scholarly journals Demand Side Management for Commercial Area Using Teaching Learning Based Optimization

2021 ◽  
Author(s):  
Dhanshri Narayane ◽  
Amarjeet S Pandey ◽  
D B Pardeshi ◽  
Renuka Rasal
Keyword(s):  
Smart Grid ◽  
Management Strategy ◽  
Optimization Problems ◽  
Demand Side Management ◽  
Demand Side ◽  
Load Shifting ◽  
Load Demand ◽  
Commercial Area ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In Smart Grid Demand side management (DSM) plays a crucial role which permits customers to form educated selections concerning their energy consumption. It allows the strength to companies lessen the height load call for and reshape the burden profile. Most of the present demand aspect management ways utilized in ancient energy management system is with specific techniques and algorithms. In addition, the present ways handle solely a restricted range of governable a lot of restricted varieties of loads. This paper covers a requirement aspect management strategy supported load shifting technique for demand aspect management of future sensible grids with an outsized range of devices of many sorts. The day-in advance load shifting technique is proposed and mathematically formulated as a minimization problem. Teaching Learning Based Optimization (TLBO) is an efficient optimization is proposed. Considering Smart Grid with commercial customer, Simulations has been carried out. The respective results emphasis that the considered demand side management strategy attains substantial savings, whereas suppresses the mark of load demand of the smart grid. The outcome is by improve in sustainability of the smart grid, in addition to reduced standard operational value and carbon emission levels. The proposed algorithms can be easily applied to various optimization problems.

Battery Management Based on Predictive Control and Demand-Side Management

2016 ◽  
pp. 149-180
Author(s):  
Deepranjan Dongol ◽  
Elmar Bollin ◽  
Thomas Feldmann
Keyword(s):  
Energy Management ◽  
Predictive Control ◽  
Management Strategy ◽  
Demand Side Management ◽  
Battery Management ◽  
Demand Side ◽  
Energy Management Strategy ◽  
Battery System ◽  
Surplus Energy ◽  
Load Demand

The chapter is intended to introduce the predictive control based energy management strategy for the grid connected renewable systems in order to achieve an effective demand side management strategy. Grid connected Photovoltaic battery system as being popular and extensively used has been discussed in this chapter .Conventionally, battery storage has been used to store surplus energy produced and meet the load demand with this stored energy. However, such systems do not respond to the grid conditions and violate grid constraints of permissible grid voltage and frequency limits. The operation of the battery depends on the forecast of photovoltaic output and the load demand and as such a predictive control based energy management strategy is needed. A simple optimization problem for such scenarios has also been formulated in great detail to provide readers with an idea for solving such problems. The results of simulations are also discussed.

Demand side management of smart grid: Load shifting and incentives

2014 ◽  
Vol 6 (3) ◽  
pp. 033136 ◽  
Author(s):  
Thillainathan Logenthiran ◽  
Dipti Srinivasan ◽  
K. W. M. Vanessa
Keyword(s):  
Smart Grid ◽  
Demand Side Management ◽  
Demand Side ◽  
Load Shifting ◽  
Grid Load

scholarly journals DEMAND SIDE MANAGEMENT OF RENEWABLE ENERGY INTEGRATED SMART GRID USING LOAD SHIFTING TECHNIQUES

2020 ◽  
pp. 3193-3199
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Smart Grid ◽  
Energy Management ◽  
Demand Side Management ◽  
Energy Resources ◽  
Management Approach ◽  
Demand Side ◽  
Load Shifting ◽  
The Impact

The Paris Agreement on Climate Change has led to introduction of new reforms for clean power plan such as decarbonization of power sector, planned decommissioning of thermal power plants and inclusion of renewable energies for power production. But this desired integration of renewable energy resources to power system faces two technical challenges: variability and uncertainty. An effective energy management with help of smart grid engineering can be the key for its beneficial use. Demand Side Management (DSM) is a valuable strategy for energy management in smart grid. It supports numerous smart grid functionalities for instance electricity market control, Load scheduling, managing decentralized distributed energy resources. Identifying energy consumption patterns and to sketch electricity load profiles can be achieved through numerous DSM based programs. Load shifting based DSM can be linked to consumer’s behavior in understanding their pattern of energy consumption. Here, the practiced load shifting based demand side management approach can help in maximizing power efficiency, sustaining power reliability and resiliency of renewable sources. This paper reviews the various energy management strategies developed to minimize the impact of renewable energy intermittency using Load Shifting Demand Side Management (DSM) approach.

scholarly journals Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 370 ◽  
Author(s):  
Mayank Singh ◽  
Rakesh Jha
Keyword(s):  
Renewable Energy ◽  
Peak Load ◽  
Object Oriented ◽  
Demand Side Management ◽  
Demand Side ◽  
Multi Objective ◽  
Service Contracts ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
The Impact

This paper proposes Object-Oriented Usability Indices (OOUI) for multi-objective Demand Side Management (DSM). These indices quantify the achievements of multi-objective DSM in a power network. DSM can be considered as a method adopted by utilities to shed some load during peak load hours. Usually, there are service contracts, and the curtailments or dimming of load are automatically done by service providers based on contract provisions. This paper formulates three indices, namely peak power shaving, renewable energy integration, and an overall usability index. The first two indices indicate the amount of peak load shaving and integration of renewable energy, while the third one combines the impact of both indices and quantifies the overall benefit achieved through DSM. The application of the proposed indices is presented through simulation performed in a grid-tied microgrid environment for a multi-objective DSM formulation. The adopted microgrid structure consists of three units of diesel generators and two renewable energy sources. Simulation has been done using MATLAB software. Teaching-Learning-Based Optimization (TLBO) is adopted as the optimization tool due to its simplicity and independency of algorithm-specific control parameters. Five different cases of renewable energy availability with results validate the efficiency of the proposed approach. The results indicate the usefulness in determining the suitable condition regarding DSM application.

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