Recent trends in power management strategies for optimal operation of distributed energy resources in microgrids: A comprehensive review
Seshu Kumar Rangu
Department of Electrical Engineering, National Institute of Technology, Silchar, India
Search for more papers by this authorPhani Raghav Lolla
Department of Electrical Engineering, National Institute of Technology, Silchar, India
Search for more papers by this authorKoteswara Raju Dhenuvakonda
Department of Electrical Engineering, National Institute of Technology, Silchar, India
Search for more papers by this authorCorresponding Author
Arvind R. Singh
School of Electrical Engineering, Shandong University, Jinan, China
Correspondence
Arvind R. Singh, School of Electrical Engineering, Shandong University, Jinan, China.
Email: [email protected]
Search for more papers by this authorSeshu Kumar Rangu
Department of Electrical Engineering, National Institute of Technology, Silchar, India
Search for more papers by this authorPhani Raghav Lolla
Department of Electrical Engineering, National Institute of Technology, Silchar, India
Search for more papers by this authorKoteswara Raju Dhenuvakonda
Department of Electrical Engineering, National Institute of Technology, Silchar, India
Search for more papers by this authorCorresponding Author
Arvind R. Singh
School of Electrical Engineering, Shandong University, Jinan, China
Correspondence
Arvind R. Singh, School of Electrical Engineering, Shandong University, Jinan, China.
Email: [email protected]
Search for more papers by this authorSummary
The current era in sustainable development is focused on the rapid integration of renewable energy sources driven by a wide range of socio-economic objectives. Due to the inherent property of time-varying weather conditions, the intermittent sources, that is, Solar PV and Wind Energy, are considered as variable energy resources. The uncertainty and variability problem of these sources has brought many complications to distributed network operators to operate and control the complex or multi-microgrids with limited fast-ramping resources in order to maintain the power system flexibility. It led many researchers to find an alternative strategy since the conventional approaches are no longer adequate to handle the economic implications of operational decision making. At first, the brief review of various deterministic and probabilistic approaches, stochastic programming and robust optimisation strategies to address the uncertainty of variable energy resources are discussed. Furthermore, in the energy management point of view, the optimal scheduling problem of distributed sources of the microgrid is considered, and a brief review of optimisation models, advanced control strategies and demand response strategies to maximise economic benefits of microgrids are also elaborately presented. Finally, the multiagent-based distributed and decentralised control strategies for seamless integration of distributed generator units are reviewed under various configurations of the power grid along with communication network topologies.
REFERENCES
- 1https://powermin.nic.in/en/content/overview.
- 2https://mnre.gov.in/img/documents/uploads/0ce0bba7b9f24b32aed4d89265d6b067.pdf.
- 3Surender Reddy S, Bijwe PR, Abhyankar AR. Real-time economic dispatch considering renewable power generation variability and uncertainty over scheduling period. IEEE Syst J. 2015; 9(4): 1440-1451.
- 4Tang Y, Zhong J, Liu J. A generation adjustment methodology considering fluctuations of loads and renewable energy sources. IEEE Trans Power Syst. 2016; 31(1): 125-132.
- 5Gu Y, Xie L. Stochastic look-ahead economic dispatch with variable generation resources. IEEE Trans Power Syst. 2017; 32(1): 17-29.
- 6Kanchev H, Colas F, Lazarov V, Francois B. Emission reduction and economical optimization of an urban microgrid operation including dispatched PV-based active generators. IEEE Trans Sustain Energy. 2014; 5(4): 1397-1405.
- 7Ying Y, Wu Y, Su Y, Fu R, Liang X, Xu H. Dispatching approach for active distribution network considering PV generation reliability and load predicting interval. J Eng. 2017; 2017(13): 2433-2437.
10.1049/joe.2017.0766 Google Scholar
- 8Conte F, D'Agostino F, Pongiglione P, Saviozzi M, Silvestro F. Mixed-integer algorithm for optimal dispatch of integrated PV-storage systems. IEEE Trans Ind Appl. 2019; 55(1): 238-247.
- 9Ahn S, Nam S, Choi J, Moon S. Power scheduling of distributed generators for economic and stable operation of a microgrid. IEEE Trans Smart Grid. 2013; 4(1): 398-405.
- 10Chakraborty S, Ito T, Senjyu T, Saber AY. Intelligent economic operation of smart-grid facilitating fuzzy advanced quantum evolutionary method. IEEE Trans Sustain Energy. 2013; 4(4): 905-916.
