Mixed-integer optimisation based approach for design and operation of distributed energy systems

Wouters, Carmen; Fraga, Eric S.; James, Ady; Polykarpou, Eleftheria M.

doi:10.1109/aupec.2014.6966501

Cited by 5 publications

(2 citation statements)

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“…A superstructure energy integrated deterministic MILP model for a small residential neighbourhood is proposed in this paper building further on efforts in the field by the authors [36] as well as by Mehleri et al [23,24], Weber and Shah [25] and Keirstead et al [29]. New functionalities lead to the following contributions to the work in the field:…”

Section: Contributions Of This Workmentioning

confidence: 93%

“…The model consists of an objective function bound by design and operational constraints of the available technologies as well as energy integration constraints and energy balance equations. The developed model builds further on the efforts in the field, in particular by Mehleri et al [23,24], Weber and Shah [25] and Keirstead et al [29], as well as of the authors [36]. The most relevant equations are detailed below and the full model is included in Appendix A.…”

Section: Energy Integrated Distributed Energy System Modelmentioning

confidence: 99%

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An energy integrated, multi-microgrid, MILP (mixed-integer linear programming) approach for residential distributed energy system planning – A South Australian case-study

Wouters

Fraga

James

2015

Energy

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151

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The integration of distributed generation units and microgrids in the current grid infrastructure requires an efficient and cost effective local energy system design. A mixed-integer linear programming model is presented to identify such optimal design. The electricity as well as the space heating and cooling demands of a small residential neighbourhood are satisfied through the consideration and combined use of distributed generation technologies, thermal units and energy storage with an optional interconnection with the centralised grid. Moreover, energy integration is allowed in the form of both optimised pipeline networks and microgrid operation. The objective is to minimise the total annualised cost of the system to meet its yearly energy demand. The model integrates the operational characteristics and constraints of the different technologies for several scenarios in a South Australian setting and is implemented in GAMS. The impact of energy integration is analysed, leading to the identification of key components for residential energy systems. Additionally, a multi-microgrid concept is introduced to allow for local clustering of households within neighbourhoods. The robustness of the model is shown through sensitivity analysis, up-scaling and an effort to address the variability of solar irradiation.

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