Modeling Price- and Incentive-Based Demand Response Strategies in the Renewable-based Energy Markets

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Hajibandeh, N (Author) Other; Ehsan, M (Author) Other; Soleymani, S (Author) Other; Shafie khah, M (Author) Other; Catalao, JPS (Author) FEUP

Conference proceedings International

Title: 2017 1ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2017 17TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC / I&CPS EUROPE)

17th IEEE International Conference on Environment and Electrical Engineering (IEEE EEEIC) / 1st IEEE Industrial and Commercial Power Systems Europe (IEEE I&CPS Europe)

Milan, ITALY, JUN 06-09, 2017

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Other information

Authenticus ID: P-00N-7R9

DOI: 10.1109/eeeic.2017.7977701

Abstract (EN): This paper models the impacts of Demand Response Programs (DRPs) on the behavior of energy market participants in the electricity markets in the presence of renewable energies. In such oligopolistic environment, market interactions are considered by using a game theoretic model, and the market transactions are cleared by means of a Security Constraint Unit Commitment program (SCUC). One sample is considered from each main category of DRPs consisting of different types of time of use tariffs, real-time pricing, critical peak pricing from Price-Based Demand Response (PBDR), and different types of emergency demand response program tariffs from Incentive Based Demand Response (IBDR) in the presence of the wind farms. It is expected that the numerical results with the presence of renewable energy resources indicate that different types of these DRPs differently affect the oligopolistic behavior of market players that should be studied by the system operators before their implementation. Using Monte Carlo simulation method, several scenarios are generated to show the possible contingencies in Day-Ahead energy market. Then some scenario reduction methods are used for reduction the numbers of scenarios. Finally, a two-stage stochastic model is applied to solve this scheduling in a mixed-integer linear programming through GAMS. Consequently, the effect of demand response in the reduction of the operation cost is proved. The proposed approach is tested on a modified IEEE six-bus system.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5