Electricity Customers Research Articles

Forecasting of Reactive Power Consumption with the Use of Artificial Neural Networks

Many electricity customers, particularly in the area of small and medium-sized enterprises, are characterized by a system of 3-phase unbalanced current consumption and the polarization of power factor in the individual phases. Strong variation of power factor in the different phases (often with both inductive and capacitive nature) causes the inability to install classic–a 3-phase reactive power compensators. Therefore, energy consumers are exposed to higher costs for the crossing of the contractual power factor. This paper describes the problem of reactive power forecasting with the use of artificial neural networks. For calculation, the Nonlinear Autoregressive (NAR) neural network was used for different input vector sizes and different numbers of neurons in the hidden layer for foreseeing reactive power generation. Results of simulations compared to real measurements confirm that it is possible to forecast the reactive power course, useful for optimal planning of reactive power compensation strategies.

Effect of Demand Response Programs on Industrial Specific Energy Consumption: Study at Three Cement Plants

Demand-side management (DSM) is the modification of consumer utilization manner for energy through various methods to smooth their power consumption curve and increase their energy efficiency. Although the DSM improves the electricity grid stability and protects the environment, it allows customers to reduce their costs. The available literature classifies DSM into two different areas: (i) energy efficiency (EE) and (ii) demand response (DR). In other words, cement plants are the largest customers of electricity, so the implementation of DSM programs in these heavy industries is very important for electricity companies. Past studies have often examined the impact of DR programs in cement factories on solving peak problems and changing load profiles but have not studied the effects of DR implementation on the energy efficiency index in the short and long term in these industries. In this paper, while examining the effect of implementing three types of common DR programs in smart grids including demand bidding (DB), ancillary services (A/S), and time of use (TOU) on the load profile of cement plants, based on the mathematical model of the load and the real data of the case study, it is shown that the implementation of the TOU program has created the highest energy efficiency by reducing specific energy consumption (SEC) in cement plants.

Net-metering and net-billing in photovoltaic self-consumption: The cases of Ecuador and Spain

Solar Photovoltaics has become one of the cheapest sources of electricity, with the potential to expand further through distributed generation. PV self-consumption can empower electricity customers, transforming them into prosumers, but its success relies on appropriate regulations, especially in the treatment of surplus electricity, being net-metering and net-billing the most common remuneration mechanisms. As the number of such PV systems increases, regulation needs to evolve and may affect profitability.The purpose of this research is to take advantage of the Spanish experience, establishing key points that can improve the regulation in Ecuador and other countries. The operation of these remuneration schemes is studied through an economic analysis for a wide range of residential customers, highlighting the low profitability of small PV installations, which are not profitable for the average consumer in Ecuador. Grid parity is reached in Spain over 3 kW and in Ecuador only for the highest electricity consumers. Although the net-billing model is more effective in promoting PV self-consumption in the medium and long term, it requires additional conditions, such as those in Spain. Some recommendations are proposed, e.g. additional support with progressive tax reductions, simplication of permits and the introduction of collective self-consumption.

Peramalan Jumlah Pelanggan PLN Menggunakan Simple Linear Regression

The rate of population growth in Lubuklinggau City continues to increase every year, this is a factor in the increasing demand for electricity. The development of the number of PLN customers in Lubuklinggau City in 2017 was 90.606 customers, increasing to 120.809 customers in 2021. PLN must be able to predict the number of electricity customers in the future in order to provide electricity needs. One of the forecasting models that can be used to predict the development of the number of PLN electricity customers is the Simple Linear Regression model. Simple Linear Regression is a forecasting model based on the effect of the causal variables on the effect variables. The variables used are the population variable and the number of PLN customers. From these two variables, the process of analyzing the influence of population on the number of PLN customers in the future will be carried out. The results showed that 90% increase in population affects the number of PLN customers. With the prediction of the development of the number of customers, PLN Lubuklinggau can prepare the necessary facilities and can estimate how much additional power and electrical energy will be in meeting customer needs in the future.

Price Formation in Auctions for Financial Transmission Rights

Financial Transmission Rights (FTRs) are financial derivatives in wholesale electricity markets that are sold in auctions. The revenue collected from FTR auctions is passed through to electricity customers to reimburse them for transmission congestion payments they make in the spot energy market. On average, electricity customers’ congestion payments greatly exceed auction reimbursements in electricity markets across the United States. We study the issue of auction revenue deficiency through the lens of Auction Revenue Rights (ARRs), which is the predominant mechanism used in U.S. electricity markets to distribute auction revenue to electricity customers. We demonstrate how the ARR process influences fundamental supply conditions in the FTR auction market and show how divergent auction equilibria emerge under different ARR decision-making regimes. Using market data from PJM, we find empirical evidence that variation in ARR management strategies helps explain differences between an FTR’s auction price and its realized ex post value.

