Uncertainty cost functions for solar photovoltaic generation, wind energy generation, and plug-in electric vehicles: mathematical expected value and verification by Monte Carlo simulation

Arevalo, J. C.; Santos, Fabian; Rivera, Sergio

doi:10.1504/ijpec.2019.098620

Cited by 26 publications

(42 citation statements)

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Section: Formulation Of Opfmentioning

confidence: 91%

“…The underestimation cost function of renewable generators includes those for photovoltaic generators, wind generators, and electric cars. The overestimation expressions of renewable generators follow the same logic and can be found in Arevalo et al 46 • Solar PDF In previous research work, [45][46][47] different PDFs were evaluated to determine which one would best mathematically model the behaviour of the irradiation emitted by the sun. Their results showed that the irradiation forecast in some places of the world follows log-normal PDF, that is to say, this PDF is the best match for the solar irradiance behaviour.…”

Section: Control Models For Controllable Pv Energy Systemsmentioning

confidence: 98%

“…In previous research work, different PDFs were evaluated to determine which one would best mathematically model the behaviour of the irradiation emitted by the sun. Their results showed that the irradiation forecast in some places of the world follows log‐normal PDF, that is to say, this PDF is the best match for the solar irradiance behaviour.…”

Section: Formulation Of Opfmentioning

confidence: 99%

“…This variable is subject to the geographical position of the point of interest. The studies showed in previous studies got that the wind speed forecast in some places of the world follows Rayleigh PDF, that is to say, this PDF is the best match for the wind speed behaviour. In this study, wind speed distribution is modelled by Rayleigh PDF as follows:

f_{v} ((), v) = ((), \frac{v}{σ^{2}}) e^{(- {((), \frac{v}{\sqrt{2} σ})}^{2})} .

…”

Section: Formulation Of Opfmentioning

confidence: 99%

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Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization

Duman

Rivera

et al. 2019

Int Trans Electr Energ Syst

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Summary The produced energy from varied sources in modern power systems is to be optimally planned for planning and operating of power system under the determined limit conditions. Recently, the rising overall people population of the world, the increasing of people requirements, improvements of technology, and ecosystem and global climate changes have caused with the increasing of electric energy demand. One of the most important solution methods to meet this energy demand is considered as utilization of renewable energy sources (RESs) in power systems. The structure of power systems has become with the usage of RESs more complex. The optimal power flow (OPF) from planning and operation problems has converted to difficult problem with RESs integrated into modern power systems. This paper presents the OPF problem of power systems with a high penetration of controllable renewable sources. These kinds of sources are able to inject a determined power since they have a back‐up unit (storage). Uncertain solar irradiance and wind speed are simulated via log‐normal and Rayleigh probability distributions, respectively. The proposed OPF problem with controllable renewable sources is solved by the differential evolutionary particle swarm optimization (DEEPSO) algorithm. Simulations conducted on various test systems illustrate the effectiveness and efficiency of DEEPSO as compared with other algorithms including moth swarm algorithm, backtracking search algorithm, and differential search algorithm. In addition, the Wilcoxon signed‐rank test is applied to show the supremacy, effectiveness, and robustness of DEEPSO algorithm.

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Section: Formulation Of Opfmentioning

confidence: 91%

Section: Control Models For Controllable Pv Energy Systemsmentioning

confidence: 98%

Section: Formulation Of Opfmentioning

confidence: 99%

f_{v} ((), v) = ((), \frac{v}{σ^{2}}) e^{(- {((), \frac{v}{\sqrt{2} σ})}^{2})} .

…”

Section: Formulation Of Opfmentioning

confidence: 99%

See 2 more Smart Citations

Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization

Duman

Rivera

et al. 2019

Int Trans Electr Energ Syst

Self Cite

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show abstract

“…To accomplish this objective, it is necessary to have battery energy storage [23]. Developing batteries that are safer, powerful, economical and with a longer useful life [24][25][26][27][28][29] has been a technological challenge in the last decade. Most battery banks are made of lithium batteries.…”

Section: Introductionmentioning

confidence: 99%

Impact of Energy Storage Useful Life on Intelligent Microgrid Scheduling

2020

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Planning the operation scheduling with optimization heuristic algorithms allows microgrids to have a convenient tool. The developments done in this study attain this scheduling taking into account the impact of energy storage useful life in the microgrid operation. The scheduling solutions, proposed for the answer of an optimization problem, are obtained by using a metaheuristic algorithm called Differential Evolutionary Particle Swarm Optimization (DEEPSO). Thanks to the optimization that is conducted in this study, it is possible to formulate dispatches of the existent microgrid (MG) by always looking for the ideal dispatch that implies a lower cost and provides a greater viability to any project related to renewable energy, electric vehicles and energy storage. These advances oblige the battery manufacturers to start looking for more powerful batteries, with lower costs and longer useful life. In this way, this paper proposes a scheduling tool considering the energy storage useful life.

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Dynamic optimal power flow with cross entropy covariance matrix adaption evolutionary strategy for systems with electric vehicles and renewable generators

2021

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Summary Modern power systems which integrate renewable energy sources (RESs), such as wind or solar energy sources and plug‐in electric vehicles (PEVs), need to carry out the uncertainty due to the access of demanded or injected power. Therefore, it is necessary to consider uncertainty costs in optimal power flow problems. To develop the uncertainty cost functions, log‐normal probability distribution function (PDF) for solar irradiance, normal PDF for unloading and loading behavior of PEVs, and Rayleigh PDF for wind speed are considered. In this work, a dynamic optimal power flow (DOPF) model is formulated by considering the uncertainties of RESs and PEVs. A newly hybrid metaheuristic algorithm named cross entropy (CE) covariance matrix adaption evolutionary strategy (CMAES) is proposed to evaluate the DOPF problem in a modified IEEE 118‐bus system. The application of adaptive step length in the CMAES method improves the local exploitation capability, while the CE method is used in the initial stage for global exploration due to its fast convergence. Simulation results indicate that the proposed technique can solve the DOPF problems with RESs and PEVs effectively and can provide high‐quality solutions compared to different techniques. The conventional statistical method called ANOVA test is performed for comparative analysis of different techniques. The results of this test show the validation of CE‐CMAES compared to different optimization techniques. Therefore, the proposed method could be easily and efficiently applied by power system operators in real‐time optimal power flow operations.

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Uncertainty cost functions for solar photovoltaic generation, wind energy generation, and plug-in electric vehicles: mathematical expected value and verification by Monte Carlo simulation

Cited by 26 publications

References 0 publications

Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization

Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization

Impact of Energy Storage Useful Life on Intelligent Microgrid Scheduling

Dynamic optimal power flow with cross entropy covariance matrix adaption evolutionary strategy for systems with electric vehicles and renewable generators

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