ارائه برنامه بهینه پخش بار واحدهای حرارتی در سیستم قدرت در حضور مزرعه بادی با ضریب نفوذپذیری بالا و بارهای انعطاف پذیر
علی حیدری
1
(
گروه برق، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی واحد گرگان، گرگان، ایران
)
رضا ابراهیمی
2
(
گروه مهندسی برق، واحد گرگان، دانشگاه آزاد اسلامی، ایران
)
محمود قنبری
3
(
گروه مهندسی برق، واحد گرگان، دانشگاه آزاد اسلامی، گرگان، ایران
)
کلید واژه: پخش بار بهینه واحدهای حرارتی, مزرعه بادی, بارهای انعطاف پذیر, الگوریتم جستجوی صاعقه,
چکیده مقاله :
در این مقاله، یک مدل تصادفی برای پخشبار اقتصادی (ED) در سیستم قدرت با حضور منابع تجدیدپذیر و بارهای انعطافپذیر پیشنهاد شده است که عدمقطعیتهای مرتبط را نیز در نظر میگیرد. این مدل، سناریو محور بوده و از شبیهسازی مونت کارلو برای تعیین سناریوها با ضرایب وزنی منحصر به هر سناریو بهره میبرد. برای حل مسئله بهینهسازی، یک الگوریتم ترکیبی جدید با نام hLSA-PSO توسعه یافته است که در آن پارامترهای الگوریتم ازدحام ذرات (PSO) توسط الگوریتم جستجوی صاعقه (LSA) تنظیم میشوند. کارایی این الگوریتم با مقایسه نتایج به دست آمده از حل توابع محک با الگوریتمهای GGO، PSO، و LSA تأیید شده است. بررسیهای شبیهسازی نشان میدهند که مدل پیشنهادی در سه حالت مختلف اجرا شده است: بدون در نظر گرفتن عدمقطعیت و پاسخ به بار، با لحاظ عدمقطعیتها بدون مشارکت بارهای انعطافپذیر، و با در نظر گرفتن هر دو عامل. نتایج نشان داد که در نظر گرفتن عدمقطعیتها هزینههای بهرهبرداری را به میزان ۲.۶٪ افزایش میدهد و همچنین به دلیل عدمقطعیت، سهم منابع بادی در تأمین بار کاهش مییابد. از سوی دیگر، مشارکت بارهای انعطافپذیر باعث کاهش تولید در ساعات اوج و کاهش هزینهها شده است. الگوریتم hLSA-PSO در تمامی موارد عملکرد بهتری نسبت به سایر روشها داشته است.
چکیده انگلیسی :
This paper proposes a stochastic model for Economic Dispatch (ED) in power systems incorporating renewable energy sources and flexible loads, taking into account associated uncertainties. The model is scenario-based and utilizes Monte Carlo simulation to determine scenarios with unique weighting factors for each scenario. To solve the optimization problem, a new hybrid algorithm named hLSA-PSO is developed, in which the parameters of the Particle Swarm Optimization (PSO) algorithm are adjusted by the Lightning Search Algorithm (LSA). The efficiency of this algorithm is validated by comparing results obtained from solving benchmark functions with GGO, PSO, and LSA algorithms. Simulation studies show that the proposed model is executed in three different modes: without considering uncertainties and load response, considering uncertainties without the participation of flexible loads, and considering both factors. The results indicate that considering uncertainties increases operational costs by 2.6%, and due to uncertainties, the share of wind resources in meeting the load is reduced. On the other hand, the participation of flexible loads reduces production during peak hours and decreases costs. The hLSA-PSO algorithm outperforms other methods in all cases.
[1] Marzbani, F. and Abdelfatah, A., 2024. Economic dispatch optimization strategies and problem formulation: A comprehensive review. Energies, 17(3), p.550.
[2] Bhavsar, S., Pitchumani, R., Maack, J., Satkauskas, I., Reynolds, M. and Jones, W., 2024. Stochastic economic dispatch of wind power under uncertainty using clustering-based extreme scenarios. Electric Power Systems Research, 229, p.110158.
[3] Dehvan, M., Mozafari, B., Muchehkhorti, D.S.S. and Vatani, D.M., Economic Dispatch Optimization in Multi-Area Power Distribution Considering Wind Farms and Hydro Power Plants. Mehrnoosh, Economic Dispatch Optimization in Multi-Area Power Distribution Considering Wind Farms and Hydro Power Plants.
[4] Gholami Dehbalaee, M.R., Shaeisi, G.H. and Valizadeh, M., 2020. A novel exclusive binary search algorithm to solve the nonlinear economic dispatch problem. Journal of Energy Management and Technology, 4(3), pp.48-56.
[5] Postolov, B. and Iliev, A., 2022. New metaheuristic methodology for solving security constrained hydrothermal unit commitment based on adaptive genetic algorithm. International Journal of Electrical Power & Energy Systems, 134, p.107163.