Presenting an economic interaction bi-level model between electricity retailer and customer in bilateral electricity market contract

Presenting an economic interaction bi-level model between electricity retailer and customer in bilateral electricity market contract

Analisis Sistem Instalasi Listrik Sebagai Sarana Penerangan Laboratorium Terpadu Bagi Mahasiswa Pendidikan Vokasional Teknik Elektro


 
 The use of electrical energy consumed by the University of Sultan Ageng Tirtayasa uses electrical energy sources managed by PLN which are included in primary consumers, where primary electricity customers are given a voltage of 20kV (medium voltage). Then from the PLN substation in Untirta, it is distributed by equipment or components to the Sultan Ageng Tirtayasa University area. Medium Voltage (TM) which is a strong current at Sultan Ageng Tirtayasa University is divided into 5 parts, where the medium voltage starts from the power house and enters the Integrated Laboratory Building, Economic Building, Transformer House, Integrated Class and Rectorate Building. The light voltage (TR) is divided into 3 zones, namely the Integrated Laboratory Building, Transformer House and Rectorate Building. The research method used is qualitative research with a descriptive approach. Data collection techniques used are observation, interviews, documentation, and literature study. Analysis of the data used is interactive analysis by Milles & Humerman. The results of the study stated that this light voltage is used as an additional voltage to compensate for medium voltages such as lighting lamps. The lighting source in the Integrated Laboratory uses the Philips brand with 5 types of lamps namely TL-LED, LED Floodlight, LED BULB, BL CG TL-5 and TL-RM 300 M1 which have IP Ratings of IP65 and IP20. For output use 10-200watt with a lighting level of 2100-28000 lm. The Schneider brand socket has a power of 200-1000 watt and a current of 16 Ampere.
 Keywords: Installation, System, Lighting, Building, Laboratory, Facilities.

Securing Grid Infrastructure Resilience Through Improved Capital Asset Planning and Management

Utilities are working to strengthen and evolve their electric grid operations to deliver power with higher reliability and safety, resulting in superior customer service. In addition to these traditional, yet still very relevant objectives, the concept of improving grid resiliency is one that electric grid operators, regulators and customers are increasingly interested in. There is a growing awareness and recognition that a more resilient electric system is essential for the modern and sustained economic prosperity of local communities and residents. Grid resilience has long been in the lexicon of utilities and their stakeholders. However, evolving legislative and regulatory mandates are increasing the urgency for focused solution planning and execution.

Customer Attitudes Towards the Role of Socio-Economic Factors in Electricity Theft

Electricity theft has become a matter of trouble for both customers and electricity distribution companies. Due to electricity theft, distribution companies are facing lots of T&D losses every year. Despite of several technical measures adopted, there is no significant reduction in losses due to electricity theft. This fact diverts the attention towards non-technical measures which include formulation and enforcement of stringent rules and regulations. As the policies need to be enforced on electricity customers, therefore, policies should be designed in accordance with the customers’ attitude towards electricity theft. Therefore, this work has analyzed their attitude towards role of certain socio-economic factors which strongly impact the practice of electricity theft. This study has surveyed the electricity customers of two distribution companies, UHBVN and DHBVN in State Haryana. The findings of this work may help the electricity distribution companies to chalk out better and more effective plans to put check upon the practice of electricity theft in the society.

Ranking Load in Microgrid System Based on the Priority Weight Calculation of Power Supply

This paper proposes a method of ranking load in Microgird system based on the calculation of priority weights on the continuity of power supply of the loads. The proposed method applies the covariance matrix of the criterion layer to determine values of each criterion. The fuzzy preference relation matrix is used to replace the pairwise comparison matrix of the scheme layer. The weight of the criterion layer and the scheme layer are combined to get the final weights of each load bus. This solves the problem of consistency factor when evaluating loads from experts and Microgrid system operators. The calculation of priority weighting on the continuity of power supply for loads supports system operators to be proactive in planning load shedding or load shedding due to system failures, thereby minimizing damage to the system and electricity customers. The proposed method is calculated on the diagram of the IEEE 16 bus Microgrid system with six sources and eight loads.

Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors

Demand flexibility (DF) has the potential to increase the saturation of renewables in the grid and reduce operating costs for both utilities and customers. However, less than 8% of U.S. residential electric customers are enrolled in DF programs. A major research gap on this topic is an uneven understanding of behavioral drivers of electricity use and DF program participation at the household level. In this study, we employ machine learning models to predict residential occupant behavior in activities relevant to DF. We model occupants’ extensive decisions (i.e., choice of action) and intensive behaviors (i.e., amount of time spent) during peak and off-peak time periods using the publicly available American Time Use Survey, which includes activities data for approximately 200,000 respondents. In our machine learning models, predictions for both extensive and intensive behavior fell within a ±20% error margin at the aggregate level. We identify 13 key sociodemographic predictors of DF-related intensive behavior using LASSO inference and beta coefficient ranking. However, these top predictors differ by activity, suggesting potential scope for differential user targeting for DF events and technologies during program design. This work also contributes to understanding when and who might adopt these DF technologies based on their daily routine activities.

