Abstract

Optimization encourages the economical and efficient operation of the electrical system. Most power system problems are nonlinear and nonconvex, and they frequently ask for the optimization of two or more diametrically opposed objectives. The numerical optimization revolution led to the introduction of numerous evolutionary algorithms (EAs). Most of these methods sidestep the problems of early convergence by searching the universe for the ideal. Because the field of EA is evolving, it may be necessary to reevaluate the usage of new algorithms to solve optimization problems involving power systems. The introduction of renewable energy sources into the smart grid of the present enables the emergence of novel optimization problems with an abundance of new variables. This study's primary purpose is to apply state-of-the-art variations of the differential evolution (DE) algorithm for single-objective optimization and selected evolutionary algorithms for multi-objective optimization issues in power systems. In this investigation, we employ the recently created metaheuristic algorithm known as the moth flow optimizer (MFO), which allows us to answer all five of the optimal power flow (OPF) difficulty objective functions: (1) reducing the cost of power generation (including stochastic solar and thermal power generation), (2) diminished power, (3) voltage variation, (4) emissions, and (5) reducing both the cost of power generating and emissions. Compared to the lowest figures provided by comparable approaches, MFO's cost of power production for IEEE-30 and IEEE-57 bus architectures is $888.7248 per hour and $31121.85 per hour, respectively. This results in hourly cost savings between 1.23% and 1.92%. According to the facts, MFO is superior to the other algorithms and might be utilized to address the OPF problem.

Keyword

Multi-objective economic-environmental power dispatch (MOEED), Probability density functions (PDFs), Combined cost and emission minimization, Success history-based adaptation technique of differential evolution (SHADE).

Cite this article

Alam MK, Sulaiman MH, Sayem MS, Khan R

Refference

[1][1]Risi BG, Riganti-fulginei F, Laudani A. Modern techniques for the optimal power flow problem: state of the art. Energies. 2022; 15(17):1-20.

[2][2]Ramesh S, Vydeki D. Rice disease detection and classification using deep neural network algorithm. In micro-electronics and telecommunication engineering: proceedings of 3rd ICMETE 2019 (pp. 555-66). Springer Singapore.

[3][3]Happ HH. Optimal power dispatch. IEEE Transactions on Power Apparatus and Systems. 1974; 3:820-30.

[4][4]Fioretto F, Mak TW, Van HP. Predicting AC optimal power flows: combining deep learning and Lagrangian dual methods. In proceedings of the AAAI conference on artificial intelligence 2020 (pp. 630-7).

[5][5]Fortenbacher P, Demiray T. Linear/quadratic programming-based optimal power flow using linear power flow and absolute loss approximations. International Journal of Electrical Power & Energy Systems. 2019; 107:680-9.

[6][6]Montoya OD, Gil-gtonzález W, Garces A. Sequential quadratic programming models for solving the OPF problem in DC grids. Electric Power Systems Research. 2019; 169:18-23.

[7][7]Mohamed AA, Kamel S, Hassan MH, Mosaad MI, Aljohani M. Optimal power flow analysis based on hybrid gradient-based optimizer with moth–flame optimization algorithm considering optimal placement and sizing of FACTS/wind power. Mathematics. 2022; 10(3):1-31.

[8][8]Obinwa CI, Nwabueze CA, Mbachu CB. Primal-dual interior-point technique for optimisation of 330kV power system on one variable. European Journal of Engineering and Technology Research. 2020; 5(2):165-70.

[9][9]Krishnasamy V. Genetic algorithm for solving optimal power flow problem with UPFC. International Journal of Software Engineering and Its Applications. 2011; 5(1):39-50.

[10][10]Singh A, Singh AD, Singh V. Optimal power flow solution of transmission line network of electric power system using genetic algorithm technique. International Research Journal of Engineering and Technology. 2018; 5(4):755-60.

[11][11]Anwer A, Almosawi AH. A dynamic optimal power flow of a power system based on genetic algorithm. Engineering and Technology Journal. 2022; 40(2):290-300.

