To support of growing demand for electrical energy, power plants must be able to supply the required electricity. The high value of greenhouse gas emissions from fossil fuel power plants has become a global concern, thus providing a separate impetus for the transition to cleaner energy sources. This problem is further complicated by environmental regulations such as Carbon Cap-and-Tax policies, which pose additional challenges due to the potential high tax burden due to carbon emissions produced by thermal power plants. This research aims to improve the operational efficiency of electric power systems from the perspective of operational costs through Multi-Objective Dynamic Optimal Power Flow using the Mixed-Integer Nonlinear Programming (MINLP) method, and evaluate the optimization of operational costs, power losses, and emissions in modern Multi-Area electric power systems with a high portion of renewable energy integration and energy storage due to consider Carbon Cap-and-Taxes value.

Reliable distribution operation is increasingly stressed by frequent disturbances and variability from distributed generation (DG). We propose a two-stage FLISR framework that decouples fast rule-based isolation from learning-based service restoration. Stage-1 uses a graph-only path-screening routine to identify the minimal set of sectionalizing switches for isolation without running power flow. Stage-2 trains a topology-aware Deep Q-Network (DQN) to restore service with a hybrid action design that mixes small micro-composite packets and single-switch moves, plus an explicit STOP action. Feasibility is enforced at decision time via execution-time masking that rejects actions causing non-radial topology, served-load degradation, or voltage-screen violations avoiding pre-mask bias while keeping exploration intact. The framework is implemented on a modified IEEE 33-bus system with embedded photovoltaics (PV) and evaluate it by load served, active-power losses, voltage profile, and training time. Experiments show the hybrid policy achieves fast, high-quality restoration under topological and voltage constraints, offering a favorable balance between the stability of decomposed actions and the rapid gains of composite packets. The results indicate a practical path toward self-healing distribution operation under DG penetration.

Integrating carbon policy into power dispatch is crucial for the decarbonization of energy-intensive industries. This study investigates the Dynamic Economic Emission Dispatch (DEED) problem within an industrial microgrid at a major fertilizer plant (Petrokimia Gresik). Unlike conventional multi-objective approaches, this research formulates a unified single-objective function by internalizing environmental externalities as a financial penalty, directly aligning with Indonesia's carbon tax regulation of IDR $\mathbf{3 0, 0 0 0} / \mathbf{t C O}_{\mathbf{2}}$ e. To solve this complex optimization, the Turbulent Flow of Water-based Optimization (TFWO) algorithm is proposed and benchmarked against the Runge-Kutta Optimizer (RUN). Two critical operational scenarios are analyzed: a conventional baseline and an integrated system utilizing Waste Heat Boilers (WHB) powered by exothermic heat from a sulfuric acid plant. Empirical results reveal scenario-dependent performance between the two algorithms. TFWO demonstrates consistent superiority in computational speed across all scenarios, while cost solution quality varies: RUN achieves a marginally lower cost in Case 1 (0.67%), whereas TFWO delivers both lower cost and faster computation in Case 2. Specifically, the integration of exothermic-based recovery units (Case 2) facilitated a substantial 26.76% reduction in total daily costs (IDR 632,613,551). Furthermore, TFWO demonstrated a 19.27% to 39.75% acceleration in convergence speed compared to the runge kutta benchmark. These findings provide a scalable framework for industrial facilities to optimize power dispatch while remaining compliant with emerging fiscal carbon frameworks.

This study investigates the operational and economic impacts of Battery Energy Storage Systems (BESS) for energy shifting in the Sulutgo power system, Indonesia. The utilization of a BESS as an energy shifting strategy and a support for low-cost power plants to operate at optimum capacity is expected to be a solution for reducing generation cost. First, an energy simulation was conducted using an energy analyzer software to obtain energy allocation of each power plant unit for baseline scenario. The results then imported into power system analyzer software to identify the system parameters such as load factor and busbar loading, which are subsequently analyzed to determine the placement and capacity of the BESS to be integrated. Next, simulation in energy analyzer and power system analyzer software are repeated for BESS integration scenario to assess its operational and economic impacts on the system. The results show that BESS can increase the utilization of coal-fired power plants and reduce gas-fired generation during peak hours. Consequently, the overall generation cost of the system decreases.