Collaborative Dynamic Task Allocation With Demand Response in Cloud-Assisted Multiedge System for Smart Grids

Collaborative cloud–edge Power Internet of Things technology is required to support the development of smart grids, which have become intelligent, green, and regionally autonomous systems. The diversity of electricity customer behaviors and different computational intensities of energy management applications present challenges for task allocation among computing resources that belong to different agents. In this article, we propose a novel trilevel collaborative optimization model to comprehensively consider the relation among various agents, including users, edge nodes (ENs), a cloud center (CC), and a multiedge league (MEL). We first formulate a Stackelberg game between users and ENs modeled as the lower level and middle level. In addition, with the assistance of the CC, we propose a MEL cooperation scheme to analyze the collaborative task allocation problem among multiple edges, which is modeled as the upper level to maximize the social welfare of the multiedge system (MES) without damaging the interests of the various ENs. The proposed trilevel model is equivalent to a bilevel program, solved by the proposed collaborative dynamic task allocation (CDTA) algorithm. Numerical simulations are presented to verify the proposed scheme and the results show that this scheme is effective for task allocation among users, ENs, the cloud, and the MEL in a cloud-assisted MES.

Community energy projects in the context of generation and transmission expansion planning

We extend multi-level generation and transmission expansion planning optimisation methods to analyse the impact of the community energy sector emergence on power system expansion. Methodologically, we do this by incorporating biform games and linear production games into the classic power transmission and generation expansion planning problem. The model is formulated as a Mathematical Program with Equilibrium Constraints (MPEC). Using a stylised example based on Chilean power market data, we show that community energy projects can be an optimal choice for residential customers. Furthermore, we find that community energy projects can widely impact the power system as a whole. Importantly, unlike some large generation projects, they can have positive effects on electricity customers who do not participate in them. Nevertheless, the implementation of net billing schemes, potential competitors for community energy projects, may be preferred when net billing injection prices are comparatively high, there are potentially high levels of confidence about the final payoffs, and there exist comparatively lower investment costs. Yet, we show that, depending on the specific case analysed with our model, it is likely that under the above circumstances nodal prices increase, social welfare decreases, and capacity limitations need to be addressed. The methodology developed in this work can be a valuable tool to examine and better understand the complexities of community energy projects and their interactions with market incumbents.

Selecting and prioritizing the electricity customers for participating in demand response programs

Demand response (DR) provides an opportunity for customers to play an important role in the operation of the electricity grid by reducing or shifting their electricity usage during peak periods. However, selecting customers to participate in DR programs is challenging. To solve this problem, typical load profiles should be characterized by data mining techniques such as clustering algorithms. Traditional clustering algorithms manually determine the centre of clusters and that the selected centre of clusters may fall into a local optimum. Here, to overcome these issues, a new clustering algorithm based on the Density Peak Clustering algorithm (DPC) and Artificial Bee Colony algorithm (ABC) which is called A-DPC, is implemented to optimally determine the representative load curves. Moreover, by introducing a new priority index, the eligible residential customers are selected for participating in DR programs. Also, to meet the sufficient load reduction in a DR event, the proposed approach suggests a plenty number of residential customers to be called. The result evidence that A-DPC has a stronger global search ability to optimally select the centre of clusters if compared to other clustering algorithms.

Complaint electricity customer clustering method based on electricity big data

In the fierce domestic market competition, the improvement of service quality is an important goal of electricity companies. This paper proposes the k-means algorithm based on the improved wolf pack algorithm, which is suitable for the power company complains, there are many factors that affect user complaints. Firstly, the PCA method is used to reduce the dimension of the complaint user’s behavior influencing factors, so as to improve the accuracy of clustering. Secondly, due to the shortcomings of falling into local optimal solution and low clustering accuracy, this paper proposes an improved wolf pack algorithm, which proposes the interactive strategy of walking behavior, calling behavior and an adaptive siege strategy for behavior, the clustering accuracy and convergence speed are improved.This algorithm overcomes the shortcomings of the original algorithm. Moreover, it improves the efficiency and clustering quality of the algorithm and it can realize the accurate classification of users.

