Capturing Chronology and Extreme Values of Representative Days for Planning of Transmission Lines and Long-Term Energy Storage Systems

TL;DR

This paper introduces a clustering-based hybrid time series aggregation method to efficiently model the variability of renewable energy sources and loads for long-term power system planning, focusing on transmission and energy storage.

Contribution

It presents a novel hybrid aggregation algorithm that captures extreme values and temporal dynamics, improving long-term planning accuracy for renewable-integrated power systems.

Findings

Effective modeling of long-term energy storage systems

Improved accuracy in transmission planning with aggregated data

Validated on a modified 6-bus system

Abstract

The growing penetration of renewable energy sources (RESs) is inevitable to reach net zero emissions. In this regard, optimal planning and operation of power systems are becoming more critical due to the need for modeling the short-term variability of RES output power and load demand. Considering hourly time steps of one or more years to model the operational details in a long-term expansion planning scheme can lead to a practically unsolvable model. Therefore, a clustering-based hybrid time series aggregation algorithm is proposed in this paper to capture both extreme values and temporal dynamics of input data by some extracted representatives. The proposed method is examined in a complex co-planning model for transmission lines, wind power plants (WPPs), short-term battery and long-term pumped hydroelectric energy storage systems. The effectiveness of proposed mixed-integer linear programming (MILP) model is evaluated using a modified 6-bus Garver test system. The simulation results confirm the proposed model efficacy, especially in modeling long-term energy storage systems.