International Journal of Advanced Technology and Engineering Exploration (IJATEE) ISSN (P): 2394-5443 ISSN (O): 2394-7454 Vol - 8, Issue - 80, July 2021
  1. 1
    Google Scholar
Hybrid BIMASGO approach based optimal scheduling of renewable microgrid with multi-period islanding constraints

Kavitha Kumari KS and Samuel Rajesh Babu R

Abstract

In this manuscript, a hybrid system for the optimal microgrid programming with multi-period island constraints was proposed. The proposed hybrid method is the combined execution of the Buyer Inspired Metaheuristic Optimization Algorithm (BIMA) and Shell Game Optimization (SGO); hence it is named as BIMASGO approach. The BIMASGO approach was utilized for optimal microgrid programming and also considerably diminishes the computational load. The main objective of the proposed work is to diminish the cost of operating the microgrid, including the cost of operating the dispatchable units, the cost of transferring power from the main network, and the cost of inconvenience incurred by consumers. The cost of power transfer from the main grid should be positive or negative based on the direction of flow in the transmission line connecting the microgrid to the main grid. A negative cost represents an export of energy to the main grid, appears as an economic benefit for the microgrid. The cost of inconvenience represents the penalty in scheduling adjustable loads outside of consumer-specified time intervals. The constant penalty factor is utilized to prioritize loads with respect to sensitivity when operating at specified time intervals, where a higher value for the penalty factor denotes less flexible load based on time interval settings of the operation. The value of the penalty factor is chosen reasonably higher than the generation cost of the units and the market price. In the proposed system, the BIMASGO approach develops the evaluation procedure to establish the exact schedule of the microgrid combinations depends on the load side of the power range. In the proposed technique, the objective function is defined by the data of the system subject to equality and inequality restrictions. During the programming process, several actual constraints associated with adjustable charges, battery charge/discharge limitations, and the on/off time of dispatchable Distributed Energy Resources (DERs) were considered. The proposed method was implemented in the MATLAB / Simulink site. It was analysed and compared with different existing methods. The calculation time of BIMASGO and the existing methods were also discussed. The calculation time of the proposed method was 4.1 seconds.

Keyword

Microgrid, Scheduling, Constraints, Buyer inspired meta-heuristic optimization algorithm, Shell game optimization, Operating cost.

Cite this article

KS KK, Rajesh Babu SR

Refference

[1][1]Sefidgar-Dezfouli A, Joorabian M, Mashhour E. A multiple chance-constrained model for optimal scheduling of microgrids considering normal and emergency operation. International Journal of Electrical Power & Energy Systems. 2019; 112:370-80.

[2][2]Faridnia N, Habibi D, Lachowicz S, Kavousifard A. Optimal scheduling in a microgrid with a tidal generation. Energy. 2019; 171:435-43.

[3][3]Hemmati M, Mohammadi-ivatloo B, Abapour M, Anvari-moghaddam A. Optimal chance-constrained scheduling of reconfigurable microgrids considering islanding operation constraints. IEEE Systems Journal. 2020; 14(4):5340-9.

[4][4]Dong B, Jiao L, Wu J. Graph‐based hybrid hyper‐heuristic channel scheduling algorithm in multicell networks. Transactions on Emerging Telecommunications Technologies. 2017; 28(1).

[5][5]Aghdam FH, Kalantari NT, Mohammadi-ivatloo B. A chance-constrained energy management in multi-microgrid systems considering degradation cost of energy storage elements. Journal of Energy Storage. 2020.

[6][6]Wen Y, Chung CY, Liu X, Che L. Microgrid dispatch with frequency-aware islanding constraints. IEEE Transactions on Power Systems. 2019; 34(3):2465-8.

[7][7]Shah P, Mehta B. Microgrid optimal scheduling with renewable energy sources considering islanding constraints. Iranian Journal of Science and Technology, Transactions of Electrical Engineering. 2019; 44:805-19.

[8][8]Abadi M, Sadeghzadeh SM. A control approach with seamless transition capability for a single-phase inverter operating in a microgrid. International Journal of Electrical Power & Energy Systems. 2019; 111:475-85.

