research-focus Research Focus International Scientific Journal Research Focus 2181-3833 Ilm-fan va innovatsiyalar akademiyasi 10.66073/researchfocus.v4i10.1794 REL-A-013111 05.00.00 LOAD FORECASTING AND EQUIPMENT OPTIMIZATION IN RENEWABLE ENERGY INTEGRATION ПРОГНОЗИРОВАНИЕ НАГРУЗКИ И ОПТИМИЗАЦИЯ ОБОРУДОВАНИЯ ПРИ ИНТЕГРАЦИИ ВОЗОБНОВЛЯЕМЫХ ИСТОЧНИКОВ ЭНЕРГИИ QAYTA TIKLANUVCHI ENERGIYA ULANISHIDA YUKLAMANI BASHORATLASH VA QURILMALARNI OPTIMALLASHTIRISH Turaxanov Sherzod Shadiyarovich “RIETER TEXTILSYSTEMEN” LLC 05 11 2025 2025 4 10 26 32 https://creativecommons.org/licenses/by/4.0 This work is licensed under a Creative Commons Attribution 4.0 International License.

This paper presents a two-layer integrated framework for load forecasting and equipment optimization under renewable energy integration. The first layer delivers day-ahead and near real-time forecasts using a hybrid LSTM–Transformer model trained on SCADA/AMI data enriched with weather and calendar features. The second layer performs multi-objective MILP optimization combined with Model Predictive Control (MPC) to coordinate capacitor banks, voltage regulators/OLTC, inverter PQ dispatch, demand response (DR) signals, and energy storage, while explicitly accounting for forecast uncertainty. Simulation results indicate reduced technical losses and voltage violations, lower renewable curtailment, transformer loading relief, and improved operational costs. By unifying the forecast–control loop within a smart-grid context, the proposed approach enhances network resiliency and overall energy efficiency.

Статья предлагает двухуровневый интегрированный подход к прогнозированию нагрузки и оптимизации оборудования в условиях интеграции ВИЭ. На первом уровне формируется суточный и ближний оперативный прогноз с использованием гибридной модели LSTM–Transformer на данных SCADA/AMI с учётом погодных и календарных факторов. На втором уровне выполняется многоцелевой MILP-оптимизатор и модельно-предиктивное управление (MPC), координирующие режимы конденсаторных установок, регуляторов напряжения/РПН, инверторов (PQ-диспетчирование), мероприятий управления спросом (Demand Response) и систем накопления энергии с учётом неопределённости прогноза. Моделирование демонстрирует снижение технических потерь и нарушений ограничений по напряжению, сокращение ограничений выработки ВИЭ (curtailment), разгрузку трансформаторов и оптимизацию эксплуатационных затрат. Предложенное решение объединяет связку «прогноз–управление» в среде smart-grid, повышая устойчивость сети и энергоэффективность.

Maqolada qayta tiklanuvchi energiya manbalari (QTM) ulanishi sharoitida yuklamani bashoratlash va elektr texnikasi qurilmalarini optimallashtirish uchun ikki pog‘onali integratsiyalashgan yondashuv taklif etiladi. Birinchi pog‘onada SCADA/AMI ma‘lumotlari, ob-havo hamda kalendar ko‘rsatkichlari asosida gibrid LSTM–Transformer modeli yordamida kun- / oldi va yaqin real vaqt yuklama prognozi olinadi. Ikkinchi pog‘onada prognoz noaniqligini inobatga oluvchi MILP asosidagi ko‘p-maqsadli optimallashtirish va modelga asoslangan prediktiv boshqaruv (MPC) orqali kompensatorlar, kuchlanish regulyatorlari/OLTC, invertorlar (PQ-dispatch), talabga javob (DR) signallari hamda saqlash tizimlari ish rejimlari muvofiqlashtiriladi. Simulyatsiya natijalari tarmoqdagi texnik yo‘qotishlarni va kuchlanish cheklovi buzilishlarini kamaytirish, RES curtailmentini qisqartirish, transformator yuklanishini pasaytirish hamda ekspluatatsion xarajatlarni optimallashtirish imkonini ko‘rsatadi. Taklif etilgan usul aqlli tarmoq (smart-grid) muhitida bashorat–boshqaruv bog‘liqligini yagona yechimga birlashtirib, tarmoq barqarorligi va energiya samaradorligini oshiradi.

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