A Review of Predictive Software for the Design of Community Microgrids

Abstract

This paper discusses adding a spatial dimension to the design of community microgrid projects in the interest of expanding the existing discourse related to energy performance optimization measures. A multidimensional vision for designing community microgrids with higher energy performance is considered, leveraging urban form (superstructure) to understand how it impacts the performance of the system’s distributed energy resources and loads (infrastructure). This vision engages the design sector in the technical conversation of developing community microgrids, leading to energy efficient designs of microgrid-connected communities well before their construction. A new generation of computational modeling and simulation tools that address this interaction are required. In order to position the research, this paper presents a survey of existing software packages, belonging to two distinct categories of modeling, simulation, and evaluation of community microgrids: the energy infrastructure modeling and the urban superstructure energy modeling. Results of this software survey identify a lack in software tools and simulation packages that simultaneously address the necessary interaction between the superstructure and infrastructure of community microgrids, given the importance of its study. Conclusions represent how a proposed experimental software prototype may fill an existing gap in current related software packages.

A real-time predictive software prototype for simulating urban-scale energy consumption based on surrogate models

“…The second reason behind the above-mentioned disengagement is the lack of urban-scale energy modeling and simulation tools that capture the presented complexity. As explained in “ A Review of Predictive Software for the Design of Community Microgrids ” from the authors of this paper (Rahimian et al , 2018), the quantity outputted from existing urban-scale energy simulation tools are the summed amount of each building's energy performance in an area, which is not an accurate reflection of the real-world energy performance of cities. Moreover, the hardcoded backends of these tools make running urban-scale energy simulations a tedious task and of expensive computation, practically impossible to use for real-world projects.…”

“…Adding the feasibility study of accessing renewable energy to this relational pattern brings the understanding of how urban form impacts energy performance in community microgrids to another level of complexity. This, previously, has not been offered to the building and urban design communities. The second reason, concluded by our research, is the lack of urban-scale energy modeling and simulation tools that capture the presented complexity (Rahimian et al , 2018). Existing urban-scale energy simulation tools use the summed amount of each building's energy performance within a region as the value of energy performance of the urban region of study.…”

“…The second reason, concluded by our research, is the lack of urban-scale energy modeling and simulation tools that capture the presented complexity (Rahimian et al , 2018). Existing urban-scale energy simulation tools use the summed amount of each building's energy performance within a region as the value of energy performance of the urban region of study.…”

Modelling of an Optimized Microgrid Model by Integrating DG Distributed Generation Sources to IEEE 13 Bus System

“…Different modelling schemes are adopted for designing and simulation of MG including mathematical modelling, real time-modelling and dynamic modelling as reviewed in literature [15]. Different software are available for designing and modelling of MG. [16]. Among them Simulink MATLAB, PSCAD, EMTDC, ATPDraw, ETAP µGrid, and RAPSim are commonly used.…”

Proposal of an Adaptive Neurofuzzy System to Control Flow Power in Distributed Generation Systems

“…According to [4], electricity DG units located in adequate places (near to users) allow reducing transmission losses and increasing the flexibility to the generation system using local renewable energy sources. A microgrid integrates heterogeneous distributed energy resources within the distribution system [5]. Microgrids represent a challenge that requires control techniques, automation, and computation for generation and distribution [6].…”