Multi-Objective Planning of Distributed Energy Resources Based on Enhanced Adaptive Weighted-Sum Algorithm

A multi-objective planning model of distributed energy resources is proposed in this article to find the balance between carbon emission and economic cost. The economic cost is in the two-stage form including investment and operation cost. A novel enhanced adaptive weighted-sum algorithm (EAWS) with only one sparse-preference parameter is proposed to generate an informative Pareto front. A new calculation method for the energy penetration rate (EPR) is given in order to indicate the integration level of PV units. The reactive power support from PV unit is modeled in the operation stage. The quasi-static time series analysis is used to provide a comprehensive view on the impact of carbon reduction on the power and carbon emission flow. Applying the proposed model to the IEEE 33-bus distribution system, the results demonstrate that more informative Pareto fronts from EAWS can be achieved compared with classical weighted-sum (CWS), classical adaptive weighted-sum (CAWS) and
$\varepsilon -{\text{constraint}}$
methods. The EPR can exceed 100% but there exists an upper limit. The reactive power support reduces the power loss, and the output of PV unit reduces carbon emission intensity with the sacrifice of security operation. The model also achieves a satisfactory convergency performance in the 69-bus and IEEE 141-bus distribution systems.