Demand-Response Based Energy Advisor for Household Energy Management

Alfaverh, Fayiz; Denaï, Mouloud; Alfaverh, Khaldoon

doi:10.1109/worlds4.2019.8904042

Cited by 5 publications

(1 citation statement)

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“…Two peak demand periods occur during morning and evening times when energy prices are higher. Whereas off-peak demand periods correspond to periods of the day where electricity prices are lower since customer's activities such as washing, cleaning, cooking, and watching TV are reduced [30]. Therefore, the aim of this study is to shift the operating time of specific appliances from peak demand hours to off-peak periods without compromising the costumer's preferences.…”

Section: Home Energy Management and Q-learning Modellingmentioning

confidence: 99%

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

2020

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As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer's preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user's preferences, user's feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.

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Section: Home Energy Management and Q-learning Modellingmentioning

confidence: 99%