[6] Zou, J., Ahmed, S. and Sun, X.A., 2018. Multistage stochastic unit commitment using stochastic dual dynamic Putz, D., Schwabeneder, D., Auer, H. and Fina, B., 2021. A comparison between mixed-integer linear programming and dynamic programming with state prediction as novelty for solving unit commitment. International Journal of Electrical Power & Energy Systems, 125, p.106426.
[7] Zhang, X., Liu, Y., Zhao, J., Liu, J., Korkali, M. and Chen, X., 2022. Short‐circuit current constrained unit commitment and transmission switching model for improving renewable integration: An MILP formulation. IET Generation, Transmission & Distribution, 16(9), pp.1743-1755.
[8] Feng, Z.K., Niu, W.J., Wang, W.C., Zhou, J.Z. and Cheng, C.T., 2019. A mixed integer linear programming model for unit commitment of thermal plants with peak shaving operation aspect in regional power grid lack of flexible hydropower energy. Energy, 175, pp.618-629.
[9] Nozarian, M., Seifi, H., Sheikh-El-Eslami, M.K. and Delkhosh, H., 2022. Hydro Thermal Unit Commitment involving Demand Response resources: a MILP formulation. Electrical Engineering, pp.1-18.
[10] Srikanth, K., Panwar, L.K., Panigrahi, B.K., Herrera-Viedma, E., Sangaiah, A.K. and Wang, G.G., 2018. Meta-heuristic framework: quantum inspired binary grey wolf optimizer for unit commitment problem. Computers & Electrical Engineering, 70, pp.243-260.
[11] Reddy K, S., Panwar, L., Panigrahi, B.K. and Kumar, R., 2019. Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets. Engineering Optimization, 51(3), pp.369-389.
[12] Navin, N.K. and Sharma, R., 2019. A fuzzy reinforcement learning approach to thermal unit commitment problem. Neural Computing and Applications, 31(3), pp.737-750.
[13] Panossian, N.V., McLarty, D. and Taylor, M.E., 2019, April. Artificial Neural Network for Unit Commitment on Networks with Significant Energy Storage. In 2019 IEEE Green Technologies Conference (GreenTech) (pp. 1-5). IEEE.
[14] Koltsaklis, N.E. and Dagoumas, A.S., 2018. Incorporating unit commitment aspects to the European electricity markets algorithm: An optimization model for the joint clearing of energy and reserve markets. Applied energy, 231, pp.235-258.
[15] Bakirtzis, E.A., Simoglou, C.K., Biskas, P.N. and Bakirtzis, A.G., 2018. Storage management by rolling stochastic unit commitment for high renewable energy penetration. Electric Power Systems Research, 158, pp.240-249.
[16] Deka, D. and Datta, D., 2019. Optimization of unit commitment problem with ramp-rate constraint and wrap-around scheduling. Electric Power Systems Research, 177, p.105948.
[17] Poncelet, K., Delarue, E. and D’haeseleer, W., 2020. Unit commitment constraints in long-term planning models: Relevance, pitfalls and the role of assumptions on flexibility. Applied Energy, 258, p.113843.
[18] Zhai, Y., Liao, X., Mu, N. and Le, J., 2020. A two-layer algorithm based on PSO for solving unit commitment problem. Soft Computing, 24(12), pp.9161-9178.
[19] Du, E., Zhang, N., Hodge, B.M., Wang, Q., Lu, Z., Kang, C., Kroposki, B. and Xia, Q., 2018. Operation of a high renewable penetrated power system with CSP plants: A look-ahead stochastic unit commitment model. IEEE Transactions on Power Systems, 34(1), pp.140-151.
[20] van Ackooij, W., Finardi, E.C. and Ramalho, G.M., 2018. An exact solution method for the hydrothermal unit commitment under wind power uncertainty with joint probability constraints. IEEE Transactions on Power Systems, 33(6), pp.6487-6500.
[21] Du, E., Zhang, N., Hodge, B.M., Wang, Q., Lu, Z., Kang, C., Kroposki, B. and Xia, Q., 2018. Operation of a high renewable penetrated power system with CSP plants: A look-ahead stochastic unit commitment model. IEEE Transactions on Power Systems, 34(1), pp.140-151.
[22] van Ackooij, W., Finardi, E.C. and Ramalho, G.M., 2018. An exact solution method for the hydrothermal unit commitment under wind power uncertainty with joint probability constraints. IEEE Transactions on Power Systems, 33(6), pp.6487-6500.
[23] Wang, M.Q., Yang, M., Liu, Y., Han, X.S. and Wu, Q., 2019. Optimizing probabilistic spinning reserve by an umbrella contingency constrained unit commitment. International Journal of Electrical Power & Energy Systems, 109, pp.187-197.
[24] Du, Y.F., Li, Y.Z., Duan, C., Gooi, H.B. and Jiang, L., 2020. An Adjustable Uncertainty Set Constrained Unit Commitment with Operation Risk Reduced through Demand Response. IEEE Transactions on Industrial Informatics.
[25] Jabari, F., Mohammadpourfard, M. and Mohammadi-Ivatloo, B., 2020. Implementation of Demand Response Programs on Unit Commitment Problem. In Demand Response Application in Smart Grids (pp. 37-54). Springer, Cham.
