Two-Stage Stochastic Optimization for Low-Carbon Dispatch in a Combined Energy System

TL;DR

This paper presents a two-stage stochastic optimization model for low-carbon energy dispatch that integrates renewable sources, traditional power plants, and battery storage, considering emissions regulation and uncertainties in renewable outputs.

Contribution

It introduces a novel two-stage stochastic model incorporating a carbon trading mechanism and scenario clustering for renewable output uncertainty in a combined energy system.

Findings

Stochastic model reduces expected operation costs compared to deterministic approaches.

Carbon trading mechanism influences system operation costs significantly.

Scenario clustering effectively captures renewable output uncertainties.

Abstract

While wind and solar power contribute to sustainability, their intermittent nature poses challenges when integrated into the grid. To mitigate these issues, renewable energy can be combined with coal fired power and hydropower sources to stabilize the energy system, with battery storage serving as a backup source to smooth the total output. This study develops a low carbon dispatch model for a combined energy system using a two stage stochastic optimization approach. The model incorporates a carbon trading mechanism to regulate emissions and addresses the uncertainty in wind and solar outputs by clustering output curves into typical scenarios to derive a joint distribution. In the initial stage of scheduling, decisions are made regarding the unit commitment for coal fired power plants. The second stage optimizes the expected operation cost of other energy generation sources. The feasibility of the model is demonstrated by comparing the results of stochastic and deterministic scenarios through simulation. Analysis of different carbon prices further explores the impact of the carbon trading mechanism on the system's operation cost.