Optimal Integrated Energy System Planning With DG Uncertainty Affine Model and Carbon Emissions Charges

Integrated energy systems (IES) with cooling, heat, electricity, and natural gas have drawn significant interest recently as we embrace more sustainable energy a midst climate change. However, the uncertain outputs of distributed generators (DGs) make it challenging for IES planning while maintaining low-cost installation and operation under carbon emission constraints. To tackle the challenge, this work proposes an optimal planning model for IES considering both DG output uncertainties and carbon emission punishments. To reduce the conservatism of the widely-adopted interval and affine algorithms, an affine model based on the matrix form is first proposed to model the uncertain DG outputs. A tiered dynamic charging cost model is further developed to introduce and minimize carbon emissions with a punishment mechanism at the planning stage. Based on these two sub-models, an optimal IES planning model is proposed to simultaneously minimize the overall costs of investment, operation, and carbon emissions. To solve the multi-dimensional nonlinear model, an improved quantum particle swarm optimization (IQPSO) algorithm is introduced with enhanced global optimization ability. Simulation results on the IEEE 33-bus and 69-bus IES network have demonstrated that the proposed method can effectively reduce the impacts of DG uncertainty and carbon emissions at the planning stage of IES with a better long-term economy.