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Accurately predicting and balancing energy generation and consumption are crucial for grid operators and asset managers in a market where renewable energy is increasing. To speed up the process, these predictions should ideally be performed based only on on-site measured data and data available within the monitoring platforms, data which are scarce for small- and medium-scale PV systems. In this study, we propose an algorithm that can now-cast the power output of a photovoltaic (PV) system with high accuracy. Additionally, it offers physical information related to the configuration of such a PV system. We adapted a genetic algorithm-based optimization approach to parametrize a digital twin of unknown PV systems, using only on-site measured PV power and irradiance in the plane of array. We compared several training datasets under various sky conditions. A mean deviation of −1.14 W/kWp and a mean absolute percentage deviation of 1.81% were obtained when we analyzed the accuracy of the PV power now-casting for the year 2020 of the 16 unknown PV systems used for this analysis. This level of accuracy is significant for ensuring the efficient now-casting and operation of PV assets.

A key aspect for achieving a high-accuracy Photovoltaic (PV) power simulation, and reliable digital twins, is a detailed description of the PV system itself. However, such information is not always accurate, complete, or even available. This work presents a novel approach to learn features of unknown PV systems or subsystems using genetic algorithm optimization. Based on measured PV power, this approach learns and optimizes seven PV system parameters: nominal power, tilt and azimuth angles, albedo, irradiance and temperature dependency, and the ratio of nominal module to nominal inverter power (DC/AC ratio). By optimizing these parameters, we create a digital twin that accurately reflects the actual properties and behaviors of the unknown PV systems or subsystems. To develop this approach, on-site measured power, ambient temperature, and satellite-derived irradiance of a PV system located in south-west Germany are used. The approach proposed here achieves a mean bias error of about 10% for nominal power, 3° for azimuth and tilt angles, between 0.01%/C and 0.09%/C for temperature coefficient, and now-casts with an accuracy of around 6%. In summary, we present a new solution to parametrize and simulate PV systems accurately with limited or no previous knowledge of their properties and features.

Heat pump (HP) units coupled to thermal storage offer flexibility in operation and hence the possibility to shift electric load. This can be used to increase PV self-consumption or optimise operation under variable electricity prices. A key question is if new sizing procedures for heat pumps, electric boilers and thermal storages are needed when heat pumps operate in a more dynamic environment, or if sizing is still determined by the thermal demand and thus sizing procedures are already well known. This is answered using structural optimisation based on mixed integer linear programming. The optimal system size of a HP, an electric back-up heater and thermal storage are calculated for 37 scenarios to investigate the impact of on-site PV, variable electricity price, space heat demand and domestic hot water demand. The results are compared to today's established sizing procedures for Germany. Results show that the thermal load profile has the strongest influence on system sizing. In most of the scenarios investigated, the established sizing procedures are sufficient. Only large PV sizes, or highly fluctuating electricity prices, create a need for lager storage. However, allowing the storage to be overheated by 10 K, the need for a larger storage only occurs in the extreme scenarios., Correction in: Energy and Buildings, vol. 152, page. 843. DOI: 10.1016/j.enbuild.2017.07.006, WOS:000411545100073QC 20200910