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

