Two-Stage Stochastic Scheduling of Integrated Electricity and Natural Gas Systems Considering Ramping Costs With Power-to-Gas Storage and Wind Power

Xiaozhe Song,Houhe Chen,Tao Jiang,Chuqiao Lin,Rufeng Zhang

doi:10.3389/fenrg.2020.596774

Abstract

Power-to-gas (P2G) facilities and natural gas fired power units provide flexibility to integrated electricity and natural gas systems (IENGS) for wind power accommodation and ramp deployment. This paper proposes a stochastic coordinated scheduling model for IENGS considering ramping costs with P2G storage and wind power. The operation model of natural gas system with P2G is presented, and the benefits of P2G integration are analyzed. To address the uncertainties of wind power and energy loads, multiple representative scenarios are generated. The flexible ramping requirements and costs are incorporated and analyzed, and flexible ramp can be provided by P2G in this work. The coordinated scheduling model for IENGS is formulated as a two-stage stochastic programming problem, in which day-ahead scheduling for electricity systems is modeled in the first stage model and scheduling of natural gas systems is carried out in the second stage model. Numerical case studies on a modified PJM 5-bus electricity system with a 7-node natural gas system and the IEEE 118-bus system with a 20-node Belgian natural gas system verify that P2G can help accommodate wind power, provide additional flexible ramping capacities, and reduce the gas supply from gas suppliers and gas load shedding.

Highlights

In the last decade, the use of natural gas for power generation has increased significantly throughout the world (Zhang et al, 2020)
The primary goal of this work is to carry out coordinated stochastic optimal scheduling of integrated electricity and natural gas systems (IENGS) utilizing P2G storage to promote wind power integration and reduce ramping costs for electricity systems
The uncertainties of wind power and energy loads are represented in multiple scenarios

Summary

INTRODUCTION

The use of natural gas for power generation has increased significantly throughout the world (Zhang et al, 2020). The primary goal of this work is to carry out coordinated stochastic optimal scheduling of IENGS utilizing P2G storage to promote wind power integration and reduce ramping costs for electricity systems. Dayahead stochastic scheduling of electricity systems is carried out in the first stage to determine the gas loads of gas-fired generation units and produced SNG of P2G for all scenarios, which are utilized in the second stage optimization model. To simulate the uncertainties of wind power and electricity and natural gas loads, the Monte Carlo method is utilized to generate proper number of uncertain scenarios. The flexible ramp provided by natural gas-fired units is constrained, especially in peak natural gas load periods In these periods, heavy nodal loads lead to violations of the natural gas network, such as the nodal pressure of an end node of long pipeline falling below its lower limit. The objective function of the first stage stochastic scheduling model for electricity system is to minimize the expected operation cost of the electricity system, including electricity bidding costs, wind power curtailment costs, ramping costs and P2G operation costs, as shown in Eq 12

Objective

CONCLUSION

Findings

DATA AVAILABILITY STATEMENT