Abstract
Distribution network capacity and services are ideally priced according to the cost-causality principle, where customers are charged according to the extent to which they are responsible for additional network capacity. As such, optimal network pricing should be forward-looking, reflecting the long-run marginal cost of providing network services. Since network investment costs are driven by peak demand, it is imperative that tariffs are demand-based and are able to provide efficient price signals to customers. In this paper, we evaluate customer's contribution to the collective peak demand of a capacity distribution network and then calculate their fair cost share using Shapley value analysis. In light of the computational requirements of these calculations, we use a sample-based approach to approximate the Shapley value with reasonable accuracy. We then employ customer yearly load profiles in the Solar Home Electricity Data to test the efficacy of our methodology.
Original language | English |
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Title of host publication | 2019 IEEE Milan PowerTech |
Editors | Federica Foiadelli |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1207-1212 |
Number of pages | 6 |
ISBN (Electronic) | 9781538647226 |
ISBN (Print) | 9781538647233 |
DOIs | |
Publication status | Published - 2019 |
Externally published | Yes |
Event | IEEE Milan PowerTech 2019 - Politecnico di Milano (Bovisa Campus), Milan, Italy Duration: 23 Jun 2019 → 27 Jun 2019 https://attend.ieee.org/powertech-2019/ https://ieeexplore.ieee.org/xpl/conhome/8792346/proceeding (Proceedings) |
Conference
Conference | IEEE Milan PowerTech 2019 |
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Abbreviated title | PowerTech 2019 |
Country/Territory | Italy |
City | Milan |
Period | 23/06/19 → 27/06/19 |
Internet address |
Keywords
- Cooperative game theory
- Cost-causality
- Demand-based tariffs
- Network tariffs
- Optimal network pricing
- Randomised sampling
- Shapley value