Comparative Application of Differential Evolution and Particle Swarm Techniques to Reactive Power and Voltage Control

This paper presents the comparative application of two metaheuristic approaches: Differential Evolution (DE) and Particle Swarm Optimization (PSO) to the solution of the reactive power and voltage control problem. Efficient distribution of reactive power in an electric network leads to minimization of the system losses and improvement of the system voltage profile. It can be achieved by varying the excitation of generators or the on-load tap changer positions of transformers as well as by switching of discrete portions of inductors or capacitors etc. This constitutes a typical mixed integer non-linear optimization problem for the solution of which metaheuristic techniques have proven well suited in principle. The feasibility, effectiveness and generic nature of both DE and PSO approaches investigated are exemplarily demonstrated on the Nigerian grid system and the New England power system. Comparisons were made between the two approaches in terms of the solution quality and convergence characteristics. The simulation results revealed that both approaches were able to remove the voltage limit violations, but PSO procured in some instances slightly higher power loss reduction as compared with DE; on the other hand DE required a lower number of function evaluations as compared with PSO. Consideration of computational effort is relevant for potential real time on line application

Swarm Intelligence and Evolutionary Approaches for Reactive Power and Voltage Control

This paper presents a comparison of swarm intelligence and evolutionary techniques based approaches for minimization of system losses and improvement of voltage profiles in a power network. Efficient distribution of reactive power in an electric network can be achieved by adjusting the excitation on generators, the on-load tap changer positions of transformers, and proper switching of discrete portions of inductors or capacitors. This is a mixed integer non-linear optimization problem where metaheuristics techniques have proven suitable for providing optimal solutions. Four algorithms explored in this paper include differential evolution (DE), particle swarm optimization (PSO), a hybrid combination of DE and PSO, and a mutated PSO (MPSO) algorithm. The effectiveness of these algorithms is evaluated based on their solution quality and convergence characteristic. Simulation studies on the Nigerian power system show that a PSO based solution is more effective than a DE approach in reducing real power losses while keeping the voltage profiles within acceptable limits. The results also show that MPSO allows for further reduction of the real power losses while maintaining a satisfactory voltage profile

Differential Evolution Approach for Reactive Power Optimization of Nigerian Grid System

The goal of reactive power dispatch is to minimize the system losses and improve the system voltage profiles at all times. This is achieved by adjusting various generating units\u27 excitation systems continuously, discrete tap positions of on-load tap changers of transformers as well as switching of correct doses of inductors or capacitors. This is a mixed integer non-linear optimization problem. In this paper, the differential evolution (DE), a novel evolutionary computation technique which was originally designed for continuous problems is applied to solve this problem. DE appears to ally qualities of established computational intelligence (CI) techniques with a more striking computational performance, thus suggesting the possibility of having the potential for on line applications in the control center; comparison work with other techniques is presently conducted. The developed tool was demonstrated on the Nigerian power system grid for three case scenarios preset on the power world simulator which was linked with DE for power flow calculation (fitness check of solutions). The results achieved revealed that DE procured a significant reduction of real power losses while simultaneously keeping the voltage profiles within the acceptable limits

Computational Enhancement of Genetic Algorithm Via Control Device Pre-Selection Mechanism for Power System Reactive Power/Voltage Control

In this paper, the application of a novel and computationally enhances genetic algorithm (GA) for solving the reactive power dispatch problem is presented. In order to attain a significant reduction in the computational time of GA, a systematic procedure of reactive power control device pre-selection mechanism is herein proposed to choose a-priori subsets of the available control devices, which maximally influence buses experiencing voltage limit violations. The GA reactive power dispatch module then accesses such judiciously pre-selected control device candidates to determine their optimal settings. A pragmatic scheme aimed at further curtailing the number of the final control actions entertained is also set forth. The far-reaching simulation results obtained for two case study scenarios using the proposed algorithmic procedures on a German utility network of Duisburg, replicated on an operator-training simulator, are presented and fully discussed in depth

Electronic excitations stabilized by a degenerate electron gas in semiconductors

Excitons in semiconductors and insulators consist of fermionic subsystems, electrons and holes, whose attractive interaction facilitates bound quasiparticles with quasi-bosonic character. In the presence of a degenerate electron gas, such excitons dissociate due to free carrier screening. Despite their absence, we found pronounced emission traces in the below-band-edge region of bulk, germanium-doped GaN up to a temperature of 100 K, mimicking sharp spectral features at high free electron concentrations (3.4E19–8.9E19 cm−3). Our interpretation of the data suggests that a degenerate, three-dimensional electron gas stabilizes a novel class of quasiparticles, which we name collexons. These many-particle complexes are formed by exchange of electrons with the Fermi gas. The potential observation of collexons and their stabilization with rising doping concentration is enabled by high crystal quality due to the almost ideal substitution of host atoms with dopants.DFG, 43659573, SFB 787: Semiconductor Nanophotonics: Materials, Models, Device

Assessing the potential for sea-based macroalgae cultivation and its application for nutrient removal in the Baltic Sea

Marine eutrophication is a pervasive and growing threat to global sustainability. Macroalgal cultivation is a promising circular economy solution to achieve nutrient reduction and food security. However, the location of production hotspots is not well known. In this paper the production potential of macroalgae of high commercial value was predicted across the Baltic Sea region. In addition, the nutrient limitation within and adjacent to macroalgal farms was investigated to suggest optimal site-specific configuration of farms. The production potential of Saccharina latissima was largely driven by salinity and the highest production yields are expected in the westernmost Baltic Sea areas where salinity is >23. The direct and interactive effects of light availability, temperature, salinity and nutrient concentrations regulated the predicted changes in the production of Ulva intestinalis and Fucus vesiculosus. The western and southern Baltic Sea exhibited the highest farming potential for these species, with promising areas also in the eastern Baltic Sea. Macroalgal farming did not induce significant nutrient limitation. The expected spatial propagation of nutrient limitation caused by macroalgal farming was less than 100–250 m. Higher propagation distances were found in areas of low nutrient and low water exchange (e.g. offshore areas in the Baltic Proper) and smaller distances in areas of high nutrient and high water exchange (e.g. western Baltic Sea and Gulf of Riga). The generated maps provide the most sought-after input to support blue growth initiatives that foster the sustainable development of macroalgal cultivation and reduction of in situ nutrient loads in the Baltic Sea.</p