Energy management of a microgrid: Compensating for the difference between the real and predicted output power of photovoltaics

Riar, Baljit; Lee, Jae-Hwa; Tosi, Alessandra; Duncan, Stephen R.; Osborne, Michael A.; Howey, David A.

doi:10.1109/pedg.2016.7527042

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…The improvement issue considers a variety of practical restrictions. The issue has been fixed piece by piece to save machine time and complexity [27].…”

Section: Energy Management Systemsmentioning

confidence: 99%

Operation and Control of Microgrids Using IoT (Internet of Things)

Voumick¹,

Deb²,

Khan³

2021

JSEA

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The current microgrid power management system is undergoing a significant and drastic overhaul. The integration of existing electrical infrastructure with an information and communication network is an inherent and significant need for microgrid classification and operation in this case. Microgrid technology's most important features: 1) Full duplex communication; 2) Advanced metering infrastructure; 3) Renewable and energy resource integration; 4) Distribution automation and complete monitoring, as well as overall power system control. A microgrid's communication infrastructure is made up of several hierarchical communication networks. Microgrid applications can frequently be found in numerous aspects of energy consumption. Because it provides a spontaneous communicational network, the Internet of Things plays a fundamental and crucial role in Microgrid infrastructure. This paper covers the deployment of a comprehensive energy management system for microgrid communication infrastructure based on the Internet of Things (IoT). This paper discusses microgrid operations and controls using the Internet of Things (IoT) architecture. Microgrids make use of IoT-enabled technologies, in conjunction with power grid equipment, which are enabling local networks to provide additional services on top of the essential supply of electricity to local networks that operate in parallel with or independently of the regional grid. Local balancing, internal blockage management, and request for support marketplace or grid operator activities are examples of auxiliary services provided by the microgrid that can add value to each end-user and other true stakeholders. Different technologies, architectures, and applications that use IoT as a key element with the main purpose of preserving and regulating innovative smart microgrids in accordance with modern optimization features and regulations are designed to update and improve efficiency, resiliency, and economics.

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“…The improvement issue considers a variety of practical restrictions. The issue has been fixed piece by piece to save machine time and complexity [27].…”

Section: Energy Management Systemsmentioning

confidence: 99%

Operation and Control of Microgrids Using IoT (Internet of Things)

Voumick¹,

Deb²,

Khan³

2021

JSEA

View full text Add to dashboard Cite

show abstract

“…This algorithm is focused on the principle: avoid wastage of the existing renewable potential at each time interval. The reduction in output power due to converter failure, shading of the panels, and accumulation of dirt on the PV panels is controlled using model predictive control scheme in [27]. The objective of the energy management system (EMS) is to minimize the energy taken from the grid.…”

Section: Power Quality Controlmentioning

confidence: 99%

Optimization Techniques for Operationand Control of Microgrids � Review

Ramprabhakar

2018

JGE

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Microgrid systems show great promise in integrating large numbers of distributed generation systems, based on renewable energy sources into future power systems. The characteristics of power generated by renewable energy sources is random, unregulated and highly unreliable, vowing to the stochastic nature of the sources like wind, solar etc. While integrating the locally generated power into grid sounds very attractive, there are many operational and planning concerns. Also, a major chunk of power generated is weather dependent, this makes an energy storage system and/or backup power generation system an imperative part of microgrids. A proper planning and designing is the first step towards integrated power. Optimization techniques justify cost of investment of a Microgrid by enabling economic and reliable usage of resources. This paper summarizes various optimization methodologies and criterion for optimization of Microgrids. Extensive study of published literature show that computational alternatives like evolutionary, heuristic and non-classical algorithms show better results when compared to other conventional methods. The study of multi-objective optimization problems shows superior performance by combining intelligent optimization algorithms with adaptive techniques.

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“…Another approach to PV production estimation is presented in [24], which calculates the parameters of the nonlinear PVUSA model based on measured power, calculated clear-sky irradiance, and forecasted temperature. The impacts of the discrepancies between the actual and the predicted PV production in a grid-connected microgrid have also been considered in [25]. In this case, a compensation factor based on the ratio of the sum of the two latest consecutive actual PV measured power and the sum of the corresponding two latest consecutive predicted PV production is proposed.…”

Section: Introductionmentioning

confidence: 99%

Reduction of Forced Outages in Islanded Microgrids by Compensating Model Uncertainties in PV Rating and Battery Capacity

Michaelson

Mahmood

Jiang

2018

IEEE Power Energy Technol. Syst. J.

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Energy management systems for islanded microgrids often rely on predictions of energy availability and usage. Such predictions can be used to plan actions, such as shedding non-essential loads, so that critical loads continue to be served. However, uncertainties in the prediction models may lead to incorrect decisions, and subsequently jeopardize reliable operation of the microgrid. For a photovoltaic (PV) and battery based microgrid, uncertainties in the PV rating and the battery capacity model parameters can lead to otherwise avoidable outages. In this paper, techniques have been developed to identify and compensate for such model uncertainties. The approach uses differences between the actual and predicted data sequences to determine compensation factors to improve prediction accuracy. The developed techniques account for operating condition changes automatically, and no additional sensors are needed for their implementation. The method has been evaluated using data from rooftop irradiance and temperature sensors and the corresponding forecasts. It has been shown that the proposed techniques can improve the accuracy of the predictions and hence lead to more effective energy management decisions. Together with a pre-emptive load shedding strategy, the total outage time of the microgrid can be shortened by as much as 11% for the chosen scenario.

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Energy management of a microgrid: Compensating for the difference between the real and predicted output power of photovoltaics

Cited by 7 publications

References 10 publications

Operation and Control of Microgrids Using IoT (Internet of Things)

Operation and Control of Microgrids Using IoT (Internet of Things)

Optimization Techniques for Operationand Control of Microgrids � Review

Reduction of Forced Outages in Islanded Microgrids by Compensating Model Uncertainties in PV Rating and Battery Capacity

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