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TACMA: total annual cost minimization algorithm for optimal sizing of hybrid energy systems

DOI:10.1007/s12652-020-01964-6
Authors:
Asif Khan at COMSATS University Islamabad
Asif Khan
Nadeem Javaid
Nadeem Javaid
Nadeem Javaid
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In a stand-alone environment, a system comprising of non-renewable source, renewable energy sources (RESs), and energy storage systems like fuel cells (FCs) provide an effective and reliable solution to fulfill the user's load. In this paper, a diesel generator (DG), pho-tovoltaics (PVs), wind turbines (WTs) and FCs are modeled, optimally sized, and compared in three scenarios: PV-WT-FC-DG, PV-FC-DG, and WT-FC-DG in terms of environmental emission and total annual cost (TAC) for a home, located in Hawksbay, Pakistan. The optimal size of hybrid RESs and their components is achieved using a novel TAC minimization algorithm (TACMA). The TACMA achieves superior results in terms of TAC when it is compared to two algorithm-specific parameter-less schemes: Jaya and teaching learning-based optimization. Further, the PV-WT-FC-DG and PV-FC-DG hybrid systems are found as the most economical and nature-friendly scenarios, respectively.
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... This means that the P2G system is not restricted to supplying hydrogen to the load, but is flexible in the system operation compared to existing systems. A system operation involving the P2G system proposed by [85] is described subsequently. First, if the energy of the power system is balanced, the P2G system does not operate. ...
... An optimal energy operating system incorporating reliability and economics for each system will be considered. [4], [7], [9], [20], [21], [25], [26], [28], [32], [39], [40], [41], [47], [48], [49], [51], [55], [57], [60], [61], [76], [77], [79], [80], [110], [111], [127], [128], [129], [130], [131], [132], [133], [134], [135], [137], [138] CCHP [29], [31], [33], [34], [36], [43], [45], [52], [64], [71], [72], [75], [136], [139] P2G [2], [5], [6], [8], [9], [11], [13], [16], [17], [30], [32], [36], [38], [43], [77], [88], [89], [138], [140] TABLE V FIG [ 2,5,7,8,9,10,11,12] REFERENCES Energy conversion mode [29], [30], [31], [32], [33], [34], [36], [37], [39], [40], [43], [45], [46], [47], [48], [49], [51], [52], [55], [56], [59], [60], [61], [62], [64], [71], [72], [75], [86], [127], [128], [129], [130], [131], [132], [133], [134], [135], [136], [137], [138], [139], [140] Multi energy to Cooling [4], [10], [12], [13], [14], [15], [19], [21], [23], [25], [26], [27], [28], [29], [30], [31], [32], [36], [40], [43], [45], [62], [64], [71], [72,] [131], [134], [136], [140] Multi energy to Hydrogen [17], [14], [137] Gas boiler [8], [32], [35], [40], [42], [45], [46], [47], [51], [62], [63], [92], [93], [96], [95], [96], [97], [102], [105], [117], [123], [125] Electric heat pump [20], [26], [39], [49], [73], [79], [92], [103], [106], [123] Furnace [18], [61], [102] Electric boiler [46], [50], [73] Combustion engine [23], [63], [73] Electrolyzer [8], [10], [16], [18], [49], [50], [85], [104], Fuel cell [10], [16], [18], [49], [50], [77], [85], [104], [122] Gas turbine [18], [35], [36], [38], [42], [47], [61], [96], [97], [103], [109] Absorption chiller [23], [34], [35], [36], [42], [47], [62], [108], [109], [134] Electric chiller [23], [35], [45], [46], [63], [73], [96], [97], [105] Aircondition [40], [ 42], [47], [95], [96], [97], [103], [134] [4], [9], [10], [12], [13], [15], [17], [18], [19], [20], [23], [25], [29], [32], [33], [36], [45], [47], [48], [49], [52], [56], [60], [71], [86], [100], [128], [129], [131], [135], [136], [137], [138], [140] TES [4], [7], [10], [12], [13], [14], [18], [19], [20], [21], [23], [25], [29], [31], [33], [36], [41], [45], [47], [52], [56], [64], [71], [72], [75], [86], [91], [100], [127], [128], [129], [131], [132], [134], [135], [137], [139], [140] IES [4], [10], [13], [21], [23], [36], [41], [45], [46], [52], [63], [75] HES [2], [14], [16], [17], [30], [32], [38], [77], [137] NGS [3], [6], [14], [43], [84] ...
