7,019 research outputs found

    HVAC-based hierarchical energy management system for microgrids

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    With the high penetration of renewable energy into the grid, power fluctuations and supply-demand power mismatch are becoming more prominent, which pose a great challenge for the power system to eliminate negative effects through demand side management (DSM). The flexible load, such as heating, ventilation, air conditioning (HVAC) system, has a great potential to provide demand response services in the electricity grids. In this thesis, a comprehensive framework based on a forecasting-management optimization approach is proposed to coordinate multiple HVAC systems to deal with uncertainties from renewable energy resources and maximize the energy efficiency. In the forecasting stage, a hybrid model based on Multiple Aggregation Prediction Algorithm with exogenous variables (MAPAx)-Principal Components Analysis (PCA) is proposed to predict changes of local solar radiance, vy using the local observation dataset and real-time meteorological indexes acquired from the weather forecast spot. The forecast result is then compared with the statistical benchmark models and assessed by performance evaluation indexes. In the management stage, a novel distributed algorithm is developed to coordinate power consumption of HVAC systems by varying the compressorsโ€™ frequency to maintain the supply-demand balance. It demonstrates that the cost and capacity of energy storage systems can be curtailed, since HVACs can absorb excessive power generation. More importantly, the method addresses a consensus problem under a switching communication topology by using Lyapunov argument, which relaxes the communication requirement. In the optimization stage, a price-comfort optimization model regarding HVACโ€™s end users is formulated and a proportional-integral-derivative (PID)-based distributed algorithm is thus developed to minimize the customerโ€™s total cost, whilst alleviating the global power imbalance. The end users are motivated to participate in energy trade through DSM scheme. Furthermore, the coordination scheme can be extended to accommodate battery energy storage systems (BESSs) and a hybrid BESS-HVAC system with increasing storage capacity is proved as a promising solution to enhance its selfregulation ability in a microgrid. Extensive case studies have been undertaken with the respective control strategies to investigate effectiveness of the algorithms under various scenarios. The techniques developed in this thesis has helped the partnership company of this project to develop their smart immersion heaters for the customers with minimum energy cost and maximum photovoltaic efficiency

    Distributed Control Strategies for Microgrids: An Overview

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    There is an increasing interest and research effort focused on the analysis, design and implementation of distributed control systems for AC, DC and hybrid AC/DC microgrids. It is claimed that distributed controllers have several advantages over centralised control schemes, e.g., improved reliability, flexibility, controllability, black start operation, robustness to failure in the communication links, etc. In this work, an overview of the state-of-the-art of distributed cooperative control systems for isolated microgrids is presented. Protocols for cooperative control such as linear consensus, heterogeneous consensus and finite-time consensus