Power Consumption Of Consumers Research Articles

Selection of optimal structure of an energy-supply system for objects in an agro-industrial complex based on renewable-energy sources

This study considers the issues in constructing optimal systems for autonomous energy supply to objects in an agro-industrial complex using renewable-energy sources (RESs). The regularities of distribution of the zones for optimal use of solar, wind, hydraulic, and integrated power plants are revealed by comparing their operational and economic indicators. To determine the zones of optimal use of various RESs types, a systematic approach is employed, which allows conducting a comprehensive analysis of the object, strictly systematizing of research, and performing synthesis, i.e., finding the optimal system under given conditions. The level of functional and structural perfection of the systems is considered by evaluating their internal characteristics through a functional and structural approach using structural, functional, and functional–structural descriptions of the systems where each system generates the corresponding model types. Synthesis of the optimal structure of a renewable energy supply system (RESs) is carried out based on the operating conditions and consumer power consumption according to an “efficiency–cost” criterion, an integral-quality indicator, a complex efficiency criterion, and private indicators of the quality and cost of the systems. An algorithm and a calculation program are developed that allows determining the optimal structure of autonomous RESs under specific cases and restrictions. The developed recommendations on the choice of an optimal system for autonomous power supply using RESs under Kazakhstan conditions provide practical importance because they can potentially minimize errors in the choice of RESs types during the design stage.

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

Fed‐NILM: A federated learning‐based non‐intrusive load monitoring method for privacy‐protection

Non-intrusive load monitoring (NILM) is essential for understanding consumer power consumption patterns and may have wide applications such as in carbon emission reduction and energy conservation. Determining NILM models requires massive load data containing different types of appliances. However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners when determining the NILM model. To address these problems, a novel NILM method based on federated learning (FL) called Fed-NILM is proposed. In Fed-NILM, instead of local load data, local model parameters are shared among multiple data owners. The global NILM model is obtained by averaging the parameters with the appropriate weights. Experiments based on two measured load datasets are performed to explore the generalization capability of Fed-NILM. In addition, a comparison of Fed-NILM with locally trained NILM models and the centrally trained NILM model is conducted. Experimental results show that the Fed-NILM exhibits superior performance in terms of scalability and convergence. Fed-NILM out performs locally trained NILM models operated by local data owners and approaches the centrally trained NILM model, which is trained on the entire load dataset without privacy protection. The proposed Fed-NILM significantly improves the co-modelling capabilities of local data owners while protecting the privacy of power consumers.

Mitigating consumer privacy breach in smart grid using obfuscation-based generative adversarial network.

Smart meters allow real-time monitoring and collection of power consumption data of a consumer's premise. With the worldwide integration of smart meters, there has been a substantial rise in concerns regarding threats to consumer privacy. The exposed fine-grained power consumption data results in behaviour leakage by revealing the end-user's home appliance usage information. Previously, researchers have proposed approaches to alter data using perturbation, aggregation or hide identifiers using anonymization. Unfortunately, these techniques suffer from various limitations. In this paper, we propose a privacy preserving architecture for fine-grained power data in a smart grid. The proposed architecture uses generative adversarial network (GAN) and an obfuscator to generate a synthetic timeseries. The proposed architecture enables to replace the existing appliance signature with appliances that are not active during that period while ensuring minimum energy difference between the ground truth and the synthetic timeseries. We use real-world dataset containing power consumption readings for our experiment and use non-intrusive load monitoring (NILM) algorithms to show that our approach is more effective in preserving the privacy level of a consumer's power consumption data.

