Real-Time Price Based Home Energy Management Scheduler

Abstract

With the recent development of advanced metering infrastructure, real-time pricing (RTP) scheme is anticipated to be introduced in future retail electricity market. This paper proposes an algorithm for a home energy management scheduler (HEMS) to reduce the cost of energy consumption using RTP. The proposed algorithm works in three subsequent phases namely realtime monitoring (RTM), stochastic scheduling (STS) and real-time control (RTC). In RTM phase, characteristics of available controllable appliances are monitored in real-time and stored in HEMS. In STS phase, HEMS computes an optimal policy using stochastic dynamic programming (SDP) to select a set of appliances to be controlled with an objective of the total cost of energy consumption in a house. Finally, in RTC phase, HEMS initiates the control of the selected appliances. The proposed HEMS is unique as it intrinsically considers uncertainties in RTP and power consumption pattern of various appliances. In RTM phase, appliances are categorized according to their characteristics to ease the control process, thereby minimizing the number of control commands issued by HEMS. Simulation results validate the proposed method for HEMS.Index Terms-Energy demand, home energy management, Markov decision process, real-time electricity price. NOMENCLATURE HEMHome energy management. HEMSHome energy management scheduler. RTP Real-time pricing. RTM Real-time monitoring. STS Stochastic scheduling. RTC Real-time control. CoEC Cost of energy consumption. SDP Stochastic dynamic programming. MDP Markov decision process.Interrupt signal from customer for th appliance.

A Market-Based Optimization Approach for Domestic Thermal and Electricity Energy Management System: Formulation and Assessment

“…In contrast with a centralized optimization-based approach [1,3,4,[6][7][8][9][10][11], the market-based is a decentralized approach where each participant bids according to its local information. This section presents and discusses the market-based optimization conditions which lead to system cost minimization.…”

“…HEMS objective function may be formulated differently depending on whether it leverages forecast uncertainty information i.e., stochastic optimization [1][2][3] or not i.e., deterministic optimization [4][5][6][7][8][9][10][11]. Among them, the most common HEMS optimization approaches formulate this problem as a linear [6,7], mixed integer linear (MILP) [4,5,8,9], quadratic [10,11] or dynamic programming [1] yielding typically 24-h schedule for the appliances.…”

A Compilation of various Home Energy Management Systems in Smart Grid Networks

“…The communication system is based on Zigbee protocol for wireless communication. Dissimilar to OREM, the i-HEM work on real time pricing [19]. The consumer may switch on any appliance to any moment of time irrespective of the peak hour concern and the i-HEM algorithm suggest an appropriate time to the consumer to start the appliance.…”

Towards the Handling Demand Response Optimization Model for Home Appliances

“…Vivekananthan et al [16] proposed an algorithm for a Home Energy Management Scheduler (HEMS) to manage the consumption of residential electrical energy in order to reduce the cost of electricity. The proposal applied stochastic dynamic programming to manage the residential appliances.…”