scholarly journals Optimal Bidding Strategies for Online Ad Auctions with Overlapping Targeting Criteria

2020 ◽  
Vol 4 (2) ◽  
pp. 1-55
Author(s):  
Erik Tillberg ◽  
Peter Marbach ◽  
Ravi Mazumdar
Keyword(s):  
Bidding Strategies ◽  
Optimal Bidding ◽  
Ad Auctions

scholarly journals Optimal Bidding Strategies for Online Ad Auctions with Overlapping Targeting Criteria

2020 ◽  
Vol 48 (1) ◽  
pp. 109-110
Author(s):  
Erik Tillberg ◽  
Peter Marbach ◽  
Ravi Mazumdar
Keyword(s):  
Bidding Strategies ◽  
Optimal Bidding ◽  
Ad Auctions

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>

scholarly journals A Stochastic MPC Based Energy Management System for Simultaneous Participation in Continuous and Discrete Prosumer-to-Prosumer Energy Markets

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3751
Author(s):  
Pablo Baez-Gonzalez ◽  
Felix Garcia-Torres ◽  
Miguel A. Ridao ◽  
Carlos Bordons
Keyword(s):  
Energy Management ◽  
Management System ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Management System ◽  
Bidding Strategies ◽  
Production And Consumption ◽  
Optimal Bidding ◽  
Continuous Trading ◽  
Stochastic Model Predictive Control

This article studies the exchange of self-produced renewable energy between prosumers (and with pure end consumers), through the discrete trading of energy packages and proposes a framework for optimizing this exchange. In order to mitigate the imbalances derived from discrepancies between production and consumption and their respective forecasts, the simultaneous continuous trading of instantaneous power quotas is proposed, giving rise to a time-ahead market running in parallel with a real-time one. An energy management system (EMS) based on stochastic model predictive control (SMPC) simultaneously determines the optimal bidding strategies for both markets, as well as the optimal utilisation of any energy storage system (ESS). Simulations carried out for a heterogeneous group of agents show that those with SMPC-EMS achieve savings of between 3% and 15% in their energy operation economic result. The proposed structures allows the peer-to-peer (P2P) energy trading between end users without ESS and constitute a viable alternative to avoid deviation penalties in secondary regulation markets.

scholarly journals Combined Bidding Strategy of Multi-owner Cascaded Hydropower Stations in Day-Ahead Market

2018 ◽  
Vol 246 ◽  
pp. 02036 ◽  
Author(s):  
Ying Yang ◽  
Weibin Huang ◽  
Guangwen Ma ◽  
Shijun Chen ◽  
Gang Liu ◽  
...  
Keyword(s):  
Electricity Market ◽  
Power Market ◽  
Bidding Strategy ◽  
Competitive Advantages ◽  
Bidding Strategies ◽  
Cascade Hydropower Stations ◽  
The Game Theory ◽  
Optimal Bidding ◽  
Hydropower Stations ◽  
Operation Characteristics

Under the background of the electricity market reform, if the multi-owner cascade hydropower stations bid separately, the overall competitive advantages of river basin cannot be exerted, and the overall benefits cannot achieve the maximization. Based on the operation characteristics of cascade hydropower stations and the rule of competitive bidding, this paper established a bi-level optimal model for bidding game in day-ahead market, and used the Nash equilibrium principle of the game theory and genetic algorithm to solve this model, the optimal bidding strategies of the multi-owner cascade hydropower stations have been solved under the circumstances of bidding by oneself and alliance. The results from the calculating examples showed that the unified price declaration of the multi-owner cascade hydropower stations in day-ahead market can improve the overall and individual generation efficiency, and proved the effectiveness and feasibility of the combined bidding strategy in power market.

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