... An optimal energy operating system incorporating reliability and economics for each system will be considered. [4], [7], [9], [20], [21], [25], [26], [28], [32], [39], [40], [41], [47], [48], [49], [51], [55], [57], [60], [61], [76], [77], [79], [80], [110], [111], [127], [128], [129], [130], [131], [132], [133], [134], [135], [137], [138] CCHP [29], [31], [33], [34], [36], [43], [45], [52], [64], [71], [72], [75], [136], [139] P2G [2], [5], [6], [8], [9], [11], [13], [16], [17], [30], [32], [36], [38], [43], [77], [88], [89], [138], [140] TABLE V FIG [ 2,5,7,8,9,10,11,12] REFERENCES Energy conversion mode [29], [30], [31], [32], [33], [34], [36], [37], [39], [40], [43], [45], [46], [47], [48], [49], [51], [52], [55], [56], [59], [60], [61], [62], [64], [71], [72], [75], [86], [127], [128], [129], [130], [131], [132], [133], [134], [135], [136], [137], [138], [139], [140] Multi energy to Cooling [4], [10], [12], [13], [14], [15], [19], [21], [23], [25], [26], [27], [28], [29], [30], [31], [32], [36], [40], [43], [45], [62], [64], [71], [72,] [131], [134], [136], [140] Multi energy to Hydrogen [17], [14], [137] Gas boiler [8], [32], [35], [40], [42], [45], [46], [47], [51], [62], [63], [92], [93], [96], [95], [96], [97], [102], [105], [117], [123], [125] Electric heat pump [20], [26], [39], [49], [73], [79], [92], [103], [106], [123] Furnace [18], [61], [102] Electric boiler [46], [50], [73] Combustion engine [23], [63], [73] Electrolyzer [8], [10], [16], [18], [49], [50], [85], [104], Fuel cell [10], [16], [18], [49], [50], [77], [85], [104], [122] Gas turbine [18], [35], [36], [38], [42], [47], [61], [96], [97], [103], [109] Absorption chiller [23], [34], [35], [36], [42], [47], [62], [108], [109], [134] Electric chiller [23], [35], [45], [46], [63], [73], [96], [97], [105] Aircondition [40], [ 42], [47], [95], [96], [97], [103], [134] [4], [9], [10], [12], [13], [15], [17], [18], [19], [20], [23], [25], [29], [32], [33], [36], [45], [47], [48], [49], [52], [56], [60], [71], [86], [100], [128], [129], [131], [135], [136], [137], [138], [140] TES [4], [7], [10], [12], [13], [14], [18], [19], [20], [21], [23], [25], [29], [31], [33], [36], [41], [45], [47], [52], [56], [64], [71], [72], [75], [86], [91], [100], [127], [128], [129], [131], [132], [134], [135], [137], [139], [140] IES [4], [10], [13], [21], [23], [36], [41], [45], [46], [52], [63], [75] HES [2], [14], [16], [17], [30], [32], [38], [77], [137] NGS [3], [6], [14], [43], [84] ...