[12][12]Duman S, Rivera S, Li J, Wu L. Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization. International Transactions on Electrical Energy Systems. 2020; 30(4):1-28.

[13][13]Widarsono K, Murdianto FD, Nur M, Mustofa A. Optimal power flow using particle swarm optimization for IEEE 30 bus. In journal of physics: conference series 2020 (pp. 1-9). IOP Publishing.

[14][14]Abido MA. Optimal power flow using particle swarm optimization. International Journal of Electrical Power & Energy Systems. 2002; 24(7):563-71.

[15][15]Wang Z, Menke JH, Schäfer F, Braun M, Scheidler A. Approximating multi-purpose AC optimal power flow with reinforcement trained artificial neural network. Energy and AI. 2022; 7:1-13.

[16][16]Moradi B, Kargar A, Abazari S. Transient stability constrained optimal power flow solution using ant colony optimization for continuous domains. IET Generation, Transmission & Distribution. 2022; 16(18):3734-47.

[17][17]Al-bahran LT, Abdulrasool AQ. Multi objective functions of constraint optimal power flow based on modified ant colony system optimization technique. In IOP conference series: materials science and engineering 2021(pp. 1-18). IOP Publishing.

[18][18]Suresh V, Janik P, Jasinski M. Metaheuristic approach to optimal power flow using mixed integer distributed ant colony optimization. Archives of Electrical Engineering. 2020; 69(2):335-48.

[19][19]Dwivedi D, Balasubbareddy M. Optimal power flow using hybrid ant lion optimization algorithm. Pramana Research Journal. 2019; 9(2):368-80.

[20][20]Addisu M, Salau AO, Takele H. Fuzzy logic based optimal placement of voltage regulators and capacitors for distribution systems efficiency improvement. Heliyon. 2021; 7(8):1-9.

[21][21]Belboul Z, Toual B, Kouzou A, Mokrani L, Bensalem A, Kennel R, et al. Multiobjective optimization of a hybrid PV/wind/battery/diesel generator system integrated in microgrid: a case study in Djelfa, Algeria. Energies. 2022; 15(10):1-30.

[22][22]Benzaouia M, Hajji B, Rabhi A, Mellit A, Benslimane A, Dubois AM. Energy management strategy for an optimum control of a standalone photovoltaic-batteries water pumping system for agriculture applications. In proceedings of the 2nd international conference on electronic engineering and renewable energy systems: ICEERE, Saidia, Morocco 2021 (pp. 855-68). Springer Singapore.

[23][23]Adetokun BB, Muriithi CM. Application and control of flexible alternating current transmission system devices for voltage stability enhancement of renewable-integrated power grid: a comprehensive review. Heliyon. 2021; 7(3):1-7.

[24][24]Sadat SA, Sahraei-ardakani M. Customized sequential quadratic programming for solving large-scale ac optimal power flow. In north American power symposium 2021 (pp. 1-6). IEEE.

[25][25]Yalman Y, Çelik Ö, Adnan TA, Bayindir KÇ. Optimum power flow by using interior point optimization method. Researcher. 2022; 2(2):131-8.

[26][26]Sun S, Li Y, Xu Z, Li M, Wang C. Research on AC/DC power flow optimization by using interior point method. In IOP conference series: materials science and engineering 2019 (pp. 1-10). IOP Publishing.

[27][27]Fang M, Xiang Y, Li J. Locational electricity–carbon price model: design and analysis. Energy Reports. 2022; 8:721-8.

[28][28]Wang Z, Anderson CL. A progressive period optimal power flow for systems with high penetration of variable renewable energy sources. Energies. 2021; 14(10):1-17.

[29][29]Hou Y, Gao H, Wang Z, Du C. Improved grey wolf optimization algorithm and application. Sensors. 2022; 22(10):1-19.

[30][30]Basetti V, Rangarajan SS, Shiva CK, Pulluri H, Kumar R, Collins RE, et al. Economic emission load dispatch problem with valve-point loading using a novel quasi-oppositional-based political optimizer. Electronics. 2021; 10(21):1-21.

