Two‐stage self‐adaption security and low‐carbon dispatch strategy of energy storage systems in distribution networks with high proportion of photovoltaics

Abstract

Abstract With the goal of achieving carbon neutrality, active distribution networks (DNs) with a high proportion of photovoltaics (PVs) are facing challenges in maintaining voltage stability and low‐carbon operation. Energy storage systems (ESSs), which have the ability to store and transfer energy temporarily, can be used as effective measures to enhance the capacity of consuming PVs and reduce carbon emissions in DNs. However, existing low‐carbon dispatch strategies for multiple sources, storages and loads fail to consider voltage violations, while the temporal carbon emission intensity of the upper‐level power grid is also often overlooked, which is an important factor that affects the dispatch strategy. Therefore, a two‐stage self‐adaptive dispatch strategy of ESSs that considers the temporal characteristics of slack nodal carbon emission intensity to minimise carbon emissions while maintaining voltage stability in DNs with high access to PVs is proposed. First, the framework of the proposed two‐stage self‐adaptive dispatch strategy of ESSs is established by taking into account the effects of ESSs on adjusting voltages and reducing carbon emissions, respectively, with the two‐stage switch principle of two operation modes being determined. On this basis, an optimization dispatch model is established to improve voltages and carbon emissions, and the optimal day‐ahead dispatch strategy of ESSs can be obtained by solving the model using genetic algorithm. Case studies of the modified 10 kV IEEE 33‐node DN and IEEE 123‐node DN verify the feasibility and superiority of the proposed two‐stage self‐adaptive security and low‐carbon day‐ahead dispatch strategy for ESSs, showing that the voltage stabilisation and lower carbon emissions of DNs are both improved.

Keywords

• distribution network
• energy storage
• genetic algorithm
• photovoltaic power systems