[9][9]Hussain A, Bui VH, Kim HM. Resilience-oriented optimal operation of networked hybrid microgrids. IEEE Transactions on Smart Grid. 2017; 10(1):204-15.

[10][10]Hussain A, Bui VH, Kim HM. Fuzzy logic-based operation of battery energy storage systems (BESSs) for enhancing the resiliency of hybrid microgrids. Energies. 2017; 10(3):1-9.

[11][11]Hussain A, Bui VH, Kim HM. A resilient and privacy-preserving energy management strategy for networked microgrids. IEEE Transactions on Smart Grid. 2016; 9(3):2127-39.

[12][12]Balasubramaniam K, Saraf P, Hadidi R, Makram EB. Energy management system for enhanced resiliency of microgrids during islanded operation. Electric Power Systems Research. 2016; 137:133-41.

[13][13]Zadsar M, Haghifam MR, Larimi SM. Approach for self-healing resilient operation of active distribution network with microgrid. IET Generation, Transmission & Distribution. 2017; 11(18):4633-43.

[14][14]Hussain A, Rousis AO, Konstantelos I, Strbac G, Jeon J, Kim HM. Impact of uncertainties on resilient operation of microgrids: a data-driven approach. IEEE Access. 2019; 7:14924-37.

[15][15]Dong J, Zhu L, Su Y, Ma Y, Liu Y, Wang F, et al. Battery and backup generator sizing for a resilient microgrid under stochastic extreme events. IET Generation, Transmission & Distribution. 2018; 12(20):4443-50.

[16][16]Gao H, Chen Y, Xu Y, Liu CC. Resilience-oriented critical load restoration using microgrids in distribution systems. IEEE Transactions on Smart Grid. 2016; 7(6):2837-48.

[17][17]Liu X, Shahidehpour M, Li Z, Liu X, Cao Y, Bie Z. Microgrids for enhancing the power grid resilience in extreme conditions. IEEE Transactions on Smart Grid. 2016; 8(2):589-97.

[18][18]Salehpour MJ, Tafreshi SM. The effect of price responsive loads uncertainty on the risk-constrained optimal operation of a smart micro-grid. International Journal of Electrical Power & Energy Systems. 2019; 106:546-60.

[19][19]Chanda S, Srivastava AK. Defining and enabling resiliency of electric distribution systems with multiple microgrids. IEEE Transactions on Smart Grid. 2016; 7(6):2859-68.

[20][20]Bektas Z, Kayalıca MO, Kayakutlu G. A hybrid heuristic algorithm for optimal energy scheduling of grid-connected micro grids. Energy Systems. 2020:1-17.

[21][21]Gu W, Wu Z, Bo R, Liu W, Zhou G, Chen W, et al. Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: a review. International Journal of Electrical Power & Energy Systems. 2014; 54:26-37.

[22][22]Belvedere B, Bianchi M, Borghetti A, Nucci CA, Paolone M, Peretto A. A microcontroller-based power management system for standalone microgrids with hybrid power supply. IEEE Transactions on Sustainable Energy. 2012; 3(3):422-31.

[23][23]Sobu A, Wu G. Dynamic optimal schedule management method for microgrid system considering forecast errors of renewable power generations. In international conference on power system technology 2012 (pp. 1-6). IEEE.

[24][24]Alqurashi A, Etemadi AH, Khodaei A. Treatment of uncertainty for next generation power systems: state-of-the-art in stochastic optimization. Electric Power Systems Research. 2016; 141:233-45.

[25][25]Carrión M, Arroyo JM. A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Transactions on Power Systems. 2006; 21(3):1371-8.

[26][26]Khodaei A. Microgrid optimal scheduling with multi-period islanding constraints. IEEE Transactions on Power Systems. 2013; 29(3):1383-92.

[27][27]Wang MQ, Gooi HB. Spinning reserve estimation in microgrids. IEEE Transactions on Power Systems. 2011; 26(3):1164-74.

[28][28]Rokni SG, Radmehr M, Zakariazadeh A. Optimum energy resource scheduling in a microgrid using a distributed algorithm framework. Sustainable Cities and Society. 2018; 37:222-31.

