loads (DLs) in the network can coordinate with the online control in maintaining V and f of the system. Thus, the present work provides an offline analysis to optimally allocate DLs in the power electronic (PE) interfaced DGs powered autonomous microgrid to minimize the V and f deviations at off-peak hours. The V and f deviations as per the droop characteristics of PE interfaced DGs have been taken into consideration in the load flow. The optimization problem is formulated and solved using heuristic techniques viz MWOA and NSGA-II. The weakly meshed standard test beds of IEEE 33 and 69 bus distribution systems have been used in the present work as autonomous microgrids.

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Abbreviations

Vf :

Voltage and frequency of microgrid

DL:

Dump load

\(V_\mathrm{min}\) :

Minimum voltage limit

\(V_\mathrm{max}\) :

Maximum voltage limit

\(I_\mathrm{max}\) :

Maximum line loading limit

\(P_{\mathrm{DL}}\) :

Active dump load

\(Q_{\mathrm{DL}}\) :

Reactive dump load

\(P_{\mathrm{DL,min}}\) :

Minimum active dump load

\(P_\mathrm{DL,max}\) :

Maximum active dump load

\(Q_{\mathrm{DL,min}}\) :

Minimum reactive dump load

\(Q_\mathrm{DL,max}\) :

Maximum reactive dump load

PQ :

Active and reactive power

\(P_\mathrm{dg}\) :

Active power given by DG

\(Q_\mathrm{dg}\) :

Reactive power given by DG

\(V_0, f_0\) :

Nominal V and f of microgrid

\(P_\mathrm{dg0}, Q_\mathrm{dg0}\) :

Nominal active and reactive power of DG

\(\mu \), \(\beta \) :

Index for bus and branch, respectively,

\({\hbox {IT}}_{1}\) :

Iteration counter for inner loop

\({\hbox {IT}}_{2}\) :

Iteration counter for outer loop

delF, delV1:

f and V deviation at regulatory bus

\(S_{\mu }\) :

Apparent power at \(\mu \)th bus

\(P_{\mathrm{DL}}\) :

Active dump load

\(Q_{\mathrm{DL}}\) :

Reactive dump load

\(I_{i}\), \(E_{i}\) :

Current at ith node and edge

snrn :

Sending end and receiving end bus

\(P_{{\hbox {dg}}(i)}\) :

Active power of DG at ith bus

\(Q_{{\hbox {dg}}(i)}\) :

Reactive power of DG at ith bus

\(P_{{\hbox {dg}}(i0)}\) :

Nominal active power of DG at ith bus

\(Q_{{\hbox {dg}}(i0)}\) :

Nominal reactive power of DG at ith bus

IT:

Iteration count for MWOA algorithm

x, \(x^{*}\) :

Feasible array and best solution in MWOA

size(d):

Dimension of the problem in MWOA

\(L(\gamma _2)\) :

Levy index in MWOA

s :

Step length of the Levy flight in MWOA

\({\hbox {IM}}_\mathrm{sn,rn}\) :

Impedance of the edge

\(N_\mathrm{nodes}\) :

Number of nodes in the network

\(N_\mathrm{dg}\) :

Number of DGs in the network

\(\hbox {max}\_\hbox {iter}\) :

Maximum iterations of the MWOA

\(I_{\hbox {dg}(i)}\) :

Current injected by the DG at ith bus

\(I_{\hbox {dump}(i)}\) :

Current drawn by the DL at ith bus

\(\hbox {SOC}_\mathrm{min}\) :

Minimum state of charge of BES

\(\hbox {SOC}_\mathrm{max}\) :

Maximum state of charge of BES

dP, dQ :

Deviation in P and Q at the DG bus

\(U_\mathrm{gbhw}\) :

Unit price of gas water boiler

\(U_\mathrm{elhw}\) :

Unit price of electric water boiler

\(\hbox {Co}_\mathrm{gbhw}\) :

Cost of hot water using gas boiler

\(\hbox {Co}_\mathrm{elhw}\) :

Cost of hot water using electric boiler

\(\hbox {Co}_\mathrm{st}\) :

Cost of battery energy storage

\(\hbox {Co}_\mathrm{ev1}\) :

Cost incurred in event 1

\(\hbox {Co}_\mathrm{ev2}\) :

Cost incurred in event 2

\(V_\mathrm{hw}^h\) :

Volume of hot water generated at hth hour

\(H_\mathrm{hw}^h\) :

Power for hot water generation at hth hour

Ef:

