A Low Complexity and Secure Demand Response Technique for Peak Load Reduction

Parizy, Ehsan Saeidpour; Bahrami, Hamid Reza; Choi, Seungdeog

doi:10.1109/tsg.2018.2822729

Cited by 86 publications

(38 citation statements)

References 22 publications

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“…The PV system with storage elements is incorporated in the proposed model to facilitate a DSM program, so that the consumers can schedule their demands with a higher degree of freedom to maximize their comfort. Unlike other approaches, Reference [43] presented a hopping DR scheme which is actually a heuristic optimization technique that has low time complexity and improved consumer's privacy by notably reducing communication between loads and grid. The proposed scheme significantly reduces the average PAR, energy prices and required frequency bandwidth for communication purposes as compared with DLC approach.…”

Section: Background Literaturementioning

confidence: 99%

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

et al. 2020

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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.

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Section: Background Literaturementioning

confidence: 99%

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

et al. 2020

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“…With a centralized coordination, a basic objective is to manage the load by one prevalent element which is an aggregator or the utility [25,26]. Centralized coordination can result in powerful outcomes; however, it requires point by point data of homes [27], which is not functional and furthermore tends to cause a substantial calculation burden.…”

Section: Central Energy Managementmentioning

confidence: 99%

Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources

et al. 2018

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Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort..

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“…Thus, the EDNs demanded power will possess more flexibility and controllability in a smart grid. Herein, demand response (DR) programs have been suggested to be implemented in EDNs …”

Section: Introductionmentioning

confidence: 99%

“…In recent years, several remarkable efforts have been conducted in order to utilize the benefits of DR programs in EDNs operation and planning . Balijepalli et al presented a comprehensive review of different advantages of executing DR programs for EDNs.…”

Section: Introductionmentioning

confidence: 99%

“…[13][14][15][16][17][18][19][20][21][22][23][24][25] In recent years, several remarkable efforts have been conducted in order to utilize the benefits of DR programs in EDNs operation and planning. [13][14][15][16][17][18][19] Balijepalli et al 13 presented a comprehensive review of different advantages of executing DR programs for EDNs. In addition, DR programs have been implemented in Wu et al 14 for management of congestion states in EDNs regarding wind power in a deregulated energy market.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A stochastic formulation for distribution automation planning incorporating emergency demand response programs

Mohammadi

Soleymani

Mozafari

et al. 2019

Int Trans Electr Energ Syst

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Summary Advanced distribution automation (ADA) is a vital basis of the smart grid, which leads to enhancement of reliability level. Moreover, demand response programs are highly proposed to be employed in the smart grid. In this paper, a cross‐sectional benefit of smart grid maturity model is proposed in which the emergency demand response programs (EDRPs) are considered in the ADA planning problem. In fact, the role of EDRPs is considered in the service restoration process that affects the ADA plan. The service restoration process possesses several uncertain parameters including the demanded power of customers during service restoration, which are considered in the proposed formulation for ADA planning. The objective function of the optimization problem includes various costs imposed to the distribution system operator due to contingency occurrence including the expected total customer interruption cost, total investment cost of automatic sectionalizing switches, circuit breakers, and fuses deployment, and the total expected cost of implementing EDRP programs in the service restoration. The presented ADA planning methodology is tested on the fourth bus number of Roy Bilinton test system (RBTS4) to investigate its effectiveness.

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A Low Complexity and Secure Demand Response Technique for Peak Load Reduction

Cited by 86 publications

References 22 publications

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources

A stochastic formulation for distribution automation planning incorporating emergency demand response programs

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