scholarly journals Demand Side Management Techniques for Peak Reduction

Author(s):  
D. Sai Kumar

Industrial growth is the back bone for the development of any nation. Industries are mainly dependent on electrical energy. But from the various studies, the sources for electrical energy are decreasing gradually, and in turn, the gap is increasing between the supplier and the load. The solution for this scenario is optimal utilization of resources. To overcome this problem , the concept Demand Side Management (DSM) has emerged in Power System Planning and Management. The principle objective of DSM is mutual understanding between the supplier and the consumer for maximizing benefits and minimizing inconvenience. The aim of this research work is selection and application of appropriate DSM techniques to industrial and domestic loads for peak load management and energy conservation, that is to control the maximum demand during the peak hours and saving the energy by using the energy efficient and intelligent appliances like air conditioners and water heaters. DSM includes techniques like the End Use Equipment Control, the Load Priority Technique, he Peak Clipping & Valley filling, the Differential Tariff and Resizing of the equipment. Depending upon the application, all the techniques may be applied sequentially, or only a few of them can be applied. There is a lot of ambiguity in the selection of DSM techniques, because the application of each DSM technique depends on the case study and the problem associated with the respective case study. After comprehensive understanding of a particular case, a thorough investigation and subsequent data analysis pave the way for the selection of appropriate DSM technique/techniques

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1618
Author(s):  
Mohanasundaram Anthony ◽  
Valsalal Prasad ◽  
Raju Kannadasan ◽  
Saad Mekhilef ◽  
Mohammed H. Alsharif ◽  
...  

This work describes an optimum utilization of hybrid photovoltaic (PV)—wind energy for residential buildings on its occurrence with a newly proposed autonomous fuzzy controller (AuFuCo). In this regard, a virtual model of a vertical axis wind turbine (VAWT) and PV system (each rated at 2 kW) are constructed in a MATLAB Simulink environment. An autonomous fuzzy inference system is applied to model primary units of the controller such as load forecasting (LF), grid power selection (GPS) switch, renewable energy management system (REMS), and fuzzy load switch (FLS). The residential load consumption pattern (4 kW of connected load) is allowed to consume energy from the grid and hybrid resources located at the demand side and classified as base, priority, short-term, and schedulable loads. The simulation results identify that the proposed controller manages the demand side management (DSM) techniques for peak load shifting and valley filling effectively with renewable sources. Also, energy costs and savings for the home environment are evaluated using the proposed controller. Further, the energy conservation technique is studied by increasing renewable conversion efficiency (18% to 23% for PV and 35% to 45% for the VAWT model), which reduces the spending of 0.5% in energy cost and a 1.25% reduction in grid demand for 24-time units/day of the simulation study. Additionally, the proposed controller is adapted for computing energy cost (considering the same load pattern) for future demand, and it is exposed that the PV-wind energy cost reduced to 6.9% but 30.6% increase of coal energy cost due to its rise in the Indian energy market by 2030.


2021 ◽  
Author(s):  
Sophie Adams ◽  
Lisa Diamond ◽  
Tara Esterl ◽  
Peter Fröhlich ◽  
Rishabh Ghotge ◽  
...  

Executive Summary of the final report of the Users TCP Social License to Automate Task findings from a 2 year project with 16 researchers in 6 countries, 26 Case studies spanning electric vehicles, home and precinct batteries, air conditioners and other heat pumps.


2014 ◽  
Vol 69 (5) ◽  
Author(s):  
Husna Syadli ◽  
Md Pauzi Abdullah ◽  
Muhammad Yusri Hassan ◽  
Faridah Hussin

When the high electricity demand growth is not matched by growth in generating sufficient capacity, deficit cannot be avoided. In Sumatera, power outages of up to 6 hours per day are part of the power crisis experienced. To date, deficits experienced by Sumatera require better management strategy and operation of electric power systems, taking into account the security system, reliability and customer service. This paper briefly discusses the impact of rolling blackouts on the community's economy and proposed demand-side management strategies as short term measure to overcome the power supply deficit in Sumatera. From the analysis, electricity savings in household equipment can save energy consumption by 98.79 MW at peak load and 97.55 MW for off peak load time. 


Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4443 ◽  
Author(s):  
Yung-Yao Chen ◽  
Yu-Hsiu Lin

Electrical energy management, or demand-side management (DSM), in a smart grid is very important for electrical energy savings. With the high penetration rate of the Internet of Things (IoT) paradigm in modern society, IoT-oriented electrical energy management systems (EMSs) in DSM are capable of skillfully monitoring the energy consumption of electrical appliances. While many of today’s IoT devices used in EMSs take advantage of cloud analytics, IoT manufacturers and application developers are devoting themselves to novel IoT devices developed at the edge of the Internet. In this study, a smart autonomous time and frequency analysis current sensor-based power meter prototype, a novel IoT end device, in an edge analytics-based artificial intelligence (AI) across IoT (AIoT) architecture launched with cloud analytics is developed. The prototype has assembled hardware and software to be developed over fog-cloud analytics for DSM in a smart grid. Advanced AI well trained offline in cloud analytics is autonomously and automatically deployed onsite on the prototype as edge analytics at the edge of the Internet for online load identification in DSM. In this study, auto-labeling, or online load identification, of electrical appliances monitored by the developed prototype in the launched edge analytics-based AIoT architecture is experimentally demonstrated. As the proof-of-concept demonstration of the prototype shows, the methodology in this study is feasible and workable.


2020 ◽  
Vol 862 ◽  
pp. 22-27
Author(s):  
Laxman S. Godse ◽  
M.J. Bhalerao ◽  
Faiz M. Khwaja ◽  
Neelima R. Kulkarni ◽  
Parshuram B. Karandikar

Ultracapacitor is a new electrical energy storage device which has high power density than conventional battery and capacitor. It offers high capacitance in small volume compared to conventional capacitors. While selecting ultracapacitors for various applications, parameters like specific resistance, internal capacitance, pulse current, energy density are required to be considered. Amongst these factors, specific capacitance of ultracapacitor depends mainly on parameters of electrode. The present paper is focused on modeling of ultracapacitor based on variations in some of the electrode parameters. The objective of present research work is to apply a statistical method to obtain an electrode material based model for prismatic type ultracapacitor. To have deep insight about the performance through modeling approach, the number of trials have been taken by doing the variations in the electrode materials of ultracapacitor and the quantity of the electrode material loaded on the current collector. The effect of both these variations is studied over the specific capacitance, which is taken as output parameter of model. Developed model is validated at selected values of input parameters.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 29767-29814 ◽  
Author(s):  
Shafiqur Rehman ◽  
Habib Ur Rahman Habib ◽  
Shaorong Wang ◽  
Mahmut Sami Buker ◽  
Luai M. Alhems ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document