This paper aims to achieve a balance of power in a group of prosumers, based on a price mechanism, i.e. to steer the difference between the total production and consumption of power to zero. We first set the information network topology such that the prosumers exchange price (power) information with their neighbors according to a choosen information network topology. Based on the exchanged information and the prosumers own measured power demand, each prosumer uses a local control strategy to turn on and off its power generator to cooperatively achieve the global balance. More specifically, the local control strategy results from a distributed model predictive control method based on dual decomposition and sub-gradient iterations. The method achieves a unique dynamic price signal for each prosumer. Simulation results with realistic data validate the method. Index Terms-Distributed decision-making, distributed MPC, energy management, intelligent networks, modeling, optimal grid control, power distribution planning, price mechanism This work is carried out as part of the Flexines project, financed by Koers Noord.

This paper considers heat and power production from micro Combined Heat and Power systems and heat storage in a network of households. The goal is to balance the local heat demand and supply in combination with balancing the power supply and demand in the network. The on-off decisions of the local generators are done completely distributed based on local information and information exchange with a few neighbors in the network. This is achieved by using an information sharing model with a distributed model predictive control method based on dual-decomposition and sub-gradient iterations. Because of the binary nature of the decisions, a mixed integer quadratic problem is solved at each agent. The approach is tested with simulation, using realistic heat and power demand patterns. We conclude that the distributed control approach is suitable for embedding the distributed generation at a household level.Index Terms-distributed model predictive control, smart energy management, distributed generation This work is carried out as part of the Flexines project, financed by Koers Noord.

C e n t r u m v o o r W i s k u n d e e n I n f o r m a t i c a PNA Probability, Networks and Algorithms Probability, Networks and AlgorithmsContinuous feedback fluid queues ABSTRACT We investigate a fluid buffer which is modulated by a stochastic background process, while the momentary behavior of the background process depends on the current buffer level in a continuous way. Loosely speaking the feedback is such that the background process behaves as a continuous-time Markov chain' with generator Q(y) at times when the buffer level is y, where the entries of Q(y) are continuous functions of y. Moreover, the fluid-flow rates for the buffer may also depend continuously on the current buffer level. First we define the feedback behavior precisely. Then we deduce the Kolmogorov forward equations for the joint background/buffer-process under some regularity assumptions. After presenting the differential equations and boundary conditions for the stationary distributions, we find an explicit solution when the background process has two states. AbstractWe investigate a fluid buffer which is modulated by a stochastic background process, while the momentary behavior of the background process depends on the current buffer level in a continuous way. Loosely speaking the feedback is such that the background process behaves 'as a continuous-time Markov chain' with generator Q(y) at times when the buffer level is y, where the entries of Q(y) are continuous functions of y. Moreover, the fluid-flow rates for the buffer may also depend continuously on the current buffer level.First we define the feedback behavior precisely. Then we deduce the Kolmogorov forward equations for the joint background/buffer-process under some regularity assumptions. After presenting the differential equations and boundary conditions for the stationary distributions, we find an explicit solution when the background process has two states.

Problem Definition: Many production systems deteriorate over time as a result of load and stress caused by production. The deterioration rate of these systems typically depends on the production rate, implying that the equipment’s deterioration rate can be controlled by adjusting the production rate. We introduce the use of condition monitoring to dynamically adjust the production rate to minimize maintenance costs and maximize production revenues. We study a single-unit system for which the next maintenance action is scheduled upfront. Academic/Practical Relevance: Condition-based maintenance decisions are frequently seen in the literature. However, in many real-life systems, maintenance planning has limited flexibility and cannot be done last minute. As an alternative, we are the first to propose using condition information to optimize the production rate, which is a more flexible short-term decision. Methodology: We derive structural optimality results from the analysis of deterministic deterioration processes. A Markov decision process formulation of the problem is used to obtain numerical results for stochastic deterioration processes. Results: The structure of the optimal policy strongly depends on the (convex or concave) relation between the production rate and the corresponding deterioration rate. Condition-based production rate decisions result in significant cost savings (by up to 50%), achieved by better balancing the failure risk and production output. For several systems a win-win scenario is observed, with both reduced failure risk and increased expected total production. Furthermore, condition-based production rates increase robustness and lead to more stable profits and production output. Managerial Implications: Using condition information to dynamically adjust production rates provides opportunities to improve the operational performance of systems with production-dependent deterioration.

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