[31][31]Li Q, Wang Z, Wei A. Research on optimal scheduling of wind-pv-hydro-storage power complementary system based on BAS algorithm. In IOP conference series: materials science and engineering 2019 (pp. 1-9). IOP Publishing.

[32][32]Bhende CN, Panda S, Mishra S, Narayanan A, Kaipia T, Partanen J. Optimal power flow management and control of grid connected photovoltaic-battery system. International Journal of Emerging Electric Power Systems. 2019; 20(5).1-16.

[33][33]Omotoso HO, Al-shaalan AM, Farh HM, Al-shamma’a AA. Techno-economic evaluation of hybrid energy systems using artificial ecosystem-based optimization with demand side management. Electronics. 2022; 11(2):1-22.

[34][34]Wais DS, Majeed WS. Performance of high voltage transmission line based on thyristor-control series compensator. Journal of Engineering and Sustainable Development. 2021; 25(5):26-38.

[35][35]Ashour A, Mohamad TI, Sopian K, Ludin NA, Alzahrani K, Ibrahim A. Performance optimization of a photovoltaic-diesel hybrid power system for Yanbu, Saudi Arabia. AIMS Energy. 2021; 9(6):1260-73.

[36][36]Biswas P, Suganthan P, Amaratunga G. Optimal power flow solutions using algorithm success history based adaptive differential evolution with linear population reduction. In international conference on systems, man, and cybernetics 2018 (pp. 249-54). IEEE.

[37][37]Sharma A, Sharma A, Averbukh M, Rajput S, Jately V, Choudhury S, et al. Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module. Energy Reports. 2022; 8:6576-92.

[38][38]Ruta W, Saulo M, Odero NA. Emission constrained economic dispatch using moth flame optimization and bat hybrid algorithm. International Journal of Engineering Research & Technology. 2018; 11(5):827–44.

[39][39]Alam MK, Sulaiman MH, Sayem MS, Imtiaz S, Khan R. Moth flame optimization for transmission loss minimization in optimal power flow using renewable energy. The 5th national conference for postgraduate research 2022 (pp. 1-6).

[40][40]Sulaiman MH, Mustaffa Z, Mohamad AJ, Saari MM, Mohamed MR. Optimal power flow with stochastic solar power using barnacles mating optimizer. International Transactions on Electrical Energy Systems. 2021; 31(5):1-19.

[41][41]Rahman SI, Hazari MR, Hani SU, Pathik BB, Mannan MA, Mahfuz A, et al. Primary frequency control of large-scale PV-connected multi-machine power system using battery energy storage system. International Journal of Power Electronics and Drive Systems. 2021; 12(3):1862-71.

[42][42]Peter G, Stonier AA, Gupta P, Gavilanes D, Vergara MM, Lung SJ. Smart fault monitoring and normalizing of a power distribution system using IoT. Energies. 2022; 15(21):1-22.

[43][43]Babu MT, Nei H, Kowser MA. Prospects and necessity of wind energy in Bangladesh for the forthcoming future. Journal of The Institution of Engineers (India): Series C. 2022; 103(4):913-29.

[44][44]Rabbani MG, Sattary CT, Mamun MRA, Rahman MM, Khan MNH. Performance analysis of non-renewable energy in Bangladesh. Indonesian Journal of Electrical Engineering and Computer Science. 2017; 5(2):290-8.

[45][45]Babayomi OO, Dahoro DA, Zhang Z. Affordable clean energy transition in developing countries: pathways and Technologies. Iscience. 2022; 25(5):1-6.

[46][46]Sakthivel VP, Sathya PD. Single and multi-area multi-fuel economic dispatch using a fuzzified squirrel search algorithm. Protection and Control of Modern Power Systems. 2021; 6(1):1-13.

[47][47]Mirjalili S. Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowledge-Based Systems. 2015; 89:228-49.