A multiple uncertainty-based Bi-level expansion planning paradigm for distribution networks complying with energy storage system functionalities

Reliability improvement is regarded as a crucial task in modern distribution network expansion planning. Compared to previous works, this paper presents a bi-level optimization model to optimize the planning of the distribution network complying with multiple renewable energy and energy storage system (ESS) functionalities to guarantee the economical and reliable operation of the distribution network. The candidate assets include substations, distribution lines, renewable energy-based distributed generations (DGs), and ESSs are systematically involved. The load level affected by seasonal change and the multiple uncertainties, including renewable energy, load fluctuation, and contingency outage, are comprehensively considered. The uncertainties caused by the stochastic of renewable energy and load demand are described using Latin Hypercube Sampling (LHS) method. To address the computational burden and complexities associated with non-linear AC power flow, the mixed-integer linear programming (MILP)-based bi-level model is proposed via piecewise linearization methodology. Therein, the upper-level optimization model is proposed to minimize the total present value cost of the planning scheme in normal operating conditions. The lower level model, which is constrained to investment decision-making of the upper-level framework, aims to minimize the total cost of expected energy not supplied (EENS) considering the uncertainties of the single contingency outage. The effectiveness of the proposed bi-level planning model is validated by numerical studies to guarantee economic and reliability improvement for distribution network.