Non-technical loss detection in limited-data low-voltage distribution feeders

Non-technical loss (NTL) is the major source of energy loss in distribution networks. Since the direct calculation of NTL is impossible, it is mostly being obtained by calculating the difference between total loss and technical loss (TL). This is rather not possible without adequate equipment for measuring the TL. However, time-worn distribution networks with high limitations and lack of equipment could identify the regions with a high share of NTL to reduce/eradicate the sources of NTL. This strategy can increase efficiency and reduce costs compared to the one-by-one inspection of customers. This paper presents a novel NTL detection procedure based on load estimation in highly limited distribution networks. First, the low-voltage (LV) distribution network will be divided into a few smaller hypothetical sub-networks through a fuzzy c-means (FCM) clustering technique. Then, a meter placement based on the maximum likelihood criterion is deployed to find the best places for installing the existing meters in each cluster. In the next stage, a linear system of equations is utilized to extract the pattern load profiles (PLPs) related to each class of customers for each hypothetical sub-network separately. These PLPs will be used to estimate the transformers’ load in all LV feeders based on their share in the total consumption of the entire network. Finally, NTL possibility per capita will be achieved for each LV feeder by defining an index that estimates the degree of NTL in those areas.©2021 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

A Price-Based Load Flow Algorithm in Decentralized Distribution Markets

With the addition of new elements such as distributed generation, demand response, and price-sensitive loads, as well as grid smartness, the operation and management of distribution networks have faced additional complexities. Hence, the electricity price has a vital role for all market participants and strongly affects the demand, generation, and subsequently, the load flow analysis in the network. In this situation, there is a need for a load flow algorithm in which price is integrated into load flow equations. Therefore, this paper seeks to present a suitable algorithm called price-based load flow which considers the electricity price as a state variable similar to technical variables. In this situation, the price is added to the load flow equations as a new state variable and the effect of location on the price is considered. Therefore, it is possible to use the radial structure of the distribution network and communication between adjacent buses in order to provide an effective and appropriate method to find the price simultaneously with technical variables. The numerical study on the IEEE 33-bus distribution network shows the performance of the proposed method. Unlike the centralized market structure, the method does not need all network information to calculate distribution locational marginal price using optimal power flow. In this method, each bus can perform its computation only by information exchange with adjacent buses

Evaluating the efficiency of divestiture policy in promoting competitiveness using an analytical method and agent-based computational economics

Choosing a desired policy for divestiture of dominant firms' generation assets has been a challenging task and open question for regulatory authority. To deal with this problem, in this paper, an analytical method and agent-based computational economics (ACE) approach are used for ex-ante analysis of divestiture policy in reducing market power. The analytical method is applied to solve a designed concentration boundary problem, even for situations where the cost data of generators are unknown. The concentration boundary problem is the problem of minimizing or maximizing market concentration subject to operation constraints of the electricity market. It is proved here that the market concentration corresponding to operation condition is certainly viable in an interval calculated by the analytical method. For situations where the cost function of generators is available, the ACE is used to model the electricity market. In ACE, each power producer's profit-maximization problem is solved by the computational approach of Q-learning. The power producer using the Q-learning method learns from past experiences to implicitly identify the market power, and find desired response in competing with the rivals. Both methods are applied in a multi-area power system and effects of different divestiture policies on market behavior are analyzed.Electricity market Divestiture policy Agent-based computational economics

Reliability-Centered Maintenance for Overhead Transmission Lines in Composite Power System

Reliable overhead transmission lines have a vital role in maintaining the reliability of power systems at a desirable level and ensuring the continuity of electrical energy. In this regard, a proper preventive maintenance program can ensure the reliable functioning of transmission lines. Based on the concept of reliability-centered maintenance, this paper proposes a preventive maintenance strategy that can be implemented in real power systems due to the limited data requirements. The priority of each line for preventive maintenance was obtained by two indices revealing the technical condition and importance of each line in the network. The proposed method was applied to a real transmission network in Khorasan province of Iran. In order to extract the condition index of lines, failure statistics during six recent years as well as technical data of lines were investigated. Moreover, this paper presents a two-stage method to determine the importance index of each line by evaluating the consequences of line failures on the network. The presented results show the efficiency of the proposed approach