- 11Liu J, Li J. A bi-level energy-saving dispatch in smart grid considering interaction between generation and load. IEEE Trans Smart Grid. 2015; 6(3): 1443-1452.
- 12Nikmehr N, NajafiRavadanegh S. Optimal power dispatch of multi-microgrids at future smart distribution grids. IEEE Trans Smart Grid. 2015; 6(4): 1648-1657.
- 13Liang H, Zhuang W. Stochastic modeling and optimization in a microgrid: a survey. Energies. 2014; 7: 2027-2050.
- 14Reddy S, Sandeep V, Jung C-M. Review of stochastic optimization methods for smart grid. Front Energy. 2017; 11(2): 197-209.
- 15Meng L, Sanseverino ER, Luna A, Dragicevic T, Vasquez JC, Guerrero JM. Microgrid supervisory controllers and energy management systems: a literature review. Renew Sust Energ Rev. 2016; 60: 1263-1273.
- 16Rafique SF, Jianhua Z. Energy management system, generation and demand predictors: a review. IET Gener Transm Distrib. 2017; 12(3): 519-530.
- 17Dkhili N, Eynard J, Thil S, Grieu S. A survey of modelling and smart management tools for power grids with prolific distributed generation. Sustain Energy Grids Netw. 2019; 21: 1-8.
- 18Ma J, Ma X. A review of forecasting algorithms and energy management strategies for microgrids. Syst Sci Contr Eng. 2018; 6: 237-248.
- 19Nosratabadi SM, Hooshmand R-A, Gholipour E. A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems. Renew Sust Energ Rev. 2017; 67: 341-363.
- 20Sasaki Y, Yorino N, Zoka Y, Wahyudi FI. Robust stochastic dynamic load dispatch against uncertainties. IEEE Trans Smart Grid. 2018; 9: 5535-5542.
- 21Safta C, Chen RL, Najm HN, Pinar A, Watson J. Efficient uncertainty quantification in stochastic economic dispatch. IEEE Trans Power Syst. 2017; 32(4): 2535-2546.
- 22Li J, Ou N, Lin G, Wei W. Compressive sensing based stochastic economic dispatch with high penetration renewables. IEEE Trans Power Syst. 2019; 34(2): 1438-1449.
- 23Peng C, Hou Y, Yu N, Yan J, Lei S, Wang W. Multiperiod risk-limiting dispatch in power systems with renewables integration. IEEE Trans Indus Inform. 2017; 13(4): 1843-1854.
- 24Nosair H, Bouffard F. Economic dispatch under uncertainty: the probabilistic envelopes approach. IEEE Trans Power Syst. 2017; 32(3): 1701-1710.
- 25Krad I, Gao DW, Ela E, Ibanez E, Wu H. Analysis of operating reserve demand curves in power system operations in the presence of variable generation. IET Renew Power Gen. 2017; 11(7): 959-965.
- 26Wang C, Bao-Sen Luh P, Navid N. Ramp requirement design for reliable and efficient integration of renewable energy. IEEE Trans Power Syst. 2017; 32(1): 562-571.
- 27Zhu J, Liu Q, Xiong X, et al. Multi-time-scale robust economic dispatching method for the power system with clean energy. J Eng. 2019; 16: 1377-1381.
- 28Nguyen TA, Crow ML. Stochastic optimization of renewable-based microgrid operation incorporating battery operating cost. IEEE Trans Power Syst. 2016; 31(3): 2289-2296.
- 29Li N, Uçkun C, Constantinescu EM, Birge JR, Hedman KW, Botterud A. Flexible operation of batteries in power system scheduling with renewable energy. IEEE Trans Sustain Energy. 2016; 7(2): 685-696.
- 30Zhi-gang L, Hao Z, Hai-feng X, Jiang-feng Z, Xue-ping L, Xiao-feng S. Robust DED based on bad scenario set considering wind, EV and battery switching station. IET Gen Transm Distrib. 2017; 11(2): 354-362.
- 31Tian Y, Fan L, Tang Y, Wang K, Li G, Wang H. A coordinated multi-time scale robust scheduling framework for isolated power system with ESU under high RES penetration. IEEE Access. 2018; 6: 9774-9784.
- 32Fan M, Vittal V, Heydt GT, Ayyanar R. Probabilistic power flow analysis with generation dispatch including photovoltaic resources. IEEE Trans Power Syst. 2013; 28(2): 1797-1805.
