scholarly journals (DA-DOPF): A Day-Ahead Dynamic Optimal Power Flow With Renewable Energy Integration in Smart Grids

2021 ◽  
Vol 9 ◽  
Author(s):  
Muhammad Arsalan Ilyas ◽  
Thamer Alquthami ◽  
Muhammad Awais ◽  
Ahmad H. Milyani ◽  
Muhammad Babar Rasheed
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Smart Grids ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Analysis ◽  
Control Variable ◽  
Constraint Handling ◽  
Optimal Power ◽  
Artificial Neural

The performance of a power system can be measured and evaluated by its power flow analysis. Along with the penetration of renewable energies such as wind and solar, the power flow problem has become a complex optimization problem. In addition to this, constraint handling is another challenging task of this problem. The main critical problem of this dynamic power system having such variable energy sources is the intermittency of these VESs and complexity of constraint handling for a real-time optimal power flow (RT-OPF) problem. Therefore, optimal scheduling of generation sources with constraint satisfaction is the main goal of this study. Hence, a renewable energy forecasting–based, day-ahead dynamic optimal power flow (DA-DOPF) is presented in this paper with the forecasting of solar and wind patterns by using artificial neural networks. Moreover, contribution factors are calculated using triangular fuzzy membership function (T-FMF) in the sub-interval time slots. Furthermore, the superiority of feasible (SF) solution constraint handling approach is used to avoid the constraint violation of inequality constraints of optimal power flow. The IEEE 30-bus transmission network has been amended to integrate a solar photovoltaic and wind farm in different buses. In this approach, the computing program is based on MATPOWER which is a tool of MATLAB for load flow analysis which uses the Newton–Raphson technique because of its rapid convergence. Meteorological information has been gathered during the time frame January 1, 2015, to December 31, 2017, from Danyore Weather Station (DWS) at Hunza, Pakistan. A Levenberg–Marquardt calculation–based artificial neural network model is utilized to foresee the breeze speed and sunlight-based irradiance in light of its versatile nature. Finally, the results are discussed analytically to select the best generation schedule and control variable values.

scholarly journals LP based solution for Security Constrained Optimal Power Flow including Power Routers

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Electrical Power ◽  
Flow Analysis ◽  
Time Frame ◽  
Electrical Power System ◽  
Piecewise Linearization ◽  
Dc Power ◽  
Optimal Power

Power Routers offers many benefits to the power system, it helps in improving the existing transmission asset utilization. Security Constrained Optimal Power Flow (SCOPF) is becoming more important in the electrical power system especially in the present deregulated environment. This paper focuses on completely linearizing the complex non-linear SCOPF problem. The objective function is linearized using Piecewise linearization technique and the constraints are framed using linear sensitivity factors. A formal extension is made to the traditional SCOPF by including power router control in the post contingency time frame. DC power flow analysis is used to calculate the real power flow in the lines.Thus, in this paper, further minimization of cost is achieved by using Power Router control and it is compared with the conventional SCOPF.

scholarly journals A Progressive Period Optimal Power Flow for Systems with High Penetration of Variable Renewable Energy Sources

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2815
Author(s):  
Zongjie Wang ◽  
C. Lindsay Anderson
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Real Time ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Renewable Energy Sources ◽  
Wind Farms ◽  
Energy Sources ◽  
Power System Operations ◽  
Optimal Power

Renewable energy sources including wind farms and solar sites, have been rapidly integrated within power systems for economic and environmental reasons. Unfortunately, many renewable energy sources suffer from variability and uncertainty, which may jeopardize security and stability of the power system. To face this challenge, it is necessary to develop new methods to manage increasing supply-side uncertainty within operational strategies. In modern power system operations, the optimal power flow (OPF) is essential to all stages of the system operational horizon; underlying both day-ahead scheduling and real-time dispatch decisions. The dispatch levels determined are then implemented for the duration of the dispatch interval, with the expectation that frequency response and balancing reserves are sufficient to manage intra-interval deviations. To achieve more accurate generation schedules and better reliability with increasing renewable resources, the OPF must be solved faster and with better accuracy within continuous time intervals, in both day-ahead scheduling and real-time dispatch. To this end, we formulate a multi-period dispatch framework, that is, progressive period optimal power flow (PPOPF), which builds on an interval optimal power flow (IOPF), which leverages median and endpoints on the interval to develop coherent coordinations between day-ahead and real-time period optimal power flow (POPF). Simulation case studies on a practical PEGASE 13,659-bus transmission system in Europe have demonstrated implementation of the proposed PPOPF within multi-stage power system operations, resulting in zero dispatch error and violation compared with traditional OPF.

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