Optimal long-term reactive power planning using decomposition techniques

Mangoli, Maurice K.W; Lee, Kwang Y.; Park, Young Moon

doi:10.1016/0378-7796(93)90067-o

Cited by 29 publications

(6 citation statements)

References 11 publications

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“…Optimization of power systems encompasses a broad spectrum of problem domains, including optimal power flow [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], LMP-base market calculations [15], [16], [17], transmission switching [18], [19], [20], real-time security-constrained dispatch [21], [22], day-ahead security-constrained unit commitment [23], [24], [25], distribution network configuration [26], [27], capacitor placement [28], [29], [30], expansion planning [31], [32], [33], [34], [35], [36], [37], [38], [39], vulnerability analysis [40], [41], [42], [43], [44], and power system restoration [45], [46] to name a few. Some of these use active power only, while others consider both active and reactive power.…”

Section: Introductionmentioning

confidence: 99%

A Linear-Programming Approximation of AC Power Flows

Coffrin

Hentenryck

2014

INFORMS Journal on Computing

246

177

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Linear active-power-only DC power flow approximations are pervasive in the planning and control of power systems. However, these approximations fail to capture reactive power and voltage magnitudes, both of which are necessary in many applications to ensure voltage stability and AC power flow feasibility. This paper proposes linear-programming models (the LPAC models) that incorporate reactive power and voltage magnitudes in a linear power flow approximation. The LPAC models are built on a convex approximation of the cosine terms in the AC equations, as well as Taylor approximations of the remaining nonlinear terms. Experimental comparisons with AC solutions on a variety of standard IEEE and MATPOWER benchmarks show that the LPAC models produce accurate values for active and reactive power, phase angles, and voltage magnitudes. The potential benefits of the LPAC models are illustrated on two "proof-of-concept" studies in power restoration and capacitor placement.Index Terms-DC power flow, AC power flow, LP power flow, linear relaxation, power system analysis, capacitor placement, power system restorationTransformer parameters V = | V |∠θ • Polar form S n AC Power at bus n S nm

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Section: Introductionmentioning

confidence: 99%

A Linear-Programming Approximation of AC Power Flows

Coffrin

Hentenryck

2014

INFORMS Journal on Computing

246

177

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“…IEEE-30 bus system network consists of six generator buses, 24 load buses, and 41 branches. The four branches (6, 9), (6,10), (4,12), and (28, 27) are under load tap changing transformer with 20 discrete steps of 0.01 p.u. each.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Another possible objective is the minimization of voltage deviation or the maximization of voltage stability margin [1].The mathematical solution to RPP is also very challenging owing to the large number of variables and uncertain parameters [2]. Many classic optimization techniques, such as linear [3], nonlinear [4], or mixed integer programming [5], and decomposition methods [6] have been applied for solving RPP problem.…”

Section: Introductionmentioning

confidence: 99%

Reactive power planning with voltage stability enhancement using covariance matrix adapted evolution strategy

Jeyadevi¹,

Baskar²,

Iruthayarajan³

2010

Int Trans Elec Energy Syst

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SUMMARYThis paper presents the application of Covariance matrix adapted evolution strategy (CMAES) to reactive power planning (RPP) problem by minimizing the combined total cost of energy loss and the allocation cost of additional reactive power sources. Voltage stability index (L-index) is considered as an additional constraint in order to include the system stability into account. To improve the search efficiency of CMAES, penalty parameter-less scheme is employed for constraint handling. To determine the performance of the CMAES method on reactive power planning problem, IEEE-30 bus test system and Tamil Nadu Electricity Board (TNEB)-69 bus system (a practical system in India) are taken. For comparison purposes, the results of real-coded GA (RGA), particle swarm optimization (PSO), sequential quadratic programming (SQP), and Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods are considered. The simulation results reveal that the CMAES algorithm performs better in terms of energy cost, investment cost, and consistency in getting optimal solution. Karush-Kuhn-Tucker (KKT) conditions are also applied on the best solution obtained using CMAES algorithm to substantiate a claim on optimality.

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“…This point represents the minimum investment cost but at the maximum CVaR. For the next iteration, the CVaR constraints are added to the problem, and a new point is obtained, setting the CVaR value obtained in the previous iteration as CVaR max (9), then proceeding to the next iteration and so on [35].…”

Section: Stmentioning

confidence: 99%

“…Several classical optimisation models and algorithms such as linear programming [1–3], quadratic programming [4] non‐linear programming [5, 6], mixed integer non‐linear programming [7, 8], decomposition methods [9, 10], methods based on successive linear programming [11, 12] and mathematical programming techniques (such as the gradient method [13] and the Newton method [14]) have been used to solve deterministic reactive power optimisation problems.…”

Section: Introductionmentioning

confidence: 99%

Reactive power planning under conditional‐value‐at‐risk assessment using chance‐constrained optimisation

Lopez¹,

Contreras

Mantovani³

2015

IET Generation, Transmission & Distribution

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This study presents a risk-assessment approach to the reactive power planning problem. Chance-constrained programming is used to model the random equivalent availability of existing reactive power sources for a given confidence level. Load shedding because of random equivalent availability of those reactive power sources is implemented through conditional-value-at-risk. Tap settings of under-load tap-changing transformers are considered as integer variables. Active and reactive demands are considered as probability distribution functions. The proposed mathematical formulation is a two-stage stochastic, multi-period mixed-integer convex model. The tradeoff between risk mitigation and investment cost minimisation is analysed. The proposed methodology is applied to the CIGRE-32 electric power system, using the optimisation solver CPLEX in AMPL. NomenclatureThe following notations used throughout this paper are described below. The operators E, Pr, Var and Std are used as expectation, probability, variance and standard deviation measures, respectively.

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Optimal long-term reactive power planning using decomposition techniques

Cited by 29 publications

References 11 publications

A Linear-Programming Approximation of AC Power Flows

A Linear-Programming Approximation of AC Power Flows

Reactive power planning with voltage stability enhancement using covariance matrix adapted evolution strategy

Reactive power planning under conditional‐value‐at‐risk assessment using chance‐constrained optimisation

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