are discussed and reviewed in this paper. Distributed cooperative algorithms for primary and secondary control systems, including (among others issues) virtual impedance, synthetic inertia, droop-free control, stability analysis, imbalance sharing, total harmonic distortion regulation, are also reviewed and discussed in this survey. Tertiary control systems, e.g., for economic dispatch of electric energy, based on cooperative control approaches, are also addressed in this work. This review also highlights existing issues, research challenges and future trends in distributed cooperative control of microgrids and their future applications

    Gather-and-broadcast frequency control in power systems

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    We propose a novel frequency control approach in between centralized and distributed architectures, that is a continuous-time feedback control version of the dual decomposition optimization method. Specifically, a convex combination of the frequency measurements is centrally aggregated, followed by an integral control and a broadcast signal, which is then optimally allocated at local generation units. We show that our gather-and-broadcast control architecture comprises many previously proposed strategies as special cases. We prove local asymptotic stability of the closed-loop equilibria of the considered power system model, which is a nonlinear differential-algebraic system that includes traditional generators, frequency-responsive devices, as well as passive loads, where the sources are already equipped with primary droop control. Our feedback control is designed such that the closed-loop equilibria of the power system solve the optimal economic dispatch problem

    ๋…๋ฆฝํ˜• ํ˜ผํ•ฉ AC/DC ๋งˆ์ดํฌ๋กœ๊ทธ๋ฆฌ๋“œ์˜ ์ „์—ญ ์ „๋ ฅ ๊ท ํ˜•์„ ์œ„ํ•œ ้ž์ค‘์•™์ง‘์ค‘ํ˜• ์ œ์–ด ๋ฐฉ์•ˆ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2021. 2. ๋ฌธ์Šน์ผ.Recently, islanded microgrids (IMGs) have been considered the most attractive solution to supply electricity to rural and remote areas where electricity is hard to be provided. An IMG is designed to actively supply and manage the power and energy itself by various distributed generators (DGs) using neighboring natural sources. On the other hand, according to the growth of DC technology, an islanded hybrid AC/DC microgrid (IHMG), which consists of AC and DC MGs that are connected by an interlinking converter (IC), has been implemented to exploit its characteristics of higher efficiency and better compatibility. Generally, in MG areas, maintaining the desired power sharing ratio between the DGs is one of the crucial issues. Maintaining power sharing between DGs allows securing more reserve for DGs, reducing stress on DGs, and minimizing the generation costs of DGs. In each AC or DC MG, DGs usually adopt droop control as conventional generators to maintain the power sharing ratio between the DGs. From droop control, the power sharing ratio between the DGs is maintained based on the frequency and common DC bus voltage in each AC MG and DC MG. For global power sharing (GPS) between all DGs in an IHMG beyond individual MGs, IC plays a key role to achieve GPS by adjusting their outputs. As the power sharing ratio between the DGs in each AC and DC MGs is based on the frequency and common DC bus voltage, respectively, GPS can be achieved by equalizing the normalized frequency and the common DC bus voltage through active power control of ICs. On the other hand, to improve the reliability of the entire IHMG