scholarly journals Robust Stochastic Dynamic Optimal Power Flow Model of Electricity-Gas Integrated Energy System considering Wind Power Uncertainty

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhengfeng Qin ◽  
Xiaoqing Bai ◽  
Xiangyang Su
Keyword(s):  
Stochastic Optimization ◽  
Wind Power ◽  
Power Output ◽  
Gas Turbines ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Model ◽  
Mixed Integer ◽  
Stochastic Dynamic ◽  
Optimal Power

The application of gas turbines and power to gas equipment deepens the coupling relationship between power systems and natural gas systems and provides a new way to absorb the uncertain wind power as well. The traditional stochastic optimization and robust optimization algorithms have some limitations and deficiencies in dealing with the uncertainty of wind power output. Therefore, we propose a robust stochastic optimization (RSO) model to solve the dynamic optimal power flow model for electricity-gas integrated energy systems (IES) considering wind power uncertainty, where the ambiguity set of wind power output is constructed based on Wasserstein distance. Then, the Wasserstein ambiguity set is affined to the eventwise ambiguity set, and the proposed RSO model is transformed into a mixed-integer programming model, which can be solved rapidly and accurately using commercial solvers. Numerical results for EG-4 and EG-118 systems verify the rationality and effectiveness of the proposed model.

Optimal Power Flow Considering Generator Number Constraint on Regulation of Active Power Output

2014 ◽  
Vol 672-674 ◽  
pp. 1042-1047
Author(s):  
Yu De Yang ◽  
Pei Xian Qu
Keyword(s):  
Power Output ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Model ◽  
Optimal Solution ◽  
Active Power ◽  
Interior Point Algorithm ◽  
Discrete Variables ◽  
Optimal Power ◽  
The Relationship

Operation number constraint of control means isn’t considered to traditional optimal power flow model, and at optimal solution all the control variables often be changed, which causes a tedious dispatching plan and is difficult to operate. In this paper, Optimal Power Flow (OPF) with constraints limiting the number of control actions was discussed, and 0-1 discrete variables of model was transformed to complementary constraint, then modern interior point algorithm was used for solving. Through simulation the relationship between generator number constraint on regulation of the active power output and optimization objective was explored, which could obtain best balance point on two, and close to conventional OPF optimization objective with reducing the number of regulation.

scholarly journals An integrated OPF dispatching model with wind power and demand response for day-ahead markets

2019 ◽  
Vol 9 (4) ◽  
pp. 2794
Author(s):  
Ricardo Moreno ◽  
Johan Obando ◽  
Gabriel Gonzalez
Keyword(s):  
Wind Power ◽  
Demand Response ◽  
Electricity Markets ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Mixed Integer ◽  
New Paradigm ◽  
Linear Quadratic ◽  
Distributed Resources ◽  
Optimal Power

In the day-ahead dispatching of network-constrained electricity markets, renewable energy and distributed resources are dispatched together with conventional generation. The uncertainty and volatility associated to renewable resources represents a new paradigm to be faced for power system operation. Moreover, in various electricity markets there are mechanisms to allow the demand participation through demand response (DR) strategies. Under operational and economic restrictions, the operator each day, or even in intra-day markets, dispatchs an optimal power flow to find a feasible state of operation. The operation decisions in power markets use an optimal power flow considering unit commitment to dispatch economically generation and DR resources under security restrictions. This paper constructs a model to include demand response in the optimal power flow under wind power uncertainty. The model is formulated as a mixed-integer linear quadratic problem and evaluated through Monte-Carlo simulations. A large number of scenarios around a trajectory bid captures the uncertainty in wind power forecasting. The proposed integrated OPF model is tested on the standard IEEE 39-bus system.

scholarly journals Real-Time Active-Reactive Optimal Power Flow with Flexible Operation of Battery Storage Systems

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1697 ◽  
Author(s):  
Erfan Mohagheghi ◽  
Mansour Alramlawi ◽  
Aouss Gabash ◽  
Frede Blaabjerg ◽  
Pu Li
Keyword(s):  
Real Time ◽  
Wind Power ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Storage Systems ◽  
Mixed Integer ◽  
Second Phase ◽  
Battery Storage ◽  
Optimal Power ◽  
Flexible Operation