Segmenting household electricity customers with quantitative and qualitative approaches

Understanding private electric utility customers is essential given their central role in the sustainability transition of the electricity system. Socio-economic attributes, environmental attitude, and electricity consumption are not enough to take the new technological, economic and regulatory bases of the residential electricity markets in many industrialized countries into account. Further attributes can help to obtain a more holistic understanding of the private electricity customer. We conduct three studies and find a good correspondence of the customer segments resulting from a survey of the literature, an expert workshop, and a survey of Swiss electricity customers. Five key customer segments are distinguished: 1) affluent and quality-oriented, 2) ecologically aware, 3) technophile, 4) regionally rooted, and 5) stable and uninterested. Due to their unique energy preferences, these customer segments represent critical boundary conditions for technology adoption driven sustainability transitions. Assessment of the strengths and weaknesses of the segmentation methods suggest that with sufficient resources a combination can produce reliable and valid segments.

An Auxiliary Decision-Making System for Electric Power Intelligent Customer Service Based on Hadoop

Aiming at the problems of low security, high occupancy rate, and long response time in the current power intelligent customer service assistant decision-making system, a power intelligent customer service assistant decision-making system based on the Hadoop big data framework is designed. By analyzing the Hadoop big data framework, according to the characteristics and core elements of the HDFS distributed file system, the MapReduce programming model, and the data mining algorithm, the basic process of power intelligent customer service assistance decision-making is established. We analyze the overall and functional requirements of the system, design the overall architecture and application architecture of the system, design the E-R diagram and table structure of the database according to the database design principle, and realize the design of power intelligent customer service auxiliary decision-making system based on Hadoop big data framework. The test results show that the proposed method has high system security and low system occupancy and can effectively shorten the system response time. The systems run more flawlessly as compared to the existing methods and give impressing results with lesser CPU utilization. The response time was recorded to be about 12.2 seconds for 1000 power intelligent customer servers, which is much lower than that of the competitors.

An Advanced Satisfaction-Based Home Energy Management System Using Deep Reinforcement Learning

Home energy management (HEM) systems optimize electricity demand of appliances according to the price-based demand response (DR) programs. Undoubtedly, customer satisfaction is of such importance that if not taken into consideration, it prevents customers from participating in the DR. HEM systems suffer from high nonlinearity due to the variety of smart appliances and different criteria for customer satisfaction. In this paper, an advanced satisfaction-based HEM system using deep reinforcement learning is proposed to hourly schedule the controllable and time-shiftable appliances, including electric vehicle, air conditioner, and lighting system as controllable loads and washing machine, and dishwasher as time-shiftable loads. The proposed framework deploys a Deep Q-Network (DQN) method. Regarding customer dissatisfaction, this paper takes into consideration nonlinear precise functions. The Kano model for EV departure SoC, charging duration and lighting system satisfaction, desired temperature span for air conditioner, and the desirable operation period, waiting time, and consecutive mode of dishwasher and washing machine are taken into account. The proposed HEM system is applied to a smart home, and the results are compared with those of the Q-Learning algorithm. Numerical results prove the effectiveness of the proposed HEM system in reducing electricity cost and customer dissatisfaction, as well as the superiority of DQN over Q-Learning as well.

Electricity Theft Detection using Machine Learning

This research work dealt with the indiscriminate theft of electric power, reported as a non-technical loss, affecting electric distribution companies and customers, triggering serious consequences including fires and blackouts. The research focused on recommending the best prediction model using Machine Learning in electrical energy theft. The source of the information on the electricity consumption of 42372 consumers was a dataset published in the State Grid Corporation of China. The method used was data imputation, data balancing (oversampling and under sampling), and feature extraction to improve energy theft detection. Five Machine Learning models were tested. As a result, the accuracy indicator of the SVM model was 81%, K-Nearest Neighbors 79%, Random Forest 80%, Logistic Regression 69%, and Naive Bayes 68%. It is concluded that the best performance, with an accuracy of 81%, is obtained by using the SVM model.

Optimal location and sizing of multiple distributed generators in radial distribution network using metaheuristic optimization algorithms

The satisfaction of electricity customers and environmental constraints imposed have made the trend towards renewable energies more essential given its advantages such as reducing power losses and enhancing voltage profiles. This study addresses the optimal sizing and setting of Photovoltaic Distributed Generator (PVDG) connected to Radial Distribution Network (RDN) using various novel optimization algorithms. These algorithms are implemented to minimize the Multi-Objective Function (MOF), which devoted to optimize the Total Active Power Loss (TAPL), the Total Voltage Deviation (TVD), and the overcurrent protection relays (OCRs)?s Total Operation Time (TOT). The effectiveness of the proposed algorithms is validated on the test system standard IEEE 33-bus RDN. In this paper is presented a recent meta-heuristic optimization algorithm of the Slime Mould Algorithm (SMA), where the results reveal its effectiveness and robustness among all the applied optimization algorithms, in identifying the optimal allocation (locate and size) of the PVDG units into RDN for mitigating the power losses, enhance the RDN system's voltage profiles and improve the overcurrent protection system. Accordingly, the SMA approach can be a very favorable algorithm to cope with the optimal PVDG allocation problem.