Robust Scheduling Optimization Model for Multi-Energy Interdependent System Based on Energy Storage Technology and Ground-Source Heat Pump

Zhongfu Tan,Hongyu Lin,De Gejirifu,Hongwu Guo,Shenbo Yang,Liwei Ju,Qingkun Tan,Xueying Song

doi:10.3390/pr7010027

Zhongfu Tan, Hongyu Lin + Show 6 more

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https://doi.org/10.3390/pr7010027

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Journal: Processes	Publication Date: Jan 8, 2019
Citations: 10	License type: CC BY 4.0

Affiliation: North China Electric Power University

Abstract

The energy consumed by buildings makes up a significant part of total social energy consumption. The energy use rate of the traditional cooling and heating unit is low. A distributed cooling, heating, and power (CHP) system can achieve cascade use of energy and reduce the long-distance transportation of energy. Along with the wide use of ground-source heat pumps and energy storage technology, the combined cooling, heating, and power (CCHP) system coupled with a ground-source heat pump and energy storage technology is increasingly being used. Firstly, we proposed the construction of a CCHP system driven by distributed energy resources (DERs) including three subsystems of an electricity subsystem, a CCHP subsystem and an auxiliary heating subsystem as the object of study in this paper. Besides, with the goals of reducing carbon emissions, increasing energy efficiency, and minimizing system cost, a constraint mechanism based on the DOM-PSO (dynamic object method/particle swarm optimization) algorithm was applied. Finally, taking Tianjin Eco-City as an example, we used the PSO algorithm to analyze the operating characteristics of the cold and power cogeneration system under the uncertainty of the wind power output. The simulation results show that the joint optimization mode operation strategy can balance the results of different optimization modes by increasing the robust coefficient of wind power. Of all scenarios examined, the CCHP system coupled with the ground-source heat pump and energy storage technology performed best.

Highlights

With the progress of society and the improvement of people’s living standards, China’s demand for energy is increasing rapidly: the resulting imbalance between the supply and demand of energy production and consumption is increasing, while the coal-based energy structure is having a serious impact on the environment
For the traditional Particle Swarm Optimization (PSO) algorithm, the group particles are updated in the solution space, some a location far from the current optimal value to ensure the diversity of the population and make up particles search nearby value areas, which speeds up the local update; the other particles search at a for the PSO algorithm
The PSO algorithm is easy to fall into the local minimum

Summary

Introduction

With the progress of society and the improvement of people’s living standards, China’s demand for energy is increasing rapidly: the resulting imbalance between the supply and demand of energy production and consumption is increasing, while the coal-based energy structure is having a serious impact on the environment. A combined cooling, heating, and power (CCHP) system has many advantages, for example, it is energy-efficient, which leads to energy savings; it is clean, which is compatible with environmental protection goals; it is a reliable energy supply, and so on. For these reasons, CCHP system is developing. In reference [3], a CCHP system including a power generation unit, absorption refrigeration equipment, electric refrigeration equipment, and heating equipment was built, enabling the hourly cooling, heating, and power load demand to be met in various buildings

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