Stochastic modeling of photovoltaic power generation and electricity prices

Benth, Fred Espen; Ibrahim, Noor ‘Adilah

doi:10.21314/jem.2017.164

Cited by 18 publications

(14 citation statements)

References 21 publications

(30 reference statements)

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“…Scenario analysis is a method which maps the diversity of different scenarios. Its aim is to explore the range of possible choices, and to test how well such choices may succeed in various possible futures [18].…”

Section: Tests and Resultsmentioning

confidence: 99%

Modelling and Simulation of Distributed Systems Using Intelligent Multi-Agents

Dragomir

2022

J. Sci. Arts

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Among the challenges in the field of the paper we have identified the necessity of control methods and tools of mobile robots (MRS) equipped with robot manipulator (RM) serving assembly/ disassembly mechatronic lines (A/DML).The framework of the work reported here is represented by SMART&ASTI A/DML served by two MRs with RM, working collaboratively and the goal is development of a multi-agent system able control interactively trajectories of MRs, working collaboratively to serve the A/DML, by avoiding the collisions between them. The advantage offered by the proposed solution consists in graphical representation of the trajectory of MRs working collaboratively, as well as the current status of them, while the users are able to interact intuitively with the MRs trough the proposed GUI The added value of the paper consists in the implementation of multi-agents systems in a complex A/DML served by two MRs equipped with RM, working collaboratively, the increased autonomy in communication between the entities of entire system, and the adaptive control of MRs trajectories.

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Section: Tests and Resultsmentioning

confidence: 99%

Modelling and Simulation of Distributed Systems Using Intelligent Multi-Agents

Dragomir

2022

J. Sci. Arts

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“…After logarithmizing the data in Eq.1, the discretized hourly cloud component v t is assumed to be characterized by an autoregressive component, as in Benth and Ibrahim (2017), and by an hourly seasonal component, as in Keles et al (2013) for the wind capacity utilization. To account for this hourly seasonal component of the cloudiness, the average value vDA…”

Section: Solar Pv Generationmentioning

confidence: 99%

Short-term risk management of electricity retailers under rising shares of decentralized solar generation

et al. 2022

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“…Since solar power gets energy from solar radiation, the output from solar power generation is usually calculated using the amount of solar radiation. Benth and Ibrahim (2017) propose a stochastic model for solar power production using cyclical solar radiation intensity. That is why it is natural to conduct volumetric risk management of solar power generators based on solar radiation or solar power generation.…”

Section: Introductionmentioning

confidence: 99%

Volumetric Risk Hedging Strategies and Basis Risk Premium for Solar Power

Kanamura

2020

JAI

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This paper studies volumetric risk hedging strategies for solar power under incomplete market settings with a twofold proposal of temperature-based and solar power generationbased models for solar power derivatives and discusses the basis risk arising from solar power volumetric risk hedge with temperature. Based on an indirect modeling of solar power generation using temperature and a direct modeling of solar power generation, we design two types of call options written on the accumulated non cooling degree days (ANCDDs) and the accumulated low solar power generation days (ALSPGDs), respectively, which can hedge cool summer volumetric risk more appropriately than those on well-known accumulated cooling degree days. We offer the pricing formulas of the two options under the good-deal bounds (GDBs) framework, which can consider incompleteness of solar power derivative markets. To calculate the option prices numerically, we derive the partial differential equations for the two options using the GDBs. Empirical studies using Czech solar power generation and Prague temperature estimate the parameters of temperature-based and solar power generation-based models, respectively. We numerically calculate the call option prices on ANCDDs and ALSPGDs, respectively, as the upper and lower price boundaries using the finite difference method. Results show that the call option prices based on a solar power generation process are bigger than the call option prices based on a temperature process. This is consistent with the fact that the solar power generation approach takes into account more comprehensive risk than the temperature approach, resulting in the bigger prices for the solar power generation approach. We finally show that the basis risk premiums, i.e., solar power generation-based call option prices minus temperature-based call option prices, decrease in line with initial temperature greater than around 25 • C. This may be because the uncertainty in solar power generation by temperature decreases due to the cancellation between the increase in solar power generation due to the increase in solar radiation and the decrease in solar power generation due to the decrease in solar panel efficiency.

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Stochastic modeling of photovoltaic power generation and electricity prices

Cited by 18 publications

References 21 publications

Modelling and Simulation of Distributed Systems Using Intelligent Multi-Agents

Modelling and Simulation of Distributed Systems Using Intelligent Multi-Agents

Short-term risk management of electricity retailers under rising shares of decentralized solar generation

Volumetric Risk Hedging Strategies and Basis Risk Premium for Solar Power

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