[Paper Review] Robust Capacity Assessment of Distributed Generation in Unbalanced Distribution Networks Incorporating ANM Techniques

Xin Chen, Wenchuan Wu+1 more|arXiv (Cornell University)|Sep 12, 2017

Optimal Power Flow Distribution24 references12 citations

TL;DR

This paper proposes a two-stage adjustable robust optimization framework for assessing the maximum hosting capacity of distributed generation (DG) in unbalanced distribution networks, integrating three-phase power flow modeling and active network management (ANM) techniques. The method robustly handles uncertainties in load and DG outputs, enabling optimal DG sizing and sitting while generating time-specific ANM strategies, with validation on an IEEE 33-bus system showing improved capacity and robustness.

ABSTRACT

To settle a large-scale integration of renewable distributed generations (DGs), it requires to assess the maximal DG hosting capacity of active distribution networks (ADNs). For fully exploiting the ability of ADNs to accommodate DG, this paper proposes a robust comprehensive DG capacity assessment method considering three-phase power flow modelling and active network management (ANM) techniques. The two-stage adjustable robust optimization is employed to tackle the uncertainties of load demands and DG outputs. With our method, system planners can obtain the maximum penetration level of DGs with their optimal sizing and sitting decisions. Meanwhile, the robust optimal ANM schemes can be generated for each operation time period, including network reconfiguration, on-load-tap-changers regulation, and reactive power compensation. In addition, a three-step optimization algorithm is proposed to enhance the accuracy of DG capacity assessment results. The optimality and robustness of our method are validated via numerical tests on an unbalanced IEEE 33-bus distribution system.

Motivation & Objective

• To address the challenge of accurately assessing the maximum distributed generation (DG) hosting capacity in unbalanced distribution networks under uncertainty.
• To integrate active network management (ANM) techniques—such as network reconfiguration, OLTC regulation, and reactive power compensation—into the capacity assessment process.
• To develop a robust optimization framework that accounts for uncertainties in load demand and renewable DG output.
• To provide system planners with optimal DG sizing and sitting decisions alongside time-specific ANM operation schemes.
• To enhance the accuracy and reliability of DG capacity assessment through a three-step optimization algorithm.

Proposed method

• A two-stage adjustable robust optimization model is formulated to handle uncertainties in load and DG outputs using adjustable robust counterparts.
• Three-phase power flow modeling is incorporated to accurately represent unbalanced distribution network conditions.
• Active network management (ANM) techniques—network reconfiguration, on-load-tap-changer (OLTC) regulation, and reactive power compensation—are embedded as decision variables in the optimization framework.
• A three-step optimization algorithm is proposed to improve the computational accuracy and convergence of the robust capacity assessment.
• The robust optimization framework generates robust optimal ANM schemes for each time period, ensuring system security under uncertainty.
• The model is solved using a robust optimization solver with uncertainty sets defined for load and DG output variations.

Experimental results

Research questions

• RQ1 What is the maximum DG hosting capacity in an unbalanced distribution network when uncertainties in load and DG output are considered?
• RQ2 How can ANM techniques be optimally coordinated with DG integration to enhance hosting capacity?
• RQ3 What is the impact of three-phase unbalanced modeling on the accuracy of DG capacity assessment compared to balanced models?
• RQ4 How does the proposed two-stage adjustable robust optimization framework improve robustness and operational reliability?
• RQ5 What is the computational and practical performance of the three-step optimization algorithm in enhancing solution accuracy?

Key findings

• The proposed method achieves a higher DG hosting capacity compared to conventional approaches by effectively leveraging ANM strategies.
• The integration of three-phase power flow modeling significantly improves the accuracy of capacity assessment in unbalanced networks.
• The two-stage adjustable robust optimization framework ensures system security across all uncertainty realizations within predefined sets.
• The three-step optimization algorithm enhances solution accuracy and convergence, reducing optimality gaps in the capacity assessment.
• Numerical tests on the IEEE 33-bus system confirm the method’s robustness and effectiveness, with stable performance under varying load and DG output scenarios.
• The method generates time-specific optimal ANM schemes, including network reconfiguration and reactive power compensation, which are critical for operational feasibility.