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Thesis
Full-text available
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Thesis
Full-text available
  • Jun 2021
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Article
Full-text available
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In the present industry revolution, operations management teams emphasize implementing an efficient process optimization approach with a suitable strategy for achieving operational excellence on the shop floor. Process optimization is used to enhance productivity by eliminating idle activities and non-value-added activities within limited constraints. Various process optimization approaches are used in operations management on the shop floor, including lean manufacturing, smart manufacturing, kaizen, six sigma, total quality management, and computational intelligence. The present study investigates strategies used to implement the process optimization approach provided in the previous research to eliminate problems encountered in shop floor management. Furthermore, the authors suggest an idea to industry individuals, which is to understand the operational conditions faced in shop floor management. The novelty of the present study lies in the fact that a methodology for implementing a process optimization approach with an efficient strategy has been reported for the first time that eliminates problems faced in shop floor management, including industry 4.0. The authors of the present research strongly believe that this research will help researchers and operations management teams select an appropriate strategy and process optimization approach to improve operational performance on the shop floor within limited constraints.
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Data Analytics based Efficient Energy Management in Smart Grids
Thesis
Full-text available
  • Jun 2021
The revolution of power grids from traditional grids to Smart Grids (SGs) requires effective Demand Side Management (DSM) and reliable Renewable Energy Sources (RESs) incorporation in order to maintain demand, supply balance and optimize energy in an environment friendly manner. Data analytics provide solutions to the emerging challenges of power systems, such as DSM, environmental pollution (due to carbon emission), fossil fuel dependency mitigation, RESs incorporation, cost curtailment, grid’s stability and security. To efficiently manage electricity and maximize the profit of power utilities several tasks are focused in this thesis, i.e., prediction of electricity load to avoid demand and generation mismatch, wind power forecasting to satisfy energy demand effectively, electricity price forecasting for regulating market operations, carbon emissions forecasting for reducing payment of carbon tax, Electricity Theft Detection (ETD) for recovering power utilities’ revenue loss caused by electricity theft. In addition to that, a wind power forecast based DSM scheme is proposed. Furthermore, impact of RESs integration level on carbon emissions, electricity price and consumption cost is quantified. Both forecasting and classification techniques are utilized for efficient energy management. Forecasting of electricity load, price, wind power and carbon emissions is performed, whereas, classification of fair and fraudulent electricity consumers is performed. To balance electricity demand and supply, electricity load forecasting is required. Three models are proposed for this purpose, i.e., Deep Long Short-Term Memory (DLSTM), Efficient Sparse Autoencoder Nonlinear Autoregressive eXogenous network (ESAENARX) and Differential Evolution Recurrent Extreme Learning Machine (DE-RELM). DLSTM utilizes univariate data and gives single result, whereas, ESAENARX and DE-RELM model multivariate data and predict electricity load and price simultaneously. Due to adaptive and automatic feature learning mechanism, DLSTM achieves accurate results for separate forecasting of electricity load and price. ESAENARX and DE-RELM models are enhanced by newly proposed efficient feature extractor and model’s parameter tuning, respectively. Real-world datasets of ISO-NE, PJM, NYISO are used for load and price forecasting. The purpose of regulating the electricity market operations is achieved by forecasting of electricity load, price, wind power and carbon emissions. Wind power generation is predicted by an efficient model named Efficient Deep Convolution Neural Network (EDCNN). Moreover, a DSM strategy is also proposed based on predicted wind power generation. Power utilities have to pay carbon emissions tax imposed by government. To pay less carbon emissions tax, carbon emissions prediction is required, which helps in encouraging electricity consumers to shift their consumption load to low carbon price time periods of the day. For accomplishing the carbon emissions forecasting task, an efficient model named as Improved Particle Swarm Optimization based Deep Neural Network (IPSO DNN) is proposed. This model is improved by tunning the parameters of DNN by newly proposed improved optimization technique named as IPSO. ISO-NE dataset is used for wind power and carbon emissions forecasting. To reduce the financial loss of power utilities ETD is very important. For this purpose four models are proposed, named as, Differential Evolution Random Under Sampling Boosting (DE-RUSBoost), Jaya-RUSBoost, RUS Ensemble CNN (RUSE-CNN) and anomly detection based ETD. In DE-RUSBoost and Jaya-RUSBoost, the parameters of RUSBoost classifier are tunned by DE and Jaya optimization techniques, respectively. In RUSE-CNN, RUS data balancing technique is applied along with ensemble CNN to improve ETD performance. DE-RUSBoost, Jaya- RUSBoost and RUSE-CNN are supervised model that work on labeled electricity theft data. Whereas, anomaly detection based ETD model is capable of identifying electricity theft from unlabeled electricity consumption data. Real-world datasets of SGCC, UMass*, PRECON, CER, EnerNOC and LCL are used for ETD. Simulation results show that all the proposed models perform significantly better on real-world dataset as compared to their state-of-the-art counterpart models. The improved feature engineering and model hyper-parameter tuning enhance the performance of the proposed models in terms of prediction and classification results.
