Work

Uncertainty reduction in power forecasting of virtual power plant: From day-ahead to balancing markets

Year: 2024

Type: article

Abstract: Adjusting prediction data before bidding is a straightforward and cost-effective method to reduce uncertainty and imbalance between bidding data and real-time power supply. To avoid profit loss for virtual power plant , this study proposes an uncertainty optimization model that minimizes the remaining uncertainty after power generation forecasts. The proposed model specifically addresses different weather conditions—rainy, overcast, cloudy, partly cloudy, and sunny—by minimizing the distance between actual and forecasted VPP generation. The model is applied to adjust prediction data of a VPP with an average generation capacity of 1.5 GW in Tokyo, Japan . Bidding data for winter 2024 are predicted using three deep neural network-based methods. The results indicate a significant reduction in both uncertainty and energy storage capacity after using the uncertainty optimization model. Moreover, the findings show that the proposed uncertainty optimization model increases the profit growth rate for prediction methods characterized by high uncertainty.

Source: Renewable Energy

DOI: 10.1016/j.renene.2024.121875

Authors: Reza Nadimi, Mika Goto

Institution: Institute of Science Tokyo

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,160