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Single-level Robust Bidding of Renewable-only Virtual Power Plant for Profit Optimization in Energy Markets


Core Concepts
The author proposes a single-level robust mathematical approach to optimize profit for a Renewable-only Virtual Power Plant in energy markets, considering uncertainties. The model outperforms existing literature and ensures computational efficiency.
Abstract
The paper introduces a novel approach for bidding optimization of a Renewable-only Virtual Power Plant in energy markets. It addresses uncertainties related to electricity prices, ND-RES production, and demand flexibility. The proposed model shows superior performance compared to existing models, emphasizing the importance of considering various uncertainties for RVPP operators and researchers.
Stats
The RVPP obtains a profit of C56 by bidding its median values of ND-RES production. In Case 2, the RVPP profit in the DAM is -C12. In Case 3, the RVPP profit in the DAM is -C166. In Case 4, the RVPP profit in the DAM is -C279.
Quotes
"The worst-case profit of RVPP due to uncertainties related to electricity prices, ND-RES production, and flexible demand is captured." "The simulation results show the superiority of the proposed robust model compared to those in the literature." "The proposed single-level MILP model has high computational efficiency and simpler implementation compared to multi-level optimization models."

Deeper Inquiries

How does considering multiple uncertainties impact decision-making for RVPP operators

Considering multiple uncertainties can have a significant impact on decision-making for RVPP operators. By taking into account uncertainties related to electricity prices, Non-dispatchable Renewable Energy Sources (ND-RES) production, and flexible demand, operators can make more informed and robust bidding decisions. These uncertainties introduce variability and risk into the market participation of RVPPs, affecting their profitability and performance. Operators need to assess these uncertainties carefully to mitigate risks, optimize profits, and ensure reliable operation in energy markets.

What are the potential drawbacks of simplifying worst-case scenarios in energy bidding models

Simplifying worst-case scenarios in energy bidding models can lead to several potential drawbacks. One major drawback is that oversimplified worst-case scenarios may not accurately capture the full range of possible outcomes under uncertainty. This could result in suboptimal bidding strategies that do not adequately protect against adverse conditions or maximize profit potential. Additionally, simplifications may overlook important nuances in the market dynamics, leading to missed opportunities or increased exposure to financial losses due to unforeseen events.

How can advancements in renewable energy forecasting technology enhance the accuracy of profit optimization models

Advancements in renewable energy forecasting technology play a crucial role in enhancing the accuracy of profit optimization models for Virtual Power Plants (VPPs). By improving the precision of forecasts for ND-RES production levels, operators can better anticipate available generation capacity and adjust their bidding strategies accordingly. More accurate forecasts enable operators to optimize resource allocation, minimize imbalance costs, and capitalize on favorable market conditions effectively. Enhanced forecasting technology also helps reduce uncertainty levels associated with renewable energy integration, leading to improved operational efficiency and profitability for VPPs.
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