CS14: Renewable Energy Prospects

The increasing penetration of utility-scale solar photovoltaic (PV) power plants introduces operational and economic challenges due to inherent variability in solar irradiance. One critical parameter affecting grid stability and economic performance is the ramp-up rate, defined as the rate at which PV output power increases over a given time interval. This study presents an analytical investigation of ramp-up rate characteristics and evaluates their operational and economic implications for modern power systems.

The methodology is based on mathematical modeling of PV power output using standard irradiance-performance relationships and scenario-based simulation under clear-sky and transient cloud conditions. Ramp-up rates are calculated using time-series power output models and evaluated using statistical analysis to determine magnitude, frequency, and operational impact.

The results indicate that ramp-up rates are directly influenced by irradiance variability, system capacity, and response characteristics. High ramp-up rates increase the need for flexible generation resources and reserve capacity, which may increase operational costs and affect economic dispatch efficiency. Mitigation strategies such as ramp-rate control, improved forecasting, and integration of energy storage systems significantly reduce variability and improve system stability.

The study concludes that proper ramp-rate management is essential for reliable and economically efficient solar PV integration. The findings support system operators, planners, and policy makers in optimizing renewable energy integration while minimizing operational and economic risks.
 

Economies are increasingly adopting renewable energy certificates as a policy mechanism to support the transition away from reliance on fossil fuels. We investigate the price of solar renewable energy certificates (SRECs) in New Jersey, allowing for the potential presence of jumps and time-varying volatility. We find that both features play an important role in the stochastic process describing SREC price returns. We also simulate the implied probability that at least one jump would occur in any given month. These implied probabilities indicate that jumps played a consistently important role in both SRECs and electricity prices. Jumps in SRECs appear to have been particularly noteworthy between late 2011 and early 2013, a period when electricity prices in New Jersey were relatively high. This result hints at the potentially important role of market structure in driving fat tails in price returns.

Wind turbine outputs are known to degrade over time (Staffell and Green, Renewable Energy, 2014), but earlier work has concentrated on onshore turbines. The UK now has enough experience of operating offshore wind farms to assess whether the harsher offshore environment means higher degradation rates. More modern turbines may also have proven more reliable than earlier models, a result that was suggested by our earlier study but can now be checked with more data. 
We follow the methodology of our earlier work but increase wind farms' measured outputs to take account of curtailment by system operators, something that has risen significantly in recent years. We calculate ideal hourly (and hence monthly) outputs from the wind speeds in NASA's MERRA reanalysis of wind speed data, using the renewables.ninja algorithms. The weather-corrected load factor for a month (our unit of observation) is given by the farm's actual output multiplied by the ratio of its ideal output for that month to the mean of its ideal output over the data period. We then analyse the trends in weather-corrected load factors for individual farms and groups of farms.
Initial results suggest degradation rates that are much lower than the 1.6% p.a. estimated in 2014: 1.17% for onshore turbines and 0.49% for offshore. This result comes despite the generally harsher conditions offshore but may reflect greater efforts to design-in resilience or more consistent maintenance practices for the higher-value offshore turbines. 
High degradation rates would reduce the lifetime output of wind turbines and therefore increase their levelised cost of electricity. Our earlier estimate suggested that the then-current estimates of LCOE might be 9% lower once degradation was taken into account. Our newer results imply that the penalty from degradation is much lower than this

The sugarcane bioenergy sector is embracing innovations by applying novel technologies (enzymatic hydrolysis and biodigestion) to unlock energy yield potentials from residues. An underexplored pathway is the carbon capture and utilization of biogenic CO₂ through autotrophic microalgae cultivation, particularly lipid-rich strains for biodiesel production. This study proposes a novel integrated biorefinery that prioritizes the production of high-energy-density liquid biofuels while consuming biogas on-site to support this purpose. The greenhouse gas abatement potential and economic performance are assessed. Moreover, uncertainties over the investment, economies of scale for microalgae cultivation, and a stochastic biodiesel price are evaluated through a Monte Carlo simulation to address the biorefinery's economic resilience. Simulations are conducted in Aspen HYSYS®, comprising an ethanol factory (2 Mt/y cane), a cogeneration system, a microalgae biodiesel plant, anaerobic digestion, and an enzymatic hydrolysis module. Two scenarios were evaluated: case 1 allocates all bagasse to cogeneration, while case 2 directs it to hydrolysis. For each tonne of sugarcane, case 1 yields 84 L of ethanol, 56 L of biodiesel, and 128 kWh of surplus electricity; whereas case 2 produces 100 L of ethanol, 48 L of biodiesel, and 56 kWh of electricity. The carbon intensities of bioenergy are 14.83 (case 1) and 16.81 gCO2eq/MJ (case 2). Deterministic internal rates of return are 14.1% (case 1) and 11.7% (case 2), whereas their respective probabilities of success are 69% and 59.6%. The biorefinery increases by more than twice the energy output of a standard ethanol plant, with the best outcomes arising from allocating bagasse to cogeneration, although both cases showed economic resilience. Nevertheless, hedging tools are recommended to manage risks, since the Conditional Values at Risk (CvaR5%) of the projects were negative. Investigation of microalgae cultivation reliability in pilots is still crucial before full-scale deployment of the proposed configurations.

High solar photovoltaic (PV) penetration is reshaping wholesale price formation, yet its effect on price risk may differ across market conditions and seasons. We investigate how solar PV generation, along with wind output and PV curtailment, affects wholesale electricity price levels and variability, and whether these effects vary across the price distribution and seasons. We study the electricity market in the Kyushu region of Japan, which has the country's highest PV penetration, limited interconnection capacity, and four distinct seasons. Extending prior daily-data analysis to hourly observations, we estimate quantile regressions of hourly JEPX spot market prices for April 2016 to March 2020 on demand, solar PV, wind generation, PV curtailment, and nuclear generation. This approach yields sharper estimates and reveals curtailment-related price responses that are largely hidden under daily aggregation. The estimates confirm a strong merit-order effect: additional PV output lowers wholesale electricity prices. Wind output and PV curtailment also exhibit effects on price variability. A central contribution is evidence of seasonal asymmetry in price variability. During summer, higher PV coincides with temperature-driven demand peaks, generating a positive supply–demand co-movement that dampens price dispersion. In autumn and winter, reduced PV output occurs alongside rising heating demand, so PV fluctuations amplify price dispersion rather than stabilize it. These results have broader implications for high-PV-penetration markets worldwide: renewable energy policies-particularly those governing PV installation location, storage valuation, and hedging strategies-should explicitly account for climate-driven seasonal variation in PV-induced price risk.