Anmm Niyam Haque

Congestion management in smart distribution network

The accelerating use of Distributed Energy Resources (DERs) and new forms of loads connected in the Medium Voltage (MV) and Low Voltage (LV) networks are posing a great challenge for the Distribution System Operators (DSOs) in the near future. The bidirectional and uncertain flow of power may result in congestions at certain points in the distribution network. Consequently, assets are overloaded; voltage deviations can occur and cascading failures may take place. Therefore, the DSOs are compelled to investigate and optimize their asset investment cost by introducing smart grid functionalities in order to mitigate investments. Out of a number of alternatives, congestion management is one of the most promising strategies to deal with the network issues. Congestion management schemes have traditionally been treated in the transmission system level. But with the widespread use of Distributed Generators (DGs) and expected severe loading conditions, the management procedure will have to be applied in the distribution network as well. This paper discusses the need and possibility of congestion management in a smart distribution network.

Real-time congestion management in active distribution network based on dynamic thermal overloading cost

The rapid proliferation of distributed energy resources (DERs) leads to capacity challenges, i.e. network congestions, in the low-voltage (LV) distribution networks. Different types of control strategies are being developed to tackle the challenges with direct switching actions such as load shedding or power curtailment. Alternatively, demand flexibility from the large number of DERs is being considered as a potential approach by influencing the individual end-users with various demand response (DR) programs. However, most of the DR-based solutions focus on scheduling phase, thus having a limitation to handle network issues in real-time grid operation. In order to improve DRs capability, besides a proper incentive scheme for involved actors, the DR-based approach needs to integrate network constraints and quantify this real-time information in its control process. In this paper, a novel method for real-time congestion management is proposed, which focuses on resolving the congestion problem at the MV/LV transformer. Detail models for different loads and thermal overloading of the MV/LV transformer are developed to realize the benefits of the demand flexibility. The overall performance of the integrated approach for the congestion management has been verified by a simulation with a typical LV network of the Netherlands.

Smart curtailment for congestion management in LV distribution network

The rapid proliferation of distributed energy resources (DERs) leads to capacity challenges, i.e. network congestions, in the low-voltage (LV) distribution networks. A number of strategies are being widely studied to tackle the challenges with direct switching actions such as load shedding or power curtailment. On the other hand, various market-based demand response (DR) programs have been developed to influence the large number of DERs to use their flexibility to deal with network congestions. However, most of the market-based solutions rely on the flexibilities of the DERs, thus cannot solve the congestion when flexibility is not available in the network. To complement the market-based solutions, a smart active power curtailment based mechanism is necessary for managing the congestions in the distribution network. In this paper, we propose a novel method for congestion management by active power curtailment based on a Mixed-Integer Programming technique. In addition, two greedy selection methods together with fair power curtailment and security constrained OPF methods have been developed for the sake of comparison. The overall performance of the proposed approach and the comparison with other methods have been verified by a simulation with a typical LV network of the Netherlands.

HEMS as network support tool: Facilitating network operator in congestion management and overvoltage mitigation

Rapid proliferation of the Distributed Energy Resources (DERs) is posing major operational challenges like congestions and voltage limit violations in the distribution networks. A number of direct and market-based control strategies are being widely developed to address these challenges. While the direct approaches aim to control the loads directly, the market-based control influences individual prosumers with price or incentive-based mechanisms. In this paper, a Multi-Agent System (MAS) based Home Energy Management System (HEMS) has been proposed to provide network support functionalities during congestions and overvoltage incidents. The proposed mechanism is comprised of a scalable market-based architecture and enhanced with an advanced power curtailment mechanism that curtails active power consumption of the residential electrical appliances and power injection of individual distributed generation (DG) units. The proposed HEMS model is simulated and verified with a case study for a typical Dutch house. To test the operation of the proposed HEMS, a Zigbee based lab prototype has also been developed.

Distributed intelligence: Unleashing flexibilities for congestion management in smart distribution networks (Invited paper)

Electrical distribution networks worldwide are facing frequent capacity challenges due to the widespread roll out of various distributed energy resources (DERs). A number of demand response (DR) mechanisms have been developed in order to circumvent the problems and enhance the flexibility of the distribution network. While the existing centralized control system remains its crucial role for reliable and secure grid operation, distributed intelligence is a complement technology with a focus on dividing the control task into a number of simpler problems and solve them with minimum exchange of information. Based on the recent developments of distributed intelligence, this paper discusses a decentralized approach to enable demand response for managing the congestions more efficiently. The approach is validated with simulations for representative Dutch low-voltage (LV) networks.

A study of congestion management in smart distribution networks based on demand flexibility

Electrical distribution networks worldwide are facing frequent capacity challenges due to the widespread roll out of various distributed energy resources (DERs). A number of demand response (DR) mechanisms have been developed in order to circumvent the problems and enhance the flexibility of the distribution network. While the existing centralised control system remains its crucial role for reliable and secure grid operation, distributed intelligence is a complement technology with a focus on dividing the control task into a number of simpler problems and solve them with minimum exchange of information. Based on the recent developments of distributed intelligence, this paper investigates a set of different congestion management approaches to effectively regulate the overloading issue of transformers or conductors. The study is validated with simulations for representative Dutch low-voltage (LV) networks.

Learning technique for real-time congestion management in an active distribution networks

During the last few decades, the concept of demand response (DR) in the energy sector has gained substantial momentum. Research has led to a range of DR solutions. These solutions mostly differ in their applications, the hosting power system, the energy market etc. Moreover, as per the EU directive, DR aggregators should be allowed to trade DR alongside supply in both day-ahead and real time electricity markets. Meanwhile, independent aggregators do not consider physical limitations of a network, thus setting up new a challenges for network operation. In this paper, an active learning technique for real-time congestion management is proposed to tackle this challenge. This enables distributed system operator (DSO) to incenticize independent aggregators efficiently in order to use DR for overloading mitigation. Lastly, a case study is simulated which verifies the performance of a new approach for congestion management.