- 33Li Z, Wu W, Shahidehpour M, Zhang B. Adaptive robust tie-line scheduling considering wind power uncertainty for interconnected power systems. IEEE Trans Power Syst. 2016; 31(4): 2701-2713.
- 34Liu Y, Nair NC. A two-stage stochastic dynamic economic dispatch model considering wind uncertainty. IEEE Trans Sustain Energy. 2016; 7(2): 819-829.
- 35Bizuayehu AW, Sánchez de la Nieta AA, Contreras J, Catalão JPS. Impacts of stochastic wind power and storage participation on economic dispatch in distribution systems. IEEE Trans Sustain Energy. 2016; 7(3): 1336-1345.
- 36Yan N, Xing ZX, Li W, Zhang B. Economic dispatch application of power system with energy storage systems. IEEE Trans Appl Supercond. 2016; 26(7): 1-5.
- 37Wu H, Krad I, Florita A, et al. Stochastic multi-timescale power system operations with variable wind generation. IEEE Trans Power Syst. Vol 32. Piscataway, NJ: IEEE; 2017: 3325-3337.
10.1109/TPWRS.2016.2635684 Google Scholar
- 38Tang Y, Luo C, Yang J, He H. A chance constrained optimal reserve scheduling approach for economic dispatch considering wind penetration. IEEE/CAA J Autom Sin. 2017; 4(2): 186-194.
10.1109/JAS.2017.7510499 Google Scholar
- 39Yan J, Li F, Liu Y, Gu C. Novel cost model for balancing wind power forecasting uncertainty. IEEE Trans Energy Convers. 2017; 32(1): 318-329.
- 40Shi L, Wang R, Yao L. Modelling and solutions of coordinated economic dispatch with wind–hydro–thermal complex power source structure. IET Renew Power Gen. 2017; 11: 262-270.
- 41Xu Y, Yin M, Dong ZY, Zhang R, Hill DJ, Zhang Y. Robust dispatch of high wind power-penetrated power systems against transient instability. IEEE Trans Power Syst. 2018; 33(1): 174-186.
- 42Che L, Liu X, Zhu X, Wen Y, Li Z. Intra-interval security based dispatch for power systems with high wind penetration. IEEE Trans Power Syst. 2019; 34(2): 1243-1255.
- 43Sachs J, Sawodny O. A two-stage model predictive control strategy for economic diesel-PV-Battery Island microgrid operation in rural areas. IEEE Trans Sustain Energy. 2016; 7(3): 903-913.
- 44Garcia-Torres F, Valverde L, Bordons C. Optimal load sharing of hydrogen-based microgrids with hybrid storage using model-predictive control. IEEE Trans Ind Electron. 2016; 63(8): 4919-4928.
- 45C. Li, Bosio, F. Chen, S. K. Chaudhary, J. C. Vasquez and J. M. Guerrero, "Economic dispatch for operating cost minimization under real-time pricing in droop-controlled DC microgrid," IEEE J Emerg Sel Top Power Electron, vol. 5, no. 1, pp. 587–595, 2017.
- 46Zhao Y, Yu J, Ban M, Liu Y, Li Z. Privacy-preserving economic dispatch for an active distribution network with multiple networked microgrids. IEEE Access. 2018; 6: 38802-38819.
- 47Ahmad J, Tahir M, Mazumder SK. Dynamic economic dispatch and transient control of distributed generators in a microgrid. IEEE Syst J. 2019; 13(1): 802-812.
- 48Maulik A, Das D. Optimal power dispatch considering load and renewable generation uncertainties in an AC–DC hybrid microgrid. IET Gen Transm Distrib. 2019; 13: 1164-1176.
- 49Shan H, Hongjing L, Linlin W, et al. Economic optimisation of microgrid based on improved quantum genetic algorithm. J Eng. 2019; 2019: 1167-1174.
- 50Du Y, Wu J, Li S, Long C, Onori S. Coordinated energy dispatch of autonomous micro grids with distributed MPC optimization. IEEE Trans Ind Inform. 2019; 15(9): 5289-5298.
- 51Ma J, Geng G, Jiang Q. Two-time-scale coordinated energy Management for Medium-Voltage DC systems. IEEE Trans Power Syst. 2016; 31(5): 3971-3983.
- 52Zeng W, Zhang Y, Chow M. Resilient distributed energy management subject to unexpected misbehaving generation units. IEEE Trans Indus Inform. 2017; 13(1): 208-216.
