Integrated Expansion Planning of Electric Energy Generation, Transmission, and Storage for Handling High Shares of Wind and Solar Power Generation

TL;DR

This paper presents a comprehensive multi-period model for long-term expansion planning of electric grids, integrating generation, transmission, and storage to accommodate high renewable energy shares and uncertainties.

Contribution

It introduces a novel integrated planning model with hierarchical clustering and Benders decomposition to efficiently optimize renewable integration and storage sizing.

Findings

Effective handling of renewable intermittency and congestion.

Reduction in transmission and generation expansion costs.

Validated model performance on IEEE RTS test system.

Abstract

In this paper, an integrated multi-period model for long term expansion planning of electric energy transmission grid, power generation technologies, and energy storage devices is introduced. The proposed method gives the type, size and location of generation, transmission and storage devices to supply the electric load demand over the planning horizon. The sitting and sizing of Battery Energy Storage (BES) devices as flexible options is addressed to cover the intermittency of Renewable Energy Sources (RESs), mitigate lines congestion, and postpone the need for new transmission lines and power plants installation. For efficient handling of RESs uncertainties, and operational flexibility, the upward and downward Flexible Ramp Spinning Reserve (FRSR) are modeled. Besides, the Low-Carbon Policy (LCP) is considered in the objective function of the proposed Transmission, Generation, and Storage Expansion Planning (TGSEP) model. A hierarchical clustering method that can preserve the chronology of input time series throughout the planning horizon periods is developed to capture the short-term uncertainties of load demand and RESs. The short-term operational flexibility requirements make the joint long-term transmission and generation planning a high computational problem. Therefore, the Mixed-Integer Linear Programming (MILP) formulation of the model is solved using an accelerated Benders Dual Decomposition (BDD) method. The IEEE RTS test system is utilized to validate the effectiveness of the proposed joint expansion planning model.