system and increase the power transfer ability between the AC and DC MGs, multiple ICs can be interconnected in an IHMG system. In this case, similar to the reasons for DGs, the desired power sharing ratio between ICs must be maintained. Thus, ICs should be controlled to achieve GPS and power sharing between ICs. This is called global power balancing (GPB) of an IHMG in this dissertation. Control strategies to achieve GPB through ICs can be categorized as centralized and non-centralized controls. In a centralized control strategy, a central control unit receives information about the IHMG system and provides control signals to individual ICs via a centralized communication network. However, as centralized control strategy is exposed to single point failure (SPF) risks, non-centralized control strategies, such as local control strategy and distributed control strategy, have attracted research attention. Without central control units, only local information is used for local control strategy, or information from distributed communication among only ICs is used for the distributed control strategy. By adopting a local control strategy, system reliability can be improved without using communication; however, the control objective cannot be achieved perfectly due to insufficient information. By contrast, by implementing a distributed control strategy, control objectives can be obtained accurately with sufficient information about an IHMG system; however, the use of communication can degrade system reliability. To implement non-centralized control, the operator usually chooses between local and distributed control strategies considering what the system needs most. Various studies have been conducted on non-centralized control methods of an IC to achieve GPB in an IHMG. By adopting a local control strategy, the normalized droop control method of ICs has been most widely employed for local control method of ICs. However, this control method causes steady-state error of GPS, and if the gains of the controller are adjusted to improve the GPS accuracy, it may cause system instability, consequently making it difficult to reduce the error. On the other hand, under a distributed control strategy, distributed control methods of ICs have been proposed. The most advanced distributed control method is based on additional proportional-integral controllers, virtual leader-followers, and distributed communication. This method makes the IHMG system achieve perfect GPS; however, it may cause the system to collapse when unexpected changes, such as communication delays and the failure of DGs, occur in the system. Therefore, in this dissertation, novel non-centralized control methods for ICs are proposed to overcome the limitations of conventional non-centralized control methods. A new local control method for ICs is proposed that drastically decrease the error of GPS accuracy without any instability issues. Based on the required active power of ICs to achieve GPS, the active power reference of each IC is determined, and a damping factor is introduced to overcome issues regarding power sharing between ICs. In addition, guidelines for designing the damping factor are presented based on analyses of stability and GPS accuracy. Furthermore, a new distributed control method for ICs is proposed, which improves the robustness against unexpected changes in the system, such as communication delay and failure of ICs and DGs. Based on the required active power of ICs to achieve GPS, the active power reference of each