Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach.

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Impacts of Renewable Sources of Energy on Bid Modeling Strategy in an Emerging Electricity Market Using Oppositional Gravitational Search Algorithm

Energies ◽

10.3390/en14185726 ◽

2021 ◽

Vol 14 (18) ◽

pp. 5726

Author(s):

Satyendra Singh ◽

Manoj Fozdar ◽

Hasmat Malik ◽

Irfan Ahmad Khan ◽

Sattam Al Otaibi ◽

...

Keyword(s):

Renewable Resources ◽

Search Algorithm ◽

Thermal Power ◽

Gravitational Search Algorithm ◽

Distribution Functions ◽

Solar Irradiation ◽

Bidding Strategy ◽

Wind Speed Profile ◽

Gravitational Search ◽

The Impact

Power suppliers in a dynamic power market can achieve full benefit by introducing a bidding strategy mechanism. In the power sector, renewable resources have significant gradual usage and their effect on the production of detailed bidding approaches is becoming further complicated in the industry. Due to the irregular nature of these renewable resources and because they are subject to several fluctuations, there is an inherent issue with generating electricity. Taking these considerations into account, attempts have been made to create a model of bidding strategy to optimize the benefit of the electricity producers using the oppositional gravitational search algorithm. The Weibull and Beta distribution functions are utilized to describe the stochastic characteristics of the wind-speed profile and solar-irradiation, respectively. For the IEEE-30 and IEEE-57 frameworks, the suggested method is being checked and explained. In comparison to other optimization approaches, the results of this approach were taken into account, and it was discovered that it outperformed other techniques in addressing bid difficulties. In addition, it is worth noting that the impact of renewable energy on the bidding strategy lowered market clearing and thermal power generating costs, and encouraged renewable influenced producers to put forward the excess electricity into the real-time market.

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Generation Bidding Strategy in a Pool-Based Electricity Market Using Oppositional Gravitational Search Algorithm

2017 14th IEEE India Council International Conference (INDICON) ◽

10.1109/indicon.2017.8487910 ◽

2017 ◽

Cited By ~ 4

Author(s):

Satyendra Singh ◽

Manoj Fozdar

Keyword(s):

Electricity Market ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Bidding Strategy ◽

Gravitational Search

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

Energy & Environment ◽

10.1177/0958305x211014875 ◽

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.

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