Which variables are essential for renewable energies?

ABSTRACT

Essential Variables are defined as a minimal set of variables that explain the state of the system. They are crucial for predicting its developments, and support metrics that measure its evolution. The variables should be relevant to meet requirements of stakeholders and be technically and economically feasible for systematic observation. A definition of Essential Renewable Energies Variables is proposed linked with their identification in several domains in renewable energy using a bottom-up and user-driven approach, and spanning over several years of documented interaction with stakeholders. Lists of variables are proposed in hydropower, solar, wind, and marine energies. It does not comprise the variables relating to social and economic aspects supporting decision making in investment nor those relating to civil engineering that are needed to erect a plant or farm.

KEYWORDS:

• Renewable energy
• solar energy
• wind energy
• hydropower energy
• renewable marine energy
• essential variables
• GEOSS

1. Introduction

Observations and monitoring are fundamental to know and characterize the environment in a broad sense, including renewable energies. To ensure quality of observational datasets as the credibility of the climate record, the Global Climate Observation System (GCOS) has introduced the concept of essential climate variables that denotes a set of variables describing the environment which are of preeminent importance for the climate community (GCOS 2003). This introduction was meant to answer the need for enhancing coordination in climate observations (Bojinski et al. 2014; Houghton et al. 2012).

The concept of the essential climate variables has been widely endorsed by scientists, operators of observing systems, programme planners and policymakers. It helps these users to focus more on these variables in terms of observational, scientific and funding priorities, and ensure that appropriate efforts are made on the dissemination of these variables, so that they are made available to the various stakeholders. The concept of essential variables is not limited to climate; it has been extended to other domains. By adopting the concept of essential variables, the exploitation of the requirements is easier and more efficient. It helps in prioritizing a minimum set of measurements to capture the essential processes in the field, and are often complementary each to the others and to other essential variables defined in other domains. As a consequence, a long-run process started to define, collect and deliver to stakeholders the domain-specific essential variables and to identify the gaps existing for covering the observation of these variables within the observation communities and in particular within the Group on Earth Observation (http://www.earthobservations.org). Different communities have published or are currently publishing similar definitions tailored to their specificities, such as in biodiversity (Pereira et al. 2013) or ocean ecosystems (Constable et al. 2016). More recently, the concept has been extended to even more comprehensive goals targets and indicators for global sustainability and human development such as the 17 Sustainable Development Goals (SDGs) set-up at the UN in September 2015. They are called essential sustainable development variables by Stafford-Smith et al. (2017) or essential SDG variables by Reyers et al. (2017).

The ConnectinGEO project funded by the European Commission under the Horizon 2020 framework program have as second objective to define essential variables for each societal benefit areas (SBA) defined by the Global Earth Observation System of Systems (GEOSS) work program, among which are renewable energies (http://connectingeo.net/Objectives.htm, Maso et al. 2015).

Within the ConnectinGEO project, the value of essential variables has been discussed (Bombelli et al. 2015) and essential variables defined as ‘a minimal set of variables that determine the system’s state and developments, are crucial for predicting system developments, and allow us to define metrics that measure the trajectory of the system’.

Collecting users’ needs for data in Earth observation is not new at all and has a long history in renewable energies. Indeed, among others, hydropower, solar, wind, and marine energies offer environmentally friendly alternatives to fossil fuels and are particularly sensitive to environmental conditions. These energy sources are intermittent, and their availability depends largely on local climate and weather. They are benefiting a lot from Earth observation data though there is no dedicated space mission or in situ network of instruments at global scale. The collection of such data have guided the development of several databases, web services, etc. that among other aspects have tackled challenges to take into account the GEOSS interoperability requirements in operational and commercial information systems for renewable energies (see e.g. Gschwind et al. 2006; Ménard et al. 2012, 2015). It does not comprise the variables relating to social and economic aspects supporting decision making in investment nor those relating to civil engineering that are needed to erect a plant or farm.

The present paper aims to propose a definition of essential renewable energies variables (EREV) and to provide lists of essential renewable energies variables (EREV) resulting of this long history/large experience and specific to four energies: hydropower, solar, wind, and marine energies. Section 2 is dedicated to the definition of the EREVs and properties. The methodology followed to identify these EREVs is described in Section 3. Section 4 lists the EREVs and discusses the differences between EREVs and ECVs even if EREVs can be considered as a subset of ECVs and Section 5 concludes the paper.

