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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 control schemes in which each agent employs a model-based predictive control approach. Coordination between the agents is used to improve decision making. This coordination can be in the form of parallel or serial schemes. We propose a novel serial coordination scheme based on Lagrange theory and compare this with an existing parallel scheme. Experiments by means of simulations on a particular type of transportation network, viz., an electric power network, illustrate the performance of both schemes. It is shown that the serial scheme has preferable properties compared to the parallel scheme in terms of the convergence speed and the quality of the solution.

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Abstract-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 micro combined 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 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.Index Terms-micro combined heat and power systems, demand response, model predictive control.

We consider the control of large-scale transportation networks, like road traffic networks, power distribution networks, water distribution networks, etc. For control of these networks, we propose a multi-agent control scheme in which each agent employs Model Predictive Control. In order to obtain coordination and to improve decision making agents communicate with each other. We compare two Lagrangian-based communication and decision making schemes. One scheme is based on serial iterations between agents, while the other is based on parallel iterations. The schemes are explained theoretically and assessed experimentally by means of simulations on a particular type of transportation network, viz., a power distribution network. The serial scheme shows to have preferable properties compared to the parallel scheme in terms of solution speed and quality.

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Irrigation canals are large-scale systems, consisting of many interacting components, and spanning vast geographical areas. For safe and efficient operation of these canals, maintaining the levels of the water flows close to prespecified reference values is crucial, both under normal operating conditions as well as in extreme situations.Irrigation canals are equipped with local controllers, to control the flow of water by adjusting the settings of control structures such as gates and pumps. Traditionally, the local controllers operate in a decentralized way in the sense that they use local information only, that they are not explicitly aware of the presence of other controllers or subsystems, and that no communication among them takes place. Hence, an obvious drawback of such a decentralized control scheme is that adequate performance at a system-wide level may be jeopardized, due to the unexpected and unanticipated interactions among the subsystems and the actions of the local controllers.In this paper we survey the state-of-the-art literature on distributed control of water systems in general, and irrigation canals in particular. We focus on the model predictive control (MPC) strategy, which is a model-based control strategy in which prediction models are used in an optimization to determine optimal control inputs over a given horizon. We discuss how communication among local MPC controllers can be included to improve the performance of the overall system. We present a distributed control scheme in which each controller employs MPC to determine those actions that maintain water levels after disturbances close to pre-specified reference values. Using the presented scheme the local controllers cooperatively strive for obtaining the best systemwide performance. A simulation study on an irrigation canal with seven reaches illustrates the potential of the approach.