COST TD 1207 : Distribution Network Reinforcement Planning (DNRP)

The DNRP problem refers to a set of actions taken by distribution network operator/planner which ensures the secure and efficient operation of the distribution network within a given planning horizon. It includes conductor replacement, installing capacitors, network reconfiguration, On Load Tap Changer settings, distributed generator connection assessment and etc. It is usually formulated as a MINLP optimization problem and it is tried to optimize the summation of investment and operating costs. The presence of uncertain parameters in electrical power systems presents an ongoing problem for system operators when it comes to making decisions. Determining the most appropriate actions relies heavily on forecasts for a number of parameters such as demand, renewable energy resources availability and more recently weather. These uncertain parameters present an even more compelling problem at the distribution level, as these networks are inherently unbalanced, and need to be represented as such for certain tasks [1] .

Optimization models and Solution methods

Different optimization models have been proposed to solve the DNRP problem. These models can be categorized as follows:

  • Single/multiple objective optimization: Single objective optimization models are the most common form of formulating the DNRP problem. These models are either naturally single objective or they combine several objectives to form a single objective function using weighted sum approach [2]. The multi objective models try to find the Pareto optimal front of solution space [3].

  • Mathematical techniques

    • Non-linear programming : the power flow equations contain the sin and cos functions [4]

    • Quadratic programming [5]

    • Mixed integer programming : benders decomposition [6]

  • Heuristic techniques Immune algorithm [3], Big Bang-Big Crunch Method [7], Genetic algorithm [8], PSO [9]

  • Uncertainty and risk consideration: robust optimization, Information gap decision theory, Stochastic methods [10]


Different optimization solvers and commercial software have been used for solving the DNRP problem such as CONOPT, DICOPT [1], OpenDSS [11], Digsilent [12], MATPOWER [13].


[1] A.O Connell; A. Soroudi; A. Keane, "Distribution Network Operation Under Uncertainty Using Information Gap Decision Theory," in IEEE Transactions on Smart Grid , vol.PP, no.99, pp.1-1doi: 10.1109/TSG.2016.2601120

[2] W. El-Khattam, K. Bhattacharya, Y. Hegazy and M. M. A. Salama, "Optimal investment planning for distributed generation in a competitive electricity market," in IEEE Transactions on Power Systems, vol. 19, no. 3, pp. 1674-1684, Aug. 2004.

[3] Alireza Soroudi, Mehdi Ehsan, Hamidreza Zareipour, A practical eco-environmental distribution network planning model including fuel cells and non-renewable distributed energy resources, Renewable Energy, Volume 36, Issue 1, January 2011, Pages 179-188, ISSN 0960-1481,

[4] S. S. Al Kaabi, H. H. Zeineldin and V. Khadkikar, "Planning Active Distribution Networks Considering Multi-DG Configurations," in IEEE Transactions on Power Systems, vol. 29, no. 2, pp. 785-793, March 2014.

[5] K. Mahmoud and N. Yorino, "Robust quadratic-based BFS power flow method for multi-phase distribution systems," in IET Generation, Transmission & Distribution, vol. 10, no. 9, pp. 2240-2250, 6 9 2016.

[6] H. M. Khodr, Z. A. Vale and C. Ramos, "Notice of Violation of IEEE Publication Principles A Benders Decomposition and Fuzzy Multicriteria Approach for Distribution Networks Remuneration Considering DG," in IEEE Transactions on Power Systems, vol. 24, no. 2, pp. 1091-1101, May 2009.

[7] M. M. Othman, W. El-Khattam, Y. G. Hegazy and A. Y. Abdelaziz, "Optimal Placement and Sizing of Distributed Generators in Unbalanced Distribution Systems Using Supervised Big Bang-Big Crunch Method," in IEEE Transactions on Power Systems, vol. 30, no. 2, pp. 911-919, March 2015.

[8] Harrison, Gareth P., et al. "Hybrid GA and OPF evaluation of network capacity for distributed generation connections." Electric Power Systems Research 78.3 (2008): 392-398.

[9] S. Ganguly, "Multi-Objective Planning for Reactive Power Compensation of Radial Distribution Networks With Unified Power Quality Conditioner Allocation Using Particle Swarm Optimization," in IEEE Transactions on Power Systems, vol. 29, no. 4, pp. 1801-1810, July 2014.

[10] Soroudi, Alireza, and Turaj Amraee. "Decision making under uncertainty in energy systems: State of the art." Renewable and Sustainable Energy Reviews 28 (2013): 376-384.

[11] Model, O. P., & Element, O. S. OpenDSS Manual. EPRI,[Online] Available at: http://sourceforge. net/apps/mediawiki/electricdss/index. php.

[12] Hansen, Anca D., et al. Dynamic wind turbine models in power system simulation tool DIgSILENT. 2004.

[13] Zimmerman, Ray Daniel, Carlos Edmundo Murillo-Sánchez, and Robert John Thomas. "MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education." IEEE Transactions on power systems 26.1 (2011): 12-19.


Dr Alireza Soroudi, University College Dublin