- 53Battistelli C, Agalgaonkar YP, Pal BC. Probabilistic dispatch of remote hybrid microgrids including battery storage and load management. IEEE Trans Smart Grid. 2017; 8(3): 1305-1317.
- 54Marzband M, Azarinejadian F, Savaghebi M, Guerrero JM. An optimal energy management system for islanded microgrids based on multiperiod artificial bee Colony combined with Markov chain. IEEE Syst J. 2017; 11(3): 1712-1722.
- 55Hu W, Wang P, Gooi HB. Toward optimal energy management of microgrids via robust two-stage optimization. IEEE Trans Smart Grid. 2018; 9(2): 1161-1174.
- 56Romero-Quete D, Cañizares CA. An affine arithmetic-based energy management system for isolated microgrids. IEEE Trans Smart Grid. 2019; 10: 2989-2998.
- 57Wu J, Wang K, Zhang B, et al. Optimal economic dispatch model based on risk management for wind-integrated power system. IET Gen Transm Distrib. 2015; 9: 2152-2158.
- 58Xing H, Cheng H, Zhang L. Demand response based and wind farm integrated economic dispatch. CSEE J Power Energy Syst. 2015; 1(4): 37-41.
- 59Yang L, He M, Vittal V, Zhang J. Stochastic optimization-based economic dispatch and interruptible load management with increased wind penetration. IEEE Trans Smart Grid. 2016; 7(2): 730-739.
- 60Wang S, Yu D, Yu J, Zhang W, Foley A, Li K. Optimal generation scheduling of interconnected wind-coal intensive power systems. IET Gen Transm Distrib. 2016; 10: 3276-3287.
- 61Hedayati-Mehdiabadi M, Hedman KW, Zhang J. Reserve policy optimization for scheduling wind energy and reserve. IEEE Trans Power Syst. 2018; 33(1): 19-31.
- 62Yang S, Zeng D, Ding H, Yao J, Wang K, Li Y. Stochastic security-constrained economic dispatch for random responsive price-elastic load and wind power. IET Renew Power Gen. 2016; 10: 936-943.
- 63Zheng Y, Song Y, Hill DJ, Meng K. Online distributed MPC-based optimal scheduling for EV charging stations in distribution systems. IEEE Trans Indus Inform. 2019; 15(2): 638-649.
- 64Liang H, Liu Y, Li F, Shen Y. Dynamic economic/emission dispatch including PEVs for peak shaving and valley filling. IEEE Trans Ind Electron. 2019; 66(4): 2880-2890.
- 65Andervazh M, Javadi S. Emission-economic dispatch of thermal power generation units in the presence of hybrid electric vehicles and correlated wind power plants. IET Gener Transm Distrib. 2017; 11(9): 2232-2243.
- 66Wikner E, Thiringer T. Extending battery lifetime by avoiding high soc. Appl Sci. 2018; 8(10): 1-16.
- 67Basu AK, Bhattacharya A, Chowdhury S, Chowdhury SP. Planned scheduling for economic power sharing in a CHP-based micro-grid. IEEE Trans Power Syst. 2012; 27(1): 30-38.
- 68Kumar A, Deng Y, He X, Kumar P, Bansal RC. Energy management system controller for a rural microgrid. J Eng. 2017; 2017(13): 834-839.
10.1049/joe.2017.0447 Google Scholar
- 69Chen C, Duan S, Cai T, Liu B, Hu G. Smart energy management system for optimal microgrid economic operation. IET Renew Power Gen. 2011; 5(3): 258-267.
- 70Fonseca M, Bezerra UH, De Almeida Brito J, Leite JC, Nascimento MHR. Pre-dispatch of load in thermoelectric power plants considering maintenance management using fuzzy logic. IEEE Access. 2018; 6: 41379-41390.
- 71Lokeshgupta B, Sivasubramani S. Multi-objective dynamic economic and emission dispatch with demand side management. Int J Electr Power Energy Syst. 2018; 97: 334-343.
- 72Abdi H, Dehnavi E, Mohammadi F. Dynamic economic dispatch problem integrated with demand response (DEDDR) considering non-linear responsive load models. IEEE Trans Smart Grid. 2016; 7(6): 2586-2595.
- 73O'Connell N, Pinson P, Madsen H, O'Malley M. Economic dispatch of demand response balancing through asymmetric block offers. IEEE Trans Power Syst. 2016; 31(4): 2999-3007.
- 74Ming H, Xie L, Campi MC, Garatti S, Kumar PR. Scenario-based economic dispatch with uncertain demand response. IEEE Trans Smart Grid. 2019; 10(2): 1858-1868.
