Optimal power flow with inter-temporal constraints as an aiding tool for demand-side management

Uturbey, Wadaed; Costa, Antonio Simões

doi:10.1049/ip-gtd:20020079

Cited by 25 publications

(12 citation statements)

References 5 publications

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“…Equation (13) represents the dynamic constraints. When (8) and (9) are both included in dynamic constraints, the size of dynamic constraints is…”

Section: Primal-dual Interior Point Methods Frameworkmentioning

confidence: 99%

“…When the power flow equations are included, the DED evolves into DOPF problem, which is developed for optimisation in a given time-horizon subject to power flow constraints, operational constraints and dynamic constraints. DOPF is critical for applications such as optimal generations dispatch [8][9][10], ancillary service dispatch [11], load management [12,13], and so on. Various models have been established with different generation characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Solving long time‐horizon dynamic optimal power flow of large‐scale power grids with direct solution method

Qin

Hou

et al. 2014

IET Generation, Transmission & Distribution

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Dynamic optimal power flow (DOPF) is an extension of optimal power flow for the optimal generation dispatch in a given time-horizon. The dynamic constraints bring tremendous numerical difficulties in solving this model. With particular attention to handle dynamic constraints, an efficient method has been presented for directly solving the large-scale DOPF Karush-Kuhn-Tucker (KKT) system arising from the primal-dual interior point method. First, the reduced KKT system is derived, showing that dynamic constraints lead to non-zeros in off-diagonal parts in the coefficient of KKT system. Then, the efficiency of the algorithm is improved by two measures: (i) to utilise the Cholesky factorisation algorithm, a constant diagonal perturbation is introduced in the positive-indefinite KKT coefficient and (ii) efficient reordering algorithms are identified and integrated in the sparse direct solver to improve the efficiency. Case studies on the IEEE 118-bus system over 24-96 time intervals are presented. These case studies show that the proposed method has a significant speed-up than decomposed interior point methods. The proposed method has also been successfully applied in Chinese realistic large-scale power grids. Two realistic case studies are reported. Both realistic cases have over 100 000 decision variables. NomenclatureF DOPF objective function f t generation cost at the tth time interval N t number of time intervals K number of blocks of dynamic constraints N d number of dynamic constraints N ineq number of inequality constraints in each time interval N eq number of equality constraints in each time interval N g number of generators N number of variables in each time interval L Lagrangian function μ barrier parameter ε convergence criteria a, b, c arrays of generation cost coefficients of all generators P gt , Q gt arrays of active and reactive power generation of generators at the tth time interval P lt , Q lt arrays of active and reactive load demands at the tth time interval V t ,V t arrays of complex bus voltage and its conjugation of all buses at the tth time interval Y conjugation of nodal admittance matrix S bt array of apparent power of branches at the tth time interval P g , P g , Q g , Q g arrays of lower and upper bound of active output of generators, lower and upper bound of reactive output of generators S b , S b arrays of lower and upper bound of apparent power of branches V , V arrays of lower and upper bound of all buses' voltages R, R arrays of lower and upper bound of generators' ramping rates C, C arrays of lower and upper bound of generators' generation contracts x t array of variables at the tth time interval x array of variables of the whole time-horizon www.ietdl.org & The Institution of Engineering and Technology 2014 h t , g tArrays of equality constraints and inequality constraints at the tth time interval g, g arrays of lower and upper bound of inequality constraints g d , g d arrays of lower and upper bound of dynamic constraints l t , u t arrays of lower and upper slack variables of ineq...

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“…Equation (13) represents the dynamic constraints. When (8) and (9) are both included in dynamic constraints, the size of dynamic constraints is…”

Section: Primal-dual Interior Point Methods Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solving long time‐horizon dynamic optimal power flow of large‐scale power grids with direct solution method

Qin

Hou

et al. 2014

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

show abstract

“…(27) and (28), when no transmission congestion is present, the multipliers are called the system marginal prices [21].…”

Section: Hybrid Approach For Determination Of Lmp and Srmcmentioning

confidence: 99%

Deregulated model and locational marginal pricing

Sood

Padhy

Gupta

2007

Electric Power Systems Research

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“…Theoretical frameworks for the development of complex models for representing consumers' behaviour can be found in [12,15,[18][19][20].…”

Section: Demand Elasticitymentioning

confidence: 99%

“…Therefore, demand elasticity has been considered as a variable parameter that can represent a "snapshot" of the time varying behaviour of this quantity. Other methodologies [12,15,[18][19][20] can be applied to yield the assessment of this parameter during time without loosing in generality.…”

Section: Case Bmentioning

confidence: 99%

Demand elasticity increase for reducing social welfare losses due to transfer capacity restriction: A test case on Italian cross-border imports

Bruno

Benedictis

Scala

et al. 2006

Electric Power Systems Research

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Optimal power flow with inter-temporal constraints as an aiding tool for demand-side management

Cited by 25 publications

References 5 publications

Solving long time‐horizon dynamic optimal power flow of large‐scale power grids with direct solution method

Solving long time‐horizon dynamic optimal power flow of large‐scale power grids with direct solution method

Deregulated model and locational marginal pricing

Demand elasticity increase for reducing social welfare losses due to transfer capacity restriction: A test case on Italian cross-border imports

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