Distributionally Robust Collaborative Planning Method for Transmission Network External Channel and Energy Storage with High Proportion of Renewable Energy

Following the proposal of ‘carbon neutrality and carbon peaking’ goals, a high proportion of renewable energy is expected to be connected to the transmission network. In response to the significant challenges posed by the strong randomness and centralized, high-capacity integration of wind and photovoltaic power, which affects the safe operation of the transmission network and the consumption of new energy, this approach considers the uncertainty and time correlation of wind and photovoltaic output. This paper proposes a method of distributed by the transmission channel of high -proportion new energy access channels and energy storage. In this method, the planning of external transmission channels and energy storage is jointly used as decision variables. Surplus renewable energy resources are dispatched through the external transmission channels, while energy storage functions are utilized for peak shaving, valley filling, and suppressing the random fluctuations of new energy, thereby promoting the full consumption of renewable energy. Then, using techniques such as second-order cone convex relaxation and Taylor series expansion, the original mixed integer non-convex nonlinear programming model is transformed into a mixed integer convex programming model to achieve efficient solution. Finally, an improved IEEE 39-bus transmission system is taken as a case study to verify the validity of the proposed model and method.