Solar photovoltaic (PV) and wind energy are now the most scalable and cost-effective electricity sources. However, their weather-dependent variability raises concerns about their ability to provide reliable, continuous power. The concept of firm power—the ability to meet demand 24/7/365—is central to addressing this challenge. Research from IEA PVPS Task 16 shows that PV and wind can deliver firm power cost-effectively when combined with battery energy storage systems (BESS), dynamic curtailment (aka implicit storage), and a small share of dispatchable thermal generation using GHG-free e-fuels. This paper quantifies how the cost of firm PV/wind power varies with the geographic scale of the generation footprint across the continental U.S., assuming a flat baseload and 5% e-fuel flexibility. Results show that localized, self-contained firm power systems within areas smaller than 50,000 km² are economically viable, with projected 2050 levelized costs ranging from 4.25 to 6.25 ¢/kWh ─ challenging the assumption that large-scale transmission is essential for reliable renewable power. The study also explores optimal PV/wind mixes, storage requirements, and how these evolve with footprint size.

The current mainstream strategy to allow a high share of variable renewable energy feed-in is mainly aimed at enabling new flexibility resources, but at high penetration, these resources are unlikely to be sufficient. On the contrary, the firmness and dispatchability of solar/wind could reduce/eliminate any demand for additional flexibility. In this work, we showed that solar/wind facilities can produce both variable/intermittent and baseload/dispatchable 24/365 energy by installing battery energy storage, grid forming inverters and suitable power plant controller. Then, we propose a new market design more suitable for this generation splitting approach. Using Italy as a case study, we have shown through energy simulations and cost optimization/analysis that the proposed market reform, combined with a firm energy feed-in tariff (always below 100 /MWh), would make it profitable to reduce variable energy feed-in from large PV/wind power plants and related induced flexibility requirements by 20 %-30 %-40 %, in 2024-2030-2050. This flexibility reduction increases to 50 %- 60 %-70 % dealing with the joint generation of an optimal mix of PV/wind farms. In addition, in 2050, for PV and the optimal mix of solar/wind systems, incentives below 100 /MWh will push producers to generate only dispatchable energy. We also showed that our approach could solve or mitigate the significant misalignments between the current market structure and the techno-economic characteristics of renewables: wholesale market price volatility and cannibalization, growth of balancing prices and system-charges required to increase grid hosting capacity and adequacy.

The growing penetration of variable renewable energy increases the need for strategies that reduce solar-induced flexibility requirements with reliable photovoltaic supply. This study investigates cost-effective trading strategies for utility-scale photovoltaic/smart inverter/battery systems (flexible PV plants) to deliver firm and predictable power to the day-ahead electricity market. An important novelty is the introduction of dedicated key performance indicators for assessing solar supply quality: certainty of supply, supply variability and solar-induced flexibility required from the power system. These metrics are used to compare annual and monthly baseload commitments, forecast-corrected/optimized offers against unconstrained PV supply. Results show that while the provision of a PV baseload 24/365 with 99 % of certainty of supply shows near zero variability and solar-induced flexibility, the minimum production cost is 320 €/MWh firming just 30 % of the unconstrained PV generation. In contrast, forecast-based strategies provide near-firm solar power at costs of 128 and 149 €/MWh while firming 62–72 % of solar generation still ensuring a high supply certainty and low system flexibility requirements. An additional strategy, based on the trading of not only firm output but also the residual variable component (generation splitting), further improves economic performance bringing total production costs closer to market parity while having solar-induced reserves.

