The Introduction of Non-Dispatchable Technologies as Decision Variables in Long-Term Generation Expansion Models

Caramanis,; Tabors, Richard D.; Nochur,; Schweppe,

doi:10.1109/mper.1982.5519756

Cited by 25 publications

(7 citation statements)

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“…We focus on investment planning models that can be formulated as linear or mixed integer linear programs. 1 Examples of such models include: Caramanis et al (1982), Bloom (1983), and Sherali and Staschus (1990) for generation expansion planning; Binato et al (2001) for transmission expansion planning; and Pereira et al (1985), Dantzig et al (1989), van der Weijde and Hobbs (2012), and Munoz et al (2014) for composite transmission and generation expansion planning. Other electricity investment planning market simulation models that are commonly used for energy and environmental policy analysis include IPM (ICF, 2013), the Electricity Market Module of NEMS (Gabriel et al, 2001) , ReEDS (Short et al, 2011), Haiku (Paul and Burtraw, 2002), and MARKAL (EIA, 2013).…”

Section: Abstract Planning Modelmentioning

confidence: 99%

“…Early planning models only considered single-area load duration curves based on time-series of historical and forecasted data (Anderson, 1972;Booth, 1972). These were later improved, e.g., through the use of Gram-Charlier series (Caramanis et al, 1982), to account for the effect of non-dispatchable generation technologies, such as wind and solar, on the optimal generation mix. A simple approach to the latter determines operating hours to be simulated via moment matching on demand, wind, solar, and hydro data (van der Weijde and Hobbs, 2012).…”

Section: Introductionmentioning

confidence: 99%

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New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints

Muñoz

Hobbs

Watson

2016

European Journal of Operational Research

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We propose a novel two-phase bounding and decomposition approach to compute optimal and near-optimal solutions to large-scale mixed-integer investment planning problems that have to consider a large number of operating subproblems, each of which is a convex optimization. Our motivating application is the planning of power transmission and generation in which policy constraints are designed to incentivize high amounts of intermittent generation in electric power systems. The bounding phase exploits Jensen's inequality to define a lower bound, which we extend to stochastic programs that use expected-value constraints to enforce policy objectives. The decomposition phase, in which the bounds are tightened, improves upon the standard Benders' algorithm by accelerating the convergence of the bounds. The lower bound is tightened by using a Jensen's inequality-based approach to introduce an auxiliary lower bound into the Benders master problem. Upper bounds for both phases are computed using a sub-sampling approach executed on a parallel computer system. Numerical results show that only the bounding phase is necessary if loose optimality gaps are acceptable. However, the decomposition phase is required to attain optimality gaps. Use of both phases performs better, in terms of convergence speed, than attempt

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Section: Abstract Planning Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints

Muñoz

Hobbs

Watson

2016

European Journal of Operational Research

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“…The general subject of renewable resources in distribution systems has received considerable attention. References [5][6][7][8][9] are a small sampling of the literature of the field. Figure 1 shows a hierarchical concept of controls and energy management in a power distribution system -and their relationship to the transmission system and the point of end use.…”

Section: Renewable Energy Resources In Distribution Powermentioning

confidence: 99%

Distribution system design enabling renewable energy resource deployment

Heydt

Sathyanarayana

Vittal

2009

2009 IEEE Power &Amp; Energy Society General Meeting

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This is a description of a panel presentation on the subject of enabling renewable energy resources in power distribution systems. The main elements of the presentation relate to: control methods; fault current management; safety considerations; improvement of system reliability; energy management; and infrastructure issues. A discussion of multiobjective management is given, and a hierarchical control infrastructure is proposed for distribution systems with renewable resources.

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“…The case of linearly dependent normally distributed nodal power injections was treated in [8] and [9]. An alternative approach for the modeling of linearly correlated system inputs has been presented in [10] and further developed in [11], where the Gram-Schmidt orthogonalization is used in order to transform the linearly correlated system inputs into a weighted sum of independent random variables. The time-dependent approach is suitable for the modeling of the system load, which by nature presents a high time-dependence on seasonality and daily patterns or for performing rough system analyses where a multistate unit approach is enough for the modeling of stochastic generation [7].…”

Section: Introductionmentioning

confidence: 99%

Using Copulas for Modeling Stochastic Dependence in Power System Uncertainty Analysis

Papaefthymiou

Kurowicka

2009

IEEE Trans. Power Syst.

389

151

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Abstract-The increasing penetration of renewable generation in power systems necessitates the modeling of this stochastic system infeed in operation and planning studies. The system analysis leads to multivariate uncertainty analysis problems, involving non-Normal correlated random variables. In this context, the modeling of stochastic dependence is paramount for obtaining accurate results; it corresponds to the concurrent behavior of the random variables, having a major impact to the aggregate uncertainty (in problems where the random variables correspond to spatially spread stochastic infeeds) or their evolution in time (in problems where the random variables correspond to infeeds over specific time-periods).In order to investigate, measure and model stochastic dependence, one should transform all different random variables to a common domain, the rank/uniform domain, by applying the cumulative distribution function transformation. In this domain, special functions, copulae, can be used for modeling dependence. In this contribution the basic theory concerning the use of these functions for dependence modeling is presented and focus is given on a basic function, the Normal copula. The case study shows the application of the technique for the study of the large-scale integration of wind power in the Netherlands.

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The Introduction of Non-Dispatchable Technologies as Decision Variables in Long-Term Generation Expansion Models

Cited by 25 publications

References 0 publications

New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints

New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints

Distribution system design enabling renewable energy resource deployment

Using Copulas for Modeling Stochastic Dependence in Power System Uncertainty Analysis

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