Efficiency gas and electric boiler

\(C_\mathrm{wr}\), \(\rho _\mathrm{wr}\) :

Specific heat and density of water

\(T_\mathrm{st}^h\) :

Temperature set point for hot water

\(T_\mathrm{in}^h\) :

Inlet temperature of water

\(\hbox {LCOE}^\mathrm{bat}\) :

Levelized cost of energy for battery storage

References

  1. Lasseter, R.H.; Paigi, P.: Microgrid: a conceptual solution. In: Proceedings of the Power Electronics Specialists Conference Germany, pp. 1–6 (2004)

  2. Renewables 2018 Global Status Report (2018)

  3. Jiang, Q.; Xue, M.; Geng, G.: Energy management of microgrid in grid-connected and stand-alone modes. IEEE Trans. Power Syst. 28(3), 3380–3389 (2013)

    Article  Google Scholar 

  4. Integrating Renewable Electricity on the grid—a report by APS panel on public affairs. https://www.aps.org/policy/reports/popa-reports/upload/integratingelec.pdf

  5. Microgrid market, Potential Size, Opportunities, Future Trends, Size, Share, Growth, Segmentation, Gross Margin, Future Demand and Leading Players Updates by Forecast to 2023 (2019). https://www.marketwatch.com/press-release/microgrid-market-2019-potential-size-opportunities-future-trends-size-share-growth-segmentation-gross-margin-future-demand-and-leading-players-updates-by-forecast-to-2023-2019-04-03

  6. Uniyal, A.; Sarangi, S.: Optimal allocation of ELC in microgrid using droop controlled load flow. IET GTD 13(20), 4566–4578 (2019)

    Google Scholar 

  7. Chris, L.: Overcapacity and the challenges of going 100% renewable (2017)

  8. Baldwin, S.: High Penetration Levels of Renewable Electricity: Challenges and Opportunities. Smart Grid Development in China and the United States: Status, Prospects and Opportunities for Bilateral Cooperation Washington, DC (2013)

  9. Gyawali, N.; Paudel, B.; Subedi, B.: Improved active power sharing strategy for ELC controlled synchronous generators based islanded micro grid application. In: 9th International Conference on Software, Knowledge, Information Management and Applications, pp. 1–6 (2015)

  10. Singh, R.R.; Kumar, A.; Shruthi, D.; Panda, R.; ThangaRaj, C.: Review and experimental illustrations of electronic load controller used in standalone micro-hydro generating plants. Eng. Sci. Technol. Int. J. 21(5), 886–900 (2018)

    Google Scholar 

  11. Roodsari, B.N.; Nowicki, E.P.; Freere, P.: An experimental investigation of the distributed electronic load controller: a new concept for voltage regulation in microhydro systems with transfer of excess power to household water heaters. In: IEEE Proceedings of the International Humanitarian Technology Conference, pp. 1–4 (2014)

  12. Roodsari, B.N.; Nowicki, E.P.: Analysis and experimental investigation of the improved distributed electronic load controller. IEEE Trans. Energy Convers. 33(3), 905–914 (2018)

    Article  Google Scholar 

  13. Chen, S.; Zhang, T.; Gooi, H.B.; Raplh, D.M.: Penetration rate and effectiveness studies of aggregated BESS for frequency regulation. IEEE Trans. Smart Grid 7(1), 167–177 (2016)

    Article  Google Scholar 

  14. Kim, Y.J.; Calaf, G.D.R.; Norford, L.K.: Analysis and experimental implementation of grid frequency regulation using behind-the-meter batteries compensating for fast load demand variations. IEEE Trans. Power Syst. 32(1), 484–498 (2017)

    Article  Google Scholar 

  15. Tayab, U.B.; Azrik, M.; Roslan, B.; Hwai, J.; Kashif, M.: A review of droop control techniques for microgrid. Renew. Sustain. Energy Rev. 76, 717–727 (2016)

    Article  Google Scholar 

  16. Hirsch, A.; Parag, Y.; Guerrero, J.: Microgrids: a review of technologies, key drivers, and outstanding issues. Renew. Sustain. Energy Rev. 90, 402–411 (2018)

    Article  Google Scholar 

  17. Diaaeldin, I.; Aleem, S.A.; El-Rafei, A.; Abdelaziz, A.; Zobaa, A.F.: Optimal network reconfiguration in active distribution networks with soft open points and distributed generation. Energies 12(4172), 1–31 (2019)

