An overview of selected topics in smart grids

doi:10.1007/s11708-016-0418-6

Front. Energy

2016, Vol. 10

Issue (4) : 441-458 https://doi.org/10.1007/s11708-016-0418-6

REVIEW ARTICLE

An overview of selected topics in smart grids

S. Hari Charan CHERUKURI,Balasubramaniyan SARAVANAN(

)

School of Electrical Engineering, VIT University, Vellore 632014, India

Download: PDF(394 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Smart technologies when used in the traditional grid infrastructure will provide a different environment and working conditions in the grid by bringing the required smartness into the grid, called the smart grid. The smart grid can play a major role in the upcoming days to come because there is a necessity to integrate coordinated renewable energy resources into the grid and to operate the grids at a higher efficiency considering many aspects including reliability of the supply. Apart from this, there is a necessity to manage the demand supply gap in the smart grid by optimally scheduling the generators or by effectively scheduling the demand side resources instead of going for the traditional methods like partial or full load shedding. This paper presents an overview on the present state-of-the-art of smart grid technologies and broadly classifies the papers referred into two major areas, papers based on improvement of operational efficiency in smart grids and papers based on smartness in maintaining the demand supply gap. Some of the papers projected in this work also give a brief overview of the necessity of the smart grid.

Keywords smart grid demand side management electric springs operational efficiency

Corresponding Author(s): Balasubramaniyan SARAVANAN

Just Accepted Date: 14 June 2016 Online First Date: 29 July 2016 Issue Date: 17 November 2016

Cite this article:

S. Hari Charan CHERUKURI,Balasubramaniyan SARAVANAN. An overview of selected topics in smart grids[J]. Front. Energy, 2016, 10(4): 441-458.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-016-0418-6
https://academic.hep.com.cn/fie/EN/Y2016/V10/I4/441

Fig.1 Brief architecture of smart grid

Tab.1 Number of customer interruptions and number of reclosers installed

Fig.2 Configuration of FREEDM system

Tab.2 Computation efficiency of the DOPF algorithm

Tab.3 Power losses in the system after reconfiguration using different algorithms Unit: kW

Tab.4 System power losses for different loadings

Fig.3 Model of the system considered in Ref. [17]

Tab.5 Power loss in the system before and after placement of DG in the distribution system

Fig.4 Five bus Irish practical system considered in Ref. [24]

Tab.6 Impact of control actions on reactive power drawn

S. No.	Brief overview of the topic	Remarks	Ref.
1	Evolution of smartness in the system in terms of reducing outages caused by momentary faults	Replacing fuses with auto re-closers in the feeder laterals will result in reducing system outages by 62% and 100% on two different lateral sections considered in the paper.	[10]
2	Looping of primary distribution systems to accommodate fluctuating power sources	The FREEDM configuration implemented will be useful to integrate renewable energy sources in distribution level and capable of handling dispatched storage devices like batteries/super capacitors.	[11]
3	Substation automation and integration of renewable energy sources and storage devices to improve grid operability	There is a necessity to develop communication and advanced control of the software like the SCADA to satisfactorily operate the integrated system.	[12]
4	Optimum power flow techniques to improve the grid operability and efficiency	In order to achieve higher computational speed for varying load conditions in smart grid environment, the DOPF algorithm can be used because it has a higher computation speed.	[13]
5	Distribution system reconfiguration in order to improve the system efficiency in terms of loss reduction	Power system reconfiguration helps reduce the real power loss in distribution systems to a great extent and it can be stated that by application of any optimization algorithms, the loss reduction is≥30 for any type of algorithm used in the literature.	[16]
6	Smart reconfiguration	The concept of smart reconfiguration which is suitable for varying load conditions can be more smarter in terms of reducing the power loss than the methodology used in Ref. [16].	[17]
7	Placement of DGs in the distribution system in order to improve the system efficiency by reducing the real power losses	Placement of DGs in the system helps reduce power consumption from the main grid because DGs supply the loads locally and hence there will be power loss reduction in the distribution system. This placement of DGs uses optimization algorithms to optimally size and from the literature it is clear that using any type of algorithms reduces the power loss by≥50%.	[19]
8	Placement of renewable DGs in the transmission system considering generation stochasticity	Placement of uncertain DGs has been attempted where there is a healthy grid interaction whenever there is deficit in power generated by the DGs which is effective and smart for current scenario.	[20]
9	Minimization of reactive power drawn from the grids to improve reactive power handling efficiency	By coordinated control of OLTCs and making the DGs run near to unity power factor there can be a substantial reduction in reactive power drawn from the main grid which is a drawback in Refs [18–20]. and the reactive power drawn from the grid is almost ‘zero’ most of the time.	[24]
10	Combined operation of DGs and base load plants	Islanding operation of renewable DGs is possible and there is a necessity to make the base load plants such as the gas turbine power plants operational whenever required to improve the quality of power supplied which means that there is a need to incorporate renewable as well as non-renewable energy sources in order to improve the system operational efficiency.	[26]