[26] Roukerd, S.P., Abdollahi, A. and Rashidinejad, M., 2020. Uncertainty-based unit commitment and construction in the presence of fast ramp units and energy storages as flexible resources considering enigmatic demand elasticity. Journal of Energy Storage, 29, p.101290.
[27] Rajamand, S., 2020. Effect of demand response program of loads in cost optimization of microgrid considering uncertain parameters in PV/WT, market price and load demand. Energy, 194, p.116917.
[28] Su, J., Dehghanian, P. and Lejeune, M.A., 2022. Price‐based unit commitment with decision‐dependent uncertainty in hourly demand. IET Smart Grid, 5(1), pp.12-24.
[29] Malekshah, S., Banihashemi, F., Daryabad, H., Yavarishad, N. and Cuzner, R., 2022. A zonal optimization solution to reliability security constraint unit commitment with wind uncertainty. Computers and Electrical Engineering, 99, p.107750.
[30] Nagarajan, K., Rajagopalan, A., Bajaj, M., Sitharthan, R., Dost Mohammadi, S.A. and Blazek, V., 2024. Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management. Scientific Reports, 14(1), p.3091.
[31] Hoseinzadeh, S., Garcia, D.A. and Huang, L., 2023. Grid-connected renewable energy systems flexibility in Norway islands’ Decarbonization. Renewable and Sustainable Energy Reviews, 185, p.113658. [32] Xu, M., Li, Q., Zhao, Z. and Sun, C., 2024. Bilinear-DRTFT: Uncertainty prediction in electricity load considering multiple demand responses. Energy, 309, p.133067.
[33] Ramezani, M., Choe, D.E., Heydarpour, K. and Koo, B., 2023. Uncertainty models for the structural design of floating offshore wind turbines: A review. Renewable and Sustainable Energy Reviews, 185, p.113610.
[34] Sukumar, B., Aslam, S., Karthikeyan, N. and Rajesh, P., 2024. A hybrid BCMPO technique for optimal scheduling of electric vehicle aggregators under market price uncertainty. IETE Journal of Research, 70(3), pp.2974-2988.
[35] Wang, L., Xiao, T., Liu, S., Zhang, W., Yang, B. and Chen, L., 2023. Quantification of model uncertainty and variability for landslide displacement prediction based on Monte Carlo simulation. Gondwana Research, 123, pp.27-40.
[36] Marzbani, F. and Abdelfatah, A., 2024. Economic dispatch optimization strategies and problem formulation: A comprehensive review. Energies, 17(3), p.550.
[37] Du, W., Ma, J. and Yin, W., 2023. Orderly charging strategy of electric vehicle based on improved PSO algorithm. Energy, 271, p.127088.
[38] Tao, Y., Mo, L., Yang, Y., Liu, Z., Liu, Y. and Liu, T., 2023. Optimization of Cascade Reservoir Operation for Power Generation, Based on an Improved Lightning Search Algorithm. Water, 15(19), p.3417.
[39] Lubinski, T., Coffrin, C., McGeoch, C., Sathe, P., Apanavicius, J., Bernal Neira, D. and Quantum Economic Development Consortium, 2024. Optimization applications as quantum performance benchmarks. ACM Transactions on Quantum Computing, 5(3), pp.1-44.
[40] El-Kenawy, E.S.M., Khodadadi, N., Mirjalili, S., Abdelhamid, A.A., Eid, M.M. and Ibrahim, A., 2024. Greylag goose optimization: nature-inspired optimization algorithm. Expert Systems with Applications, 238, p.122147.
[41] Kumar, L., Pandey, M. and Ahirwal, M.K., 2023. Parallel global best-worst particle swarm optimization algorithm for solving optimization problems. Applied Soft Computing, 142, p.110329.
[42] Qu, C., Peng, X. and Zeng, Q., 2024. Learning search algorithm: framework and comprehensive performance for solving optimization problems. Artificial Intelligence Review, 57(6), p.139.
[43] Afroozeh, M., Abdolmohammadi, H. and Nazari, M.E., 2024. Apply a mutation in gray wolf optimization algorithm to solve the economic-environmental dispatch problem of integrated power plants including thermal and wind. Journal of Intelligent Procedures in Electrical Technology, 14(56), pp.59-76.
[44] Alhasnawi, B.N., Jasim, B.H., Bureš, V., Sedhom, B.E., Alhasnawi, A.N., Abbassi, R., Alsemawai, M.R.M., Siano, P. and Guerrero, J.M., 2023. A novel economic dispatch in the stand-alone system using improved butterfly optimization algorithm. Energy Strategy Reviews, 49, p.101135.
[45] Ozkaya, B., Duman, S., Kahraman, H.T. and Guvenc, U., 2024. Optimal solution of the combined heat and power economic dispatch problem by adaptive fitness-distance balance based artificial rabbits optimization algorithm. Expert Systems with Applications, 238, p.122272.
[46] Habib, S., Kamarposhti, M.A., Shokouhandeh, H. and Colak, I., 2023. Economic dispatch optimization considering operation cost and environmental constraints using the HBMO method. Energy Reports, 10, pp.1718-1725.