# Long-Term Forecasting Framework for Renewable Energy Technologies’ Installed Capacity and Costs for 2050

^{*}

## Abstract

**:**

## 1. Introduction

- Best-fit temporal quadratic regressions on available annual capacity data provide narrower decadal forecasts than those published for wind and solar energy technologies.
- New long-term forecasts for capacity and cost are provided for the more novel battery energy storage systems.
- Insights into the manufacturing infrastructure required for renewable technologies’ continued growth are shown as among the most influential drivers of capacity expansion.

## 2. Materials and Methods

#### 2.1. Global Installed Electricity Generation Capacity Scenarios

#### 2.2. Global Installed Electricity Generation Capacity Growth Profile of Solar and Wind Technologies

^{2}) included in Table 2 are particularly low for linear regressions, which do not reflect the generation capacity acceleration over time for solar and wind technologies. While this analysis determined high R

^{2}(greater than 0.9) for exponential regressions, the divergence in early and later years, which can be seen in Figure 4, led to large divergence for periods longer than 20 years. Third-degree polynomials were found to have the highest R

^{2}; however, distortions tended to be amplified when extrapolating over long periods. For example, PV generation capacity for 2050 was determined to be twice the level of IRENA’s REmap scenario, while the result for concentrating solar power was negative. Quadratic extrapolations (second-order polynomials) were found to model reality more accurately for 20 years or more with R

^{2}of more than 0.95 for the 2000–2020 IRENA dataset. Given the use of annual capacity data, the multi-year extrapolation is not subject to seasonality as would be electricity generation data.

#### 2.3. Learning Rates as a Means to Determine Cost Trends

^{b}

_{0}for a doubling in installed capacity and defining X

_{0}as the reference capacity, Y

_{0}as the reference unit cost, and Y

_{doubling}as the unit cost at the time of the doubling of capacity, the learning rate can be derived as follows [34]:

_{0}− Y

_{doubling})/Y

_{0}= 1 − (Y

_{doubling/}Y

_{0}) = 1 − (2X

_{0})

^{b}/

**X**

_{0}

^{b}= 1 − 2

^{b}

- Improvements to production processes such as productivity, process innovations, standardization, and economies of scale;
- Changes in product design, namely, redesign and the use of novel materials;
- Real-term reductions in the input price of materials and labor.

#### 2.4. Empirical and Theoretical Underpinnings

_{0}[GW] of installed generating capacity and P

_{0}[GW/annum] in manufacturing capacity for a given reference year t

_{0}. Assuming a constant rate of change in manufacturing capacity $\dot{P}$ over a relatively stable period, then the increase in manufacturing capacity as a function of time would be as follows:

_{0}for the installed manufacturing capacity in the year 2000 for all the technologies. Except for the onshore wind, these regressions did not yield fitted G

_{0}values consistent with the known installed generating capacity in the reference year. Constrained regressions were then obtained by forcing P

_{0}and G

_{0}values for the reference year (the year 2000 in the case of the 20-year regressions and the year 2010 in the case of the 10-year regressions). The installed manufacturing capacity coefficients P

_{0}values were estimated using a three-year average growth in generation capacity from the reference year. Global electricity generation capacity coefficients G

_{0}values were set to the installed generation capacity for the reference year.

- Onshore wind showed the highest level of coherence over the 10- and 20-year time frames, with a relatively constant rate of increase in manufacturing capacity. Whether unconstrained or forced, the 20-year regressions yielded R
^{2}greater than 0.994. - For both PV and offshore wind, the correlation weakened with R
^{2}reducing from 0.98 to 0.92 when forcing reference manufacturing and generation capacity for the year 2000. The 2010–2020 regression better predicted future trends for both technologies. This result can be attributed to the scaling of manufacturing infrastructure over the last decade. There have been notable shifts for PV since 2010, with Chinese PV production aggressively coming onstream, which is accommodated by a rebasing in manufacturing capacity. - CSP showed a robust correlation between actual generation capacity and the 10- and 20-year regressions. However, the erratic capacity addition provided distortions to the shorter 10-year time series, an unlikely predictor of future trends.