Tab.7 Key factors essential to improve smartness in terms of efficiency in electric grids

Fig.5 Methods for reducing demand supply gap in grids

Tab.8 Results of different test cases obtained

Tab.9 Results obtained by FDP and BPSO [35]

Fig.6 Layout of energy hub [40]

Tab.10 Different investment options and their costs involved

Fig.7 General block diagram of electric spring

Fig.8 Detailed architecture of power electronic block

S.No.	Brief overview of the topic	Remarks	Ref.
1	Generation scheduling	Effective in managing demand supply gap; the methods covered are useful and effective in scheduling the generation considering the fact that generation stochasticity is more important for future smart grids	[27,28]
2	DSM	One of the powerful techniques useful to manage demand supply gap and useful for achieving control over fuel consumption	[29]
3	GUI based DSM model	A GUI based DSM model which is capable of changing the characteristics of the load curve has been developed which is user friendly and results in an electricity bill reduction of 33%	[33]
4	Online model for DSM	DSM model which uses two way communication for its operation and effectively solves the proposed mathematical model using linear programming which is quite easier than the use of BPSO as in Ref. [33]	[34]
5	Better optimization model in DSM	Although fuzzy dynamic programming is capable of solving complex problems, the use of BPSO can be recommended for solving DSM problems because BPSO proves better in many cases than FDP	[35]
6	DSM based on price prediction	DSM and price prediction methodologies if used hand in hand will give better results in terms of reducing the demand supply gap	[36]
7	Linearized DSM model for energy cost reduction	Use of convex energy cost model for DSM problems will make the methodology simple and the cost reduction is around 15% and the peak to average ratio for the considered curve is reduced to a little extent	[37]
8	Introduction of storage	Introduction of storage in the grids and by installing energy hubs, the demand supply gap can be tackled, which is different from generation scheduling and DSM	[38-40]
9	Energy hub	By looking at the different investment options available, if the gap in demand and supply is less, one can simply go for installation of CHP unit alone in the energy hub and if the system is vulnerable for larger variations and if the cost is a constraint, the CHP unit and heat DSM scheme can be preferred	[40]
10	Energy storage by means of electric vehicles	Electric vehicles are also capable of contributing their part in managing the demand supply gap because most of the time EVs will be in parking lots	[42-44]
11	Use of secondary batteries to reduce the demand supply gap	Using batteries from the EVs after their gridable period is an excellent option to improve the storage and battery purchase cost in the system	[45]
12	Electric springs	Very new technology presented in the literature which will be useful in reducing the demand supply gap in a different way. For a system which runs on battery support during un-certainties in power generation, the battery storage requirement is reduced by 50% and 10% for the cases considered, which means that the demand supply gap can be effectively handled with reduced storage requirement	[52-53]

Tab.11 Key aspects contributing to smartness in the grids in terms of demand supply gap reduction

