Real-Time Stochastic Optimization of Energy Storage Management Using Deep Learning-Based Forecasts for Residential PV Applications

Hafiz, Faeza; Awal, MA; Queiroz, Anderson Rodrigo de; Husain, Iqbal

doi:10.1109/tia.2020.2968534

Cited by 90 publications

(55 citation statements)

References 32 publications

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“…Although there are also initiatives dealing with complex control problems in different fields (e.g., [24], [16], and [47]), our primary goal is to propose a parallel swarm-based method for optimizing final arrival segment design considering the presence of aircraft of multiple Technology Maturity Levels (TML).…”

Section: Related Workmentioning

confidence: 99%

Swarm-Based Optimization of Final Arrival Segments Considering the UAS Integration in the National Airspace System

et al. 2021

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In the past few years, there has been a growth in Unmanned Aircraft Systems (UAS) numbers in segregated airspace. However, although there is an interest in integrating large UAS in the National Airspace System (NAS), safety challenges regarding this insertion arise from the inclusion of new ways of reaching unsafe states. Although UAS may be used in different situations and brings several advantages to the airspace (e.g., efficiency), it may bring uncertainties due to the lack of familiarity of Air Traffic Controllers (ATCos) in these operations. Furthermore, the Terminal Maneuvering Area (TMA) is a critical control area generally established at the confluence of Air Traffic Service (ATS) routes in which the aircraft tend to be closer to each other. Besides, defining a final arrival segment for a set of aircraft in a complex environment is challenging. Thereupon, the main objective of this research is to propose a parallel swarmbased method for optimizing final aircraft arrival segment design (i.e., routes that connect the final sector to the Initial Approach Fix -IAF) considering the presence of aircraft of multiple Technology Maturity Levels (TML) -including the UAS. This is conducted from two perspectives: ATCo workload (which is related to safety) and sequencing duration (which is related to efficiency). Furthermore, different phases of UAS integration are considered using the Technology Maturity Levels (TMLs). Finally, the solutions consider airspace restrictions (e.g., minimum separation between aircraft and bad weather conditions). The experiments conducted show that this approach can build safe and efficient solutions, even in situations with many aircraft.

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Section: Related Workmentioning

confidence: 99%

Swarm-Based Optimization of Final Arrival Segments Considering the UAS Integration in the National Airspace System

et al. 2021

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“…The operation of different types of DG to meet the load needs a parallel operation but causes improper load sharing between converters and circulating current due to the abrupt variations in the source, sudden changes in load, and parametric differences due to various constraints [14]. Studies in the literature have discussed the most popular techniques of load sharing such as active current sharing [15][16][17] and droop control [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. The most familiar one is the master/slave technique, where, in the master/slave current method, a common bus is employed between DC converters for proper current sharing [15] and the generation of the required base voltage.…”

Section: Introductionmentioning

confidence: 99%

“…In [29], the proposed improved-mode adaptive droop control strategy for the DC microgrid considered various operating conditions and disturbance scenarios using the DC microgrid study system. The impact of distributed control methods was discussed in [30][31][32]. The importance and advantages of optimization techniques such as the stochastic optimization, consensus algorithm, and improved equal incremental principle (IEIP) in the distributed control methods were discussed in detail.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Adaptive Droop Control Applied to Low-Voltage DC Microgrid

2021

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In a DC microgrid, droop control is the most common and widely used strategy for managing the power flow from sources to loads. Conventional droop control has some limitations such as poor voltage regulation and improper load sharing between converters during unequal source voltages, different cable resistances, and load variations. This paper addressed the limitations of conventional droop control by proposing a simple adaptive droop control technique. The proposed adaptive droop control method was designed based on mathematical calculations, adjusting the droop parameters accordingly. The primary objective of the proposed adaptive droop controller was to improve the performance of the low-voltage DC microgrid by maintaining proper load sharing, reduced circulating current, and better voltage regulation. The effectiveness of the proposed methodology was verified by conducting simulation and experimental studies.

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“…For example, PV generation forecasting was studied with both machine learning and physics based models. Hafiz et al described an energy management system to optimize energy purchasing cost and to reduce peak power usage for residences based on solar-PV forecasts using a Long Short-Term Memory (LSTM) model [5]. The study in [6] compared several forecasting methods for PV generation and reported that LSTM models provided the lowest prediction errors.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Method for the Forecast and Separation of Total and HVAC Loads With Application to Energy Management of Smart and NZE Homes

et al. 2021

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Real-Time Stochastic Optimization of Energy Storage Management Using Deep Learning-Based Forecasts for Residential PV Applications

Cited by 90 publications

References 32 publications

Swarm-Based Optimization of Final Arrival Segments Considering the UAS Integration in the National Airspace System

Swarm-Based Optimization of Final Arrival Segments Considering the UAS Integration in the National Airspace System

Investigation of Adaptive Droop Control Applied to Low-Voltage DC Microgrid

Artificial Intelligence Method for the Forecast and Separation of Total and HVAC Loads With Application to Energy Management of Smart and NZE Homes

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