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Article

Assessing Financial and Flexibility Incentives for Integrating Wind Energy in the Grid Via Agent-Based Modeling

1
Electrical Energy Laboratory (EELAB), Department of Electromechanical, Systems and Metal Engineering, Ghent University, Tech Lane Ghent Science Park—Campus A, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium
2
Sharif Policy Research Institute (SPRI), Sharif University of Technology, Tehran 1459986131, Iran
*
Author to whom correspondence should be addressed.
Energies 2019, 12(22), 4314; https://doi.org/10.3390/en12224314
Received: 12 September 2019 / Revised: 21 October 2019 / Accepted: 7 November 2019 / Published: 12 November 2019
(This article belongs to the Special Issue 100% Renewable Energy Transition: Pathways and Implementation)
This article provides an agent-based model of a hypothetical standalone electricity network to identify how the feed-in tariffs and the installed capacity of wind power, calculated in percentage of total system demand, affect the electricity consumption from renewables. It includes the mechanism of electricity pricing on the Day Ahead Market (DAM) and the Imbalance Market (IM). The extra production volumes of Electricity from Renewable Energy Sources (RES-E) and the flexibility of electrical consumption of industries is provided as reserves on the IM. Five thousand simulations were run by using the agent-based model to gather data that were then fit in linear regression models. This helped to quantify the effect of feed-in tariffs and installed capacity of wind power on the consumption from renewable energy and market prices. The consumption from renewable sources, expressed as percentage of total system consumption, increased by 8.17% for every 10% increase in installed capacity of wind power. The sharpest increase in renewable energy consumption is observed when a feed-in tariff of 0.04 €/kWh is provided to the wind farm owners, resulting in an average increase of 9.1% and 5.1% in the consumption from renewable sources while the maximum installed capacity of wind power is 35% and 100%, respectively. The regression model for the annualized DAM prices showed an increase by 0.01 €cents/kWh in the DAM prices for every 10% increase in the installed wind power capacity. With every increase of 0.01 €/kWh in the value of feed-in tariffs, the mean DAM price is lowered as compared to the previous value of the feed-in tariff. DAM prices only decrease with increasing installed wind capacity when a feed-in tariff of 0.04 €/kWh is provided. This is observed because all wind power being traded on DAM at a very cheap price. Hence, no volume of electricity is being stored for availability on IM. The regression models for predicting IM prices show that, with every 10% increase in installed capacity of wind power, the annualized IM price decreases by 0.031 and 0.34 €cents/kWh, when installed capacity of wind power is between 0 and 25%, and between 25 and 100%, respectively. The models also showed that, until the maximum installed capacity of wind power is less than 25%, the IM prices increase when the value of feed-in tariff is 0.01 and 0.04 €/kWh, but decrease for a feed-in tariff of 0.02 and 0.03 €/kWh. When installed capacity of wind power is between 25 and 100%, increasing feed-in tariffs to the value of 0.03 €/kWh result in lowering the mean IM price. However, at 0.04 €/kWh, the mean IM price is higher, showing the effect of no storage reserves being available on IM and more expensive reserves being engaged on the IM. The study concludes that the effect of increasing installed capacity of wind power is more significant on increasing consumption of renewable energy and decreasing the DAM and IM prices than the effect of feed-in tariffs. However, the effect of increasing values of both factors on the profit of RES-E producers with storage facilities is not positive, pointing to the need for customized rules and incentives to encourage their market participation and investment in storage facilities. View Full-Text
Keywords: agent-based modelling; flexibility; renewable energy; electricity markets agent-based modelling; flexibility; renewable energy; electricity markets
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MDPI and ACS Style

Maqbool, A.S.; Baetens, J.; Lotfi, S.; Vandevelde, L.; Van Eetvelde, G. Assessing Financial and Flexibility Incentives for Integrating Wind Energy in the Grid Via Agent-Based Modeling. Energies 2019, 12, 4314. https://doi.org/10.3390/en12224314

AMA Style

Maqbool AS, Baetens J, Lotfi S, Vandevelde L, Van Eetvelde G. Assessing Financial and Flexibility Incentives for Integrating Wind Energy in the Grid Via Agent-Based Modeling. Energies. 2019; 12(22):4314. https://doi.org/10.3390/en12224314

Chicago/Turabian Style

Maqbool, Amtul Samie, Jens Baetens, Sara Lotfi, Lieven Vandevelde, and Greet Van Eetvelde. 2019. "Assessing Financial and Flexibility Incentives for Integrating Wind Energy in the Grid Via Agent-Based Modeling" Energies 12, no. 22: 4314. https://doi.org/10.3390/en12224314

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