Logo EEM11
Session 22: Market Power and Market Strategies
Time: Friday, 27/May/2011: 2:00pm - 3:30pm
Session Chair: Jorge de Sousa
Location: Paris


Impacts of Electric Vehicles’ Charging Strategies in the Electricity Prices

Cristina Inês Camus, Tiago Farias, Jorge Esteves

ISEL, Portugal

Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs), which obtain their fuel from the grid by charging a battery, are set to be introduced into the mass market and expected to contribute to oil consumption reduction. In this research, scenarios for 2020 EVs penetration and charging profiles are studied integrated with different hypotheses of power sources expected in electricity generation for year 2020. Simulations of the impacts in load profiles and spot prices are obtained for the Portuguese case study as well as the emissions balance between the transportation and the electricity generation sectors. Simulations made for year 2020 in a scenario of low hydro production and high costs, estimate that the price could reach the 17 cents/kWh (including wholesale, plus the net access, plus retailer revenue) for a 2 Million EVs charging mainly at peak hours. In an off-peak recharge the price reduces to about 7 cents/kWh. In a high hydro production and low wholesale prices, an off peak recharge could reach 5.6 cents/kWh. Reductions in primary energy consumption, fossil fuels use and CO2 emissions of 4%, 12% and 9% respectively were verified from the transportation and electricity generation sectors together when compared with a Business as usual (BAU) scenario without EVs and the same electricity production mix. In these extreme cases, EV energy prices were between 0.9€ to 2.8€ per 100 km.

Price Formation and Market Power in a Low Carbon Electricity System

Iain Digby Morrow1, Derek Bunn2

1Cambridge Economic Policy Associates, United Kingdom; 2London Business School, United Kingdom

We consider an agent-based model of forward trading in electricity. Electricity suppliers and generators buy and sell power in three stylized markets: the forward market, the intra-day or prompt market and the real-time spot or ancillary services market. Using computational learning, we develop a model whereby agents' strategies are determined by evolved neural networks of arbitrary size and topology. In a high carbon system similar to today's conventional fossil-fuel based supply stacks, simple strategies for both agents emerge. When substantial wind generation is included, however, these strategies are seen to be no longer appropriate. New insights relating to the impact of wind on fossil generator market power have substantial implications for price formation and the investment signals regarding peaking capacity.

Assessment of the Market Power Cost in Liberalised Electricity Markets Using SMPI, PMPI, and NMPI Indicators

Mohammad Reza Hesamzadeh

Royal Institute of Technology, Sweden

Market power analysis is one of the major issues facing regulators of wholesale electricity markets. The exercise of market power both distorts wholesale price signals and reduces the efficiency of the operation of and investment in the wholesale electricity market. This paper deals with a systematic way for quantifying and visualising market power. The paper first proposes three indicators termed the System Market Power Indicator, SMPI, the Producer Market Power Indicator, PMPI, and the Nodal Market Power Indicator, NMPI. The game theory in applied mathematics and the concept of social welfare in microeconomics are used in formulating of these indicators. The SMPI finds the total cost of exercising market power by generating companies. The contribution of a specific generating company in system market power is calculated using the PMPI. The NMPI finds the contribution of each power system node in the total market power cost.

Then after, a colour contour map is used to visualise the exercise of market power and its associated cost. The proposed market power indicators are applied to the modified Garver’s example system to show the promising performance of these indicators.

Comparison and Empirical Validation of Optimizing and Agent-Based Models of the Italian Electricity Market.

Eric Guerci1, Sandro Sapio2

1GREQAM, France; 2Università di Napoli Parthenope

In recent years, several oligopolistic models of the liberalized power exchanges have been proposed, within two paradigms based upon radically different assumptions on rationality, learning and cognition: optimal choice and agentbased computational modeling. This paper is a first attempt to compare the explanatory performances of an agent-based model with a supply function equilibrium model on the same dataset. The models are designed in such a way that differences in performance between them are mainly due to their different behavioral assumptions, and are validated on a unique plantlevel dataset on the Italian power exchange. As suggested by our findings, the agent-based model is better able to capture the intraday profile of power prices, but both models tend to overestimate the degree of competition among generating companies.

Application of a Game Theory Model to Analyze the Competitive Behavior in the Iberian Electricity Market

João Hermínio Lagarto1,2,3, Jorge Alberto de Sousa1,2, Álvaro Martins4

1Lisbon Engineering Superior Institute, Portugal; 2CIEEE, IST; Portugal; 3MIT Portugal Program in Sustainable Energy Systems, Portugal; 4Economics and Management Institute (ISEG) of the Technical University of Lisbon, Portugal

Electricity market prices can be influenced by many drivers, such as fuel costs, CO2 emission prices, hydro and other renewable production and strategic behavior of firms that participate in the market. One of the aims of the liberalization process of the electricity industry was to bring electricity prices more in line with costs. Therefore, the influence that the strategic behavior of firms might have in market prices is a concern.

This paper analyzes the strategic behavior of three medium size firms acting in the Iberian electricity market (IBELM) in the first year after its implementation, that is, from July 2007 to June 2008. This strategic behavior is analyzed by computing an hourly competitive parameter which is obtained from a conjectural variation model. Then the evolution of the monthly average of this conjectural variation parameter is studied.

Results showed that some of the analyzed firms did not reflect in market prices the increase in fuel costs and in CO2 emission prices that occurred in the first six months of 2008.

Bidding in an Electricity Multi-Market Using a Q-Learning Approach

Habib Rajabi Mashhadi1, Javid Khorasani2

1Ferdowsi University of Mashhad; 2Islamic Azad University

Due to interaction between different commodities in electricity markets, the bidding problem in an electricity multimarket is a challenging task from the view point of generation companies. In this paper a Q-learning algorithm, which is usually used in single environment problems, is applied to model the bidding behavior of a GenCo in a multi-environment problem. The proposed method is compared with a model-based approach in order to confirm the method. Afterwards, the proposed Q-learning approach is used in a multi-agent problem to analyze an electricity multimarket. The results show that Q-learning method can model the market agent’s behavior. Therefore the proposed method can be used to design the optimal bidding strategy in a multimarket environment, when an exact model of market prices is not available. The method is capable of finding the optimal strategy in different market conditions.