This study examines the cost-effectiveness of residential photovoltaic (PV) systems in the UK by considering the changes occurring in the supporting mechanism, the Feed-in Tariff (FiT). The metric used is the levelised cost of energy. The analysis stresses the importance of the FiT scheme and demonstrates the lowest cost of produced energy that domestic PV systems can achieve with the current policies. In this study, the term grid parity is used when the levelised cost of the PV generated energy is lower than the retail electricity cost that the consumer pays. It is observed that, for certain scenarios and in certain UK cities, a domestic PV system can reach grid parity without using the FiT scheme, but it might not constitute a sufficient reason to invest in a PV system.
The study discusses the short-term performance variations of grid-connected photovoltaic (PV) systems installed in Kanpur, India. The analysis presents a holistic view of the performance variations of three PV array technologies [multi-crystalline (multi-Si), copper indium gallium diselenide and amorphous silicon] and two inverter types (high-frequency transformer and low-frequency transformer). The analysis considers the DC-AC conversion efficiency of the inverter, system performance through performance ratio (PR) calculations, energy variations between fixed and tracking systems and the comparison between calculated and simulated data for the examined period. The energy output difference between the tracking and fixed systems of the same PV technology show that these are dependent on differences in temperature coefficient, shading and other system related issues. The PR analysis shows the effect of temperature on the multi-Si system. The difference between the simulated and measured values of the systems was mostly attributed to the irradiance differences. Regarding the inverter evaluation, the results showed that both inverter types underperformed in terms of the conversion efficiency compared with nameplate values.
Technological advances, environmental awareness and, in several countries (including the UK), financial incentives lead to the adoption of PV (photovoltaic) systems. Economic viability, an important consideration for investment in residential PV, is dependent on the annual energy yield which is affected by distribution network based factors such as point of connection to network, network hosting capacity, load profiles etc. in addition to the climate of the location. A computational algorithm easy on resources is developed in this work to evaluate the effects of distribution network on the annual energy yield of residential PV systems under scenarios of increasing PV penetration. A case study was conducted for residential PV systems in Newcastle upon Tyne with a generic UK distribution network model. Results identified penetration levels at which PV generation curtailment would occur as a consequence of network voltage rise beyond grid limits and the variation in the percentage of annual energy yield curtailed among the systems connected to the network. The volatility of economic performance of the systems depending on its location within the network is also analysed. The study also looked at the impact of the resolution of PV generation profiles on energy yield estimates and consequently economic performance.
A key aspect for high photovoltaic (PV) system penetration is financial viability, the assessment of which is dependent on a reliable prediction of the lifetime energy output of the system. For installation in a particular location, the lifetime energy prediction depends on a range of parameters, including system design, system technology and the prevailing climatic conditions. It is also important to consider how the system losses vary with time and any degradation of system components. A variety of aspects can influence PV system performance including the PV module technology used and the location where the system is installed. Other main influencing parameters are solar irradiation levels, temperature, PV system conversion efficiency, degradation factors during the lifetime, reliability and operational issues (e.g. shading) (Huld et al., 2011). In addition, there is also the uncertainty of how these parameters have been measured or estimated. The Canada Centre for Mineral and Energy Technology found that the combined uncertainty over a PV system's lifetime could be up to 7.9% for an average modelled energy yield (Thevenard and Pelland, 2013). Hence, the uncertainty value cannot be neglected in PV system performance predictions as it can play a key role in the judgement of the system's economic viability. It is well documented in the literature that uncertainties in the lifetime energy generation and solar output degradation can lead to significant investment risk (
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