Management of Losses and Indicators in Photovoltaic Systems. Current Challenges

Abstract: Solar systems are foreseen as favorites among new energy generation technologies. Energy losses management seems relevant in this coming competitive context. On one hand, losses detection and its quantification, broken-down by each type, is necessary. On the other hand, identifying loss causes (new Machine learning and Bigdata approaches) is also needed. Contractual KPIs need to be deeper covered too, as far as the classic contractual Availability, PR and others classical indicators keep their star role. In this sense, calculation methods of real time and aggregated data of producible (possible) energy, for a better losses’ estimation is essential. Comparing Wind and PV contexts, the former enjoys better standardization in contractual KPIs than the last, whose companies, owners, EPC, O&M, and Asset Managers seems to proceed with a wide variety of contractual KPIs. In addition, contractual losses characterization uses not to be susceptible of an automatic classification, creating another need in terms of how to digitize these “manual” re-categorization, and swiftly linking them to the system, monitoring and analysis tools, or, for instance, work orders.

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Introduction

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Studies in collaboration with Energy International Agency, forecasted how relevant solar energy will become in next years (SolarPower Europe (2020): Global Market Outlook for Solar Power 2020-2024) arriving to Terawatts in terms of total installed power locating Photovoltaic as one of favorites generation technologies, and in general renewable energy satisfying 80% of 2030 energy demand. For the sake of simplicity this paper will focus on grid connected systems, without local charges.

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Simple empirical models are constructed to describe the performance of the PV systems under ‘normal operation” (S.K. Firth, 2010). Losses do not tell us everything about a site, despite of getting: suitable indicators, detection, estimation even forecasting processes or early warning of failures, they are just an incomplete picture of what is happening in a PV plant. There is a huge amount of very different events with similar symptoms, due to output power from operating PV systems is highly dependent on weather and environmental conditions and consists of many interconnected units, being inevitable to encounter too many faulty situations (Mellit, 2018). However, is essential at any asset management the continuous use of proper key indicators and a suitable losses management system.

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Likely causes of wrong losses estimation/detection

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After two years data/conclusions gathering at ISOTROL, this list was worked out as likely causes of wrong losses estimation/detection:

-Indicators: variety related to PV losses
-Producibles: How to estimate energy you expect
-Categorization: methods of detection/classification
-Data treatment: Storing, cleaning and validation
-Centralized platform for data acquisition / supervision
-Sensorization and inputs
-Others

The first three sources will be briefly commented along the present study, and a case covered to illustrate some conclusions.

Indicators. Main contractual indicators impose a cleaning of resource signals, calculation thresholds, and to be able excluding certain events or periods, from calculations. Therefore, we will be managing operating KPIs and, on the other side, contractual ones. The former mostly related to monitoring real time data/events and the last to manage contractual liabilities, likely to demand penalties due to bad performance or guarantee management. Therefore, classical/contractual indicators are relevant in terms of benefits, leading us to identify improvement actions and thus enhance our profitability.

In terms of time-based availability, which -in general- it is measuring within a time interval the available portion of the asset DC Power, we may find in the market two main approaches when excluding external events: a) to add the excluded time as Operative time, (in numerator) or b) to deduct the excluded time from the Useful time to be considered (denominator).

In terms of energy-based availability, which -in general- it is measuring within a time interval the performance, a common practice for contractual KPIs is to add (in numerator) the external loss as an extra produced energy, so to eliminate its external effect, and achieving the contractual figure.

Another exhaustive way is to extract both from numerator and denominator (energy produced & producible) all intervals affected by any external event. If we now talk about another king of indicators, the PR, its usage has been very challenged, and many modern discussions claim PR as not enough representative as indicator, pointing other technical performance indicators as more relevant depending on the actor of the process (for instance, diffuse component, gain, tailored producible algorithms, subelements energy/time availability, etc).

Contractual PR used to be the net-adjusted-PR, i.e. the PR corrected (with temperature, module degradation and availability) plus cleaned from the impact of any external event, or sometimes, furthermore, (for the sake of a spotless operational output), even cleaned from any unavailability interval (deducting both irradiation and produced energy) or any interval with outliers.

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