Optimal Dispatch of Integrated Energy System Considering Energy Hub Technology and Multi-Agent Interest Balance

Zeng, Chengyu; Jiang, Yuechun; Liu, Yuqing; Tan, Zhuhua; He, Zhongnan; Wu, Shuhong

doi:10.3390/en12163112

Cited by 11 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimisation goals for an IES could vary depending on the specific interests of multiple stakeholders. Such scenarios could be analysed by considering an IES governed by multiple agents such as energy service providers, renewable energy owners, and users, and using multi-objective optimisation such as the non-dominated sorting genetic algorithm-III to balance the interests of each agent (Zeng et al, 2019). Besides, a hierarchical framework for trading IDR resources among users in IESs using blockchain and an energy management system could increase user participation, reduce costs, minimise resource loss, and improve system flexibility, as discussed in .…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic verification of an optimisation algorithm for power dispatch of integrated energy systems

Morales Sandoval,

De La Cruz-Loredo,

Saikia

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

The urgent need to achieve net-zero carbon emissions by 2050 has led to a growing focus on innovative approaches to producing, storing, and consuming energy. Integrated energy systems (IES) have emerged as a promising solution, capitalising on synergies between energy networks and enhancing efficiency. Such a holistic approach enables the integration of renewable energy sources and flexibility provision from one energy network to another, reducing emissions while facilitating strategies for operational optimisation of energy systems. However, emphasis has been mostly made on steady-state methodologies, with a dynamic verification of the optimal solutions not given sufficient attention. To contribute towards bridging this research gap, a methodology to verify the outcomes of an optimisation algorithm is presented in this paper. The methodology has been applied to assess the operation of a civic building in the UK dedicated to health services. This has been done making use of real energy demand data. Optimisation is aimed at improving power dispatch of the energy system by minimising operational costs and carbon emissions. To quantify potential discrepancies in power flows and operational costs obtained from the optimisation, a dynamic model of the IES that better captures real-world system operation is employed. By incorporating slow transients of thermal systems, control loops, and non-linearity of components in the dynamic model, often overlooked in traditional optimisation modules, the methodology provides a more accurate assessment of energy consumption and operational costs. The effectiveness of the methodology is assessed through model-in-the-loop co-simulations between MATLAB/Simulink and Apros alongside a series of scenarios. Results indicate significant discrepancies in power flows and operational costs between the optimisation and the dynamic model. These findings illustrate potential limitations of conventional operational optimisation modules in addressing real-world complexities, emphasising the significance of dynamic verification methods for informed energy management and decision-planning.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Dynamic verification of an optimisation algorithm for power dispatch of integrated energy systems

Morales Sandoval,

De La Cruz-Loredo,

Saikia

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…[140], [141], [142], [143]. Among them, the evolutionary and meta-heuristic algorithms applied to EH energy management and planning problems include: genetic algorithms [144], [145], [146], shuffled frog leaping algorithm [147], grey wolf optimization [148], improved water wave optimization algorithm [149], ϵ-domination based multi-objective evolutionary algorithm [150], differential evolution quantum particle swarm optimization algorithm [151], group search optimizer [152], [153], nondominated sorting genetic algorithm [154], [155], [156], time varying acceleration coefficient gravitational search algorithm [157], [158], time varying acceleration coefficients particle swarm optimization algorithm [159], flower pollination algorithm [160], particle swarm optimization [161], [162], [163], [164], modified teaching-learning based optimization [165], [166], [167], [168], and quantum artificial bee colony algorithm [169]; and, their hybrid versions, such as combination of the multiple-mutations adaptive genetic algorithm with an interior point optimization solver [170], hybrid genetic particle swarm optimization [171], combination of adaptive neuro-fuzzy inference system and genetic algorithms [172], hybrid algorithm of ant-lion optimizer and krill herd optimization [81], hybrid teaching-learning-based optimization and crow search algorithm [173], and hybrid particle swarm -neurodynamic algorithm…”

Section: Appendix a Application Of Evolutionary Algorithms To Operati...mentioning

confidence: 99%

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

et al. 2023

View full text Add to dashboard Cite

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.

show abstract

“…Many research debates have been conducted concerning optimal energy management in decentralized and centralized control multi-hub. The literature [27] presents a multi-agent method for energy trading in grid-connected hubs.…”

Section: Energy Hub Systemsmentioning

confidence: 99%

Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework

et al. 2021

View full text Add to dashboard Cite

During the last few years, attention has overwhelmingly focused on the integrated management of urban services and the demand of customers for locally-based supply. The rapid growth in developing smart measuring devices has made the underlying systems more observable and controllable. This exclusive feature has led the system designers to pursue the implementation of complex protocols to provide faster services based on data exchanges. On the other hand, the demands of consumers for locally-based supply could cause a disjunction and islanding behavior that demands to be dealt with by precise action. At first, keeping a centralization scheme was the main priority. However, the advent of distributed systems opened up new solutions. The operation of distributed systems requires the implementation of strong communication links to boost the existing infrastructure via smart control and supervision, which requires a foundation and effective investigations. Hence, necessary actions need to be taken to frustrate any disruptive penetrations into the system while simultaneously benefiting from the advantages of the proposed smart platform. This research addresses the detection of false data injection attacks (FDIA) in energy hub systems. Initially, a multi-hub system both in the presence of a microgrid (the interconnected smart energy hub-based microgrid system) and without it has been modeled for energy management in a way that allows them to cooperate toward providing energy with each other. Afterward, an FDIA is separately exerted to all three parts of the energy carrier including the thermal, water, and electric systems. In the absence of FDIA detection, the impact of FDIA is thoroughly illustrated on energy management, which considerably contributes to non-optimal operation. In the same vein, the intelligent priority selection based reinforcement learning (IPS-RL) method is proposed for FDIA detection. In order to model the uncertainty effects, the unscented transformation (UT) is applied in a stochastic framework. The results on the IEEE standard test system validate the system’s performance.

show abstract

Optimal Dispatch of Integrated Energy System Considering Energy Hub Technology and Multi-Agent Interest Balance

Cited by 11 publications

References 17 publications

Dynamic verification of an optimisation algorithm for power dispatch of integrated energy systems

Dynamic verification of an optimisation algorithm for power dispatch of integrated energy systems

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework

Contact Info

Product

Resources

About