APLIKASI ALGORITMA SIMULATED ANNEALING PADA SISTEM KOORDINASI PEMBANGKITAN UNIT THERMAL

Salah satu permasalahan dalam sistem tenaga listrik adalah menentukan kombinasi unit pembangkit (unit commitment) dan pembebanan ekonomis (economic dispatch) unit pembangkit dengan mematuhi kendala-kendala operasi pembangkit dan meminimalkan biaya bahan bakar sehingga diperoleh biaya produksi total paling rendah. Metode yang digunakan dalam penelitian ini adalah simulated annealing. Metode ini merupakan suatu teknik optimasi yang didasarkan pada pendinginan logam atau kristal yang dapat menyelesaikan masalah kombinatorial dengan ukuran yang besar. Algoritma simulated annealing mencari kombinasi unit pembangkit yang paling optimum dioperasikan dalam setiap penurunan temperatur. Dengan menggunakan algoritma simulated annealing penyelesaian permasalahan kombinasi unit pembangkit dengan biaya pembangkitan yang paling murah dapat diselesaikan dengan cepat dan dengan hasil yang optimal Kata kunci : unit commitment, economic dispatch, simulated annealing,optimasi One of the problems of electrical power system is determining the combination of unit generation (unit commitment) and load economical of unit generation (economic dispatch) by obeying the constraints operation unit generation and minimize fuel cost in order to achieve minimum total production cost. The method used in this research is simulated annealing. This method is appeared as an optimization technique which is based on metal cooling or crystal that is able to solve a large scale of combinatorial problem. Simulated annealing algorithm search the most optimal unit generation combination in every decrease temperature. By using simulated annealing algorithm the problem of combination unit generation with the lowest cost could be solved quickly and with the optimal result. Keyword : unit commitment, economic dispatch, simulated annealing, optimizatio

ESTIMASI BEBAN PUNCAK HARIAN BERDASARKAN KLUSTER TIPE HARI BERBASIS ALGORITMA HYBRID SWARM PARTICLE-ARTIFICIAL NEURAL NETWORK

Prediksi beban listrik jangka pendek merupakan salah satu perencanaan operasi sistem tenaga listrik yang memiliki peranan penting dalam hal mewujudkan operasi yang ekonomis. Hasil prediksi tersebut dapat dijadikan masukan utama dalam unit commitment, economic dispatch, ataupun studi aliran daya. Skripsi penelitian ini bertujuan untuk melakukan studi penggunaan algoritma hibrid PSO-JST dalam melakukan prediksi beban puncak harian jangka pendek dengan membuat kluster data berdasarkan tipe hari yang berbeda. Data historis menggunakan data pengeluaran beban listrik dari P3B PT.PLN (Persero) Area III Jawa Barat UPB-Cigereleng. Perhitungan dilakukan dengan menggunakan perangkat lunak MATLAB untuk mengetahui tingkat akurasi prediksi dan nilai MAPE (Mean Absolute Percentage Error) pada algoritma HPSO-JST dibandingkan dengan data Rencana Beban Sistem (RBS) PT.PLN dan algoritma backpropagation-jaringan syaraf tiruan (BP-JST) tanpa dikombinasikan dengan algoritma particle swarm optimization (PSO). Hasil simulasi menunjukkan bahwa hasil prediksi beban puncak berbasis algoritma HPSO-JST memberikan tingkat akurasi yang baik dan nilai MAPE yang kecil serta stabil dibawah 2%, jika dibandingkan dengan prediksi RBS-PLN dan BP-JST. Hasil prediksi beban yang akurat akan menghasilkan efisiensi kepada perusahaan listrik sehingga dapat menekan biaya operasional pembangkitan dan tentunya secara tidak langsung akan berdampak pada murahnya biaya produksi listrik. Kata Kunci : Prediksi Beban Puncak Jangka Pendek, Tipe Hari, Algoritma Hybrid Particle Swarm Optimization Jaringan Syaraf Tiruan, Mean Absolute Percentage Error. Short-term electrical load prediction is one of the operation planning of electric power system has an important role in terms of realizing the economical operation. The prediction results can be used as a major input in unit commitment, economic dispatch, or load flow studies. Final research aims to study the use of hybrid PSO-ANN algorithm to predict the short-term daily peak loads by creating clusters of data based on different types of days. Historical data using expenditure data from the electrical load PT PLN P3B (Persero) Area III West Java UPB-Cigereleng. Calculations were performed using MATLAB to determine the level of accuracy of prediction and the value of MAPE (Mean Absolute Percentage Error) HPSO-ANN algorithm compared with the data Rencana Beban Sistem (RBS) PT.PLN and algorithm-back propagation neural network (BP-ANN) without algorithm combined with particle swarm optimization (PSO). The simulation results prove that the results predicted peak load HPSO algorithm-based ANN gave a good degree of accuracy and MAPE values are small and stable below 2%, when compared with the RBS-PLN predictions and BP-ANN. The results are accurate load prediction will result in efficiencies to the electric company so as to reduce the operational costs of generation and certainly will indirectly have an impact on the low cost of electricity production. Keywords : Prediction of Short-Term Peak Load, Type Day, Hybrid Particle Swarm Optimization Algorithm Neural Network, Mean Absolute Percentage Error