1	Dovi V G, Friedler F, HuisinghD, K<?Pub Caret1?>lemeš J J. Cleaner energy for sustainable future. Journal of Cleaner Production, 2009, 17(10): 889–895 https://doi.org/10.1016/j.jclepro.2009.02.001
2	Battaglini A, Lilliestam J, Haas A, Patt A. Development of Super Smart Grids for a more efficient utilisation of electricity from renewable sources. Journal of Cleaner Production, 2009, 17(10): 911–918 https://doi.org/10.1016/j.jclepro.2009.02.006
3	Mukhopadhyay S, Soonee S K, Joshiand R, Rajput A K. On the progress of renewable energy integration into smart grids in India. In: IEEE Power and Energy Society General Meeting. San Diego, USA, 2012, 1–6
4	Acharjee P. Strategy and implementation of smart grids in India. Energy Strategies Reviews, 2013, 1(3): 193–204 https://doi.org/10.1016/j.esr.2012.05.003
5	Sinha A, Neogi S, Lahiri R N, Chowdhury S, Chowdhury S P, Chakraborty N. Smart grid initiative for power distributionutility in India. In: IEEE Power and Energy Society General Meeting. Detroit, USA, 2011, 1–8
6	Phuangpornpitak N, Tia S. Opportunities and challenges of integrating renewable energy in smart grid system. Energy Procedia, 2013, 34: 282–290 https://doi.org/10.1016/j.egypro.2013.06.756
7	Cardenas J A, Gemoets L, Ablanedo Rosas J H, Sarfi R. A literature survey on smart grid distribution: an analytical approach. Journal of Cleaner Production, 2014, 65: 202–216 https://doi.org/10.1016/j.jclepro.2013.09.019
8	Marnay C, Venkataramanan G. Microgrids in the Evolving Electricity Generation and Delivery Infrastructure. In: IEEE Power Engineering Society General Meeting. Montreal, Canada, 2006
9	Short T. Electric Power Distribution. CRC Press, 2004
10	Carlson N, Asgeirsson H, Lascu R, Benaglio J, Ennis M G. Application of cutout type reclosers on distribution lateral circuits—a field study. In: IEEE Power and Energy Society General Meeting. Calgary, Canada, 2009, 1–4
11	Heydt G T. Future renewable electrical energy delivery and management systems: energy reliability assessment of FREEDM systems. In: IEEE Power and Energy Society General Meeting. Minneapolis, USA, 2010, 1–4
12	Miller M T, Johns M B, Sortomme E, Venkata S S. Advanced integration of distributed energy resources. In: IEEE Power and Energy Society General Meeting. San Diego, USA, 2012, 1–2
13	Lin S Y, Chen J F. Distributed optimal power flow for smart grid transmission system with renewable energy sources. Energy, 2013, 56: 184–192 https://doi.org/10.1016/j.energy.2013.04.011
14	Saadat H. Power System Analysis. McGraw Hill, 2004
15	Srinivasa Rao R, Narasimham S V L, Ramalinga Raju M, Srinivasa Rao A. Optimal network reconfiguration of large-scale distribution system using harmony search algorithm. IEEE Transactions on Power Systems, 2011, 26(3): 1080–1088 https://doi.org/10.1109/IDAMS.2010.2076839
16	Mohamed Imran A M, Kowsalya M. A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using fireworks algorithm. International Journal of Electrical Power & Energy Systems, 2014, 62: 312–322 https://doi.org/10.1016/j.ijepes.2014.04.034
17	Dukpa A, Venkatesh B. Smart reconfiguration using fuzzy graphs. In: IEEE Power and Energy Society General Meeting. Minneapolis, USA, 2010
18	El-Fergany A. Study impact of various load models on DG placement and sizing using backtracking search algorithm. Applied Soft Computing, 2015, 30: 803–811 https://doi.org/10.1016/j.asoc.2015.02.028
19	Mohamed Imran A, Kowsalya M. Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization. Swarm and Evolutionary Computation, 2014, 15: 58–65 https://doi.org/10.1016/j.swevo.2013.12.001
20	Najjar M B, Ghoulam E, Fares H. Mini renewable hybrid distributed power plants for lebanon. Energy Procedia, 2012, 18: 612–621 https://doi.org/10.1016/j.egypro.2012.05.074
21	Li H Y, Ge S Y, Liu H. Analysis of the effect of distributed generation on power grid. In:Asia-Pacific Power and Energy Engineering Conference. 2012,1–5
22	Keane A, Ochoa L F, Vittal E, Dent C J, Harrison G P. Enhanced utilization of voltage control resources with distributed generation. IEEE Transactions on Power Systems, 2011, 26(1): 252–260 https://doi.org/10.1109/TPWRS.2009.2037635
23	Ochoa L F, Keane A, Dent C, Harrison G P. Minimizing transmission reactive support required by high penetration of distributed wind power generation. In: the 8th International Workshop Large-Scale Integration of Wind Power into Power Systems. Bremen, Germany, 2009
24	Ochoa L F, Keane A, Harrison G P. Minimizing the reactive support for distributed generation: enhanced passive operation and smart distribution networks. IEEE Transactions on Power Systems, 2011, 26(4): 2134–2142 https://doi.org/10.1109/TPWRS.2011.2122346
25	Suryanarayanan S, Ren W, Steurer M, Ribeiro P F, Heydt G T. A real-time controller concept demonstration for distributed generation interconnection. In: IEEE Power Engineering Society General Meeting. Montreal, Canada, 2006
26	Vokony I, Dan A. Examination of smart grids in island operation. In: IEEE Bucharest Power Technology Conference. Bucharest, Romania, 2009, 1–7
27	Varaiya P P, Wu F F, Bialek J W. Smart operation of smart grid: risk-limiting dispatch. Proceedings of the IEEE, 2011, 99(1): 40–57 https://doi.org/10.1109/JPROC.2010.2080250
28	Koutsopoulos I, Tassiulas L. Challenges in demand load control for the smart grid. IEEE Network, 2011, 25(5): 16–21 https://doi.org/10.1109/MNET.2011.6033031
29	Masters G M. Renewable and Efficient Electric Power Systems. Hoboken, NJ: John Wiley, 2004
