Abstract
We formulate an Optimal Real-Time Power Flow (ORPF) problem that integrates renwable energy generation and energy storage. In the ORPF problem, we seek to minimize the costs of energy storage and of power generation from fossil fuel that are required to balance the loads and generation from renewable sources. We present a novel decentralized algorithm for this problem, using tie-set graph theory. Tie-set graph theory significantly reduces the complexity of the ORPF problem by dividing a power network into a set of independent loops referred to as “tie-sets.” Simulation results demonstrate real-time power production responses and flow controls that lead to reliable use of battery systems and reduce the cost of using fossil fuel.
Highlights
Integration of renewables with energy storage systems has been motivated by the increasing availability of renewable energy from solar and wind power and excess generation by customers
Many of the earlier models for Optimal power flow (OPF) focused on static optimizations, i.e., optimizations for isolated periods of time in which power supply and demand must be balanced at every period
Simulation results show that our algorithm reduces the costs of battery usage and power production by Controllable Generation Facility (CGF) at every time step, and demonstrates flow distribution that leads to reliable use of battery systems
Summary
Integration of renewables with energy storage systems has been motivated by the increasing availability of renewable energy from solar and wind power and excess generation by customers. Many of the earlier models for OPF focused on static optimizations, i.e., optimizations for isolated periods of time in which power supply and demand must be balanced at every period. Today, it is increasingly common for energy storage devices such as batteries to be installed and used in power grids [5]. Based on the monitored data, each tie-set independently optimizes the power production response and distributes power flow at each time step to minimize both CGF and battery cost. Simulation results show that our algorithm reduces the costs of battery usage and power production by CGF at every time step, and demonstrates flow distribution that leads to reliable use of battery systems
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