Home Energy Management Systems under Effects of Solar‐Battery Smart Inverter Functions

Abstract

Rooftop solar panels paired with home‐scale batteries are increasingly popular in the residential area and they bring a fantastic opportunity to households, considered as prosumers, to make benefits from trading electricity. Unlike research in the literature, we consider the trading with technical influences of utility requirements: Volt‐Watt and Volt‐Var functions. As possible active power curtailments may occur and lead to economic losses to households, our work reveals how a home energy management system (HEMS) maximizes profit from trading energy with an aggregator and an electricity retailer. Appropriate optimization problems are formulated with technical constraints, these functions, and home appliances. HEMS responses are optimized in two phases. The first phase is for planning an optimal day‐ahead energy commitment to the aggregator with a consideration of the predicted power curtailments. On the day of operation, the second phase tries to follow the commitment by reoptimizing the responses in the real condition. Simulation of a practical power system shows that addressing these functions can mitigate power curtailment, reduce economic loss, and improve home profits. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Recent Techniques Used in Home Energy Management Systems: A Review

“…Category Technique Simulation Platform [19] Traditional techniques Linear programming MATLAB [20] Traditional techniques Mixed-integer linear programming MATLAB [21] Traditional techniques Mixed-integer linear programming - [22] Traditional techniques Mixed-integer linear programming - [23] Traditional techniques Mixed-integer linear programming MATLAB [24] Traditional techniques Mixed-integer linear programming GAMS [25] Traditional techniques Mixed-integer linear programming IBM ILOG CPLEX optimization studio [26] Traditional techniques Mixed-integer linear programming MATLAB [27] Traditional techniques Non-linear programming MATLAB [28] Traditional techniques Mixed-integer non-linear programming AIMMS [29] Traditional techniques Mixed-integer non-linear programming MATLAB [30] Traditional techniques Dynamic programming MATLAB [31] Traditional techniques Dynamic programming - [32] Traditional techniques Stochastic programming GAMS [33] Traditional techniques Stochastic programming GAMS [34] Traditional techniques Stochastic programming - [35] Traditional techniques Stochastic programming MATLAB [36] Traditional techniques Stochastic programming GAMS [37] Traditional techniques Stochastic programming Java [38] Traditional techniques Stochastic programming GAMS [39] Traditional techniques Stochastic programming GAMS [40] Traditional techniques Stochastic programming - [41] Traditional techniques Stochastic programming GAMS [42] Traditional techniques Robust programming Python [43] Traditional techniques Robust programming GAMS [44] Traditional techniques Robust programming GAMS [45] Model predictive control - [46] Model predictive control - [47] Model predictive control MATLAB The number of selected articles according to these categories is shown in Table 4. The category, technique, and simulation platform of the articles are shown in Table…”

“…Other studies considered more technical aspects of the residence energy management. An example may be found in [26], where the trading with technical influences of utility requirements is considered: Volt-Watt and Volt-Var functions.…”

Arisk‐basedenergy management system design for grid‐connectedsmart homes

“…The aggregator explores maximum gains due to prices on the power pool market, distribution system constraints, and requirements to determine economic incentives for demand response. Recent research 11 has shown that using solar panels and batteries for optimal EMS in SHs has had a significant impact on optimal energy management. A new study looks at the presence of this equipment in the SH due to making a profit to participate in the power market.…”

A systematic review of energy management system based on various adaptive controllers with optimization algorithm on a smart microgrid

“…High peak demand is likely to occur often, posing a danger to system functioning. The electric utility and system operators may use EMS to minimize the risk of excessive peak demand 7 . The EMS is essential for a smarter grid during peak hours since it is automated controlling of the load and does not need human involvement to provide accurate findings and predictions.…”

“…The electric utility and system operators may use EMS to minimize the risk of excessive peak demand. 7 The EMS is essential for a smarter grid during peak hours since it is automated controlling of the load and does not need human involvement to provide accurate findings and predictions. The energy loss in the network and lines may be reduced by optimizing the functioning of the generating units.…”