30	Ramanathan B, Vittal V. A framework for evaluation of advanced direct load control with minimum disruption. IEEE Transactions on Power Systems, 2008, 23(4): 1681–1688 https://doi.org/10.1109/TPWRS.2008.2004732
31	Gellings C W, Chamberlin J H. Demand Side Management: Concepts and Methods, 2nd ed. Tulsa, OK: Penn Well Books, 1993
32	Jazayeri P, Schellenberg A, Rosehart W D, Doudna J, Widergren S, Lawrence D, Mickey J, Jones S. A survey of load control programs for price and system stability. IEEE Transactions on Power Systems, 2005, 20(3): 1504–1509 https://doi.org/10.1109/TPWRS.2005.852147
33	Gudi N, Wang L, Devabhaktuni V. A demand side management based simulation platform incorporating heuristic optimization for management of household appliances. Electric Power and Energy Systems, 2012, 43(1): 185–193 https://doi.org/10.1016/j.ijepes.2012.05.023
34	Conejo A J, Morales J M, Baringo L. Real-time demand response model. IEEE Transactions on Smart Grid, 2010, 1(3): 236–242 https://doi.org/10.1109/TSG.2010.2078843
35	Pedrasa M A A, Spooner T D, Mac Gill I F. Scheduling of demand side resources using binary particle swarm optimization. IEEE Transactions on Power Systems, 2009, 24(3): 1173–1181 https://doi.org/10.1109/TPWRS.2009.2021219
36	Mohsenian-Rad A H, Leon-Garcia A. Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Transactions on Smart Grid, 2010, 1(2): 120–133 https://doi.org/10.1109/TSG.2010.2055903
37	Mohsenian-Rad A H, Wong V W S, Jatskevich J, Schober R, Leon-Garcia A. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid, 2010, 1(3): 320–331 https://doi.org/10.1109/TSG.2010.2089069
38	Geidl M, Koeppel G, Favre-Perrod P, Klöckl B, Andersson G, Fröhlich K. Energy hubs for the future. IEEE Power and Energy Magazine, 2007, 1: 25–30
39	Chicco G. Challenges for smart distribution systems: data representation and optimization objectives. In: the 12th International Conference on Optimization of Electrical and Electronic Equipment. Brasov, Romania, 2010, 1236–1244
40	Kienzle F, Ahcin P, Andersson G. Valuing investments in multi-energy conversion, storage, and demand-side management systems under uncertainty. IEEE Transaction on Sustainable Energy, 2011, 2(2): 194–202 https://doi.org/10.1109/TSTE.2011.2106228
41	Kanchev H, Lu D, Colas F, Lazarov V, Francois B. Energy management and operational planning of a microgrid with a PV-based active generator for smart grid applications. IEEE Transactions on Industrial Electronics, 2011, 58(10): 4583–4592 https://doi.org/10.1109/TIE.2011.2119451
42	Kempton W, Tomic J. Vehicle-to-grid power implementation: from stabilizing the grid to supporting large-scale renewable energy. Journal of Power Sources, 2005, 144(1): 280–294 https://doi.org/10.1016/j.jpowsour.2004.12.022
43	Kempton W, Tomic J. Vehicle-to-grid power fundamentals: calculating capacity and net revenue. Journal of Power Sources, 2005, 144(1): 268–279 https://doi.org/10.1016/j.jpowsour.2004.12.025
44	Andreotti A, Carpinelli G, Mottola F, Proto D. A review of single-objective optimization models for plug-in vehicles operation in smart grids Part I: theoretical aspects. In: IEEE Power and Energy Society General Meeting. San Diego, USA, 2012,1–8.
45	Debnath U K, Ahmad I, Habibi D. Quantifying economic benefits of second life batteries of gridable vehicles in the smart grid. Electrical Power and Energy Systems, 2014, 63: 577–587 https://doi.org/10.1016/j.ijepes.2014.05.077
46	Hui S Y, Lee C K, Wu F F. Electric springs—a new smart grid technology. IEEE Transactions on Smart Grid, 2012, 3(3): 1552–1561 https://doi.org/10.1109/TSG.2012.2200701
47	Lee C K, Chaudhuri B, Hui S Y. Hardware and control implementation of electric springs for stabilizing future smart grid with intermittent renewable energy sources. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2013, 1(1): 18–27 https://doi.org/10.1109/JESTPE.2013.2264091
48	Chaudhuri N R, Lee C K, Chaudhuri B, Hui S Y R. Dynamic modeling of electric springs. IEEE Transactions on Smart Grid, 2014, 5(5): 2450–2458 https://doi.org/10.1109/TSG.2014.2319858
49	Luo X, Akhtar Z, Lee C K, Chaudhuri B, Tan S C, Hui S Y R. Distributed voltage control with electric springs: comparison with STATCOM. IEEE Transactions on Smart Grid, 2015, 6(1): 209–219 https://doi.org/10.1109/TSG.2014.2345072
50	Chen X, Hou Y, Tan S C, Lee C K, Hui S Y R. Mitigating voltage and frequency fluctuation in microgrids using electric springs. IEEE Transactions on Smart Grid, 2015, 6(2): 508–515 https://doi.org/10.1109/TSG.2014.2374231
51	Yan S, Tan S C, Lee C K, Chaudhuri B, Hui S Y. Electric springs for reducing power imbalance in three-phase power systems. IEEE Transactions on Power Electronics, 2015, 30(7): 3601–3609 https://doi.org/10.1109/TPEL.2014.2350001
52	Lee C K, Shu Y H. Reduction of energy storage requirements in future smart grid using electric springs. IEEE Transactions on Smart Grid, 2013, 4(3): 1282–1288 https://doi.org/10.1109/TSG.2013.2252208
53	Cherukuri S H C, Saravanan B. Performance analysis of electric spring in reducing the power drawn from the supply system during generation uncertainties. International Journal of Renewable Energy Research, 2015, 5(4): 1133–1145
54	Lee C K, Li S N, Hui S Y. A design methodology for smart LED lighting systems powered by weakly regulated renewable power girds. IEEE Transactions on Smart Grid, 2011, 2(3): 548–554 https://doi.org/10.1109/TSG.2011.2159631

