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

Abstract

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.

Resilience‐oriented expansion planning of multi‐carrier microgrid utilizing bi‐level technique

“…Of course, the basic requirement of this method is the convexity of the lower-level problem; however, this problem is non-convex due to the use of power flow equations [20,21]. Thus, to compensate for this challenge, a linear approximation model is first developed for the lower-level problem constraints [29][30][31]. Then a mixed-integer quadratically constrained program (MIQCP) model with quadratic objective function and linear constraints is obtained for the single-level problem.…”

Smart Frequency Control of a Multicarrier Microgrid in the Presence of V2G Electric Vehicles

“…Te authors of reference [15] suggested a two-tier optimization model to defne the sales approach in the previous day's market considering comprehensive technologies of renewable wind energy. Te uncertainty of wind and load sources is modeled by the Monte Carlo scenario generation method considering their interdependence and the Copula method.…”

“…Te proposed model also provided a linearized model of IC load distribution to reduce the complexity of the problem. Daealhaq et al [15] proposed a top-of-the-line DC-DC solid-state transformer for two-way parking of photovoltaics/EV batteries with network vehicle service (V2G-PVBP). By considering the advantages of energy storing capacity of EVs, V2G-PVBP showed efciency in both satisfying the usual needs of electric vehicle owners and preparing load handling and load adjustment performance to the MG. Qin et al [16] introduced the optimal performance of a fuzzy controller to obtain balancing between energy consumption and energy generation in an independent microgrid (S-MG) in the presence of EVs.…”