Kabupaten Magelang merupakan salah satu kabupaten dengan produksi padi yang melimpah bahkan melebihi target menjadikannya daerah dengan produksi padi tertinggi di Jawa Tengah. Desa Krogowanan, Kecamatan Sawangan, Magelang merupakan salah satu desa yang berada di Kabupaten Magelang yang memiliki pengembangan komoditas padi terbesar dengan luas lahan sawah mencapai 1756 Ha. Di Desa Krogowanan, praktek pertanian masih didominasi olah pertanian monokultur dan belum banyak memanfaatkan teknologi pertanian modern secara optimal. Pola tanam ini rentan terhadap serangan hama dan belum memanfaatkan teknologi modern dalam budidaya padi mereka. Ketergantungan pada metode tradisional ini tidak hanya meningkatkan risiko kegagalan panen akibat hama tetapi juga membatasi kemampuan petani untuk merespon cepat terhadap gangguan tersebut. Serangan hama wereng coklat yang dapat mengakibatkan kerugian produksi hingga 70% telah menjadi masalah serius di beberapa daerah di Kabupaten Magelang, termasuk Desa Krogowanan. Penanganan yang tidak efektif dan kurangnya penggunaan teknologi dalam pengendalian hama dapat mengurangi kualitas dan kuantitas produksi padi. Berdasarkan latar belakang permasalahan yang telah dijelaskan di atas, Laboratorium Simulasi Sistem Tenaga Listrik Departemen Teknik Elektro ITS berencana untuk melaksanakan pengabdian masyarakat berbasis produk dengan mengimplementasikan sistem smart farming berupa integrasi power supply dan aktuator dengan pengendalian melalui website Internet of Things (IoT). Hal ini bertujuan untuk memudahkan petani untuk monitoring kondisi lahan mereka dari mana saja dan kapan saja untuk mengelola hasil tani menjadi lebih efisien.

Pulau Bawean merupakan pulau yang terletak di Laut Jawa, berjarak sekitar 80 mil atau 120 kilometer di sebelah utara Gresik. Pulau Bawean memiliki jumlah penduduk sebanyak 80.289 jiwa pada tahun 2020 dan dikelilingi oleh fishing ground seluas 27.000 km². Pulau yang termasuk dalam wilayah Kabupaten Gresik, Jawa Timur ini memiliki potensi besar dalam bidang kelautan dan energi surya. Masyarakat Bawean yang mayoritas bekerja di sektor pertanian, peternakan, dan perikanan sering mengalami kendala pasokan listrik untuk penyimpanan hasil tangkapan ikan, terutama saat terjadi pemadaman listrik. Sebagai solusi, diusulkan integrasi cooling box dan freezer dengan sistem photovoltaic di Desa Daun, Kecamatan Sangkapura. Sistem ini bertujuan untuk memenuhi kebutuhan listrik para pengepul ikan agar dapat meningkatkan produktivitas dan menjaga kualitas hasil tangkapan. Potensi energi surya di Pulau Bawean mencapai rata-rata intensitas radiasi sebesar 5,5 kWh/m² per hari, menjadikannya sumber energi alternatif yang tepat. Rangkaian kegiatan meliputi survei lokasi, pengadaan dan pemasangan sistem photovoltaic, serta edukasi masyarakat mengenai manfaat energi terbarukan. Saat terjadi pemadaman, suplai listrik tetap terjaga karena adanya dukungan baterai sebagai cadangan untuk malam hari. Selain itu, kegiatan ini diharapkan dapat mengurangi emisi melalui pemanfaatan energi terbarukan serta meningkatkan kesadaran masyarakat terhadap energi ramah lingkungan dan keberlanjutan.

