Abstract
Storage and Demand Side Management (DSM) are key in integrating renewable energy into community energy systems. There are many modelling tools which support design of such systems. In order to select an appropriate tool it is essential to understand tool capabilities and assess how these match requirements for a specific situation. The aim of this paper is to provide a process to be used to make such a selection consisting of: (i) a tool capability categorisation, (ii) a stepwise tool selection process.Capabilities of 13 tools (screened from 51) for community scale were categorised covering: input data characteristics; supply technologies; design optimisation; available outputs; controls and DSM; storage; and practical considerations.A stepwise selection process is defined, adapted from software engineering, in which tools are scored based on ‘essential’, ‘desirable’, or ‘not applicable’ technical capabilities for the specific situation. Tools without essential capabilities are eliminated. Technical scores and practical considerations are then used to select the tool. The process is demonstrated for a simple case study.The future applicability of the selection process is discussed. Findings from the capability categorisation process are highlighted including gaps to be addressed and future trends in modelling of such systems.
Highlights
They accounted for 22% of installed renewable electricity capacity in 2012 in Germany (Romero-Rubio & de Andrés Díaz, 2015), UK policy is for these systems to provide 8% of renewable electricity capacity by
The specific aims of this paper are: (i) to categorise and document capabilities of tools suitable for modelling community systems for the planning design stage with focus on incorporation of storage and Demand Side Management (DSM), and (ii) develop a selection process based on these documented capabilities to identify tools suitable for modelling in a specific situation
HOMER historically has executed a grid search based on user defined inputs specifying the system options to be included but recently provided an update allowing users to only input upper and lower limits to the grid search. iHOGA was the only identified tool with multi-objective function capability, it includes a choice of available objective functions and embedded genetic algorithms (DufoLopez, Cristobal-Monreal, & Yusta, 2016)
Summary
One impact is increasing use of renewable energy through community scale energy systems. These systems have been the subject of a range of research including technical analysis (Ahadi, Kang, & Lee, 2016; Bhattacharyya, 2012; Chmiel & Bhattacharyya, 2015; Deshmukh & Deshmukh, 2008), socio-economic studies (Rogers, Simmons, Convery, & Weatherall, 2008; Walker, Devine-Wright, Hunter, High, & Evans, 2010), and environmental and institutional studies (Koirala, Koliou, Friege, Hakvoort, & Herder, 2016; Rae & Bradley, 2012) which identify important roles for such systems in the future. Community scale energy systems are being promoted by policy. In Denmark, local communities attract preferential shares in local wind projects (Danish Government, 2008)
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