Samarjit PATNAIK, Manas Ranjan NAYAK, Meera VISWAVANDYA

Strategic integration of battery energy storage and photovoltaic at low voltage level considering multiobjective cost-benefit

Renewable energy sources, such as solar photovoltaic (PV) systems and battery energy storage systems (BESS), help reduce greenhouse gas emissions while fulfilling the world’s growing energy demand. The inclusion of BESS reduces the peak hour demand, and control of charging and discharging of BESS can be economical for distributors facing time-based energy pricing. This paper discusses a novel multiobjective Horse herd optimisation algorithm (MOHHOA) approach, which is inspired by the social behaviour of horses in herds for PV and BESS optimal allocation in the radial distribution system. The proposed algorithm combines multiple benefits like benefits from economic gain per day, reduction in $CO_2$ emission per day, and reduction in energy loss per day. IEEE 69-bus radial distribution system (RDS) is used for testing the suggested approach. The case studies and simulation results show that the suggested model effectively accommodated PV power generation in IEEE 69-bus RDS without violating any system constraints. Results indicate improvement in node voltage profiles, security margins and energy losses, and peak energy savings, and system characteristics as a whole, and there were significantly improved techno-economic performance of the distribution system.

PDF

___

[1] Wong L, Ramachandaramurthy V, Taylor P, Ekanayake J, Walker S et al. Review on the optimal placement, sizing and control of an energy storage system in the distribution network; Journal of Energy Storage 2019, 21: 489-504. doi: 10.1016/j.est.2018.12.015
[2] Pawan S, Lata G. An environmental based techno-economic assessment for battery energy storage system allocation in distribution system using new node voltage deviation sensitivity approach. International Journal of Electrical Power & Energy Systems 2021. 128. 106665. doi: 10.1016/j.ijepes.2020.106665
[3] Alam M, Arefifar S. Energy Management in Power Distribution Systems: Review, Classification, Limitations and Challenges; IEEE Access 2019, vol. 7, pp. 92979-93001. doi: 10.1109/ACCESS.2019.2927303
[4] Kalantari N, Hassas M, Pourhossein K. Bibliographic Review and Comparison of Optimal Sizing Methods for Hybrid Renewable Energy Systems; Journal of Energy Management and Technology 2018; 2 (2): 66-79. doi: 10.22109/jemt.2018.102306.1039
[5] Nick M, Cherkaoui R, Paolone M. Optimal Planning of Distributed Energy Storage Systems in Active Distribution Networks Embedding Grid Reconfiguration; IEEE Transactions on Power Systems 2018; 33 (2): 1577-1590 doi: 10.1109/TPWRS.2017.2734942
[6] Saboori H, Hemmati R. Maximizing DISCO profit in active distribution networks by optimal planning of energy storage systems and distributed generators; Renewable and Sustainable Energy Reviews 2017; 71: 365-372 doi: 10.1016/j.rser.2016.12.066
[7] Talent O, Du H, Optimal sizing and energy scheduling of photovoltaic-battery systems under different tariff structures; Renewable Energy 2018; 129: 513-526: doi: 10.1016/j.renene.2018.06.016
[8] Salehi J, Esmaeilpour S, Samadi F, Safari A. Risk Based Battery Energy Storage and Wind Turbine Allocation in Distribution Networks Using Fuzzy Modeling; Journal of Energy Management and Technology 2018; 2 (2): 53-65. doi: 10.22109/jemt.2018.104786.1046
[9] Mahdavi S, Hemmati R, Mehdi A. Two-level planning for coordination of energy storage systems and wind-solardiesel units in active distribution networks; Energy Elsevier 2018; 151: 954-965. doi: 10.1016/j.energy.2018.03.123
[10] Li Y, Feng B, Li G, Qi J, Zhao D et al. Optimal distributed generation planning in active distribution networks considering integration of energy storage; Applied Energy 2018; 210: 1073-1081 doi: 10.1016/j.apenergy.2017.08.008