Firm power generation refers to renewable energy that can provide dispatchable power comparable to conventional coal-fired generation, satisfying the load profile on a 24/365 basis. Achieving firm power generation relies on flexibility resource configurations that integrate generation diversity, energy storage, and other strategies to compensate for the inherent variability of renewable energy. Although firm power generation represents the ultimate form of a zero-carbon power system, the transition pathway from coal-fired generation to firm power generation remains underexplored because coal-fired generation continues to serve as the backbone of power system operation. In this regard, this work proposes a multi-stage optimization framework that combines coal plant retirement with firm renewable deployment, enabling a systematic decarbonization pathway for the power system. The optimal results illustrate that the system levelized cost of electricity increases from 48.26 $/MWh in the coal-dominated system to 94.01 $/MWh in the firm renewable system to meet load demand on a 24/365 basis. Additionally, a firm kWh premium is introduced to quantify the additional cost of delivering firm power generation from renewable energy. The results show that the premium reaches 2.50 in the firm renewable system, while the combined deployment of multiple storage (e.g., battery, pumped hydro, and hydrogen) reduces it by 13.20%. The proposed framework provides an evolutionary pathway for coordinating coal power phaseout with firm renewable expansion, offering valuable insights for cost-effective power system decarbonization.

The variability of large-scale, distributed wind and PV generation across the continental US is evaluated and contrasted. We analyze single year of hourly-interval, timesynchronous wind power production simulated from ERA-5 and PV production simulated with SolarAnywhereTM. We examine the way in which the variability (as identified with a COV) of each resource changes with both temporal scale (time-averaging intervals from 1-8760 hours), spatial scale (spatial-averaging from 10 km(2) to 8 million km(2)) and location (across the CONUS). We empirically show that, although the variability of solar power is much more significant than that of wind at sub-24-h timescale, wind exhibits significantly more variability than solar at all timescales longer than a day. We show that spatial averaging reduces the variability of the aggregate wind resource much more so than for solar. The implications with respect to the energy transition are discussed.

We leverage multiple data sources to build a holistic solar resource map for the entirety of Canada in 2023. By comparing modeled clear-sky irradiance between historical period and 2023, the effects of the record-setting 2023 wildfire season on surface solar irradiance have been quantified. We have also studied how the smoke impacts the annual yield of leading solar PV sites in Canada.

The variable nature of solar power has hitherto been regarded as a major barrier preventing large-scale high-penetration solar energy into the power grid.Based on decades of research,particularly those advances made over the recent few years,it is now believed that dispatchable solar power is no longer a conception but will soon become techno-economically feasible.The policy-driven information exchange among the weather centers,grid operators,and photovoltaic plant owners is the key to realizing dispatchable solar power.In this paper,a five-step forecasting framework for enabling dispatchable solar power is introduced.Among the five steps,the first three,name-ly numerical weather prediction(NWP),forecast post-process-ing,and irradiance-to-power conversion,have long been famil-iar to most.The last two steps,namely hierarchical reconcilia-tion and firm forecasting,are quite recent conceptions,which have yet to raise widespread awareness.The proposed frame-work is demonstrated through a case study on achieving effec-tively dispatchable solar power generation at plant and substa-tion levels.

This article builds upon a preliminary evaluation of firm variable renewable power generation solutions for the province of Nova Scotia, Canada. This showed that an optimized blend of variable renewable resources (VREs), battery storage (BESS), implicit storage (i.e., overbuilt and dynamically curtailed VREs) and small fraction of dispatchable e-fuel thermal generation could firmly meet the entire load requirements of the province 24/365 at an unsubsidized cost of 7.7 cent/kWh in 2025 and at an anticipated cost of 4.5 cent/kWh in 2050. This preliminary study considered no change in future provincial load size or shape. We present here the results of an enhanced, more robust evaluation that: (1) considers future (2050) load requirements transformed by a complete electrification of the province's building and transportation sectors, (2) evaluates interconnection options with other Canadian provinces within existing energy and capacity allowances, and (3) provides a sensitivity analysis on the impact of future BESS capital and operational cost assumptions.

