Neuro-Fuzzy Logic Applications for Grid Energy Management

Abstract

Grid management is becoming increasingly complex due to continuous growth in consumer demand and distributed energy resources (DERs). These two rapidly changing areas present a unique challenge when taking into consideration the mission of the United States Department of Energy being to ensure energy security through efficient, environmentally friendly, reliable, and affordable electric energy [1]. As consumer demand and DERs continue to saturate the electric grid, the problem that arises is data measurement and processing. The traditional electric grid had a one-way flow of power, whereas the modern electric grid can have bi-directional power flow via DERs and other load resources such as curtailment, load shifting programs, energy storage, to name a few. This new and rapidly developing electric grid has many more data points that will need to be processed due to the exponentially growing amount of energy resources throughout it. This means that more sophisticated management software is needed to help balance an increasingly complex electric grid. This paper will expand on traditional load management technology compared to the new concepts of neuro-fuzzy logic system managing the grid. A neuro fuzzy logic-based energy management system could be a new option to use for grid management and planning. This would allow grid operators to add additional decision processing capabilities. In this manner, the grid would be able to make complex decisions across the network rather than in specifically localized areas. This capability would allow grid operators the ability to make more efficient and reliable decisions since the system is able to process more data. This additional data could open options for the network to utilize complex responses to grid scenarios by utilizing more resources available to the network. The critical concept here is to allow more data to be processed, therefore opening more resources to be used creatively for complex grid response.