Optimal energy management of a grid-connected multiple energy carrier micro-grid. (April 2019)

• Record Type: Journal Article
• Authors: Moghaddas-Tafreshi, Seyed Masoud; Mohseni, Soheil; Karami, Mohammad Ehsan; Kelly, Scott
• Is Part Of: Applied thermal engineering. Volume 152(2019)
• Page Start: 796
• Page End: 806
• Publication Date: 2019-04
• Subjects: Optimal energy management -- Optimal operation -- Multi-agent system -- Micro-grid -- Multiple energy carriers -- Renewable energy sources; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2019.02.113
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: An agent-based model is proposed for the optimal operation of a multicarrier micro-grid. The proposed model enables the consideration of uncertainties and demand response. The multi-agent system model simplifies the complexity of the modeling task. The daily operating cost of the micro-grid is saved by 15% compared to conventional models. The computational cost of simulating the model is reduced by 83% compared to those of conventional models. Abstract: This paper presents a novel modeling approach to optimize the electrical and thermal energy management of a multiple energy carrier micro-grid with the aim of minimizing the operation cost such that system constraints are satisfied. The proposed micro-grid includes a micro-turbine, a fuel cell, a rubbish burning power plant, a wind turbine generator system, a boiler, an anaerobic reactor-reformer system, an inverter, a rectifier, and some energy storage units. The model uses day-ahead forecasting (24 h) to estimate the electrical and thermal loads on a micro-grid network. A day-ahead forecast is also used to estimate electricity generation from wind turbines. Due to the uncertainty associated with day-ahead forecasts, a Monte Carlo simulation is used to estimate thermal loads, electrical loads, and wind power generation. Also, a real-time pricing demand response program is used to shift non-vital loads. The operating cost of the micro-grid is minimized through the particle swarm optimization algorithm. The simulation results demonstrate the proposed modeling framework is superior over conventional centralized optimal scheduling models widely used in the literature in terms of reducing operating cost and computational complexity. In addition, the results obtained by applying the proposed modeling framework are analyzed and validated through scenario testing.