An Inventive Method for Eco-Efficient Operation of Home Energy Management Systems

Abstract

A demand response (DR) based home energy management systems (HEMS) synergies with renewable energy sources (RESs) and energy storage systems (ESSs). In this work, a three-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of HEMS. The proposed method provides the trade-off between the net cost of energy ( C E n e t ) and the time-based discomfort ( T B D ) due to shifting of home appliances (HAs). At step-1, primary trade-offs for C E n e t , T B D and minimal emissions T E M i s s are generated through a heuristic method. This method takes into account photovoltaic availability, the state of charge, the related rates for the storage system, mixed shifting of HAs, inclining block rates, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. A filtration mechanism (based on the trends exhibited by T E M i s s in consideration of C E n e t and T B D ) is devised to harness the trade-offs with minimal emissions. At step-2, a constraint filter based on the average value of T E M i s s is used to filter out the trade-offs with extremely high values of T E M i s s . At step-3, another constraint filter (made up of an average surface fit for T E M i s s ) is applied to screen out the trade-offs with marginally high values of T E M i s s . The surface fit is developed using polynomial models for regression based on the least sum of squared errors. The selected solutions are classified for critical trade-off analysis to enable the consumer choice for the best options. Furthermore, simulations validate our proposed method in terms of aforementioned objectives.

A Cost-Effective Optimization for Scheduling of Household Appliances and Energy Resources

“…In the recent decade, various studies [29][30][31][32][33][34][35][36][37][38][39][40][41][42] also have been documented to solve home energy scheduling problems for the smart home with only RESs integration, or both RESs and ESS integration. In study [29], authors proposed a hybrid of stochastic programming and robust optimization model for the HEM system by incorporating the uncertainties of solar photovoltaic (PV) electricity output and electricity tariffs.…”

“…In [38], authors proposed a three-step simulation method based on a heuristic approach to design the HEM system for eco-efficient operations of the household appliances in a smart home by utilizing deployed RESs and ESS at home. In the proposed approach, household appliances have been scheduled to achieve objectives such as reduce the total cost of electricity, emission, and time-based user's discomfort.…”

A Weighted-Sum PSO Algorithm for HEMS: A New Approach for the Design and Diversified Performance Analysis

“…The proposed model for HEMS operation is included with the PV unit based on the forecasted value of solar irradiations. The power obtained from the PV unit is formulated as follows [24]:…”

Enhanced whale optimization algorithm for sizing of hybrid wind/photovoltaic/diesel with battery storage in Algeria desert

“…However, there are important growing concerns caused by these sources, which are used mainly and abundantly in domestic, industrial, and transportation applications, such as air pollution, greenhouse gas emissions, and climate change all over the globe (Khojasteh et al, 2018; Paris, 2020; Yousefi et al, 2017). Therefore, the actual tendency of the energy production and consumption at all level, is to find new alternative sources mostly environmental friendly to overcome this gap such as renewable energy sources, which have been already exploited during the last two decades in ascendant manner (Ahmad et al, 2017; Hussain et al, 2018). At the end of 2019, the global electrical energy capacity generation reached 2537 GW with an increase rate of 7.45% and 15.6% in respect to 2018 and 2017 respectively (which provided approximately 26.5% of global electricity) (REN21, 2018).…”