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Muhammad Awais
Department of Technology, The University of Lahore, Lahore 54000, Pakistan

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Journal article
Published: 27 May 2021 in Sustainability
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Price based demand response is an important strategy to facilitate energy retailers and end-users to maintain a balance between demand and supply while providing the opportunity to end users to get monetary incentives. In this work, we consider real-time electricity pricing policy to further calculate the incentives in terms of reduced electricity price and cost. Initially, a mathematical model based on the backtracking technique is developed to calculate the load shifted and consumed in any time slot. Then, based on this, the electricity price is calculated for all types of users to estimate the incentives through load shifting profiles. To keep the load under the upper limit, the load is shifted in other time slots in such a way to facilitate end-users regarding social welfare. The user who is not interested in participating load shifting program will not get any benefit. Then the well behaved functional form optimization problem is solved by using a heuristic-based genetic algorithm (GA), wwhich converged within an insignificant amount of time with the best optimal results. Simulation results reflect that the users can obtain some real incentives by participating in the load scheduling process.

ACS Style

Thamer Alquthami; Ahmad Milyani; Muhammad Awais; Muhammad Rasheed. An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective. Sustainability 2021, 13, 6066 .

AMA Style

Thamer Alquthami, Ahmad Milyani, Muhammad Awais, Muhammad Rasheed. An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective. Sustainability. 2021; 13 (11):6066.

Chicago/Turabian Style

Thamer Alquthami; Ahmad Milyani; Muhammad Awais; Muhammad Rasheed. 2021. "An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective." Sustainability 13, no. 11: 6066.

Journal article
Published: 29 February 2020 in Energies
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In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company.

ACS Style

Zubair Khalid; Ghulam Abbas; Muhammad Awais; Thamer Alquthami; Muhammad Babar Rasheed. A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid. Energies 2020, 13, 1062 .

AMA Style

Zubair Khalid, Ghulam Abbas, Muhammad Awais, Thamer Alquthami, Muhammad Babar Rasheed. A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid. Energies. 2020; 13 (5):1062.

Chicago/Turabian Style

Zubair Khalid; Ghulam Abbas; Muhammad Awais; Thamer Alquthami; Muhammad Babar Rasheed. 2020. "A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid." Energies 13, no. 5: 1062.

Journal article
Published: 14 July 2016 in Energies
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This paper presents real time information based energy management algorithms to reduce electricity cost and peak to average ratio (PAR) while preserving user comfort in a smart home. We categorize household appliances into thermostatically controlled (tc), user aware (ua), elastic (el), inelastic (iel) and regular (r) appliances/loads. An optimization problem is formulated to reduce electricity cost by determining the optimal use of household appliances. The operational schedules of these appliances are optimized in response to the electricity price signals and customer preferences to maximize electricity cost saving and user comfort while minimizing curtailed energy. Mathematical optimization models of tc appliances, i.e., air-conditioner and refrigerator, are proposed which are solved by using intelligent programmable communication thermostat ( iPCT). We add extra intelligence to conventional programmable communication thermostat (CPCT) by using genetic algorithm (GA) to control tc appliances under comfort constraints. The optimization models for ua, el, and iel appliances are solved subject to electricity cost minimization and PAR reduction. Considering user comfort, el appliances are considered where users can adjust appliance waiting time to increase or decrease their comfort level. Furthermore, energy demand of r appliances is fulfilled via local supply where the major objective is to reduce the fuel cost of various generators by proper scheduling. Simulation results show that the proposed algorithms efficiently schedule the energy demand of all types of appliances by considering identified constraints (i.e., PAR, variable prices, temperature, capacity limit and waiting time).

ACS Style

Muhammad Babar Rasheed; Nadeem Javaid; Muhammad Awais; Zahoor Ali Khan; Umar Qasim; Nabil Alrajeh; Zafar Iqbal; Qaisar Javaid. Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes. Energies 2016, 9, 542 .

AMA Style

Muhammad Babar Rasheed, Nadeem Javaid, Muhammad Awais, Zahoor Ali Khan, Umar Qasim, Nabil Alrajeh, Zafar Iqbal, Qaisar Javaid. Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes. Energies. 2016; 9 (7):542.

Chicago/Turabian Style

Muhammad Babar Rasheed; Nadeem Javaid; Muhammad Awais; Zahoor Ali Khan; Umar Qasim; Nabil Alrajeh; Zafar Iqbal; Qaisar Javaid. 2016. "Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes." Energies 9, no. 7: 542.