Optimal Bidding Strategy in Deregulated Power Market Using Krill Herd Algorithm

Chandram, K.; Rajababu, Durgam; Raghuram, K.

doi:10.1007/978-981-13-1819-1_5

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…For deciding MCP and calculation of the amount of bid power, ISO considered Equations (3) and ( 4) and ignored Equation (5). The MCP and amount of bid power are calculated by Equations ( 7) and (8), respectively.…”

Section: Statistical Modeling Of Bidding Strategy Problemmentioning

confidence: 99%

“…First, in a restructured power market setting, for traditional power traders, the optimum bidding problem was solved with the help of a complex programming approach [3]. Furthermore, for the purpose of resolving power sector bidding strategy considering variables that influence the bidding strategy challenge, such as generation costs, market restrictions, and competitor bidding behavior, many researchers have used numerous mathematical approaches, e.g., Monte Carlo simulation [4], particle swarm optimization algorithms (PSO) [5], genetic algorithms (GA) [6], bat inspired techniques [7], krill herd algorithms (KHA) [8], gravitational search algorithms [9], etc. In the above-mentioned works, the action of competitors is first explained using the normal probability distribution function (PDF), followed by the solution of the benefit maximization problem.…”

Section: Introductionmentioning

confidence: 99%

“…According to the literature review, there are many optimization strategies that can be applied to the issue of bidding approach and optimize supplier benefit. Heuristic optimization techniques such as the particle swarm optimization algorithm (PSO) [5], genetic algorithm (GA) [6], bat inspired technique (BIT) [7], krill herd algorithm (KHA) [8], gravitational search algorithm [9] have main limitations in their sensitivity to the choice of parameters, such as the inertia weight and learning factors in PSO, crossover and mutation probabilities in GA, the requirement of parameter tuning, a poor control strategy the and lack of exploration capability in BIT, poor exploitation capability in KHM, and population initialization in GSA.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

Singh

Fozdar

Malik³

et al. 2021

Applied Sciences

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It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach.

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Section: Statistical Modeling Of Bidding Strategy Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

Singh

Fozdar

Malik³

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…After using classical methods for solving bidding problem, the authors in refs. [5][6][7][8][9][10] uses different optimization techniques considering the factors like cost of generations, market constraints and rival's bidding behaviour that affect the bidding strategy problem. The risk issue is chosen as the key issues for bidding problem [11] where the risk is minimized for profit maximization in a competitive electricity market.…”

Section: Introductionmentioning

confidence: 99%

Impact of renewable energy sources on modelling of bidding strategy in a competitive electricity market using improved whale optimization algorithm

Sahoo

Hota

2021

IET Renewable Power Gen

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In a competitive power market, generating utilities can be enhanced to achieve maximum profit by implementing a process of bidding strategy. Now‐a‐days renewable sources like solar and wind have become better alternatives significantly than other sources prior to power generation. These sources have extensive utilisation day‐by‐day in power sector and their impact in developing precise bidding strategies is getting more challenging aspect in the market. Since these renewable sources possess intermittent nature and undergo many uncertainties, the generating utilities encounter an unavoidable problem. Taking these aspects into consideration, attempts have been made using improved whale optimization algorithm to make the bidding strategy model for maximizing the power supplier's profit. Weibull and Beta distribution functions are used for modelling the stochastic characteristics of wind‐speed profile and solar‐irradiation, respectively. The proposed technique is tested and clarified with an IEEE‐30, IEEE‐57 and practical 75‐bus Indian system. The outcomes of this method were taken into comparison with other optimization techniques and found that it has an advantage upon other methods in solving bidding problems. Further, it is observed that the impact of renewable sources on bidding strategy reduces the market clearing price and the generation of thermal power while increases the total bidding power.

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“…Strategic bidding problem for competitive power suppliers in the England -wale electricity markets is provided in [11,12]. These algorithms such as genetic algorithm (GA) [13], particle swarm optimization algorithm (PSO) [14], differential evolution (DE) [15], invasive weed optimization (IWO) [16] and krill herd algorithm (KHA) [17], bacterial foraging algorithm [18], agent-based algorithm [19], bat inspired algorithm [20] have been applied in solving the DSOBS problem. Advantages and limitations of these methods are explained by few researchers.…”

Section: Introductionmentioning

confidence: 99%

Supplier side optimal bidding strategy for electricity market using bacterial foraging optimization algorithm

L¹,

Raju²,

Chandram

2021

IJAPE

Self Cite

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<p class="Title1">In this article, bacterial foraging optimization (BFO) algorithm is developed for single side optimal bidding strategy in an electricity market. Optimal bidding strategy is one of the important functions in the electricity market along with forecasting of the electricity price and the profit based unit commitment. The prime objective of generating company (Genco) is to maximize their profit when they participate in the bidding process. The BFO algorithm has been used to maximize the probability density function (pdf). In the second stage the BFO algorithm is again applied to maximize the profit of the suppliers. The Proposed algorithm is developed in MATLAB (Version, 2019) and tested on standard test case available in the literature. Also, the simulation results are presented and compared. It is noticed that the proposed method yields the best results in terms of profit.</p>

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Optimal Bidding Strategy in Deregulated Power Market Using Krill Herd Algorithm

Cited by 7 publications

References 12 publications

Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

Impact of renewable energy sources on modelling of bidding strategy in a competitive electricity market using improved whale optimization algorithm

Supplier side optimal bidding strategy for electricity market using bacterial foraging optimization algorithm

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