Abstract

With the increasing penetration of wind power, the uncertainty associated with it brings more challenges to the operation of the integrated energy system (IES), especially the power subsystem. However, the typical strategies to deal with wind power uncertainty have poor performance in balancing economy and robustness. Therefore, this paper proposes a distributionally robust joint chance-constrained dispatch (DR-JCCD) model to coordinate the economy and robustness of the IES with uncertain wind power. The optimization dispatch model is formulated as a two-stage problem to minimize both the day-ahead and the real-time operation costs. Moreover, the ambiguity set is generated using Wasserstein distance, and the joint chance constraints are used to ensure that the safety constraints (e.g., ramping limit and transmission limit) can be satisfied jointly under the worst-case probability distribution of wind power. The model is remodeled as a mixed-integer tractable programming issue, which can be solved efficiently by ready-made solvers using linear decision rules and linearization methods. Case studies on an electricity–gas–heat regional integrated system, which includes a modified IEEE 24-bus system, 20 natural gas-nodes, and 6 heat-node system, are investigated for verification. Numerical simulation results demonstrate that the proposed DR-JCCD approach effectively coordinates the economy and robustness of IES and can offer operators a reasonable energy management scheme with an acceptable risk level.

Highlights

  • Published: 28 February 2022In order to achieve the 1.5 ◦ C temperature control target set by the Paris Climate Agreement [1], the proportion of global power generation via renewable sources will continue to rise

  • The tricity–gas–heat integrated energy system (IES) with wind power uncertainty is investigated in this paper

  • The wind wind power generation uncertainty is captured in the model by employing the worst-case power generation uncertainty is captured in the model by employing the worst‐case prob‐

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Summary

Introduction

In order to achieve the 1.5 ◦ C temperature control target set by the Paris Climate Agreement [1], the proportion of global power generation via renewable sources will continue to rise. Using the same moment information, a distributionally robust optimal power flow problem is formulated to solve renewable energy and load uncertainties [14]. With a Wasserstein-distance-based ambiguity set to deal with renewable energy uncertainties, a power-flow DRO problem with multi-stage feedback policies is formulated in [18]. A joint chance-constrained DRO model is proposed for the combined electricity and natural gas system to address renewable energy uncertainty while using the ambiguity set with the confidence bands of the true density function [26]. A two-stage distributionally robust joint chance-constrained dispatch (DRJCCD) model is proposed for the electricity–gas–heat IES with the Wasserstein distancebased ambiguity set, considering the wind power uncertainty. For the electricity–gas–heat IES, a distributionally robust joint chance-constrained dispatch model is proposed to boost the system flexibility while considering wind power uncertainty.

Framework for Electricity–Gas–Heat IES
Formulation of Day‐Ahead
Constraints for the Power System
Constraints for Natural Gas System
Constraints for Heating System
Constraints for Multiple Energy Converters
Formulation of Real-Time Operation
Proposed Solution Method
Basic Formulation
Wasserstein Distance-Based Ambiguity Set
Reformulation of Objective Function
Approximation of Joint Chance Constraints
Robust
The Influence of Different Confidence Levels
Comparisons among DR‐JCCD, RO, and SP
Analysis
Effect of Gas-Fired Generations on Electric Peak Shaving
Findings
Conclusions
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