IC is determined and an estimator to obtain the required data for control is proposed with a dynamic consensus algorithm and distributed communication between ICs. In addition, a design method for the parameters of the estimator is proposed based on stability analysis. To demonstrate the effectiveness of the proposed control methods, stability analyses based on the small-signal model and Simulink-based simulations are performed for each proposed control method. Furthermore, the proposed control methods are implemented as hardware-in-the-loop experiments through real-time simulators and real controllers of ICs and case studies are performed. The effectiveness of the proposed methods is observed under an actual hardware experimental environment. Owing to the proposed control methods, GPB in an IHMG can be achieved more accurately and robustly and can ultimately help improve the reliability of the system. Finally, we hope that this study will make a significant contribution to achieve stable power supply to rural and remote areas, where people frequently face power shortages and blackouts.์ตœ๊ทผ๋“ค์–ด ๋„์„œ์ง€์—ญ ๋ฐ ๋ฏธ์ „ํ™”์ง€์—ญ ๋“ฑ ๊ณ ๋ฆฝ๋œ ์†Œ๊ทœ๋ชจ ์ง€์—ญ์— ๋Œ€ํ•œ ์ „๋ ฅ๊ณต๊ธ‰์˜ ์ฃผ์š” ํ•ด๊ฒฐ์ฑ…์œผ๋กœ ๋‹ค์–‘ํ•œ ๋ถ„์‚ฐ์ „์›์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๋…๋ฆฝํ˜• ๋งˆ์ดํฌ๋กœ๊ทธ๋ฆฌ๋“œ๊ฐ€ ํฐ ๊ด€์‹ฌ์„ ๋ฐ›๊ณ  ์žˆ๋‹ค. ๋™์‹œ์— ์ง๋ฅ˜ ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์œผ๋กœ AC์™€ DC ๋งˆ์ดํฌ๋กœ๊ทธ๋ฆฌ๋“œ๊ฐ€ ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ๋ฅผ ํ†ตํ•ด ์—ฐ๊ฒฐ๋˜๋Š” ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง„ ๋…๋ฆฝํ˜• ํ˜ผํ•ฉ AC/DC ๋งˆ์ดํฌ๋กœ๊ทธ๋ฆฌ๋“œ๊ฐ€ ๋น ๋ฅด๊ฒŒ ํ™•์‚ฐ๋˜๊ณ  ์žˆ๋‹ค. ๋งˆ์ดํฌ๋กœ๊ทธ๋ฆฌ๋“œ ๋‚ด์—์„œ ๋ถ„์‚ฐ์ „์›๊ฐ„ ์ถœ๋ ฅ ๋ถ„๋ฐฐ์œจ์„ ์›ํ•˜๋Š” ๋Œ€๋กœ ์œ ์ง€ํ•˜๋Š” ๊ฒƒ์€ ๋งค์šฐ ์ค‘์š”ํ•œ ์ด์Šˆ ์ค‘ ํ•˜๋‚˜๋กœ, ์ด๋ฅผ ํ†ตํ•ด ์˜ˆ๋น„๋ ฅ์˜ ์•ˆ์ •์ ์ธ ํ™•๋ณด์™€ ๋ถ„์‚ฐ์ „์›์˜ ์ˆ˜๋ช…์ฆ์ง„ ๋ฐ ๋ฐœ์ „๋น„์šฉ์„ ์ตœ์†Œํ™”์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ๊ฐ AC ๋ฐ DC ๋งˆ์ดํฌ๋กœ๊ทธ๋ฆฌ๋“œ ๋‚ด ๋ถ„์‚ฐ์ „์›๋“ค์€ ๊ธฐ์„ฑ ๋ฐœ์ „์›๊ณผ ์œ ์‚ฌํ•˜๊ฒŒ ๋“œ๋ฃน์ œ์–ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋™์ž‘ํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋ถ„์‚ฐ์ „์› ๊ฐ„ ์ถœ๋ ฅ ๋ถ„๋ฐฐ์œจ์ด ์ฃผํŒŒ์ˆ˜์™€ ๊ณตํ†ต DC ๋ชจ์„  ์ „์••์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์›ํ•˜๋Š” ๋น„์œจ๋กœ ์œ ์ง€๋œ๋‹ค. ๊ฐœ๋ณ„ ๋งˆ์ดํฌ๋กœ๊ทธ๋ฆฌ๋“œ๋ฅผ ๋„˜์–ด ๋…๋ฆฝํ˜• ํ˜ผํ•ฉ AC/DC ๋งˆ์ดํฌ๋กœ๊ทธ๋ฆฌ๋“œ ์ „์—ญ์˜ ๋ถ„์‚ฐ์ „์› ์ถœ๋ ฅ ๋ถ„๋ฐฐ์œจ์„ ์œ ์ง€ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ์˜ ์ถ”๊ฐ€์ ์ธ ์ œ์–ด๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ ์œ ํšจ์ „๋ ฅ ์ถœ๋ ฅ ์ œ์–ด๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ ์ฃผํŒŒ์ˆ˜์™€ ๊ณตํ†ต DC ๋ชจ์„  ์ „์••์˜ ์ •๊ทœํ™”๋œ ๊ฐ’์„ ์ผ์น˜์‹œํ‚ค๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋…๋ฆฝํ˜• ํ˜ผํ•ฉ AC/DC ๋งˆ์ดํฌ๋กœ๊ทธ๋ฆฌ๋“œ ์ „์—ญ์˜ ๋ชจ๋“  ๋ถ„์‚ฐ์ „์› ์ถœ๋ ฅ ๋ถ„๋ฐฐ์œจ์„ ์œ ์ง€์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ํ•œํŽธ, ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ ํƒˆ๋ฝ์— ๋Œ€๋น„ํ•˜๊ณ , AC์™€ DC ๋งˆ์ดํฌ๋กœ๊ทธ๋ฆฌ๋“œ ๊ฐ„ ๋” ๋งŽ์€ ์ „๋ ฅ์„ ์ „์†กํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค๊ธฐ์˜ ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ๊ฐ€ ๋ณ‘๋ ฌ๋กœ ๊ตฌ์ถ•๋˜๋Š” ์‚ฌ๋ก€๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ์ด ๊ฒฝ์šฐ, ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ๊ฐ„ ์ถœ๋ ฅ ๋ถ„๋ฐฐ์œจ๋„ ๋ถ„์‚ฐ์ „์›์˜ ๊ฒฝ์šฐ์™€ ์œ ์‚ฌํ•œ ์ด์œ ๋กœ ์›ํ•˜๋Š” ๋Œ€๋กœ ์œ ์ง€๋  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ๋Š” ๋ชจ๋“  ๋ถ„์‚ฐ์ „์›์˜ ์ถœ๋ ฅ ๋ถ„๋ฐฐ์œจ๊ณผ ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ๊ฐ„ ์ถœ๋ ฅ ๋ถ„๋ฐฐ์œจ์„ ์›ํ•˜๋Š” ๋Œ€๋กœ ์œ ์ง€ํ•˜๋„๋ก ์ œ์–ด๋ฅผ ์ˆ˜ํ–‰ํ•ด์•ผ ํ•˜๋ฉฐ, ์ด๋ฅผ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋…๋ฆฝํ˜• ํ˜ผํ•ฉ AC/DC ๋งˆ์ดํฌ๋กœ๊ทธ๋ฆฌ๋“œ์˜ ์ „์—ญ ์ „๋ ฅ ๊ท ํ˜• ๋‹ฌ์„ฑ์ด๋ผ ์ •์˜ํ•œ๋‹ค. ์ „์—ญ ์ „๋ ฅ ๊ท ํ˜•์˜ ๋‹ฌ์„ฑ์„ ์œ„ํ•œ ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ์˜ ์ œ์–ด ์ „๋žต์œผ๋กœ๋Š” ์ค‘์•™์ง‘์ค‘ํ˜• ์ œ์–ด ์ „๋žต๊ณผ ๋น„์ค‘์•™์ง‘์ค‘ํ˜• ์ œ์–ด ์ „๋žต์ด ์„ ํƒ๋  ์ˆ˜ ์žˆ๋‹ค. ์ค‘์•™์ง‘์ค‘ํ˜• ์ œ์–ด ์ „๋žต์—์„œ๋Š” ์ค‘์•™ ํ†ต์‹  ๋„คํŠธ์›Œํฌ๋ฅผ ํ†ตํ•ด ์ค‘์•™ ์ œ์–ด๊ธฐ๊ฐ€ ๊ณ„ํ†ต ์ •๋ณด๋ฅผ ๋ฐ›์•„ ๊ฐ ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ์—๊ฒŒ ์ œ์–ด ์‹ ํ˜ธ๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ํ•˜์ง€๋งŒ ์ค‘์•™์ง‘์ค‘ํ˜• ์ œ์–ด ์ „๋žต์€ ์ค‘์•™ ์ œ์–ด๊ธฐ ํƒˆ๋ฝ๊ณผ ๊ฐ™์€ ๋‹จ์ผ ์ง€์  ํƒˆ๋ฝ์— ๋Œ€ํ•œ ํฐ ์œ„ํ—˜์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ์–ด, ๋น„ํ†ต์‹  ์ œ์–ด ์ „๋žต๊ณผ ๋ถ„์‚ฐํ˜• ์ œ์–ด ์ „๋žต๊ณผ ๊ฐ™์ด ์ค‘์•™ ์ œ์–ด๊ธฐ๋ฅผ ์ด์šฉํ•˜์ง€ ์•Š๋Š” ๋น„์ค‘์•™์ง‘์ค‘ํ˜• ์ œ์–ด ์ „๋žต์— ๋Œ€ํ•ด ์ตœ๊ทผ ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์ด๋ค„์ง€๊ณ  ์žˆ๋‹ค. ์ค‘์•™ ์ œ์–ด๊ธฐ ์—†์ด, ๋น„ํ†ต์‹  ์ œ์–ด ์ „๋žต์˜ ๊ฒฝ์šฐ๋Š” ์ฃผ๋ณ€ ์ธก์ • ์ •๋ณด๋งŒ์„ ์ด์šฉํ•˜์—ฌ ์ œ์–ดํ•˜๋ฉฐ ๋ถ„์‚ฐํ˜• ์ œ์–ด ์ „๋žต์˜ ๊ฒฝ์šฐ๋Š” ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ๊ฐ„์˜ ๋ถ„์‚ฐ ํ†ต์‹  ์ •๋ณด๋งŒ์„ ์ถ”๊ฐ€์ ์œผ๋กœ ์ด์šฉํ•˜์—ฌ ์ œ์–ด๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ฒŒ ๋œ๋‹ค. ๋น„ํ†ต์‹  ์ œ์–ด ์ „๋žต์€ ํ†ต์‹ ์„ ์ด์šฉํ•˜์ง€ ์•Š๊ธฐ์— ๋†’์€ ์‹ ๋ขฐ์„ฑ์„ ๊ฐ€์ง€๋‚˜ ์ •๋ณด์˜ ํ•œ๊ณ„๋กœ ์™„๋ฒฝํ•œ ์ œ์–ด๊ฐ€ ๋ถˆ๊ฐ€๋Šฅํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•œํŽธ, ๋ถ„์‚ฐํ˜• ์ œ์–ด ์ „๋žต์˜ ๊ฒฝ์šฐ ์ถฉ๋ถ„ํ•œ ์ •๋ณด๋ฅผ ํ™•๋ณดํ•˜์—ฌ ์™„๋ฒฝํ•œ ์ œ์–ด๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋‚˜ ํ†ต์‹ ์„ ์ด์šฉํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ๊ณ„ํ†ต ์‹ ๋ขฐ์„ฑ์„ ํ•˜๋ฝ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ์˜ ๋น„์ค‘์•™์ง‘์ค‘ํ˜• ์ œ์–ด๋ฐฉ์•ˆ์œผ๋กœ ์‹œ์Šคํ…œ์˜ ์ƒํ™ฉ์— ๋”ฐ๋ผ ๋น„ํ†ต์‹  ๋ฐ ๋ถ„์‚ฐํ˜• ์ œ์–ด ์ „๋žต ์ค‘ ํ•œ ๊ฐ€์ง€๊ฐ€ ์„ ํƒ๋˜์–ด ์ ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ์ „์—ญ ์ „๋ ฅ ๊ท ํ˜• ๋‹ฌ์„ฑ์„ ์œ„ํ•œ ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ์˜ ๋น„์ค‘์•™์ง‘์ค‘ํ˜• ์ œ์–ด์— ๋Œ€ํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์„ ํ–‰์—ฐ๊ตฌ๊ฐ€ ์ˆ˜ํ–‰๋œ ๋ฐ” ์žˆ๋‹ค. ๋จผ์ €, ๋น„ํ†ต์‹  ์ œ์–ด ์ „๋žต์„ ์ ์šฉํ•œ ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ ์ œ์–ด ๋ฐฉ์•ˆ์œผ๋กœ๋Š” ์ •๊ทœํ™” ๋“œ๋ฃน ๊ธฐ๋ฐ˜์˜ ๋ฐฉ์‹์ด ๊ฐ€์žฅ ๋„๋ฆฌ ์ด์šฉ๋œ๋‹ค. ํ•˜์ง€๋งŒ, ์ด ์ œ์–ด ๋ฐฉ์•ˆ์€ ์ „์—ญ ์ „๋ ฅ ๊ท ํ˜•์— ๋Œ€ํ•œ ์ •์ƒ์ƒํƒœ ์˜ค์ฐจ๋ฅผ ์•ผ๊ธฐํ•˜๊ณ  ๋งŒ์•ฝ ์ „์—ญ ์ „๋ ฅ ๊ท ํ˜•์˜ ์ •ํ™•๋„๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•ด ์ œ์–ด ์ด๋“๊ฐ’์„ ๋ณ€๊ฒฝํ•˜๊ฒŒ ๋˜๋ฉด ์‹œ์Šคํ…œ์˜ ๋ถˆ์•ˆ์ •์„ฑ์„ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ์–ด ์˜ค์ฐจ๋ฅผ ํฌ๊ฒŒ ์ค„์ด๊ธฐ ์–ด๋ ต๋‹ค. ํ•œํŽธ, ๋ถ„์‚ฐํ˜• ์ œ์–ด ์ „๋žต ํ•˜์—์„œ๋„ ๋Œœ์•™ํ•œ ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ์˜ ์ œ์–ด ๋ฐฉ์•ˆ์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. ๊ฐ€์žฅ ๊ณ ๋„ํ™”๋˜์—ˆ๋‹ค๊ณ  ์—ฌ๊ฒจ์ง€๋Š” ๋ถ„์‚ฐํ˜• ์ œ์–ด ๋ฐฉ์‹์€ ์ถ”๊ฐ€ PI ์ œ์–ด๊ธฐ๋“ค๊ณผ ๊ฐ€์ƒ ๋ฆฌ๋”-์ถ”์ข…์ž, ๊ทธ๋ฆฌ๊ณ  ๋ถ„์‚ฐํ˜• ํ†ต์‹ ์„ ์ด์šฉํ•œ ๊ฒƒ์ด๋‹ค. ์ด ๋ฐฉ์•ˆ์€ ๋…๋ฆฝํ˜• ํ˜ผํ•ฉ AC/DC ๋งˆ์ดํฌ๋กœ๊ทธ๋ฆฌ๋“œ ์‹œ์Šคํ…œ์ด ์™„๋ฒฝํ•œ ์ „์—ญ ์ „๋ ฅ ๊ท ํ˜•์„ ๋‹ฌ์„ฑํ•˜๋„๋ก ํ•˜์ง€๋งŒ, ํ†ต์‹  ์ง€์—ฐ๊ณผ ๋ถ„์‚ฐ์ „์› ํƒˆ๋ฝ๊ณผ ๊ฐ™์€ ์˜ˆ๊ธฐ์น˜ ๋ชปํ•œ ๋ณ€ํ™”์— ๋Œ€ํ•ด ์‹œ์Šคํ…œ ๋ถ•๊ดด๋ฅผ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ธฐ์กด ์ œ์–ด ๋ฐฉ์•ˆ๋“ค์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋Š” ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ์˜ ์ƒˆ๋กœ์šด ๋น„ํ†ต์‹  ๋ฐ ๋ถ„์‚ฐํ˜• ์ œ์–ด ๋ฐฉ์•ˆ๋“ค์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. ๋จผ์ €, ๊ธฐ์กด ๋น„ํ†ต์‹  ๊ธฐ๋ฐ˜ ์ •๊ทœํ™” ๋“œ๋ฃน์ œ์–ด ๋ฐฉ์•ˆ์œผ๋กœ๋Š” ํฌ๊ฒŒ ์ค„์ด๊ธฐ ์–ด๋ ค์› ๋˜ ์ „์—ญ ์ „๋ ฅ ๊ท ํ˜• ์˜ค์ฐจ๋ฅผ ํฌ๊ฒŒ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด ์ƒˆ๋กœ์šด ๋น„ํ†ต์‹  ๊ธฐ๋ฐ˜ ์ œ์–ด ๋ฐฉ์•ˆ์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ „์—ญ ์ „๋ ฅ ๊ท ํ˜•์„ ์œ„ํ•ด ํ•„์š”ํ•œ ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ์˜ ์œ ํšจ์ „๋ ฅ๋Ÿ‰ ์‚ฐ์ •์„ ํ†ตํ•ด ์ œ์–ด๊ธฐ๊ฐ€ ๋„์ถœ๋˜๋ฉฐ, ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ๊ฐ„ ์ถœ๋ ฅ ๋ถ„๋ฐฐ์œจ ์ด์Šˆ๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ๋Œํ•‘ ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ ๋„์ž…๋˜์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์•ˆ์ •๋„์™€ ์ „์—ญ ์ „๋ ฅ ๊ท ํ˜•์˜ ์ •ํ™•๋„ ๋ถ„์„์„ ํ†ตํ•ด ๋Œํ•‘ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์„ค๊ณ„ ๊ฐ€์ด๋“œ๋ผ์ธ์ด ์ œ์‹œ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ๋ณ‘๋ ฌ ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ ๊ตฌ์ถ• ์‹œ ๊ธฐ์กด ๋ถ„์‚ฐํ˜• ํ†ต์‹  ๊ธฐ๋ฐ˜ ์ œ์–ด ๋ฐฉ์•ˆ์˜ ํ•œ๊ณ„์ธ ์˜ˆ๊ธฐ์น˜ ๋ชปํ•œ ๊ณ„ํ†ต ๋ณ€ํ™”์— ๋Œ€ํ•œ ์ทจ์•ฝ์„ฑ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ์ƒˆ๋กœ์šด ๋ถ„์‚ฐํ˜• ์ œ์–ด ๋ฐฉ์•ˆ์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ „์—ญ ์ „๋ ฅ ๊ท ํ˜•์„ ์œ„ํ•ด ํ•„์š”ํ•œ ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ์˜ ์œ ํšจ์ „๋ ฅ๋Ÿ‰ ์‚ฐ์ •์„ ํ†ตํ•ด ์ œ์–ด๊ธฐ๊ฐ€ ๋„์ถœ๋˜์—ˆ์œผ๋ฉฐ, ์—ฐ๊ณ„์ปจ๋ฒ„ํ„ฐ๋ผ๋ฆฌ์˜ ๋ถ„์‚ฐํ˜• ํ†ต์‹ ๊ณผ ๋™์  ํ•ฉ์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ œ์–ด์— ํ•„์š”ํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์ถ”์ •ํ•˜๋Š” ์ถ”์ •๊ธฐ๋ฅผ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ „์ฒด ํ†ตํ•ฉ ์‹œ์Šคํ…œ์˜ ์•ˆ์ •๋„ ๋ถ„์„์„ ํ†ตํ•ด ์ œ์–ด๊ธฐ์™€ ์ถ”์ •๊ธฐ ํŒŒ๋ผ๋ฏธํ„ฐ ์„ค๊ณ„ ๋ฐฉ์•ˆ์ด ์ œ์‹œ๋˜์—ˆ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ์•ˆ๋“ค์˜ ์„ฑ๋Šฅ์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ ์ œ์•ˆ๋œ ์ œ์–ด๊ธฐ ๋งˆ๋‹ค ์†Œ์‹ ํ˜ธ ๋ชจ๋ธ ๊ธฐ๋ฐ˜์˜ ์•ˆ์ •๋„ ๋ถ„์„๊ณผ Simulink๋ฅผ ํ†ตํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์ด ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ œ์•ˆ๋œ ๋ฐฉ์•ˆ๋“ค์€ ์‹ค์‹œ๊ฐ„ ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ์™€ ์‹ค์ œ ์ œ์–ด๊ธฐ๋ฅผ ํ†ตํ•ด Hardware-in-the-loop ์‹คํ—˜์œผ๋กœ ๊ตฌํ˜„๋˜์—ˆ๊ณ  ์ด์— ๋Œ€ํ•œ ์‚ฌ๋ก€์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ ์ œ์•ˆ๋œ ๋ฐฉ์•ˆ๋“ค์˜ ํšจ๊ณผ๋ฅผ ์‹ค์ œ ํ•˜๋“œ์›จ์–ด ์‹คํ—˜ ํ™˜๊ฒฝํ•˜์—์„œ ํ™•์ธํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ์•ˆ์„ ํ™œ์šฉํ•˜๋ฉด ๋…๋ฆฝํ˜• ํ˜ผํ•ฉ AC/DC ๋งˆ์ดํฌ๋กœ๊ทธ๋ฆฌ๋“œ ๋‚ด ์ „์—ญ ์ „๋ ฅ ๊ท ํ˜•์ด ๋ณด๋‹ค ์•ˆ์ •์ ์ด๋ฉฐ ์ •ํ™•ํ•˜๊ฒŒ ์ˆ˜ํ–‰๋˜์–ด ๊ณ„ํ†ต์˜ ์‹ ๋ขฐ์„ฑ ํ–ฅ์ƒ์— ๋„์›€์ด ๋  ๊ฒƒ์œผ๋กœ ์—ฌ๊ฒจ์ง„๋‹ค. ์ตœ์ข…์ ์œผ๋กœ๋Š”, ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ๋„์„œ์ง€์—ญ ๋ฐ ๋ฏธ์ „ํ™”์ง€์—ญ์œผ๋กœ์˜ ์›ํ™œํ•œ ์ „๋ ฅ๊ณต๊ธ‰์— ํฐ ๊ธฐ์—ฌ๋ฅผ ํ•  ์ˆ˜ ์žˆ๊ธธ ๊ธฐ๋Œ€ํ•œ๋‹ค.Chapter 1 Introduction 1 1.1 Motivations and purposes 1 1.2 Highlights and contributions 5 1.3 Dissertation organization 7 Chapter 2 Islanded Hybrid AC/DC Microgrid 8 2.1 Concept of islanded hybrid AC/DC microgrid 8 2.2 Control strategy of DGs in AC microgrid 11 2.3 Control strategy of DGs in DC microgrid 20 2.4 Technical issues on control of interlinking converter 26 Chapter 3 Local Control Method of Interlinking Converters 35 3.1 Review of conventional local control method for ICs 36 3.2 Required active power of interlinking converters for GPS 38 3.2.1 Equivalent model for the IHMG 38 3.2.2 Required active power