A key step in the design of cyclo-static real-time systems is the determination of buffer capacities. In our multi-processor system, we apply back-pressure, which means that tasks wait for space in output buffers. Consequently buffer capacities affect the throughput. This requires the derivation of buffer capacities that both result in a satisfaction of the throughput constraint, and also satisfy the constraints on the maximum buffer capacities. Existing exact solutions suffer from the computational complexity that is associated with the required conversion from a cyclo-static dataflow graph to a single-rate dataflow graph. In this paper we present an algorithm, with polynomial computational complexity, that does not require this conversion and that obtains close to minimal buffer capacities. The algorithm is applied to an MP3 play-back application that is mapped on our multi-processor system. For this application, we see that a cyclo-static dataflow model can reduce the buffer capacities by 50% compared to a multi-rate dataflow model.

Abstract

By combining a cluster of microCHP appliances, a virtual power plant can be formed. To use such a virtual power plant, a good heat demand prediction of individual households is needed since the heat demand determines the production capacity. In this paper we present the results of using neural networks techniques to predict the heat demand of individual households. This prediction is required to determine the electricity production capacity of the large fleet of microCHP appliances. All predictions are short-term (for one day) and use historical heat demand and weather influences as input.

Background: For a new district in the Dutch city Meppel, a hybrid energy concept is developed based on bio-gas co-generation. The generated electricity is used to power domestic heat pumps which supply thermal energy for domestic hot water and space heating demand of households. In this paper, we investigate direct control of the heat pumps by the utility and how the large-scale optimization problem that is created can be reduced significantly. Methods: Two different linear programming control methods (global MILP and time scale MILP) are presented. The latter solves large-scale optimization problems in considerably less computational time. For simulation purposes, data of household thermal demand is obtained from prediction models developed for this research. The control methods are compared with a reference control method resembling PI on/off control of each heat pump. Results: The reference control results in a dynamic electricity consumption with many peak loads on the network, which indicates a high level of simultaneous running heat pumps at those times. Both methods of mix integer linear programming (MILP) control of the heat pumps lead to a much improved, almost flat electricity consumption profile. Conclusions: Both optimization control methods are equally able to minimize the maximum peak consumption of electric power by the heat pumps, but the time scale MILP method requires much less computational effort. Future work is dedicated on further development of optimized control of the heat pumps and the central CHP.

No abstract