A hybrid approach based on IGDT–MPSO method for optimal bidding strategy of price-taker generation station in day-ahead electricity market

Efeito do atraso de pagamentos nos Custos de Transação em contratações públicas

“…Zhu et al (2006) explicam que o processamento do PSO se assemelha ao comportamento dos seres humanos ao incluir aleatoriedade e evolução. Essas características fazem do PSO um método muito útil para selecionar pregões que maximizam os lucros do fornecedor considerando atributos como incerteza (Nojavan et al, 2015), informação incompleta (Soleymani, 2011) e o comportamento e racionalidade limitada dos agentes (Zhu et al, 2006). Machado-Coelho et al (2017) explicam que o PSO funciona por meio da geração aleatória r de s_size soluções para o problema dentro de um espaço n-dimensional chamadas de partículas.…”

Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model

“…According to the provided reports in [1], more than 80 % of the US electricity is supplied by energy sources such as petroleum, natural gas, and coal that can be implemented by thermal units. The impact of possibilistic reserve deployment and forced outages of thermal units on the SS problem have been studied in [10] while the same problem of a thermal GenCo has been addressed in [11] based on the information gap decision theory.…”

“…Equalities (10) and (11) calculate the total energy and spinning reserve offers by thermal units. Constraints ( 12)-( 14) are employed to model the limitations related to the maximum and minimum value of produced and offered energies by thermal units.…”

Electric Power Bid Determination and Evaluation for Price Taker Units Under Price Uncertainty

“…As bidding problem has dynamic interaction and market operations, both optimization and heuristic approaches are used to model and solve the bidding problem for market players. Optimal control (Liu and Wu, 2006), game theory (Song et al, 2003) and , Lagrangian relaxation (Zhang et al, 2000), dynamic programming (Jiang and Powell, 2015), bilevel programming and swarm (Zhang et al, 2010), information gap decision theory (Nojavan et al, 2015), Shuffled Frog Leaping Algorithm (Kumar and Kumar, 2014), point estimate method (Peik-Herfeh et al, 2013), stochastic cournot model (Sharma et al, 2014), stochastic optimization (Davatgaran et al, 2018), and (Song and Amelin, 2017), and simulation (Yucekaya, 2013) are some of the recent research areas on the bidding problem. It is also possible to use hybrid models and include operational characteristics to model and solve the bidding problem.…”