A multiobjective optimization approach to the operation and investment of the national energy and transportation systems

Ibáñez, Eduardo

doi:10.31274/etd-180810-618

Cited by 12 publications

(44 citation statements)

References 31 publications

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“…This formulation represents a planning tool named National Energy and Transportation Infrastructure Planning (NETPLAN) tool, which is being developed by the authors and their colleagues at Iowa State University [8]. The tool models the integrated energy and transportation infrastructure systems for the entire U.S. and is capable of performing 40-year infrastructure planning studies.…”

Section: Long-term Planning Problem Formulationmentioning

confidence: 99%

“…together, the model lends itself for a formulation that represents multiperiod network flow optimization [19], wherein the energy flows in all the arcs from supply arcs to demand nodes are optimized. In [8] and [9], further details on the NETPLAN model is provided, including the transportation sector and its interdependence with the energy and fuel sectors.…”

Section: Long-term Planning Problem Formulationmentioning

confidence: 99%

“…1. The optimization is subject to the constraints described in (2)- (8). According to (2), at each node j, the energy inflow across arc (i, j) must be equal to outflow across arc (j, k), accounting for the nonzero demand (dj) for nodes that model energy consumption (D).…”

Section: Long-term Planning Problem Formulationmentioning

confidence: 99%

“…NETPLAN was used to investigate the national long-term infrastructure planning for U.S. Two cases were simulated: Case A that plans a 40-year (T = 40) generation portfolio for a small-scale national model represented by a five-node system as shown in [8] and Case B that plans a 40-year generation portfolio for a 13-node national model, as defined by the U.S. Department of Energy's (DOE) Energy Information Administration (EIA) for National Energy Modeling Systems (NEMS). NETPLAN's reference year load and generation data are shown in [9].…”

Section: Numerical Analysismentioning

confidence: 99%

“…This proxy value is usually the undepreciated value of the infrastructure at the final period of the simulation and is typically assumed a prorated value of the capital based on its remaining useful life after period T . There have been many planning tools such as BALMOREL [7], Iowa state university's NETPLAN [8], [9], and Potsdam Institute for Climate Impact Research's (PotsdamInstituts für Klimafolgenforschung) LIMES [10] that use some form of salvage values. There are also planning models such as Lawrence Livermore National Laboratory's Meta-Net [11], National Renewable Energy Laboratory's ReEDS [12], EPRI's Prism2.0 [13], and the University of California Berkeley's SWITCH [14], [15] that optimize investments over multiple years by considering the annualized investment cost of assets (or prorated operational costs over the estimated lifetime), an approach similar to the salvage value.…”

Section: Introductionmentioning

confidence: 99%

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Building Foresight in Long-Term Infrastructure Planning Using End-Effect Mitigation Models

Krishnan

McCalley

2017

IEEE Systems Journal

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This paper applies the end-effect mitigation models to build foresight into long-term infrastructure planning problems. This paper describes the phenomenon of end effects; presents four different models that address it, namely extended simulation, salvage value, and primal and dual equilibria; and applies them to long-term energy system planning problems with finite resources for investments. The planning model is a simultaneous multiperiod linear optimization model that plans for energy system infrastructures at the national scale. Two instances of this model are considered: 1) a small-scale five-node model; and 2) a medium-to-large-scale 13-node model. These instances are used to assess and quantify: 1) the end effects of using investment solutions; and 1) the efficacy of each mitigation methods in terms of accuracy and computational time. The illustrations demonstrate that, without attributing the long-term infrastructure planning with some level of foresight about the future cost and performance of the technologies, the resulting portfolio will have end effects in terms of: 1) investment bias toward low-cost resources at the end years; and 2) adopting low-cost quick fixes (such as excess cofiring) to meet near-term emission targets, both of which may render the model to lose sight of long-term targets and economics.Index Terms-Carbon policy, computational time, dual equilibrium, end effects, foresight, infrastructure planning. NOMENCLATURE TPlanning horizon (years). N Set representing all network nodes, where D is the set of regional demand nodes. A Set representing all network arcs, which consists of generation (G), interregional transmission (T ), and fuel (F ) arcs. i, j, k Energy sector nodes, where arcs connecting them represent various infrastructures. t, z Time period. e (i,j) (t)Energy flow in the arc (i, j) at time t. eInv (i,j) Number of hours in a time period t. IR Inflation rate. Arc operational efficiency parameter, which models energy conversion and transmission efficiencies. E.g., fuel to generation output conversion efficiency (generation heat rate) and transmission losses.Nodal energy demanded at region j at time t.The energy demanded may be electricity, petroleum, or natural gas depending upon the subsystem of the energy system where the node j is situated. pkd j (t) Peak demand at time t in region j. RM j (t)Reserve margin at time t in region j. lbe (i,j) (t),Lower and upper existing capacities (accounts ube (i,j) (t) periodic retirements for infrastructures) of energy arc at time t. uf (i,j)

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Section: Long-term Planning Problem Formulationmentioning

confidence: 99%

Section: Long-term Planning Problem Formulationmentioning

confidence: 99%

Section: Long-term Planning Problem Formulationmentioning

confidence: 99%

Section: Numerical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Building Foresight in Long-Term Infrastructure Planning Using End-Effect Mitigation Models

Krishnan

McCalley

2017

IEEE Systems Journal

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The role of bio-renewables in national energy and transportation systems portfolio planning for low carbon economy

Krishnan

McCalley

2016

Renewable Energy

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Bio-power and biofuels are promising alternative energy resources. This paper investigates their role in the long-term U.S. national energy and transportation portfolio planning, while considering the competition among other energy options. The paper presents a systematic modeling framework for integrating biomass pathways to the energy and transportation systems, and also captures the geographical variation in the feedstock availability and cost across the U.S. The paper then presents two different case studies-energy sector planning and integrated energy & transportation sectors planning. The studies reveal long-term cost and emission savings from bio-renewables, where the bulk of benefits are observed due to biofuels (with bio-power production limited by feedstock prices). Under a 40% CO 2 emissions reduction scenario over the next 40 years, penetration of bio-renewables promise up to 10-Trillion USD (2010$) savings in system costs (investments and operational). Simulations also show that the impediment with bio-renewable penetration is mostly influenced by the availability of low-cost feedstock, specifically for bio-power production. According to current estimation of long-term feedstock availability, U.S will be able to power upto 150 Billion Gallons Year (BGY) (or approx. 560*10 9 liters per year) bio-refinery capacity around 2020s, and about 200 BGY (or approx. 750*10 9 liters per year) by 2050.

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Using NETPLAN to analyze environmental impacts on generation expansion planning

McCalley

2012

2012 IEEE Power and Energy Society General Meeting

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A multiobjective optimization approach to the operation and investment of the national energy and transportation systems

Cited by 12 publications

References 31 publications

Building Foresight in Long-Term Infrastructure Planning Using End-Effect Mitigation Models

Building Foresight in Long-Term Infrastructure Planning Using End-Effect Mitigation Models

The role of bio-renewables in national energy and transportation systems portfolio planning for low carbon economy

Using NETPLAN to analyze environmental impacts on generation expansion planning

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