Аннотации:
Distributed renewable energy systems are now widely installed in many buildings, transforming the buildings into 'electricity prosumers'. Additionally, managing shared energy usage and trade in smart buildings continues to be a significant difficulty. The main goal of solving such problems is to flatten the aggregate power consumption-generation curve and increase the local direct power trading among the participants as much as possible. This study provides a coordinated smart building energy-sharing concept for smart neighborhood buildings integrated with renewable energy sources and energy storage devices within the building itself. This neighborhood energy management model's primary objective is to reduce the total power cost of all customers of smart buildings in the neighborhood by increasing the use of locally produced renewable energy. In the first stage, a group of optimum consumption schedules for each HEMS is calculated by an Improved Butterfly Optimization Algorithm (IBOA). A neighborhood energy management system (NEMS) is established in the second stage based on a consensus algorithm. A group of four smart buildings is used as a test system to evaluate the effectiveness of the suggested neighborhood smart building energy management model. These buildings have varying load profiles and levels of integration of renewable energy. In this paper, the proposed framework is evaluated by comparing it with the Grey Wolf optimization (GWO) algorithm and W/O scheduling cases. With applying GWO, the total electricity cost, peak load, PAR, and waiting time are improved with 3873.723 cents, 21.6005 (kW), 7.162225 (kW), and 87 s respectively for ToU pricing and 11217.57 (cents), 18.0425(kW), 5.984825 (kW), and 98 s respectively for CPP tariff. However, using the IBOA Improves the total electricity cost, peak load, PAR, and waiting time by 3850.61 (cents), 20.1245 (kW), 6.7922 (kW), and 53 s respectively, for ToU and 10595.8 (cents), 17.6765(kW), 5.83255(kW), and 74 s for CPP tariff. Also, it is noted that the run time is improved using GWO and IBOA by 13% and 47%, respectively, for ToU and 2% and 26% for CPP. However, the number of iterations required to obtain the optimal solution is reduced using the GWO and IBOA by 60% and 81% for ToU and 55% and 80% for CPP tariffs. The results show significant improvements obtained by applying just intelligent programming and management.
