Two-Stage Energy Management of Multi-Smart Homes With Distributed Generation and Storage

Abstract

This study presents a new two-stage hybrid optimization algorithm for scheduling the power consumption of households that have distributed energy generation and storage. In the first stage, non-identical home energy management systems (HEMSs) are modeled. HEMS may contain distributed generation systems (DGS) such as PV and wind turbines, distributed storage systems (DSS) such as electric vehicle (EV), and batteries. HEMS organizes the controllable appliances considering user preferences, amount of energy generated/stored and electricity price. A group of optimum consumption schedules for each HEMS is calculated by a Genetic Algorithm (GA). In the second stage, a neighborhood energy management system (NEMS) is established based on Bayesian Game (BG). In this game, HEMSs are players and their pre-determined optimal schedules are their actions. NEMS regulates the total power fluctuations by allowing the energy transfer among households. In the proposed algorithm, HEMS decreases the electricity cost of the users, while NEMS flats the load curve of the neighborhood to prevent overloading of the distribution transformer. The proposed HEMS and NEMS models are implemented from scratch. A survey of 250 participants was conducted to determine user habits. The results of the survey and the proposed system were compared. In conclusion, the proposed hybrid energy management system saves power by up to 25% and decreases cost by 8.7% on average.

A New Pricing Scheme for Intra-Microgrid and Inter-Microgrid Local Energy Trading

“…The constraint Equation (8) limits the stored energy between S min and S max . To ensure that the storages are charged and discharged at the corresponding rates, constraints Equations (10) and (9) are applied.…”

“…Moreover, bi-level optimization problems have been investigated in Reference [8] for cloud based demand management in two different tiers. Similarly, the authors of Reference [9] presents a two-stage hybrid optimization problem for controlling appliance operation at the first stage and and neighbourhood energy management at the second stage.…”

Hybrid risk‐averse energy management optimizer for large‐scale industrial building microgrids

“…The linear framework was solved in general algebraic modeling software (GAMS) 12 . Okumus et al presented a hybrid optimization algorithm for scheduling power consumption in households using GA to minimize cost, shave the load demand peak and its fluctuations to flatten the load curve 13 …”