Load Management Systems

We propose, design, and construct a smart load management (SLM) system that can be effectively utilized to meet up emergency demand (light and fan) of consumers when power generation is not sufficient with respect to its demand. The connectivity among power distribution authority, SLM device, and user are established by authority identification number, de­ vice identification number, and user identification number using GSM-based mobile network. The SLM device can be configured and reconfigured load simply by SMS without changing hardware or software. Using the device, the power distribution authority will be able to provide power for emergency loads of all con­ sumers and also to monitor maximum allowable permitted load. Instead of complete blackout, SLM device facilitates switching over from permitted load to emergency and vice versa depending on the availability of the power supply. The SLM system can be effectively applied in a country where demand of electricity is more than its generation.

The International Journal of Wireless & Mobile Networks (IJWMN) is a bi monthly open access peer-reviewed journal that publishes
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With the rapid development of science and technology, the problem of energy load monitoring and decomposition of electrical equipment has been receiving widespread attention from academia and industry. For the purpose of improving the performance of non-intrusive load decomposition, a non-intrusive load decomposition method based on a hybrid deep learning model is proposed. In this method, first of all, the data set is normalized and preprocessed. Secondly, a hybrid deep learning model integrating convolutional neural network (CNN) with long short-term memory network (LSTM) is constructed to fully excavate the spatial and temporal characteristics of load data. Finally, different evaluation indicators are used to analyze the mixture. The model is fully evaluated, and contrasted with the traditional single deep learning model. Experimental results on the open dataset UK-DALE show that the proposed algorithm improves the performance of the whole network system. In this paper, the proposed decomposition method is compared with the existing traditional deep learning load decomposition method. At the same time, compared with the obtained methods: spectral decomposition, EMS, LSTM-RNN, and other algorithms, the accuracy of load decomposition is significantly improved, and the test accuracy reaches 98%.

ABSTRACT Thermostatically controlled loads, which represent the major portion of consumptions, have considerable shifting potential for Demand Response (DR) applications. In this study an event-driven intelligent control system for rescheduling thermostatic loads is worked on. In addition to the discussion of areas of use for the proposed system, a number of terms that can be used in decision making processes are defined. The performance of the proposed system for working with refrigerators according to the power output of a local photovoltaic system is simulated by the use of field data. The results are discussed from both grid-side and customer-side perspectives.

Optimal load shedding is a very critical issue in power systems. It plays a vital role, especially in third world countries. A sudden increase in load can affect the important parameters of the power system like voltage, frequency and phase angle. This paper presents a case study of Pakistan's power system, where the generated power, the load demand, frequency deviation and load shedding during a 24-hour period have been provided. An artificial neural network ensemble is aimed for optimal load shedding. The objective of this paper is to maintain power system frequency stability by shedding an accurate amount of load. Due to its fast convergence and improved generalization ability, the proposed algorithm helps to deal with load shedding in an efficient manner.

Electricity crisis in Pakistan is mounting day by day. From 2007 onwards, Businesses and consumers alike wonder each year if there will be enough electricity to go around. It continues throughout the year but the period of concern, referred to as times of “peak electric load”, is a real threat as the demand for electricity exceeds the supply. Results are blackouts, crippling businesses and household activities within a sector, city, state, and unquestionably in entire region. Load management is the process of balancing the supply of electricity on the network with the electrical load by adjusting or controlling the load rather than the power station output. This can be achieved by direct intervention of the utility in real time. This paper discusses a management plan for distribution of electricity. The idea of the research is to create such a balance in the load distribution to avoid total shutdown in any region. Power is distributed in such a way that all users can at least enjoy basic
household devices in all times of the day without the use of personal energy producing (generators) and energy storing
(Uninterrupted Power Supply) devices. In this paper,systematic empirical investigation of data was performed. Moreover the
disadvantages of alternative energy devices are explored and alternates are identified

Thermostatically controlled loads, which represent the major portion of consumptions, have considerable shifting potential for Demand Response (DR) applications. In this study an event-driven intelligent control system for rescheduling thermostatic loads is worked on. In addition to the discussion of areas of use for the proposed system, a number of terms that can be used in decision making processes are defined. The performance of the proposed system for working with refrigerators according to the power output of a local photovoltaic system is simulated by the use of field data. The results are discussed from both grid-side and customer-side perspectives.