This study investigates the optimization of deep neural network (DNN) architectures for photovoltaic (PV) power forecasting through a systematic evaluation of 50 configurations with varying numbers of neurons and layer depths. The objective is to identify an architecture that achieves high forecasting accuracy while maintaining good generalization under changing solar and environmental conditions. Accurate PV power forecasting is essential for supporting grid stability, improving energy management, and enabling effective integration of renewable energy sources. Model performance was evaluated using mean square error (MSE) on both training and testing datasets to assess learning accuracy and generalization capability. The results show that training MSE values remain consistently low, ranging from $8.8 \times 10^{-5}$ to $3.0 \times 10^{-4}$, indicating effective learning across all configurations. In contrast, testing MSE values vary from $5.23 \times 10^{-4}$ to $4.63 \times 10^{-2}$, demonstrating noticeable differences in generalization performance. To obtain a balanced evaluation, a combined performance metric is proposed by assigning 70 percent weight to testing MSE and 30 percent to training MSE. Based on this metric, the optimal configuration consisting of 9 neurons and 8 layers achieved a training MSE of $9.80 \times 10^{-5}$, a testing MSE of $1.00 \times 10^{-3}$, and the lowest combined score of 0.1163. The results confirm that moderately scaled DNN architectures provide better forecasting accuracy and stability than deeper or more complex models, offering a practical and data-driven framework for PV power forecasting applications.

Harmonic filters are essential components in electric power systems, serving to reduce harmonic distortion that, if uncontrolled, can cause equipment failure, lower system efficiency, and increase power losses. Nonlinear loads, especially those with semiconductor-based devices, are a significant source of harmonics in today’s distribution networks. These harmonics degrade power quality and raise the chances of equipment damage and energy inefficiencies. This study investigates the impact of nonlinear loads on harmonic distribution within a radial distribution network, focusing on Substation 2 Tonasa IV at PT. Semen Tonasa. By applying the Whale Optimization Algorithm (WOA) in MATLAB 2021b, the research seeks to optimize harmonic reduction techniques. Simulation results indicate that positioning a harmonic filter at bus 6 is most effective for reducing harmonic spread in the Tonasa IV system. With a single-tuned filter rated at 1109.747 kVAR, the system realizes a total loss reduction of about 4.55%, underscoring the importance of targeted filter placement to improve power quality and operational efficiency in industrial power distribution

This paper presents a dynamic, self-healing system for smart microgrids that addresses the harmonic distortion, voltage imbalance, and Renewable Energy Source (RES) intermittency using an Improved Whale Optimization Algorithm (IWOA). The IWOA simultaneously optimizes four equally weighted objectives — active power losses, voltage deviation, Total Harmonic Distortion (THD), and Phase Voltage Unbalance Rate (PVUR) — thereby overcoming the limitations of the conventional methods. An enhanced IWOA movement mechanism prevents the premature convergence in complex, unbalanced harmonic scenarios. When validated on a modified IEEE 33-bus system under diverse fault, load, and generation conditions, with realistic RES and harmonic modeling, the IWOA was found to significantly improve THD and PVUR compared to the conventional approaches. Furthermore, the IWOA outperforms the standard Whale Optimization Algorithm (WOA) by converging faster and providing a superior final solution, thus demonstrating its effectiveness in enhancing the microgrid resilience and power quality.

Minimizing transmission losses is essential for ensuring efficient and reliable operation in large-scale power systems. The Java-Madura-Bali grid, Indonesia’s largest transmission network, presents significant operational challenges due to its high demand and complex generation structure. This paper proposes a Selective Multi-Objective Economic Dispatch (SMOED) model, utilizing the B-loss coefficient method to accurately evaluate and reduce transmission losses while simultaneously minimizing generation costs. Simulation results demonstrate that single-objective optimization focusing on loss reduction achieves a maximum transmission loss reduction of 37.26%, with substantial increases in generation cost. In contrast, the SMOED approach delivers a slightly lower loss reduction of up to 32.93%, but achieves significant cost savings during peak load conditions. These results have shown that the developed SMOED from the traditional ED model could achieve an outstanding result by using a selective dispatch mechanism, technical economic trade-offs, and accurate loss modelling in large power systems.

