that is based on DG as an alternative to centralised generation, so these studies go well beyond cost benefit analyses of individual distributed energy plants. BERR and WADE [20] quantify the costs and benefits (including the social cost of carbon emissions) of using a decentralised electricity generation system (that encompasses a range of different technologies) to meet electricity demand needs for the whole of the UK for the next 20 years, and compare this with the relative costs and benefits of equivalent centralised generation. They compare alternative ‘scenarios’ (exogenously determined “bundles” of electricity generating technologies) and a wide range of model input assumptions by the user for a DG compared with a CG system (regarding, for example: transmission and distribution infrastructure costs; electricity output losses associated with transmission and distribution; fuel use; electricity demand growth over time). Given the complexities of modelling DG, the exercise is necessarily rather assumption-driven. As a result, there are significant uncertainties associated with the ‘rule of thumb’-type assumptions made in the study. For example, the authors note that the WADE framework incorporates a single cost to reflect the cost of infrastructure updates required for transmission and distribution under each of the DG and CG scenarios. In practice, however, such costs vary significantly from project to project. Furthermore, the model adopts averysimplistic treatment of CHP: although the generation sector is explicitly modelled in WADE, heat is not explicitly identified and so instead the authors attempt to quantify the benefits of better fuel efficiency associated with CHP simplistically.
that is based on DG as an alternative to centralised generation, so these studies go well beyond cost benefit analyses of individual distributed energy plants. BERR and WADE [20] quantify the costs and benefits (including the social cost of carbon emissions) of using a decentralised electricity generation system (that encompasses a range of different technologies) to meet electricity demand needs for the whole of the UK for the next 20 years, and compare this with the relative costs and benefits of equivalent centralised generation. They compare alternative ‘scenarios’ (exogenously determined “bundles” of electricity generating technologies) and a wide range of model input assumptions by the user for a DG compared with a CG system (regarding, for example: transmission and distribution infrastructure costs; electricity output losses associated with transmission and distribution; fuel use; electricity demand growth over time). Given the complexities of modelling DG, the exercise is necessarily rather assumption-driven. As a result, there are significant uncertainties associated with the ‘rule of thumb’-type assumptions made in the study. For example, the authors note that the WADE framework incorporates a single cost to reflect the cost of infrastructure updates required for transmission and distribution under each of the DG and CG scenarios. In practice, however, such costs vary significantly from project to project. Furthermore, the model adopts averysimplistic treatment of CHP: although the generation sector is explicitly modelled in WADE, heat is not explicitly identified and so instead the authors attempt to quantify the benefits of better fuel efficiency associated with CHP simplistically.
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