Optimal nanogrid planning at building level

Abstract

This paper presents a planning framework for active buildings as an Energy Nano-Grid (ENG), determining the optimal size and generation mix of distributed energy resources (DERs) and battery energy storage (BES) system, the type of ENG that can be either AC or DC, and the optimal energy management (EM). Due to the increasing penetration of battery energy storage devices, electric vehicles (EVs) and even DC loads on the utility side, DC ENGs would potentially be more useful than AC ENGs by reducing the number of converters, facilitating the connection of various types of distributed energy resources and loads to the common bus with simplified interfaces, and mitigating the losses associated with AC/DC energy conversion. Therefore, the selection of the type of ENG is an economic issue where the planning objective includes the investment, operation and maintenance costs of energy resources, the investment costs of battery energy storage (BES) and converters, and the costs/revenues for buying/selling energy from/to the upstream grid or neighbor ENGs. In this way, the proposed program achieves an optimal load sharing. Optimal results might be affected in terms of some system specifications such as the ratio of DC load (from 0.4 to 1) at ENG, the maximum permissible installation capacity of BESs (from 200 to 800 kWh), and maximum discharge power that EVs can deliver to the ENG or upstream network (from 50 to 200 kW). Using some numerical case studies associated with three residential ENGs, result show that increase in the rate of DC load has the highest effect on the type of ENG (DC feeder is adopted for DC load rate 0.6 at ENG 1 and ENG 2, and 0.8 at the third one) through decrease in investment and operation costs, meanwhile, the capacity of BES directly affect the size of generation units, and the maximum discharging power of EVs just support peak load supply due to being out of the park lot during the day. The proposed planning model is analyzed in detail to demonstrate its applicability, effectiveness and control.