A Strategy for System Risk Mitigation Using FACTS Devices in a Wind Incorporated Competitive Power System

Das, Amlan; Dawn, Subhojit; Gope, Sadhan; Ustun, Taha Selim

doi:10.3390/su14138069

Cited by 31 publications

(14 citation statements)

References 37 publications

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“…Here, ω is the confidence level and g(x,y) is the loss associated with the decision vector Q, to be chosen from a certain subset x of Ṙ, and the random vector y in Ṙ. The probability of g(x,y) is denoted by p(y) not exceeding a threshold ζ is then given by [9]:…”

Section: F Risk Assessmentmentioning

confidence: 99%

“…Gandoman [7] analyzed and addressed FACTS device uses in renewable integrated smart grids. The studies in [8,9] provided risk reduction methods for a windintegrated competitive electricity market through the application of FACTS technology. Among FACTS devices, TCSC can effectively rebalance the flow of power which enhances the system's transfer efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Risk Mitigation & Profit Improvement of a Wind-Fuel Cell Hybrid System With TCSC Placement

Basu¹,

Dawn

Saha³

et al. 2023

IEEE Access

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The incorporation of renewable energy into the existing electrical system is vital in a competitive electrical system. The unpredictable nature of renewable sources is the main obstacle to energy source integration. Since wind energy is unpredictable, integrating it into an existing thermal system requires some additional operating procedures to maintain the economic and functioning sustainability of the system. In a competitive power network, renewable energy uncertainty creates an imbalance cost (IC) which directly affects the system economy. This study investigates system generation costs, voltage profiles, and electric losses in a deregulated power market incorporating wind farms (WF) & fuel cells (FC). The fuel cell has been used here as a reserve generating unit to mitigate the deficit of power in the renewable incorporated system. To check the efficacy of the presented method, two locations in India are chosen at random. To assess the imbalance cost caused by the discrepancy between forecasted (FWS) and actual wind speeds (AWS), several charge rates (i.e. surplus and deficit) were established. The electrical system has been restructured, so consumers are continually looking for efficient and stable economic power which is only possible by reducing the system risk. This paper outlines a strategy for the optimal operation of a Thyristor-Controlled Series Compensator (TCSC) and fuel cell in a wind-integrated system to maximize system profit and minimize the system risk. In this work, different algorithms like Sequential Quadratic Programming (SQP), Artificial Bee Colony Algorithms (ABC), and Moth Flame Optimization Algorithms (MFO) are used to analyze the economic and functional risk of the system. Additionally, it explains how the fuel cell system is employed to offset the wind farm integration's deviation in the realtime power market. Value-at-Risk (VaR) and conditional Value-at-Risk (cVaR) have been used for risk analysis. A modified IEEE 14-bus test system is considered to validate the entire study whereas any small, large as well as hybrid systems can be considered to perform this methodology.

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Section: F Risk Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk Mitigation & Profit Improvement of a Wind-Fuel Cell Hybrid System With TCSC Placement

Basu¹,

Dawn

Saha³

et al. 2023

IEEE Access

Self Cite

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“…A magnetotactic-based optimization technique (MBO) is applied for controller gain tuning in [42][43][44][45][46][47]. New optimization techniques such as flower pollination algorithm [48], satin bowerbird optimization [49], butterfly optimization algorithm [50], artificial gorilla troops [51],honey badger algorithm [52] are also used in the literature.…”