In this paper, a multi-phase multi-time-scale real-time dynamic active-reactive optimal power flow (RT-DAR-OPF) framework is developed to optimally deal with spontaneous changes in wind power in distribution networks (DNs) with battery storage systems (BSSs). The most challenging issue hereby is that a large-scale ‘dynamic’ (i.e., with differential/difference equations rather than only algebraic equations) mixed-integer nonlinear programming (MINLP) problem has to be solved in real time. Moreover, considering the active-reactive power capabilities of BSSs with flexible operation strategies, as well as minimizing the expended life costs of BSSs further increases the complexity of the problem. To solve this problem, in the first phase, we implement simultaneous optimization of a huge number of mixed-integer decision variables to compute optimal operations of BSSs on a day-to-day basis. In the second phase, based on the forecasted wind power values for short prediction horizons, wind power scenarios are generated to describe uncertain wind power with non-Gaussian distribution. Then, MINLP AR-OPF problems corresponding to the scenarios are solved and reconciled in advance of each prediction horizon. In the third phase, based on the measured actual values of wind power, one of the solutions is selected, modified, and realized to the network for very short intervals. The applicability of the proposed RT-DAR-OPF is demonstrated using a medium-voltage DN.

scholarly journals A Novel Linear Optimal Power Flow Model for Three-Phase Electrical Distribution Systems

2020 ◽  
Author(s):  
Juan Sebastian Giraldo ◽  
Pedro Pablo Vergara ◽  
Juan Camilo Lopez ◽  
Phuong Nguyen ◽  
Nikolaos Paterakis
Keyword(s):  
Distribution System ◽  
Power Flow ◽  
Distribution Systems ◽  
Optimal Power Flow ◽  
Flow Model ◽  
Mixed Integer ◽  
Electrical Distribution Systems ◽  
Optimal Power ◽  
Electrical Distribution ◽  
Three Phase

This paper presents a new linear optimal power flow model for three-phase unbalanced electrical distribution systems considering binary variables. The proposed formulation is a mixed-integer linear programming problem, aiming at minimizing the operational costs of the network while guaranteeing operational constraints. Two new linearizations for branch current and nodal voltage magnitudes are introduced. The proposed branch current magnitude linearization provides a discretization of the Euclidean norm through a set of intersecting planes; while the bus voltage magnitude approximation uses a linear combination of the L1 and the L∞ norm. Results were obtained for an unbalanced distribution system, in order to assess the accuracy of the linear formulation when compared to a nonlinear power flow with fixed power injections, showing errors of less than 4\% for currents and 0.005\% for voltages.

Research on interval multi-objective optimal power flow in AC-DC systems considering wind power fluctuation

2021 ◽  
pp. 0309524X2199277
Author(s):  
Hongfen Zhang ◽  
Youchao Zhang
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Model ◽  
Power Fluctuation ◽  
Affine Arithmetic ◽  
Multi Objective ◽  
Optimal Power ◽  
Generation Cost

Aiming at the influence of the uncertainty of power system operating parameters such as wind power fluctuation on AC-DC hybrid system, an interval optimal power flow calculation method based on interval and affine arithmetic is proposed in this paper. First, AC and DC interval power flow model is constructed based on the relationship between interval and affine arithmetic, and the uncertainties such as the new energy generation output of the system are expressed as interval variables; static security performance index (PI) is introduced in AC-DC multi-objective optimal power flow objective functions, which take the system’s power generation cost and network loss into account; the Pareto optimal solution set is distributed uniformly in space by using the particle swarm algorithm to solve the interval optimal power flow model. Finally, MATLAB simulation examples are used to verify that the method can optimize the system’s power generation cost, network loss and static safety index while considering wind power fluctuation.

scholarly journals A Novel Linear Optimal Power Flow Model for Three-Phase Electrical Distribution Systems

2020 ◽  
Author(s):  
Juan Sebastian Giraldo ◽  
Pedro Pablo Vergara ◽  
Juan Camilo Lopez ◽  
Phuong Nguyen ◽  
Nikolaos Paterakis
Keyword(s):  
Distribution System ◽  
Power Flow ◽  
Distribution Systems ◽  
Optimal Power Flow ◽  
Flow Model ◽  
Mixed Integer ◽  
Electrical Distribution Systems ◽  
Optimal Power ◽  
Electrical Distribution ◽  
Three Phase

This paper presents a new linear optimal power flow model for three-phase unbalanced electrical distribution systems considering binary variables. The proposed formulation is a mixed-integer linear programming problem, aiming at minimizing the operational costs of the network while guaranteeing operational constraints. Two new linearizations for branch current and nodal voltage magnitudes are introduced. The proposed branch current magnitude linearization provides a discretization of the Euclidean norm through a set of intersecting planes; while the bus voltage magnitude approximation uses a linear combination of the L1 and the L∞ norm. Results were obtained for an unbalanced distribution system, in order to assess the accuracy of the linear formulation when compared to a nonlinear power flow with fixed power injections, showing errors of less than 4\% for currents and 0.005\% for voltages.

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