An optimal scheduling of renewable energy in flexible interconnected distribution networks considering extreme scenarios

Abstract

As renewable power penetration gradually increases in hierarchical distribution networks, certain regions have started to lack the ability to consume. How to improve the consumption capacity of a hierarchical distribution network through optimal dispatching has become a hot topic in the current research on distribution system operation. Firstly, the article makes an accurate and rapid assessment of the consumption capacity of the station area through the limit scenario analysis method; secondly, based on the assessment results, the main network renewable power sources are prioritized for consumption, while the main network and the station area; thirdly, the main network renewable power sources are prioritized for consumption, considering the assessment results while the main network and the station area reach the optimal power flow simultaneously through a multi-layer dispatching model. Then, through the flexible interconnection system, load balancing and energy optimization are performed for unconsumed distributed generation (DG) and incomplete energy storage system (ESS) in the station area to further improve the efficiency of renewable energy. Finally, the effectiveness of the proposed model is verified by simulation tests.

Optimized allocation of microgrids’ distributed generations and electric vehicle charging stations considering system uncertainties by clustering algorithms

“…Therefore, there is no need to consider additional states for the output power of DGs. However, the generation of PVs and WTs would be uncertain, and it is influenced by solar irradiance and wind speed (stochastic parameters) [42,43]. In addition, the loads of EVCSs are not deterministic, and their multi-state conditions should be considered.…”

“…The outputs of the clustering of the WT output power are mathematically expressed in Equation (42). The OF for the WT output power clustering is similar to EVCS and PV output power modelling, as shown in Equation (43).…”

The Multiobjective Control Based on Tolerance Optimization in a Multienergy System

“…However, in multiobjective optimization problems, there is often a complex coupling relationship between multiple objective functions, which poses signifcant challenges for the multilevel decomposition process. Moreover, this approach generally results in a trade-of among multiple objective functions, lacking an analysis of the importance level between diferent objective functions [21].…”