Emerging new technologies like distributed generation, distributed storage, and demand side load management will change the way we consume and produce energy. These techniques enable the possibility to reduce the greenhouse effect and improve grid stability by optimizing energy streams. By smartly applying future energy production, consumption and storage techniques, a more energy efficient electricity supply chain can be achieved. In this paper a three-step control methodology is proposed to manage the cooperation between these technologies, focused on domestic energy streams. In this approach, (global) objectives like peak shaving or forming a Virtual Power Plant can be achieved without harming the comfort of residents. As shown in this work, using good predictions, in advance planning and realtime control of domestic appliances, a better matching of demand and supply can be achieved.
In this paper we study the following generalization of the job-shop scheduling problem. Each operation can be performed by one machine out of a set of machines given for this operation. The processing time does not depend on the machine which has been chosen for processing the operation. This problem arises in the area of flexible manufacturing. As a generalization of the jobshop problem it belongs to the hardest problems in combinatorial optimization. We show that an application of tabu search techniques to this problem yields excellent results for benchmark problems. Zusammenfassung. In dieser Arbeit behandeln wir die folgende Verallgemeinerung des Job-Shop Scheduling Problems. Jede Operation kann auf einer beliebigen Maschine aus einer Menge yon Maschinen, die fiir diese Operation gegeben ist, bearbeitet werden. Die Bearbeitungszeit h~ingt dabei nicht yon der gew~ihlten Maschine ab. Das in dieser Arbeit behandelte Problem tritt im Bereich der flexiblen Fertigung auf. Als Verallgemeinerung des klassischen Job-Shop Problems geh6rt es zu den schwierigsten Problemen aus dem Bereich der kombinatorischen Optimierung. Wir zeigen, dab eine Anwendung der Tabu-Search Metaheuristik hervorragende Ergebnisse fiir die yon uns untersuchten Testprobleme liefert.
This paper addresses the problem of operating room (OR) scheduling at the tactical level of hospital planning and control. Hospitals repetitively construct operating room schedules, which is a time-consuming, tedious, and complex task. The stochasticity of the durations of surgical procedures complicates the construction of operating room schedules. In addition, unbalanced scheduling of the operating room department often causes demand fluctuation in other departments such as surgical wards and intensive care units. We propose cyclic operating room schedules, so-called master surgical schedules (MSSs) to deal with this problem. In an MSS, frequently performed elective surgical procedure types are planned in a cyclic manner. To deal with the uncertain duration of procedures we use planned slack. The problem of constructing MSSs is modeled as a mathematical program containing probabilistic constraints. Since the resulting mathematical program is computationally intractable we propose a column generation approach that maximizes the operation room utilization and levels the requirements for subsequent hospital beds such as wards and
Abstract-Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of domestic technologies have been developed to improve this efficiency. These technologies on their own already improve the efficiency, but more can be gained by a combined management. Multiple optimization objectives can be used to improve the efficiency, from peak shaving and Virtual Power Plant (VPP) to adapting to fluctuating generation of wind turbines.In this paper a generic management methology is proposed applicable for most domestic technologies, scenarios and optimization objectives. Both local scale optimization objectives (a single house) and global scale optimization objectives (multiple houses) can be used. Simulations of different scenarios show that both local and global objectives can be reached.
Many Demand Side Management (DSM) approaches use energy prices as steering signals. This paper shows that such steering signals may result in power quality problems and high losses. As an alternative, this paper proposes to use desired (e.g., flat) power profiles as steering signals and presents an efficient scheduling algorithm that can follow desired power profiles. This paper investigates the complexity of price and profile steering, and presents an algorithm for profile steering.The evaluation of this algorithm studies the results of a best possible uniform pricing and profile steering for a case of 121 houses, each with an electrical vehicle of which the power consumption can be controlled and shifted in time. In contrast to the other evaluated approaches, our profile steering algorithm results in a much flatter profile and keeps the voltage between 220 V and 235 V at each node. It reduces distribution losses by 57 % compared to no control, and by 48 % compared to uniform pricing.
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