A Multi-objective Chance-constrained Information-gap Decision Model for Active Management to Accommodate Multiple Uncertainties in Distribution Networks

Abstract

The load demand and distributed generation (DG) integration capacity in distribution networks (DNs) increase constantly, and it means that the violation of security constraints may occur in the future. This can be further worsened by short-term power fluctuations. In this paper, a scheduling method based on a multi-objective chance-constrained information-gap decision (IGD) model is proposed to obtain the active management schemes for distribution system operators (DSOs) to address these problems. The maximum robust adaptability of multiple uncertainties, including the deviations of growth prediction and their relevant power fluctuations, can be obtained based on the limited budget of active management. The systematic solution of the proposed model is developed. The max term constraint in the IGD model is converted into a group of normal constraints corresponding to extreme points of the max term. Considering the stochastic characteristics and correlations of power fluctuations, the original model is equivalently reformulated by using the properties of multivariate Gaussian distribution. The effectiveness of the proposed model is verified by a modified IEEE 33-bus distribution network. The simulation result delineates a robust accommodation space to represent the adaptability of multiple uncertainties, which corresponds to an optional active management strategy set for future selection.

An Uncertain Optimal Energy Flow Model for CCHP Campus Microgrid Using Parameterized Probability Boxes

“…And the optimization of the mass flow p-boxes is achieved by adjusting the supply temperature of the CCHP unit. As can be observed from (8), by increasing the temperature difference between the supply temperature and the ambient temperature, the mass flow can be reduced to meet the requirement of its pbox constraint.…”

“…The thermal model includes the power balance equation of nodes (8), the temperature drop equation of pipelines (9), and the temperature mixing equation of nodes (10) [21]. In (8), for the heating network,…”

“…Reference [7] adopted both risk-averse and risk-seeking IGDT methods to the optimal bidding problem of electric vehicles. Reference [8] developed a multi-objective chance-constrained IG-DT model to ensure the secure operation of distribution network. However, these three methods have their shortcomings.…”