RETRACTED: A new converged Emperor Penguin Optimizer for biding strategy in a day-ahead deregulated market clearing price: A case study in China

A Modified Emperor Penguin Algorithm for Solving Stagnation in Multi-Model Functions

“…This is done by creating a new solution with more components from the current solution. Conversely, when Author Modification Application Dhiman and Kumar [4] First Proposed Test Optimization Problems Jia et al [9] disruptive polynomial mutation Levy flight thermal exchange operator Satellite image segmentation Xing [12] Gaussian mutation Levy flight multilevel threshold for color image segmentation Kaur et al [6] Muti-objective optimization Yang and Gao [8] Face recognition Kaur et al [11] Multi-objective optimization Lu et al [13] Sequential quadratic programming Market clearing price Dhiman et al [5] Binary emperor penguin optimizer Sameh et al [7] Photovoltaic control system Tang et al [10] Energy consumption of the residential buildings the higher threshold is reached, the process aims to enhance diversity by incorporating more components from the relocated solution. The current position, 𝑃 𝑖 , and the position generated using equation ( 5) are used to create the information vector, 𝑃 𝐼𝑉 .…”

“…Xing [12] proposed an EPO algorithm for solving the multilevel threshold for color image segmentation. Lu et al [13] improved the EPO algorithm by optimizing its output using the sequential quadratic programming. They used the improved algorithm to minimize the market clearing price probability function.…”

Day-Ahead Risk Averse Market Clearing Considering Demand Response with Data-Driven Load Uncertainty Representation: A Singapore Electricity Market Study

“…Because of the significant role in electricity markets, numerous studies have investigated DR participation in the MC [6][7][8][9]. For instance, a price-based self-scheduling scheme for the multi-energy market is proposed in [6], which aims to maximize the total profit while considering characteristics of various DR consumers.…”

“…For instance, a price-based self-scheduling scheme for the multi-energy market is proposed in [6], which aims to maximize the total profit while considering characteristics of various DR consumers. Similarly, [7] presents an optimization framework, with the adoption of a newly developed heuristic algorithm, to obtain the optimal bidding strategy in the dayahead market. A competition framework in a retail energy market is formulated in the presence of DR [8], which verifies that it could reduce prosumers cost and increased retailers profit with DR. [9] proposed an improved incentive-based DR model, in which the incentive value is not a fix value and relates to the peak intensity of each hour.…”