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A new data driven long-term solar yield analysis model of photovoltaic power plants

- Ray, Biplob, Shah, Rakibuzzaman, Islam, Md Rabiul, Islam, Syed

  • Authors: Ray, Biplob , Shah, Rakibuzzaman , Islam, Md Rabiul , Islam, Syed
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 8, no. (2020), p. 136223-136233
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  • Description: Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs). © 2013 IEEE.

Enhanced profitability of photovoltaic plants by utilizing cryptocurrency-based mining load

- Eid, Bilal, Islam, Md Rabiul, Shah, Rakibuzzaman, Nahid, Abdullah, Kouzani, Abbas, Mahmud, M.

  • Authors: Eid, Bilal , Islam, Md Rabiul , Shah, Rakibuzzaman , Nahid, Abdullah , Kouzani, Abbas , Mahmud, M.
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Applied Superconductivity Vol. 31, no. 8 (2021), p.
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  • Description: The grid connected photovoltaic (PV) power plants (PVPPs) are booming nowadays. The main problem facing the PV power plants deployment is the intermittency which leads to instability of the grid. In order to stabilize the grid, either energy storage device - mainly batteries - or a power curtailment technique can be used. The additional cost on utilizing batteries make it not preferred solution, because it leads to a drop in the return on investment (ROI) of the project. A good alternative, is using a customized load (such as; cryptocurrency-based loads) which consumes the surplus energy. This paper investigating the usage of a customized load - cryptocurrency mining rig - to create an added value for the owner of the plant and increase the ROI of the project. These devices are widely used to perform the required calculations for validating the transactions on the network of the Blockchain. A comparison between the ROI of the mining rig and the battery have been conducted in this study. Based on this study the mining rig has superior ROI of 7.7% - in the case with the lowest ROI - compared to 4.5% for battery. Moreover, an improved controlling strategy is developed to combine both the battery and mining rig in the same system. The developed strategy is able to keep the profitability as high as possible during the fluctuation of the mining network. © 2002-2011 IEEE.

Impact of MMC-HVDC Control Parameter Selection on the Dynamic Performance of AC System

- Shah, Rakibuzzaman, Islam, Syed, Islam, Md Rabiul

  • Authors: Shah, Rakibuzzaman , Islam, Syed , Islam, Md Rabiul
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices; ASEMD 2020, Virtual Tianjin ;16 to 18 October 2020
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  • Description: High penetration of voltage source converter (VSC) based system is anticipated in future power systems. This study discusses the interaction of modular multilevel converter (MMC) based high voltage dc (HVDC) system with the dynamic of the host ac network in the electromechanical time frame. The respective controls for MMC-HVDC system suitable for the stability studies in the electromechanical time-frame are considered. This paper gives insight into the effects of several important sensitivities such as (i) MMC control implementation, (ii) non linearity associated with the fault ride through implementation, and (iii) phase locked loop parameters on ac system dynamics. The comprehensive analysis has been performed using an equivalent model of Great British transmission system. © 2020 IEEE.

An extreme learning machine based adaptive VISMA for stability enhancement of renewable rich power systems

- Setiadi, Herlambang, Shah, , Rakibuzzaman, Islam, Md Rabiul, Asfani, Dimas, Nasution, Tigor, Abdillah, Muhammad, Megantoro, Prisma, Krismanto, Awan

  • Authors: Setiadi, Herlambang , Shah, , Rakibuzzaman , Islam, Md Rabiul , Asfani, Dimas , Nasution, Tigor , Abdillah, Muhammad , Megantoro, Prisma , Krismanto, Awan
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 11, no. 2 (2022), p.
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  • Description: Maintaining power system stability in renewable-rich power systems can be a challenging task. Generally, the renewable-rich power systems suffer from low and no inertia due to the integration of power electronics devices in renewable-based power plants. Power system oscillatory stability can also be affected due to the low and no inertia. To overcome this problem, additional devices that can emulate inertia without adding synchronous machines can be used. These devices are referred to as virtual synchronous machines (VISMA). In this paper, the enhancement of oscillatory stability of a realistic representative power system using VISMA is proposed. A battery energy storage system (BESS) is used as the VISMA by adding an additional controller to emulate the inertia. The VISMA is designed by using Fruit Fly Optimization. Moreover, to handle the uncertainty of renewable-based power plants, the VISMA parameters are designed to be adaptive using the extreme learning machine method. Java Indonesian Power Grid has been used as the test system to investigate the efficacy of the proposed method against the conventional POD method and VISMA tuning using other methods. The simulation results show that the proposed method can enhance the oscillatory stability of the power system under various operating conditions. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Multi-mode damping control approach for the optimal resilience of renewable-rich power systems

- Setiadi, Herlambang, Mithulananthan, Nadarajah, Shah, Rakibuzzaman, Islam, Md Rabiul, Fekih, Afer, Krismanto, Awan, Abdillah, Muhammad

  • Authors: Setiadi, Herlambang , Mithulananthan, Nadarajah , Shah, Rakibuzzaman , Islam, Md Rabiul , Fekih, Afer , Krismanto, Awan , Abdillah, Muhammad
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Energies Vol. 15, no. 9 (2022), p.
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  • Description: The integration of power-electronics-based power plants is developing significantly due to the proliferation of renewable energy sources. Although this type of power plant could positively affect society in terms of clean and sustainable energy, it also brings adverse effects, especially with the stability of the power system. The lack of inertia and different dynamic characteristics are the main issues associated with power-electronics-based power plants that could affect the oscillatory behaviour of the power system. Hence, it is important to design a comprehensive damping controller to damp oscillations due to the integration of a power-electronics-based power plant. This paper proposes a damping method for enhancing the oscillatory stability performance of power systems with high penetration of renewable energy systems. A resilient wide-area multimodal controller is proposed and used in conjunction with a battery energy storage system (BESS) to enhance the damping of critical modes. The proposed control also addresses resiliency issues associated with control signals and controllers. The optimal tuning of the control parameters for this proposed controller is challenging. Hence, the firefly algorithm was considered to be the optimisation method to design the wide-area multimodal controllers for BESS, wind, and photovoltaic (PV) systems. The performance of the proposed approach was assessed using a modified version of the Java Indonesian power system under various operating conditions. Both eigenvalue analysis and time-domain simulations are considered in the analysis. A comparison with other well-known metaheuristic methods was also carried out to show the proposed method’s efficacy. Obtained results confirmed the superior performance of the proposed approach in enhancing the small-signal stability of renewable-rich power systems. They also revealed that the proposed multimodal controller could enhance the penetration of renewable energy sources in the Javan power system by up to 50%. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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