Renewable Energy Research

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1. 01Enquiry and requirement assessment
2. 02Technical scoping and proposal
3. 03Execution
4. 04Quality review and delivery, with an agreed round of revisions

Read the full project workflow.

In depth: how the work is done

1. Resource assessment

   Solar assessment uses irradiance measurements or satellite-derived estimates, with corrections for tilt, shading and soiling. Wind assessment uses measured or modelled wind speeds, extrapolation to hub height, and long-term correction of short records. We compare data sources, quantify differences and express expected yield as a distribution, such as P50 and P90 values, with the basis of each stated.

2. Performance modelling

   Photovoltaic models combine irradiance, temperature, module characteristics and losses; wind turbine models apply power curves with corrections for air density, turbulence and wakes. Bioenergy and solar-thermal systems require conversion efficiency and thermal-loss models. We validate against measured production where available and show how sensitive predictions are to assumptions such as soiling or availability.

3. System sizing and optimisation

   Sizing studies choose capacities of generators, storage and converters to meet demand at minimum cost or maximum reliability. Time-series simulation over a representative year or several years evaluates performance, and optimisation searches among configurations. Results depend on resource data, load profile, prices and component costs, so sensitivity analysis is essential.

4. Forecasting generation

   Forecasts use numerical weather prediction, satellite imagery, sky imagers or statistical models, depending on horizon. Evaluation should compare with persistence and climatology baselines, use time-respecting validation and report skill scores. Probabilistic forecasts support decisions about reserves and storage.

5. Techno-economic and life-cycle analysis

   Levelised cost of energy, net present value and payback depend on discount rate, degradation, operating cost and incentives, all uncertain. Life-cycle assessment adds embodied energy and emissions from manufacturing, transport and end of life. We present ranges and sensitivities and avoid precise-looking numbers that rest on weak assumptions.

6. Integration with grids and other sectors

   Renewables interact with networks, storage, heating, transport and hydrogen production. Studies of these couplings need consistent time resolution and scenarios. We help define system boundaries and keep model scope proportionate to the question.

7. Quality of meteorological and production data

   Measured irradiance, wind and production data contain errors from sensor soiling, shading, calibration drift and data-logger gaps. Before modelling, data should be screened with physical limits, consistency checks between related variables and comparison with nearby stations or satellite estimates. Documenting corrections and the fraction of data removed shows readers how reliable the validation is, and supports honest uncertainty estimates for predicted yield.

8. Reporting uncertainty in yield and cost

   Energy yield and cost estimates combine resource uncertainty, model uncertainty, loss assumptions and financial parameters. A simple uncertainty budget lists each source and its estimated magnitude. Combined uncertainty can be computed under stated independence assumptions. Presenting a central estimate with a range, and showing which inputs dominate, makes the analysis more useful for decisions and more credible to reviewers.

9. Degradation and lifetime of renewable systems

   Photovoltaic modules degrade, turbine components wear and bioenergy catalysts deactivate, so performance declines over time. Lifetime models use measured degradation rates with uncertainty, and the choice of rate can change economic results more than many technical details. We recommend using ranges from the literature or measurements, stating their source, and testing how the conclusions change when degradation is faster or slower than assumed.

Common pitfalls this service helps avoid

• Using one year of resource data. Inter-annual variability can change yield estimates considerably.
• Ignoring losses such as soiling and availability. These reduce real yield relative to ideal models.
• Comparing forecasts without a persistence benchmark. Skill cannot be judged without baselines.
• Quoting levelised cost without ranges. Results are sensitive to discount rate and assumptions.

What good reporting looks like

A renewable energy report describes the resource data and sources, the models and parameters, the validation, the yield or sizing results with uncertainty, the forecast evaluation, the economic and life-cycle assumptions with sensitivity and the limitations. It is not a bankable energy-yield assessment or investment advice, which are performed by qualified independent engineers under project-specific scopes.

An illustrative scenario

Hypothetical example written for explanation. It is not a client case, a result or a testimonial.

Suppose a researcher sizes a solar-plus-battery system for a remote site. A simulation using a typical year indicates a battery size that meets demand, but using three years of data shows the worst year requires more storage to avoid unmet demand. The report presents cost against reliability for each year, a sensitivity on battery cost and degradation, and states that load data came from a short measurement campaign.

Questions to settle before you begin

• How many years of resource data represent the site?
• Which losses should I include?
• What benchmark will my forecasts beat?
• Which economic assumptions drive my result?
• What system boundary applies to my life-cycle claims?

Key terms

P50 and P90

Yield values exceeded with probability 50 and 90 percent.

Levelised cost of energy

Lifetime cost divided by lifetime energy.

Persistence forecast

A forecast assuming future equals recent conditions.

Capacity factor

Actual energy produced divided by maximum possible.

Embodied energy

Energy used to make and deliver a product.

Working together on Environmental, Energy & Sustainability Research projects

Environmental, energy and sustainability engagements begin with the system boundary and the question: emissions, efficiency, resource use, performance of a technology or the effect of a policy or design choice. We ask for data, assumptions and the standards or frameworks you intend to follow. Analyses document boundaries, data sources and uncertainty, and they avoid presenting model outputs as measured facts. Reports state clearly which results depend on assumptions you supplied, and they do not constitute regulatory submissions or formal environmental assessments.

Few projects need a single service in isolation. These are the services most often combined with this one.

• Energy Systems & Storage

  Modelling and analysis of energy systems and storage technologies including batteries, thermal storage and hydrogen, covering operation, degradation and system integration.

• AI-Based Engineering Forecasting

  Forecasting of engineering quantities such as load, generation, wear or demand using machine-learning models, compared against classical baselines with proper out-of-sample evaluation.

• Sustainability & Life-Cycle Assessment

  Life-cycle assessment and sustainability analysis of materials, products and processes, with transparent system boundaries, inventory data and impact-assessment choices.

• Electrical Power Systems & Smart Grids

  Modelling and data analysis for power systems and smart grids: load flow and stability studies, renewable integration, and analytics on grid or consumption data.