This paper presents a demand response (DR) framework that intertwines thermodynamic building models with a genetic algorithm (GA)-based optimization method. The framework optimizes heating/cooling schedules of end-users inside a business park microgrid with local distributed generation from renewable energy sources (DG-RES) based on two separate objectives: net load minimization and electricity cost minimization. DG-RES is treated as a curtailable resource in anticipation of future scenarios where the infeed of DG-RES to the regional distribution network could be limited. We test the DR framework with a case study of a refrigerated warehouse and an office building located in a business park with local PV generation. Results show the technical potential of the DR framework in harnessing the flexibility of the thermal masses from end-user sites in order to: (1) reduce the energy exchange at the point of connection; (2) reduce the cost of electricity for the microgrid end-users; and (3) increase the local utilization of DG-RES in cases where DG-RES exports to the grid are restricted. The results of this work can aid end-users and distribution network operators to reduce energy costs and energy consumption.
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DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:
This paper optimizes the thermodynamic behavior of buildings through demand response (DR) by operating their mechanical heating/cooling systems at 50% or 100% output capacity on a 15-minute basis. The optimization's objective is either minimizing cost or net electricity consumption, considering hourly prices and renewable energy resource availability in the local microgrid. The proposed DR framework combines thermodynamic models with an automated, genetic-algorithm based optimization, resulting in demonstrable benefits in terms of cost and energy efficiency for the end-users. The optimal DR schedule with multiple heating/cooling output capacity is compared against an unoptimized, business-as-usual scenario and against a DR schedule which allows only a binary operation. Results show that flexibility can be harnessed from the buildings' thermal mass, and that a finer temporal granularity not only improves the cost-and energy performance of the system, but also the utilization of renewable energy sources in the microgrid.
Abstract-The goal of this paper is to propose a design approach to transform the current distribution network of the Eindhoven University of Technology campus into a smart grid. First, the needs and interests of different stakeholders are translated into a local definition of the smart grid concept. This definition is the starting point for outlining the values and services that the smart grid should provide, and the goals it needs to fulfill. Future campus loads, distributed generators, and mobile storage capabilities are modeled and simulated in order to assess their impact on the distribution grid and determine hosting capacity. Recommendations are given on the infrastructure needed for enabling the transition to smart grids, not only for the university as a concrete case study, but rather as a blueprint for future smart grid pilots.
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