To ensure sustainability in design and capacity expansion, some researchers have used the findings from LCA studies to inform, critique, and compare energy policies and decisions. For instance, authors in [40] analyzed the effectiveness of energy transition plans of UK’s largest utility. Authors in [70] adopted a similar approach but compared proposed grid mixes in 2030 versus 2010. In [49], an LCA-based method was developed to help policymakers with deploying energy storage systems. In another study, authors in [71] developed an LCA-based methodology for determining carbon tax incentives that only considered environmental impacts and not cost. Lastly, LCA can help influence policies and regulations within electricity markets by coupling with other models such as optimization, geospatial informatics, net energy analysis, building information, and network theory [44,50,54,58,65,72].

Alternatively, many researchers have proposed sustainability-focused power grid capacity expansion planning with embedded objectives and/or constraints that model environmental impacts. These can be designed to encourage renewable generation technologies or to place limits on non-renewable alternatives (Figure 3). Naturally, one of the easiest ways to expand the power grid in a sustainable fashion is to limit generation options to only renewables and disregard higher-polluting ones. This approach has been adopted by some authors, for instance, in [10,63,73,74]. Alternatively, authors in [75] proposed to incorporate the external costs of energy generation and transmission (e.g., costs due to environmental and societal damages) into the marginal production costs so that it impacts the supply and demand equilibrium. Authors in [76,77,78,79] incorporated a cost term in the objective function to reflect emissions. In [80], a carbon tax term was added to the cost objective function.

Authors in [81] introduced cost and revenue functions associated with carbon capture and storage (CSS) for non-renewable technologies. The cost was associated with the installation of the CSS technology, which also uses a nonnegligible portion of the electricity produced by the plant. The revenue was modeled to reflect selling the captured CO₂ as a commodity or tax credits received for capturing carbon.

If financial incentives are allocated to new renewable technologies, their costs need to be included in the model as well. For instance, authors in [82] included a term in their cost function to reflect the incentive payments made by the utility. This term was a linear function of the level of demand reduction (in MW) and energy conservation (in MWh). The former variable was then included in the adequacy constraint, whereas the latter was included in the energy balance constraint. The authors argued that this enables the planning authority to design optimal rates of renewable integration and energy conservation targets.

Alternatively, installing new renewable plants can be incentivized indirectly by enforcing limits on emissions. Authors in [79,83,84] modeled the problem as minimizing the cost of new technologies to be installed and introduced an additional constraint to limit the emissions from generation. However, as pointed out in the previous section, emissions only account for one aspect of the environmental impacts of new technologies, and an LCA-based approach is the only viable option to model all important undesired impacts. This has been the focus of some researchers in recent years. For instance, authors in [72] developed an optimization framework for a case study in China to allocate energy resources that can supply the load with lower environmental impacts than the business as usual (BAU) model. They considered material input, GWP, and water deprivation as the impact categories. Their objective function was defined as the sum of environmental impacts per unit of each energy resource used to supply the demand, and the model was solved subject to constraints listed as the entire demand being met (overall power balance with no network constraints), limits on the levels of each of the environmental impact categories, and each environmental impact must reduce compared to BAU. The environmental factors for each technology were determined based on a cradle-to-grave LCA analysis. Authors in [71] compared the cost-optimal study of the European power system based on carbon tax incentives with one in which the goal was to minimize environmental impacts, modeled based on a total of 18 indicators. They calculated the annual emissions as a function of the energy mix for 12 representative days in the year and concluded that shifting from an economic to an environment-focused model increased the share of renewables by 2.65% and decreased the overall emissions by 9%. Further, it resulted in upgrading the power grid to accommodate the renewables, leading to a 1.5% increase in the overall cost of the power system. In [85], GHG emissions were incorporated into the grid capacity expansion planning by introducing a new objective function alongside the traditional cost minimization objective. Emission categories were calculated using LCA and combined into investment (for new generation capacity) and operation (for new and existing generation) categories. In their case study, the authors noted an 82% reduction in emissions at the expense of a 63% increase in cost. A similar approach was proposed in [86], where generation expansion planning was combined with LCA into a multi-objective optimization framework. The impacts of the energy supply were divided into a fixed portion, reflecting the costs and impacts associated with the infrastructure construction as well as the upstream processes necessary, and a variable portion, which included costs and impacts associated with the production of electricity. The model was solved subject to constraints associated with power balance, unit capacity limits, and shutdown and start-up of generators, to name a few.

Sometimes the push towards sustainability is guided by a renewable energy target to be met. Authors in [79] introduced constraints that reflected the desired renewable installed capacity and lower/upper bounds for regional expansion of capacity. A similar target for the actual (not nominal) renewable energy capacity was adopted as a constraint in [78]. Authors in [10] introduced a constraint in their model to reflect the wind energy target to be met each year: In each year, the capacity of each wind farm multiplied by its capacity factor, summed over all wind farms, must exceed that year’s wind penetration requirement.

Renewable energy resources are intermittent and stochastic in nature, making them different from fossil fuel plants from a reliability perspective. The capacity value of a generation resource, which is defined as the fraction of its rated capacity that is considered firm, should be included in the calculations for determining the amount of reserve margin and/or energy storage capacity needed. To address this, authors in [79] modeled constraints that ensured that secured capacity was higher than peak load of the year. Authors in [87] incorporated the capacity value of variable generation in the form of resource adequacy constraints. Alternatively, the uncertainties of the renewable energy resources can also be modeled in the form of stochastic-based optimization, where first-stage variables indicate installation decisions and second-stage variables concern the operation of the resource under different scenarios. One such example is reported in [15].

Some researchers have also studied the suitability of various generation technologies. For instance, authors in [79] included a suitability objective function modeled as distance to the river for cooling purposes of steam power plants, infrastructure for gas distribution/storage for gas turbines, etc. As another example, authors in [88] developed a fuzzy logic model to rank various green energy alternatives for capacity expansion. They considered social (number of jobs created, balanced development across regions), technical (technology maturity, production capacity), environmental (land requirement, emission reduction), and economic factors (investment cost, affordability of cost of energy).

Other incentives have also been included in the capacity expansion model. As an example, authors in [89] considered the potential of DERs (general resources, not focused on renewables) in reducing the peak load, which could allow for avoiding or delaying the need for capacity expansion (referred to as non-wire alternatives (NWAs)). They considered the timing of grid expansion as a variable, which could be affected by the contribution of those DERs, and investigated whether delaying or avoiding capacity expansion investments would balance the costs of NWAs.

Finally, as the modern distribution grid becomes equipped with a larger number of distributed energy resources, some authors argue that transmission and distribution planning problems need to be combined because, if solved in isolation, the solutions to each one would likely be suboptimal. Such approaches have been proposed, for instance, in [15,90].