Day‐ahead demand side management using symbiotic organisms search algorithm

Niharika,; Mukherjee, V.

doi:10.1049/iet-gtd.2018.0106

Cited by 34 publications

(11 citation statements)

References 28 publications

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“…Most of the researches are based on algorithms, strategies, techniques and hybrid algorithms for the DSM in smart grid [8,9]. The behaviour of pricing signals such as RTP, TOU, and CPP are analyzed for optimized consumption scheduling of HEMS [10,11], and [12].…”

Section: Related Workmentioning

confidence: 99%

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Energy Consumption Scheduling Using Adaptive Differential Evolution Algorithm in Demand Response Programs

Muthuselvi¹,

Balasubramanian²

2019

IJIES

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Demand Side Management (DSM) provides a better solution in order to manage increased electricity demand in the power system network. The DSM program relieves the stress on the electrical network for maintaining power system reliability during peak hours. This work proposes a new scheduling approach based on an Adaptive Differential Evolution algorithm (ADEA) by considering a new recombination probability factor (CP) and real mutation factor (F) for analysis. This proposed method is analyzed for industrial, commercial and residential network loads. The main aim of this demand control technique is to reduce the difference between the target curve and total load consumption curve. This paper provides a better solution compared to other algorithms like Evolutionary Approach (EA) and Symbiotic Organism Search (SOS) to reduce the peak load and electricity cost in industrial, commercial and residential distribution networks. The proposed method gives 8.2 % peak load reduction compared to EA for residential area and 2.28% reduction in peak load when compared to SOS for commercial sector. Also it reduces the electricity cost at 17.25%, 20.76% and 21.15 % for residential, commercial and industrial sectors when compared to without DSM. The participation factor of a consumer may be increased by price factor and the less violation in their scheduled demand. The described concept provides a relatively accurate fit to the target curve after load shifting. This concept will increase consumer participation in the DR Program.

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Section: Related Workmentioning

confidence: 99%

“…This approach supports the shifting of peak load to some other off peak hour in a day. In [9] Symbiotic organisms search (SOS) algorithm is presented and also considered residential, industrial and commercial end users. It gives the better peak load reduction only for industrial area compared to logarithmic approach.…”

Section: Related Workmentioning

confidence: 99%

Energy Consumption Scheduling Using Adaptive Differential Evolution Algorithm in Demand Response Programs

Muthuselvi¹,

Balasubramanian²

2019

IJIES

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“…A detailed review of various DR models for optimization related to energy management system isreported in various literature as presented in (Fallahi and Smith, 2017;Hussain and Gao, 2018;Jordehi, 2019;Khalili et al, 2019;Mizutani et al, 2018;Mohammed and Albadi, 2020;Robert et al, 2018;Sedighizadeh et al, 2018;Shariatzadeh et al, 2015). Researchers have proposed various energy management systems and models (EMS) to solve the generation scheduling problems either by using the traditional approach (Kiptoo et al, 2020;Mehdizadeh et al, 2018;Nwulu and Xia, 2017;SoltaniNejad Farsangi et al, 2018) or by using metaheuristic algorithms (Aghajani et al, 2015;da Silva et al, 2020;Esther et al, 2016;Jordehi, 2020;Kakran and Chanana, 2019;Niharika and Mukherjee, 2018;Nosratabadi and Hooshmand, 2020;Roy et al, 2019;Sarker et al,2020;Shahryari et al, 2019) with the objective to either minimize operational cost or minimize both cost and emission.Grid-tied MiG with DR is seen as an excellent option by independent system operators (ISO) to achieve solutions to a large spectrum of modern-day electrical distribution problems.Futuristic MiG control systems are encouraged to include demand-side management (DSM) as an indispensable component (Imani et al, 2020;Ajoulabadi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The multi-objective scheduling of MiG coupled with incentive-based DR has been investigated in (Aghajani et al, 2015;da Silva et al, 2020;Esther et al, 2016;Jordehi, 2020;Kakran and Chanana, 2019;Niharika and Mukherjee, 2018;Nosratabadi and Hooshmand, 2020;Olorunfemi and Nwulu, 2020;Roy et al, 2019;Shahryari et al, 2019)for minimization of operational cost and emissions released to the atmosphere. The augmented ɛ-constraint approach has been used to solve the multi-objective problem to minimize operation costs and emissions (Sedighizadeh et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Incentive-based demand response in grid-connected microgrid using quasi-opposed grey wolf optimizer

Dubey¹,

Dubey²,

Pandit³

2021

Int. J. Eng. Sci. Tech

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The paradigm shifts in the electrical industry from demand-driven generation to supply-driven generation due to the incorporation of renewable generating sources is a growing research field. Implementing demand response in present-day distribution schemes is anattractive approach often adopted by microgrid (MiG) operator.This paper incorporates an incentivebased demand response (IBDR) method in a grid-connected microgrid (MiG) comprising of conventional generators (CGs), wind turbines (WTs), and solar PV units. The main aim is to collectively minimize the fossil fuel cost of CGs, lower the transaction cost of portable power from the grid, and maximize theMiG operator's profitafter implementing demand response. This multi-objective problem combining optimal economic load dispatch of MiG with an efficient demand-side response is solved using a proposed Quasi-opposed Grey Wolf Optimizer (QOGWO) algorithm. The effect of the proposed algorithm on demand-side management (DSM) is analyzed for two cases, (i) varying the value of power interruptibility (ii) varying the maximum limit of curtained power. Performance of QOGWO is compared with original GWO and a variant of GWO, Intelligent Grey Wolf Optimizer (IGWO). Results show the superior global search capability and complex constrained handling capability of QOGWO.

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“…In [16], the SOS algorithm is applied to solve the transmission congestion problem in power system. Article [17] suggested the SOS algorithm for the demand side management modelling in the power industry. In [18], the SOS algorithm is applied to determine the coefficients of an optimal fractional order digital integrator.…”

Section: Introductionmentioning

confidence: 99%

Symbiotic organisms search algorithm for optimal design of CMOS two‐stage op‐amp with nulling resistor and robust bias circuit

Ghosh

Kar

et al. 2019

IET Circuits, Devices & Systems

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This study suggests an evolutionary technique namely symbiotic organisms search (SOS) algorithm based optimal designs of two different analogue very-large-scale integration circuits. The configurations considered here are nulling resistor compensation based complementary metal-oxide-semiconductor (CMOS) two-stage op-amp and two-stage CMOS op-amp with robust bias circuit. The prime goal of this work is the sizing of metal-oxide-semiconductor (MOS) transistors employing the SOS algorithm to optimise the area occupied by the individual circuit. Design results based on the SOS algorithm are authenticated with SPICE simulation. SPICE simulation results reveal that all the design specifications are firmly satisfied for both the circuits. Moreover, SPICE based results show that the SOS algorithm provides much better results compared to the earlier reported techniques regarding the gain, MOS area and power dissipation for the abovementioned op-amp circuits.

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Day‐ahead demand side management using symbiotic organisms search algorithm

Cited by 34 publications

References 28 publications

Energy Consumption Scheduling Using Adaptive Differential Evolution Algorithm in Demand Response Programs

Energy Consumption Scheduling Using Adaptive Differential Evolution Algorithm in Demand Response Programs

Incentive-based demand response in grid-connected microgrid using quasi-opposed grey wolf optimizer

Symbiotic organisms search algorithm for optimal design of CMOS two‐stage op‐amp with nulling resistor and robust bias circuit

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