Oana Pop

Transmission cost allocation using the distribution factors method

One of the key problems of energy transmission system in an independent environment refers to necessity to establish a cost for system services on nondiscriminatory bases. The prices must to be simple and transparent. A properly established cost provides economical signals for short-term and long-term recovery current expenses and for a fair cost allocation for participants. This paper presents allocation of transmission costs to consumers and to generator using the distribution factors method. A detailed comparison between regime with active losses and the regime without active losses are presented. A case study based on a 12-node system is provided.

Comparison of power system tracing cost allocation methods

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.

Probabilistic distribution factors assessment using OptimalPowerPrice Mathematica software, case study: Test 25 buses test power system

Competition within the electric power systems has proven the importance of distribution factors assessment. In addition, load characteristic is unpredictable, leading to a diversity of supply paths. As a result, probabilistic power flow analysis can provide better tools and information for determining the tracing of the generator-consumer path. In this paper, the authors analyze the probabilistic distribution power factors method using the OptimalPowerPrice Mathematica software. Moreover, a correlation between the generalized distribution factors is obtained by means of probabilistic and deterministic methods. The case study in Section IV presents the above approach using a 25 buses test power system.

Artificial intelligence based TNEP. Part 1: Mathematical models

The paper is focusing on transmission network expansion planning (TNEP) problem solved using artificial intelligence techniques. It is divided into two parts. The 1st part is dedicated to the particle swarm optimization (PSO) and genetic algorithm (GA) concepts and mechanisms. The mathematical models and the associated software tool are also presented. Practical considerations are discussed. The 2nd part is focusing on case studies. 13 buses test power system, developed by the authors and IEEE 24 RTS have been used. The research work is going to be used in case of the Romanian power system (over 1000 buses).

Comparison of transmission cost allocation methods case study: DET WEST power system

In most countries the electric power industry has been undergoing through drastic and dramatic changes. The electrical utilities have been structured in separate generation, transmission and distribution segments bringing in a transparency in the utility business. Depending on the market structure of each country, the individual segments have also to compete in liberalized markets.

Network usage with probabilistic distribution factors method

This paper presents a probabilistic network usage computing using distribution factors method. A software tool is developed in Mathlab environment and contains a part dedicated to probabilistic power flow computing and another part allocated to network usage allocated to generators and consumers. Case study is applied on West, South-West and North-West parts of Romanian Power System. Useful results have been provided.

Probabilistic network usage by means of equivalent bilateral exchanges. Case study for the Romanian Power System

This paper analysis allocation method based on equivalent bilateral exchanges considering the existence of uncertainties from electric power systems. The theoretical part address on a software tool developed in Mathlab environment and contains a part dedicated to probabilistic power flow computing and another part allocated to network usage allocated to generators and consumers. This software tool is tested on a real system based on West, South-West and North-West parts of Romanian Power System and it provide useful results.

Artificial intelligence based TNEP. Part 2: Case studies

The paper is focusing on transmission network expansion planning (TNEP) problem solved using artificial intelligence techniques. It is divided into two parts. The 1st part is dedicated to the particle swarm optimization (PSO) and genetic algorithm (GA) concepts and mechanisms. The mathematical models and the associated software tool are also presented. Practical considerations are discussed. The 2nd part is focusing on case studies. 13 buses test power system, developed by the authors and IEEE 24 RTS have been used. The research work is going to be used in case of the Romanian power system (over 1000 buses).

Probabilistic network usage. Case study for the Romanian Power System

Pro rata allocation method based probabilistic network usage computing is presented within the paper. A software tool has been developed in Matlab environment. It contains a part dedicated to probabilistic power flow computing and another part corresponding to network usage allocated to generating units and consumers. The Romanian Power System is used as case study. Useful results have been provided.

ANN techniques for power consumption forecasting

The authors are focusing on developing a software tool designed for power consumption forecasting based on artificial neural networks (ANN). The hourly and peak consumed power and daily load curves are forecasted. The backpropagation algorithm is presented within the paper. Also, some practical considerations are highlighted, necessary to be known to develop a real application. The base propagation algorithm is completed with conjugated gradient and parabolic interpolation. A software tool has been developed in Matlab environment. The results are compared with the ones provided by classic forecasting methods. The analyses and the corresponding conclusions highlight the fact that the ANN based forecasting algorithm has a better behaviour.