Hierarchical and cooperative model predictive control of electrical grids by using overlapping information

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The presented study deals with hierarchical and cooperative model predictive control (MPC) of electrical grids. The aim of this study is minimizing electrical frequency deviation while ensuring power levels do not rise too much. The original system is a simply interconnected one divided in several areas and, in order to control eventually disconnected areas due to communication blackouts, an expansion of the original system to a hierarchical version of itself by overlapping original system’s areas.Peer ReviewedPostprint (author's final draft

A hierarchical MPC scheme for interconnected systems

This paper describes a hierarchical control scheme for interconnected systems. The higher layer of the control structure is designed with robust Model Predictive Control (MPC) based on a reduced order dynamic model of the overall system and is aimed at optimizing long-term performance, while at the lower layer local regulators acting at a higher frequency are designed for the full order models of the subsystems to refine the control action. A simulation experiment concerning the control of the temperature inside a building is reported to witness the potentialities of the proposed approach

A multiobjective-based switching topology for hierarchical model predictive control applied to a hydro-power valley

In a Hierarchical Model Predictive Control (H-MPC) framework, this paper explores suitable time-variant structures for the hierarchies of different local MPC controllers. The idea is to adapt to different operational conditions by changing the importance of the local controllers. This is done by defining the level of the hierarchy they belong to, and solving within each level the local MPC problem using the information provided by the higher levels at the current time step and the predicted information from the lower levels obtained in the previous time step. As selecting a hierarchy results in a combinatorial optimization problem, it is explicitly solved for a limited number of relevant topologies only and then the switching between topologies is defined with a multiobjective optimizer, so as to decide the best H-MPC scheme according to the expected performance. A comparison with fixed-topology H-MPC controllers is done, showing the effectiveness of the proposed approach for the power control of a hydro-power valley.Peer ReviewedPostprint (author’s final draft

A Hierarchical Model Predictive Control Approach For Battery Systems

Applications in energy systems often require to simultaneously miti- gate long-term peak and short-term electricity costs. The long-term peak electricity demand cost, known as demand charge, constitutes an important component of the electricity bills for large consumption units like building campuses or manufacturing plants. This poses a challenging multiscale planning problem that should make decisions at fine timescales while mitigating long-term costs. We present a hierarchical model predictive control (MPC) approach to tackle this problem in the context of stationary battery systems. The goal is to determine the optimal charge-discharge policy for the battery to minimize the monthly demand charge. We also perform comparative studies of the proposed hierarchical MPC scheme and standard MPC schemes that use ad-hoc approaches to handle the multiple timescales. In the proposed hierarchical MPC approach, we assume that the state of charge (SOC) policy is periodic, which allows us to cast the long-term planning problem as a tractable stochastic programming problem. Here, very period (e.g., a day or week) represents an operational scenario and we seek to determine targets for the periodic SOC levels and the peak cost. The long-term planner MPC communicates the periodic SOC targets and peak cost to a short-term MPC controller. The short-term MPC determines the intra-period charge/discharge policies (at high resolution) while meeting the targets of the long-term planning. We use a case study for a university campus to demonstrate that the hierarchical MPC scheme yields optimal demand charge and charge-discharge policy under nominal (perfect forecast) conditions. Under imperfect forecasts, we show that the hierarchical MPC scheme results in significant improvements in demand charge reduction over a standard MPC scheme that uses a discounting factor to capture long-term effects

Hierarchical distributed model predictive control based on fuzzy negotiation

This work presents a hierarchical distributed model predictive control approach for multiple agents with cooperative negotiations based on fuzzy inference. Specifically, a fuzzy-based two-layer control architecture is proposed. In the lower control layer, there are pairwise negotiations between agents according to the couplings and the communication network. The resulting pairwise control sequences are sent to a coordinator in the upper control layer, which merges them to compute the final ones. Furthermore, conditions to guarantee feasibility and stability in the closed-loop system are provided. The proposed control algorithm has been tested on an eightcoupled tank plant via simulation