[11] Mahmood D, Javaid N, Ahmed G, Khan S, Monteiro V. A review on optimization strategies integrating renewable energy sources focusing uncertainty factor – Paving path to eco-friendly smart cities; Sustainable Computing: Informatics and Systems 2021; 30: 100559 doi: 10.1016/j.suscom.2021.100559
[12] Li C, Yu X, Huang T, Chen G, He X. A Generalized Hopfield Network for Non-smooth Constrained Convex Optimization: Lie Derivative Approach; IEEE Transactions on Neural Networks and Learning Systems 2016; 27 (2): 308-321. doi: 10.1109/TNNLS.2015.2496658
[13] Li C, Yu X, Huang T, He X. Distributed Optimal Consensus Over Resource Allocation Network and Its Application to Dynamical Economic Dispatch; IEEE Transactions on Neural Networks and Learning Systems 2018; 29 (6): 2407- 2418. doi: 10.1109/TNNLS.2017.2691760
[14] Naeimi F, Azizyan G, Rashki M. Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems; Knowledge-Based Systems 2021; 213: 106711. doi: 10.1016/j.knosys.2020.106711
[15] Mikaeel A, Mohammed L, Shah D, Ryuto S, Atsushi Y et al. Optimal Multi-Configuration and Allocation of SVR, Capacitor, Centralized Wind Farm, and Energy Storage System: A Multi-Objective Approach in a Real Distribution Network; IET Renewable Power Generation 2019; 13 (5): 762-773 doi: 13.10.1049/iet-rpg.2018.5057
[16] Das CK, Bass O, Kothapalli G, Mahmoud T, Habibi D. Overview of energy storage systems in distribution networks: Placement, sizing, operation, and power quality; Renewable and Sustainable Energy Reviews 2018; 91:1205-1230 doi: 10.1016/j.rser.2018.03.068
[17] Duchaud J, Darras C, Notton G, Cyril V. Multi-Objective Particle Swarm Optimal Sizing of a Renewable Hybrid Power Plant with Storage; Renewable Energy 2018; 131:1156-1167 doi: 10.1016/j.renene.2018.08.058
[18] Bakhtiari H, Naghizadeh R. Multi-criteria optimal sizing of hybrid renewable energy systems including wind, photovoltaic, battery, and hydrogen storage with ϵ -constraint method; IET Renewable Power Generation 2018; 12: 883-892. doi: 10.1049/iet-rpg.2017.0706
[19] Sawle Y, Gupta S, Bohre A. Socio-techno-economic design of hybrid renewable energy system using optimization techniques; Renewable Energy 2018; 119: 459-472 doi: 10.1016/j.renene.2017.11.058
[20] Bhoi SK, Nayak MR, Kasturi K. Energy scheduling of grid-tied photovoltaic-wind-diesel-battery hybrid system for load management under real-time pricing mechanism using game theory; Turkish Journal of Computer and Mathematics Education 2021; 12 (6): 5033-5044.
[21] Bhoi SK, Nayak MR. Optimal scheduling of battery storage with grid tied PV systems for trade-off between consumer energy cost and storage health; Microprocessors and Microsystems 2020; 79: 103274. doi:10.1016/j.micpro.2020.103274
[22] Leou RC. An economic analysis model for the energy storage system applied to a distribution substation, International Journal of Electrical Power & Energy Systems 2012; 34 (132-137). doi:10.1016/j.ijepes.2011.09.016
[23] Jannesar M, Sedighi A, Savaghebi M, Guerrero J. Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration; Applied Energy 2018; 226: 957-966 doi: 10.1016/j.apenergy.2018.06.036
[24] Talaei A, Begg K, Jamasb T. The large scale roll-out of electric vehicles: the effect on the electricity sector and CO2 emissions, EPRG Working Paper 2012 doi:10.17863/CAM.1017
[25] Ali H, Khan FA. Attributed multi‐objective comprehensive learning particle swarm optimization for optimal security of networks. Applied Soft Computing. 2013; 13 (9):3903‐3921. doi:10.1016/j.asoc.2013.04.015
[26] Van Veldhuizen DA, Lamont GB. Evolutionary computation and convergence to a pareto front. In Late breaking papers at the genetic programming 1998 conference 1998 Jul 22 (pp. 221-228).
[27] Deb K, Pratap A, Agarwal S, Meyarivan TA. A fast and elitist multi-objective genetic algorithm: NSGA‐II. IEEE Transactions on Evolutionary Computation 2002;6 (2):182‐197. doi:10.1109/4235.996017