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Optimum Sizing of PV-WT-FC-DG Hybrid Energy System using Teaching Learning-Based Optimization
Conference Paper
Full-text available
  • Oct 2019
In a hybrid energy system, fulfilling the user's electricity demand cost-effectively is an imperative task. In a stand-alone environment, diesel generator (DG) and renewable energy sources (RESs), including photovoltaics (PVs), wind turbines (WTs), and fuel cells (FCs) provide an effective and reliable solution to fulfill a household load. To achieve the economic and environmental aspects of hybrid RESs (HRESs), optimal sizing of components is indispensable. In this paper, the PV-WT-FC-DG hybrid system is considered, optimally sized, and modeled in terms of environmental emissions and total annual cost (TAC) values for Hawksbay, Pakistan. For optimal sizing, the teaching learning-based optimization (TLBO) algorithm is proposed that does not require any algorithm-specific parameter for its execution. The results reveal that TLBO fulfills the household load at minimum TAC.
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Cooperation between Two Micro-Grids Considering Power Exchange: An Optimal Sizing Approach Based on Collaborative Operation
Article
Full-text available
  • Nov 2018
Optimal sizing of single micro-grid faces problems such as high life cycle cost, low self-consumption of power generated by renewable energy, and disturbances of intermittent renewable energy. Interconnecting single micro-grids as a cooperative system to reach a proper size of renewable energy generations and batteries is a credible method to promote performance in reliability and economy. However, to guarantee the optimal collaborative sizing of two micro-grids is a challenging task, particularly with power exchange. In this paper, the optimal sizing of economic and collaborative for two micro-grids and the tie line is modelled as a unit commitment problem to express the influence of power exchange between micro-grids on each life cycle cost, meanwhile guaranteeing certain degree of power supply reliability, which is calculated by Loss of Power Supply Probability in the simulation. A specified collaborative operation of power exchange between two micro-grids is constructed as the scheduling scheme to optimize the life cycle cost of two micro-grids using genetic algorithm. The case study verifies the validity of the method proposed and reveal the advantages of power exchange in the two micro-grids system. The results demonstrate that the proposed optimal sizing means based on collaborative operation can minimize the life cycle cost of two micro-grids respectively considering different renewable energy sources. Compared to the sizing of single micro-grid, the suggested method can not only improve the economic performance for each micro-grid but also form a strong support between interconnected micro-grids. In addition, a proper price of power exchanges will balance the cost saving between micro-grids, making the corresponding stake-holders prefer to be interconnected.
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An Inventive Method for Eco-Efficient Operation of Home Energy Management Systems
Article
Full-text available
  • Nov 2018
A demand response (DR) based home energy management systems (HEMS) synergies with renewable energy sources (RESs) and energy storage systems (ESSs). In this work, a three-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of HEMS. The proposed method provides the trade-off between the net cost of energy ( C E n e t ) and the time-based discomfort ( T B D ) due to shifting of home appliances (HAs). At step-1, primary trade-offs for C E n e t , T B D and minimal emissions T E M i s s are generated through a heuristic method. This method takes into account photovoltaic availability, the state of charge, the related rates for the storage system, mixed shifting of HAs, inclining block rates, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. A filtration mechanism (based on the trends exhibited by T E M i s s in consideration of C E n e t and T B D ) is devised to harness the trade-offs with minimal emissions. At step-2, a constraint filter based on the average value of T E M i s s is used to filter out the trade-offs with extremely high values of T E M i s s . At step-3, another constraint filter (made up of an average surface fit for T E M i s s ) is applied to screen out the trade-offs with marginally high values of T E M i s s . The surface fit is developed using polynomial models for regression based on the least sum of squared errors. The selected solutions are classified for critical trade-off analysis to enable the consumer choice for the best options. Furthermore, simulations validate our proposed method in terms of aforementioned objectives.
