Bidding strategy of hybrid power plant in day‐ahead market as price maker through robust optimization

Darvishi, Sajede; Sheisi, Gholamhossein; Aghaei, Jamshid

doi:10.1002/2050-7038.12426

Cited by 9 publications

(6 citation statements)

References 50 publications

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“…In this work, the presented optimization problem tries to simultaneously maximize the profit of coordinated energy sources and minimize the emission of GHG. e paper in [18] studied both price-maker roles in the day-ahead market and pricetaker roles as balancing market in real time. Authors in [19] developed an optimization model to determine the optimal bidding strategy of a GENCO with coordinated windpumped storage-thermal system.…”

Section: Aimmentioning

confidence: 99%

Bidding Strategy of a Wind-Thermal GENCO considering Piecewise Linear AC Power Flow and Correlated Uncertainties

Daealhaq

Hassan

Ghanimi

et al. 2022

International Transactions on Electrical Energy Systems

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In deregulated electricity markets, generation companies (GENCO) try to maximize their economic benefits considering the electricity demand, transmission network condition, and other participants’ behaviors. The increasing penetration of renewable sources such as wind power generation with intermittent nature poses several challenges to the participation of GENCOs in the electricity market. Thus, this paper presents a stochastic bilevel optimization model to determine the coordinated bidding strategy of a wind-thermal GENCO with the aim of maximizing its profit in the day-ahead and real-time balancing market. Herein, the model aims to maximize the profit of GENCO in the day-ahead and the balancing market in the upper-level problem while minimizing the operation cost of the system in the lower-level problem. The uncertainties of wind power generation and electricity demand are modeled by defining a set of scenarios considering their mutual correlation using the copula technique. Additionally, incorporating AC power flow constraints in the proposed optimization model offers a better solution to the coordinated bidding strategy of the wind-thermal GENCO. Further, the nonlinear AC power flow equations are linearized using the piecewise approximation technique to reduce the computational complexity and enhance the accuracy of the optimal solution. In the end, the developed algorithm is implemented on the IEEE 24-bus RTS, and the simulation results are provided to validate the efficiency and applicability of the proposed coordinated bidding strategy model. The results advocate that the participation of the thermal unit along with the wind farm might mitigate the risk of uncertainties, but it causes an intense increase in the locational marginal price of the system. Importantly, the simulation results indicate the computational efficiency of the model by developing an exact AC power flow model without compromising the results. Notably, it has been found that the profit of the wind-thermal GENCO would be increased by 35.2% employing the copula technique to model the mutual correlation of uncertain parameters.

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Section: Aimmentioning

confidence: 99%

Bidding Strategy of a Wind-Thermal GENCO considering Piecewise Linear AC Power Flow and Correlated Uncertainties

Daealhaq

Hassan

Ghanimi

et al. 2022

International Transactions on Electrical Energy Systems

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“…To address the uncertainties associated with wind power generation and loads, and to obtain the optimal behavior of a virtual power plant, a stochastic adaptive RO method has been presented in [22]. The optimal bidding strategy of a hybrid power plant, which acts as a price-maker in the DA market and a price-taker in the balancing market, has been derived in [23]. It addressed the uncertainty of the price quota curve (PQC) with the RO.…”

Section: B Parametersmentioning

confidence: 99%

Developing Robust Bidding Strategy for Virtual Bidders in Day-Ahead Electricity Markets

Mehdipourpicha

Wang

2021

IEEE Open J. Power Energy

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Purely financial players without any physical assets can participate in day-ahead electricity markets as virtual bidders. They can arbitrage the price difference between day-ahead (DA) and real-time (RT) markets to maximize profits. Virtual bidders encounter various monetary risks and uncertainties in their decision-making due to the high volatility of the price difference. Therefore, this paper proposes a max-min two-level optimization model to derive the optimal bidding strategy of virtual bidders. In this model, the risks of uncertainties associated with the rivals' strategies and RT market prices are managed by robust optimization. The proposed max-min two-level model is turned into a single-level mixed integer linear programming model through duality theory (DT), strong duality theory (SDT), and Karush-Kuhn-Tucker (KKT) conditions. An illustrative case is designed to demonstrate the advantages of the proposed model over the deterministic model. Moreover, case studies on the IEEE 24-bus test system validate the applicability of the proposed model.

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“…To incorporate the uncertainty of renewable generation and electricity prices in the power bidding strategy, stochastic programming including scenario-based stochastic optimization [8], robust optimization [9], and chance-constrained optimization [10] have been applied for the development of an optimal bidding strategy. Maneesha et al [11] developed a two-stage stochastic joint bidding framework for a wind power plant and pumped storage plant.…”

Section: Introductionmentioning

confidence: 99%

Wind Power Bidding Based on an Ensemble Differential Evolution Algorithm with a Problem-Specific Constraint-Handling Technique

Huang,

Zhao,

et al. 2024

Energies

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The intermittent nature of wind power generation induces great challenges for power bidding in the electricity market. The deployment of battery energy storage can improve flexibility for power bidding. This paper investigates an optimal power bidding strategy for a wind–storage hybrid power plant in the day-ahead electricity market. To handle the challenges of the uncertainties of wind power generation and electricity prices, the optimal bidding problem is formulated as a risk-aware scenario-based stochastic programming, in which a number of scenarios are generated using a copula-based approach to represent the uncertainties. These scenarios consider the temporal correlation of wind power generation and electricity prices between consecutive time intervals. In the stochastic programming, a more practical but nonlinear battery operation cost function is considered, which leads to a nonlinear constrained optimization problem. To solve the nonlinear constrained optimization problem, an ensemble differential evolution (EDE) algorithm is proposed, which makes use of the merits of an ensemble of mutant operators to generate mutant vectors. Moreover, a problem-specific constraint-handling technique is developed. To validate the effectiveness of the proposed EDE algorithm, it is compared with state-of-the-art DE-based algorithms for constrained optimization problems, including a constrained composite DE (C2oDE) algorithm and a novel DE (NDE) algorithm. The experimental results demonstrate that the EDE algorithm is much more reliable and much faster in finding a better bidding strategy against benchmarking algorithms. More precisely, the average values of the success rate are 0.893, 0.667, and 0.96 for C2oDE, NDE, and EDE, respectively. Compared to C2oDE and NDE, the average value of the mean number of function evaluations to succeed with EDE is reduced by 76% and 59%, respectively.

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Bidding strategy of hybrid power plant in day‐ahead market as price maker through robust optimization

Cited by 9 publications

References 50 publications

Bidding Strategy of a Wind-Thermal GENCO considering Piecewise Linear AC Power Flow and Correlated Uncertainties

Bidding Strategy of a Wind-Thermal GENCO considering Piecewise Linear AC Power Flow and Correlated Uncertainties

Developing Robust Bidding Strategy for Virtual Bidders in Day-Ahead Electricity Markets

Wind Power Bidding Based on an Ensemble Differential Evolution Algorithm with a Problem-Specific Constraint-Handling Technique

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