Distributed Model Predictive Control for Hybrid Energy Resource System With Large-Scale Decomposition Coordination Approach

Model Predictive Control of Distributed Energy Resources with Predictive Set-Points for Grid-Connected Operation

This paper proposes an MPC - based (model predictive control) scheme to control active and reactive powers of DERs (distributed energy resources) in a grid - connected mode (either through a bus with its associated loads as a PCC (point of common coupling) or an MG (micro - grid)). DER may be a DG (distributed generation) or an ESS (energy storage system). In the proposed scheme, the set - poin...

A Decomposition-Coordination Approach for Large-Scale Optimization

Resource Allocation for Energy Efficient Large-Scale Distributed Systems

Global warming and climate change trends call for urgent action to manage information and communication technologies in a sustainable manner by minimizing energy consumption and utilizing resources more efficiently. Distributed computing environments have become the de facto platforms for many applications. These systems bring a range of heterogeneous resources that should be able to function c...

Distributed multi-agent Load Frequency Control for a Large-scale Power System Optimized by Grey Wolf Optimizer

This paper aims to design an optimal distributed multi-agent controller for load frequency control and optimal power flow purposes. The controller parameters are optimized using Grey Wolf Optimization (GWO) algorithm. The designed optimal distributed controller is employed for load frequency control in the IEEE 30-bus test system with six generators. The controller of each generator is consider...

Improved Dual Decomposition for Distributed Model Predictive Control ⋆

In dual decomposition, the dual to an optimization problem with a specific structure is solved in distributed fashion using (sub)gradient and recently also fast gradient methods. The traditional dual decomposition suffers from two main short-comings. The first is that the convergence is often slow, although fast gradient methods have significantly improved the situation. The second is that comp...