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.
Highlights
From the previous decades, the energy requirement has grown to a critical level; the generation units have not been maintained at a sufficient rate to manage this increasing demand.The balance between demand and generation is a vital requirement for stable power system operation.The problem to maintain this balance has conventionally been addressed in the past; utilities have upgraded their centralized generation units and transmission capabilities through some supply side management methodologies
Horizon divided into 4 windows; home appliances (HAs) classified in terms of occupancy, activity and delay tolerance are operated in designated windows; Objectives for cost of energy (CE) and time-based discomfort (TBD) are combined through the weighted sum method (WSMD) for user comfort
The simulations are conducted using MATLAB 2015 and are reported in Section 6.1 based on Algorithm 1. These results show the validity of multi-objective genetic algorithm or Pareto optimization (MOGA/PO) based heuristic for DRSREODLDG-based
Summary
The energy requirement has grown to a critical level; the generation units have not been maintained at a sufficient rate to manage this increasing demand.The balance between demand and generation is a vital requirement for stable power system operation.The problem to maintain this balance has conventionally been addressed in the past; utilities have upgraded their centralized generation units and transmission capabilities through some supply side management methodologies. The energy requirement has grown to a critical level; the generation units have not been maintained at a sufficient rate to manage this increasing demand. The balance between demand and generation is a vital requirement for stable power system operation. The problem to maintain this balance has conventionally been addressed in the past; utilities have upgraded their centralized generation units and transmission capabilities through some supply side management methodologies. During the previous decade, demand-side management (DSM) has become a substituent scheme to manage the increasing requirement of energy which focuses on the consumer side. The home energy management system (HEMS) is used to implement. Major approaches for HEMS operation include price-based demand response (DR), Energies 2018, 11, 3091; doi:10.3390/en11113091 www.mdpi.com/journal/energies
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