Impact of Emission Trading on Optimal Bidding of Price Takers in a Competitive Energy Market

2018 ◽  
pp. 171-180 ◽  
Author(s):  
Somendra P. S. Mathur ◽  
Anoop Arya ◽  
Manisha dubey
Keyword(s):  
Emission Trading ◽  
Energy Market ◽  
Optimal Bidding

Impacts of Emission Trading on Optimal Bidding Strategies of Generation Companies in Day-Ahead Electricity Markets

2014 ◽  
Vol 521 ◽  
pp. 476-479 ◽  
Author(s):  
Guo Zhong Liu
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Emissions Trading ◽  
Emission Trading ◽  
Distribution Functions ◽  
Bidding Strategy ◽  
Chance Constrained Programming ◽  
Optimization Approach ◽  
Generation Company ◽  
Optimal Bidding

The impacts of Emission trading on building the optimal bidding strategy for a generation company participating in a day-ahead electricity market is investigated. The CO2 emission price in an emissions trading market is evaluated by using an optimization approach similar to the well-developed probabilistic production simulation method. Then upon the assumption that the probability distribution functions of rivals bidding are known, a stochastic optimization model for building the risk-constrained optimal bidding strategy for the generation company in the framework of the chance-constrained programming is presented. Finally, a numerical example is served for demonstrating the feasibility of the developed model and method, and the optimal bidding results are compared for the two situations with and without the CO2 emissions trading.

Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production

Energy ◽  
2016 ◽  
Vol 106 ◽  
pp. 194-202 ◽  
Author(s):  
Gabriella Ferruzzi ◽  
Guido Cervone ◽  
Luca Delle Monache ◽  
Giorgio Graditi ◽  
Francesca Jacobone
Keyword(s):  
Renewable Energy ◽  
Energy Production ◽  
Energy Market ◽  
Renewable Energy Production ◽  
Optimal Bidding ◽  
Micro Grid

scholarly journals Does carbon asset add value to clean energy market? Evidence from EU

Green Finance ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 495-507
Author(s):  
Memoona Kanwal ◽  
◽  
Hashim Khan
Keyword(s):  
Emission Trading ◽  
Clean Energy ◽  
Energy Market ◽  
The European Union ◽  
Value Maximization ◽  
Trading Scheme ◽  
Generalized Orthogonal ◽  
Stock Portfolio ◽  
Allocation Strategies ◽  
The Eu

<abstract> <p>This paper examines if clean energy stocks help investors in managing carbon risk. We use the price of the European Union Allowance (EUA) and European clean energy index (ERIX) for the three phases of the EU-Emission Trading Scheme. Analyzing the time-varying correlation and volatility of EUA stock and ERIX through generalized orthogonal GO-GARCH model, the empirical results reveal relative independence of the European renewable energy market from the carbon market providing diversification benefits and value addition by including carbon assets in clean energy stock portfolio. Furthermore, three portfolios with different weight allocation strategies reveal that the carbon asset provides risk and downside risk benefits when mixed with a clean energy stock portfolio. These results are useful for investors who enter the market for value maximization and the regulators striving to make strategies for managing carbon risk.</p> </abstract>

Optimal bidding strategy for an energy hub in energy market

Energy ◽  
2018 ◽  
Vol 148 ◽  
pp. 482-493 ◽  
Author(s):  
Vahid Davatgaran ◽  
Mohsen Saniei ◽  
Seyed Saeidollah Mortazavi
Keyword(s):  
Bidding Strategy ◽  
Energy Market ◽  
Energy Hub ◽  
Optimal Bidding

Optimal Bidding Strategy in the Uncertain Energy Market

2003 ◽  
Vol 36 (20) ◽  
pp. 147-152
Author(s):  
Pathom Attaviriyanupap ◽  
Keita Ogata ◽  
Hiroyuld Kita ◽  
Eiichi Tanalta ◽  
Jun Hasegawa
Keyword(s):  
Bidding Strategy ◽  
Energy Market ◽  
Optimal Bidding

scholarly journals GENCO Optimal Bidding Strategy and Profit based Unit Commitment using Evolutionary Particle Swarm Optimization Illustrating the Effect of GENCO Market Power

2018 ◽  
Vol 8 (4) ◽  
pp. 1997
Author(s):  
Adline Bikeri ◽  
Christopher Muriithi ◽  
Peter Kihato
Keyword(s):  
Particle Swarm Optimization ◽  
Market Power ◽  
Unit Commitment ◽  
Particle Swarm ◽  
Bidding Strategy ◽  
Energy Market ◽  
Market Demand ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Optimal Bidding

<p>In deregulated electricity markets, generation companies (GENCOs) make unit commitment (UC) decisions based on a profit maximization objective in what is termed profit based unit commitment (PBUC). PBUC is done for the GENCOs demand which is a summation of its bilateral demand and allocations from the spot energy market. While the bilateral demand is known, allocations from the spot energy market depend on the GENCOs bidding strategy. A GENCO thus requires an optimal bidding strategy (OBS) which when combined with a PBUC approach would maximize operating profits. In this paper, a solution of the combined OBS-PBUC problem is presented. An evolutionary particle swarm optimization (EPSO) algorithm is implemented for solving the optimization problem. Simulation results carried out for a test power system with GENCOs of differing market strengths show that the optimal bidding strategy depends on the GENCOs market power. Larger GENCOs with significant market power would typically bid higher to raise market clearing prices while smaller GENCOs would typically bid lower to capture a larger portion of the spot market demand. It is also illustrated that the proposed EPSO algorithm has a better performance in terms of solution quality than the classical PSO algorithm.</p>

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