Abstract
The medium and long-term electricity trading approach considering uncertain renewable energy participation is established based on the bi-level model in this paper. The upper-level is designed to maximize the profitability of power generation companies, while the lower-level aims to maximize the social welfare. Afterwards, a hybrid algorithm combining the discrete and continuous particle swarm optimization is proposed to solve the upper-level model, and the nonlinear programming method is used to address the lower-level model. Finally, verification is performed on the improved IEEE 39-bus system, and simulation results demonstrate that the proposed approach could indeed improve the profit of renewable energy as well as increase the social welfare of power generation enterprises.
Highlights
With increasingly prominent energy issues, renewable power generation has received more and more attention
With the help of sequence operation algorithm for describing the uncertainty of wind power and photovoltaic power output, this paper proposes a medium and long-term power trading approach based on a bi-level model considering renewable energy participation
COMPREHENSIVE MODEL OF UNCERTAINTY OF RENEWABLE ENERGY POWER OUTPUT An improved sequence operation algorithm is used to deal with uncertain wind power and photovoltaic output
Summary
With increasingly prominent energy issues, renewable power generation has received more and more attention. Most of the above work focuses on the establishment of equilibrium models, and there are few studies on medium and long-term power trading methods considering renewable energy participation. With the help of sequence operation algorithm for describing the uncertainty of wind power and photovoltaic power output, this paper proposes a medium and long-term power trading approach based on a bi-level model considering renewable energy participation. Step 3) The electricity trading organization adopts a reasonable algorithm to centrally solve the clear model of medium and long-term power transaction considering renewable energy participation. Based on the above interactive procedures that the upperlevel optimization program solves power output and on-off states of units while the lower-level program optimization optimizes the market clearing price, the two layers are interacted with each other and solved iteratively for the medium and long-term transactions. Where a and b are fitting parameters of wind power curve respectively
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