An Analysis of Multi Objective Energy Scheduling in PV-BESS System Under Prediction Uncertainty

Nair, Unnikrishnan Raveendran; Sandelić, Monika; Sangwongwanich, Ariya; Dragičević, Tomislav; Costa‐Castelló, Ramon; Blaabjerg, Frede

doi:10.1109/tec.2021.3055453

Cited by 21 publications

(9 citation statements)

References 31 publications

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“…In order to apply an MPC controller, it is necessary to transform this generic objective into a cost function and restrictions on the system variables. As usual in the MPC [19], [23], in this work the cost function will be constructed as the linear combination of different cost functions, each of which will be directly related to a specific concept. All norms used represent 1-norm.…”

Section: Control Objectivesmentioning

confidence: 99%

Combined heat and power using high-temperature proton exchange membrane fuel cells for housing facilities

López

Costa‐Castelló

López³

et al. 2021

2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )

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Recently, new alternatives to conventional energy sources such as fossil fuels are arising due to global problems related to climate change effect and energy shortage. In this context, fuel cells and combined heat and power technologies appear as a possible solution due to their ability to provide both electrical and thermal energy more efficiently compared to traditional methods. Related to this, high-temperature proton exchange membrane fuel cells offer the possibility of implementing combined heat and power systems, and they are also considered an efficient technology that emits less greenhouse gases. In this article a model predictive control based energy management system for a specific house is presented. Simulation and control models of the system are presented, together with dimensions and energy profiles used. Finally, control objectives and the proposed control algorithm are detailed, and the results when trying to match residential heat and power demands are discussed.

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Section: Control Objectivesmentioning

confidence: 99%

Combined heat and power using high-temperature proton exchange membrane fuel cells for housing facilities

López

Costa‐Castelló

López³

et al. 2021

2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )

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“…A mixed-integer linear programming-based robust cost-optimal scheduling algorithm is developed to enhance the overall revenue of a PV-BESS integrated system using RNN and CNN algorithms as a forecasting model 32 . Moreover, model predictive control 33 and a predictive management strategy 34 are applied to maximize the self-consumption rate of PV-BESS energy through energy curtailment and scheduling schemes.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore 30 – 34 , did not explore a DL-based day-ahead prediction scheme, whereas the system proposed in this study implements a new, highly accurate prediction model (i.e., Bi-LSTM) for power generation and consumption forecasts. Previous studies considered the power curtailment scheme 33 , 34 appliance scheduling scheme 6 , 30 , and feed-in tariff and TOU scheme 29 , whereas this study uses the constraints drawn from the predicted generation and consumption, real-time state of charge (SoC), and charging and discharging allowance.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based optimal energy management for photovoltaic and battery energy storage integrated home micro-grid system

Alam

Rahman

Ahmed

et al. 2022

Sci Rep

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The development of the advanced metering infrastructure (AMI) and the application of artificial intelligence (AI) enable electrical systems to actively engage in smart grid systems. Smart homes with energy storage systems (ESS) and renewable energy sources (RES)-known as home microgrids-have become a critical enabling technology for the smart grid. This article proposes a new model for the energy management system of a home microgrid integrated with a battery ESS (BESS). The proposed dynamic model integrates a deep learning (DL)-based predictive model, bidirectional long short-term memory (Bi-LSTM), with an optimization algorithm for optimal energy distribution and scheduling of a BESS-by determining the characteristics of distributed resources, BESS properties, and the user’s lifestyle. The aim is to minimize the per-day electricity cost charged by time-of-use (TOU) pricing while considering the day-basis peak demand penalty. The proposed system also considers the operational constraints of renewable resources, the BESS, and electrical appliances. The simulation results from realistic case studies demonstrate the validation and responsibility of the proposed system in reducing a household’s daily electricity cost.

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“…Traditional control approaches depending on the detailed parameters of the system model can expose various challenges under harsh disturbances. To this end, non-model based approaches such as model predictive controller (MPC) [17], artificial neural networks (ANNs) [18], and fuzzy logic [19], a neural network supported MPC design [20] methodologies are presented for the robust system operations, which do not need any system model but also have been observed to outperform with satisfying results. In this context, a great number of AI-based smarter aspects with artificial and deep neural networks are utilized for better performance such as control of DC-DC converter using ANNs [21] for providing stable output voltage.…”

Section: Introductionmentioning

confidence: 99%

“…or more time series are utilized to estimate future values. According to the conventional equivalent circuit of PV panel, IPV is stated as(20)…”

mentioning

confidence: 99%

Deep Learning-Aided Sensorless Control Approach for PV Converters in DC Nanogrids

2021

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In a microgrid, photovoltaic (PV) systems are broadly preferred with energy storage systems (ESSs) that form small-sized direct current (DC) microgrids. They are also termed local grids i.e., DC nanogrids, which feed the local consumers to some extent in the next decades. Therefore, ESSs enable the DC nanogrids more flexible and stable by preserving the intermittent nature of renewables. Yet still, feeding local consumers smoothly with PV-battery-based systems is exceedingly a considerable theme. In this context, proper control of power electronics converters as the main carrier of the system is essential. Besides, the rise of PV applications challenges possible issues upon integrating the conventional grid. Emerging possible issues and the ways of dealing with them have been developing day by day. Thus, it is inevitable that innovative methods will be put into practice. To achieve this goal, the deep learning aidedsensorless control approach is adopted. To validate the proposed control method, the training phase is presented elaborately with the help of the experimental setup of a DC nanogrid. From the obtained results, it is concluded that the deep learning-based approach reaches very small error values, captures the system dynamics with a high accuracy rate successfully, and allows the possibility to apply practically.INDEX TERMS Deep neural network (DNN), sensorless control, deep supervised learning, photovoltaics, power electronic converters, DC microgrid, DC nanogrid.

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An Analysis of Multi Objective Energy Scheduling in PV-BESS System Under Prediction Uncertainty

Cited by 21 publications

References 31 publications

Combined heat and power using high-temperature proton exchange membrane fuel cells for housing facilities

Combined heat and power using high-temperature proton exchange membrane fuel cells for housing facilities

Deep learning based optimal energy management for photovoltaic and battery energy storage integrated home micro-grid system

Deep Learning-Aided Sensorless Control Approach for PV Converters in DC Nanogrids

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