Enhanced higher-order interior-point method to minimise active power losses in electric energy systems

This paper proposes a probabilistic model for optimal joint allocation of energy and spinning reserve to determine energy and spinning reserve capacities. The model takes into consideration both the hourly changes of load demands and the probability of generators' contingencies. The objective function aims at minimizing not only fuel costs caused by power generation but also the costs associated with spinning reserve supply. The proposed model is tackled using the Primal-Dual Interior Point Method (PDIPM), which is capable of solving optimization problems with nonlinear equality and inequality constraints efficiently. This paper reports on the simulation results obtained by using a benchmark IEEE Reliability Test System (IEEE-RTS).The simulation studies also include a comparison between the proposed probabilistic model and the conventional deterministic model, which suggests that the joint allocation of energy and spinning reserve can be optimized by the proposed model for economical and reliable purposes. Consequently, it can be applied to minimize the costs of running the power system with adequate spinning reserve.

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“…λ and μ are the Lagrange multipliers associated with the inequality and equality constraints, respectively [16].…”

Section: A Primal-dual Interior Point Methodsmentioning

confidence: 99%

A probabilistic model for optimal joint allocation of energy and spinning reserve using Primal-Dual Interior Point Method

Ding¹,

Zheng

Jing

et al. 2013

2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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“…The system is composed of: 226 buses, 293 branches, 20 voltage adjustable generators, 77 tap adjustable transformers, and 46 capacitors; the lower/upper limit of the load bus voltage are 1.0 and 1.1, respectively; the lower/upper limit for all the transformer tap stalls and terminal voltage of PV buses are based on the practical engineering setting and not presented for the limitation of the space; the total load of system is 3 From the table3, it could be seen that for the practical system, the reduced loss is about 8.6% of the pre-optimization system loss with the voltage of all the buses being within the limit, which further demonstrates the validity of the method proposed.…”

Section: Simulation Examplesmentioning

confidence: 99%

“…There are many solutions for reactive power optimization, such as linear programming, nonlinear programming, secondary programming, sensitive analysis, and mixed integer planning [1][2][3][4] . These methods are generally based on some presumptions and have some defects.…”

Section: Introductionmentioning

confidence: 99%

The Application of Adaptive Immune Algorithm for Reactive Power Optimization

Lin

Wang

2009

2009 Fifth International Conference on Natural Computation

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The adaptive immune algorithm (AIA) developed from immune algorithm (IA), owns faster computation speed and better convergence than that of GA and other stochastic type algorithms, due to its characteristic of having two layers optimization. The paper proposes to apply adaptive immune algorithm for reactive power optimization. The coding method for the control variables based on decimal system is introduced in detail. The test results of example systems demonstrate its feasibility and validity.

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“…After solving (7) at each iteration k an estimate of the variables is obtained according to [7] and [9], calculating the maximum primal and dual step sizes at each iteration. In iteration k the value of k µ is computed based on a decrement of the residual value of the complementary condition, which is computed as in [9]. Applying IPM to (2) the following augmented Lagrangean function is obtained: …”

Section: Regional Decompositionmentioning

confidence: 99%