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Ziel und Tempo der Energiewende sind gesetzt. Der Ausstieg aus der Stromproduktion in Kernkraftwerken soll bis 2022 geschafft sein. Eine Elektrizitätserzeugung, die auf erneuerbaren Energien beruht, soll die bisherige Erzeugung auf der Grundlage von Kohle, Kernbrennstoffen und Erdgas bis 2050 stufenweise weitgehend ablösen und damit maßgeblich zu den Klimaschutzzielen der Bundesregierung beitragen. Der Weg zu diesen Zielen ist für die Beteiligten hingegen noch nicht deutlich einsehbar. Viele offene Fragestellungen technischer, ökonomischer, legislativer und gesellschaftlicher Natur verstellen den Blick auf eine klare Strategie zur Erreichung der energiepolitischen Ziele. Vielschichtige Aufgaben und immense Herausforderungen kommen mit der Mammutaufgabe „Energiewende“ auf Politik, Wirtschaft, Wissenschaft und Bevölkerung zu. Ein wichtiger Enabler für die erfolgreiche Integration von Wind- und Sonnenenergie sowie für neue Prozesse, Marktrollen und Technologien ist die Informations- und Kommunikationstechnologie (IKT). An diesem Punkt setzt die hier vorliegende Studie an.
Optimal microgrid scheduling with peak load reduction involving an electrolyzer and flexible loads
(2016)
This work consists of a multi-objective mixed-integer linear programming model for defining optimized schedules of components in a grid-connected microgrid. The microgrid includes a hydrogen energy system consisting of an alkaline electrolyzer, hydrogen cylinder bundles and a fuel cell for energy storage. Local generation is provided from photovoltaic panels, and the load is given by a fixed load profile combined with a flexible electrical load, which is a battery electric vehicle. The electrolyzer has ramp-up constraints which are modeled explicitly. The objective function includes, besides operational costs and an environmental indicator, a representation of peak power costs, thus leading to an overall peak load reduction under optimized operation. The model is used both for controlling a microgrid in a field trial set-up deployed in South-West Germany and for simulating the microgrid operation for defined period, thus allowing for economic system evaluation. Results from defined sample runs show that the energy storage is primarily used for trimming the peak of electricity drawn from the public grid and is not solely operated with excess power. The flexible demand operation also helps keeping the peak at its possible minimum.
Meeting the requirements of smart grids local, decentralized subnets will offer additional potentials to stabilize and compensate the utility grid mainly on the low voltage level. In a quite complex configuration these decentralized energy systems are combined power, heat and cooling power distributions. According to the regional and local availability of renewable energy sources advanced energy management concepts should consider climatic conditions as well as the state of the interacting utility grid and consumption profiles. The approach uses demonstrational setups to develop a forecast based energy management for trigeneration subnets by taking into account the running conditions of local electrical and thermal energy conversion units. This should lead to the best coverage of the demand and supporting/stabilizing the utility grid at the same time. For the first of three demonstrational projects the priorities of the subnet are given with the maximization of the CHP operation to substitute a major part of the heating and cooling power delivered by electric heaters or compression chillers.