In this work, we develop simple linear models that allow users to predict solar irradiance forecast errors based solely on solar variability at a specific location on Earth. These straightforward yet actionable models enable solar forecasters to quickly estimate forecast errors for a given site, providing a clear indication of how well their forecasting models are likely to perform. The error in deterministic solar irradiance forecasts is measured by the Root Mean Square Error (RMSE), while solar variability is quantified by the standard deviation of an hourly time series of changes in the dimensionless clear sky index. Sixty sites distributed around the globe are used to build two types of RMSE prediction models. The first type is for intra-day forecasts (1-hour to 6-hour forecast horizons), while the second is for day-ahead forecasts (24-hour horizon). The derivation of the intra-day forecast error prediction model leverages on a non-linear time series approach whereas the one for day-ahead forecast error relies on forecasts issued by the European Centre for Medium-Range Weather Forecasts (ECMWF). For each type of model, we calculate also the 2.5% and 97.5% percentiles of the distribution in order to estimate the 95% uncertainty interval associated with the prediction. This uncertainty interval defines the bounds of the RMSE within which 95% of future RMSE values are expected to fall. These error bounds can provide solar forecasters with valuable insights into the performance of their solar forecasting methods in relation to the forecast challenges posed by site-specific variability. Verification against published results in the literature, specifically for seven sites of the SURFRAD network, demonstrates that these models can satisfactorily predict intra-day and day-ahead forecast RMSEs using only site-specific solar variability data.

The recent literature has demonstrated that overbuilding & proactive curtailment of renewables is a necessary puzzle piece in achieving the lowest-cost firm power, which is the kind of power from renewables that can satisfy load demand with 100% certainty-firming up renewables, which can be gauged via firm kWh premium, is synonymous with turning the business-as-usual variable renewables into effectively dispatchable ones and implies curtailed power. Although some studies have examined the benefits of utilizing curtailed electricity for hydrogen production, there is currently a lack of in-depth exploration on how to economically configure a fully renewables-dominated firm generation system and a hydrogen production system while ensuring 100% load fulfillment. Besides, various firm power enablers, which refer to technologies that help achieve firm generation, such as wind-solar blending or the aforementioned overbuilding, are often limited to quantitative analyses. Thus, this work evaluates the feasibility of utilizing nearly zero-cost curtailed power for hydrogen production in the context of firm power generation. Two highlights of this work that differentiate it from previous studies are that: (1) a theoretical paradigm for the coordinated deployment of a firm wind-photovoltaic-battery system under a firm-power condition and a hydrogen system has been established from the perspectives of joint optimization and separate optimization; and (2) the deep coupling relationships among various enablers are investigated, and a qualitative analysis is conducted on the impacts of battery storage sharing and wind-solar blending in reducing the system premium in the domain of firm power delivery. The case study demonstrates the economic viability of including hydrogen production in a firm renewable system. The firm kWh premium of the joint strategy reaches a value of 1.82, whereas the separate strategy sees an increase to 2.23. Compared to the absence of battery storage sharing, utilizing shared storage reduces the system premium by 44.85%. Besides, the premium increases by 27.47% when the wind farm is not installed. These results reveal that joint optimization, battery storage sharing, and wind-solar blending can all contribute beneficially towards a firm-power setup.

As part of Italy's National Recovery and Resilience Plan (PNRR), the "Rome Technopole" innovation ecosystem focuses on Energy Transition. Within this initiative, the RES4TECH project aims to meet the electricity demand of the future Rome Technopole campus through energy-flexible photovoltaic (PV) systems with battery energy storage systems (BESS). These energy-flexible PV systems with BESS, integrated with smart inverters and remote control, are designed to deliver power reliably under two scenarios: one delivering forecasted generation, and the other matching a continuous demand profile (24/365 firm generation). Since the Rome Technopole will be completed by 2030, the expected electrical load was modeled using data from the Engineering Macro-area at the University of Rome Tor Vergata (2013-2021). Solar and climate data from the university's EsterLab enabled simulations of PV generation at various tilt angles, identifying 30 degrees south-facing as optimal. An optimization process was developed to determine the ideal balance between PV and BESS capacity to minimize energy costs. Simulations show that oversizing PV capacity (3.9 times the annual electrical demand) and integrating storage can fully cover electricity needs. The optimal system includes 9.5 MWp of PV and 16.8 MWh of storage to serve an average daily demand of 9.84 MWh. A financial analysis indicates that 65 % self-production is already cost-effective today (LCOE = 112 /MWh, payback period = 11 years). Achieving 80 % self-generation would currently require 28 years, but this could drop to 9 years if 2050 technology costs are reached by 2030.

