Scenario Analysis
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2019-10-25
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Springer, Cham
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Abstract
The performance of TSO-DSO coordination schemes depends on the characteristics of the electricity systems in which regulation reserves can be provided by both transmission and distribution resources. In order to investigate the main aspects influencing TSO-DSO interactions, three detailed electricity scenarios (related to the expected 2030 evolution of Denmark, Italy and Spain) are defined. In particular, these scenarios are designed by assuming various distribution network conditions, determining different volumes of flexibility requested by the DSO in order to carry out local services. Then, the structure of a simulation platform, aimed at testing TSO-DSO coordination schemes, is proposed. It is based on three main blocks, integrating actual market clearing, bidding and dispatching algorithms, together with a simulator for the network and all the physical power resources. Finally, the simulation results are processed by means of a cost-benefit analysis which identified the market architectures providing the highest economic benefits to the system and the main factors that influence the profitability of one TSO-DSO coordination scheme among the others.
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2030 electricity scenario , Simulation of TSO-DSO coordination schemes , Cost-benefit analysis , ICT cost estimation , 2030 electricity scenario , Simulation of TSO-DSO coordination schemes , Cost-benefit analysis , ICT cost estimation , General Engineering , Economics, Econometrics and Finance (all) , General Business,Management and Accounting , Project ID , info:eu-repo/grantAgreement/EC/H2020/691405/EU/Smart TSO-DSO interaction schemes, market architectures and ICT Solutions for the integration of ancillary services from demand side management and distributed generation/SmartNet , info:eu-repo/grantAgreement/EC/H2020/691405/EU/Smart TSO-DSO interaction schemes, market architectures and ICT Solutions for the integration of ancillary services from demand side management and distributed generation/SmartNet , Funding Info , H2020, n. 691405, SmartNet , H2020, n. 691405, SmartNet
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Rossi , M , Merino , J , Madina , C , Turienzo , E , Svendsen , H , Kuusela , P & Koponen , P 2019 , Scenario Analysis . in unknown : Modeling, Analysis and Case-Studies . Springer, Cham , pp. 93-139 . https://doi.org/10.1007/978-3-030-29203-4_5