A Coordinated Bidding Model for Wind Plant and Compressed Air Energy Storage Systems in the Energy and Ancillary Service Markets Using a Distributionally Robust Optimization Approach

Aldaadi, Mohsen; Al–Ismail, Fahad Saleh; Al‐Awami, Ali T.; Muqbel, Ammar

doi:10.1109/access.2021.3123792

Cited by 18 publications

(14 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reference [21] proposed a stochastic multiobjective bidding strategy model for participation of wind-thermal-photovoltaic GENCO in the wholesale electricity market. Authors in [22] developed a coordinated bidding strategy model for participation of a combined energy system composed of wind farm and compressed air energy storage (CAES) in the day-ahead and ancillary service markets. e authors in [23] proposed a stochastic risk-constrained optimization problem to determine the bidding strategy of a GENCO owing wind-thermal-CAES 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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Moreover, the work has modeled the uncertainty of DA price via a robust approach while it has used the stochastic method to model uncertainties related to intra-day and balancing market prices as well as wind power. CAES and WPP participation in energy and ancillary service markets is modeled through the distributionally robust method in [31]. A risk-constrained strategy has been provided in reference [32] in which CAES and wind power aggregator have participated as a hybrid power plant in electricity markets (DA, intra-day and balancing markets).…”

Section: B Literature Reviewmentioning

confidence: 99%

“…In equation (31), 𝜑 ℎ is an uncertain parameter that varies from 𝜑 𝑚𝑖𝑛 ℎ to 𝜑 𝑚𝑎𝑥 ℎ . Therefore, the optimization problem ( 31)-( 34) can be reformulated as a robust model.…”

Section: Robust Optimization Approachmentioning

confidence: 99%

Optimal Look-Ahead Strategic Bidding/Offering of Integrated Renewable Power Plants and CAES With Stochastic-Robust Approach

et al. 2022

View full text Add to dashboard Cite

Today, due to the high penetration of renewable energy resources and restructuring of power systems, photovoltaic power plants (PVPPs) and wind power plants (WPPs) as renewable power plants (RPPs) can participate in the electricity markets. However, the intermittent power generation of RPPs may be challenging for the owners of these power plants. In order to mitigate the unpredictable and intermittent power generation of RPPs, energy storage systems like compressed air energy storage (CAES) can be an appropriate solution. In this paper, the optimal day-ahead and look-ahead strategic offering and bidding of integrated RPPs and CAES in the electricity market are investigated. Also, a stochastic-robust approach is proposed for modeling renewable generation and electricity price uncertainty. The proposed mixed-integer linear program (MILP) is formulated in GAMS software under the CPLEX solver. Three case studies are investigated to validate the proposed method. According to numerical results, in the optimistic strategy, the coordinator of RPPs and CAES has more opportunities to participate in the electricity market. But in This article has been accepted for publication in IEEE Access.

show abstract

“…In [27], an offering strategy of MCP has been developed for working with solar energy and CAES. Aldaadi et al [28] depict a robust optimization bidding method to optimally coordinate wind farms and CAES in a power system with the presence of ancillary services. Paper [29] proposed the mathematical modelling of price-taker CAES to represent the system uncertainties in self-functioning models.…”

Section: Introductionmentioning

confidence: 99%

“…28) where t denotes the current iteration, ⃖⃖⃗ ℜ b is a parameter with a range of [−a, a], ⃖⃖⃗ ℜ c decreases linearly from one to zero, ⃖⃗ X denotes the location of slime mould, ⃖⃗ X b denotes the individual location with the highest odour concentration, ⃖⃗ W represents the weight, ⃖⃖⃗ X A and ⃖⃗ X B represent the randomly selected two individuals from slime mould.…”

mentioning

confidence: 99%

Social welfare improvement using market clearing mechanism of a wind farm‐CAES hybrid congested transmission system

Das

Dawn

Gope

et al. 2022

IET Renewable Power Gen

View full text Add to dashboard Cite

The goal of any well-established deregulated power sector is to satisfy their customer by delivering the power with maintaining the best quality at a reasonable price. This paper presents the impression of the wind farm (WF) and compressed air energy storage (CAES) hybrid systems in the congested deregulated power system. The main motto of this work is to enhance social welfare by using a market-clearing mechanism of the WFCAES hybrid system. To determine the effect of the hybrid system, initially the market-clearing power (MCPO) problem is measured without considering the WF-CAES hybrid system, and then the same work has been carried out with considering the WF-CAES hybrid system. The bus sensitivity factor (BSF) decides the optimal position of the WF-CAES hybrid system in the congested day-ahead electrical system. The study is followed out through generator sensitivity factor (GSF) with the application of artificial bee colony algorithm (ABC) and slime mould algorithm (SMA) to calculate optimal market clearing price (MCP) and market clearing volume (MCV). The market-clearing power problem has been solved in a modified IEEE-30 bus test system. The SMA algorithm is been implemented in the market-clearing mechanism for the first time which is the novelty of this paper.

show abstract

A Coordinated Bidding Model for Wind Plant and Compressed Air Energy Storage Systems in the Energy and Ancillary Service Markets Using a Distributionally Robust Optimization Approach

Cited by 18 publications

References 37 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

Optimal Look-Ahead Strategic Bidding/Offering of Integrated Renewable Power Plants and CAES With Stochastic-Robust Approach

Social welfare improvement using market clearing mechanism of a wind farm‐CAES hybrid congested transmission system

Contact Info

Product

Resources

About