Perturb and Observe (P&O) is a commonly used algorithm for Maximum Power Point Tracking (MPPT) in wind turbines. MPPT plays a critical role in enhancing wind turbine efficiency by dynamically adjusting operating parameters to adapt to fluctuating wind conditions. Although P&O is favored for its simplicity and adaptability, its performance is hindered by step size selection issues, which lead to inefficiency, oscillations, and slow convergence. To overcome these limitations, this research proposes a modified P&O algorithm that automates step size selection based on divided sectors of wind speed and normalized power in region two. Additionally, an integration of the pitch-angle control from region three was employed to maintain the optimal power output under variable wind conditions. The proposed approach reduces tracking time, minimizes perturbation errors, and ensures a stable power output. The proposed modifications enhance the efficiency and reliability of Wind Energy Conversion Systems (WECS) by addressing the shortcomings of the conventional P&O methods.

Provinsi Jawa Tengah merupakan salah satu provinsi dengan angka pertumbuhan pertanian yang sangat tinggi, tidak terkecuali Kabupaten Magelang. Sensus Pertanian tahap I 2023 (ST2023) yang merupakan agenda rutin setiap 10 tahun sekali (pada tahun berakhiran angka 3) menunjukkan jumlah petani di level rumah tangga di Magelang meningkat hampir 100%. Selain itu, kontribusi pertanian juga mencapai angka 12,4% terhadap produk domestik bruto (PDB) berdasarkan harga berlaku (ADHB) dan juga menyerap tenaga kerja hingga 27%. Kabupaten Magelang menjadi salah satu kabupaten dengan produksi padi yang melimpah bahkan melebihi target, menjadikannya daerah dengan produksi padi tertinggi di Jawa Tengah. Desa Krogowanan, merupakan salah satu desa yang berada di Kecamatan Sawangan, Kabupaten Magelang yang memiliki pengembangan komoditas padi terbesar dengan luas lahan sawah mencapai 1756 Ha. Namun, sebagian besar petani padi di Desa Krogowanan masih menerapkan polatanam-monokultur dan belum memanfaatkan teknologi secara optimal. Masih banyak petani yang kurang familiar terkait teknologi yang dapat mereka gunakan untuk membantu mengelola lahan pertanian. Dengan memanfaatkan teknologi Agriculture Multicopter Drone serta mengenalkan inovasi smart farming, drone tersebut nantinya akan dilengkapi dengan alat semprot pestisida. Selain itu, drone ini juga dirancang untuk mempermudah para petani melakukan monitoring dan controlling pada lahan sawah yang sedang dikerjakan.

The Indonesian archipelago still relies on conventional power plants. Buton Island has significant wind energy potential. Rongi Village, located in South Buton Regency, is considered an optimal location with suitable wind speeds. The Indonesian government's target to achieve a renewable energy mix by 2050 has driven this research to investigate the impact of wind turbine penetration on the quality of electrical power in the Buton Island distribution system. Wind turbine penetration in the distribution system can affect power quality and system stability. Therefore, this study focuses on analyzing the impact of wind turbine penetration on power quality and system stability in the distribution system. Power flow and transient simulations will be conducted to assess performance under steady-state and dynamic conditions, using ETAP software to assist with the modeling and simulation of the Buton Island distribution system. The wind turbine penetration scheme involves the use of 15 wind turbine units, each with a capacity of 2.1 MW at constant wind speeds. The research findings indicate that the increased power supply from wind turbines in the system leads to a decrease in the power factor and voltage at several buses, as well as an increase in power losses in the lines. The overall transient analysis shows that the system experiences voltage and frequency fluctuations during short-circuit disturbance periods. However, the system successfully recovers to stable operating conditions after these disturbances are resolved.