The International Energy Agency (IEA) defines firm power generation as the ability for an ensemble of generating resource to meet a given electrical load 24x365. A growing body of work undertaken under the aegis of the IEA demonstrates that PV and wind can cost-effectively meet this firm power criterion if operating with optimal amounts of battery storage (BESS) and overbuilding (aka implicit storage). The firm power cost-effectiveness of these variable renewable (VRE) resources can be further enhanced with optimal PV/wind blending and by including a small fraction of dispatchable thermal generation operated with clean (albeit expensive) GHG-free e-fuels in the generation mix. The IEA results also suggest that, almost anywhere on the planet, cost-effective firm VRE solutions could be achieved locally without requiring major transmission build-up over large distances (to both capture high VRE resource regions and mitigate their variability.) In this article, we systematically quantify this assertion for the continental US (CONUS) by analyzing how the cost of firm PV/wind generation evolves as a function of the renewable generating footprint from a single point to a subcontinental scale. Results indicate that self-contained localized 100% renewable solutions can be viable for footprints of less than 50,000 km2 (i.e., the size of a small US state).

Providing firm/dispatchable solar generation round the clock will be a prerequisite for the complete transition to Renewable Energy Source (RES). This work shows how the evolution of photovoltaic (PV) generation from variable/unconstrained to firm/dispatchable can be driven by regulatory and market reforms and appropriate incentives. First, we showed that solar farms can split their generation into baseload round the clock and variable/intermittent depending on the cost of storage. Then, by dividing the spot market into a variable renewable energy market and a day-ahead firm energy market, we showed through energy simulations and cost-benefit analysis that reasonable incentives for firm PV generation could currently reduce the flexibility requirement induced by large solar farms by 20%, by 30% by 2030, reaching full dispatchability by 2050.

A firm photovoltaic (PV) plant differs from a conventional unconstrained PV plant in terms of its ability to satisfy load demand on a 24/365 basis. Amongst various firm power enablers, overbuilding & proactive curtailment is the most counter-intuitive yet indispensable one. Although the cost-effectiveness of firm PV plants has been studied numerous times, few studies have evaluated the utilization of curtailed energy. To that end, this work advocates using the curtailed energy for hydrogen production, which is not impacted by the intermittency and variability of the curtailed power. A new mathematical optimization model that minimizes the firm kWh premium of the PV–battery–hydrogen hybrid system is put forth. Instead of using just generic modeling for the energy components (i.e., PV, battery, and electrolyzer), refined modeling, which could introduce bilinearity and nonlinearity, is herein considered. To address such optimization difficulty, a new algorithm, which hybridizes the particle swarm optimization and the branch-and-bound method, is proposed. The analysis reveals that the additional inclusion of a hydrogen production system within a firm PV plant is techno-economically attractive, and can lower the curtailment rate by 36%, and the overall firm kWh premium by almost 7%. What this implies is that, under the current market economics, the hydrogen production system becomes entirely free when used with firm PV plants.

Renewable Energy Communities are an important strategy of the EU, to promote and optimize the distributed deployment of renewable generation systems. At current incentives and costs, RECs that share the energy produced by PV/battery systems are expected to take the lion's share of distributed generation. In this paper, we highlight some important critical issues in electric system management that could result from the deployment of RECs if PV/battery system generation cannot meet the entire community demand. Thus, we show how a flexible PV system (PV/battery systems able to provide firm/dispatchable generation) can be cost-effectively dimensioned to supply 24/365 96%-97 % of the demand of a residential users' community. We demonstrate that flexible PV systems of 1/10 MWp with 1.2/12 MWh of storage capacity can supply communities of 142/1420 residential users at a least production cost of 0.12/0.096 /kWh. We further provide two business models based on power purchase agreement to demonstrate that, at these production costs, fully solar RECs are currently techno-economically feasible and provide mutual benefits to both flexible PV producers and REC members. Suitable virtual corporate PPAs can reduce the electric bill of the REC members by 11%-5% without requiring any investments and increase the producer incomes by 18%-22 %.