2. Definition of the EREVs

Though GCOS (2003) lists a series of 44 variables considered as ECV, it did not provide a clear definition of an essential climate variable. The concept of essential variables is flexible vis-à-vis changing priorities, application needs, and scientific and technological innovation but can be specific to a domain. Bojinski et al. (2014) proposed a definition of an ECV as ‘a physical, chemical, or biological variable or a group of linked variables that critically contributes to the characterization of Earth’s climate’. Pereira et al. (2013) define an essential biodiversity variable as ‘a measurement required for study, reporting, and management of biodiversity change’. As they noted that many variables may fit this definition, they added that a process was held to ‘identify the most essential’. Constable et al. (2016) wrote that ecosystem essential ocean variables are ‘defined biological or ecological quantities derived from field observations, rather than being a product of an assessment. Their importance is determined by how well they contribute to assessments of the ecosystems’.

One may note that these definitions are close to each other, though the first and third ones mention variables or quantities while the second one says measurements. One may also note that the variables are critical, essential or important. It stresses the preeminent role of the users of these variables as they will determine themselves what is important/critical/essential. It also means that there may not be a unique set of essential variables depending on what is critical for a given community of stakeholders. It will be seen later that with respect to renewable energies and due to the overlapping nature of the various domains and SBA, there are common needs and that it is possible to converge towards a limited set of variables.

In a given domain, several variables are possible candidates to be essential. Once an initial list of variables set-up, Bojinski et al. (2014) or the ConnectinGEO project (Maso et al. 2015) recommend to select only those that are feasible, cost-effective for systematic observation and relevant. Variables must obey the following principles:

• Feasibility: quantifying the variable, either from observations or derived methods, on a global scale is, in principle, technically feasible using proven, scientifically understood methods;

• Cost effectiveness: the cost of generating and archiving data on a variable is affordable; observation should mainly rely on coordinated observing systems using proven technology, taking advantage where possible of historical datasets.

• Relevance: the variable is critical for the objective to be achieved, i.e. the goals and targets defined by the community of stakeholders.

In renewable energies, stakeholders are numerous and do not form any structured ensemble. Hence their expressions of needs result in a fragmentation of requirements and a fragmentation of listed observation variables. Nevertheless, analysing their expressions of needs, it happens that in the various domains in renewable energies (hydropower, solar, wind, marine), the path towards the essential variables was the same, allowing to group observation variables and to express a common definition of EREVs.

Hence the definition for EREVs proposed by the authors is: ‘The observations that meet important requirements from Renewable Energy stakeholders and are technically feasible and cost effective for systematic observation and global implementation’.

This definition allows EREVs to be used for evaluation of the performances of single power system or power farms, their modelling, the modelling of their behaviours, the due diligence of power systems … 

3. Methodology to identify EREVs

The GCOS is an efficient forum to debate on observation systems and opportunities, hence of the ECVs, in climate and its three components: atmospheric, oceanic, and terrestrial. It gathers numerous researchers, operators of observation systems, and policy bodies from all over the world. There is no such a forum in renewable energies. Because of the very strong influence of the climate on renewable energies, research institutes in renewable energies such as MINES ParisTech are able to make their concerns expressed in the GCOS. This is not the case of the numerous investors, banks, small and medium enterprises (SMEs), consultants and governmental agencies working in renewable energies. Setting up the list of users’ requirements and hence of the EREVs has been made in a different way.

Collecting users’ requirements is the first step. Surveys of users’ requirements were launched by research institutes, governmental or international agencies, or association of SMEs in order to elicit the views of different communities of users. The targeted audiences offer diversity: researchers, academics, enterprises, consultants, non-governmental organizations, local to international agencies. Table 1 lists the various documents relating to the collection of users’ requirements. Surveys have taken various forms: face-to-face interviews, small to medium size meetings, on-line questionnaires. MINES ParisTech has been involved in many such surveys since 1980. One of the many lessons learnt is that requirements from SMEs are best captured in face-to-face meetings with open but well-prepared questionnaires.

Table 1. List of documents and projects for users’ requirements.

