A Stochastic Transactive Energy Model for Optimal Dispatch of Integrated Low-Carbon Energy Hubs in the Incorporated Electricity and Gas Networks

Daneshvar, Mohammadreza; Ivatloo, Behnam Mohammadi; Zare, Kazem; Asadi, Somayeh; Anvari‐Moghaddam, Amjad

doi:10.1109/sges51519.2020.00106

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…By extension, (i) electrical energy reliability, (ii) enhanced service quality, and iii) reduced expenses, can be achieved [35]. In an attempt to efficiently organize and plan the interconnections within an SG, the National Institute for Standards and Technology (NIST) and the Smart Grid Interoperability Panel (SGIP) have developed a new framework, covering seven important aspects of the SG, among others, energy transmission and generation, service providers, distribution network, customers, market domain, and network operators [36,37].…”

Section: Smart Grid Trendsmentioning

confidence: 99%

Reactive Power Optimization Based on the Application of an Improved Particle Swarm Optimization Algorithm

Mourtzis

Angelopoulos

2023

Machines

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Climate change, improved energy efficiency, and access to contemporary energy services are among the key topics investigated globally. The effect of these transitions has been amplified by increased digitization and digitalization, as well as the establishment of reliable information and communication infrastructures, resulting in the creation of smart grids (SGs). A crucial aspect in optimizing energy production and distribution is reactive power optimization, which involves the utilization of algorithms such as particle swarm optimization (PSO). However, PSO algorithms can suffer from premature convergence and being trapped in local optima. Therefore, in this research the design and development of an improved PSO algorithm for minimization of power loss in the context of SGs is the key contribution. For digital experimentation and benchmarking of the proposed framework, the IEEE 30-bus standardized model is utilized, which has indicated that an improvement of approximately 11% compared to conventional PSO algorithms can be achieved.

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Section: Smart Grid Trendsmentioning

confidence: 99%

Reactive Power Optimization Based on the Application of an Improved Particle Swarm Optimization Algorithm

Mourtzis

Angelopoulos

2023

Machines

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“…Where 𝐸 and 𝐸 are the capacities of the gas and electricity networks in ( 5) and (6). The transformer, CHP capacities and boiler𝐸 in (7), 𝐸 in (8) and 𝐸 in ( 9) are limited by the amount of energy conversion scaled by each equipment efficiency. Equation ( 10) limits the battery charge at any hour 𝐸 (𝑡) by its maximum capacity𝐸 .…”

Section: 𝑂𝑓 = 𝑃𝑐(𝑡)𝐸 (𝑡) + 𝑃𝑔(𝐸 (𝑡) + 𝐸 (𝑡)) (1)mentioning

confidence: 99%

“…But nowadays some scholars have introduced sufficient constraints for the network side (usually input side). Mohammadreza Daneshvar et al in [8] has taken into consideration the gas pipe constraints, by which compressors ratings and pressure limits were implemented.…”

Section: Introductionmentioning

confidence: 99%

Transformers Efficiency Variability in Energy Hub Modelling

Mohammad¹,

Jamjoum²,

Negry³

et al. 2022

Preprint

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This paper presented an energy hub model considering the impact of transformer efficiencies, by taking the difference in transformer efficiency based on the load demand. This can be achieved by taking the transformer efficiency as a dynamic value according to the transformer loading efficiency curve, as the transformer efficiency differs due to the copper losses. The following energy hub model was solved using Mixed Integer Linear Programming (MILP) with a CPLEX solver in GAMS software. Final findings showed that there is only a slight difference in the final overall when taking transformer efficiency variety into consideration for one transformer.

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“…The Smart Grid Interoperability Panel (SGIP) and National Institute for Standards and Technology (NIST) designed an algorithm or methodology for organizing and/or planning the different interconnections of a Smart Grid network, describing their seven main domains: service providers, transmission, generation, customer, distribution, market, and operators. This scheme is represented in Figure 2, and shows several reasons for discussion about the characteristics, behaviors, uses, interfaces, requirements, and concepts of an intelligent network [13,14].…”

Section: Taxonomymentioning

confidence: 99%

A Comprehensive Review on Smart Grids: Challenges and Opportunities

Escobar

Matamoros

Tejeida-Padilla

et al. 2021

Sensors

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Recently, the operation of distribution systems does not depend on the state or utility based on centralized procedures, but rather the decentralization of the decisions of the distribution companies whose objectives are the efficiency of interconnectivity. Therefore, distribution companies are exposed to greater risks, and due to this, the need to make decisions based on increasingly reliable models has grown up considerably. Therefore, we present a survey of key aspects, technologies, protocols, and case studies of the current and future trend of Smart Grids. This work proposes a taxonomy of a large number of technologies in Smart Grids and their applications in scenarios of Smart Networks, Neural Networks, Blockchain, Industrial Internet of Things, or Software-Defined Networks. Therefore, this work summarizes the main features of 94 research articles ranging the last four years. We classify these survey, according Smart Grid Network Topologies, because it can group as the main axis the sensors applied to Smart Grids, as it shows us the interconnection forms generalization of the Smart Networks with respect to the sensors found in a home or industry.

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A Stochastic Transactive Energy Model for Optimal Dispatch of Integrated Low-Carbon Energy Hubs in the Incorporated Electricity and Gas Networks

Cited by 6 publications

References 22 publications

Reactive Power Optimization Based on the Application of an Improved Particle Swarm Optimization Algorithm

Reactive Power Optimization Based on the Application of an Improved Particle Swarm Optimization Algorithm

Transformers Efficiency Variability in Energy Hub Modelling

A Comprehensive Review on Smart Grids: Challenges and Opportunities

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