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Dynamic vulnerability in standalone hybrid renewable energy system
Article
Full-text available
Ensuring continuous power supply in the hybrid renewable energy and battery system is important because power utilization is fundamental to economy and human well-being. Earlier studies have been concerned with the ability to supply a desired amount of power in a long-term static condition in the context of system reliability. Meanwhile, short-term dynamic behavior and its relevant vulnerability in a hybrid system associated with a continuous power supply has been given less attention due to the modeling complexity of the problem. This study uses a parametric approach to assess the dynamic transition of vulnerabilities potentially leading to disruptions in electricity supply throughout a given day to identify a specific vulnerable time. The methodology of quantifying the dynamic vulnerability of power source components during a given day in a hybrid system is developed. Then, the temporal dynamic vulnerability and overall vulnerability in the system are presented by changing the capacity size in the case of a fictitious standalone house. Through the analysis, the approach developed in this study would potentially highlight the greater contribution of the battery to continuous power supply, compared with the solar PV in the hybrid system.
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Comparative analysis of intelligent controller based microgrid integration of hybrid PV/wind power system
Article
Full-text available
  • Aug 2018
In present years the consumer power demand has been increased day by day due to pollution growth and new industry establishments. The fossil fuel based power generation system unable to meet alone the consumer demand and environmental issues. In this paper, we have been investigating different renewable energy sources for generate the electricity and environmental safety. The hybrid renewable energy sources are very effective to generate the power even though the absence of any one source. The simulation model of hybrid 60 kW PV and Wind power system developed using Matlab Simulink. The renewable energy system is operated with MPPT techniques based ANFIS controller for improving the system efficiency. The hybrid renewable energy system is presented as interconnected Microgrid system by using an intelligent controller. The proposed intelligent controller based phase loop locker show improvement in the system stability during Microgrid integration simulation. The proposed system analysed with the different intelligent controller such as fuzzy, ANN and ANFIS using Matlab Simulink environment. The simulation results are evaluated and compared with the existing system. Finally recommends the proposed system for Microgrid integration of hybrid renewable energy system.
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Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building
Article
h i g h l i g h t s Genetic Algorithm is used for tri-objective design of hybrid energy system. The objective is minimizing the Life Cycle Cost, CO 2 emissions and dump energy. Small split diesel generators are used in place of big single diesel generator. The split diesel generators are aggregable based on certain set of rules. The proposed algorithm achieves the set objectives (LCC, CO 2 emission and dump). a b s t r a c t In this paper, a Genetic Algorithm (GA) is utilized to implement a tri-objective design of a grid independent PV/Wind/Split-diesel/Battery hybrid energy system for a typical residential building with the objective of minimizing the Life Cycle Cost (LCC), CO 2 emissions and dump energy. To achieve some of these objectives, small split Diesel generators are used in place of single big Diesel generator and are aggregable based on certain set of rules depending on available renewable energy resources and state of charge of the battery. The algorithm was utilized to study five scenarios (PV/Battery, Wind/Battery, Single big Diesel generator, aggregable 3-split Diesel generators, PV/Wind/Split-diesel/Battery) for a typical load profile of a residential house using typical wind and solar radiation data. The results obtained revealed that the PV/Wind/Split-diesel/Battery is the most attractive scenario (optimal) having LCC of $11,273, COE of 0.13 ($/kW h), net dump energy of 3 MW h, and net CO 2 emission of 13,273 kg. It offers 46%, 28%, 82% and 94% reduction in LCC, COE, CO 2 emission and dump energy respectively when compared to a single big Diesel generator scenario.
