An overview on network cost allocation methods

https://doi.org/10.1016/j.epsr.2008.10.005Get rights and content

Abstract

This work is devoted to study and discuss the main methods to solve the network cost allocation problem both for generators and demands. From the presented, compared and discussed methods, the first one is based on power injections, the second deals with proportional sharing factors, the third is based upon Equivalent Bilateral Exchanges, the fourth analyzes the power flow sensitivity in relation to the power injected, and the last one is based on Zbus network matrix. All the methods are initially illustrated using a 4-bus system. In addition, the IEEE 24-bus RTS system is presented for further comparisons and analysis. Appropriate conclusions are finally drawn.

Introduction

The cost of the transmission network can be interpreted as the cost of maintenance, planning and operation of the infrastructure that involves the transmission system. It is the responsibility of the generators and demands, that are the users of the transmission system, to pay for the network usage of this system. For example, in Brazil, the net basic cost of the transmission network, TUST-RB, whose elements have a voltage equal or above 230 kV, exceeded 3.1 billion dollars in the period of 2005–2006, according to [1].

One of the main challenges to allocate the cost of transmission is how to establish a criterion to split it between generators and demands. According to [15] the methods of network transmission cost allocation should, beyond ensuring the quality of the transmission service (voltage control, static and dynamic stability, etc.), satisfy a set of restrictions for its correct application:

  • no cross-subsidies;

  • transparency of the cost allocation procedure;

  • simple regulatory method;

  • adequate remuneration of present and future transmission investments;

  • economic signaling for future dimensioning;

  • continuity of existing network charges.

Different proposals for transmission network cost allocation have appeared in recent years. The pro rata method, presented in [10] and [11], allocates costs to generators and demands according to the sum of active power produced/consumed by each generator/demand.

Other methods, a bit more complex, distribute the costs based on the active power flow produced by generators and demands through the transmission lines. These methods use the proportional sharing principle, where the flows attributed to each generator and demand in “upstream” lines determine the power flows through “downstream” lines. These flows are associated with the origins and destinations, i.e., generators and demands. Examples of this method can be found in [15], [3], [4], [12].

The network usage method presented in [7] and [8] uses Equivalent Bilateral Exchanges (EBEs) to allocate costs to generators and demands. In order to create an EBE, each demand is attributed a generation fraction and, in the same way, a fraction of each demand is attributed to each generator. The attribution of costs by the network usage method occurs considering the impact, in terms of power flow, of each EBE in each transmission line, determined by the DC power flow solution.

The Zbus method [5] presents a solution based on the Zbus matrix and considers the current injection at each bus. The combination of these two elements (Zbus matrix and current injections) determines a measure of sensitivity that indicates what is the individual contribution of each current injection to produce the power flow through of a transmission line.

The nodal method, used in many countries, allocates the network usage costs and provides a measure to determine this allocation based on the power flow sensitivity in each line due to the power injected at each bus. This method can be found in [1].

In the last years, several studies about network allocation considering cross-border exchanges in Europe and Asia have appeared. For example, in Europe, there is a need to find a robust and fair mechanism for Inter-Transmission System Operators (Inter-TSO) compensation in the European internal electricity market to replace the provisional European Transmission System Operators (ETSO) mechanism. One of the main challenges is that the system operator of each country does not have information about the electrical networks of other countries, making the application of any network cost allocation method difficult. These issues can be found in [16], [17], [18], [19]. However, these studies are out of the scope of the paper.

The main objective of this work is to study and discuss the main methods used to allocate the network usage costs. Note that a less exhaustive work that also studies and analyzes several methods to allocate network usage costs in transmission systems is presented in [14]. This paper is organized as follows: Section 2 introduces the main methods present in the literature; Section 3 illustrates the methods using a 4-bus system; a more complex IEEE 24-bus RTS case study is presented in Section 4, and Section 5 presents the conclusions reached with the different methods that are analyzed.

Section snippets

Network usage cost allocation methods

Any network usage cost method must be both calculated and defined for a certain period, but there are different proposals to allocate this cost. In this section five ways to allocate this cost are presented: the pro rata method(PR), the proportional sharing method (PS), the Equivalent Bilateral Exchange (EBE) method, the nodal method, and the Zbus method.

Example of application

Some premises presented in section 1 indicate certain subjectivity in the evaluation (for example, easiness to promote regulation and transparency of the cost allocation procedure). However, the last three premises can be further evaluated. To illustrate them, several analyses regarding the locational viewpoint, the remuneration of new investments and the stability of tariffs, are presented. All analyses are done for the 4-bus system depicted in Fig. 1.

Fig. 1 presents the result of the power

Case study

The IEEE 24-bus RTS system shown in Fig. 2, whose data are depicted in [9], is presented in this case study. The same five methods applied to the 4-bus system are used in this section. The converged power flow corresponds to the IEEE RTS peak load, on the Tuesday of week 51 from 5 p.m to 6 p.m, as in [5]. The aspects referring to location, new investments and statistics rates are also discussed.

Conclusions

A comparison of the main methods of network cost allocation present in the literature is presented in this paper. An analysis of the main characteristics of several methods and recommendations for their use in different situations follows.

The PR method is not sensitive to the transmission system, i.e., this method can be considered poor in the locational aspect, and does not make an adequate remuneration method with regard to new investments. However, and for this reason, is a stable method

References (19)

  • Agência Nacional de Energia Elétrica (in Portuguese). Online access from October, 2006. Available at...
  • A.R. Bergen et al.

    Power Systems Analysis

    (1999)
  • J. Bialek

    Tracing the flow of electricity

    IEE Proc. Gen. Trans. Distrib.

    (1996)
  • J. Bialek

    Topological generation and load distribution factors for supplement charge allocation in transmission open access

    IEEE Trans. Power Syst.

    (1997)
  • A.J. Conejo et al.

    Zbus transmission network cost allocation

    IEEE Trans. Power Syst.

    (2007)
  • A.G. Expósito

    Análisis y Operación de Sistemas de Energía Eléctrica (in Spanish)

    (2002)
  • F.D. Galiana et al.

    Transmission network cost allocation based on Equivalent Bilateral Exchanges

    IEEE Trans. Power Syst.

    (2003)
  • H.A. Gil et al.

    Multiarea transmission network cost allocation based on Equivalent Bilateral Exchanges

    IEEE Trans. Power Syst.

    (2005)
  • IEEE Task Force, The IEEE Reliability Test System 1996. IEEE Trans. Power Syst., 14 (3):1010–1020, August...
There are more references available in the full text version of this article.

Cited by (0)

1

Supported in part by FAPESP (Foundation of Assistance to the Research of the State of São Paulo), project no. 07/01543-7.

2

Supported partially by CNPq (National Council for Scientific and Technological Development), project no. 308010/2006-0.

View full text