CO2 intensities and primary energy factors in the future European electricity system

The European Union strives for sharp reductions in both CO2 emissions as well as primary energy use. Electricity consuming technologies are becoming increasingly important in this context, due to the ongoing electrification of transport and heating services. To correctly evaluate these technologies, conversion factors are needed—namely CO2 intensities and primary energy factors (PEFs). However, this evaluation is hindered by the unavailability of a high-quality database of conversion factor values. Ideally, such a database has a broad geographical scope, a high temporal resolution and considers cross-country exchanges of electricity as well as future evolutions in the electricity mix. In this paper, a state-of-the-art unit commitment economic dispatch model of the European electricity system is developed and a flow-tracing technique is innovatively applied to future scenarios (2025–2040)—to generate such a database and make it publicly available. Important dynamics are revealed, including an overall decrease in conversion factor values as well as considerable temporal variability at both the seasonal and hourly level. Furthermore, the importance of taking into account imports and carefully considering the calculation methodology for PEFs are both confirmed. Future estimates of the CO2 emissions and primary energy use associated with individual electrical loads can be meaningfully improved by taking into account these dynamics

Genetic algorithms for scheduling generation and maintenance in power systems

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Penerapan Batas Ramp-Rate dengan Menggunakan Kombinasi Metode FDP (Forward Dynamic Programming) dan QP (Quadratic Programming) Pada Unit Commitment-Economic Dispatch

Kebutuhan akan energi listrik terus menigkat seiring dengan perkembangan teknologi. meningkatnya beban listrik ini harus diimbangi dengan penambahan daya yang dibangkitkan. Hal ini sangat berpengaruh pada penjadwalan unit pembangkit yang harus ditentukan dengan baik agar didapatkan pembangkitan yang optimal. Pada Tugas Akhir ini mengambil topik mengenai unit commitment dan economic dispatch dengan mempertimbangkan nilai dari batasan generator (ramp-rate). Metode yang digunakan adalah complete enumeration dengan forward dynamic programming pada unit commitment dan quadratic programming pada economic dispatch. Metode - metode tersebut diterapkan dalam pemrograman matlab sehingga dapat dijadikan suatu program perhitungan unit commitment dan economic dispatch dengan mempertimbangkan nilai batasan ramp-rate. Dengan metode tersebut, diharapkan permasalahan penjadwalan unit pembangkit dapat terselesaikan dengan baik dan optimal sehingga memperoleh total biaya pembangkitan yang ekonomis

Review on the cost optimization of microgrids via particle swarm optimization

Economic analysis is an important tool in evaluating the performances of microgrid (MG) operations and sizing. Optimization techniques are required for operating and sizing an MG as economically as possible. Various optimization approaches are applied to MGs, which include classic and artificial intelligence techniques. Particle swarm optimization (PSO) is one of the most frequently used methods for cost optimization due to its high performance and flexibility. PSO has various versions and can be combined with other intelligent methods to realize improved performance optimization. This paper reviews the cost minimization performances of various economic models that are based on PSO with regard to MG operations and sizing. First, PSO is described, and its performance is analyzed. Second, various objective functions, constraints and cost functions that are used in MG optimizations are presented. Then, various applications of PSO for MG sizing and operations are reviewed. Additionally, optimal operation costs that are related to the energy management strategy, unit commitment, economic dispatch and optimal power flow are investigated. © 2019, The Author(s)

Generation scheduling using genetic algorithm based hybrid techniques

The solution of generation scheduling (GS) problems involves the determination of the unit commitment (UC) and economic dispatch (ED) for each generator in a power system at each time interval in the scheduling period. The solution procedure requires the simultaneous consideration of these two decisions. In recent years researchers have focused much attention on new solution techniques to GS. This paper proposes the application of a variety of genetic algorithm (GA) based approaches and investigates how these techniques may be improved in order to more quickly obtain the optimum or near optimum solution for the GS problem. The results obtained show that the GA-based hybrid approach offers an effective alternative for solving realistic GS problems within a realistic timeframe