RNM is a very large-scale planning tool, which plans the electrical distribution network using the GPS coordinates and power of every single customer and distributed energy resource (DER)
The Reference Network Model (RNM) is a very large-scale planning tool, which plans the electrical distribution network using the GPS coordinates and power of every single customer and distributed energy resource (DER). It designs the high, medium and low voltage networks, planning both substations and feeders. For planning the network, it considers technical constraints, such as voltage limits, capacity constraints and continuity of supply targets. It also considers geographical constraints such as the street map, the topography and forbidden ways through such as nature reserves or lakes. The objective of the RNM is not to design the real network of the company, but rather to build a reference network, whose cost is indicative of the efficient cost required for building a network. Initially this type of models were designed to serve as a regulation tool for assessing the distribution network costs under incentive regulation. More recently, they are being applied to assess the impact of integrating distributed energy resources in the distribution networks, as shown in the list of projects and publications.
There are two versions: i) greenfield and ii) brownfield. The greenfield builds the network from scratch and is used to model the initial network. The brownfield expands the network in order to accommodate additional demand or new distributed energy resources. The brownfield version is used to estimate which are the reinforcements required in future scenarios. Both models have been programmed in C++, to be able to deal with very large-scale networks comprising millions of customers.
Both RNMs are organized in four layers, which account for different levels of information: (i) The logical layer, (ii) the topological layer, (iii) the electrical layer and (iv) the continuity of supply layer. Each layer comprises both data and algorithms.
The logical layer includes the concept of graph, nodes and branches, for modeling a network. It only considers connection at a logical level, without including node coordinates or electrical parameters. The topological layer incorporates the coordinates and algorithms for obtaining the best path for a new distribution line, considering forbidden ways through (such as lakes or nature reserves) and a street map which is automatically calculated considering the input location of customers. The electrical layer incorporates all the electrical parameters such as bus voltages, resistances and reactances of the equipment. It includes power flows algorithms (backward forward method for radial network, and Gauss-Seidel and Newton-Raphson for meshed networks). It also incorporates planning algorithms for determining the location of transformer substations and branch-exchange methods for planning the network. Finally the continuity of supply layer includes protective equipment as well as the planning algorithms for achieving the ASIDI and ASIFI targets.
Regarding the information required by the RNMs, the most critical data are the location and power of every single customer and distributed energy resource (DER). If daily profiles are used it also has to be specified for every single element, the active and reactive power either consumed or injected into the network at each hour. With this information, the models plan the network for connecting these customers and DERs to the transmission substations.
The typical methodology is summarized in Fig. 1. For each distribution network, the first step is to identify the location of customers and DERs and use this information in the Greenfield RNM whose output is a model of the initial network. The next step is to define the scenarios to be analyzed. Then, the initial networks are expanded to connect the additional resources, through the brownfield model. Finally the costs are assessed using an standardized equipment library. Resulting costs are broken down in substations and feeders.
Figure 1: Methodology for impact assessment
Fig. 2 shows an example network built with the model in an urban area. Thick red lines are Medium Voltage (MV) feeders, thin black lines are Low Voltage (LV) feeders, green circles are MV/LV Transformer substations and small points are LV customers.
Figure 2: Example network built with the model in urban areas
Fig. 3 shows an example network built with the model in a rural area. Thick red lines are Medium Voltage (MV) feeders, thin black lines are Low Voltage (LV) feeders, the green triangle is the HV/MV substation, green circles are MV/LV Transformer substations and small points are LV customers.
Figure 3: Example network built with the model in rural areas
- Feeder layout.
- Substation location.
- Technical and economical parameters of each equipment: resistances, reactances, voltages, capcities, lengths, costs, ...
- Customers and DG per voltage level.
- Length of the low, medium and high voltage networks indicating aerial and underground ratios.
- Total costs of substations and feeders per voltage level broken down in investment cost, preventive and corrective maintenance cost, energy losses and protective equipment.
- Estimation of ditches, cables in fachades and posts.
- Continuity of supply indexes (SAIDI, SAIFI) in each area.
- Substations, feeders and protective equipments installed for each voltage level, indicating the number of installations and their main technical paremeters and costs.
It has been developed a module to select automatically the location of the customers based on a street map image. Using this module it is possible to use a street map image as input to the model, automatically selecting the location of the customers and building the network for these customers using the RNM. Executing the model in this mode, it is possible to obtain a reference network for a given area using very few input data. This is illustrated in Fig. 4 for the greek town of Katerini.
Figure 4: Katerini street map and reference distribution network in Katerini.
