A Multi-Objective Method to Design Demand Response Strategies for Power Systems Including Wind Power Generation

2018 ◽  
Author(s):  
Miadreza Shafie-Khah ◽  
Marta Ribeiro ◽  
Neda Hajibandeh ◽  
Gerardo J. Osorio ◽  
Joao P. S. Catalao
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Wind Power ◽  
Demand Response ◽  
Wind Power Generation ◽  
Objective Method ◽  
Multi Objective ◽  
Response Strategies

scholarly journals Multi-Objective Optimal Reactive Power Planning under Load Demand and Wind Power Generation Uncertainties Using ε-Constraint Method

2020 ◽  
Vol 10 (8) ◽  
pp. 2859 ◽  
Author(s):  
Amir Hossein Shojaei ◽  
Ali Asghar Ghadimi ◽  
Mohammad Reza Miveh ◽  
Fazel Mohammadi ◽  
Francisco Jurado
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Wind Power ◽  
Reactive Power ◽  
Test System ◽  
Wind Power Generation ◽  
Multi Objective ◽  
Load Demand ◽  
Reactive Power Planning ◽  
Constraint Method

This paper presents an improved multi-objective probabilistic Reactive Power Planning (RPP) in power systems considering uncertainties of load demand and wind power generation. The proposed method is capable of simultaneously (1) reducing the reactive power investment cost, (2) minimizing the total active power losses, (3) improving the voltage stability, and (4) enhancing the loadability factor. The generators’ voltage magnitude, the transformer’s tap settings, and the output reactive power of VAR sources are taken into account as the control variables. To solve the probabilistic multi-objective RPP problem, the ε-constraint method is used. To test the effectiveness of the proposed approach, the IEEE 30-bus test system is implemented in the GAMS environment under five different conditions. Finally, for a better comprehension of the obtained results, a brief comparison of outcomes is presented.

scholarly journals A secondary option market design for reserve procurement by renewable energy sources.

2021 ◽  
Author(s):  
Reza Ghaffari
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Real Time ◽  
Wind Power ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Wind Power Generation ◽  
Reliable Operation ◽  
Actual Load ◽  
Balancing Services

Wind power generation is uncertain and intermittent accentuating variability. Currently in many power systems worldwide, the total generation-load unbalance caused by mismatch between forecast and actual wind power output is handled by automatic governor control and real-time 5-minute balancing markets, which are operated by the independent system operators for maintaining reliable operation of power systems. Mechanisms such as automatic governor control and real-time 5-minute balancing markets are in place to correct the mismatch between the load forecast and the actual load. They are not designed to address increased uncertainty and variability introduced by large-scale wind power or solar power generation expected in the future. Thus, large-scale wind power generation with increased uncertainty and intermittency causing variability poses a techno-economic challenge of sourcing least cost load balancing services (reserve).

Unbiased optimal power flow for power systems with wind power generation

2014 ◽  
Vol 50 (18) ◽  
pp. 1312-1314 ◽  
Author(s):  
S.J. Plathottam ◽  
P. Ranganathan ◽  
H. Salehfar
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Wind Power ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Wind Power Generation ◽  
Optimal Power

scholarly journals Demand Flexibility Management for Buildings-to-Grid Integration with Uncertain Generation

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6532
Author(s):  
Vahab Rostampour ◽  
Thom S. Badings ◽  
Jacquelien M. A. Scherpen
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Wind Power ◽  
Distribution System ◽  
Large Scale ◽  
Wind Power Generation ◽  
Forecast Errors ◽  
Monte Carlo Simulation Study ◽  
System Operator ◽  
The Impact

We present a Buildings-to-Grid (BtG) integration framework with intermittent wind-power generation and demand flexibility management provided by buildings. First, we extend the existing BtG models by introducing uncertain wind-power generation and reformulating the interactions between the Transmission System Operator (TSO), Distribution System Operators (DSO), and buildings. We then develop a unified BtG control framework to deal with forecast errors in the wind power, by considering ancillary services from both reserves and demand-side flexibility. The resulting framework is formulated as a finite-horizon stochastic model predictive control (MPC) problem, which is generally hard to solve due to the unknown distribution of the wind-power generation. To overcome this limitation, we present a tractable robust reformulation, together with probabilistic feasibility guarantees. We demonstrate that the proposed demand flexibility management can substitute the traditional reserve scheduling services in power systems with high levels of uncertain generation. Moreover, we show that this change does not jeopardize the stability of the grid or violate thermal comfort constraints of buildings. We finally provide a large-scale Monte Carlo simulation study to confirm the impact of achievements.

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