Stochastic versus Robust Optimization of wind-hydro power plant's operational strategy

2016 ◽  
Author(s):  
M. S. Javaid ◽  
Numan Saeed ◽  
A.T. Al-Awami ◽  
Zorays Khalid
Keyword(s):  
Robust Optimization ◽  
Hydro Power ◽  
Operational Strategy

Micro-hydro Power

1991 ◽  
Author(s):  
Peter Fraenkel ◽  
Oliver Parish ◽  
Varis Bolkalders ◽  
Adam Harvey ◽  
Andrew Brown ◽  
...  
Keyword(s):  
Hydro Power

Virtual Synchronous Generator Control of Power System including Large-scale Wind Farm by Cooperative Operation between Battery and LFC Hydro Power Plant

2020 ◽  
Vol 140 (6) ◽  
pp. 531-538
Author(s):  
Kotaro Nagaushi ◽  
Atsushi Umemura ◽  
Rion Takahashi ◽  
Junji Tamura ◽  
Atsushi Sakahara ◽  
...  
Keyword(s):  
Power Plant ◽  
Power System ◽  
Large Scale ◽  
Wind Farm ◽  
Synchronous Generator ◽  
Hydro Power ◽  
Virtual Synchronous Generator

Network Congestion Minimization Models Based on Robust Optimization

2018 ◽  
Vol E101.B (3) ◽  
pp. 772-784 ◽  
Author(s):  
Bimal CHANDRA DAS ◽  
Satoshi TAKAHASHI ◽  
Eiji OKI ◽  
Masakazu MURAMATSU
Keyword(s):  
Robust Optimization ◽  
Network Congestion

The Economic Price of Natural Gas in Pakistan (Notes & Comments)

1974 ◽  
Vol 13 (4) ◽  
pp. 481-484
Author(s):  
J. Faaland ◽  
J. R. Parkinson
Keyword(s):  
Natural Gas ◽  
World Bank ◽  
Electricity Generation ◽  
Irrigate Land ◽  
Hydro Power ◽  
Power Resources ◽  
The World Bank ◽  
The World ◽  
Economic Price ◽  
Tarbela Dam

The World Bank Study," Water and Power Resources of West Pakistan" [1], is one of the most thorough-going and sophisticated of its type. In re¬reading it we have been struck by a curious argument related to the real benefits to be expected from the construction of the Tarbela dam. It was designed to produce electricity as well as to irrigate land and it was necessary to estimate the benefits that the electricity would confer. One way of doing this was to estimate the saving that would be made by using hydro-power instead of natural gas or imported fuel, for electricity generation. This meant that an appropriate set of prices had to be estimated for Pakistan's supply of natural gas. The way in which this was done was, to say the least, unusual. The relevant passage justi¬fying the approach adopted is as follows:

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

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