Two-stage robust optimization of regional integrated energy systems considering uncertainties of distributed energy stations - Zong, Xuanjun; Yuan, Yue (Frontiers in Energy Research, 2023)
For the regional integrated energy system (RIES) composed of energy supply network and distributed energy station, the uncertainty of distributed photovoltaic (PV) output and the fluctuation of various loads bring great challenges to the stability of system operation and the accuracy of optimal scheduling. In order to enhance the operational reliability of regional integrated energy systems and reduce the impact of photovoltaic and load uncertainties on distributed energy stations, this paper proposes a robust optimization of regional integrated energy systems that takes into account the uncertainty of distributed energy stations method. Firstly, the regional integrated energy system is divided into the upper electric-gas energy supply network and the lower distributed energy station. The upper model mainly realizes energy transmission, the lower model is a two-stage robust optimization model of distributed energy stations in the form of min-max-min, which mainly realizes flexible energy supply of different types of energy. Then, the lower two-stage robust optimization model is simplified and solved by using a column and constraint generation (CCG) algorithm. After that, alternating direction method of multipliers (ADMM) is used to solve the upper and lower models of the regional integrated energy system, and the solution scale is reduced while ensuring the correlation between the energy transmission network and the distributed energy stations. Finally, a test example is provided to illustrate the effectiveness and usefulness of the proposed method. It follows from simulation results that the robust optimization method can effectively reduce the instability of the system operation caused by uncertainty factors and improve the system’s anti-interference ability, in addition, systems with high penetration levels of photovoltaic will benefit more from robust optimization.