The increasing penetration of distributed energy resources in distribution networks poses new challenges to the secure and efficient operation of distribution networks. One major challenge is the network congestion caused by the non-coordinated operation of flexible demands, such as electrical vehicles and heat pumps. In general, there are two categories of market-based day-ahead congestion management methods for distribution networks: price-based method and incentive-based method. In order to use the synergy of the two types of methods and to resolve potential conflicts against regulations when one type of method is implemented solely, a coordination scheme of the two types of methods is proposed for efficient day-ahead congestion management. In the proposed coordination scheme, the dynamic tariff (DT) as a price signal is used to partly resolve congestion firstly, and the scheduled reprofiling product (SRP) as an incentive-based flexibility service product is used to deal with the remaining congestion. By employing the coordination scheme, the distribution system operator (DSO) holds the profit neutral position in terms of congestion management, denoting that the DSO does not have the congestion management cost or revenue. Based on the DT model and SRP-based model, the coordination problem is formulated as a two-level non-convex mixed-integer non-linear programming (MINLP) model that is transformed into a one-level MINLP model with linear constraints and is solved by a proposed particle swarm optimization-based solution strategy. The Roy Billinton Test System was used to conduct case studies to validate the effectiveness of the proposed coordination scheme for day-ahead congestion management in distribution networks. The case study results demonstrate that the proposed coordination scheme can efficiently resolve congestion by the coordination of the DTs and SRPs while ensuring that the DSO is in a profit neutral position.
|Journal||International Journal of Electrical Power and Energy Systems|
|Number of pages||12|
|Publication status||Published - 2022|
- Congestion management
- Dynamic tariff
- Incentive-based method
- Particle swarm optimization