Application of ANN and DSM techniques for peak load management a case study

Babu, P. Ravi; Divya, V.; Venkatesh, Kareenhalli V.; Kodad, S. F.; Ram, B. V. Sankar

doi:10.1109/sibircon.2008.4602568

Cited by 7 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although there is no universal definition of the SG, it can simply be an 'intelligent' grid. In contrast to traditional grids that can only transmit and distribute power, SGs can store, communicate, and make decisions [6]- [9]. Another advantage of SGs is that there is no need to build new infrastructure, rather improvements to existing setups to make them more autonomous and improve their power delivery is all that is required.…”

Section: Gcp(i T)mentioning

confidence: 99%

“…Over the years, some computational methods have been proposed to optimize scheduling of DER problems in SGs. In this respect, an optimization method was developed using an artificial neural network (ANN) and demand side management (DSM) for industrial peak load to optimize available energy resources, and an improvement in energy system efficiency was achieved with reference to the load factor [6]. The genetic algorithm (GA) was applied to optimally design an SG using a generalized optimization formulation of distributed generators [29].…”

Section: Gcp(i T)mentioning

confidence: 99%

“…The objective function of this problem, written to be minimized, is given by OF = OC − In (6) If the objective function is negative it means that the system is profitable because the income is greater than the operation cost. Alternatively, if the operation cost is greater than the income it means that there are no profits.…”

Section: Objective Functionmentioning

confidence: 99%

See 2 more Smart Citations

Solving the Problem of Large-Scale Optimal Scheduling of Distributed Energy Resources in Smart Grids Using an Improved Variable Neighborhood Search

Ramli

Bouchekara

2020

IEEE Access

View full text Add to dashboard Cite

Since the last decade, power systems have been evolving dynamically due to smart grid technologies. In this context, energy management and optimal scheduling of different resources are very important. The main objective of this paper is to study the optimal scheduling of distributed energy resources (OSDER) problem. This problem is a challenging, complex and very large-scale mixed-integer non-linear programming (MINLP) problem. Its complexity escalates with incorporation of uncertain and intermittent renewable sources, electric vehicles, variable loads and markets which makes it hard to be solved using traditional optimization algorithms and solvers. However, it can be handled efficiently and without approximation or modification of the original formulation using modern optimization algorithms such as metaheuristics. In this paper, an improved version of the variable neighborhood search (IVNS) algorithm is proposed to solve the OSDER problem. The proposed algorithm was tested on two large-scale centralized day-ahead energy resource scenarios. In the first scenario, the 12.66 kV, 33-bus test system with a total of 49,920 design variables is used whilst in the second scenario, the 30 kV, 180-bus test system is used with a total of 154,800 design variables. The optimization results using the proposed algorithm were compared with five existing optimization algorithms, i.e., chaotic biogeography-based optimization (CBBO), cross-entropy method and evolutionary PSO (CEEPSO), chaotic differential evolution with PSO (Chaotic-DEEPSO), Levy differential evolution with PSO (Levy-DEEPSO), and the variable neighborhood search (VNS). For the first test system, the IVNS has achieved a score of-5598.89 while for the second test system it has achieved a score of-3180.15. A comparative study of the results has shown that the proposed IVNS algorithm performs better than the remaining algorithms for both cases.

show abstract

Section: Gcp(i T)mentioning

confidence: 99%

Section: Gcp(i T)mentioning

confidence: 99%

See 1 more Smart Citation

Solving the Problem of Large-Scale Optimal Scheduling of Distributed Energy Resources in Smart Grids Using an Improved Variable Neighborhood Search

Ramli

Bouchekara

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…This work led to a reduction of peak demand of 5.7%. Similar work by [10] applied Artificial Neural Networks (ANN) to manage peak load in DSM was reviewed from which the fuzzy logic controller was developed using Matlab software for this paper.…”

Section: 0mentioning

confidence: 99%

A Fuzzy Controller based Demand-Side Management System Design for Optimization of Induction Furnaces

Nyanga¹,

Mhlanga²

2011

Power and Energy Systems

View full text Add to dashboard Cite

The paper is a design of an automated Demand-side management system that will optimize electricity usage in a manufacturing plant using induction furnaces, through a multi-furnace controller. The multi-furnace controller controls two furnaces which alternate in between being a melting and a holding furnace. The control system selectively delivers preselected percentages of available power to furnaces. The power supply delivers power to both furnaces. A capacitor station in parallel connection to the power supply and the furnaces is tuned to form a tank circuit therewith and acts as the power factor correction device. Switches control the selected power delivered to the furnaces respectively and control the delivery of a first portion of the power for holding molten product in the hold furnace as the master control. Simulation model was design using fuzzy logic controller. The results show that using the multi-furnace controller results in a 30% decrease in the operating costs of the furnaces as demonstrated by the model plots.

show abstract