_{0}and G

_{0}measurably reduced the accuracy of generation capacity forecasts. The 10-year 2000 2010 best-fit regression capacity forecasts for the subsequent ten years presented in Figure 6 highlight that the unconstrained regression better predicted global generation capacity evolution for the subsequent decade.

## 3. Results

#### 3.1. A Narrower 2050 Global Electricity Generation Capacity Outlook

- The more representative forecast using the unconstrained 20-year quadratic extrapolation is the primary marker on the graph (yellow bar). The constrained quadratic provides more conservative extrapolations of generation capacity for PV and onshore wind, reflected in the lower value marked with crosses (yellow X).
- PV estimates using the 2010–2020 ten-year regressions are also shown. The forecasts using the shorter, later time series point to an acceleration in global installations reaching or exceeding the upper end of reviewed ranges, in line with IRENA’s 2050 “REmap” scenario [29]. The 133GW or 19% increase in PV power generation capacity in 2021 [3] aligns with this higher forecast.
- Concentrating solar power with a projected 40–50 GW capacity for 2050 is below published scenarios, at less than half of the JRC’s baseline expectation. The marked reduction in capacity addition in later years drives this lower level of adoption.

#### 3.2. Cost Forecasts of Established Renewable Energy Technologies

#### 3.3. Cost Forecasts of Nascent Renewable Energy Technologies

^{2}of 0.992, quadratic regressions were found to be well suited to model the growth of the total lithium-ion battery market since 2011. The quadratic best-fit is also strongly correlated with stationary application growth since 2014, with an R

^{2}of 0.999. When forcing P

_{0}and G

_{0}values for the estimated installed manufacturing and storage capacity for reference years 2011 and 2014, the second-degree coefficient and the R

^{2}marginally decreased, as was the case for PV and wind. For capacity extrapolation, the unconstrained best-fit coefficients were again adopted.

#### 3.4. Limitations

## 4. Discussion

^{2}> 0.9). The forecasts presented in this research are, therefore, based on historical growth drivers and constraints, which may be subject to change over time. Therefore, regular regression updates should be conducted as longer time series become available to ensure that discontinuities or shifts in manufacturing capacity and capabilities are built into future modelling. Furthermore, coherent datasets on generation and manufacturing capacity should enable more advanced analytical techniques to validate the assumption of relatively constant growth in manufacturing capacity. Piecewise linear approximation of quadratic regressions could be an additional tool to address discontinuities observed for solar thermal technologies since 2015 and for the step function increase in offshore wind generating capacity in 2021.

## 5. Conclusions

- Quadratic best-fit regressions on historical global electricity generation capacity help develop narrower, data-driven growth forecasts.
- The multi-year forecasting approach on global installed capacity was shown to apply to solar and wind renewable energy technologies and high-growth novel technologies such as battery energy storage systems.
- By relying on embedded growth dynamics, shown to be strongly correlated to the manufacturing infrastructure of each technology, the approach helps reduce assumption-driven biases.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Global average levelized cost of energy for solar and wind energy technologies [5].

**Figure 2.**Global average Capex projections for major renewable energy technologies [5,10,14] (IEA net zero scenario using US, EU, and China average for PV and wind; JRC utility-scale technology interquartile maximum forecasts for PV and wind adjusted for exchange rate and inflation; and CSP from JRC range only).

**Figure 3.**Schematic of approach to improve forecasts through best-fit regression modelling (dark-grey-shaded boxes are new contributions from this research).

**Figure 4.**Best-fit regressions for the global installed generation capacity of onshore wind [1].

**Figure 5.**Global installed generation capacity quadratic best-fit regressions 2000–2020. (

**a**) Onshore wind and solar photovoltaic. (

**b**) Offshore wind and concentrating solar power.

**Figure 6.**Onshore wind global installed generation capacity forecasts using 2000–2010 best-fit regressions.

**Figure 9.**Global average Capex forecasts from this analysis compared to current and published scenarios [10] (PV: one axis tracking; Onshore: medium specific capacity, medium hub height; Offshore: monopole, medium distance; and CSP: tower with storage).

