Decentralised power systems with a high portion of power generated from renewable energy sources and cogeneration units (CHP) are emerging worldwide. Optimising the energy usage of such systems is a difficult task as the stochastic fluctuations of generation from renewable sources, the coupling of electrical and thermal power generation by CHP and the time dependence of necessary storage devices require new approaches. Evolutionary algorithms are able to solve the optimisation task of the energy management. They use the principles of erroneous replication and cumulative selection that can be observed in biological processes, too. Very often recombination is included in the optimisation process. Using these quite simple principles the algorithm is able to explore difficult, large and high dimensional solution spaces. It will converge to the optimal solution in most of the cases quite fast, compared to other types of optimisation algorithms. At the example of an one dimensional replicator it is derived that the convergence speed in optimising convex functions increases by several orders of magnitude even after a few cycles compared to Monte-Carlo-simulation. For several types of equipment models are developed in this work. The cost to operate a given power system for a given time span is chosen as objective function. There is a variety of parameters (more than 15) that can be set in the algorithm. With quite extensive investigations it could be shown that the product of number of replicators and the number of calculated cycles has the most important influence on the quality of the solution but the calculation time is also proportional to this number. If there are reasonable values chosen for the remaining parameters the algorithm will find appropriate solutions in adequate time in most of the cases. Although a pure evolutionary algorithm will converge to a solution the convergence speed can be greatly enhanced by extending it to a hybrid algorithm. Grouping the replicators of the first cycle in suggestive regions of the solution space by an intelligent initialisation algorithm and repairing bad solutions by introducing a Lamarckian repair algorithm makes the optimisation converge fast to good optima. The algorithm was tested using data of several existing energy systems of different structure. To optimise the energy usage in a power system with 15 different types of units the required computation time is in the range of 15 minutes. The results of this work show that extended hybrid evolutionary algorithms are suitable for integrated optimisation of energy usage in combined local energy systems. They reach better results with the same or less effort than many other optimisation methods. The developed method of optimisation of energy usage can be applied in energy systems of small and large size and complexity as optimisation computations of energy systems on the island of Cape Clear, at FH Offenburg and in the Allgäu demonstrate.

Decentralised power systems with a high portion of power generated from renewable energy sources and cogeneration units (CHP) are emerging worldwide. Optimising the energy usage of such systems is a difficult task as the stochastic fluctuations of generation from renewable sources, the coupling of electrical and thermal power generation by CHP and the time dependence of necessary storage devices require new approaches. Evolutionary algorithms are able to solve the optimisation task of the energy management. They use the principles of erroneous replication and cumulative selection that can be observed in biological processes, too. Very often recombination is included in the optimisation process. Using these quite simple principles the algorithm is able to explore difficult, large and high dimensional solution spaces. It will converge to the optimal solution in most of the cases quite fast, compared to other types of optimisation algorithms. At the example of an one dimensional replicator it is derived that the convergence speed in optimising convex functions increases by several orders of magnitude even after a few cycles compared to Monte-Carlo-simulation. For several types of equipment models are developed in this work. The cost to operate a given power system for a given time span is chosen as objective function. There is a variety of parameters (more than 15) that can be set in the algorithm. With quite extensive investigations it could be shown that the product of number of replicators and the number of calculated cycles has the most important influence on the quality of the solution but the calculation time is also proportional to this number. If there are reasonable values chosen for the remaining parameters the algorithm will find appropriate solutions in adequate time in most of the cases. Although a pure evolutionary algorithm will converge to a solution the convergence speed can be greatly enhanced by extending it to a hybrid algorithm. Grouping the replicators of the first cycle in suggestive regions of the solution space by an intelligent initialisation algorithm and repairing bad solutions by introducing a Lamarckian repair algorithm makes the optimisation converge fast to good optima. The algorithm was tested using data of several existing energy systems of different structure. To optimise the energy usage in a power system with 15 different types of units the required computation time is in the range of 15 minutes. The results of this work show that extended hybrid evolutionary algorithms are suitable for integrated optimisation of energy usage in combined local energy systems. They reach better results with the same or less effort than many other optimisation methods. The developed method of optimisation of energy usage can be applied in energy systems of small and large size and complexity as optimisation computations of energy systems on the island of Cape Clear, at FH Offenburg and in the Allgäu demonstrate.