Optimal location-allocation of storage devices and renewable-based DG in distribution systems

• Published in: Electric power systems research Vol. 172; pp. 11 - 21
• Main Authors: Home-Ortiz, Juan M., Pourakbari-Kasmaei, Mahdi, Lehtonen, Matti, Sanches Mantovani, José Roberto
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.07.2019; Elsevier Science Ltd
• Subjects: Alternative energy; Cluster analysis; Clustering; Conic programming; Convexity; Decision making; Distributed generation; Energy storage; Gas turbines; Mixed integer; Multistage distribution system planning; Radial distribution; Renewable energy sources; Sensitivity analysis; Stochastic programming; Turbines; Uncertainty; Vector quantization; Wind power; Wind turbines; Renewable energy sources; Distributed generation; Multistage distribution system planning; Conic programming; Energy storage; Stochastic programming
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2019.02.013

•Proposing Stochastic multi-stage model for distribution system planning.•Applying scenario reduction from historical data by using k-means.•Using convex relaxation to ease simultaneous allocation of RES and storage devices. This paper proposes a mixed integer conic programming (MICP) model to find the optimal type, size, and place of distributed generators (DG) over a multistage planning horizon in radial distribution systems. The proposed planning framework focuses on the optimal siting and sizing of wind turbines, photovoltaic panels, gas turbines, and energy storage devices (ESD). Inherently, renewable energy sources and electricity demands are subject to uncertainty. To handle such probabilistic situations in decision-making, the MICP model is extended into a two-stage stochastic programming model. To obtain more practical results, annual historical data are used to generate the scenarios. For the sake of tractability, the k-means clustering technique is used to reduce the number of scenarios while keeping the correlation between the uncertain data. Due to convexity, the proposed MICP model guarantees to find the global optimal solution. To show the potential and performance of the proposed model a 69-bus radial distribution system under different conditions is dully studied and a sensitivity analysis is conducted. Results and comparisons approve its effectiveness and usefulness.