A CVaR-Robust-Based Multi-Objective Optimization Model for Energy Hub Considering Uncertainty and E-Fuel Energy Storage in Energy and Reserve Markets

Mokaramian, Elham; Shayeghi, Hossein; Sedaghati, Farzad; Safari, Amin; Alhelou, Hassan Haes

doi:10.1109/access.2021.3100336

Cited by 51 publications

(6 citation statements)

References 41 publications

(53 reference statements)

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“…The work in [100] used the Wasserstein distance for the DRO formulation, the considered uncertainty again being renewable generation. In addition, [3] has used a CVar-Based DRO to deal with the uncertainties of loads and renewables. Article [108] has applied the DRO approach to a water-energy nexus management problem, which indicates an operational problem of a networked multi-EH system under renewable energy uncertainty.…”

Section: ) Distributionally Robust Optimizationmentioning

confidence: 99%

“…The joint co-optimization of different energy carriers, traditionally operated and planned separately, forms a prominent endeavor towards efficient energy and infrastructure utilization [1], since co-optimizing decisions across multiple energy carriers, features a synergistic effect [2]. Hence, the Energy Hub (EH), an energy management unit that encompasses multiple energy carriers and technologies, is envisioned as a new concept that enhances the integrated system's efficiency [3].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: ) Distributionally Robust Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…A possible solution for the integrated management of MES is the energy hub (EH), which is the location where the production, conversion, storage, and consumption of various energy carriers occur (Mohammadi et al, 2017). This inspired the author (Mokaramian et al, 2021) to develop a day-ahead scheduling paradigm for energy hubs (EH) in the energy and reserve markets, which takes into account the economy and pollution emission as the two primary goals. An innovative hybrid energy storage facility based on a fuel cell, wind power, photovoltaic energy, and a specific fuel cell unit in the presence of elastic demand make up the energy hub under study (Ragab et al, 2020(Ragab et al, , 2021.…”

Section: Introductionmentioning

confidence: 99%

Introducing Optimal Energy Hub Approach in Smart Green Ports based on Machine Learning Methodology

Tawfik,

Shehata,

Hassan

et al. 2024

Preprint

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The integration of renewable energy systems in port facilities is essential for achieving sustainable and environmentally friendly operations. This paper presents the implementation of an Optimal Energy Hub approach in smart green ports, with a focus on the case study of Egypt and the Middle East Oil Transmission & Pipelines Company (MIDTAP) company. The study explores the integration of photovoltaic (PV) and wind energy systems within the port's operations. Machine learning methodologies are utilized to optimize energy management and decision-making processes in the port. The proposed approach leverages historical data, weather forecasts, and real-time energy demand to predict and optimize the utilization of PV and wind energy resources. By utilizing machine learning techniques, the energy hub can efficiently balance energy supply and demand, ensuring optimal utilization of renewable sources while minimizing reliance on conventional energy sources. Furthermore, this paper discusses a scenario of increasing load and renewable energy generation in the port facility. A comprehensive energy flow-based mixed integer linear programming (MILP)-based optimization framework implemented in MATLAB is presented. This framework aims to minimize electricity consumption costs while providing considerable flexibility and adaptability to accommodate the changing energy landscape. The results of the study provide valuable insights into the implementation of sustainable energy solutions in smart green ports, specifically in the context of Egypt and the MIDTAP company. The integration of PV and wind energy systems, along with the proposed MILP-based optimization framework, offers a practical and efficient approach to minimize electricity consumption costs and reduce the carbon footprint of port operations. The findings demonstrate the flexibility and adaptability of the Optimal Energy Hub approach in accommodating increasing loads and renewable energy generation. The research findings encourage the adoption of similar energy hub approaches in other port facilities, contributing to the global transition towards greener and more sustainable practices. The utilization of machine learning methodologies and MILP-based optimization frameworks in smart green ports can facilitate cost-effective and environmentally friendly energy management, promoting a more sustainable future for port operations. Several scenarios were used to show how the proposed notion was perceived. The primary objective is to maximize the use of renewable energy sources while reducing expenses and emissions. The MIDTAP Company evaluated the effects of adding more renewable energy systems in smart green ports through a study involving six scenarios. Additionally, MIDTAP Company conducted research with six scenarios to assess the impact of increasing the number of renewable energy systems in smart green ports. In these scenarios, the renewable energy systems (PV + Wind) are increased by 10%, 20%, 30%, and 40%, while the load is increased by 25% and 50%. The primary objective of these scenarios is to maximize the use of renewable energy sources while reducing expenses and emissions, and the results provide valuable insights into the potential benefits and challenges of scaling up renewable energy systems in smart green ports. The significance of grid connectivity and sustainable energy solutions for smart green ports was also emphasized by the study. The findings show how economical and sustainable energy solutions may be achieved in smart green ports through the application of machine learning and optimization based on MILP. The selection of a 40% increase in renewable energy systems (PV + Wind) in the sixth scenario, which aims to explore the potential for further scaling up renewable energy infrastructure within the MIDTAP port, is based on the available area in MIDTAP. Considering the spatial limitations and resource availability of the port, careful assessment and planning are crucial to determine the optimal capacity for renewable energy systems. By selecting a 40% increase, the study takes into account the available area within MIDTAP and the potential for accommodating additional renewable energy installations. This approach ensures that the scenario aligns with the port's physical constraints while still pushing the boundaries of renewable energy integration. Evaluating this scenario provides insights into the feasibility and benefits of leveraging a larger share of renewable energy sources within the port, ultimately contributing to its sustainable and environmentally friendly operations.

