NDLI: Portfolio Optimization for Electricity Market Participation with Particle Swarm

Content Provider	IEEE Xplore Digital Library
Author	Faia, Ricardo Pinto, Tiago Vale, Zita Pires, E.J. Solteiro
Copyright Year	2015
Abstract	The liberalization of energy markets has imposed several modifications in the electricity market environment. The paradigm of monopoly market ceased to exist, and new models have been put into practice. The new models have increased the incentive on competitiveness, making market players struggle to achieve the best outcomes out of market participation. Producers aim at reaching the maximum profit on the sale of energy, while consumers try to minimize their spending on electrical energy. The proposed methodology considers the optimization of players' participation in multiple market opportunities. Reference prices that are expected in each market type at each moment are achieved through the application of neural networks. Using the forecasted prices, the proposed portfolio optimization method allocates the sale and purchase of electrical energy to different markets throughout the time, with the aim at achieving the most advantageous participation profile. A particle swarm approach is used to reduce the execution time while guaranteeing the minimum degradation of the results. Results of the swarm methodology are compared to those of a deterministic approach, using real data from the Iberian electricity market -- MIBEL.
Starting Page	62
Ending Page	67
File Size	317921
Page Count	6
File Format	PDF
ISBN	9781467375818
ISSN	23783915
e-ISBN	9781467375825
DOI	10.1109/DEXA.2015.31
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-01
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization Portfolios Electricity supply industry Adaptation models Particle swarm optimization Artificial neural networks Context Portfolio Optimization Electricity Markets Multi-Agent Simulation Particle Swarm Optimization
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

Electricity Markets Portfolio Optimization Using a Particle Swarm Approach

Hybrid PSO-ANFIS approach for short-term electricity prices prediction

Realistic Multi-agent Simulation of Competitive Electricity Markets

Data mining approach to support the generation of Realistic Scenarios for multi-agent simulation of electricity markets

Optimal Generation Portfolio Allocation in Competitive Electricity Market

A reinforcement learning algorithm for Agent-based Computational Economics (ACE) model of electricity markets

Improved particle swarm algorithm for portfolio optimization problem

Multi-agent Simulation of Continental, Regional, and Micro Electricity Markets

Metalearner Based on Dynamic Neural Network for Strategic Bidding in Electricity Markets

Portfolio Optimization for Electricity Market Participation with Particle Swarm

Similar Documents

Electricity Markets Portfolio Optimization Using a Particle Swarm Approach

Hybrid PSO-ANFIS approach for short-term electricity prices prediction

Realistic Multi-agent Simulation of Competitive Electricity Markets

Data mining approach to support the generation of Realistic Scenarios for multi-agent simulation of electricity markets

Optimal Generation Portfolio Allocation in Competitive Electricity Market

A reinforcement learning algorithm for Agent-based Computational Economics (ACE) model of electricity markets

Improved particle swarm algorithm for portfolio optimization problem

Multi-agent Simulation of Continental, Regional, and Micro Electricity Markets

Metalearner Based on Dynamic Neural Network for Strategic Bidding in Electricity Markets

Portfolio Optimization for Electricity Market Participation with Particle Swarm