Abstract
This paper develops the coordination structure and method for utilizing flexibilities in a Micro-Grid (MG), an Active Distribution Network (ADN) and a Transmission Grid (TG), which can play an essential role in addressing the uncertainties caused by renewable energy power generation (REPG). For cooperative dispatching, both flexibilities and uncertainties on the interface of MG–ADN and ADN–TG are portrayed in unified forms utilizing robust optimization (RO), based on the modified equipment-level model of flexible resources. The Constraint-and-Column Generation method is adopted to solve the RO control problems. Simulations on the modified IEEE case-6 and case-33 systems are carried out. The results suggest that the proposed algorithm can exploit flexible resources in both an MG and an ADN, improving the economy and promoting REPG consumption within each level (MG, ADN and TG) while reducing uncertainties and providing flexibilities for superior operators.
Highlights
The continuous growth of renewable energy power generation (REPG) represented by wind power (WP) and photovoltaics (PV) is an important support for peaking carbon dioxide emissions before 2030 and achieving carbon neutrality [1]
In studies [11,12], the high penetration of plug-in electric vehicles, demand response and energy storage were considered as flexible resources, and network loss was included in the optimization goals
This study set out to exploit the flexibilities of a multi-level power grid, which can play a crucial role in dealing with the uncertainties caused by the widely developed REPGs
Summary
The continuous growth of renewable energy power generation (REPG) represented by wind power (WP) and photovoltaics (PV) is an important support for peaking carbon dioxide emissions before 2030 and achieving carbon neutrality [1]. Network (ADN) and a Transmission Grid (TG), causing problems of multi-level scheduling for REPG consumption and economic operation [2,3]. Dealing with the uncertainties of REPG utilizing various kinds of flexible resources plays a significant role in improving REPG consumption and optimal operation, despite the different emphases of scheduling in multiple levels [4,5]. A two-layer control scheme operating at two different timescales was illustrated in [8] for the energy management of an MG based on stochastic model predictive control. In studies [11,12], the high penetration of plug-in electric vehicles, demand response and energy storage were considered as flexible resources, and network loss was included in the optimization goals. Stochastic methods were adopted in [13,14,15] to comprise fluctuations of WP and load
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