Search

Demand side management (DSM) is one of the trending economic strategies which shifts the elastic demand to the off-peak hours from the peak hours so as to reduce the overall generation cost of the system. The work done in this paper can be categorized in three phases. In the first phase, various wind speed to power conversion mathematical models available in literature are analysed to find out the one with maximum level of wind penetration. For second phase, an economic DSM strategy is implemented to restructure the forecasted load demand model for various participation levels. In the final phase the cost-effective optimization of two microgrid distribution systems are percolated. As an optimization tool, novel hybrid CSAJAYA has been used to carry on the study. Different types of grid participating and pricing strategies along with valve point loading effect and wind energy uncertainty are considered to amplify the complexity and practicality of the study. The generation costs reduced from 3 to 5% when the forecasted demand was reformed with 20% DSM participation for both the test systems. A detailed comparison with the results from various optimization tools studied confirms the effectiveness of the proposed hybrid approach. The hybrid optimization tool presented in this paper performs better in terms of central tendencies, nonparametric statistical analysis, and algorithm execution time. • Five different wind speed-to-power mathematical modelling have been studied. • Effects of DSM in reducing the microgrid generation cost was analysed. • Grid participation strategies and their impact on microgrid generation cost was analysed. • Grid pricing strategies and their impact on microgrid generation cost was analysed. [ABSTRACT FROM AUTHOR]

The conception of electromechanical oscillations initiates in the power network when there is an installation of the generator in parallel with the existent one. Further, the interconnection of multiple areas, extension in transmission, capricious load characteristics, etc. causes low-frequency oscillations in the consolidated power network. This paper proposes variants of a booming population-based grey wolf optimization (GWO) algorithm in the tuning of power system stabilizer parameters of a multi-machine system in damping low-frequency oscillations. The parameters have been tuned by framing an objective function considering the improving damping ratios for the system states with lesser damping ratios and shifting the system eigenvalues towards the left-hand side of s-plane for the improved settling characteristics for the oscillations in the system. The requisites of stabilizer strategy are mapped with the hallmarks of prevalent algorithms and designed hybrid versions of GWO for the enhancement of the multi-machine power system stability. Four variants of GWO technique are nominated based on the competent stabilizer performance namely, modified grey wolf optimization (MGWO), hybrid MGWO particle swarm optimization (MGWOPSO), hybrid MGWO sine cosine algorithm (MGWOSCA) and hybrid MGWO crow search algorithm (MGWOCSA) for the designed multi-machine power network. The proposed methods have been realized with the statistical analysis on the 23 benchmark functions. Nonparametric statistical tests, namely, Feidman test, Anova test and Quade tests, have been performed on the test system, further analysed in detail. A detailed comparative analysis under the self-clearing fault is presented to illustrate the suitability of the proposed techniques. For the analysis purpose, the location of system eigenvalues has been observed along with their oscillating frequencies and corresponding damping ratios. Further, the damping nature offered with considered system uncertainty for the system states also presented with the PSS parameters obtained by the proposed algorithms. • Amendments were made by hybridizing GWO with PSO, SCA and CSA and recommended for the electrical power system stability enhancement. • Realization of these amended algorithms was done on 23 benchmark functions. • Algorithms mentioned above were utilized to tune traditional power system stabilizer considering system uncertainties. • Eigenvalue analysis and damping characteristics of system states have been presented under system uncertainties. • Non-parametric statistical analysis was performed for the amended GWO algorithms. [ABSTRACT FROM AUTHOR]

