Community Energy Networks With Storage: Modeling Frameworks for Distributed Generation

Abstract

With the rapid decline in cost of distributed generation (DG) systems such as PV power and a similar projection for storage (S) systems such as batteries, accelerated uptake of DGS systems is observed and projected. When operated individually, DGS systems are expected to perform suboptimally due to variability and mismatches of demand and generation timing. Surplus generation should be curtailed when the user is disconnected from the grid, or else sold to grid at low feed-in tariffs when grid-connected. An alternative is the installation of an oversized storage system that would be under-utilized most of the time. A solution, analogous to distributed or cloud computing, is the development of a local network of individual DGS systems (called nanogrids) that cooperate with each other dynamically using a procedure whereby maximum efficiency is achieved for the network. Here, we introduce a state-of-the-art structure of a “cooperative community energy network.” We also develop a methodology for dynamic scheduling of the network with a local energy market mechanism so that all community members benefit fairly from the cooperation.