A Monte Carlo Approach to Represent Uncertainty in the European Electricity Market

Orgaz, Alberto; Bello, Antonio; Reneses, Javier

doi:10.1109/eem.2018.8469938

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…Reference [30] models the uncertainties of wind power production and market price with fuzzy scenario-based approaches. Reference [31] uses Monte Carlo methodology to solve a European multi-area market equilibrium [31]. To generate various scenarios for modeling the probabilistic nature of wind and PV generations, probability density functions are required in Monte Carlo methodology [32], [33].…”

Section: Nomenclaturementioning

confidence: 99%

“…Time slots in net metering are longer than those in net purchase and sale. Therefore, the production of the solar panel consists of two parts: power sold to the grid (Go i,h ) and power consumed by subscribers (Gi i,h ), as shown in (31), and load balance is considered in constraint (32). The cost of selling electricity to the power grid is calculated in (33) [1], [8].…”

Section: ) Net Metering and Net Purchase And Sale Billing Mechanismsmentioning

confidence: 99%

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Optimal Day-ahead Dynamic Pricing of Grid-connected Residential Renewable Energy Resources Under Different Metering Mechanisms

Parandeh

Bagheri

Jadid

2023

Journal of Modern Power Systems and Clean Energy

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Nowadays, grid-connected renewable energy resources have widespread applications in the electricity market. However, providing household consumers with photovoltaic (PV) systems requires bilateral interfaces to exchange energy and data. In addition, residential consumers' contribution requires guaranteed privacy and secured data exchange. Dayahead dynamic pricing is one of the incentive-based demand response methods that has substantial effects on the integration of renewable energy resources with smart grids and social welfare. Different metering mechanisms of renewable energy resources such as feed-in tariffs, net metering, and net purchase and sale are important issues in power grid operation planning. In this paper, optimal condition decomposition method is used for dayahead dynamic pricing of grid-connected residential renewable energy resources under different metering mechanisms: feed-intariffs, net metering, and net purchase and sale in conjunction with carbon emission taxes. According to the stochastic nature of consumers' load and PV system products, uncertainties are considered in a two-stage decision-making process. The results demonstrate that the net metering with the satisfaction average of 68% for consumers and 32% for the investigated electric company leads to 28% total load reduction. For the case of net purchase and sale mechanism, a satisfaction average of 15% for consumers and 85% for the electric company results in 11% total load reduction. In feed-in-tariff mechanism, in spite of increased social welfare, load reduction does not take place.

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Section: Nomenclaturementioning

confidence: 99%

Section: ) Net Metering and Net Purchase And Sale Billing Mechanismsmentioning

confidence: 99%

Optimal Day-ahead Dynamic Pricing of Grid-connected Residential Renewable Energy Resources Under Different Metering Mechanisms

Parandeh

Bagheri

Jadid

2023

Journal of Modern Power Systems and Clean Energy

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“…Advanced decisions are made by these companies in the electricity markets (Đogić, 2017). In real-time situations, several unpredictable factors affect the prices of electricity, which results in high market uncertainty (Orgaz et al, 2018). Owing to this uncertainty in the markets, the contracts and agreements in the electricity markets also gets uncertain, which leads to price-volatility in for each contract.…”

Section: Introductionmentioning

confidence: 99%

A Promising Scheme for Portfolio Selection to Gain Pragmatic Pool-based Electricity Market Returns under Uncertain Circumstances

Wattoo¹,

Feng²,

Yousif³

et al. 2018

STUD INFORM CONTROL

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The prospective trading position of an independent power production company (IPPCo) is fortified by handling its portfolio between numerous trading preferences. Owing to uncertain circumstances, the future profits of these trading preferences are estimated by applying fuzzy or probabilistic methods. Conventional schemes are usually found inappropriate in computing the uncertainty and revealing the deviation between apparent and actual market profits. Based on this deviation, a non-probabilistic Information Gap Decision (IGD) theory is applied in this paper to present the optimum scheme for IPPCo's portfolio selection. The performance of IPPCo is modeled and critically analyzed in determining its trading scheme. Numerous decisions are validated for IPPCo through various criteria in results to plump for the best outcome. The risk averting behavior of IPPCo's framework leads to offer robust decisions towards losses, whereas, its risk captivating behavior provides the facility to acquire windfall gain. The presented scheme is also corroborated through real time scenarios of PJM market.

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A Monte Carlo Approach to Represent Uncertainty in the European Electricity Market

Cited by 2 publications

References 14 publications

Optimal Day-ahead Dynamic Pricing of Grid-connected Residential Renewable Energy Resources Under Different Metering Mechanisms

Optimal Day-ahead Dynamic Pricing of Grid-connected Residential Renewable Energy Resources Under Different Metering Mechanisms

A Promising Scheme for Portfolio Selection to Gain Pragmatic Pool-based Electricity Market Returns under Uncertain Circumstances

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