Multi-period wind integrated optimal dispatch using series PSO-DE with time-varying Gaussian membership function based fuzzy selection

Pandit, Manjaree; Chaudhary, Vishal; Dubey, Hari Mohan; Panigrahi, B. K.

doi:10.1016/j.ijepes.2015.05.017

Cited by 45 publications

(14 citation statements)

References 50 publications

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“…The environmental concerns due to the emission of hazardous pollutants from the fossil fuel fired thermal plants force us to consider minimization of emission as another objective. Many strategies like installation of air filters, pollutant‐cleaning equipment, switching to low emission fuels, replacement of aged equipment and generating units, integration of renewable energy sources, and environmental dispatching have taken into considerations . All of them except environmental dispatching need considerable amount of capital and huge replacement/modification/upgrade strategy that can be considered as a long‐term solution.…”

Section: Introductionmentioning

confidence: 99%

“…Many strategies like installation of air filters, pollutant-cleaning equipment, switching to low emission fuels, replacement of aged equipment and generating units, integration of renewable energy sources, and environmental dispatching have taken into considerations. [4][5][6][7][8] All of them except environmental dispatching need considerable amount of capital and huge replacement/modification/ upgrade strategy that can be considered as a long-term solution. Thus, environmental dispatching is an attractive short-term solution to reduce the emission of pollutants, which can be considered with economic load dispatch problem.…”

Section: Introductionmentioning

confidence: 99%

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A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function

Mahdi

Vasant

Abdullah-Al-Wadud

et al. 2017

Int Trans Electr Energ Syst

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Summary In electrical power dispatch problem, economic dispatch (ED) and environmental dispatch problems play a crucial part. Economic dispatch problem refers to the minimization of generation cost, where environmental dispatch problem refers to the minimization of emission of pollutants like CO2, SO2, and NOx from the power generation system. A quantum‐inspired particle swarm optimization (QPSO) technique is presented in this paper to solve many‐objective environmental economic dispatch (EED) problems. Emissions of CO2, SO2, and NOx are considered 3 different objectives, thus making it a 4‐objective problem considering ED. Many‐objective EED problems are defined by using a cubic criterion function, and a max/max price penalty factor is considered to convert all the objectives into a single objective to compare the final results with other well‐known methods found in the literature like Lagrangian relaxation, particle swarm optimization, simulated annealing, and quantum‐behaved bat algorithm. Quantum‐inspired particle swarm optimization is implemented on a 6‐unit system to solve many‐objective EED problems, and at the same time, to show the effectiveness of QPSO in large systems, it is also implemented in a 26‐unit power generation system for ED problem. The obtained results demonstrate and verify the effectiveness and robustness of QPSO to solve many‐objective EED problems. This also shows that QPSO can effectively be implemented in such power dispatch problems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function

Mahdi

Vasant

Abdullah-Al-Wadud

et al. 2017

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…As such, traditional optimization techniques can occasionally fail or encounter numerical difficulties with objective functions. In contrast, both particle swarm optimization (PSO) and differential evolution are powerful population-based searching algorithms [21]. However, transforming a typical single-objective PSO into a multi-objective PSO requires reestablishing both the best local and global individuals to seek a front of optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Voltage-Stability Based on Congestion Management for Integrating Wind Power into the Electricity Market

Choi

Kim

2017

Applied Sciences

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This paper proposes voltage-stability based on congestion management (CM) for electricity market environments and considers the incorporation of wind farms into systems as well. A probabilistic voltage-stability constrained optimal power flow (P-VSCOPF) is formulated to maximize both social welfare and voltage stability. To reflect the probabilistic influence of CM in the presence of wind farms on voltage stability, Monte Carlo simulations (MCS) are used to analyze both the system load and the wind speed from their probability distribution functions. A multi-objective particle-swarm optimization (MOPSO) algorithm is implemented to solve the P-VSCOPF problem. A contingency analysis based on the voltage stability index (VSI) for line outages is employed to find the vulnerable line of congestion in power systems. The congestion distribution factor (CDF) is also used to find the optimal location of a wind farm in CM. The optimal pricing expression, which is obtained, with respect to preserving voltage stability, by calculating both the locational marginal prices (LMPs) and the nodal congestion prices (NCPs), is demonstrated in terms of congestion solutions. Simultaneously, the voltage stability margin (VSM) is considered within the CM framework. The proposed approach is implemented on a modified IEEE 24-bus system, and the results obtained are compared with the results of other optimal power flow methods.

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“…Later, regulations forced utilities to minimize emissions along with cost and MO models came into existence [8e22]. Both, SO [23e25] as well as MO [26,27] models can be found for wind integrated thermal scheduling problems. However, very few researchers have addressed the joint dispatch of hydro-thermal and wind units [28e30].…”

Section: Introductionmentioning

confidence: 99%

“…do not work satisfactorily for complex practical problems. Lately, nature inspired (NI) methods have gained popularity against gradient based algorithms, due to their numerous advantages [10,27] such as (i) Non dependence on nature of optimization problem (ii) Non dependence on initial solution (iii) Global search capability due to population based direct search (iv) Simple implementation and (v) Effective constraint handling. Among popular NI techniques, EP [9], PSO [10,15], DE [8,11,12], ABC [13,18,32], GS [14,17] PPO [15], and cultural algorithm with PSO [16], are successfully implemented.…”

Section: Introductionmentioning

confidence: 99%

Hydro-thermal-wind scheduling employing novel ant lion optimization technique with composite ranking index

2016

Self Cite

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Adaptive boundary shrinking Composite ranking index (CRI) Random walk mechanism Multi-objective hydro-thermal-wind scheduling (MOHTWS) Wind reserve and penalty coefficients a b s t r a c t A solution to the combined hydro-thermal-wind scheduling problem of multi reservoir cascaded hydro plants is presented employing a novel ant lion optimization (ALO) algorithm. Five objectives, cost, various emissions and power loss, are simultaneously optimized. The optimal schedules of thermal, hydro and wind power (WP) units are determined for continuously varying load subject to a large number of practical operational constraints. The effect of reserve and penalty coefficients and WP uncertainty is also investigated for the multi-objective (MO) problem. The newly proposed ALO algorithm has unique features like random walk, roulette wheel, and boundary shrinking. These operations provide a judicious balance between exploration and exploitation, and create a powerful optimization technique for complex real-world problems.Finding the best compromise solution (BCS) is a tedious task when multiple objectives are involved. A composite ranking index (CRI) is proposed as a performance metrics for MO problems. The CRI helps the decision maker in ranking the large number of Pareto-optimal solutions. The developed model is tested on three standard systems, having a mix of hydro, thermal and wind generators. The performance is found to be superior to published results and comparable with established algorithms like artificial bee colony (ABC) and differential evolution (DE).

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Multi-period wind integrated optimal dispatch using series PSO-DE with time-varying Gaussian membership function based fuzzy selection

Cited by 45 publications

References 50 publications

A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function

A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function

Multi-Objective Optimization of Voltage-Stability Based on Congestion Management for Integrating Wind Power into the Electricity Market

Hydro-thermal-wind scheduling employing novel ant lion optimization technique with composite ranking index

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