An Online Multi-Level Energy Management System for Commercial Building Microgrids With Multiple Generation and Storage Systems

Abstract

This paper presents an online multi-level energy management system for local microgrids of commercial buildings that integrate roof-top photovoltaic sources, battery storage systems, utility grids, diesel generators, supercapacitors, and commercial buildings consisting of active orchestrated loads, to solve the uncertainty problem of sources and loads, while also optimizing the local microgrid operation cost of commercial buildings and the utilization rate of local renewable energy. The energy management system includes a long-term rolling optimization level, rule-based optimization level, and load demand optimization level. At the long-term rolling optimization level, an online rolling method of data restructuring is proposed, where measurement data, short-term prediction data, and day-ahead prediction data are reconstructed to reduce the uncertainty in photovoltaic source prediction and load demand prediction. Four methods are proposed for the energy management system and simulated in MATLAB/Simulink under three typical weather conditions, cloudy, sunny, and rainy. Simulation results show that the performance of Method 3 is closest to that of Method 4, whose data conditions are ideal; Method 3 reduces the operational cost of the commercial building microgrid and improves the utilization rate of photovoltaic sources, at the slight cost of non-critical load shedding.

The Design and Application of Microgrid Supervisory System for Commercial Buildings Considering Dynamic Converter Efficiency

“…A boost/buck converter is applied as the DC/DC converter in the commercial building microgrid. Using boost/buck converters as an example, a converter power loss model is formulated as shown by Equations ( 10) and (11).…”

“…C t over a time horizon  , as shown in Equation ( 12), while considering the constraints of the components in Equations ( 1)-( 7), (11), and ( 19):…”

“…The objective of the energy management layer is to minimize the operating cost of the microgrid C DAO total (t) over a time horizon H, as shown in Equation ( 12), while considering the constraints of the components in Equations ( 1)-( 7), (11), and ( 19):…”

“…A microgrid is a small power generation and distribution system that comprises distributed power sources, energy storage systems, energy conversion devices, loads, supervisory systems, protection devices, and other components that can promote integration and consumption of local renewable energy, ensure the reliability of the power supply, and optimize grid operation [10][11][12][13]. Microgrids can be viewed from a triple perspective in terms of energy, economy, and society.…”

Stochastic Economic–Resilience Management of Combined Cooling, Heat, and Power-Based Microgrids in a Multi-Objective Approach

“…The power management in AC-DC hybrid MGs is also investigated in [52], where RESs and battery energy storage systems (BESSs) are employed. An online bi-level system is used in [53] for MG power management. Multi-energy MGs are studied in [54], in which energy management is addressed using the game-theoretic model.…”