of the ideal IC 39 3.3 Proposed local control method of single IC 41 3.4 Proposed local control method of multiple ICs 43 3.4.1 Active power reference for each IC 43 3.4.2 Proposed local control method of each IC with damping factor 43 3.5 System configuration of test system 45 3.6 Effectiveness of damping factor 47 3.6.1 Simplified small-signal state space model 47 3.6.2 Stability analysis with damping factor 50 3.6.3 GPS error due to damping factor 52 3.6.4 Guideline to determine the damping factor 55 3.7 Simulation results 57 3.7.1 Case I: load variation scenario with single IC 58 3.7.2 Case II: DG failure with single IC 60 3.7.3 Case III: multiple ICs of the proposed method without damping factor 62 3.7.4 Case IV: multiple ICs of the proposed method with damping factor 64 3.7.5 Case V: comparison with the conventional method 68 Chapter 4 Distributed Control Method of Interlinking Converters 71 4.1 Review of conventional distributed control method for ICs 73 4.2 Proposed distributed control method of ICs 76 4.2.1 Active power reference for each IC 76 4.2.2 Dynamic consensus-based estimator for PICs and ฯk 77 4.2.3 Calculation method for the parameters from local measurement 78 4.2.4 Structure of the entire proposed controller 79 4.3 System configuration 80 4.4 Design method of the consensus-based estimator 82 4.4.1 Simplified small-signal state space model 82 4.4.2 Parameter determination method for the estimator 85 4.5 Examining improved robustness with stability analysis 87 4.5.1 Stability with respect to communication delay 87 4.5.2 Stability with respect to DG status 88 4.6 Simulation results 90 4.6.1 Case I: IC failure 91 4.6.2 Case II: varying communication delay 92 4.6.3 Case III: DG failure 94 4.6.4 Case IV: GPS for cost minimization 98 4.6.5 Case V: power sharing between ICs to improve efficiency 101 Chapter 5 Hardware-in-the-loop (HIL) Experiments 105 5.1 HIL setup 107 5.2 HIL experimental results 108 5.2.1 Implementation for the proposed local control method of single IC 108 5.2.2 Implementation for the proposed local control method of multiple ICs 110 5.2.3 Implementation for the proposed distributed control method of ICs 112 Chapter 6 Conclusions and Future Extensions 119 6.1 Conclusions 119 6.2 Future extensions 121 Bibliographies 122 Appendix A. Design Method of the Normalized Droop Constants Considering GPS Error 133 ์ดˆ๋ก 134Docto

    DC Microgrids โ€“ Part I:A Review of Control Strategies and Stabilization Techniques

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    Resilience-driven planning and operation of networked microgrids featuring decentralisation and flexibility

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    High-impact and low-probability extreme events including both man-made events and natural weather events can cause severe damage to power systems. These events are typically rare but featured in long duration and large scale. Many research efforts have been conducted on the resilience enhancement of modern power systems. In recent years, microgrids (MGs) with distributed energy resources (DERs) including both conventional generation resources and renewable energy sources provide a viable solution for the resilience enhancement of such multi-energy systems during extreme events. More specifically, several islanded MGs after extreme events can be connected with each other as a cluster, which has the advantage of significantly reducing load shedding through energy sharing among them. On the other hand, mobile power sources (MPSs) such