Название: Enhancement of Grid-Connected Photovoltaic Systems Using Artificial Intelligence Автор: Amal M. Abd El-Hameid, Adel A. Elbaset Издательство: Springer Год: 2023 Страниц: 243 Язык: английский Формат: pdf (true), epub Размер: 33.8 MB
Enhancement of Grid-Connected Photovoltaic Systems Using Artificial Intelligence presents methods for monitoring transmission systems and enhancing distribution system performance using modern optimization techniques considering different multi-objective functions such as voltage loss sensitivity indexes, reducing total annual cost, and voltage deviation. The authors offer a comprehensive survey of distributed energy resources (DERs), explain the backward/forward sweep (BFS) power flow algorithm, and present simulation results on the optimal integration of photovoltaic-based distributed generators (PV-DG) and distribution static synchronous compensators (DSTATCOM) in different transmission and distribution systems. This book will be a valuable academic and industry resource for electrical engineers, students, and researchers working on optimization techniques, photovoltaic systems, energy engineering, and Artificial Intelligence.
Distributed generation (DG) refers to a variety of technologies that generate electricity at or near where it will be used, such as solar panels and combined heat and power. Distributed generation may serve a single structure, such as a home or business, or it may be part of a microgrid (a smaller grid that is also tied into the larger electricity delivery system), such as at a major industrial facility, a military base, or a large college campus. When connected to the electric utility lower-voltage distribution lines, distributed generation can help support the delivery of clean, reliable power to additional customers and reduce electricity losses along transmission and distribution lines.
Photovoltaic-based distributed generation (PV-DG) injected into the power system is considered a highly promising solution due to the advantage of clean energy use. However, the investigation of the optimal PV-DG allocation (site and size) is a significant task for power system requirements and assessment of PV potential. Recent research on PV-DG allocation is reviewed from two perspectives: (1) DG, optimization algorithms, and objectives; and (2) PV potential assessment methodologies.
Distributed flexible AC transmission systems (D-FACTS) devices are static power-electronic devices installed in AC distribution networks to increase the power transfer capability, stability, and controllability of the networks through series and/or shunt compensation. D-FACTS comprises several devices, including D-STATCOM, Distribution Static Var Compensator (D-SVC), Distributed Static Series Compensator (DSSC) and Unified Power Quality Conditioner (UPQC).
D-STATCOM is an efficient member of the D-FACTS devices that consists of a voltage source converter (VSC), a DC bus capacitor, a ripple filter, and a coupling transformer. VSC is constructed by using insulated gate bipolar transistors (IGBT) and metal-oxide-semiconductor field-effect transistors (MOSFET), where the switching of component is based on pulse-width modulation (PWM) sequences. D-STATCOM can inject or absorb both active and reactive power at a point of common coupling connection (PCC) by injecting a variable magnitude and phase angle voltage at PCC. D-STATCOM is incorporated in electric systems for enhancing the power quality, (a) improving the power factor, (b) balancing the loading, (c) mitigating the harmonic, (d) reactive power compensation, (e) reducing the power fluctuations of photovoltaic units minimizing the voltage sag, (f) mitigating the flicker in the electric system, and (g) minimizing the power losses.
1. Introduction 2. Literature Review and Power Quality Issues 3. Stochastic Optimal Planning of Distribution System Considering Integrated Photovoltaic-Based DG and D-STATCOM 4. Optimal Allocation of Distributed Energy Resources Using Modern Optimization Techniques 5. Results and Discussion 6. Conclusions and Future Work
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