Load scheduling with Maximum Demand using Binary Particle Swarm Optimization

Remani, T.; Jasmin, E A; Ahamed, T. P. Imthias

doi:10.1109/tapenergy.2015.7229634

Cited by 18 publications

(7 citation statements)

References 12 publications

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“…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]. Cost and process optimisation including minimisation of the VPP costs as in [26], aggregator's cost as in [7] and operational costs as in [6, 26, 27]. Consumer satisfaction and loss reduction have been optimised in [28].…”

Section: Existing Approaches For the Management Of Multiple Drpsmentioning

confidence: 99%

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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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Section: Existing Approaches For the Management Of Multiple Drpsmentioning

confidence: 99%

“…Cost and process optimisation including minimisation of the VPP costs as in [26], aggregator's cost as in [7] and operational costs as in [6, 26, 27]. Consumer satisfaction and loss reduction have been optimised in [28].…”

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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“…A través de algoritmos heurísticos de desplazamiento de cargas, reduciendo el costo del consumo de energía, minimizando así la factura de electricidad del cliente [61] y analizando el comportamiento del cliente desde la perspectiva de la respuesta a la demanda [69]. Dada las características del mercado de electricidad, el precio varía sustancialmente durante el día, no obstante, se encuentra literatura que refleja esquemas de precios dinámicos o en tiempo real [62], [63] acentuada por programas como son: el precio en tiempo real (Real Time Pricing, RTP), critical-peak pricing (CPP) y ToU, siempre en búsqueda de la reducción del costo de la energía del usuario con un nivel de incomodidad tolerable.…”

Section: Gestión De La Demandaunclassified

“…, 𝑎 , 𝑎 … . 𝑎 [62] de tal manera que el costo diario de la electricidad sea minimizado, donde 𝐶 es el costo del monomio durante el t-ésimo periodo de acuerdo al modelo presentado en la sección 2.1.…”

Section: Modelo Base Residencialunclassified

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Programación óptima de cargas flexibles para el mercado eléctrico regulado incorporando fuentes no convencionales de energía

Salas¹

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En este trabajo se presenta la generación de modelos matemáticos que buscan el menor valor final de la factura de energía para usuarios residenciales e industriales, a través de la programación de cargas flexibles y con inclusión de sistemas de almacenamiento con inyección de sistemas fotovoltaicos, sin alterar la demanda final energética. Apalancado por la estructuración de cargos horarios de la Resolución CREG 015 del 2018 obteniendo tarifas horarias en tres niveles diferentes siguiendo una lógica similar a los esquemas de respuesta de la demanda denominados Time of Use (ToU)

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Multi-agent Autonomous Decision Making in Smart Micro-Grids’ Energy Management: A Decentralized Approach

Ghorbani

Rahmani

Unland

2017

Lecture Notes in Computer Science

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Load scheduling with Maximum Demand using Binary Particle Swarm Optimization

Cited by 18 publications

References 12 publications

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

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

Programación óptima de cargas flexibles para el mercado eléctrico regulado incorporando fuentes no convencionales de energía

Multi-agent Autonomous Decision Making in Smart Micro-Grids’ Energy Management: A Decentralized Approach

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