**Table 1.**Global installed electricity generation capacity for major renewable energy technologies—current base and scenarios.

Global Installed Electricity Generation Capacity (GW) | ||||||
---|---|---|---|---|---|---|

IRENA 2020 Base [5] | EU Joint Research Centre (JRC) 2050 Outlook [10] | IRENA 2050 REmap [30] | IEA 2050 Net Zero [14] | |||

Actual | Baseline | Diversified | ProRES | <2 °C | <1.5 °C | |

Hydropower | 1153 | 1656 | 2193 | 1503 | 1500 | 2599 |

Solar photovoltaic | 707 | 1464 | 4424 | 6745 | 8520 | 14,458 |

Onshore wind | 698 | 1521 | 3037 | 4444 | 5440 | 7439 |

Offshore wind | 34 | 108 | 437 | 1131 | 600 | 827 |

Concentrating solar | 6 | 134 | 939 | 1473 | 300 | 426 |

**Table 2.**Coefficient of determination from best-fit regressions for IRENA’s 2000–2020 global electricity generation capacity dataset.

R^{2} from Regressions on Global Installed Electricity Generation Capacity | ||||
---|---|---|---|---|

Exponential ${{\mathit{a}}_{0}(1+\mathit{C}\mathit{A}\mathit{G}\mathit{R})}^{(\mathit{t}-{\mathit{t}}_{0})}$ | Linear c _{1} (t − t_{0}) + c_{0} | Quadratic c _{2} (t − t_{0})^{2} + c_{1} (t − t_{0}) + c_{0} | Polynomial c _{3} (t − t_{0})^{3} + c_{2} (t − t_{0})^{2} +c_{1} (t − t_{0}) + c_{0} | |

Solar photovoltaic | 0.993 | 0.746 | 0.981 | 0.999 |

Onshore wind | 0.983 | 0.927 | 0.998 | 0.998 |

Offshore wind | 0.996 | 0.760 | 0.978 | 0.999 |

Concentrating solar | 0.916 | 0.880 | 0.956 | 0.976 |

_{0}is the year 2000 (the baseline year).

**Table 3.**The 2020 Onshore wind generation capacity predictions using 5-, 10-, and 15-year regressions (t

_{0}: year 2000).

Period | Regression Coefficients | R^{2} | 2020 Capacity Extrapolation (GW) | 2020 Deviation |
---|---|---|---|---|

2020 Actual | 698 | |||

2000–2015 (15 y) | 1.8 (t − t_{0})^{2} − 3.1 (t − t_{0}) + 25 | 0.998 | 707 | 1% |

2000–2015 (15 y) | 27 ${\left(1.200\right)}^{(\mathit{t}-{\mathit{t}}_{0})}$ | 0.993 | 1038 | 49% |

2000–2010 (10 y) | 1.6 (t − t_{0})^{2} − 0.8 (t − t_{0}) + 22 | 0.995 | 650 | −7% |

2000–2010 (10 y) | 18.9 ${\left(1.253\right)}^{(\mathit{t}-{\mathit{t}}_{0})}$ | 0.999 | 1713 | 145% |

2000–2005 (5 y) | 0.5 (t − t_{0})^{2} + 5.6 (t − t_{0}) + 17 | 0.999 | 325 | −54% |

2000–2005 (5 y) | 18.9 ${\left(1.254\right)}^{(\mathit{t}-{\mathit{t}}_{0})}$ | 0.994 | 1752 | 151% |

Total Installed Costs or Capex (USD/kW Improvement) | LCOE (USD/kWh Improvement) | |||
---|---|---|---|---|

LR from Compiled Literature Review [10] | IRENA Reported LR [5] | IRENA Reported LR [5] | ||

