A Mixed Integer Linear Programming Approach for Optimal DER Portfolio, Sizing, and Placement in Multi-Energy Microgrids

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Optimal microgrid design is a challenging problem, especially for multi-energy microgrids with electricity, heating, and cooling loads as well as sources, and multiple energy carriers. To address this problem, this paper presents an optimization model formulated as a mixed-integer linear program, which determines the optimal technology portfolio, the optimal technology placement, and the associated optimal dispatch, in a microgrid with multiple energy types. The developed model uses a multi-node modeling approach (as opposed to an aggregate single-node approach) that includes electrical power flow and heat flow equations, and hence, offers the ability to perform optimal siting considering physical and operational constraints of electrical and heating/cooling networks. The new model is founded on the existing optimization model DER-CAM, a state-of-the-art decision support tool for microgrid planning and design. The results of a case study that compares single-node vs. multi-node optimal design for an example microgrid show the importance of multi-node modeling. It has been shown that single-node approaches are not only incapable of optimal DER placement, but may also result in sub-optimal DER portfolio, as well as underestimation of investment costs.

Citation Formats

TY - DATA AB - Optimal microgrid design is a challenging problem, especially for multi-energy microgrids with electricity, heating, and cooling loads as well as sources, and multiple energy carriers. To address this problem, this paper presents an optimization model formulated as a mixed-integer linear program, which determines the optimal technology portfolio, the optimal technology placement, and the associated optimal dispatch, in a microgrid with multiple energy types. The developed model uses a multi-node modeling approach (as opposed to an aggregate single-node approach) that includes electrical power flow and heat flow equations, and hence, offers the ability to perform optimal siting considering physical and operational constraints of electrical and heating/cooling networks. The new model is founded on the existing optimization model DER-CAM, a state-of-the-art decision support tool for microgrid planning and design. The results of a case study that compares single-node vs. multi-node optimal design for an example microgrid show the importance of multi-node modeling. It has been shown that single-node approaches are not only incapable of optimal DER placement, but may also result in sub-optimal DER portfolio, as well as underestimation of investment costs. AU - Mashayekh, Salman A2 - Stadler, Michael A3 - Cardoso, Gonçalo A4 - Heleno, Miguel DB - C-MIX - Community Microgrid Information Exchange DP - Open EI | National Laboratory of the Rockies DO - 10.1016/j.apenergy.2016.11.020 KW - Battery energy storage KW - Combined heat and power (CHP) KW - Solar KW - Photovoltaics KW - PV KW - Diesel generators KW - Other liquid-fuel generators KW - Planning and design KW - Planning KW - Design KW - Case studies KW - Performance KW - Local energy resources (LER) LA - English DA - 2017/02/01 PY - 2017 PB - Lawrence Berkeley National Laboratory T1 - A Mixed Integer Linear Programming Approach for Optimal DER Portfolio, Sizing, and Placement in Multi-Energy Microgrids UR - https://doi.org/10.1016/j.apenergy.2016.11.020 ER -
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Mashayekh, Salman, et al. A Mixed Integer Linear Programming Approach for Optimal DER Portfolio, Sizing, and Placement in Multi-Energy Microgrids. Lawrence Berkeley National Laboratory, 1 February, 2017, C-MIX - Community Microgrid Information Exchange. https://doi.org/10.1016/j.apenergy.2016.11.020.
Mashayekh, S., Stadler, M., Cardoso, G., & Heleno, M. (2017). A Mixed Integer Linear Programming Approach for Optimal DER Portfolio, Sizing, and Placement in Multi-Energy Microgrids. [Data set]. C-MIX - Community Microgrid Information Exchange. Lawrence Berkeley National Laboratory. https://doi.org/10.1016/j.apenergy.2016.11.020
Mashayekh, Salman, Michael Stadler, Gonçalo Cardoso, and Miguel Heleno. A Mixed Integer Linear Programming Approach for Optimal DER Portfolio, Sizing, and Placement in Multi-Energy Microgrids. Lawrence Berkeley National Laboratory, February, 1, 2017. Distributed by C-MIX - Community Microgrid Information Exchange. https://doi.org/10.1016/j.apenergy.2016.11.020
@misc{CMIX_Dataset_190, title = {A Mixed Integer Linear Programming Approach for Optimal DER Portfolio, Sizing, and Placement in Multi-Energy Microgrids}, author = {Mashayekh, Salman and Stadler, Michael and Cardoso, Gonçalo and Heleno, Miguel }, abstractNote = {Optimal microgrid design is a challenging problem, especially for multi-energy microgrids with electricity, heating, and cooling loads as well as sources, and multiple energy carriers. To address this problem, this paper presents an optimization model formulated as a mixed-integer linear program, which determines the optimal technology portfolio, the optimal technology placement, and the associated optimal dispatch, in a microgrid with multiple energy types. The developed model uses a multi-node modeling approach (as opposed to an aggregate single-node approach) that includes electrical power flow and heat flow equations, and hence, offers the ability to perform optimal siting considering physical and operational constraints of electrical and heating/cooling networks. The new model is founded on the existing optimization model DER-CAM, a state-of-the-art decision support tool for microgrid planning and design. The results of a case study that compares single-node vs. multi-node optimal design for an example microgrid show the importance of multi-node modeling. It has been shown that single-node approaches are not only incapable of optimal DER placement, but may also result in sub-optimal DER portfolio, as well as underestimation of investment costs.}, url = {https://cmix.openei.org/submissions/190}, year = {2017}, howpublished = {C-MIX - Community Microgrid Information Exchange, Lawrence Berkeley National Laboratory, https://doi.org/10.1016/j.apenergy.2016.11.020}, note = {Accessed: 2026-08-06}, doi = {10.1016/j.apenergy.2016.11.020} }
https://dx.doi.org/10.1016/j.apenergy.2016.11.020

Details

Data from Feb 1, 2017

Last updated Mar 30, 2026

Submitted Jun 2, 2026

Organization

Lawrence Berkeley National Laboratory

Contact

Salman Mashayekh

Authors

Salman Mashayekh

Lawrence Berkeley National Laboratory

Michael Stadler

Lawrence Berkeley National Laboratory

Gonçalo Cardoso

Lawrence Berkeley National Laboratory

Miguel Heleno

Lawrence Berkeley National Laboratory
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