The Equilibrium Model of Electricity Market with Consideration of the Pumped Storage GenCo

2012 ◽  
Vol 614-615 ◽  
pp. 1966-1972
Author(s):  
Jian Lin Yang ◽  
Hui Qing Lu ◽  
Fang Di Shi
Keyword(s):  
Electricity Market ◽  
Test System ◽  
Bidding Strategy ◽  
Simulation Tool ◽  
Storage Unit ◽  
Bidding Strategies ◽  
Efficient Simulation ◽  
Proposed Model ◽  
Pumped Storage ◽  
Mixed Linear Complementarity Problem

Pumped storage is the largest-capacity form of grid energy storage available. A multi-period oligopolistic model for analyzing the bidding strategies of pumped storage GenCo (PSG) is proposed in this paper. In the pumping periods, the pumped storage unit (PSU) is simulated as a special load. While in generating periods, PSU is treated as a normal generator. In this model, all GenCos are assumed to exercise Cournot strategies to maximize their own profits. The resulting equilibrium formulation is established in terms of a mixed linear complementarity problem. The purpose of this paper is to provide an efficient simulation tool for the PSG to determine its bidding strategy in an oligopolistic environment. The proposed model can also be used to study various factors that may impact PSG’s profit. Results of a six-bus test system are analyzed to illustrate the characteristics of the proposed model.

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 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.

Spinning Reserve Optimization Model Based on Bi-Level Programming

2011 ◽  
Vol 354-355 ◽  
pp. 1047-1050
Author(s):  
Da Wei Huang ◽  
Ming Lei
Keyword(s):  
Power Systems ◽  
Electricity Market ◽  
Cost Minimization ◽  
Test System ◽  
Spinning Reserve ◽  
Proposed Model ◽  
Generation Cost ◽  
Security And Reliability ◽  
Upper Level

How to determine spinning reserve reasonably in electricity market is one of the key questions which guarantee the security and reliability of power systems. For this issue, based on bi-level programming theory, an optimal model of spinning reserve is established in this paper. In this model the generation cost minimization and the reserve cost minimization are the upper level and lower level optimization object separately, and the network and “N-1”security constraints of the power system are also considered. The determination of reserve capacity and the reliability level are related, and the reserve rate of each unit is determined. And an IEEE-14 nodes test system case is used to demonstrate the feasibility and rationality of this proposed model.

scholarly journals Research on Basic Characteristics and Bidding Strategy of Thermal Power Units in Fujian Spot Market

2021 ◽  
Vol 252 ◽  
pp. 02005
Author(s):  
Yuhui Song ◽  
Zhanhua Pan ◽  
Baike Chen ◽  
Zhaoxia Jing
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Thermal Power ◽  
Spot Market ◽  
Bidding Strategy ◽  
Cost Calculation ◽  
Market Environment ◽  
Service Market ◽  
Electric Generator ◽  
Bidding Strategies

In the electricity market environment, thermal power units have changed from the executors of power production under the monopoly mechanism to the decision-makers of production and operation under the competitive environment. The merits and demerits of bidding schemes for thermal power units are directly related to self-interest of themselves. The bidding decisions of power plants are not only affected by economic factors, but also by technical factors peculiar to the power system and the electric generator. In recent years, research on bidding strategies of thermal power units based on the power market environment has been paid more and more attention in the field of electric market. This paper first introduces the basic peak regulation and frequency modulation technical characteristics of thermal power units and cost calculation. Then, from the angle of quotation, paper analyzes characteristics of quotation mechanism of units in Fujian Electric Power day-ahead, real-time and peak regulation auxiliary service market. Furthermore, the research status of bidding strategies of thermal power units participating in electricity market at home and abroad in cost analysis, market clearing price prediction, game theory and so on are summarized. Finally, the bidding strategy for units in Fujian Spot Market is put forward.

scholarly journals 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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