Robust energy systems scheduling considering uncertainties and demand side emission impacts

Distributed Companies (DISCOs) with distributed energy resources (DERs) are considered as one of the most flexible forms of power supply towards decarbonization society. However, to achieve this low carbon goal, it would not be effective without appropriate incentives and the effective demand side involvement. Meanwhile, uncertainties in the system operation such as outputs of intermittent renewable energy, electric vehicles (EVs), and demand side behaviors introduce difficulty for scheduling, and further influence the system's carbon emission. This pa-per proposes a two-stage model to investigate the dynamic carbon impacts caused by the above uncertainties to system scheduling through active demand side from both the economic aspect and environmental aspect. The carbon impacts are reflected upon a defined integrated selling price of DISCO based on the result of carbon emission flow (CEF) and real-time carbon price from the carbon trading market. The uncertainties are modeled as individual probability distributions, and the electricity consumption is forecasted by the least absolute shrinkage and selection operator (LASSO)-quantile regression neural network (QRNN). To address the system uncertainties, a probability-weighted robust optimization (PWRO) approach is applied. Case studies are tested on a modified IEEE-33 bus system, the simulation result indicates that the proposed model with PWRO can effectively handle uncertainties, while a fairer bill plan for customers towards a low-carbon economy can be obtained. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a two-stage model to investigate the dynamic carbon impacts caused by uncertainties in system scheduling, considering factors such as demand side involvement, carbon emissions, and carbon prices. Through economic and environmental analysis, it reveals the influence of uncertainties on system scheduling and proposes a probability-weighted robust optimization method to address system uncertainties.