Flexible power system operation accommodating uncertain wind power generation using transmission topology control: an improved linearised AC SCUC model

Nikoobakht, Ahmad; Mardaneh, Mohammad; Aghaei, Jamshid; Guerrero-Mestre, Victoria; Contreras, Javier

doi:10.1049/iet-gtd.2016.0704

Cited by 62 publications

(56 citation statements)

References 20 publications

(45 reference statements)

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“…Equations (22) and (23) indicate the maximum and minimum charging/discharging power. Equations (24) and (25) represent the energy capacity each fleet limits. Constraint (26) is given as hourly energy balance in PEV battery.…”

Section: Pev Modelmentioning

confidence: 99%

Optimal scheduling of electric vehicle in stochastic AC SCUC problem for large‐scale wind power penetration

Gupta

Jain

Sharma

et al. 2019

Int Trans Electr Energ Syst

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Summary Integration of highly volatile wind generation causes reliability and grid issues for system operator (SO). Plug‐in electric vehicles (PEVs) are mobile distributed source of active power that provides opportunity to use their battery storage for wind integration. The coordinated integration of wind volatility and PEVs fleet is studied under security‐constrained unit commitment (SCUC) model. In this regard, a stochastic SCUC with PEVs considering wind integration and line contingency is proposed. Wind volatility and PEVs driving behavior uncertainty is modeled through Monte Carlo simulations (MCS) of large number of scenarios with associated probabilities. This scenario has been reduced by Kantorovich distance (KD) matrix–based backward reduction technique. Moreover, pre‐line and post‐line contingency AC optimal power flow is used for network constraints in SCUC (AC SCUC). Due to consideration of N‐1 security criteria and wind power scenarios, the proposed model is mixed integer nonlinear programming (MINLP), which is computationally heavy and is thus solved by a two‐stage programming Benders decomposition (BD) approach. Different case studies are examined on modified IEEE reliability test system (RTS). Comparative analysis explores the impact on overall operational costs, PEV cost, wind curtailment, and locational marginal price (LMP) for congestion management. Simulation results validate that the proposed model is technoeconomically suitable for large‐scale wind power penetration.

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Section: Pev Modelmentioning

confidence: 99%

Optimal scheduling of electric vehicle in stochastic AC SCUC problem for large‐scale wind power penetration

Gupta

Jain

Sharma

et al. 2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…Though the decomposition is a decent approximation of the ACPF model when the networks is with lines of low X/R ratio, the reactive power of load nodes is needed at first. In addition, Benders decomposition is applied to deal with the SCUC problem with ACPF constraints considering wind power uncertainty, however, computational efficiency may decrease significantly as the scale of the network expands [11][12][13][14]. Since the reactive power of load nodes cannot be exactly forecast at present, the reactive power of load nodes is difficult to consider in the practical scheduling of day-ahead generation dispatch.…”

Section: Introductionmentioning

confidence: 99%

A Mixed-Integer Convex Programming Algorithm for Security-Constrained Unit Commitment of Power System with 110-kV Network and Pumped-Storage Hydro Units

Lin

Fan

et al. 2019

Energies

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The secure operation of 110-kV networks should be considered in the optimal generation dispatch of regional power grids in large central cities. However, since 110-kV lines do not satisfy the premise of R << X in the direct current power flow (DCPF) model, the DCPF, which is mostly applied in the security-constrained unit commitment (SCUC) problem of high-voltage power grids, is no longer suitable for describing the active power flow of regional power grids in large central cities. Hence, the quadratic active power flow (QAPF) model considering the resistance of lines is proposed to describe the network security constraints, and an SCUC model for power system with 110-kV network and pumped-storage hydro (PSH) units is established. The analytical expressions of the spinning reserve (SR) capacity of PSH units are given considering different operational modes, and the SR capacity of PSH units is included in the constraint of the SR capacity requirement of the system. The QAPF is a set of quadratic equality constraints, making the SCUC model a mixed-integer nonlinear non-convex programming (MINNP) model. To reduce the computational complexity of solving the model when applied in actual large-scale regional networks, the QAPF model is relaxed by its convex hull, and the SCUC model is transformed into a mixed-integer convex programming (MICP) model, which can be solved to obtain the global optimal solution efficiently and reliably by the mature commercial solver GUROBI (24.3.3, GAMS Development Corporation, Guangzhou, China). Test results on the IEEE-9 bus system, the PEGASE 89 bus system and the Shenzhen city power grid including the 110-kV network demonstrate that the relaxed QAPF model has good calculation accuracy and efficiency, and it is suitable for solving the SCUC problem in large-scale regional networks.

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“…MCS will generate some scenarios (several hundred or thousand) from random number following designated probability distribution and find the best and worst scenario to be handled by an operator. (Nikoobakht et al, 2017) presents the stochastic wind power generation with possible scenarios generated by MCS. Wind power uncertainty is assumed as discrete distribution in the form of Weibull distribution and continuous probability distribution functions for security constrained unit commitment problem.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Error Modelling for Improving PV Generation Prediction

Aprillia

Yang

2019

sjt

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Accurate forecasting of Photovoltaic (PV) generation output is important in operation of high PV-penetrated power systems. In this paper, an adaptive uncertainty modelling method for forecasting error is proposed to improve the prediction accuracy of PV generation. The proposed method models the uncertainty in forecast data using Kernel Density Estimator and guarantee the provision of accurate expected value. Neural Network model is then constructed by the developed uncertainty model to forecast the PV output. The actual confidence level is traced within the day and injected as an input to the Neural Network model by observing the Mean Absolute Prediction Error (MAPE) and Unscaled Mean Bounded Relative Absolute Error (UMBRAE). The proposed method is tested with various significant changes of weather condition and proved to have promising performance on PV generation forecasting. Thus, the developed adaptive uncertainty model can be further used in power system planning that have high-penetration energy sources with stochastic behavior.

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Flexible power system operation accommodating uncertain wind power generation using transmission topology control: an improved linearised AC SCUC model

Cited by 62 publications

References 20 publications

Optimal scheduling of electric vehicle in stochastic AC SCUC problem for large‐scale wind power penetration

Optimal scheduling of electric vehicle in stochastic AC SCUC problem for large‐scale wind power penetration

A Mixed-Integer Convex Programming Algorithm for Security-Constrained Unit Commitment of Power System with 110-kV Network and Pumped-Storage Hydro Units

Forecasting Error Modelling for Improving PV Generation Prediction

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