as mobile energy storage systems (MESSs), electric vehicles (EVs), and mobile emergency generators (MEGs) have been gradually deployed in current energy systems for resilience enhancement due to their significant advantages on mobility and flexibility. Given such a context, a literature review on resilience-driven planning and operation problems featuring MGs is presented in detail, while research limitations are summarised briefly. Then, this thesis investigates how to develop appropriate planning and operation models for the resilience enhancement of networked MGs via different types of DERs (e.g., MGs, ESSs, EVs, MESSs, etc.). This research is conducted in the following application scenarios: 1. This thesis proposes novel operation strategies for hybrid AC/DC MGs and networked MGs towards resilience enhancement. Three modelling approaches including centralised control, hierarchical control, and distributed control have been applied to formulate the proposed operation problems. A detailed non-linear AC OPF algorithm is employed to model each MG capturing all the network and technical constraints relating to stability properties (e.g., voltage limits, active and reactive power flow limits, and power losses), while uncertainties associated with renewable energy sources and load profiles are incorporated into the proposed models via stochastic programming. Impacts of limited generation resources, load distinction intro critical and non-critical, and severe contingencies (e.g., multiple line outages) are appropriately captured to mimic a realistic scenario. 2. This thesis introduces MPSs (e.g., EVs and MESSs) into the suggested networked MGs against the severe contingencies caused by extreme events. Specifically, time-coupled routing and scheduling characteristics of MPSs inside each MG are modelled to reduce load shedding when large damage is caused to each MG during extreme events. Both transportation networks and power networks are considered in the proposed models, while transporting time of MPSs between different transportation nodes is also appropriately captured. 3. This thesis focuses on developing realistic planning models for the optimal sizing problem of networked MGs capturing a trade-off between resilience and cost, while both internal uncertainties and external contingencies are considered in the suggested three-level planning model. Additionally, a resilience-driven planning model is developed to solve the coupled optimal sizing and pre-positioning problem of MESSs in the context of decentralised networked MGs. Internal uncertainties are captured in the model via stochastic programming, while external contingencies are included through the three-level structure. 4. This thesis investigates the application of artificial intelligence techniques to power system operations. Specifically, a model-free multi-agent reinforcement learning (MARL) approach is proposed for the coordinated routing and scheduling problem of multiple MESSs towards resilience enhancement. The parameterized double deep Q-network method (P-DDQN) is employed to capture a hybrid policy including both discrete and continuous actions. A coupled power-transportation network featuring a linearised AC OPF algorithm is realised as the environment, while uncertainties associated with renewable energy sources, load profiles, line outages, and traffic volumes are incorporated into the proposed data-driven approach through the learning procedure.Open Acces
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