  18. Liu, H.; Yang, Y.; Qi, J.; Li, J.; Wei, H.; Li, P.: Frequency droop control with scheduled charging of electric vehicles. IET Gener. Transm. Distrib. 11(3), 649–656 (2016)

    Article  Google Scholar 

  19. Rana, R.; Singh, M.; Mishra, S.: Design of modified droop controller for frequency support in microgrid using fleet of electric vehicles. IEEE Trans. Power Syst. 32(5), 3627–3636 (2017)

    Article  Google Scholar 

  20. Elsisi, M.; Soliman, M.; Aboelela, M.A.S.; Mansour, W.: Model predictive control of plug-in hybrid electric vehicles for frequency regulation in a smart grid. IET Gener. Trans. Distrib. 11(16), 3974–3983 (2017)

    Article  Google Scholar 

  21. Roorkee University Proceeding of International workshop on Hybrid Micro-Hydro Energy Systems, Water Recourses Development and Training Centre Roorkee University, India (1982)

  22. Kormilo, S.; Robinson, P.: Electronic control of small hydroelectric schemes using a microcomputer. IEE Proc. Comput. Digit. Tech. 131(4), 132–136 (1984)

    Article  Google Scholar 

  23. Roodsari, B.N.; Nowicki, E.P.; Freere, P.: A new electronic load controller for the self-excited induction generator to decrease stator winding stress. Energy Procedia ISES Solar World Congress 57, 1455–1464 (2013)

    Article  Google Scholar 

  24. Dolla, S.; Bhatti, T.S.: Load frequency control of an isolated small-hydro power plant with reduced dump load. IEEE Trans. Smart Grid 21(4), 1912–1919 (2006)

    Google Scholar 

  25. Kalla, U.K.; Singh, B.; Murthy, S.S.: Modified electronic load controller for constant frequency operation with voltage regulation of small hydro-driven single-phase SEIG. IEEE Trans. Ind. Appl. 52(4), 2789–2800 (2016)

    Article  Google Scholar 

  26. Rajagopal, V.; Singh, B.; Kansal, G.K.: Electronic load controller with power quality improvement of isolated induction generator for small hydro power generation. IET Renew. Power Gener. 5(2), 202–213 (2011)

    Article  Google Scholar 

  27. Freere, P.: Electronic load/excitation controller for a self-excited squirrel cage generator micro-hydro scheme. In: Proceedings of the International Electric Machines and Drives Conference, pp. 266–270 (1991)

  28. Bhatti, T.S.; Al-Ademi, A.A.F.; Bansal, N.K.: Load-frequency control of isolated wind-diesel-microhydro hybrid power systems WDHPS. Energy 5(22), 461–470 (1997)

    Article  Google Scholar 

  29. Nehrir, M.H.; LaMeres, B.J.; Venkataramanan, G.; Gerez, V.; Alvarado, L.A.: An approach to evaluate the general performance of stand-alone wind/photovoltaic generating systems. IEEE Trans. Energy Convers. 15(4), 433–439 (2000)

    Article  Google Scholar 

  30. Mufaris, A.L.M.; Baba, J.: Coordinated consumer load control by use of heat pump water heaters for voltage rise mitigation in future distribution system. In: Seventh Annual IEEE Green Technologies Conference, pp. 176–182 (2015)

  31. Hinov, N.L., Stanev, R.H., Vacheva, G.I.: A power electronic smart load controller for nanogirds and autonomous power systems. In: Proceedings of the XXV International Scientific Conference Electronics-ET2016, Bulgaria (2016)

  32. Saleh, K.A.; Zeineldin, H.H.; Al-Hinai, A.; El-Saadany, E.F.: Optimal coordination of directional overcurrent relays using a new time-current-voltage characteristic. IEEE Trans. Power Deliv. 30(2), 537–544 (2015)

    Article  Google Scholar 

  33. Hameed, F.; Hosani, M.A.; Zeineldin, H.H.: A modified backward/forward sweep load flow method for islanded radial microgrids. IEEE Trans. Smart Grid 10(1), 910–918 (2019)

    Article  Google Scholar 

  34. Díaz, G.; Aleixandre, J.G.; Coto, J.: Direct backward/forward sweep algorithm for solving load power flows in AC droop-regulated microgrids. IEEE Trans. Smart Grid 7(5), 2208–2217 (2016)

    Article  Google Scholar 

  35. Mirjalili, S.; Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  36. Sun, Y.: A modified whale optimization algorithm for large-scale global optimization problems. Expert Syst. Appl. 114, 563–577 (2018)