A growing body of work demonstrates that firm wind/solar power generation capable of meeting current and future electric demand 24/365 can be affordable if enabled by effective regulations. The question we pose here is whether DPV hosting capacities would be increased if DPV systems actively participated in the larger grid's firm power generation objective. We show, based on 20 years of hourly wind/solar data and two Central US case studies, that this is indeed the case with the possibility of multifold DPV hosting capacity increases.

Solar forecasts lower the cost of solar power and reduce the barriers to firm power generation. Solar forecasting conventionally has relied on advecting near real time observations and numerical weather predictions (NWPs). Observation based methods have limitations in cost and operational feasibility. NWPs generally have coarse spatial and temporal resolution, resulting in forecasts that may be overly general for a solar plant's location. Machine learning (ML) based forecasts have the potential to extract and blend observations and NWP data in an optimal blend, adding forecast skill. A primary drawback is ML based forecasts usually require training data. This paper will quantify the ML based forecast skill of using satellite derived irradiance data in lieu of ground, and the relationship between length of input training data and trained forecast skill gained. Climate and regional effects will be investigated by testing sites across the globe. Importantly these ML based forecasts will be compared to persistence forecasts and current NWP forecasts as a baseline.

Islands in tropical regions have high potential for solar energy, but the weather conditions in these areas are complex, with high fluctuations in the amount of sunlight received over time and across different locations, making it difficult to predict solar irradiance accurately.In a preliminary study, two spatio-temporal technics STVAR (spatio-temporal autoregressive) and CMV (cloud motion vector) showing a good predictive performance in literature, were assessed in this challenging environment. The strengths and the weaknesses of different models for different conditions/locations were presented. In this paper, we focus on the validation STVAR/CMV blends for the same satellite-derived irradiance dataset. In a first step, the research of the equation defining the blended model is investigated, highlighting a linear combination of irradiance predicted from CMV and STVAR by least-squares fit, as being optimal. A benchmarking illustration as a function of the orographic context exhibits the reduction of their respective gaps forced by their separate application. Then, the analysis of spatial evolution of the linear combination coefficients, led us to propose a model that quantifies coefficients of the blended model as a function of site elevation that represents an effective proxy for the microclimatological/topographical nature of the considered location. The proposed model shows good performance with an averaged relative RMSE of 16.50% in the entire study area. This model can be an appropriate choice for short-term forecasting even under complex orography conditions.

In a 2016 study, Clean Power Research presented a statistically sound methodology for quantifying the uncertainty of satellite-derived solar resource datasets adapted to ground-measured data. Since that time, Clean Power Research has made numerous improvements to SolarAnywhere® data and reference data quality control (QC) procedures. Using the same methodology and site-adaptation procedure as the original study, we demonstrate a 40% reduction in the uncertainty of site-adapted solar resource data in North America. 83% of the reduction is attributable to the increased temporal consistency of Solar Anywhere V3.6 relative to prior versions. The remaining reduction in uncertainty (17%) is due to improved data QC. Together, the results demonstrate how using high-quality datasets reduces the uncertainty of site-adapted solar resource data.

Grid‐connected photovoltaic electricity production steadily grows at the margin of conventional power generation, but its management becomes more complex. To overcome this challenge, a transformation of variable renewable energy (VRE) resources into firm power generation is proposed. Drawing on insights from the International Energy Agency Photovoltaic Power System Task 16 case studies, it becomes evident that achieving nearly 100% VRE power grids that reliably meet demand year‐round can be economically viable through optimal VRE transformation. This transformation involves various traditional methods, e.g., storage, VRE blending, geographical dispersion, and load flexibility. However, overbuilding VRE capacity and controlled curtailment, acting as implicit energy storage, are now seen as essential prerequisites for this transformation. Nevertheless, aligning this vision with the current market rules poses a dilemma as it doesn't necessarily align with VRE producers’ interests. This predicament calls for a reconsideration of VRE market regulations. Current designs based on marginal energy production signals do not suffice. Instead, it is advocated for market rules grounded in the capacity of firmly enabled VREs rather than their energy output. Ultimately, the economic model should harmonize with the variability of VRE resources, rather than forcing VRE resources to adapt to existing market structures.