Abstract
The manufacturing industry consumes electricity and natural gas to provide the power and heat required for manufacturing. Additionally, large amounts of electric energy and heat energy are used, and the electricity cost, amount of environmental pollution, and equipment maintenance cost are high. Thus, optimizing the management of equipment with new energy is important to satisfy the load demand from the system. This paper formulates the scheduling problem of these multiple energy systems as a multi-objective linear regression model (MLRM), and an energy management system is designed focusing on the economy and on greenhouse gas emissions. Furthermore, a variety of optimization objectives and constraints are proposed to make the energy management scheme more practical. Then, grey theory is combined with the common MLRM to accurately represent the uncertainty in the system and to make the model better reflect the actual situation. This paper takes load fluctuation, total grid operation cost, and environmental pollution value as reference standards to measure the effect of the gray optimization algorithm. Lastly, the model is applied to optimize the energy supply plan and its performance is demonstrated using numerical examples. The verification results meet the optimized operating conditions of the multi-energy microgrid system.
Highlights
With the acceleration of industrialization and urbanization, the energy crisis, air pollution, and other problems have become increasingly serious [1]
In order to reduce environmental pollution and to improve power quality, the traditional power grid is gradually transforming into a smart grid [2]
The authors of Reference [6] combined the optimization of the operation of a microgrid with the game analysis method and proposed an optimal configuration model of an intermediate microgrid in the distribution network based on game theory
Summary
With the acceleration of industrialization and urbanization, the energy crisis, air pollution, and other problems have become increasingly serious [1]. A microgrid is composed of a distributed power supply, energy storage, and load and has unique advantages in improving the utilization rate of renewable energy. It can reduce the power interaction with a superior power grid, can alleviate the impact on a superior power grid [4], and plays an important role in lowering carbon emissions and in improving economic benefits. The authors of Reference [5] researched the optimization of operations of a microgrid based on the chaotic particle swarm optimization algorithm. The authors of Reference [6] combined the optimization of the operation of a microgrid with the game analysis method and proposed an optimal configuration model of an intermediate microgrid in the distribution network based on game theory. The authors of Reference [7]
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