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“…They used CO to handle the problem. In Mokaramian et al., 20 a similar study is conducted for a residential EH, which also considered the conditional value at risk (CVaR) approach to enhance the EH operation and efficiency. In Faraji et al, 12 the authors researched on proposing a residential EH to address electrical and heat loads.…”

Section: Introductionmentioning

confidence: 99%

Bi‐level optimization of the integrated energy systems in the deregulated energy markets considering the prediction of uncertain parameters and price‐based demand response program

Toolabi

Soheyli

Sanei

et al. 2022

Energy Science & Engineering

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The proliferation of multienergy systems (MESs) in recent years has led to the higher efficiency of energy consumption in different sectors. MESs are generally studied in the context of energy hubs (EHs). Most of the previous works in the field of EH operation and scheduling are at the scale of residential households or communities. There is a research gap in proposing a framework in which the EH actively interacts with different energy networks. This article, however, is focused on the optimal operation and scheduling of the EH and two integrated energy grids such as power and heat distribution systems. The EH consists of combined heat and power (CHP), electric heat pump (EHP), thermal and electric energy storage systems, which are operated by an independent entity. The proposed optimization problem is based on the bi-level optimization method in which the EH is the leader and two networks are the followers. Nonlinear variables are also linearized using binary expansion and Big M methods. As another research novelty, uncertainties of load demand and photovoltaic (PV) systems' output power are taken into account using a mixed machine learning (ML)-based forecasting method. To enhance the flexibility of the system, a price-based demand response (DR) program is proposed based on the predicted load pattern of the mixed ML method. Simulation results show the efficiency of the optimization model. Also, the DR program has a significant impact on the system's cost by 40% improvement in the profit of the system.

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A CVaR-Robust-Based Multi-Objective Optimization Model for Energy Hub Considering Uncertainty and E-Fuel Energy Storage in Energy and Reserve Markets

Cited by 51 publications

References 41 publications

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

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

Introducing Optimal Energy Hub Approach in Smart Green Ports based on Machine Learning Methodology

Bi‐level optimization of the integrated energy systems in the deregulated energy markets considering the prediction of uncertain parameters and price‐based demand response program

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