This chapter presents different methods and tools for microgrid optimal investment and planning problem, focusing on specific methodological aspects addressing the challenges of rural microgrids design. In particular, three aspects of rural microgrids planning are analyzed: (1) the multi-energy nature of rural microgrids, where electricity coexists with other energy vectors (such as heat distribution); (2) the occupation of large portions of the rural territory, which requires planning methods to consider the microgrid internal network constraints; (3) the remote (and sometimes off-the-grid) locations of rural microgrids, which require security criteria and multi-objective approaches to be considered in planning problem. These three methodological aspects are discussed using the example of a real microgrid in Alaska.

1 aHeleno, Miguel1 aDomenech, Carmen, Bas1 aCardoso, Gonçalo1 aMashayekh, Salman uhttps://gridintegration.lbl.gov/publications/microgrid-investment-and-planning01346nas a2200145 4500008004100000245007300041210006800114260003900182520081100221100002001032700001901052700002201071700002101093856008601114 2020 eng d00aA Multi- Period Investment Model for Behind-the-Meter PV and Storage0 aMulti Period Investment Model for BehindtheMeter PV and Storage aWashington, DC, USAbIEEEc02/20203 aBehind -the-meter photovoltaic (PV) systems, especially when combined with storage units, are becoming an attractive solution for individual prosumers to decrease electricity costs and reduce the dependence from the utility grid. The technology costs of PV and batteries are expected to continue to decrease during the next decade, while utility energy costs and feed-in remuneration will face regulatory changes. In these scenarios of intense variations of technology and operation costs, multi-period investment approaches become relevant, by allowing for an optimal schedule of the investments throughout the years. In this paper, we propose a multi-period optimal investment model for behind-the-meter PV and storage considering multi-year variations of technology and energy costs.

1 aLindberg, Julia1 aHeleno, Miguel1 aCardoso, Gonçalo1 aValenzuela, Alan uhttps://gridintegration.lbl.gov/publications/multi-period-investment-model-behind02116nas a2200229 4500008003900000245012400039210006900163260004700232300001200279490000800291520128100299653002301580653003201603653003601635653003401671653001501705100002201720700002101742700002201763700001901785856008201804 2017 d00aA Mixed Integer Linear Programming Approach for Optimal DER Portfolio, Sizing, and Placement in Multi-Energy Microgrids0 aMixed Integer Linear Programming Approach for Optimal DER Portfo bTo be published in Applied Energyc02/2017 a154-1680 v1873 aOptimal 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.

10aelectrical network10aheating and cooling network10amixed-21 integer linear program10aMulti-energy microgrid design10apower flow1 aMashayekh, Salman1 aStadler, Michael1 aCardoso, Gonçalo1 aHeleno, Miguel uhttps://gridintegration.lbl.gov/publications/mixed-integer-linear-programming02936nas a2200241 4500008003900000245008000039210006900119260001200188300001200200490000800212520213200220653003402352653003302386653001802419653002302437653002802460653003502488100002102523700002102544700002202565700002202587856008502609 2015 d00aModelling of Non-linear CHP Efficiency Curves in Distributed Energy Systems0 aModelling of Nonlinear CHP Efficiency Curves in Distributed Ener c06/2015 a334-3470 v1483 aDistributed energy resources gain an increased importance in commercial and industrial building design. Combined heat and power (CHP) units are considered as one of the key technologies for cost and emission reduction in buildings. In order to make optimal decisions on investment and operation for these technologies, detailed system models are needed. These models are often formulated as linear programming problems to keep computational costs and complexity in a reasonable range. However, CHP systems involve variations of the efficiency for large nameplate capacity ranges and in case of part load operation, which can be even of non-linear nature. Since considering these characteristics would turn the models into non-linear problems, in most cases only constant efficiencies are assumed. This paper proposes possible solutions to address this issue. For a mixed integer linear programming problem two approaches are formulated using binary and Special-Ordered-Set (SOS) variables. Both suggestions have been implemented into the optimization model DER-CAM to simulate investment decisions of CHP micro-turbines and CHP fuel cells with variable efficiencies. The approaches have further been applied successfully in a case study with four different commercial buildings. Comparison of the results between the standard version and the new approaches indicate that total annual system costs remain almost unchanged. System performance is subject to change and storage technologies become more important. Part load operation has mainly been found important for fuel cell units. The micro-turbine is found almost exclusively in full load, thus rendering the application of the new approaches for this technology unnecessary for the considered unit sizes and building types. The approach using binary variables was the most promising method to model variable efficiencies in terms of computational costs and results. It should especially be considered for specific fuel cell technologies. Further investigation on the impacts of this approach on the prediction of fuel cell and micro-turbine performance is suggested.