[1] C. Mateo, T. Gómez, A. Sánchez, J. Peco, A. Candela "A reference network model for large-scale distribution planning with automatic street map generation", IEEE Transactions on Power Systems. vol. 26, no. 1, pp. 190-197, February 2011. DOI: 10.1109/TPWRS.2010.2052077
[2] L. González, C. Mateo, A. Sánchez, M. Alvar, "Large-scale MV/LV transformer substation planning considering network costs and flexible area decomposition", IEEE Transactions on Power Delivery. vol. 28, no. 4, pp. 2245-2253, Octubre 2013. DOI: 10.1109/TPWRD.2013.2258944
[3] P. Frías, C. Mateo, I.J. Pérez-Arriaga "Assessment of the impact of electric vehicles in electrical distribution networks (Spanish)", Lychnos. Cuadernos de la Fundaci&ocaute;n General CSIC. no. 6, pp. 56-61, Septiembre 2011.
[4] R. Cossent, L. Olmos, T. Gómez, C. Mateo, P. Frías "Distribution network costs under different penetration levels of distributed generation", European Transactions on Electrical Power. vol. 21, no. 6, pp. 1869-1888, Septiembre 2011. DOI: 10.1002/etep.503
[5] T. Gómez, C. Mateo, A. Sánchez, J. Reneses, M. Rivier "Retribution of electricity distribution in Spain and the Network Reference Model (Spanish)", Estudios de Economía Aplicada. vol. 29, no. 2, pp. 661-(24 pág), August 2011.
[6] L. Pieltain, T. Gómez, R. Cossent, C. Mateo, P. Frías "Assessment of the impact of plug-in electric vehicles on distribution networks", IEEE Transactions on Power Systems. vol. 26, no. 1, pp. 206-213, February 2011. DOI: 10.1109/TPWRS.2010.2049133
[7] I. Momber, T. Gómez, M. Rivier, C. Mateo "Benefits of EV supplier-aggregators and distribution system operators from applying smart charging of plug-in electric vehicles", Cigrè International Symposium on The Electric Power System of the Future: Integrating Supergrids and Microgrids. Bolonia, Italia, 13-15 Septiembre 2011.
[8] L. González, C. Mateo, T. Gómez, J. Reneses, M. Rivier, A. Sánchez "Assessing the impact of distributed generation on energy losses using reference network models", Cigrè International Symposium on The Electric Power System of the Future: Integrating Supergrids and Microgrids. Bolonia, Italia, 13-15 Septiembre 2011.
[9] C. Mateo, T. Gómez, A. Sánchez, A. Candela, L. Maqueda Hernando "Large-scale planning models to assess economies of scale in distribution grids in the presence of distributed generation", 17th Power Systems Computation Conference - PSCC'11. Estocolmo, Suecia, 22-26 Agosto 2011.
[10] R. Cossent, L. Olmos, T. Gómez, C. Mateo, P. Frías "Mitigating the impact of distributed generation on distribution network costs through advanced response options", 7th Conference on the European Energy Market - EEM10. ISBN: 978-1-4244-6838-6, Madrid, España, 23-25 Junio 2010.
[11] R. Cossent, T. Gómez, L. Olmos, C. Mateo, P. Frías "Assessing the impact of distributed generation on distribution network costs", 6th International Conference on the European Energy Market - EEM'09. pp. 586-593, Leuven, Bélgica, 27-29 Mayo 2009.
[12] C. Mateo, A. Rodriguez, J. Reneses, P. Frías, A. Sánchez "Cost-benefit analysis of battery storage in medium voltage distribution networks", IET Generation Transmission & Distribution. [Online: Octubre 2015].
[13] F. Postigo, C. Mateo, T. Gómez, B. Palmintier, B.M. Hodge, V. Krishnan, F. de Cuadra, B. Mather. "A review of power distribution test feeders in the United States and the need for synthetic representative networks", Energies. vol. 10, no. 11, pp. 1896-1-1896-14, Noviembre 2017. [Online: Noviembre 2017]
[14] C. Mateo, G. Prettico, T. Gómez, R. Cossent, F. Gangale, P. Frías, G. Fulli. "European representative electricity distribution networks", International Journal of Electrical Power & Energy Systems. vol. 99, pp. 273-280, Julio 2018. [Online: Febrero 2018]
[15] C. Mateo, R. Cossent, T. Gómez, G. Prettico, P. Frías, G. Fulli, A. Meletiou, F. Postigo. "Impact of solar PV self-consumption policies on distribution networks and regulatory implications", Solar Energy. vol. 176, pp. 62-72, Diciembre 2018. [Online: Octubre 2018]
[16] C. Mateo, P. Frías, K. Tapia Ahumada. "A comprehensive techno-economic assessment of the impact of natural gas-fueled distributed generation in European electricity distribution networks", Energy. [Online: Noviembre 2019]