This paper presents a comprehensive study on the characteristics and implications of large-scale photovoltaic (PV) penetration in a hybrid on-grid system incorporating diesel generators and Waste Heat Recovery Power Generation (WHRPG) units. The research focuses on a study case of practical application within a Cement Factory located in Tuban, Indonesia, where energy demand is substantial and diverse. Through extensive simulation and analysis, the paper evaluates the dynamic behavior and operational challenges associated with integrating PV systems into the existing power infrastructure. Various scenarios are examined to assess the impact of PV penetration levels on system stability, grid reliability, fuel consumption, and operational costs. The study investigates the potential benefits of hybridizing 25 MW PV with existing diesel generators and WHRPG units to optimize overall system performance and reduce environmental footprint. The findings highlight the importance of proper system design, control strategies, and grid integration techniques to effectively manage the intermittent nature of solar generation and ensure seamless operation alongside conventional power sources. Furthermore, the paper addresses the economic feasibility and investment attractiveness of deploying PV systems in industrial settings. From the research it is shown by simulation that photovoltaic penetration to the similar scale localized on-grid system could make huge difference in power output. It is necessary also to use a higher capacity of battery for more stabilized system in transient and short-time penetration effect terms.

As time passes, the number generators gas-fired electricity will continue to increase. Power plants Gas fuel is very reliable for fulfilling load requirements and supporting peak loads. Besides its high efficiency, gas fired units are very profitable because of low capital costs, fast installation and minimal pollutant emissions, as well the start and shutdown speeds are the characteristics of the unit gas fuel is suitable for basic, medium, and heavy loads. Peaks provide backup for renewable generators. Intermittent an increases interdependence thus the electricity and gas energy sectors. Optimal and Gas Flow (OPGF) was the most basic problem in the mix these two systems. Gas consumption rates vary form over time, causing pressure fluctuations in the tissue gas transmission pipelines, which can have a negative impact on security and reliability of gas delivery. In addition, the gas system has slower dynamics than the system electricity and requires a longer stabilization time processing load changes and disturbances. Therefore, the optimal power and gas flow at integrated electrical and natural gas system for minimize operating cost. The OPGF problems were solved using Mixed-Integer Linear Programming (MILP) with Piecewise RMSE values the linear approximation is 5.57

Power quality is a crucial aspect of designing a large-scale photovoltaic power plant, particularly regarding harmonics caused by inverter switching. This research aimed to analyze harmonics in a system using electrical transient analyzer program (ETAP) Power Station 20.5.0 to uncover the effect of irradiance on the inverters’ power quality running at 85% and 100% power factors. We analyzed both voltage and current total harmonic distortion (THDi and THDv) from the simulation and compared them with the mathematical model. Moreover, we analyzed the effect of changes in irradiance level on harmonics and reactive power penetration, which influenced power losses in transformers and cables. Inverters at 85% power factor experienced an increase in THDi, whereas those at 100% power factor decreased. Inverters with 85% power factor experienced more frequent switching, causing more prominent distortion. The magnitude of THDv increased proportionally with the rise of irradiance level. Inverters at 85% had a higher THDv value because of the excessive reactive power compensation when irradiance rose. Irradiance level had an inverse relationship with system losses since high irradiance levels led to lower losses as less power was required through transmission lines and transformers. Moreover, losses at 85% power factor were higher since the high harmonics caused additional losses.

The use of non-linear loads and the integration of renewable energy in electricity network can cause power quality problems, especially harmonic distortion. It is a challenge in the operation and design of the radial distribution system. This can happen because harmonics that exceed the limit can cause interference to equipment and systems. This study will discuss the determination of the optimal location and capacity of distributed generation (DG) and network reconfiguration in the radial distribution system to improve the quality of electric power, especially the suppression of harmonic distribution. This study combines the optimal location and capacity of DG and network reconfiguration using the particle swarm optimization method. In addition, this research method is implemented in the distribution system of Bandar Lampung City by considering the effect of using nonlinear loads to improve power quality, especially harmonic distortion. The inverter-based DG type used considers the value of harmonic source when placed. The combination of the proposed methods provides an optimal solution. Increased efficiency in reducing power losses up to 81.17% and %total harmonic distortion voltage (THDv) is below the allowable limit.