Noisy Immune Optimization for Chance-Constrained Programming Problems

2011 ◽  
Vol 48-49 ◽  
pp. 740-744 ◽  
Author(s):  
Zhu Hong Zhang
Keyword(s):  
Adaptive Sampling ◽  
Chance Constrained Programming ◽  
Noisy Environments ◽  
Immune Optimization ◽  
Noise Compensation ◽  
Noisy Information ◽  
Objective Chance ◽  
And Performance ◽  
Constrained Programming ◽  
Optimization Mechanism

This work puts forward a parameter-less and practical immune optimization mechanism in noisy environments to deal with single-objective chance-constrained programming problems without prior noisy information. In this practical mechanism, an adaptive sampling scheme and a new concept of reliability-dominance are established to evaluate individuals, while three immune operators borrowed from several simplified immune metaphors in the immune system and the idea of fitness inheritance are utilized to evolve the current population, in order to weaken noisy influence to the optimized quality. Under the mechanism, three kinds of algorithms are obtained through changing its mutation rule. Experimental results show that the mechanism can achieve satisfactory performances including the quality of optimization, noise compensation and performance efficiency.

scholarly journals A Chance-Constrained Multistage Planning Method for Active Distribution Networks

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4154
Author(s):  
Nikolaos Koutsoukis ◽  
Pavlos Georgilakis
Keyword(s):  
Distribution System ◽  
Net Present Value ◽  
Distribution Networks ◽  
Planning Method ◽  
Chance Constrained Programming ◽  
Expansion Plan ◽  
Total Investment ◽  
And Performance ◽  
Constrained Programming ◽  
Active Distribution Networks

This paper introduces a multistage planning method for active distribution networks (ADNs) considering multiple alternatives. The uncertainties of load, wind and solar generation are taken into account and a chance constrained programming (CCP) model is developed to handle these uncertainties in the planning procedure. A method based on a k-means clustering technique is employed for the modelling of renewable generation and load demand. The proposed solution methodology, which is based on a genetic algorithm, considers multiple planning alternatives, such as the reinforcement of substations and distribution lines, the addition of new lines, and the placement of capacitors and it aims at minimizing the net present value of the total operation cost plus the total investment cost of the reinforcement and expansion plan. The active network management is incorporated into planning method in order to exploit the control capabilities of the output power of the distributed generation units. To validate its effectiveness and performance, the proposed method is applied to a 24-bus distribution system.

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