Abstract
In recent years, optimization-based design techniques are proposed for building-integrated photovoltaic systems (BIPV), or urban PV systems. Urban PV systems are subjected to an inter-play of shading and self-consumption issues such that it makes sense to determine the functional capacity and positioning for the system before to simulate it. This chapter, therefore, analyzes the effectiveness of one optimization approach and matches it against more traditional BIPV dimensioning methods. Three design methods are described and compared to a benchmark (i.e., the ideal optimum design): The minimum capacity required by the current Italian law, the PV capacity which has an annual cumulative production equal to the cumulative demand of the building and an optimization technique using a constant energy demand (e.g., in case the user has only energy bills or cumulative forecasts). The methods were all tested on a case study located in Firenze (Italy) consisting of a residential four stories building. The case study is currently undergoing energy improvement works for experimental purposes within the H2020 Energy Matching project. The results show that the optimization approach easily outperforms the other methods despite the simplified input data enabling a sensible improvement in Net Present Value (NPV). The optimization method, when fed constant data in absence of more realistic one, still leads to an improvement of NPV from + 24 to + 85% compared to the highest yielding traditional one (i.e., the Italian law) and can, after all, achieve from 93 to 98% of the actual optimum. In some countries, the net billing (or net metering) incentives are still in place: In such economic frameworks, because the self-consumption becomes less relevant, the optimization technique is not required.
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Lovati, M., Zhang, X. (2023). Impact of the Demand Profile and the Normative Framework on a Residential Photovoltaic System. In: Zhang, X., Huang, P., Sun, Y. (eds) Future Urban Energy System for Buildings. Sustainable Development Goals Series. Springer, Singapore. https://doi.org/10.1007/978-981-99-1222-3_13
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