Modelling Wind in the Electricity Sector

We represent hourly, regional an wind data and transmission constraints in an investment planning model calibrated to the UK and test sensitivities of least cost expansions to fuel and technology prices. Thus we can calculate the value of transmission expansions to the system. We represent limited public acceptance of wind and regional network constraints by maximum built rates per region and year. Thus we calculate the marginal value of improved planning and grid connection regimes. It is likely that some constraints will remain. Market designs that do not allow for regional differentiation to reflect transmission and planning constraints can increase overall costs to consumers

Implications of intermittency and transmission constraints for renewables deployment

We represent hourly, regional wind data and transmission constraints in an investment planning model calibrated to the UK and test sensitivities of least cost expansions to fuel and technology prices. Thus we can calculate the value of transmission expansions to the system. We represent limited public acceptance of wind and regional network constraints by maximum built rates per region and year. Thus we calculate the marginal value of improved planning and grid connection regimes. It is likely that some constraints will remain. Market designs that do not allow for regional differentiation to reflect transmission and planning constraints can increase overall costs to consumers.Investment planning model, wind power, constraint land, Network constraints.

Alien Registration- Koski, Cust (Paris, Oxford County)

https://digitalmaine.com/alien_docs/21057/thumbnail.jp

Differentiation and Dynamics of Competitiveness Impacts from the EU ETS

We summarise the main factors that differentiate impacts of the EU ETS on profitability and market share. By examining and sampling a range of sectors, we present some simple metrics and indicators to help judge the nature of potential impacts. We also consider briefly the mitigation response to these impacts by sectors, and how they may evolve over time. The broad conclusion confirms the aggregate findings presented in the existing literature - more participating sectors are likely to profit under the current ETS structure out to 2012 at the cost of a modest loss of market share, but this may not hold for individual companies and regions. The period 2008-12 can assist technology investments and diversification, providing the continuation and basic principles of the EU ETS post-2012 is quickly defined and incentives are in place for sectors to pursue this

The Elephant Experiment in Africa

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Differentiation and dynamics of competitiveness impacts from the EU ETS

We summarises the main factors that differentiate impacts of the EU ETS on profitability and market share. By examining sampling a range of sectors, we present some simple metrics and indicators to help judge the nature of potential impacts. We also consider briefly the mitigation response to these impacts by sectors, and how they may evolve over time. The broad conclusion confirms the aggregate findings presented in the existing literature - most participating sectors are likely to profit under the current ETS structure out to 2012 at the cost of a modest loss of market share, but this may not hold for individual companies and regions. The period 2008-12 can assist participating sectors to build experience and financial reserves for longer term technology investments and diversification, providing the continuation and basic principles of the EU ETS post-2012 is quickly defined and incentives are in place for sectors to pursue this.Emissions trading, industrial competitiveness, spillovers, allowance allocation, perverse incentives.

Space and Time: Wind in an Investment Planning Model

Investment planning models inform investment decisions and government policies. Current models do not capture the intermittent nature of renewable energy sources, restricting the applicability of the models for high penetrations of renewables. We provide a methodology to capture spatial variation in wind output in combination with transmission constraints. The representation of wind distributions with stochastic approaches or an extensive historic data set would exceed computational constraints for real world application. Hence we restrict the amount of input data, and use boot-strapping to illustrate the robustness of the results. For the UK power system we model wind deployment and the value of transmission capacity