Internet-based advanced metering and control infrastructure of smart grid

Existing advanced metering infrastructures (AMI) use three levels of bi-directional communication between the customer and utility. Given the increase of the available amount of data provided by smart meters in smart grid, existing AMI network may not have the required bandwidth to perform the monitoring and control in real time. The increase of the AMI network bandwidth is very expensive. On the other hand, the internet employs a set of universal communication protocols and provides a wide communication bandwidth. A new internet-based advanced metering and control infrastructure (IBAMCI) is provided in this paper. It uses only two level of communication: the local area network (LAN) and the internet. Both LAN and internet platforms already exist in most consumer’s premise. The internet infrastructure when utilized eliminates the need for expensive bandwidth upgrades to the utility wide area network (WAN). The proposed IBAMCI is equipped with an integrated internet/cloud-based data management and control server, a user interface and display module. The consumer power consumption can be displayed from anywhere using the internet. Because the proposed IBAMCI offloads the utility WAN, real-time smart grid load monitoring and control becomes possible without delays. Simulations were carried out to illustrate the benefit of the proposed IBAMCI in monitoring large number of customer loads in real time. For existing AMIs, whenever the communication bandwidth becomes insufficient, the only option is to increase the utility network bandwidth, which can be prohibitively expensive.

Загальні засади забезпечення електропостачання в локальних децентралізованих енергосистемах

There has been carried out the analysis of current trends in the development of distributed generation systems, a general approach to ensuring reliable and stable operation of decentralized energy systems based on renewable energy sources, taking into account the capabilities of energy storage systems and climate forecasting generation. Based on the analysis of the peculiarities of the technological process of electricity generation by alternative power sources inherent in the Ukrainian realities, their classification is carried out on the basis of controllability and predictability. On the basis of the specified classification the generalized approach to construction of the combined local power system which would meet requirements of reliability of power consumption of consumers is offered. The conditional division of the elements of the local energy system into information-logical components and technological subsystems is proposed. Information and technological connections between the elements of the local energy system are determined. Preliminary analysis of shunting capabilities of available capacities of alternative power sources is carried out. Based on the analysis, it is proposed to use the method of combining schedules of generation and consumption of electricity for further formation to form a graph of the mismatch between the balance of consumed and produced electricity. It is proposed to use the graph of mismatch of consumed and generated capacity as a mathematical function of control of the process of generation and consumption of electricity in local electrical networks. It is pointed out the need to apply regulatory means not only for generation facilities, but also for consumers and energy storage systems in order to eliminate inconsistencies in the balances of produced and consumed electricity.

TAI: A Threshold-Based Anonymous Identification Scheme for Demand-Response in Smart Grids

Smart grids offer benefits compared to the current power grid by using technologies, such as advanced metering infrastructure and demand-response schemes. However, the introduction of these technologies also leads to challenges in the areas of privacy and identification of disobedient users. Current solutions to these challenges heavily rely on a trusted third party, which may lead to scenarios where the privacy of obedient consumers cannot be preserved. To tackle these concerns, anonymity provides a promising approach to obviating privacy preservation in smart grids. In this paper, a threshold-based anonymous identification scheme (TAI) for overload audit and privacy preservation in smart grids is proposed, where the use of a trusted third party is no longer required. Privacy preservation depends on the power consumption of consumers in the presence of a demand-response request from the power provider that defines an acceptable consumption threshold at periods of power shortage. Consumers must follow the instruction and curtail their consumption to meet the threshold. By doing so, the consumers who adhere to the power providers’ instructions keep their anonymity, whilst the disobedient are be identified. According to our security and performance analysis, TAI significantly improves efficiency compared to previous anonymous identification schemes, while providing anonymity and identification.

Real-Time Pricing by Data Fusion on Networks

This paper presents a framework of real-time pricing by data fusion of power consumption, i.e., estimating the total power consumption of consumers by sharing the information on local power consumption that each power source has. The problem addressed here is to find power estimators embedded in power sources (or embedded in access points provided in advance) and a power price controller such that the total power consumption tracks a given reference input. As a solution to this problem, we present power estimators with a consensus protocol and a proportional–integral type power price controller. We then show that our solution achieves the reference tracking of the total power consumption in a distributed manner.