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Multi-objective unit commitment (MOUC) considers concurrently both economic and environmental objectives, then finds the best trade-off with respect to these objectives. This paper proposes a novel model for MOUC, and a decomposition coordination approach is presented to solve the model. The economic objective is to reduce the fuel cost while the environmental objective is to reduce the CO 2 emission. The MOUC model considers these objectives by minimizing the distance to the Utopian point, which avoids generating Pareto optimal solutions. The model is solved by a decomposition coordination approach, which decomposes the whole system into subsystems and performs an iterative process. During each iteration step, the tie-line power flow is updated based on the margin price in connected subsystems, then, each subsystem is solved by branch and bound method, and the result is improved during iterations as shown in case studies. Besides, as the process does not require uploading units parameters, it protects the privacy of generating companies. Numerical case studies conducted using the proposed multi-objective model are applied to illustrate the performance of the approach.
Shaopeng Zhai; Zhihua Wang; Jia Cao; Guangyu He. A New Multi-Objective Unit Commitment Model Solved by Decomposition-Coordination. Applied Sciences 2019, 9, 829 .
AMA StyleShaopeng Zhai, Zhihua Wang, Jia Cao, Guangyu He. A New Multi-Objective Unit Commitment Model Solved by Decomposition-Coordination. Applied Sciences. 2019; 9 (5):829.
Chicago/Turabian StyleShaopeng Zhai; Zhihua Wang; Jia Cao; Guangyu He. 2019. "A New Multi-Objective Unit Commitment Model Solved by Decomposition-Coordination." Applied Sciences 9, no. 5: 829.
The Levenberg-Marquardt (L-M) method and the path following interior point (PFIP) method are two most popular methods to solve the optimal power flow (OPF) problem. The performance comparison between the L-M method and the PFIP method applied to solve the OPF problem has not been made so far. The main objective of this paper provides a more comprehensive reference for researcher by analysing, comparing and evaluating the performances of the two methods.
Xue Li; Jia Cao; Dajun Du. Comparison of Levenberg-Marquardt Method and Path Following Interior Point Method for the Solution of Optimal Power Flow Problem. ENERGYO 2018, 1 .
AMA StyleXue Li, Jia Cao, Dajun Du. Comparison of Levenberg-Marquardt Method and Path Following Interior Point Method for the Solution of Optimal Power Flow Problem. ENERGYO. 2018; ():1.
Chicago/Turabian StyleXue Li; Jia Cao; Dajun Du. 2018. "Comparison of Levenberg-Marquardt Method and Path Following Interior Point Method for the Solution of Optimal Power Flow Problem." ENERGYO , no. : 1.
With wind power integrated into a grid, the impact of wind power on a power system becomes a major concern. This paper mainly investigates the impact of wind power integration considering three issues – wind speed, number of wind turbines or wind power penetration (WPP) level. The impact of wind power is analysed by the optimal power flow (OPF) tool. First, the OPF model with WPP is constructed. The Levenberg–Marquardt method was then employed to solve the OPF problem. Finally, numerical simulations for the modified IEEE-30 node system with WPP are carried out. According to the simulation results, the impacts of wind speed, wind turbine number and WPP level on the static performance of the power system are systematically analysed.
Xue Li; Jia Cao; Dajun Du. Impact evaluation of wind power integration on power system using optimal power flow tool. Transactions of the Institute of Measurement and Control 2014, 37, 362 -371.
AMA StyleXue Li, Jia Cao, Dajun Du. Impact evaluation of wind power integration on power system using optimal power flow tool. Transactions of the Institute of Measurement and Control. 2014; 37 (3):362-371.
Chicago/Turabian StyleXue Li; Jia Cao; Dajun Du. 2014. "Impact evaluation of wind power integration on power system using optimal power flow tool." Transactions of the Institute of Measurement and Control 37, no. 3: 362-371.
Xue Li; Jia Cao; Pan Lu. Probabilistic Load Flow Computation in Power System Including Wind Farms with Correlated Parameters. 2nd IET Renewable Power Generation Conference (RPG 2013) 2013, 1 .
AMA StyleXue Li, Jia Cao, Pan Lu. Probabilistic Load Flow Computation in Power System Including Wind Farms with Correlated Parameters. 2nd IET Renewable Power Generation Conference (RPG 2013). 2013; ():1.
Chicago/Turabian StyleXue Li; Jia Cao; Pan Lu. 2013. "Probabilistic Load Flow Computation in Power System Including Wind Farms with Correlated Parameters." 2nd IET Renewable Power Generation Conference (RPG 2013) , no. : 1.