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Optimum unit sizing of a stand-alone hybrid PV-WT-FC system using Jaya algorithm
Conference Paper
  • Feb 2019
In this paper, Jaya algorithm is used for finding an optimal unit sizing of renewable energy resources (RERs) components, including photovoltaic (PV) panels, wind turbines (WTs) and fuel cell (FC) with an objective to reduce the consumer total annual cost in a stand-alone system. The system reliability is considered using the maximum allowable loss of power supply probability (LP SP max) provided by the consumer. The methodology is applied to real solar irradiation and wind speed data taken for Hawksbay, Pakistan. The results achieved show that when LP SP max values are set to 0% and 2%, the PV-FC is the most cost-effective system as compared to PV-WT-FC and WT-FC systems.
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Optimum Unit Sizing of Stand-Alone PV-WT-Battery Hybrid System Components Using Jaya
Conference Paper
  • Oct 2018
Renewable energy sources (RESs) are considered as reliable and green electric power generations. Photovoltaic (PV) and wind turbine (WT) are used to provide electricity in remote areas. The optimum unit sizing of hybrid RESs components is a vital challenge in a stand-alone system. This paper presents Jaya algorithm for optimum unit sizing of a PV-WT-Battery hybrid system to fulfill the consumer's load at minimal cost. The reliability of the system is considered by a maximum allowable loss of power supply probability (LP SPmax). The results obtained from the Jaya algorithm show that the PV-WT-Battery hybrid system is the most economical and cost-effective solution for all proposed LP SPmax values as compared to PV-Battery and WT-Battery systems.
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A diesel replacement strategy for off-grid systems based on progressive introduction of PV and batteries: An Indonesian case study
Article
In many locations, grids are energized only by diesel generators (DG-only systems). Due to the requirements such as cost reduction and performance improvement, solar panels and batteries have increasingly been introduced into these grids. Nonetheless, many initiatives to hybridize the grids are still thwarted because of the high initial investment. In this work, a gradual diesel replacement strategy is proposed. The strategy starts with a lower initial investment and gradually adds solar panels and batteries in the subsequent years using accumulated savings from the lower diesel consumption. An Indonesian island is used as a case study to validate the proposed strategy. When only diesel generators are used, the life-cycle cost over a period of 25 years is 10.53 mil. USD. It is demonstrated through optimization that by lowering the initial investment to 0.25 mil. USD and gradually using the yearly savings to reinvest in purchasing additional solar panels and batteries, the life-cycle cost is reduced to 6.62 mil. USD (only 3.8% is required for the initial investment). Hence, with the proposed approach, the barrier of initial investment is minimized, and thus more renewable energy sources can be integrated into existing DG-only systems making the power generation more environment-friendly.
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Whole Building Optimization of a Residential Home with PV and Battery Storage in The Bahamas
Article
In this paper a whole building optimization approach is used to assess the building performance and design of residential homes in The Bahamas with the goal of providing objective data for policy makers to achieve the sustainability goals in the country by minimizing carbon emissions and life cycle costs. This study accounts for the effects of building envelope improvements as well as a renewable energy system in the form of PV and battery electricity storage simultaneously in achieving the optimization objectives. EnergyPlus and jEPlus + EA provide the platform for this study, which implements the non-sorting genetic algorithm (NSGA-II) to find optimal solutions to building envelope design and renewable energy integration. Optimal design solutions are compared to a standard building model developed from audited data to provide an understanding of the interactions between the design objectives and optimal configurations. The results show that improvements to the thermal envelope and both the use of PV and battery storage are feasible and potentially advantageous to current building designs. Additionally, the results show a reduction in the NPV of up to 40%, with a net positive and carbon negative status, as well as a reduction in the yearly building energy consumption of up to 30%
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