    Article  Google Scholar 

  37. Deb, K.; Pratap, A.; Agarwal, S.; Meyaivan, T.: A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  38. Sriniwas, N.; Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)

    Article  Google Scholar 

  39. Teng, J.A.: Direct approach for distribution system load flow solutions. IEEE Trans. Power Deliv. 18(3), 882–887 (2003)

    Article  Google Scholar 

  40. Kersting, W.H.: Distribution System Modelling and Analysis. CRC Press, Boca Raton (2012)

    MATH  Google Scholar 

  41. Farzin, H.; Firuzabad, M.F.; Aghatie, M.M.: A stochastic multi-objective framework for optimal scheduling of energy storage systems in microgrids. IEEE Trans. Smart Grid 8(1), 117–127 (2017)

    Article  Google Scholar 

  42. Soroudi, A.R.; Ehsan, M.: Multi-objective planning model for integration of distributed generations in deregulated power systems. Iran. J. Sci. Technol. B Eng. 34(B3), 307–324 (2010)

    MATH  Google Scholar 

  43. MATLAB R2018a 1994–2018 The Math Works

  44. Baran, M.E.; Wu, F.F.: Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. Power Deliv. 4(2), 1401–1407 (1989)

    Article  Google Scholar 

  45. Satsangi, S.; Kumbhar, G.B.: Effect of load models on energy loss reduction using volt-vAr optimization. In: National Power System Conference, pp. 1–6 (2016)

  46. Nutkani, N.U.; Loh, P.C.; Weng, P.; Blaabjerg, F.: Decentralized economic dispatch scheme with online power reserve for microgrids. IEEE Trans. Smart Grid 8(1), 139–148 (2017)

    Article  Google Scholar 

  47. Baran, M.E.; Wu, F.F.: Optimal capacitor placement on radial distribution systems. IEEE Trans. Power Deliv. 4(1), 725–734 (1989)

    Article  Google Scholar 

  48. Li, Z.; Xu, Y.; Feng, X.; Wu, Q.: Optimal stochastic deployment of heterogenous energy storage in a residential multi-energy microgrid with demand-side management. IEEE Trans. Ind. Inform. 17, 1–13 (2020)

    Google Scholar 

  49. Liu, M.; Shi, Y.; Fang, F.: Optimal power flow and PGU capacity of CCHP systems using a matrix modeling approach. Appl. Energy 102, 794–802 (2013)

    Article  Google Scholar 

  50. Mostafa, H.M.; H., AbdelAleem, S.H.E., Ali, S.G., Ziad, M.A., Abdelaziz, A.Y., : Techno-economic assessment of energy storage systems using annualized life cycle cost of storage (LCCOS) and levelized cost of energy (LCOE) metrics. J. Energy Storage 29, 101345 (2020)

  51. Li, Z.; Xu, Y.: Temporally-coordinated optimal operation of a multi-energy microgrid under diverse uncertainties. Appl. Energy 240, 719–729 (2019)

    Article  Google Scholar 

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Correspondence to Ankit Uniyal.

Analytical Proof for Modified DCLF

Analytical Proof for Modified DCLF

An important analysis has been conducted on 33 bus weakly meshed autonomous microgrid to justify the assumptions in the proposed work related to the convergence behavior of modified DCLF.

Table 8 delF values for four iterations with various load levels
Table 9 delV1 values for four iterations with various load levels

The DCLF is run for various loading conditions (from 0.2 pu to 0.7 pu) of the system. It is shown in Tables A. 1 and A. 2, that minimum values of delF/delV1 have occurred for the same loading (0.7 pu) whether iteration is 1, 2, 3 or 4. Thus, when DCLF is used to minimize the objectives delF/delV1 for optimal DL allocations, it is not required to proceed till convergence to obtain the minimum value of delF/delV1. It can be observed just from first iteration values of delF/delV1. The inference is that, even though DCLF has not converged, we still can obtain DL allocations for minimized delF/delV1 values from first iteration itself. These optimal DL sizes and their corresponding locations when employed in the system and the DCLF is run till convergence, then final converged delF/delV1 values are obtained which can be recorded for analysis.

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Uniyal, A., Sarangi, S. & Rawat, M.S. Optimal Dump Load Allocations in High RBDG Penetrated Microgrid for Voltage and Frequency Regulation. Arab J Sci Eng 46, 1511–1528 (2021). https://doi.org/10.1007/s13369-020-05240-9

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