A Data-Driven Short-Term PV Generation and Load Forecasting Approach for Microgrid Applications

Trivedi, Rohit; Patra, Sandipan; Khadem, Shafiuzzaman K.

doi:10.1109/jestie.2022.3179961

Cited by 17 publications

(3 citation statements)

References 33 publications

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“….Te tree structure estimation method is an intelligent algorithm designed based on the Bayesian optimization framework. In the process of parameter optimization, the probability density of the proxy function is used to describe the domain space of parameters [19][20][21]. In this study, the loss function is used as the proxy function, and the identifcation parameters are selected by referring to the historical result evaluation of the loss function.…”

Section: Loss Functionmentioning

confidence: 99%

Identification Method of Dynamic Parameters of Distribution Network Lines

et al. 2022

International Transactions on Electrical Energy Systems

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Accurate network parameters are of great importance for the accurate control of the power distribution network (PDN). In fact, the line parameters of the PDN are always affected by external operating conditions. However, most of the line parameters in the PDN account are static parameters. In order to obtain the dynamic parameters that reflect the line operating condition, this study presents a method that uses only the RMS voltage of the first section of the line and the RMS voltage and power at the low-voltage side of the transformer. This study introduces the processing method of abnormal measurement data, constructs a derivative-free identification equation represented by a matrix, and uses the designed loss function combined with a heuristic method to solve the equation. An actual feeder is used in the experimental part. The experimental data show that the method has some antipower noise ability, and the identification accuracy of this method is better than that of the genetic algorithm and random search algorithm.

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Section: Loss Functionmentioning

confidence: 99%

Identification Method of Dynamic Parameters of Distribution Network Lines

et al. 2022

International Transactions on Electrical Energy Systems

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“…The methodology proposed in this paper can be used also in Microgrids (MGs), which are normally based on renewable/non-conventional distributed energy resources (DERs). For instance, in [16] and [17] some applications for facing different challenges introduced by MGs, such as PV Generation and Load Forecasting, Power System Problems, Energy Management and Fault detection and Protection in MGs are reviewed. In [18] a recurrent neural network (RNN) is proposed for performing islanding detection at MGs.…”

Section: Introductionmentioning

confidence: 99%

Detection of Abnormal Operation of PV Inverters Based on Regressive Prediction Models With Recursive Least Squares Training

Moreno,

Laguna,

Cipriano

et al. 2024

IEEE J. Emerg. Sel. Top. Ind. Electron.

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The large scale integration of photovoltaic (PV) power plants has launched the massive deployment of PV inverters. In fact, just a single multi-MW PV plant may have thousands of them, which can be also found isolated in low scale distributed generation applications. The number of grid-connected assets can be no longer managed and maintained effectively without using AI tools, able to analyze their operation, detect faults and support decision-making maintenance tools. In this paper, a Linear Regression method able to detect abnormal operation in PV systems, based on a Recursive Least Squares (RLS) training algorithm, which requires a low amount of data, mainly energy generation measurements and meteorological data, is proposed. In addition, two different applications of this methodology will be presented, one based on issuing a simplified model for realtime analysis, and another one consisting of a complex model for long-term diagnosis. The first one is focused on detecting faults and abnormal operation profiles in real-time, while the second one permits assessing the historical efficiency decay of PV plants in longer periods. Both will be used for detecting abnormal operation of PV inverters and panels. The performance and behavior of these algorithms will be tested using the data of 22 PV plants, placed at different climatic areas and with different peak powers. The results will show the good performance of the proposed fault detection method in both applications.

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“…Load forecasting is an important component of microgrid operation, as it can help predict the power demand of a microgrid in advance. Accurate load forecasting allows microgrid operators to plan and allocate resources effectively, which can improve the efficiency and reliability of microgrid operation [18,19]. In recent years, deep learning algorithms have shown promising results in solving various optimization problems in power systems.…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning Method for Economic Power Dispatch of Microgrid in OPAL-RT Environment

et al. 2023

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This paper focuses on the economic power dispatch (EPD) operation of a microgrid in an OPAL-RT environment. First, a long short-term memory (LSTM) network is proposed to forecast the load information of a microgrid to determine the output of a power generator and the charging/discharging control strategy of a battery energy storage system (BESS). Then, a deep reinforcement learning method, the deep deterministic policy gradient (DDPG), is utilized to develop the power dispatch of a microgrid to minimize the total energy expense while considering power constraints, load uncertainties and electricity price. Moreover, a microgrid built in Cimei Island of Penghu Archipelago, Taiwan, is investigated to examine the compliance with the requirements of equality and inequality constraints and the performance of the deep reinforcement learning method. Furthermore, a comparison of the proposed method with the experience-based energy management system (EMS), Newton particle swarm optimization (Newton-PSO) and the deep Q-learning network (DQN) is provided to evaluate the obtained solutions. In this study, the average deviation of the LSTM forecast accuracy is less than 5%. In addition, the daily operating cost of the proposed method obtains a 3.8% to 7.4% lower electricity cost compared to that of the other methods. Finally, a detailed emulation in the OPAL-RT environment is carried out to validate the effectiveness of the proposed method.

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A Data-Driven Short-Term PV Generation and Load Forecasting Approach for Microgrid Applications

Cited by 17 publications

References 33 publications

Identification Method of Dynamic Parameters of Distribution Network Lines

Identification Method of Dynamic Parameters of Distribution Network Lines

Detection of Abnormal Operation of PV Inverters Based on Regressive Prediction Models With Recursive Least Squares Training

A Deep Reinforcement Learning Method for Economic Power Dispatch of Microgrid in OPAL-RT Environment

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