Showing items related by title, author, creator and subject.

• AI-UBREM Model For Positive Energy District, Case Study: City of Vienna

  Sharma, Naitik; lopez, Alexander; Khean, Naridh; Jastrzebska, Aleksandra; Duering, Serjoscha; Chronis, Angelos; Leon, David; Allam, Sammar; College Collaboration; Architecture (25/6/2022)

  Housing in Austria contributes to 34% of Vienna's Energy consumption. Urban Building Energy modeling (UBEM) demonstrated a reliable tool to visualize city fabric and its complex systems use patterns. This tool enables visualizing and prediction of energy and solar potential of buildings as an AI-based UBREM. U-value is an index that measures building envelop/ structure heat transmittance. The lower the u-value shows better insulation, thus decreasing buildings' energy consumption. Use the sliders to alter u-values for walls, windows, roof, and basement. A single good insulation layer added to your wall can decrease building energy consumption by almost 30%. Hence, building energy demand (BED) can be met using PV cells on rooftops, the PV potential can even exceed BED and support neighboring units manifesting a Positive Energy District (PED) with renewable energy (RE).
• Smart energy solutions: two-way energy information exchange between utility companies, consumers, and prosumers

  Kashef, M, O, A Troisi, Visvisi; External Collaboration; NA; 0; 0; Architecture; 0; Kashef, Mohamad (Routledge, 2023-06-01)

  Smart cities are gradually but surely developing the infrastructure and system architecture required for integrating public and private energy services. With the mounting evidence that fossil fuels are detrimental to the environment, it is imperative to integrate renewable energy sources with existing utility infrastructure. The monopoly of utility companies on energy production and distribution is being eroded due to the proliferation of renewable energy sources (RES) from private prosumers (producers/consumers). Prosumers have developed some capacity to generate a power surplus that exceeds their immediate needs. Individuals and group prosumers have created energy communities with infrastructural and technological ecosystems that allow them to generate, control, monitor, and trade power over private and public utility networks. Multi-layered wireless mesh networks (WMN) that connect multi-sensor modules (MSM) and big data analytics servers with built in AI capacity are facilitating the development of smart energy solutions. They will revolutionize the energy sector and reconfigure the process of energy production, distribution, and information sharing among individuals, communities, and existing utility companies. Considering the fact that (i) the pace of urbanization increases, (ii) energy demand in (smart) urban spaces grows, and (iii) prosumers and, so energy communities, play an ever more important role also in the (smart) city context space, the objective of this chapter is to review the existing smart energy systems and the prospect of their application in the smart city space. The notions of energy supply and demand for energy and the role of energy communities will form the thread of the discussion in this chapter.
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  Model Predictive Control of Consensus-based Energy Management System for DC Microgrid

  Syed, Umaid Ali; Waqar, Asad; Aamir, Muhammad; Mian Qaisar, Saeed; Iqbal, Jamshed; External Collaboration; Energy Lab; 0; 0; Electrical and Computer Engineering; et al. (Plos, 2023-01-20)

  The increasing deployment and exploitation of distributed renewable energy source (DRES) units and battery energy storage systems (BESS) in DC microgrids lead to a promising research field currently. Individual DRES and BESS controllers can operate as grid-forming (GFM) or grid-feeding (GFE) units independently, depending on the microgrid operational requirements. In standalone mode, at least one controller should operate as a GFM unit. In grid-connected mode, all the controllers may operate as GFE units. This article proposes a consensus-based energy management system based upon Model Predictive Control (MPC) for DRES and BESS individual controllers to operate in both configurations (GFM or GFE). Energy management system determines the mode of power flow based on the amount of generated power, load power, solar irradiance, wind speed, rated power of every DG, and state of charge (SOC) of BESS. Based on selection of power flow mode, the role of DRES and BESS individual controllers to operate as GFM or GFE units, is decided. MPC hybrid cost function with auto-tuning weighing factors will enable DRES and BESS converters to switch between GFM and GFE. In this paper, a single hybrid cost function has been proposed for both GFM and GFE. The performance of the proposed energy management system has been validated on an EU low voltage benchmark DC microgrid by MATLAB/SIMULINK simulation and also compared with Proportional Integral (PI) & Sliding Mode Control (SMC) technique. It has been noted that as compared to PI & SMC, MPC technique exhibits settling time of less than 1μsec and 5% overshoot.