Domain	Reference
All renewable energies, or more than one energy	ECEM project (Troccoli et al. 2018)
	ENDORSE project (2013)
	ESMAP (2010)
	EO-Windfarm project (Furevik et al. 2004)
	GEO (2010)
	Hasager et al. (2007)
	IRENA Global Atlas (2013); Estima et al. (2013)
	Kaltschmitt, Streicher, and Wiese (2007)
	Renewable Energy Data Explorer service ((REDE) 2018)
	Schroedter-Homscheidt, Stackhouse, and Ranchin (2007)
	SWERA project (Renné et al. 2005)
	Zell et al. (2012)
Solar energy only	CAMS (Copernicus Atmosphere Monitoring Service, Holzer-Popp, Kluser, and Schnell 2014)
	Cros, Mayer, and Wald (2004)
	EnviSolar (Environmental information services for solar energy industries) project (Schroedter-Homscheidt et al. 2006)
	ESRA (European Solar Radiation Atlas, Greif et al. 2000; Page, Albuisson, and Wald 2001)
	IEA SHC Task 36, 46 and Mesor projects (Hoyer-Klick et al. 2008; Huld et al. 2008)
	Lefèvre et al. (2014)
	SoDa Service (Gschwind et al. 2006; Wald et al. 2002, 2004)
	Satel-Light project (Fontoynont et al. 1998)
	Vernay, Pitaval, and Blanc (2014)
Wind energy only	ADB (2014)
Marine energy only	Dubranna and Ranchin (2015)
	Dubranna et al. (2015)
	Elliott et al. (2012)
	Fichaux, Poglio, and Ranchin (2005)
	Zelle et al. (2013)

Once users’ requirements are collected, expertise is needed to harmonize the requirements, convert them into needs with priorities and then into indicators that are relevant to the domain and can be observed a priori with current means (Figure 1). Further knowledge on Earth observations capabilities is required to check if the observations are feasible and cost-effective. Eventually, one obtains lists of EREVs. At any of these steps and possibly during the survey, interactions with users are required to check if the proposed observation is understood and agreed upon by them. In addition, it is known from experience that users’ views evolve as offers evolve and a constant interaction with them is a means to remain at the leading edge.

Figure 1. Schematic view of the path followed to define EREVs.

The specific case of the SoDa service dedicated to solar energy can be used to explain Figure 1. The SoDa Service (http://www.soda-pro.com) is a broker to a list of services and web services related to Solar Radiation proposed by several providers in Europe and abroad. It offers a one-stop access to a large set of information (most of it free of charge) relating to solar radiation and its use. The SoDa project (2000–2003) funded by the European Commission (Wald et al. 2002, 2004) has been successfully turned into a sustainable service daily used by professionals, and managed since 2009 by the French company Transvalor. Gschwind et al. (2006) have expressed the lessons learnt during this transformation. Three prototypes of the SoDa Service were built during the project. There were definite advantages in iterative prototyping for the team and users. In particular, it created a better acceptance of the outcomes of the project by users. Users got acquainted to new tools progressively with a smooth learning curve, discovered new opportunities in business or others, proposed more focused specifications, and proposed to their own users services tailored to needs and opportunities. Users were in a key position in the SoDa project. However, they were initially perceived as contributors to the technological development and the SoDa team was more focused on providing very accurate services that they can use instead of thinking how the whole SoDa Service could serve them to develop their business. Later on, a long and difficult reflection process enriched by further more focused interviews of users and a better understanding of their concerns permitted a move towards a better expression of the EREVs in the SoDa Service. It leads to an increase in the use of the SoDa Service by professionals and at a further stage, an increase of paying customers, ensuring the economic sustainability of the service. A lesson learnt from the SoDa project is that it takes time, resources and expertise to express users’ requirements in EREVs ready to be delivered and that the closer to the EREVs the delivered product, the greater the chance users will adopt the product.

4. EREVs in hydropower, solar, wind, and marine energies

Table 2 gives EREVs for hydropower, solar, wind and marine energy that have been extracted from the documents of Table 1. It does not comprise the variables relating to social and economic aspects supporting decision making in investment nor those relating to civil engineering that are needed to erect a plant or farm. Only are listed those needed for resource assessment and operations and related to the state of environment.

Table 2. List of essential renewable energies variables.