Low | High | 2010–2020 | 2010–2020 | |

Solar photovoltaic | 10% | 35% | 34% | 39% |

Onshore wind | 8% | 20% | 17% | 32% |

Offshore wind | 8% | 15% | 9% | 15% |

Concentrating solar | 5% | 20% | 13–22% | 36% |

Solar Photovoltaic | Onshore Wind | ||||||||
---|---|---|---|---|---|---|---|---|---|

$\dot{\mathit{P}}$ | P_{0} | G_{0} | R^{2} | $\dot{\mathit{P}}$ | P_{0} | G_{0} | R^{2} | ||

(GW) | (GW) | (GW) | (GW) | (GW) | (GW) | ||||

20 y 2000–2020 | Unconstrained | 6.2 | −32 | 54 | 0.981 | 3.4 | −1 | 21 | 0.998 |

P_{0} and G_{0} 2000 estimates | 3.2 | 0.4 | 0.8 | 0.921 | 2.6 | 7 | 17 | 0.994 | |

10 y 2010–2020 | Unconstrained | 11.6 | 7 | 53 | 0.999 | 3.0 | 35 | 181 | 0.995 |

P_{0} and G_{0} 2010 estimates | 6.9 | 32 | 40 | 0.989 | 2.3 | 38 | 178 | 0.995 | |

Offshore wind | Concentrating solar | ||||||||

$\dot{P}$ | P_{0} | G_{0} | R^{2} | $\dot{P}$ | P_{0} | G_{0} | R^{2} | ||

(GW) | (GW) | (GW) | (GW) | (GW) | (GW) | ||||

20 y 2000–2020 | Unconstrained | 0.29 | −1.4 | 2.6 | 0.978 | 0.04 | −0.03 | 0.1 | 0.956 |

P_{0} and G_{0} 2000 estimates | 0.14 | 0.15 | 0.07 | 0.917 | 0.02 | 0.12 | 0.4 | 0.946 | |

10 y 2010–2020 | Unconstrained | 0.6 | 0.2 | 3.4 | 0.999 | −0.07 | 0.9 | 1.2 | 0.971 |

P_{0} and G_{0} 2010 estimates | 0.4 | 1.4 | 3.1 | 0.989 | −0.07 | 0.9 | 1.3 | 0.971 |

_{0}uses year 2000 for 20 year regressions and year 2010 for 10 year regressions.

LCOE Learning Rate | 2050 LCOE Estimates 2000–2020 Capacity Forecasts 20 y Regression | 2050 LCOE Estimates 2010–2020 Capacity Forecasts 10 y Regression | 2050 LCOE Published Forecast and Scenario Range | |||
---|---|---|---|---|---|---|

2020 USD/kWh | Reduction % | 2020 USD/kWh | % of 2020 LCOE | 2020 USD/kWh | ||

Solar photovoltaic | 35% | 0.015 | −74% | 0.011 | −80% | 0.015–0.025 |

39% * | 0.012 | −79% | 0.009 | −84% | ||

Onshore wind | 32% * | 0.014 | −63% | 10 y regression outcome consistent with 20y | 0.03–0.04 | |

35% | 0.013 | −67% | ||||

Offshore wind | 15% * | 0.051 | −39% | 0.045 | −46% | 0.035–0.040 |

20% | 0.042 | −50% | 0.036 | −57% | ||

Concentrating solar | 25% | 0.069 | −36% | 10 y regression distorted with the slowdown | 0.06 | |

36% * | 0.044 | −59% |

**Table 7.**Best-fit regression for lithium-ion global capacity in operation (Total t

_{0}: 2011; Stationary t

_{0}: 2014).

