GIDROISSIQLIK ENERGETIKA TIZIMLARINING HOLATLARINI EVOLYUTSION ALGORITMLAR YORDAMIDA OPTIMALLASHTIRISH

Educational Research in Universal Sciences ISSN: 2181-3515 VOLUME 4 | SPECIAL ISSUE 12 | 2025 https://t.me/Erus_uz Multidisciplinary Scientific Journal October, 2025 12 DOI: https://10.5281/zenodo.17308122 GIDROISSIQLIK ENERGETIKA TIZIMLARINING HOLATLARINI EVOLYUTSION ALGORITMLAR YORDAMIDA OPTIMALLASHTIRISH Pulatov Behzod Mannonovich PhD, dots., Toshkent davlat texnika universiteti, Toshkent, 100095, Oʻzbekiston; E-mail: [email protected] https://orcid.org/0000-0002-7008-4639 Dolzarbligi: bugungi kunda gidroissiqlik energetika tizimlari (GIET) muhim energiya manbai bo‘lib, ularning samaradorligini oshirish energiya resurslaridan optimal foydalanishni ta’minlaydi. Ushbu maqolada GIET holatlarini evolyutsion algoritmlar, xususan Tadqiqotda algoritmlarning matematik modellashtirish asosida ishlash prinsiplari, maqsadli funksiyani minimallashtirish (yoki maksimallashtirish) holatida samaradorlik ko‘rsatkichlari, shuningdek, ularni real tizimlarga tatbiq qilish imkoniyatlari tahlil qilingan. Maqsad gidroissiqlik energetika tizimlarining holatlarini evolyutsion algoritmlar yordamida optimallashtirishni tadqiq qilish, ularning afzalliklarini aniqlash va amaliy qo‘llanilish imkoniyatlarini baholashdan iborat. Usullari: tadqiqotda genetik algoritm (GA), zarracha to‘plami optimallashtirish (PSO) va differensial evolyutsiya (DE) kabi usullar yordamida optimallashtirish masalasini MATLAB dasturiy tizimidan foydalanilib, olingan natijalar aniqlik va samaradorlik bo‘yicha taqqoslanib, tahlil qilindi. Natijalar: evolyutsion algoritmlar yordamida GIET tizimlarini optimallashtirish energiya tizimining barqarorligi va samaradorligini oshirishda katta imkoniyat yaratadi. Ushbu algoritmlar real vaqt rejimida moslashuvchan boshqaruv tizimlarini yaratishda foydali bo‘lishi mumkin. Kelajakda AI asosidagi gibrid algoritmlar orqali yanada aniqroq natijalarga erishish mumkinligi ko‘rsatilgan. Kalit so‘zlar: gidroissiqlik energetika, optimallashtirish, evolyutsion algoritmlar, genetik algoritm, zarracha to‘plami optimallashtirish, differensial evolyutsiya. Educational Research in Universal Sciences ISSN: 2181-3515 VOLUME 4 | SPECIAL ISSUE 12 | 2025 https://t.me/Erus_uz Multidisciplinary Scientific Journal October, 2025 13 ОПТИМИЗАЦИЯ РЕЖИМОВ ГИДРОТЕПЛОВЫХ ЭНЕРГЕТИЧЕСКИХ СИСТЕМ С ИСПОЛЬЗОВАНИЕМ ЭВОЛЮЦИОННЫХ АЛГОРИТМОВ Пулатов Беҳзод Маннонвич PhD, доц., Ташкентский государственный технический университет, Ташкент, 100095, Узбекистан; E-mail: [email protected] https://orcid.org/0000-0002-7008-4639 Актуальность: в настоящее время гидротермические энергетические системы (ГТЭС) являются важным источником энергии, и повышение их эффективности обеспечивает оптимальное использование энергетических ресурсов. В данной статье рассматриваются состояния ГТЭС с использованием эволюционных алгоритмов, в частности, на основе математического моделирования принципов работы алгоритмов, показателей эффективности при минимизации (или максимизации) целевой функции, а также возможности их применения к реальным системам. Цель: исследование оптимизации состояний гидротермических энергетических систем с использованием эволюционных алгоритмов, определение их преимуществ и оценка возможностей практического применения. Методы: в исследовании задачи оптимизации решались с использованием таких методов, как генетический алгоритм (GA), оптимизация роя частиц (PSO) и дифференциальная эволюция (DE), с применением программной системы MATLAB. Полученные результаты были сопоставлены по точности и эффективности и проанализированы. Результаты: оптимизация ГТЭС с помощью эволюционных алгоритмов предоставляет