• Consideration of the optimal phasor measurement unit placement problem without radial buses. • Phasor measurement unit is pre-assigned at each bus connected to radial bus. • A two-phase branch-and-bound algorithm is used to determine the strategic locations for placement of phasor measurement unit. • The redundancy measurement is performed for ranking the obtained multiples solutions. This paper proposes a mixed integer nonlinear programming (MINLP) formulation with binary-valued variables for the optimal installation of the phasor measurement units (PMUs) in a power network. The MINLP framework has been solved by a two-phase branch-and-bound algorithm (BBA) for the optimal PMU placement (OPP) problem. The present work deals with the solution of the OPP problem without considering the radial buses including in the optimal solution. PMU is pre-assigned to each bus connected to a radial bus such that the radial buses are excluded from the list of potential PMU locations. The programming technique is used to determine the optimum number of PMUs and their locations to make the interconnected power network completely observable. The proposed BBA may provide multiple solutions at the lowest objective value, each time the optimizer routine restarts. Therefore, the obtained optimal solutions are being ranked regarding the measurement redundancy index. The two-phase branch-and-bound algorithm is applied to IEEE standard test systems as well as to New England 39- bus and a northern regional power grid 246- bus system. The proposed algorithm has been compared with the existing programming techniques in the recent literature. The simulation results obtained by the proposed BBA indicate the effectiveness and the efficiency to detect the desired target. [ABSTRACT FROM AUTHOR]

Reactive power planning is one of the most challenging problem for efficient and source operation of an interconnected power network. It requires effective and optimum co-ordination of all the reactive power sources present in the network. Recently, Teaching Learning Based Optimization (TLBO) algorithm is evolved and finds its application in the field of engineering optimization. In the proposed work TLBO based optimization algorithm is used for reactive power planning and applied in IEEE 30 and IEEE 57 bus system. The results obtained by this method are compared with the results obtained by other optimization techniques like PSO (Particle swarm optimization), Krill heard, HSA (Harmony search algorithm) and BB-BC (Big Bang-Big Crunch). At the end, TLBO appears as the most effective method for reactive power planning among all the methods discussed and can be considered as one of the standard method for reactive power optimization. [ABSTRACT FROM AUTHOR]

This paper presents binary particle swarm optimization (BPSO) technique for the optimal allocation of phasor measurement units (PMUs) for the entire observability of connected power network. Phasor measurement units are considered as one of the most important measuring devices in the prospect of connected power network. PMUs function may be incorporated to the wide-area connected power networks for monitoring and controlling purposes. The optimal PMU placement (OPP) problem provides reference to the assurance of the minimal number of PMUs and their analogous locations for observability of the entire connected power networks. Binary particle swarm optimization (BPSO) algorithm is developed for the solution of OPP problem. The efficacy and robustness of the proposed method has been tested on the IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, IEEE 118-bus and Northern Regional Power Grid (NRPG) 246-bus test system. The results obtained by proposed approach are compared with other standard methods and it is observed that this BPSO based placement of phasor measurement units is found to be the best among all other techniques discussed. [ABSTRACT FROM AUTHOR]

In the proposed work, authors have applied swarm intelligence based algorithms for the effective Co-ordination of Flexible AC transmission system (FACTS) devices with other existing Var sources present in the network. IEEE 30 and IEEE 57 bus systems are taken as standard test systems. SPSO (Simple Particle Swarm Optimization) and other two swarm based intelligence approaches like APSO (Adaptive Particle Swarm Optimization) and EPSO (Evolutionary Particle Swarm Optimization) are used for the optimal setting of the Var sources and FACTS devices. The result obtained with the proposed approach is compared with the result found by the conventional RPP (Reactive power planning) approach where shunt capacitors, transformer tap setting arrangements and reactive generations of generators are used as planning variables. It is observed that reactive power planning with FACTS devices yields much better result in terms of reducing active power loss and total operating cost of the system even considering the investment costs of FACTS devices. [ABSTRACT FROM AUTHOR]

In the present work, GSA (gravitational search algorithm) based optimization algorithm is applied for the optimal allocation of FACTS devices in transmission system. IEEE 30 & IEEE 57 test bus systems are taken as standards. Both active and reactive loading of the power system is considered and the effect of FACTS devices on the power transfer capacity of the individual generator is investigated. The proposed approach of planning of reactive power sources with the FACTS devices is compared with other globally accepted techniques like GA (Genetic Algorithm), Differential Evolution (DE), and PSO (Particle Swarm Optimization). From the results obtained, it is observed that incorporating FACTS devices, loadability of the power system increases considerably and each generator present in the system is being able to dispatch significant amount of active power under different increasing loading conditions where the steam flow rate is maintained corresponding to the base active loading condition. The active power loss & operating cost also reduces by significant margin with FACTS devices at each loading condition and GSA based planning approach of reactive power sources with FACTS devices found to be the best among all the methods discussed in terms of reducing active power loss and total operating cost of the system under all active and reactive loading situations. [ABSTRACT FROM AUTHOR]