Li-Ion Total | Li-Ion Stationary | ||||||||
---|---|---|---|---|---|---|---|---|---|

$\dot{\mathit{P}}$ | P_{0} | G_{0} | R^{2} | $\dot{\mathit{P}}$ | P_{0} | G_{0} | R^{2} | ||

(GW) | (GW) | (GW) | (GW) | (GW) | (GW) | ||||

8 y 2011–2019 | Unconstrained | 5.5 | −1.8 | 38 | 0.992 | ||||

P_{0} and G_{0} 2011 estimates | 3.6 | 6.5 | 35 | 0.981 | |||||

6 y 2014–2020 | Unconstrained | 0.82 | 0.14 | 1.0 | 0.999 | ||||

P_{0} and G_{0} 2014 estimates | 0.65 | 0.70 | 0.9 | 0.996 |

Learning Rate | 2020 | 2030 | 2050 | ||||
---|---|---|---|---|---|---|---|

Min | Max | Min | Max | Min | Max | ||

BESS Capex 2020 USD/kWh | 20% | 15% | 430 | 210 | 290 | 125 | 210 |

PV + BESS Capex 2020 USD/kW | 30% | 20% | 2044 | 1100 | 1400 | 600 | 950 |

PV + BESS LCOE 2020 USD/kWh | 30% | 25% | 0.065 | 0.034 | 0.043 | 0.017 | 0.026 |

_{0})

^{2}+ 0.14(t − t

_{0}) + 0.98 with t

_{0}: 2014 and 20% LR; maximum (Max) calculated using Li-ion quadratic 2011–2019 regression 2.74(t − t

_{0})

^{2}− 1.76(t − t

_{0}) + 37.5 with t

_{0}: 2010 and 15% LR; PV + BESS outlook derived from quadratic regression PV capacity extrapolation presented in Table 5 using for Min 5.8(t − t

_{0})

^{2}+ 7.1(t − t

_{0}) + 53 with t

_{0}: 2010 and a 30% LR, and for Max 3.1(t − t

_{0})

^{2}– 32(t − t

_{0}) + 54 with t

_{0}: 2000 and a 20% LR for Capex and a 25% LR for LCOE.

Generation Capacity | ||||
---|---|---|---|---|

Actual | Forecast | Deviation | ||

GW | GW | % | ||

Solar Photovoltaic | 2020 | 714 | 704 | −1.3% |

2021 | 855 | 833 | −2.6% | |

2022 | 1047 | 974 | −7.0% | |

Onshore wind | 2020 | 697 | 681 | −2.3% |

2021 | 770 | 750 | −2.6% | |

2022 | 836 | 822 | −1.7% | |

Offshore wind | 2020 | 34 | 35 | 0.3% |

2021 | 54 | 41 | −24.9% | |

2022 | 63 | 48 | −24.6% | |

Concentrating solar | 2020 | 6.5 | 6.3 | −3.1% |

2021 | 6.4 | 6.4 | 0.4% | |

2022 | 6.5 | 6.5 | −0.3% |

_{0})

^{2}+ 7.1 (t − t

_{0}) + 53. Onshore 20-year regression 1.7 (t − t

_{0})

^{2}− 1.0 (t − t

_{0}) + 21. Offshore: 10-year regression 0.29 (t − t

_{0})

^{2}+ 0.21 (t − t

_{0}) + 3.4. CSP: 10-year regression 0.035 (t − t

_{0})

^{2}+ 0.86 (t − t

_{0}) + 1.2.

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**MDPI and ACS Style**

Rozon, F.; McGregor, C.; Owen, M.
Long-Term Forecasting Framework for Renewable Energy Technologies’ Installed Capacity and Costs for 2050. *Energies* **2023**, *16*, 6874.
https://doi.org/10.3390/en16196874

**AMA Style**

Rozon F, McGregor C, Owen M.
Long-Term Forecasting Framework for Renewable Energy Technologies’ Installed Capacity and Costs for 2050. *Energies*. 2023; 16(19):6874.
https://doi.org/10.3390/en16196874

**Chicago/Turabian Style**

Rozon, Francois, Craig McGregor, and Michael Owen.
2023. "Long-Term Forecasting Framework for Renewable Energy Technologies’ Installed Capacity and Costs for 2050" *Energies* 16, no. 19: 6874.
https://doi.org/10.3390/en16196874