Rudy R. Negenborn

Demand Response With Micro-CHP Systems

With the increasing application of distributed energy resources and novel information technologies in the electricity infrastructure, innovative possibilities to incorporate the demand side more actively in power system operation are enabled. A promising, controllable, residential distributed generation technology is a microcombined heat and power system (micro-CHP). Micro-CHP is an energy-efficient technology that simultaneously provides heat and electricity to households. In this paper, we investigate to what extent domestic energy costs could be reduced with intelligent, price-based control concepts (demand response). Hereby, first the performance of a standard, so-called heat-led micro-CHP system is analyzed. Then, a model-predictive control (MPC) strategy aimed at demand response is proposed for more intelligent control of micro-CHP systems. Simulation studies illustrate the added value of the proposed intelligent control approach over the standard approach in terms of reduced variable energy costs. Demand response with micro-CHP lowers variable costs for households by about 1%-14%. The cost reductions are highest with the most strongly fluctuating real-time pricing scheme.

Distributed Model Predictive Control: An Overview and Roadmap of Future Research Opportunities

Model-predictive control (MPC) is an optimization-based control technique that uses 1) a mathematical model of a system to predict the systems behavior over a given horizon, 2) an objective function that represents what system behavior is desirable, 3) a mathematical formalization of operational constraints that have to be satisfied, 4) measurements of the state of the system at each time step, and 5) any information regarding upcoming disturbances that may be available. This article surveyed and categorized 35 distributed MPC approaches. Subsequently, several of the insights gained from the survey were presented. This study provides a picture of what features have received more or less attention over the last years, bringing about potential research niches for new approaches.

Distributed Model Predictive Control Made Easy

The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, trafficand intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems. This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those who want to gain a deeper insight in the wide range of distributed MPC techniques available.

Kalman Filter-Based Distributed Predictive Control of Large-Scale Multi-Rate Systems: Application to Power Networks

In this paper, a novel distributed Kalman filter (KF) algorithm along with a distributed model predictive control (MPC) scheme for large-scale multi-rate systems is proposed. The decomposed multi-rate system consists of smaller subsystems with linear dynamics that are coupled via states. These subsystems are multi-rate systems in the sense that either output measurements or input updates are not available at certain sampling times. Such systems can arise, e.g., when the number of sensors is smaller than the number of variables to be controlled, or when measurements of outputs cannot be completed simultaneously because of practical limitations. The multi-rate nature gives rise to lack of information, which will cause uncertainty in the systems performance. To circumvent this problem, we propose a distributed KF-based MPC scheme, in which multiple control and estimation agents each determine actions for their own parts of the system. Via communication, the agents can in a cooperative way take one anothers actions into account. The main task of the proposed distributed KF is to compensate for the information loss due to the multi-rate nature of the systems by providing optimal estimation of the missing information. A demanding two-area power network example is used to demonstrate the effectiveness of the proposed method.

Model-based predictive control applied to multi-carrier energy systems

The optimal operation of an integrated electricity and natural gas infrastructure is investigated. The couplings between the electricity system and the gas system are modeled by so-called energy hubs, which represent the interface between the loads on the one hand and the transmission infrastructures on the other. To increase reliability and efficiency, storage devices are present in the multi-carrier energy system. In order to optimally incorporate these storage devices in the operation of the infrastructure, the capacity constraints and dynamics of these have to be taken into account explicitly. Therefore, we propose a model predictive control approach for controlling the system. This controller takes into account the present constraints and dynamics, and in addition adapts to expected changes of loads and/or energy prices. Simulations in which the proposed scheme is applied to a three-hub benchmark system are presented.

Distributed Predictive Control for Energy Hub Coordination in Coupled Electricity and Gas Networks

In this chapter, the operation and optimization of integrated electricity and natural gas systems is investigated. The couplings between these different infrastructures are modeled by the use of energy hubs. These serve as interface between the energy consumers on the one hand and the energy sources and transmission lines on the other hand. In previous work, we have applied a distributed control scheme to a static three-hub benchmark system, which did not involve any dynamics. In this chapter, we propose a scheme for distributed control of energy hubs that do include dynamics. The considered dynamics are caused by storage devices present in the multi-carrier system. For optimally incorporating these storage devices in the operation of the infrastructure, their capacity constraints and dynamics have to be taken into account explicitly. Therefore, we propose a distributed Model Predictive Control (MPC) scheme for improving the operation of the multi-carrier system by taking into account predicted behavior and operational constraints. Simulations in which the proposed scheme is applied to the three-hub benchmark system illustrate the potential of the approach.

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 of the network. As potential control methodology we consider model predictive control (MPC) in a multi-agent setting. We first outline a framework for modeling transportation networks into subsystems using external variables and then discuss issues that arise when controlling these networks with multi-agent MPC. Several approaches to these issues are structured and discussed in terms of the outlined framework

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

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 first step, in this paper a decentralized controller based on model predictive control is proposed. For an individual household using a micro combined heat and power (muCHP) plant in combination with heat and electricity storages the controller determines what the actions are that minimize the operational costs of fulfilling residential electricity and heat requirements subject to operational constraints. Simulation studies illustrate the performance of the proposed control scheme, which is substantially more cost effective compared with a control approach that does not include predictions on the system it controls.

Supervisory hybrid model predictive control for voltage stability of power networks

Emergency voltage control problems in electric power networks have stimulated the interest for the implementation of online optimal control techniques. Briefly stated, voltage instability stems from the attempt of load dynamics to restore power consumption beyond the capability of the transmission and generation system. Typically, this situation occurs after the outage of one or more components in the network, such that the system cannot satisfy the load demand with the given inputs at a physically sustainable voltage profile. For a particular network, a supervisory control strategy based on model predictive control is proposed, which provides at discrete time steps inputs and set-points to lower-layer primary controllers based on the predicted behavior of a model featuring hybrid dynamics of the loads and the generation system.

Predictive Control for National Water Flow Optimization in The Netherlands

The river delta in The Netherlands consists of interconnected rivers and large water bodies. Structures, such as large sluices and pumps, are available to control the local water levels and flows. The national water board is responsible for the management of the system. Its main management objectives are: protection against overtopping of dikes due to high river flows and high sea tides, supply of water during dry periods, and navigation. The system is, due to its size, divided into several subsystems that are managed by separate regional divisions of the national water board. Due to changes in local land-use, local climate, and the need for energy savings, the currently existing control systems have to be upgraded from local manual control schemes to regional model predictive control (MPC) schemes. In principle, the national objectives for the total delta require a centralized control approach integrating all regional MPC schemes. However, such centralized control is on the one hand not feasible, due to computational limitations, and on the other hand unwanted, due to the existing regional structure of the organization of the national water board. In this chapter the application of MPC is discussed for both individual regional control and coordinated national control. Results of a local MPC scheme applied to the actual water system of the North Sea Canal/Amsterdam-Rhine Canal are presented and a framework for coordination between several distributed MPC schemes is proposed.