10acombined heat and power (chp)10aDistributed energy resources10aLinearization10aMicrogrid modeling10aNon-linear optimization10aRenewable energy supply system1 aMilan, Christian1 aStadler, Michael1 aCardoso, Gonçalo1 aMashayekh, Salman uhttps://gridintegration.lbl.gov/publications/modelling-non-linear-chp-efficiency02436nas a2200253 4500008003900000245008300039210006900122260002200191300001200213490000800225520162400233653003901857653002401896653002401920653001501944653002701959100001701986700002102003700002202024700002402046700002302070700001802093856007102111 2014 d00aModeling of Thermal Storage Systems in MILP Distributed Energy Resource Models0 aModeling of Thermal Storage Systems in MILP Distributed Energy R bElsevierc01/2015 a782-7920 v1373 aThermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO_{2} emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations are based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids.

This paper describes the introduction of stochastic linear programming into Operations DER-CAM, a tool used to obtain optimal operating schedules for a given microgrid under local economic and environmental conditions. This application follows previous work on optimal scheduling of a lithium-iron-phosphate battery given the output uncertainty of a 1 MW molten carbonate fuel cell. Both are in the Santa Rita Jail microgrid, located in Dublin, California. This fuel cell has proven unreliable, partially justifying the consideration of storage options. Several stochastic DER-CAM runs are executed to compare different scenarios to values obtained by a deterministic approach. Results indicate that using a stochastic approach provides a conservative yet more lucrative battery schedule. Lower expected energy bills result, given fuel cell outages, in potential savings exceeding 6%.

1 aCardoso, Gonçalo1 aStadler, Michael1 aSiddiqui, Afzal, S.1 aMarnay, Chris1 aDeForest, Nicholas1 aBarbosa-Póvoa, Ana1 aFerrão, Paulo uhttps://gridintegration.lbl.gov/publications/microgrid-reliability-modeling-and00637nas a2200169 4500008003900000245010800039210006900147260001200216100002100228700002200249700002400271700002000295700001800315700002400333700002400357856008600381 2012 d00aMicrogrid Modeling Using the Stochasting Distributed Energy Resources Customer Adoption Model (DER-CAM)0 aMicrogrid Modeling Using the Stochasting Distributed Energy Reso c10/20121 aStadler, Michael1 aCardoso, Gonçalo1 aBozchalui, Mohammad1 aSharma, Ratnesh1 aMarnay, Chris1 aSiddiqui, Afzal, S.1 aGroissböck, Markus uhttps://gridintegration.lbl.gov/publications/microgrid-modeling-using-stochasting01659nas a2200193 4500008003900000245006400039210006400103260003200167520101200199100002101211700001801232700002001250700002101270700002401291700002201315700002001337700002401357856008401381 2011 d00aModeling Electric Vehicle Benefits Connected to Smart Grids0 aModeling Electric Vehicle Benefits Connected to Smart Grids aChicago, IL bLBNLc09/20113 aConnecting electric storage technologies to smartgrids will have substantial implications in building energy systems. Local storage will enable demand response. Mobile storage devices in electric vehicles (EVs) are in direct competition with conventional stationary sources at the building. EVs will change the financial as well as environmental attractiveness of on-site generation (e.g. PV, or fuel cells). In order to examine the impact of EVs on building energy costs and CO_{2} emissions in 2020, a distributed-energy-resources adoption problem is formulated as a mixed-integer linear program with minimization of annual building energy costs or CO_{2} emissions. The mixed-integer linear program is applied to a set of 139 different commercial buildings in California and example results as well as the aggregated economic and environmental benefits are reported. The research shows that considering second life of EV batteries might be very beneficial for commercial buildings.