Reactive power source planning problem has significant role for secure and economic operation of power system. Reactive power planning problem is nothing but the proper coordination of existing Var sources which lead to loss minimization and cost economic operation of the system. In the proposed approach several bio-inspired algorithms like Particle Swarm Optimization (PSO), Evolutionary Particle Swarm Optimization (EPSO), Adaptive Particle Swarm Optimization (APSO), Hybrid Particle Swarm Optimization (HPSO) and Bacterial Foraging Algorithm (BFA) are used for the reactive power planning problem. Finally a comparison of all these techniques are made on the basis of the results obtained when applied to different standard test system. [ABSTRACT FROM AUTHOR]

Highlights: [•] Fuzzy based evolutionary algorithm. [•] FACTS devices for reactive power optimization. [•] FACTS devices for minimum operating cost of a power system. [Copyright &y& Elsevier]

Generation of electricity comes with the emission of toxic gases into the atmosphere by the fossil fueled generators. Along with the promotion in the utilization of renewable energy sources (RES), it is also the duty of the power engineers to arrive at a compromised solution such that less emission of toxic gases occurs with economic generation of electricity. This paper proposes a balanced trade-off method for solving environment constrained economic dispatch (ECED) problems. A novel comparative analysis is performed among proposed ECED method with existing price-penalty-factor (PPF) and fractional programming (FP) methods for solving combined economic emission dispatch (CEED) problems on a 3-unit dynamic test system to sort out the method, which yields a better trade off solution between generation cost and pollutants emitted. An algorithm, following the hunting strategy of wolves, is improvised by incorporating strategies from population-based sine-cosine algorithm along with position updating methods of crows to form a robust hybrid algorithm, which was used as the optimization tool for the study. Involvement of RES diminished the generation cost to 5.5% for both economic dispatch and PPF based CEED, and 6.5% decrease in emission of pollutants was observed due to the same. The generation cost and amount of emitted pollutants, evaluated using proposed ECED approach, were much closer to the economic dispatch and emission dispatch values respectively compared to PPF based and FP based CEED solution. Furthermore, statistical analysis endorses the superiority of the proposed hybrid optimizer over other algorithms presented in the state-of-art literature. • Three different types of CEED methods are studied. • Subject test system is a 3-units RES integrated microgrid system. • Comparative analysis among the generation costs and pollutants emitted is performed. • A novel HMGWO algorithm was proposed as the optimization tool for the study. [ABSTRACT FROM AUTHOR]

Topological insulators represent a new electronic phase which comes from the topological character of the bulk wave functions in some special materials. These exotic phases can be attributed to the strong spin-orbit interaction and can be explained with the band theory of solids. Recent studies show that topological insulators (TIs) such as Bi 2 Te 3 or Bi 2 Se 3 have promising candidature for optoelectronic industry and some of these interesting properties have been already demonstrated such as strong light absorption, photocurrent sensitivity to the polarization of light, layer thickness and size dependent tuning in the band gap. Theoretically, it has been proposed that TIs based broad spectral photodetector has potential in analogy with that of a graphene based photodetector. Combining TIs with carbon-based nanomaterials provides novel and promising technique for photodetection, and can show broad spectral properties. Along this line, we have grown photodetector based on hybrid of TIs with carbon nanotubes. Efficient light absorption and electron hole pair generation has been observed under the illumination of visible (532 nm) and NIR (1064 nm) light. High photoresponsivity and photoconductive gain of 79.4 AW-1 and 185, respectively, along with very good detector response time were observed in our device under visible laser (532 nm) illumination. • Novel optical device based on Topological Insulator - Carbon Nanotubes hybrid for broadband photodetection was developed. • Efficient light absorption and electron-hole pair generation observed under illumination of visible and NIR laser light. • Excellent photodetection properties with high photoresponsivity and photoconductive gain was demonstrated by the hybrid. [ABSTRACT FROM AUTHOR]