Introduction

In 2024, global renewable energy capacity surpassed 3,700GW, with solar and wind accounting for the majority of new power installations. As per IEA[1], in the 2024-2030 projections, global annual renewable capacity additions will rise from 666 GW in 2024 to almost 935 GW in 2030. Solar PV and wind are forecast to account for 95% of all renewable capacity additions through 2030. It is because their generation costs are lower than for both fossil and non‑fossil alternatives in most countries, and both national and international policies continue to support them.

With over $700 billion invested annually in renewable energy projects, sophisticated modelling of energy and financial scenarios has become mission-critical. These complex simulations are essential to align the sustainability objectives and financial interests of all stakeholders - from energy consumers and independent power producers to financial institutions backing the energy transition. Traditionally such modelling workflows were met using spreadsheets based models, which are often more time consuming, sub-optimal, and have limited computing capabilities. 

As the grid incorporates more renewable assets, integrating generation sources (solar, wind) and storages (battery, pumped storage) becomes increasingly complex. This complexity largely stems from two key factors: (i) the intermittent output of solar and wind, which can threaten grid frequency stability, which requires demand and supply to match at time-intervals set forth by the grid (typically sub-hourly), and (ii) the need to minimise curtailment or excess of renewable generation to avoid financial risks, necessitating smart integration with battery storage and participation in open energy markets.

To address these growing complexities, modern modelling tools form the backbone of renewable energy projects, governing the parameters on how clean energy assets are financed, developed, and operated. By integrating technical, economic, policy, and impact metrics, robust energy modelling becomes indispensable in meeting project objectives. 

Thorough energy and financial modelling during the planning phase is not merely beneficial but it is also crucial for developing resilient, efficient, and high-value energy systems. Accurate modelling provides a single, reliable source of truth for all stakeholders, ensuring their expectations and requirements are aligned long before any renewable project is built or commercialised.

How stakeholders benefit from renewable energy modelling

1. Energy Consumers
   
   

   • Energy and costs baseline analysis: modelling provides consumers with a clear analysis of their current energy mix and associated costs, serving as a foundation for exploring how additional renewables can support both sustainability and financial objectives

   • Cost transparency: robust modelling enables accurate projections of long-term energy expenses, giving consumers the confidence of stable and transparent pricing for their business operations

   • Energy savings evaluation: by comparing future renewable scenarios against the established baseline, consumers can clearly quantify potential energy and cost savings, both of which are crucial metrics for strategic planning and investment decisions

   • Evaluation of policy incentives: renewable energy projects often qualify for various policy-driven benefits, such as cost rebates or energy settlement credits. Modelling quantifies the value of these incentives, enabling consumers to accurately assess their impact on energy savings and the overall attractiveness of future renewable energy options
     
     

2. Equity Investors / Individual Power Producers (IPPs)
   
   

   • Reduction of equity risk: comprehensive modelling helps quantify and manage both operational and financial risks, enabling more informed and data-driven investment decisions

   • Optimal capacity sizing: precisely determining the capacities of solar, wind, and battery storage hybrids is critical for evaluating project financial returns while aligning with the sustainability and financial goals of end consumers

   • Maximising profitability: by analysing multiple scenarios within technical and financial constraints, modelling identifies strategies to enhance asset performance and boost overall returns

   • Enhancing bankability: accurate, transparent models that clearly outline technical and financial risks strengthen business cases, increasing the likelihood of project approval and access to funding
     
     

3. Debt Providers / Financial Institutions
   
   

   • Informed lending decisions: lenders rely on credible models to assess project viability, repayment ability, and cash flow projections, enabling data-driven risk assessment and underwriting

   • Enhanced financial security: by simulation various scenarios of renewable assets, modelling helps identify potential technical or market challenges early, reducing the likelihood of defaults or non-performing assets before commercialisation

   • Streamlined due diligence: Transparent and well-documented models facilitate thorough project evaluation, accelerating the decision-making process for funding approval

In conclusion, rigorous renewable energy modelling is the backbone of successful planning and deployment of solar, wind, and battery storage assets. It empowers all stakeholders with the insights and confidence needed to make informed decisions that balance technical feasibility, financial viability, and sustainability goals. By embracing modelling as a core function in the planning phase, we pave the way for resilient, efficient, and economically sound energy systems that will drive the transition to a cleaner, more reliable grid for the future.

EarthSync simulation module enables optimally sizing renewable energy capacities by simultaneously modelling sustainability and financial objectives, while controlling various technical, economic, and policy constraints seamlessly within a single interface, ensuring data integrity throughout the process. Know more about our simulation module here.