This paper investigates the stochastic scheduling of a Distributed Energy Resources Aggregator participating in wholesale day-ahead energy and flexibility markets. The proposed framework focuses on maximizing the aggr...
详细信息
ISBN:
(数字)9798350377941
ISBN:
(纸本)9798350377958
This paper investigates the stochastic scheduling of a Distributed Energy Resources Aggregator participating in wholesale day-ahead energy and flexibility markets. The proposed framework focuses on maximizing the aggregator’s profit while representing entities such as PV-based prosumers, electric vehicles, and large-scale battery within an unbalanced distribution network. It incorporates uncertainties related to PV generation, market prices, and flexibility deployment requests. In addition to profit maximization, the framework manages the distribution network’s voltage and line congestion while meeting the basic energy demand of the players. The aggregator leverages the flexibility of these players to participate in the flexibility electricity market, enabling it to adjust its total power production as requested by the flexibility market operator. The study evaluates two cases: Case 1, where the aggregator participates only in the energy market, and Case 2, where the aggregator joins both energy and flexibility markets. The results show that participating in both markets significantly increases profit, while maintaining compliance with the distribution network’s voltage and thermal limits and meeting participants’ demand requirements.
We demonstrate fully passive optical isolators in silicon nitride nanophotonics using the intrinsic Kerr nonlinearity. These devices serve to both stabilize and isolate on-chip lasers, reducing the linewidth of DFB la...
详细信息
This study explores the transformative potential of chatbots in the retail industry, highlighting their role in enhancing customer engagement, optimizing business processes, and revolutionizing the nature of retail in...
详细信息
Wireless Sensor Networks(WSN)has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of *** spite of this,it is challenging to de...
详细信息
Wireless Sensor Networks(WSN)has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of *** spite of this,it is challenging to design energy-efficient *** routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of *** order to solve the restricted energy problem,it is essential to reduce the energy utilization of data,transmitted from the routing protocol and improve network *** this background,the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol(DEAOA-MHRP)for *** aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in *** accomplish this,DEAOA-MHRP model initially integrates the concepts of Different Evolution(DE)and Arithmetic Optimization Algorithms(AOA)to improve convergence rate and solution ***,the inclusion of DE in traditional AOA helps in overcoming local optima *** addition,the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and *** order to ensure the energy efficient performance of DEAOA-MHRP model,a detailed comparative study was conducted and the results established its superior performance over recent approaches.
The possibility of employing a light source with a small wavelength bandwidth (35 nm) and a coarsely resolved spectrometer (~166 pm) for the interrogation of a Vernier effect-based high-sensitivity optical fiber senso...
详细信息
In this work, we present an arbitrary-scale super-resolution (SR) method to enhance the resolution of scientific data, which often involves complex challenges such as continuity, multi-scale physics, and the intricaci...
详细信息
In this work, we present an arbitrary-scale super-resolution (SR) method to enhance the resolution of scientific data, which often involves complex challenges such as continuity, multi-scale physics, and the intricacies of high-frequency signals. Grounded in operator learning, the proposed method is resolution-invariant. The core of our model is a hierarchical neural operator that leverages a Galerkin-type self-attention mechanism, enabling efficient learning of mappings between function spaces. Sinc filters are used to facilitate the information transfer across different levels in the hierarchy, thereby ensuring representation equivalence in the proposed neural operator. Additionally, we introduce a learnable prior structure that is derived from the spectral resizing of the input data. This loss prior is model-agnostic and is designed to dynamically adjust the weighting of pixel contributions, thereby balancing gradients effectively across the model. We conduct extensive experiments on diverse datasets from different domains and demonstrate consistent improvements compared to strong baselines, which consist of various state-of-the-art SR methods. Copyright 2024 by the author(s)
This paper focuses on the maintenance deficiencies of disaster resilient infrastructure on behalf of engineers that contributed to the impacts of Hurricane Katrina, Fukushima, and the 2023 Hawaii Wildfires. These disa...
详细信息
The possibility of employing a light source with a small wavelength bandwidth (35 nm) and a coarsely resolved spectrometer (~166 pm) for the interrogation of a Vernier effect-based high-sensitivity optical fiber senso...
详细信息
A high-resolution 64-channel arrayed-waveguide-grating-assisted silicon-nitride integrated optical phase array is demonstrated for 2-D beam steering. A field of view of 5◦ × 25◦ with 0.3◦ vertical resolution is a...
详细信息
This paper presents an analysis of peak demand reduction and electricity cost savings for commercial and industrial (C&I) customers deploying behind-the-meter battery storage systems (BSSs). To do so, a BSS schedu...
详细信息
ISBN:
(数字)9798350377941
ISBN:
(纸本)9798350377958
This paper presents an analysis of peak demand reduction and electricity cost savings for commercial and industrial (C&I) customers deploying behind-the-meter battery storage systems (BSSs). To do so, a BSS scheduling strategy is proposed that facilitates battery charging, both from the solar system and the grid, as well as discharging during demand charge-, peak time-of-use (ToU) rate-, and shoulder ToU rate-applicable periods in succession to peak demand and maximize cost savings. The proposed grid charging is conducted during off-peak ToU periods by taking factors like possible solar supply, BSS size and available BSS capacity, and demand during peak and shoulder periods into account. Finally, the performance of the proposed charge and discharge scheduling of the BSS is investigated for three C&I customers with different demand profiles. The simulation results are also compared with the business as usual, in which C&I customers buy and sell energy at ToU and feed-in-tariff (FiT) rates, respectively, without possessing any BSS at their premises. It is observed from the case study that the reductions in peak demand and electricity costs vary between 35.62% and 98.07% and between 4.61% and 11.01%, respectively.
暂无评论