[1]	S. L. ARUN, M. P. SELVAN. Smart residential energy management system for demand response in buildings with energy storage devices[J]. Front. Energy, 2019, 13(4): 715-730.
[2]	S. Surender REDDY, Vuddanti SANDEEP, Chan-Mook JUNG. Review of stochastic optimization methods for smart grid[J]. Front. Energy, 2017, 11(2): 197-209.
[3]	Balasubramaniyan SARAVANAN. DSM in an area consisting of residential, commercial and industrial load in smart grid[J]. Front. Energy, 2015, 9(2): 211-216.
[4]	M. M. RAJAN SINGARAVEL,S. ARUL DANIEL. Sizing of hybrid PMSG-PV system for battery charging of electric vehicles[J]. Front. Energy, 2015, 9(1): 68-74.
[5]	Pathirikkat GOPAKUMAR, G. Surya CHANDRA, M. Jaya Bharata REDDY, Dusmata Kumar MOHANTA. Optimal redundant placement of PMUs in Indian power grid — northern, eastern and north-eastern regions[J]. Front Energ, 2013, 7(4): 413-428.
[6]	Pathirikkat GOPAKUMAR, G. Surya CHANDRA, M. Jaya Bharata REDDY, Dusmata Kumar MOHANTA. Optimal placement of PMUs for the smart grid implementation in Indian power grid—A case study[J]. Front Energ, 2013, 7(3): 358-372.

Viewed

Full text

Abstract

Cited

Shared

Discussed