Implementation Of Emissions Trading Scheme Research Articles

Impact of emission trading and renewable energy support scheme on the optimality of generator side bidding

As a major Green House Gases (GHG) producer, CO2in particular, the electricity industry’s emissions have turned in to a matter of immense concern in many countries, especially in India. India’s economy and fast economic development has attracts the attention of the world. Emission trading schemes (ETS) and renewable energy support schemes (RESS) are implemented by the various developed countries to alleviate the affect of GHG emissions. In this paper, an optimization based market simulation approach is proposed with the consideration of emission trading schemes and renewable support schemes. To simulate the bidding strategy and for profit maximization, a particle swarm optimization (PSO) algorithm is used. As above problem is a multi-objective optimization problem, Where, in the first level each Genco submit the bid to the independent system operator and in the next level a optimization method is used for the determination of optimal bidding with the implementation of emission trading schemes and renewable support schemes. It is assumed that each generator should submit bid as a price taker’s in sealed auction based on pay-as-bid market clearing price mechanism. The practicability of proposed optimization method is checked by an IEEE-30 bus test system consists of six suppliers.

Impacts of emission trading schemes on GENCOs’ decision making under multimarket environment

The implementation of Emission Trading Schemes (ETS) in the electricity supply industry has driven generation companies (GENCOs) to put efforts to reduce their produced emissions. Under a multimarket environment, a GENCO generates electricity subject to physical, fuel and environmental constraints. Separate and evolving research efforts are currently shaping electricity market, fuel market and carbon market without paying adequate attentions to how each market affects the others, though the markets have overlapping goals with respect to the global economic and environmental benefits. Under this background, this paper investigates the impacts of carbon policies on a GENCO's decision making under multimarket environment. A dynamic decision making model is proposed to deal with the multimarket trading problem for a GENCO during each trading period. Differential Evolution (DE) algorithm is employed to solve the multi-period optimization problem for each time interval. Comparisons between different scenarios demonstrate the economic and environmental influences of different policies on a GENCO. With the proposed model, a GENCO can make a rational tradeoff between profit making and emission reduction under the three interactive markets environment. Policies defining the three interactive markets can accurately reflect the intended goals such as reducing emissions, promoting renewable and keeping electricity cost at a reasonable level.

Emission trading and the role of learning-by-doing spillovers in the ''bottom-up'' energy-system ERIS model

In this paper, using the "bottom-up" energy-system optimisation ERIS model, we examine the effects of emission trading on technology deployment, emphasising the role of technology learning spillovers. That is, the possibility that the learning accumulated in a particular technology in a given region may spill to other regions as well, leading to cost reductions there also. The effects of different configurations of interregional spillovers of learning in ERIS and the impact of the emission trading mechanism under those different circumstances are analysed. Including spatial spillovers of learning allows capturing the possibility that the imposition of greenhouse gas emission constraints in a given region may induce technological change in other regions, such as developing countries, even if the latter regions do not face emission constraints. Our stylised results point out the potential benefits of sound international cooperation between industrialised and developing regions on research, development, demonstration and deployment (RD3) of clean energy technologies and on the implementation of emission trading schemes.