большие возможности для повышения стабильности и эффективности энергетической системы. Эти алгоритмы могут быть полезны при создании адаптивных систем управления в режиме реального времени. В будущем возможно достижение ещё более точных результатов с помощью гибридных алгоритмов на базе искусственного интеллекта. Ключевые слова: гидротермическая энергетика, оптимизация, эволюционные алгоритмы, генетический алгоритм, оптимизация роя частиц, дифференциальная эволюция. Educational Research in Universal Sciences ISSN: 2181-3515 VOLUME 4 | SPECIAL ISSUE 12 | 2025 https://t.me/Erus_uz Multidisciplinary Scientific Journal October, 2025 14 Relevance: nowadays, hydrothermal energy systems (HTES) are a vital source of energy, and improving their efficiency ensures optimal use of energy resources. This article analyzes the states of HTES using evolutionary algorithms, particularly based on mathematical modeling of algorithm functioning, performance indicators in the context of minimizing (or maximizing) the objective function, as well as their applicability to real systems. OPTIMIZATION OF HYDROTHERMAL ENERGY SYSTEM STATES USING EVOLUTIONARY ALGORITHMS Behzod M. Pulatov PhD., assoc.prof,, Tashkent State Technical University, Tashkent, 100095, Uzbekistan; E-mail: b.pulatov[email protected]om https://orcid.org/0000-0002-7008-4639 Aim: to investigate the optimization of hydrothermal energy system states using evolutionary algorithms, identify their advantages, and assess their practical implementation potential. Methods: the research addressed optimization problems using methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE), utilizing MATLAB software. The obtained results were compared in terms of accuracy and efficiency and analyzed. Results: optimization of HTES through evolutionary algorithms creates significant opportunities to enhance the stability and efficiency of energy systems. These algorithms may be useful in developing adaptive control systems operating in real-time. It is suggested that hybrid algorithms based on AI may lead to even more accurate results in the future. Keywords: hydrothermal energy, optimization, evolutionary algorithms, genetic algorithm, particle swarm optimization, differential evolution. international experience and methods of comparative analysis of demand management are used. 1. Kirish (Introduction) Bugungi kunda energiya tizimlarining samaradorligini oshirish, ularning barqaror ishlashini ta’minlash hamda tabiiy resurslardan oqilona foydalanish dolzarb muammolardan biri hisoblanadi. Gidroissiqlik energetika tizimlari (GIET) — gidroelektr stansiyalar va issiqlik elektr stansiyalarining kombinatsiyasi bo‘lib, Educational Research in Universal Sciences ISSN: 2181-3515 VOLUME 4 | SPECIAL ISSUE 12 | 2025 https://t.me/Erus_uz Multidisciplinary Scientific Journal October, 2025 15 ularning integratsiyalashgan ishlashini optimallashtirish katta energiya tejamkorligini keltirib chiqarishi mumkin. Bunday tizimlarni optimallashtirish murakkab, noaniq va ko‘p parametrli masala bo‘lib, an’anaviy matematik metodlar har doim ham samarali natija bermaydi. Shu sababli evolyutsion algoritmlar, ya’ni biologik evolyutsiya prinsiplariga asoslangan usullar, bu borada katta imkoniyatlarga ega.