Cost benefit analysis of smart grid: A case study from India

Padmini, V.; Omran, Shaymaa; Chatterjee, Kaustav; Khaparde, S.A.

doi:10.1109/naps.2017.8107212

Cited by 6 publications

(8 citation statements)

References 5 publications

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“…European Commission considered smart grids as a key component towards a low-carbon energy strategy [11]. Smart Grid is an umbrella term to describe a number of technologies, such as advanced metering infrastructure (AMI) or the smart meters; the two-way communication; and the data analytics tools [12], this part focuses on the electricity AMI. The important aspect of the AMI is the provision of two-way digital communications between the utility and the household, which helps in the better participation in the demand response program; smart charging of plug-in electric vehicles (PEV); integration of distributed generation resources as mentioned above; as well as in operation and management of meter remotely [5,12].…”

Section: Electricity Smart Metermentioning

confidence: 99%

“…Many studies illustrate that the predominant cost driver is the meter, including installation costs followed by IT and communications technology then other auxiliary costs, which differ between case studies. It is important to mention that Faruqui et al (2011) assumes that the prices will decline significantly as innovations occur, economies of scale take hold, and manufacturing costs decline [5,12,14,16].…”

Section: Costmentioning

confidence: 99%

“…some countries suffer from energy theft, others suffer from the high levels of technical and commercial losses. The value of benefits of smart electricity meter differs from study to another according to the energy price; energy resources; whether there was a previous investment in automatic meter reading (AMR); the weather; the population density; the local labor costs; and, the operation and management of the meter [5,12,14,16].…”

Section: Quantitative Benefitsmentioning

confidence: 99%

“…The last group is the avoidance in investing in standard meters; and old IT cost related to AMR that will be replaced by AMI. Actually, this last group is considered as a negative cost [5,12,14,16].…”

Section: Quantitative Benefitsmentioning

confidence: 99%

See 3 more Smart Citations

Is Smart Housing a Good Deal? An Answer Based on Monte Carlo Net Present Value Analysis

Elariane

Dubé

2019

Sustainability

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The smart cities are considered to be an engine of economic and social growth. Most countries started to convert their existing cities into smart cities or construct new smart cities in order to improve the quality of life of their inhabitants. However, the problem that facing those countries while applying the concept of smart cities is the costs, especially for the residential sector. Despite the high initial and even operation costs for adopting different technologies in smart housing; the benefits could exceed those costs within the lifespan of the project. This article is shedding the light on the economics of smart housing. This study aims to evaluate the net present value (NPV) of a smart economic housing model to check the viability and feasibility of such projects. The calculation of the NPV based on Monte Carlo simulation provides an interesting methodological framework to evaluate the robustness of the results as well as providing a simple way to test for statistical significance of the results. This analysis helps to evaluate the potential profitability of smart housing solutions. The research ends up by proving the feasibility of this type of project.

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Section: Electricity Smart Metermentioning

confidence: 99%

Section: Costmentioning

confidence: 99%

Section: Quantitative Benefitsmentioning

confidence: 99%

Section: Quantitative Benefitsmentioning

confidence: 99%

See 2 more Smart Citations

Is Smart Housing a Good Deal? An Answer Based on Monte Carlo Net Present Value Analysis

Elariane

Dubé

2019

Sustainability

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show abstract

“…For the SG development at transmission level, PMU deployment plays a vital role for achieving SG goals such as increased system security, efficiency and reliability. PMUs measure synchronously various components such as phase voltages and currents, positive sequence voltages and currents, local frequency and the rate of change of frequency and circuit breaker status, etc [27][28].…”

Section: Phasor Measurement Unit (Pmu)mentioning

confidence: 99%

Control and communication of renewable energy based smart grid: an overview

Reddy

2018

IJET

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Smart Grid (SG) involves complex controls and communications. These controls include both the local as well as global controls. SG in-volves a two-way communication system at each stage and that is one of the advantages over the conventional utility grid. However, due to this two-way communications, the issues such as complexity, data gathering and management, etc, are introduced into the SG system. Therefore, this new grid requires a more complex and control operation. The objective of this paper is to evaluate various control systems, communication technologies, and real time measurement tools that are involved in the SG. This paper also performs the cost benefit analysis (CBA) of the control and communication schemes involved in the SG. The paper evaluates the latest tools for control and communications that are available, and it also evaluates the issues and challenges relative to it.

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Locational Marginal Price Forecasting Using Deep Learning Network Optimized by Mapping-Based Genetic Algorithm

2020

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The convolutional neural network (CNN) is commonly used in visual recognitions and classifications. However, CNN can also be applied as a forecaster that can extract features from spatiotemporal data. This paper proposes a 24h ahead electricity price forecasting method, which integrates CNN with an evolutionary algorithm and utilizes spatiotemporal data. The optimal structure of the CNN network for the locational marginal price (LMP) forecasting was obtained using a genetic algorithm (GA). A gene mapping scheme was initially encoded to represent the search space and the process of selection, mutation, and crossover eliminated structures that did not satisfy the validation fitness function and then competitive individuals were generated. The evolution process uses the root mean square error (RMSE) as the validation fitness function, which is optimzed by training the created CNN network. The proposed gene mapping scheme can be used to design an optimal CNN structure once the mapping between gene binary bits and parameters/hyperparameters of CNN is given. Day-ahead LMP and demand datasets from Pennsylvania-New Jersey-Maryland (PJM) power market were used to demonstrate the evolutionary capability of the proposed method and the finding of optimal CNN structures. Each studied dataset was grouped into 4 subsets corresponding to various seasonal characteristics (different types of situations in real life). Experimental results revealed that the proposed GA-CNN always yielded a higher forecasting accuracy and lower error rates than other forecasting methods. INDEX TERMS Convolutional neural network, deep learning, electricity price forecasting, genetic algorithm, locational marginal price.

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Cost benefit analysis of smart grid: A case study from India

Cited by 6 publications

References 5 publications

Is Smart Housing a Good Deal? An Answer Based on Monte Carlo Net Present Value Analysis

Is Smart Housing a Good Deal? An Answer Based on Monte Carlo Net Present Value Analysis

Control and communication of renewable energy based smart grid: an overview

Locational Marginal Price Forecasting Using Deep Learning Network Optimized by Mapping-Based Genetic Algorithm

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