Research On Service Oriented Source And Network Adaptive Collaboration Mechanism For Energy Internet

With the depletion of traditional fossil energy and increasingly serious environmental pollution,the utilization and development of distributed renewable energy has attracted wide attention and research in the world.However,due to the intermittent and fluctuating nature of renewable energy,renewable energy will cause great interference and adverse effects on the existing energy supply network when it is incorporated into the network.Energy Internet is the way to access and manage distributed renewable energy,and it has been widely studied at home and abroad.The main purpose of source and network collaboration in the Energy Internet is to meet the load demand while ensuring the more economical,efficient and safe operation of the Energy Internet.This paper mainly studies the problem of source and network collaboration and the implementation of adaptive collaboration mechanism.The following work has been completed:(1)A service-oriented source and network collaborative model of energy Internet is established.In this model,the economic cost service goal,energy quality service goal and environmental impact service goal are comprehensively considered.And this model meets the needs of the Energy Internet for economy,reliability and green.(2)An adaptive cooperative mechanism of the source and network based on ranking multiobjective particle swarm optimization for Energy Internet is established.Based on the serviceoriented source and network collaborative model,this mechanism considers the related constraints in detail and proposes a ranking multi-objective particle swarm optimization to adaptively solve the model,providing the optimal source and network coordination scheme for every hour of the next day.(3)An adaptive cooperative mechanism of the source and network based on model predictive control for Energy Internet is established.This mechanism is a further improvement on the adaptive cooperative mechanism of the source and network based on ranking multi-objective particle swarm optimization.By introducing the model predictive control algorithm in the adaptive mechanism,increasing the reliability,accuracy and robustness of the adaptive mechanism,the optimal source and network coordination scheme for each control moment can be provided.