Investment & generation costs vs CO2 emissions in the distribution system expansion planning: A multi-objective stochastic programming approach

Currently there is a great concern about climate change and its mitigation is one of the main reasons to encourage the development of more sustainable energy systems. Advanced methods are needed to support the planning process in which not just economic criteria are considered but also environmental issues such CO2 emissions related to energy generation. Hence, renewable distributed generation (DG) has been increasing in the last years to provide sustainable energy with low environmental impacts. Nevertheless, renewable DG introduces new challenges in the distribution system expansion planning problem (DSEP) due to its uncertain nature. To deal with those issues, this paper proposes a multi-objective approach based on Stochastic Programming for the DSEP, which addresses the minimization of two conflicting objectives: investment & generation costs and CO2 emissions. The uncertainties related to wind, irradiation, and demand are modeled through representative scenarios under a mixed-integer linear programming formulation. Multi-period investments on substations, circuits, and DG allocation are considered to maintain the feasible operation. The multi-objective formulation is solved using off-the-shelf commercial software and the well-established epsilon-constraint method. Tests in a 54-node distribution system show that robust expansion plans considering CO2 emissions result in larger penetration of renewable resources; the found set of Pareto solutions represents the trade-off between cost and emission objectives that can be used by the expansion-planner to accomplish specific needs (e.g., budget limitations, emissions reduction target, or environmental constraints).

Automated summary

This paper proposes a multi-objective approach based on Stochastic Programming for Distribution System Expansion Planning (DSEP) to minimize investment & generation costs and CO2 emissions by modeling uncertainties through mixed-integer linear programming. Tests on a 54-node distribution system show that considering CO2 emissions in robust expansion plans leads to larger penetration of renewable resources and offers a trade-off between cost and emission objectives for expansion planners to make informed decisions based on specific needs.