NDLI: LMP based bid formation for virtual power players operating in smart grids

Content Provider	IEEE Xplore Digital Library
Author	Vale, Z.A. Morais, H. Faria, P. Soares, J. Sousa, T.
Copyright Year	2011
Description	Author affiliation: GECAD - Knowledge Engineering and Decision-Support Research Group of the Electrical Engineering Institute of Porto - Polytechnic Institute of Porto (ISEP/IPP), Rua Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal (Vale, Z.A.; Morais, H.; Faria, P.; Soares, J.; Sousa, T.)
Abstract	Power system organization has gone through huge changes in the recent years. Significant increase in distributed generation (DG) and operation in the scope of liberalized markets are two relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased importance, and is being seen as a paradigm able to support power system requirements for the future. This paper proposes a computational architecture to support day-ahead Virtual Power Player (VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation module. Due to the involved problems characteristics, the implementation of this architecture requires the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource scheduling. The paper presents a case study that considers a 33 bus distribution network that includes 67 distributed generators, 32 loads and 9 storage units.
Starting Page	1
Ending Page	8
File Size	1569736
Page Count	8
File Format	PDF
ISBN	9781457710001
ISSN	19449925
e-ISBN	9781457710025
e-ISBN	9781457710018
DOI	10.1109/PES.2011.6039853
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wind forecasting Artificial neural networks Smart grids Electricity supply industry Energy resources Contracts Particle Swarm Optimization Artificial Intelligence Artificial Neural Networks Energy Resources Management Intelligent Power Systems Locational Marginal Prices (LMP)
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Application of Neural Networks on Next-Day Electricity Prices Forecasting

ANN-based LMP forecasting in a distribution network with large penetration of DG

VPP's multi-level negotiation in smart grids and competitive electricity markets

Multilevel Negotiation in Smart Grids for VPP Management of Distributed Resources

Investigation of LMP forecasting for congested power systems

MASGriP — A Multi-Agent Smart Grid Simulation Platform

Locational marginal price forecasting in deregulated electric markets using a recurrent neural network

Sensitivity Analysis of Similar Days Parameters for Predicting Short-Term Electricity Price

Neural Networks and Wavelet Transform for Short-Term Electricity Prices Forecasting

LMP based bid formation for virtual power players operating in smart grids

Similar Documents

Application of Neural Networks on Next-Day Electricity Prices Forecasting

ANN-based LMP forecasting in a distribution network with large penetration of DG

VPP's multi-level negotiation in smart grids and competitive electricity markets

Multilevel Negotiation in Smart Grids for VPP Management of Distributed Resources

Investigation of LMP forecasting for congested power systems

MASGriP — A Multi-Agent Smart Grid Simulation Platform

Locational marginal price forecasting in deregulated electric markets using a recurrent neural network

Sensitivity Analysis of Similar Days Parameters for Predicting Short-Term Electricity Price

Neural Networks and Wavelet Transform for Short-Term Electricity Prices Forecasting

LMP based bid formation for virtual power players operating in smart grids