A Solution to Non-convex/Convex and Dynamic Economic Load Dispatch Problem Using Moth Flame Optimizer

Bhadoria, Ashutosh; Kamboj, Vikram Kumar; Sharma, Manisha; Bath, S. K.

doi:10.1007/s41403-018-0034-3

Cited by 17 publications

(13 citation statements)

References 66 publications

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“…There is a possibility to vitiate the exploitation of the best favorable solutions during position updating mechanism among n different locations. 56 This concern is resolved by using an adaptive mechanism for the flames, which can be expressed as follows:…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…where P denotes the current number of iteration, the maximum number of iterations can be represented by T, and "round" represents the number of rounds covered by the moth around the flames during fly. Also, the total number of flames is signified by N. 56 Moth-flame optimization algorithm has the ability to deliver the optimal solution quickly while adaptively balancing the exploration and exploitation. This approach is easy to implement, needs lesser parameters to be fine-tuned compared with other methods, converges to global optima, and do not require gradient calculation.…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…Hence, the moths ultimately converge in the direction of the flame. This phenomenon based on moths convergence behavior is developed as an MFO algorithm …”

Section: Introduction To Mfo Algorithmmentioning

confidence: 99%

“…Figure represents the position updating behavior of moth with respect to t in a logarithmic spiral path around the flame. There is a possibility to vitiate the exploitation of the best favorable solutions during position updating mechanism among n different locations . This concern is resolved by using an adaptive mechanism for the flames, which can be expressed as follows:

Flame number = P ((), \frac{N}{P} - \frac{N - 1}{T}) round,

where P denotes the current number of iteration, the maximum number of iterations can be represented by T , and “round” represents the number of rounds covered by the moth around the flames during fly.…”

Section: Introduction To Mfo Algorithmmentioning

confidence: 99%

“…This concern is resolved by using an adaptive mechanism for the flames, which can be expressed as follows:

Flame number = P ((), \frac{N}{P} - \frac{N - 1}{T}) round,

Section: Introduction To Mfo Algorithmmentioning

confidence: 99%

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Grid frequency enhancement using coordinated action of wind unit with redox flow battery in a deregulated electricity market

Dhundhara

Verma

2020

Int Trans Electr Energ Syst

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Summary Growing electricity demand, environmental issues, and challenge to decrease dependence on fossil fuel resources have increased the inception of wind generation into the power system. However, higher penetration of wind‐based generating units into the existing grid can affect the working of the power system. Traditionally, a wind unit does not provide inertia, but collectively, wind units can have a notable impact on the dynamic performance of the power system. However, the battery energy storage systems have the potential to offer flexibility and ancillary supports to the power system. This article evaluates the impact of redox flow battery (RFB) in coordination with a doubly fed induction generator (DFIG)–based wind turbine unit (WTU) to enrich the dynamic performances of a multi‐source interconnected power system in a deregulated electricity market. The modified inertial control scheme is proposed for the DFIG unit that responds in the event of frequency deviation in the grid. The turbine sheds its kinetic energy and provides active power injection, thereby enhancing the frequency response of the system. The recently developed moth‐flame optimization (MFO) algorithm is employed for optimal tuning of the proportional‐integral (PI) controller and the speed regulator of a DFIG‐WTU. The simulation studies have been executed to analyze the impact of the RFB with WTU on the system frequency, tie‐line and different generating units power through a comparative study in terms of the settling time, and peak overshoot/undershoot in a deregulated electricity market. The analysis reveals that the WTU effectively contributes to sustaining the frequency and tie‐line power oscillations during abrupt load disturbances in the proposed power system. Moreover, the inclusion of RFB in coordination with the WTU helps to reduce the stress on a wind turbine during inertial control scheme. It can also reduce wind curtailments by absorbing excess power flowing through transmission lines, reduces wastage of green energy, and gives better dynamic response under different operating conditions of the deregulated electricity market.

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Section: Mathematical Formulationmentioning

confidence: 99%

Section: Mathematical Formulationmentioning

confidence: 99%

“…Hence, the moths ultimately converge in the direction of the flame. This phenomenon based on moths convergence behavior is developed as an MFO algorithm …”

Section: Introduction To Mfo Algorithmmentioning

confidence: 99%