Long-term Electricity Market Research Articles

The impact of electricity demand reduction policies on the EU-ETS: Modelling electricity and carbon prices and the effect on industrial competitiveness

The European electricity market is linked to a carbon market with a fixed cap that limits greenhouse gas emissions. At the same time, a number of energy efficiency policy instruments in the EU aim at reducing the electricity consumption. This article explores the interactions between the EU's carbon market on the one hand and instruments specifically targeted towards energy end-use efficiency on the other hand. Our theoretical analysis shows how electricity demand reduction triggered by energy efficiency policy instruments affects the emission trading scheme. Without adjustments of the fixed cap, decreasing electricity demand (relative to business-as-usual) reduces the carbon price without reducing total emissions. With lower carbon prices, costly low emission processes will be substituted by cheaper high emitting processes. Possible electricity and carbon price effects of electricity demand reduction scenarios under various carbon caps are quantified with a long-term electricity market simulation model. The results show that electricity efficiency policies allow for a significant reduction of the carbon cap. Compared to the 2005 emission level, 30% emission reductions can be achieved by 2020 within the emission trading scheme with similar or even lower costs for the industrial sector than were expected when the cap was initially set for a 21% emission reduction.

Assessing demand response and smart metering impacts on long-term electricity market prices and system reliability

This paper proposes a long-term electricity market analysis framework to assess the impacts of the demand response and smart metering infrastructure implementation on market price fluctuations and system reliability. Based on the probabilistic production cost simulation method that has been widely used in conventional power system planning, the suggested framework considers the uncertainties of the demand and the generator availability in a probabilistic manner. Furthermore, the framework considers the strategic interactions between generators (or, equivalently generation companies) and incorporates price responsive demand enabled with smart metering. To demonstrate how market equilibrium price changes and the system reliability enhances as the demand response with smart metering increases, the framework is applied to an experimental system based on the IEEE 1996 Reliability Test System (RTS) data, followed by a case study of 2010 Korean electricity markets.

Simulation of the European Electricity Market and CCS Development with the HECTOR Model

In this paper we introduce HECTOR, a new and advanced long-term electricity market model that simulates market behavior bottom-up through opportunistic, variable cost-based bidding of individual power plants into auction-based national markets with international interconnection capacities. Unlike most other approaches, we implement the objective function on an hourly level. This allows for a reduction of the solution space, and enables a higher modeling resolution, including opportunistic bidding behavior of power plants based on expected supply scarcity, and ex-post investment decisions based on NPV considerations. The model simulates the electricity markets of 19 European countries, with over 400 groups of power plants, and is able to closely approximate historic electricity prices. The average base load price computed by the model for 2006-2008 and across the largest regions in Europe is 54.5 €/MWh, compared to 54.8 €/MWh in reality, using 2005 as training period. In a projection until 2040, we find that conventional fossil fuel-fired power plants are replaced both by renewable energy technologies and large quantities of CCS, the latter of which almost fully utilize available CO2 storage capacities in some of the regions studied.

Multi-Regional Long-Term Electricity Supply Scenarios with Fusion

This paper examines the global potential for deployment of fusion power through elaboration of multi-regional long-term electricity market scenarios for the time horizon 2100. The probabilistic simulation dynamic programming model PLANELEC-Pro was applied in order to determine the expansion plans of the power generation systems in different world regions that adequately meet the projected electricity demand at minimum cost given the quality-of-service and CO2 emissions constraints. It was found that the deployment of total 330-950 GWe of fusion power world-wide could allow for reducing 1.8-4.3 % of global CO2 emissions from electricity generation, while entailing a slight increase of levelized system electricity cost (by approx. 0.1-0.4 €cents/kWh). By the end of century, the estimated share of fusion in regional electricity mixes varies from 1.5 to 23% depending on the region. It is concluded that economic analysis of fusion technology should be complemented with the evaluation of the whole fusion RTD program in terms of social rate of return taking into account its external “spillover” benefits.

Forecasting electricity market pricing using artificial neural networks

Electricity price forecasting is extremely important for all market players, in particular for generating companies: in the short term, they must set up bids for the spot market; in the medium term, they have to define contract policies; and in the long term, they must define their expansion plans. For forecasting long-term electricity market pricing, in order to avoid excessive round-off and prediction errors, this paper proposes a new artificial neural network (ANN) with single output node structure by using direct forecasting approach. The potentials of ANNs are investigated by employing a rolling cross validation scheme. Out of sample performance evaluated with three criteria across five forecasting horizons shows that the proposed ANNs are a more robust multi-step ahead forecasting method than autoregressive error models. Moreover, ANN predictions are quite accurate even when the length of the forecast horizon is relatively short or long.