Model predictive control for residential energy resources using a mixed-logical dynamic model

With the increase in the number of distributed energy resources and the amount of intelligence in electricity infrastructures, the possibilities for minimizing costs of household energy consumption increase. Household systems are hybrid systems, in the sense that they exhibit both continuous and discrete dynamics. In this paper the mixed-logical...

Adaptive prediction model accuracy in the control of residential energy resources

With the increasing use of distributed energy resources and intelligence in the electricity infrastructure, the possibilities for minimizing costs of household energy consumption increase. Technology is moving toward a situation in which automated energy management systems could control domestic energy generation, storage, and consumption. In...

Least-cost model predictive control of residential energy resources when applying ?mCHP

With an increasing use of distributed energy resources and intelligence in the electricity infrastructure, the possibilities for minimizing costs of household energy consumption increase. Technology is moving toward a situation in which households manage their own energy generation and consumption, possibly in cooperation with each other. As a...

Multi-area control of overlapping areas in power systems for FACTS control

As power systems generally are large interconnected systems controlled by several parties, centralized optimal power flow (OPF) control taking the entire grid into account is often not feasible. To use optimal control in power systems nevertheless, the overall system is decomposed into areas with associated subproblems, which are solved in an...

Efficient implementation of serial multi-agent model predictive control by parallelization

We discuss an extension of a scheme recently proposed for multi-agent control of large-scale networks, like power networks, road traffic networks, water networks, etc. The original scheme uses serial sequences of agent interactions that under some assumptions make agents locally choose actions that are globally optimal. However, some weaknesses...

Multi-agent model predictive control for transportation networks with continuous and discrete elements

For several reasons, control of transportation networks like road traffic networks, power transmission networks, water distribution networks, etc. has to be done using a multi-agent control approach. We consider a multi-agent control approach in which each control agent uses Model Predictive Control (MPC) to determine its actions. Each control...

Multi-agent model predictive control for transportation networks: Serial versus parallel schemes

We consider the control of large-scale transportation networks, like road traffic networks, power distribution networks, water distribution networks, etc. Control of these networks is often not possible from a single point by a single intelligent control agent; instead control has to be performed using multiple intelligent agents. We consider...

Multi-Agent Model Predictive Control of Transportation Networks

We consider multi-agent, or distributed, control of transportation networks, like traffic, water, and power networks. These networks typically have a large geographical span, modular structure, and a large number of components that require control. We discuss the necessity of a multi-agent control setting in which multiple agents control parts...

Learning-based model predictive control for Markov decision processes

We propose the use of Model Predictive Control (MPC) for controlling systems described by Markov decision processes. First, we consider a straightforward MPC algorithm for Markov decision processes. Then, we propose value functions, a means to deal with issues arising in conventional MPC, e.g., computational requirements and sub-optimality of...

Multi-Agent Model Predictive Control: A Survey

In this report we define characteristic control design elements and show how conventional single-agent MPC implements these. We survey recent literature on multi-agent MPC and discuss how this literature deals with decomposition, problem assignment, and cooperation.

Experience-based model predictive control using reinforcement learning

Model predictive control (MPC) is becoming an increasingly popular method to select actions for controlling dynamic systems. TraditionallyMPC uses a model of the system to be controlled and a performance function to characterize the desired behavior of the system. The MPC agent finds actions over a finite horizon that lead the system into a...