	Variables	Already an essential variable in:
All energies	Elevation/orography/bathymetry	Common to all domains
Hydropower (operating plants)	Precipitation	Climate/Atmospheric
	River discharge	Climate/Terrestrial
	Water use
	Snow cover
	Glaciers
	Land cover, incl. vegetation type
	Above-ground biomass
	Surface moisture
Solar energy	Downwelling solar radiation at surface, possibly with its direct and diffuse components	Climate/Atmospheric
	Wind speed and direction at surface Surface air temperature
	Solar irradiance at the top of the atmosphere, also known as total solar irradiance
	Cloud properties
	Albedo	Climate/Terrestrial
	Snow cover
Wind energy	Wind speed and direction at surface	Climate/Atmospheric
	Surface air temperature
	Surface water vapour
	Surface air pressure
	Land cover, incl. vegetation type	Climate/Terrestrial
Marine energy	Wind speed and direction at surface	Climate/Atmospheric
	Surface air temperature
	Surface water vapour
	Surface air pressure
	Sea surface temperature	Climate/Oceanic
	Sea level
	Sea state
	Sea ice
	Surface current

In this table, the requirements for frequency, resolution, uncertainty … are not reported. The characteristics of the EREVs are directly linked to their usage for a specific type of power system. As an example, the requirements for solar radiation are quite different when considering a solar farm or a photovoltaic system on the roof of a building in a town. In the first case, the spatial resolution and the time-frequency of Meteosat data (3 km at nadir and 15 min) are sufficient to fulfil users’ needs for a solar farm. In the second case, considering the shadows in a rooftop due to all small elements built on roof, a resolution of less than one metre is required and a time-frequency of a minute allows providing relevant information. Looking to snow cover, of interest for hydropower and solar system, in one hand, the aim is to evaluate the reserve for hydropower, and on the other hand to evaluate the coverage of the solar panels by snow that will prevent the production of energy. For each case, the time of information requested, depends on their future use. All the EREVs’ characteristics should be set-up in accordance with the users’ needs. As an example, Dubranna et al. (2015) reviewed the parameters needed for renewable marine energy systems and described the characteristics required for the variables requested by the users.

The EREVs proposed in Table 2, form a subset of the ECVs and are common. But the observation requirements for the same variable for ECVs and EREVS depend upon their usage as explained through few examples above.

The proposal of EREVs is based on different exchanges with users which occurred during many projects since decades. Different lessons have been learnt from these exchanges. One of them consists of the differentiation of users’ needs depending of the phase of the project in its life cycle. It could be considered that the EREVs and their features depend on the RE technology and on the phase of the project. These phases can be summarized in seven phases: site selection, feasibility study, permit stage, design and engineering, construction, operation and maintenance, and decommissioning. Each of these phases corresponds to particular user profiles, and so to user requirement (Ranchin et al. 2004). As a consequence, observational requirements of EREVs could be built for each stage of the project in order to fit more precisely different users’ needs.

Zell et al. (2012) performed a meta-analysis of observation needs expressed in publicly available documents, including scientific journal articles, scientific reports and workshop summaries. They published a list of critical Earth observation priorities for all SBA that may be considered as a list of essential variables. Though less detailed, their list for the SBA ‘energy’ is close to Table 2.

5. Conclusion

This work has proposed a definition for Essential Renewables Energies Variables (EREVs). These EREVs form a subset of Essential Climate Variables and diverge on the observation requirements due to their usage by the stakeholders of the renewables energies domain. The cases of hydropower, solar, wind, and marine energy have been considered. These variables should to be considered for the long-term measurement, assuming that it is feasible to measure them in terms of cost, effort, and impact. Moreover, considering than most of the EREVs are comprised in the ECVs data set, it could promote collaboration. The main difference consists in the observational requirements in time and spatial resolution. The ECVs observational requirements can evolve during time and converge with those of EREVs. It could help to develop a consistent way to communicate the need for long-term acquisition with funding agencies as well as the messages to decision maker. The EREVs are a basis to commit resources and to support progress towards an evidence-based knowledge base for renewable energies stakeholders.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Thierry Ranchin http://orcid.org/0000-0001-9514-045X

Lucien Wald http://orcid.org/0000-0002-2916-2391

Additional information

Funding

The authors are acknowledging the support of the H2020 program through the ConnectinGEO project. This project was funded by the EU FP for Research and Innovation (SC5-18a-2014-H2020) under grant agreement n° 641538.