[29][29]Ebrahimi MR, Amjady N. Adaptive robust optimization framework for day-ahead microgrid scheduling. International Journal of Electrical Power & Energy Systems. 2019; 107:213-23.

[30][30]Vahedipour‐dahraei M, Najafi HR, Anvari‐moghaddam A, Guerrero JM. Security‐constrained unit commitment in AC microgrids considering stochastic price‐based demand response and renewable generation. International Transactions on Electrical Energy Systems. 2018; 28(9):1-26.

[31][31]Kiptoo MK, Lotfy ME, Adewuyi OB, Conteh A, Howlader AM, Senjyu T. Integrated approach for optimal techno-economic planning for high renewable energy-based isolated microgrid considering cost of energy storage and demand response strategies. Energy Conversion and Management. 2020; 215:112917.

[32][32]Banaei M, Rezaee B. Fuzzy scheduling of a non-isolated micro-grid with renewable resources. Renewable Energy. 2018; 123:67-78.

[33][33]Gazijahani FS, Ajoulabadi A, Ravadanegh SN, Salehi J. Joint energy and reserve scheduling of renewable powered microgrids accommodating price responsive demand by scenario: a risk-based augmented epsilon-constraint approach. Journal of Cleaner Production. 2020.

[34][34]Kumar RS, Raghav LP, Raju DK, Singh AR. Intelligent demand side management for optimal energy scheduling of grid connected microgrids. Applied Energy. 2021; 285:1-14.

[35][35]Wei J, Zhang Y, Wang J, Cao X, Khan MA. Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method. Applied Energy. 2020.

[36][36]Kumari KK, Babu RS. An efficient modified dragonfly algorithm and whale optimization approach for optimal scheduling of microgrid with islanding constraints. Transactions of the Institute of Measurement and Control. 2021; 43(2):421-33.

[37][37]Sefidgar‐dezfouli A, Davatgaran V. Smart microgrid optimal scheduling with stable and economic islanding capability using optimal load contribution as spinning reserve. International Transactions on Electrical Energy Systems. 2020; 30(11).

[38][38]Aghdam FH, Kalantari NT, Mohammadi-ivatloo B. A stochastic optimal scheduling of multi-microgrid systems considering emissions: a chance constrained model. Journal of Cleaner Production. 2020.

[39][39]Lee J, Lee S, Lee K. Multistage stochastic optimization for microgrid operation under islanding uncertainty. IEEE Transactions on Smart Grid. 2020; 12(1):56-66.

[40][40]Vahedipour-dahraie M, Rashidizadeh-kermani H, Anvari-moghaddam A, Siano P. Flexible stochastic scheduling of microgrids with islanding operations complemented by optimal offering strategies. CSEE Journal of Power and Energy Systems. 2020; 6(4):867-77.

[41][41]Jafari A, Ganjehlou HG, Khalili T, Bidram A. A fair electricity market strategy for energy management and reliability enhancement of islanded multi-microgrids. Applied Energy. 2020; 270:115170.

[42][42]Lei H, Huang S, Liu Y, Zhang T. Robust optimization for microgrid defense resource planning and allocation against multi-period attacks. IEEE Transactions on Smart Grid. 2019; 10(5):5841-50.

[43][43]Liu Y, Guo L, Hou R, Wang C, Wang X. A hybrid stochastic/robust-based multi-period investment planning model for island microgrid. International Journal of Electrical Power & Energy Systems. 2021.

[44][44]Elrayyah A, Cingoz F, Sozer Y. Construction of nonlinear droop relations to optimize islanded microgrid operation. IEEE Transactions on Industry Applications. 2015; 51(4):3404-13.

[45][45]Kim HM, Kinoshita T, Shin MC. A multiagent system for autonomous operation of islanded microgrids based on a power market environment. Energies. 2010; 3(12):1972-90.

[46][46]Debnath S, Arif W, Baishya S. Buyer inspired meta-heuristic optimization algorithm. Open Computer Science. 2020; 10(1):194-219.