Multi-period automated targeting and optimisation for net zero

Abstract

Net-zero targets are not likely to be achievable without the use of negative emission technologies (NETs). Various energy planning tools rarely consider NETs for net-zero emissions planning. Marginal abatement cost (MAC) curves, which plot different emissions reduction options based on specific cost and cumulative emissions reduction, are popular tools for communication and decision support. However, this graphical approach is tedious and hard to use for the calculation of the total system cost. Hence, the automated marginal abatement cost (AMAC) method was recently developed to overcome the limitation of the graphical method. The AMAC was based on the MAC curve but implemented on an optimization platform, which allows it to set rigorous performance targets. In this work, the AMAC is extended for multi-period net-zero emissions planning. The methodology is illustrated with a case study of multiple fossil fuel power plants and two NETs (biochar and bioenergy with carbon capture and storage) with a three-decade planning horizon. In the baseline scenario, it is not possible to achieve net zero due to a deficit of 4.38 Mt CO₂/y of negative emissions. The rest of the scenarios achieved net-zero emissions during their target period. Targeting net-zero emissions in every period is at a higher risk of obtaining extreme values of the total system cost ($ 13,824–32,046 M) compared to targeting net-zero emissions only in the last period ($ 10,312–27,116 M). Multi-period decision support models are critical because of the dynamic nature of inputs needed to achieve net-zero emissions.

Graphical abstract

Appendix

A. Emissions sample calculation

The emissions calculation given the desired power rating in period 2 is demonstrated here. Since no future data on the emissions and power rating is available, the data in period 1 is used to estimate the values for period 2. It is also assumed that there is a linear relationship between the power rating and emissions within the scale of the study. For BECCS, a negative emission capacity of 2.99 Mt CO₂/y is assumed for a 500-MW BECCS power rating (Donnison et al. 2020). The general equation is given by Eq. 13. The subscripts $\mathrm{t}1$ and $\mathrm{t}2$ refer to period 1 and 2, respectively.

$${\mathrm{Emissions}}_{\mathrm{t}2} =\frac{{\mathrm{Desired Power Rating}}_{\mathrm{t}2}\left({\mathrm{Emissions }}_{\mathrm{t}1}\right)}{{\mathrm{Power Rating}}_{\mathrm{t}1}}$$

(13)

Using the data in Table 3 and the data for BECCS, the emissions for coal, NG, and BECCS in period 2 are calculated in Eqs. 14–16.

$$350\left(4.14\right)/500=2.90 \mathrm{Mt }{\mathrm{CO}}_{2}/\mathrm{y}$$

(14)

$$500\left(1.69\right)/500=1.69 \mathrm{Mt }{\mathrm{CO}}_{2}/\mathrm{y}$$

(15)

$$250\left(-2.99\right)/500=-1.50 \mathrm{Mt }{\mathrm{CO}}_{2}/\mathrm{y}$$

(16)

B. Graphical total system cost sample calculation

The following is a sample total system cost calculation for case study Scenario 1.

The areas under the curve for period 1 as shown in Fig.

Fig. 6

Graphical total system cost calculation for case study Scenario 1 for period 1 (a), period 2 (b), and period 3 (c)

6a are summed up following Eq. 17. Similarly, the areas under the curve for period 2 as shown in Fig. 6b, and period 3 as shown in Fig. 6c are summed up following Eqs. 18 and 19.

$$4.14 \left( {9.91} \right) + 1.69\left( {12.21} \right) + 1.25\left( {30} \right) = \$ 99.16 {\text{M}}/{\text{y}}$$

(17)

$$2.9\left(58\right)+1.69\left(58\right)+1.25\left(30\right)+1.5(100)=\$ 453.72 \mathrm{M}/\mathrm{y}$$

(18)

$$0.99\left(70\right)+1.69\left(70\right)+1.25\left(30\right)+3.47(150)=\$ 745.6 \mathrm{M}/\mathrm{y}$$

(19)

Finally, the total system cost is added for all periods and multiplied by the length of one period as shown in Equation 20. The result of the graphical method is identical to that of the automated approach.

$$(99.16+453.72+745.6)10=\$ 12984 \mathrm{M}$$

(20)