A novel generic architecture for the implementation of demand response programs in a smart grid

Ibrahim, Charles; Mougharbel, Imad; Daher, Nivine Abou; Kanaan, Hadi Y.; Saad, Maarouf; Semaan, Georges

doi:10.1109/icit.2017.7915419

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…This paper is an extension of the previous works conducted in [38] where global architecture along with its layers is identified. Suppose that industrial, commercial and residential customers are managed through multiple DRPs types like TOU, DLC and RTP tariffs.…”

Section: Proposed Methodsmentioning

confidence: 96%

Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution

et al. 2020

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Industrial Customers are dispersed at various levels of the electrical network and fed together with other customers' categories in a distributed environment. Optimizing industrial processes in the presence of other customers' categories supplied by the same infrastructure is a challenging issue. Existing studies have analyzed the effect of different Industrial Demand Response Programs on the distribution network, which also supplies other customers' categories. They show the need for improving the distribution performance although multiple demand response programs have been suggested for this purpose. In this paper, a new approach is presented considering an optimal synchronized process among all consumers' categories. It shows that the balance between generation and demand is maintained, the customer satisfaction is guaranteed, the profit is maximized and the cost is minimized for all customers. Various time constraints set by different industry productions are considered in the optimization process. Fairness problems, multiple pricing schemes and formulation for the same are elaborated. The method is validated through a simulation on Matlab using K-Means Clustering and multi-objective particle swarm optimization (MOPSO) along with data prediction. INDEX TERMS Multiple demand response programs, MOPSO, group method of data handling (GMDH), Kmeans, clustering, smart grid, industrial customers.

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Section: Proposed Methodsmentioning

confidence: 96%

Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution

et al. 2020

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“…Obtained results in this paper have been achieved further to the exploration of the following areas, which were essential and part of the current conducted works: Clustering and data mining techniques serving the multiple DRP application when assessing customers and predicting their patterns thus helping in (a) detection of prospect customers and their accurate consumption behaviour suitable for implementing DRPs as in [14–16], (b) development of criteria for a good baseline for DRP as in [17]. Architectures suiting the multiple DRP applications and their characteristics (a) global along with its layers as in [18], (b) generic including functionalities like prediction, learning and other topics as in [19], (c) multiple agent system based with local and global control hubs as in [20] or macro, micro grids connectivity based as in [21]. Pricings methodology ensuring customer‐guaranteed response (a) joint online learning and time slots pricing as in [22], (b) generic demand model capturing the aggregated effect of price‐responsive prosumers as in [23], (c) scheduling day‐ahead model for elastic price‐based demand bidding as in [24], (d) Two‐stage model for the retailer energy pricing as in [25].…”

Section: Existing Approaches For the Management Of Multiple Drpsmentioning

confidence: 99%

“…Architectures suiting the multiple DRP applications and their characteristics (a) global along with its layers as in [18], (b) generic including functionalities like prediction, learning and other topics as in [19], (c) multiple agent system based with local and global control hubs as in [20] or macro, micro grids connectivity based as in [21].…”

Section: Existing Approaches For the Management Of Multiple Drpsmentioning

confidence: 99%

Two stages K‐means and PSO‐based method for optimal allocation of multiple parallel DRPs application & deployment

et al. 2020

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Different types of Demand Response Programmes (DRPs) exist and can be simultaneously offered by the electrical utilities through established contracts with customers. Operating simultaneously multiple types of DRPs might lead to undesired results. DRPs might have different responses to objectives, time-based ones tend to maximise consumption during lowest tariffs periods while incentive-based ones tend to reduce the usage based on peak events, accordingly contradiction might occur. Thus, synchronising these DRPs and their parameters through an optimised process including customer selection for the appropriate one is a mandatory step. A fair allocation of the various types of DRPs including their execution's priority at a specific time is the main objective of this study. An original approach based on clustering technique for predicting customers' behaviour coupled with a particle swarm optimisation (PSO) to reach an optimal solution for relocation is presented. In this study, an optimal solution is developed; it provides the various DRPs with the most convenient parameters for the best demand/ generation balance, utility profit maximisation and operational cost minimisation. The method is validated through a simulation applying a time-based with two incentive-based DRPs in the presence of conventional and renewable generation while using Kmeans clustering and PSO on Matlab.

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“…Director mainly commands the network/socket application programming interfaces to generate and select the most suitable interfaces depending on the application requirements. In [ 18 ], Ibrahim et al demonstrated a generic architecture for demand response programs in a smart grid. The architecture is divided into four concentric layers, namely context, operation, infrastructure, and platform, which are related to technical, environmental, and economic aspects to improve energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

Infrastructure for Integration of Legacy Electrical Equipment into a Smart-Grid Using Wireless Sensor Networks

Araújo

Filho

Rodrigues

et al. 2018

Sensors

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At present, the standardisation of electrical equipment communications is on the rise. In particular, manufacturers are releasing equipment for the smart grid endowed with communication protocols such as DNP3, IEC 61850, and MODBUS. However, there are legacy equipment operating in the electricity distribution network that cannot communicate using any of these protocols. Thus, we propose an infrastructure to allow the integration of legacy electrical equipment to smart grids by using wireless sensor networks (WSNs). In this infrastructure, each legacy electrical device is connected to a sensor node, and the sink node runs a middleware that enables the integration of this device into a smart grid based on suitable communication protocols. This middleware performs tasks such as the translation of messages between the power substation control centre (PSCC) and electrical equipment in the smart grid. Moreover, the infrastructure satisfies certain requirements for communication between the electrical equipment and the PSCC, such as enhanced security, short response time, and automatic configuration. The paper’s contributions include a solution that enables electrical companies to integrate their legacy equipment into smart-grid networks relying on any of the above mentioned communication protocols. This integration will reduce the costs related to the modernisation of power substations.

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A novel generic architecture for the implementation of demand response programs in a smart grid

Cited by 6 publications

References 21 publications

Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution

Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution

Two stages K‐means and PSO‐based method for optimal allocation of multiple parallel DRPs application & deployment

Infrastructure for Integration of Legacy Electrical Equipment into a Smart-Grid Using Wireless Sensor Networks

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