No abstract

This paper provides an empirical analysis of market behaviour under 'Tradable White Certificate' (TWC) schemes. It focuses on the entire set of 'flexibilities' granted to obliged parties to meet a mandatory energy-saving target cost-effectively, i.e. range eligible measures, eligible end-use sectors, banking provision, market engagement of non-obliged parties, and trading as such. We found that market behaviour responds to the unique design and context in which TWC schemes are implemented. Contrary to expectations, limited trading is observed so the 'to-trade-or-not-to-trade' dilemma is further analysed. A real TWC market has emerged only in Italy, where obliged parties (i.e. energy distributors) show preference towards 'to-trade'. In Great Britain and France, an autarky compliance approach is identified, with obliged parties (i.e. energy suppliers) showing preference towards 'not-to-trade' driven by, among many factors, commercial benefits of non-trading (e.g. increased competitiveness). At the same time, resultsshow clearer indications of cost-effectiveness for Great Britain than for Italy. In general, high energy-saving effectiveness is observed, but low ambitious saving targets and pitfalls in the regulatory framework need to be considered to further develop TWC markets. Initial market and institutional conditions strongly suggest that trading might not be an immediate outcome. Ambitious energy targets can trigger a more dynamic usage of all flexibilities by eligible parties and thus active behaviour in TWC markets.

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Urban energy models (UEM) are useful to evaluate energy efficiency policies at district or city scale and to make the best decisions in terms of financial and environmental impacts. Probabilistic and simple physical models coexist in the UEM often with several dozens of parameters per building. Parameters coming from different databases with no consistency in the levels of uncertainty make the necessary validation difficult. This article proposes a method as a first attempt to validate UEM in a data scarcity context which requires only the annual electricity consumption of several carefully chosen cities. The first step aims to identify one parameter per energy end-use which has the largest impact on the energy consumption and the highest uncertainty. The second step aims to calibrate the model at country scale or at large territory scale to set parameters' values. The third step consists in selecting several cities (inside the territory previously used for calibration) to evaluate particular aspects of the UEM. The last step aims to simulate the calibrated model on the selected cities and to compare the simulation with the energy consumption information. The method is illustrated through the case of the electricity consumption in the French residential building sector with Smart-E, a bottom-up UEM tool.