- 75Liu J, Li J. Interactive energy-saving dispatch considering generation and demand side uncertainties: a Chinese study. IEEE Trans Smart Grid. 2018; 9(4): 2943-2953.
- 76Xu Y, Zhang W, Liu W. Distributed dynamic programming-based approach for economic dispatch in smart grids. IEEE Trans Indus Inform. 2015; 11(1): 166-175.
- 77Binetti G, Davoudi A, Naso D, Turchiano B, Lewis FL. A distributed auction-based algorithm for the nonconvex economic dispatch problem. IEEE Trans Indus Inform. 2014; 10(2): 1124-1132.
- 78Elsayed WT, El-Saadany EF. A fully decentralized approach for solving the economic dispatch problem. IEEE Trans Power Syst. 2015; 30(4): 2179-2189.
- 79Hao R, Lu T, Wu Q, Chen X, Ai Q. Distributed piecewise approximation economic dispatch for regional power systems under non-ideal communication. IEEE Access. 2019; 7: 45533-45543.
- 80Li F, Qin J, Kang Y. Multi-agent system based distributed pattern search algorithm for non-convex economic load dispatch in smart grid. IEEE Trans Power Syst. 2019; 34(3): 2093-2102.
- 81Bai L, Ye M, Sun C, Hu G. Distributed economic dispatch control via saddle point dynamics and consensus algorithms. IEEE Trans Control Syst Technol. 2019; 27(2): 898-905.
- 82Mudumbai R, Dasgupta S, Cho BB. Distributed control for optimal economic dispatch of a network of heterogeneous power generators. IEEE Trans Power Syst. 2012; 27(4): 1750-1760.
- 83Trip S, De Persis C. Distributed optimal load frequency control with non-passive dynamics. IEEE Trans Control Netw Syst. 2018; 5: 1232-1244.
- 84Li Q, Gao DW, Zhang H, Wu Z, Wang F. Consensus-based distributed economic dispatch control method in power systems. IEEE Trans Smart Grid. 2019; 10(1): 941-954.
- 85Lü P, Zhao J, Yao J, Yang S. A decentralized approach for frequency control and economic dispatch in smart grids. IEEE J Emerg Sel Top Circ Syst. 2017; 7(3): 447-458.
- 86Srikantha P, Kundur D. Distributed optimization of dispatch in sustainable generation systems via dual decomposition. IEEE Trans Smart Grid. 2015; 6(5): 2501-2509.
- 87Shiwei X, Weiwei Z, Qian Z, Gengyin L. On-line decentralised economical dispatch for power system with highly penetrated uncertain renewables. J Eng. 2017; 2017(13): 1019-1023.
10.1049/joe.2017.0483 Google Scholar
- 88Cherukuri A, Cortés J. Distributed coordination of DERs with storage for dynamic economic dispatch. IEEE Trans Autom Control. 2018; 63(3): 835-842.
- 89Hu J, Chen MZQ, Cao J, Guerrero JM. Coordinated active power dispatch for a microgrid via distributed lambda iteration. IEEE J Emerg Sel Top Circ Syst. 2017; 7(2): 250-261.
- 90Liu W, Zhuang P, Liang H, Peng J, Huang Z. Distributed economic dispatch in microgrids based on cooperative reinforcement learning. IEEE Trans Neural Netw Learn Syst. 2018; 29(6): 2192-2203.
- 91Hamdi M, Chaoui M, Idoumghar L, Kachouri A. Coordinated consensus for smart grid economic environmental power dispatch with dynamic communication network. IET Gen Transm Distrib. 2018; 12: 2603-2613.
- 92Zhang Z, Chow M. Convergence analysis of the incremental cost consensus algorithm under different communication network topologies in a smart grid. IEEE Trans Power Syst. 2012; 27(4): 1761-1768.
- 93Wen G, Yu X, Liu Z, Yu W. Adaptive consensus-based robust strategy for economic dispatch of smart grids subject to communication uncertainties. IEEE Trans Indus Inform. 2018; 14(6): 2484-2496.
- 94Liang Y, Liu F, Mei S. Distributed real-time economic dispatch in smart grids: a state-based potential game approach. IEEE Trans Smart Grid. 2018; 9(5): 4194-4208.
- 95Ji T, Hong D, Zheng J, Wu Q, Yang X. Wind power forecast with error feedback and its economic benefit in power system dispatch. IET Gen Transm Distrib. 2018; 12: 5730-5738.