[1,2] Gidroissiqlik energetika tizimlari va ularning boshqaruvi. IET quyidagi asosiy elementlardan tashkil topgan: • Gidroelektr stansiyalar (GES). • Issiqlik elektr stansiyalar (IES). • Energiyani taqsimlash va balanslash tizimi. Ularning samarali ishlashi uchun quyidagi cheklovlar va maqsadli funksiyalar hisobga olinadi: • Ishlab chiqarilgan quvvatni maksimal qilish. • Yo‘qotishlarni minimallashtirish. • Suv resursidan oqilona foydalanish. • Issiqlik energiyasini optimal taqsimlash. Evolyutsion algoritmlar va ularning mohiyati. Evolyutsion algoritmlar — bu optimallashtirishning global yechimlarini topishga mo‘ljallangan probabilistik (tasodifiy) usullardir. Ular tabiiy tanlanish, mutatsiya, krossover va tanlov mexanizmlariga asoslanadi [1,3]. Genetik algoritm (GA): • Populyatsiya yaratish. • Moslashuv funksiyasi orqali baholash. • Krossover (urug‘lanish) va mutatsiya orqali yangi avlodlar hosil qilish. • Iteratsion takrorlash. Zarracha to‘plami optimallashtirish (PSO): • Har bir zarracha pozitsiyasi va tezligini yangilash. • Jamoaviy va individual eng yaxshi yechimlarni hisobga olish. • Global konvergensiyaga erishish. Differensial evolyutsiya (DE): • Vektorlar asosida farqni hisoblash. • Krossover va selektsiya mexanizmlaridan foydalanish. • Yaxshi yechimlar tomon harakat. 2. Materiallar va usullar (Methods and materials) Optimallashtirishda quyidagi matematik model ishlatiladi: Sutka davomida IESlarda yoqilg‘i sarfi bilan bog‘liq xarajatlar: Educational Research in Universal Sciences ISSN: 2181-3515 VOLUME 4 | SPECIAL ISSUE 12 | 2025 https://t.me/Erus_uz Multidisciplinary Scientific Journal October, 2025 16 min;)( 24 1 1 →=  = =t n iitit PBB (1) minimallashtirilsin, quyidagi chegaraviy shartlarni e’tiborga olgan holda: energetika tizimida aktiv quvvat balansi bo‘yicha: ;24,...,2,1, 1 1   = = =+=+ n i m jtнtjtit tPPP  (2) rostlanuvchan stansiyalarning minimal va maksimal bera olishi mumkin bo‘lgan quvvatlari bo‘yicha: ,24...,,2,1,...,,2,1, maxmin == tniPPP ititit (3) ;24...,,2,1,...,,2,1, maxmin == tmjPPP jtjtjt (4) optimallashda qatnashuvchi GESlarda sutka davomidagi suv sarfi bo‘yicha: .,...,2,1,0),( 24 1  = ==−= tjзjtjtjtjmjQPHQQ (5) 3. Natijalar (Results) Sodda misol orqali hisoblashlarni amalga oshirsak. Faraz qilaylik, gidroissiqlik tizimi quyidagi ikki elektr stansiyadan iborat: • GES (1-stansiya): Quvvat: P1∈[20,100] MW; • IES (2-stansiya): Quvvat: P2∈[50,200] MW; • Umumiy yuklama: P1+P2=250 MW. 1-jadval. Algoritmlar bo‘yicha hisoblash Algoritm Topilgan optimal quvvatlar Iteratsiyalar soni Afzalliklari Tavsiya etiladi GA (Genetik algoritm) P1=80.2 P2=169.8 100 Kuchli global qidiruv Katta tizimlar uchun PSO (Zarracha to‘plami) P1=81.1 P2=168.9 70 Tez konvergensiya Real vaqt boshqaruv uchun DE (Differensial evolyutsiya) P1=80.9 P2=169.1 80 Barqaror va moslashuvchan Noto‘g‘ri modellarda foydali 4. Xulosa (Conclusion) Kichik va o‘rta tizimlar uchun PSO eng samarali natija beradi. Noaniqlik va murakkablik ko‘p bo‘lgan holatlarda DE foydalanish tavsiya etiladi. GA esa struktura o‘zgarishi mumkin bo‘lgan modellar uchun yaxshi ishlaydi. Evolyutsion algoritmlar yordamida GIET tizimlarini optimallashtirish energiya tizimining barqarorligi va samaradorligini oshirishda katta imkoniyat yaratadi. Ushbu algoritmlar real vaqt rejimida moslashuvchan boshqaruv tizimlarini yaratishda foydali Educational Research in Universal Sciences ISSN: 2181-3515 VOLUME 4 | SPECIAL ISSUE 12 | 2025 https://t.me/Erus_uz Multidisciplinary Scientific Journal October, 2025 17 bo‘lishi mumkin. Kelajakda AI asosidagi gibrid algoritmlar orqali yanada aniqroq natijalarga erishish mumkin. FOYDALANILGAN ADABIYOTLAR RO‘YXATI: (REFERENCES) 1. Goldberg D.E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989. 2. Kennedy J., Eberhart R. Particle Swarm Optimization, Proc. of IEEE International Conf. on Neural Networks, 1995. 3. Price K., Storn R., Lampinen J. Differential Evolution: A Practical Approach to Global Optimization, Springer, 2005. 4. Sargsyan A. va boshq. 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Evolutionary Programming Based Optimal Power Flow Algorithm. IEEE Transaction on power Systems 14(4).