Michèle Arnold

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.

On Decisive Storage Parameters for Minimizing Energy Supply Costs in Multicarrier Energy Systems

Energy storage is one possibility to cope with increasing fluctuating renewable generation in power systems. Especially when considering a number of different energy carriers, synergies enable a reduction in energy supply costs and an increase in operational flexibility. However, the devices have to be selected carefully since installation and operation are generally costly. This paper examines the influence of storage capacity and prediction horizon on the cost optimal multienergy supply of a single-family house and a network of three interconnected houses. Similarities and differences of the two cases are assessed. The energy hub concept is chosen to model the conversion and storage of the energy carriers electricity, gas, and heat. Then, model predictive control is applied to determine the cost optimal control strategy of the available conversion and storage technologies. The results show that storage capacity and the selection of the prediction horizon strongly depend on each other, both in the case of private customers and interconnected houses. Thereby, the prediction horizon is more crucial as it determines the amount of available information to operate the storage devices optimally.

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.

Improvement of OPF Decomposition Methods Applied to Multi-Area Power Systems

Large power systems are nowadays mostly operated as interconnected subsystems, each not always based on identical legislative, historical and geographical regulations. Within this interconnection, each power system is controlled by its respective control authority, forming a decentralized structure. In this paper methods are presented, decomposing a central optimal power flow (OPF) problem into distributed subproblems. These subproblems are then solved in an iterative way, independently but coordinated. Based on an available decomposition method, an improved decomposition procedure is proposed. With the new method, the convergence rate is considerably enhanced and no parameter tuning is required. Simulation results are presented, applying the procedures to an illustrative 9-bus as well as to the IEEE 39-bus system.

Multi-energy delivery infrastructures for the future

This paper presents an overview of the methods and modeling concepts developed in the framework of the project ¿Vision of Future Energy Networks¿. It outlines the fundamentals of the project comprising (a) the development of modeling and analysis tools for systems involving multiple energy carriers (e.g. electricity, heat and gas) and (b) a Greenfield approach for the integrated planning and realization of future energy infrastructures. This paper especially focusses on a layout procedure for the so-called Energy Interconnector being a device for combined transportation of electrical, chemical and thermal energy. Concluding the paper, the potential application of the Energy Interconnector is illustrated with an exemplary case study. The presented modeling and analysis framework can be used to evaluate multi-energy networks as development option for future energy networks.

Distributed control applied to combined electricity and natural gas infrastructures

The optimization of combined electricity and natural gas systems is addressed in this paper. The two networks are connected via energy hubs. Using the energy hub concept, the interactions between the different infrastructures can be analyzed. A system consisting of several interconnected hubs forms a distributed power generation structure where each hub is controlled by its respective control agent. Recently, a distributed control method has been applied to such a system. The overall optimization problem including the entire system is decomposed into subproblems according to the control agents. In this paper, a parallel and serial version of that method is discussed. Simulation results are obtained through experiments on a three-hub benchmark system.

Framework for Multiple Time-Scale Cascaded MPC Application in Power Systems

Abstract A framework for the application of cascaded Model predictive control (MPC) in power systems for controlling processes on multiple time-scales is presented. Power system control and optimisation is traditionally accomplished on time-scales ranging from milliseconds (protection systems, primary voltage and frequency control) to several years (grid expansion planning). Employing an MPC scheme for controlling or optimising power system properties over several time-scales is in general computationally prohibitive. Instead, separate MPC schemes can be implemented, each designed for one time-scale, working in parallel on the same or different tasks, acting on separate time-scales and interacting via updates of each others’ constraints and cost terms. The resulting cascaded MPC scheme remains computationally tractable. Its operational principles are illustrated by simulation examples.

Investigating renewable infeed in residential areas applying model predictive control

The operation and optimization of integrated electricity and natural gas systems is investigated. The couplings between these different infrastructures are modeled by energy hubs, which can convert and store different types of energy. In previous work, the interaction between three interconnected hubs has been analyzed. In order to take into account predicted system behavior and operational constraints, a model predictive control approach is proposed for optimal operation. In this paper, the hubs, representing residential areas, are also connected to a grid. The energy exchange not only between hubs but also between hubs and the grid is investigated. The hubs contain solar PV installations wherewith they have the ability to feed in energy to the grid. Simulations are presented where the proposed scheme is applied to the hub system. Operational costs are compared for system operation with and without renewable infeed.