Impacts of Emission Trading on Optimal Bidding Strategies of Generation Companies in Day-Ahead Electricity Markets

2014 ◽  
Vol 521 ◽  
pp. 476-479 ◽  
Author(s):  
Guo Zhong Liu
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Emissions Trading ◽  
Emission Trading ◽  
Distribution Functions ◽  
Bidding Strategy ◽  
Chance Constrained Programming ◽  
Optimization Approach ◽  
Generation Company ◽  
Optimal Bidding

The impacts of Emission trading on building the optimal bidding strategy for a generation company participating in a day-ahead electricity market is investigated. The CO2 emission price in an emissions trading market is evaluated by using an optimization approach similar to the well-developed probabilistic production simulation method. Then upon the assumption that the probability distribution functions of rivals bidding are known, a stochastic optimization model for building the risk-constrained optimal bidding strategy for the generation company in the framework of the chance-constrained programming is presented. Finally, a numerical example is served for demonstrating the feasibility of the developed model and method, and the optimal bidding results are compared for the two situations with and without the CO2 emissions trading.

Strategic Bidding and Risk Assessment Using Genetic Algorithm in Electricity Markets

2009 ◽  
Vol 10 (5) ◽  
Author(s):  
Arvind Kumar Jain ◽  
S.C. Srivastava
Keyword(s):  
Genetic Algorithm ◽  
Electricity Markets ◽  
Electricity Market ◽  
Value At Risk ◽  
Financial Risk ◽  
Congestion Management ◽  
Bidding Strategy ◽  
Strategic Bidding ◽  
Optimal Bidding ◽  
Sensitivity Factors

In an electricity market, suppliers are more concerned with maximizing their profit and minimizing the financial risk, which can be achieved through strategic bidding. In this paper, Equal Incremental Cost Criteria (EICC) has been used for developing the optimal bidding strategy. The rival's bidding behavior has been formulated using a stochastic optimization model. Genetic Algorithm (GA), along with ac sensitivity factors, has been used to decide the optimal bidding strategy including congestion management to maximize the profit of the suppliers, considering single sided as well as double sided bidding. Both pure as well as probabilistic strategies have been simulated. Results with Sequential Quadratic Programming (SQP), a classical optimization method, and dc sensitivity factors have also been obtained to compare and establish the effectiveness of proposed method. Value at Risk (VaR) has been calculated as a measure of financial risk.

scholarly journals Bidding Model Based on Balance Programming between Target and Chance

2012 ◽  
Vol 6-7 ◽  
pp. 226-232
Author(s):  
Gang Lu ◽  
Fu Shuan Wen
Keyword(s):  
Electricity Market ◽  
Programming Model ◽  
Numerical Test ◽  
Optimal Decision ◽  
Bidding Strategy ◽  
Bidding Behavior ◽  
Bidding Strategies ◽  
Generation Company ◽  
Optimal Bidding ◽  
New Research

In a pool-based single-buyer electricity market, a Generation Company (GENCO) is required to considering the decision risk when building the optimal bidding strategy due to the stochastic bidding behavior of the rivals. The optimal decision is to maximize the profit while minimizing the risk, however, they are contradicting targets. This paper proposes a new research framework about risk-constrained optimal bidding strategies based on the stochastic programming method, termed as balance programming between target and chance (BPTC). And this method can favor the GENCO to make the stochastic decision in a more rational, flexible, and applicable manner. A genetic algorithm with Monte Carlo simulation is employed to solve the programming model. The effectiveness of the proposed method is shown through a numerical test.

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>

Bilateral bidding strategy in joint day-ahead energy and reserve electricity markets considering techno-economic-environmental measures

2021 ◽  
pp. 0958305X2110148
Author(s):  
Mojtaba Shivaie ◽  
Mohammad Kiani-Moghaddam ◽  
Philip D Weinsier
Keyword(s):  
Electricity Market ◽  
Large Scale ◽  
Search Algorithm ◽  
Power Grid ◽  
Mathematical Formulation ◽  
Test System ◽  
Bidding Strategy ◽  
Level Problem ◽  
Optimal Bidding ◽  
Environmental Measures

In this study, a new bilateral equilibrium model was developed for the optimal bidding strategy of both price-taker generation companies (GenCos) and distribution companies (DisCos) that participate in a joint day-ahead energy and reserve electricity market. This model, from a new perspective, simultaneously takes into account such techno-economic-environmental measures as market power, security constraints, and environmental and loss considerations. The mathematical formulation of this new model, therefore, falls into a nonlinear, two-level optimization problem. The upper-level problem maximizes the quadratic profit functions of the GenCos and DisCos under incomplete information and passes the obtained optimal bidding strategies to the lower-level problem that clears a joint day-ahead energy and reserve electricity market. A locational marginal pricing mechanism was also considered for settling the electricity market. To solve this newly developed model, a competent multi-computational-stage, multi-dimensional, multiple-homogeneous enhanced melody search algorithm (MMM-EMSA), referred to as a symphony orchestra search algorithm (SOSA), was employed. Case studies using the IEEE 118-bus test system—a part of the American electrical power grid in the Midwestern U.S.—are provided in this paper in order to illustrate the effectiveness and capability of the model on a large-scale power grid. According to the simulation results, several conclusions can be drawn when comparing the unilateral bidding strategy: the competition among GenCos and DisCos facilitates; the improved performance of the electricity market; mitigation of the polluting atmospheric emission levels; and, the increase in total profits of the GenCos and DisCos.

scholarly journals Optimal bidding strategy for price takers and customers in a competitive electricity market

2017 ◽  
Vol 4 (1) ◽  
pp. 1358545 ◽  
Author(s):  
Somendra P.S. Mathur ◽  
Anoop Arya ◽  
Manisha Dubey ◽  
Gongnan Xie
Keyword(s):  
Electricity Market ◽  
Bidding Strategy ◽  
Optimal Bidding

Capturing Electricity Market Dynamics in the Optimal Trading of Strategic Agents using Neural Network Constrained Optimization

2021 ◽  
Author(s):  
Mihály Dolányi ◽  
Kenneth Bruninx ◽  
Jean-François Toubeau ◽  
Erik Delarue
Keyword(s):  
Neural Network ◽  
Electricity Markets ◽  
Electricity Market ◽  
Mathematical Program ◽  
Market Dynamics ◽  
Equilibrium Constraints ◽  
Market Outcomes ◽  
Optimal Trading ◽  
Optimal Bidding ◽  
Upper Level

In competitive electricity markets the optimal trading problem of an electricity market agent is commonly formulated as a bi-level program, and solved as mathematical program with equilibrium constraints (MPEC). In this paper, an alternative paradigm, labeled as mathematical program with neural network constraint (MPNNC), is developed to incorporate complex market dynamics in the optimal bidding strategy. This method uses input-convex neural networks (ICNNs) to represent the mapping between the upper-level (agent) decisions and the lower-level (market) outcomes, i.e., to replace the lower-level problem by a neural network. In a comparative analysis, the optimal bidding problem of a load agent is formulated via the proposed MPNNC and via the classical bi-level programming method, and compared against each other.

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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