Distributed energy resources (DERs) play a vital role in the smart grid development. The services handled by the DERs have different response times based on their criticality and the type of application. However, meeting the real-time deadlines of these services is cumbersome. The study seeks to address this issue by developing and implementing network slicing in the 5G network deployed in the DER environment. The research aims at identifying bottlenecks that cause delays in DERs and adopting network slicing for better communication, coordination, and resource allocation. The study employs a literature review, machine learning algorithm implementation, and validation for quantifying improvement in terms of efficiency in reaction time. The services are categorized into three: enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC), depending upon the application and response time that are to be met by the services.