Abstract
With the rapid growth of electricity demands, many traditional distributed networks cannot cover their peak demands, especially in the evening. Additionally, with the interconnection of distributed electrical and thermal grids, system operational flexibility and energy efficiency can be affected as well. Therefore, by adding a portable energy system and a heat storage tank to the traditional distributed system, this paper proposes a newly defined distributed network to deal with the aforementioned problems. Simulation results show that by adding a portable energy system, fossil fuel energy consumption and daily operation cost can be reduced by 8% and 28.29%, respectively. Moreover, system peak load regulating capacity can be significantly improved. However, by introducing the portable energy system to the grid, system uncertainty can be increased to some extent. Therefore, chance constrained programming is proposed to control the system while considering system uncertainty. By applying Particle Swarm Optimization—Monte Carlo to solve the chance constrained programming, results show that power system economy and uncertainty can be compromised by selecting appropriate confidence levels α and β. It is also reported that by installing an extra heat storage tank, combined heat and power energy efficiency can be significantly improved and the installation capacity of the battery can be reduced.
Highlights
In order to fully develop the benefits of renewable energy generation systems, the integration and optimization of microgrids have become hotspots of recent research [1]
The simulation results will be demonstrated in three aspects: (1) the influence of introducing confidence levels; (2) network performance with installation of an extra portable energy system; and (3) the influence of decoupling heat and power by installing a heat storage tank
By installing an extra portable energy system, the distributed network can obtain additional benefits by exporting electricity to the main grid at the peak demand time, which leads to a 28.29% reduction of system daily operational cost
Summary
In order to fully develop the benefits of renewable energy generation systems, the integration and optimization of microgrids have become hotspots of recent research [1]. This paper develops a combined Particle Swarm Optimization—Monte Carlo (PSO–MC) algorithm to optimize the chance constrained programming, which eliminates stochastic variables and reduces operation parameters. The main contributions of this paper can be summarized as follows: (1) it is the first time that a portable energy storage system is installed in the microgrid to increase power system peak load regulating capacity, taking thermal demand into consideration; (2) the combined PSO–MC algorithm is proposed to optimize power system operation, which reduces the stochastic variables and accelerates computation speed; (3) a heat storage tank is introduced to the distributed microgrid to decouple heat and power, and by doing that fossil energy efficiency is significantly improved and battery installation capacity is reduced as well
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