Section: Related Workmentioning

confidence: 99%

Novel COVID-19 Based Optimization Algorithm (C-19BOA) for Performance Improvement of Power Systems

et al. 2022

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The ongoing pandemic due to novel coronavirus disease-2019 (COVID-19) has rapidly unsettled the health sector with a considerable fatality rate. The main factors that help minimize the spread of this deadly virus are the proper use of masks, social distancing and antibody growth rate in a person. Based on these factors, we propose a new nature-inspired meta-heuristic algorithm named COVID-19 Based Optimization Algorithm (C-19BOA). The proposed C-19BOA mimics the spread and control behavior of coronavirus disease centered on three containment factors: (1) social distancing, (2) use of masks, and (3) antibody rate. Initially, the mathematical models of containment factors are presented, and further, the proposed C-19BOA is developed. To ascertain the effectiveness of the developed C-19BOA, its performance is verified on standard IEEE mathematical benchmark functions for the minimization of these benchmark functions and convergence to the optimal values. These performances are compared with established bio-inspired optimization algorithms available in the literature. Finally, the developed C-19BOA is applied on an electrical power system load–frequency–control model to test its effectiveness in optimizing the power system parameters and to check its applicability in solving modern engineering problems. A performance comparison of the proposed C-19BOA and other optimization algorithms is validated based on optimizing the controller gains for reducing the steady-state errors by comparing the effective frequency and tie-line power regulation ability of an industrially applied Proportional–Integral–Derivative controller (PID) and Active Disturbance Rejection controller (ADRC). Moreover, the robustness of C-19BOA optimized PID and ADRC gains is tested by varying the system parameters from their nominal values.

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“…By making reasonable assumptions, the authors conduct a comparative analysis of three market mechanisms and demonstrate the similarity between semi-centralized and deregulated approaches. Das et al 18 provide a technique for decreasing power network risks by merging renewable energy sources with FACTS devices. Using various optimization approaches, the influence of WF and FACTS controllers on system economy and risk is explored.…”

Section: Introductionmentioning

confidence: 99%

Economic enhancement and grid‐frequency control of a wind farm‐CAES‐fuel cell‐based hybrid system

Das,

Kumar

2024

Energy Storage

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In the present era, the electrical network faces difficulty owing to the restricted supply of conventional energy resources. Solar and wind power are supremely acceptable renewable energy around the globe due to their cheap availability, zero fuel costs, and environmental safety features. Maintaining power balance in a renewable combined day‐ahead power network becomes challenging owing to the uncertainty of renewable sources, resulting in economic instability. An energy‐storing device is essential for combining renewable sources with existing thermal power plants to control the energy supply and maintain system stability. Any government agency in the deregulated market does not set up energy pricing. The ISO (independent system operator) is the primary organization in the day‐ahead power network that is responsible for collecting energy bids from DISCOs (distribution companies), TRANSCOs (transmission companies), and GENCOs (generation companies). The energy pricing is set by the market regulator at a rate known as Location‐based marginal pricing (LBMP), which provides benefits to GENCOs, and DISCOs as well as the customers. In a deregulated market, the unpredictability of renewable sources affects system profit owing to the creation of instability cost (ISC), induced by mismatching the bidding power production from the renewable plant. To lessen these concerns, the presented approach has proposed an innovative model that employs ideal scheduling of the WF (wind‐farm)‐CAES (compressed‐air‐energy‐storage)‐fuel cell (FC) hybrid systems to enhance system economic profitability while keeping the grid frequency (GF) in the safe zone. The fuel cell acts as the energy‐storing device that can be utilized during high‐demand times on the grid, with the ultimate goal of maximizing profits for the overall system. The CAES system's energy level is separated into four dissimilar points, and this study presents an optimum approach for efficiently using it to maintain grid frequency. The new strategy outperformed the previous methods in all categories. The study was performed using the IEEE 30 bus test system.

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A Strategy for System Risk Mitigation Using FACTS Devices in a Wind Incorporated Competitive Power System

Cited by 31 publications

References 37 publications

Risk Mitigation & Profit Improvement of a Wind-Fuel Cell Hybrid System With TCSC Placement

Risk Mitigation & Profit Improvement of a Wind-Fuel Cell Hybrid System With TCSC Placement

Novel COVID-19 Based Optimization Algorithm (C-19BOA) for Performance Improvement of Power Systems

Economic enhancement and grid‐frequency control of a wind farm‐CAES‐fuel cell‐based hybrid system

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