Optimized utilization of distributed renewable energies for island microgrid clusters considering solar-wind correlation

Li, Zhenlong; Li, Peng; Zhen, Yuan; Xia, Jing; Tian, De

doi:10.1016/j.epsr.2022.107822

Cited by 23 publications

(14 citation statements)

References 32 publications

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“…As explained, the stochastic behaviours on the supply and demand sides of the physical layer of the SMG and the uncertainties of the cyber layer and ITS are studied in the introduced model. Some of these stochastic parameters and sub-systems, particularly in the physical layer, are chronologically dependent, and interactive correlations exist [81,82]. The time-sequence, auto-correlation, and cross-correlation of DERs and loads in the physical layer of the SMG might affect the developed analytical reliability evaluation model, considering various uncertainties [83,84].…”

Section: Headermentioning

confidence: 99%

“…

\begin{equation}ES{M}_{ESS} = \left[ { \def\eqcellsep{&}\begin{array}{@{}*{2}{c}@{}} 0&{{U}_{ESS} + CDF\left( {SOC^{\min }} \right)}\\[6pt] {P_{rated}^{ESS}}&{{A}_{ESS} \times \left( {1 - CDF\left( {SOC^{\min }} \right)} \right)} \end{array} } \right]\end{equation}

As explained, the stochastic behaviours on the supply and demand sides of the physical layer of the SMG and the uncertainties of the cyber layer and ITS are studied in the introduced model. Some of these stochastic parameters and sub‐systems, particularly in the physical layer, are chronologically dependent, and interactive correlations exist [81, 82]. The time‐sequence, auto‐correlation, and cross‐correlation of DERs and loads in the physical layer of the SMG might affect the developed analytical reliability evaluation model, considering various uncertainties [83, 84].…”

Section: Modellingmentioning

confidence: 99%

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Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

Aslani

Hashemi‐Dezaki

Ketabi

2022

IET Renewable Power Gen

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The reliability of smart micro‐grids (SMGs), as a cyber‐physical system (CPS), might be influenced by cyber failures and information transmission faults. Several Monte Carlo simulation (MCS)‐based approaches have been reported to assess the reliability of SMGs and smart grids. On the other hand, analytical reliability assessment methods have been presented in some research works, while the cyber system has not been concerned. However, the literature shows a research gap in developing an accurate and fast reliability evaluation method for SMGs based on the unavailability of cyber elements and information transmission faults. This article tries to fill the discussed gap by adding the analytical modelling of cyber‐physical interdependencies and information transmission faults to available analytical methods, focusing on physical uncertainties. Comparing the proposed model with existing MCS‐based and analytical reliability evaluation methods illustrates the advantages of this research. Test results show that less than 5.7% expected energy not supplied (EENS) error occurs by the proposed method, which would be much faster than MCS‐based ones. Moreover, the sensitivity analyses highlight the impacts of the cyber network topologies on the cyber‐physical interdependencies.

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Section: Headermentioning

confidence: 99%

“…

\begin{equation}ES{M}_{ESS} = \left[ { \def\eqcellsep{&}\begin{array}{@{}*{2}{c}@{}} 0&{{U}_{ESS} + CDF\left( {SOC^{\min }} \right)}\\[6pt] {P_{rated}^{ESS}}&{{A}_{ESS} \times \left( {1 - CDF\left( {SOC^{\min }} \right)} \right)} \end{array} } \right]\end{equation}

Section: Modellingmentioning

confidence: 99%

Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

Aslani

Hashemi‐Dezaki

Ketabi

2022

IET Renewable Power Gen

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“…The authors presented a scalable and flexible hybrid AC/DC microgrid clustering structure in [12], with distributed systems for coordinated operation. A solar-wind correlation-based efficient utilization of distributed sources for island microgrid clusters is presented in [13]. For the best energy management of interconnected clusters of microgrids with net-zero energy multi-greenhouses, model predictive control is detailed in [14].…”

Section: Related Workmentioning

confidence: 99%

A Review of Grid Code Requirements for the Integration of Renewable Energy Sources in Ethiopia

et al. 2022

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Rapid integration of renewable energy into the electric grid has ramifications for grid management and planning. Therefore, system operators have formulated grid code requirements to ensure that the grid continues to operate in a secure, safe, and cost-effective manner. The current state of grid code in Ethiopia, as well as the need for it, is discussed in this article. It lays out the technological grid integration requirements, with a focus on small and microgrids, which are especially important for the integration of renewable. The barriers to grid code normalization and renewable energy grid compatibility testing are identified, and suggestions for continued grid code development in Ethiopia based on Danish observations are provided. Further, a detailed comparative analysis of the Ethiopian grid code with the IEEE 1547-2003 and IEEE 1547-2018 standards is presented.

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“…For any programming style, such as mixed-integer programming, dynamic programming, or stochastic programming, there are a variety of options to choose from. These strategies can be used for a variety of problems, including sequence optimization problems [20][21][22][23][24][25]. Despite this, it is required to start over with each iteration of this strategy because the results are so complex.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System

Prasad

Isaac

Ponsudha³

et al. 2022

International Journal of Photoenergy

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Performance, cost, and aesthetics are all difficult to beat in today’s expanding distributed rooftop solar sector, and flat-plate PV is no exception. Photovoltaics will be able to take advantage of some of their most significant advantages as a result of this marketplace, including the elimination of transmission losses and the generation of power at the point of sale. Concentrated photovoltaic (CPV) technology, on the other hand, represents a viable alternative in the quest for ever-lower normalised energy costs and ever-shorter energy payback times. Material, components, and manufacturing techniques from allied sectors, particularly the power electronics industry, have been adapted to lower system costs and time-to-market for the system under development. The LFR is less than 30 mm wide to maximise thermal efficiency, and a densely packed cell array has been used to maximise electrical output. The Matlab simulations show that the proposed machine learning-based LFR technique has a greater concentration rate than the present LFR method, as demonstrated by the results.

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Optimized utilization of distributed renewable energies for island microgrid clusters considering solar-wind correlation

Cited by 23 publications

References 32 publications

Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

A Review of Grid Code Requirements for the Integration of Renewable Energy Sources in Ethiopia

A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System

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