Large Scale Renewable Energy Integration: Issues and Solutions

Kumar, G. Hemath; Sarojini, Ratnam Kamala; Palanisamy, K.; Padmanaban, Sanjeevikumar; Holm‐Nielsen, Jens Bo

doi:10.3390/en12101996

Cited by 60 publications

(30 citation statements)

References 39 publications

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“…The higher fluctuations in the injected power will seriously affect the power system stability. Hence, the energy storage system (ESS) is an essential element to mitigate these variations and improves the system power quality [2,3].…”

Section: Introductionmentioning

confidence: 99%

Effective Management System for Solar PV Using Real-Time Data with Hybrid Energy Storage System

et al. 2020

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This paper proposes an effective management system for stand-alone solar photovoltaic (PV) using real-time data with Hybrid Energy Storage System (HESS). The abrupt movement of fleeting clouds often gives rise to PV power output fluctuations which in turn affect the power quality and system stability due to scattered solar radiation reception. These variations can limit through a ramp-limit controller and employing a DC link controller to maintain the stable DC link voltage. The battery is used in the system for continuous power application and the sudden variations in charging and discharging of battery power can create stress on the battery. These sudden changes in a battery will be removed by the super-capacitor (SC) unit and achieves a fast DC link voltage regulation. Hence, the high energy and power density devices such as battery and SC units will deliver more stable power into the system. The control scheme is tested in Matlab/Simulink and validated by Real-Time Hardware-in-Loop (HIL) simulator using periodic one-minute data for one year from the solar PV power plant from real-time.

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Section: Introductionmentioning

confidence: 99%

Effective Management System for Solar PV Using Real-Time Data with Hybrid Energy Storage System

et al. 2020

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“…Although Germany's grid is currently one of the most stable grids in the world, some of its essential components will have to be replaced. There is an unusually high renovation demand on the medium voltage range because increased electric mobility and decentralized electricity production mainly affect switching patterns and peak performances within the medium voltage range [7,8]. We must put in place an efficient monitoring system for both the existing and the new components to avoid disturbances and failures.…”

Section: Introductionmentioning

confidence: 99%

Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

Hoffmann¹,

Wildermuth²,

Gitzel³

et al. 2020

Sensors

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The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.

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“…Due to the serious concerns related to environmental health, the leading countries are focusing on increasing the penetration of RESs into the existing power system. 1 The microgrid (MGs) consists of the distributed generations (DGs) 2 based upon renewable energy sources (RESs) such as solar and wind. The power output of RESs depends upon the weather dependent parameters such as solar radiation and wind speed resulting into a highly intermittent and uncertain system.…”

Section: Introductionmentioning

confidence: 99%

Forecast‐based modeling and robust frequency control of standalone microgrids considering high penetration of renewable sources

Kumar

Mukherjee

Mathur

et al. 2020

Int Trans Electr Energ Syst

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Summary This article proposes forecast‐based modeling and robust frequency control strategy in isolated microgrids (MGs) to improve its stability. The intermittency and variability in renewable generation is problem for its smooth integration to MGs considering frequency stability. Continuous rise in penetration levels of renewable energy sources (RESs) is the main motivation behind forecast‐based modeling and controller design for MGs. The disturbances that affect the frequency in the MG may come from the load side and/or the generation sides. In MGs, at the generation side, the forecast of power from RESs is usually obtained to get a rough estimate of available renewable power. The forecasted power always differs from the actual one, so the secondary frequency controller may get overburdened due to forecast error resulting in abnormal frequency deviation that may lead to unstable power system. The proposed H∞ based robust control design considers the forecast error which improves the system stability and performance against disturbances coming from load/generation side. First, a long short term memory based recurrent neural network has been used to forecast the renewable power availability. Thereafter a H∞ controller is designed, and it is shown through extensive simulation studies that the H∞ controller outperforms optimized PID controllers so far as rejecting the effects of uncertainties in RESs, forecasting errors, and model parameter variations are concerned. The proposed robust controller design is also validated with real time simulator (OP4510) made by Opal‐RT. The controller hardware in the loop (CHIL) test has been done taking the simulation time step of 50 μS.

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Large Scale Renewable Energy Integration: Issues and Solutions

Cited by 60 publications

References 39 publications

Effective Management System for Solar PV Using Real-Time Data with Hybrid Energy Storage System

Effective Management System for Solar PV Using Real-Time Data with Hybrid Energy Storage System

Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

Forecast‐based modeling and robust frequency control of standalone microgrids considering high penetration of renewable sources

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