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Copyright of Scientia Iranica. Transaction D, Computer Science & Engineering & Electrical Engineering is the property of Scientia Iranica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

— This paper introduces a comprehensive modeling approach for optimizing the capacities of various energy converters and storage systems within a residential Energy Hub (EH). The model focuses on minimizing energy supply costs to customers while maximizing the utilization of renewable and hydrogen-based technologies. Through the optimization process, we determined that implementing CHP units in the EH could reduce total costs by approximately 78% with green tax incentives and by 46.7% without them. Additionally, the inclusion of an incentive to the EH resulted in a 27.2% reduction in total costs imposed on subscribers. The study also evaluates the impact of uncertainties in photovoltaic (PV) electricity generation, demonstrating the benefits of using stochastic optimization over deterministic methods. The results indicate that the economic advantage of this approach over deterministic methods is 86.75%–95.74% greater, depending on the scenario. Sensitivity analysis further reveals that the investment costs of fuel cells significantly influence the optimal capacity design and economic viability of the EH model. • Proposing a modeling procedure for hydrogen within renewable-based energy hub studies. • Extracting a probabilistic model for solar radiation and ambient temperature. • Presenting various operating strategies in planning studies of residential energy hubs. • Developing a stochastic optimization algorithm for running the proposed model. [ABSTRACT FROM AUTHOR]

The combination of different energy vectors like electrical energy, hydrogen, methane, and water is a crucial aspect to deal with in integrated local energy communities (ILECs). The ILEC stands for a set of active energy users that maximise benefits and minimise costs using optimisation procedures in producing and sharing energy. In particular, the proper management of different energy vectors is fundamental for achieving the best operating conditions of ILECs in terms of both energy and economic perspectives. To this end, different solutions have been developed, including advanced control and monitoring systems, distributed energy resources, and storage. Energy management planning software plays a pivotal role in developing ILECs in terms of performance evaluation and optimisation within a multi-carrier concept. In this paper, the state-of-the-art of ILECs is further enhanced by providing important details on the critical aspects related to the overall value chain for constituting an ILEC (e.g., conceptualisation, connecting technologies, barriers/limitations, control, and monitoring systems, and modelling tools for planning phases). By providing a clear understanding of the technical solutions and energy planning software, this paper can support the energy system transition towards cleaner systems by identifying the most suitable solutions and fostering the advancement of ILECs. [ABSTRACT FROM AUTHOR]

A promising energy carrier and storage solution for integrating renewable energies into the power grid currently being investigated is hydrogen produced via electrolysis. It already serves various purposes, but it might also enable the development of hydrogen-based electricity storage systems made up of electrolyzers, hydrogen storage systems, and generators (fuel cells or engines). The adoption of hydrogen-based technologies is strictly linked to the electrification of end uses and to multicarrier energy grids. This study introduces a generic method to integrate and optimize the sizing and operation phases of hydrogen-based power systems using an energy hub optimization model, which can manage and coordinate multiple energy carriers and equipment. Furthermore, the uncertainty related to renewables and final demands was carefully assessed. A case study on an urban microgrid with high hydrogen demand for mobility demonstrates the method's applicability, showing how the multi-objective optimization of hydrogen-based power systems can reduce total costs, primary energy demand, and carbon equivalent emissions for both power grids and mobility down to −145%. Furthermore, the adoption of the uncertainty assessment can give additional benefits, allowing a downsizing of the equipment. [ABSTRACT FROM AUTHOR]

Abstract The proliferation of renewable energy resources and the incorporation of various types of energy bring opportunities as well as new challenges to the planning and operations within integrated energy systems (IESs). To better characterize multiple uncertain variables within IES, reducing wind power curtailment and minimizing costs, this paper proposes an IES collaborative planning model with EHs, in which uncertain variables are depicted by Z-numbers. Firstly, Z-number incorporates both fuzzy and probabilistic uncertainties, allowing the representation of uncertain variables with credibility information in constraints. Secondly, simplifying Z-numbers into an engineering-applicable method reduces the complexity of model resolution. Thirdly, to fully leverage the complementary transformation characteristics among different energy sources, the optimization model includes decisions on the capacity of energy conversion and energy storage components within EHs. Moreover, outside the EHs, conventional generator dispatches, and the hardening of electric-gas grid decisions are also included. Finally, the model is verified on an IES that involves the coupling of a six-node power system and a six-node gas system, and numerical results validate that wind power output values represented by Z-numbers have a standard deviation of only 0.33 from actual values. Furthermore, different credibility information in Z-numbers significantly impacts planning outcomes; an increased DR credibility can be conducive to reducing wind curtailment with 45KW and help cost-savings with 4.05%.

Abstract Hot dry rock (HDR) is regarded as a promising resource of geothermal energy and becomes an important field for future geothermal development due to its advantages of high temperature, wide distribution and huge reserves. At present, HDR research is mainly focused on the modeling and efficiency evaluation of power generation cycle, but its relationship with the source side of the system has not been considered in the field of integrated energy systems. Therefore, this paper proposes a day‐ahead scheduling method for regional integrated energy systems (RIES) with HDR based on information gap decision theory (IGDT). First, the heat transfer system model of HDR is established according to the energy flow model and basic structure of the HDR enhanced geothermal system (EGS). Second, a comprehensive geothermal energy system scheduling model is established from HDR based on the energy hub modeling structure. Then, the IGDT is introduced to analyze the renewable energy output uncertainty in the model. Finally, through a real RIES analysis, the simulation results verified the correctness and effectiveness of the proposed model. The scheduling cost was ¥47,073 when EGS participated in the scheduling. Access to EGS reduced the system's total 24‐h energy purchase by 8305 kW, natural gas consumption by 3051.9 m3, and total carbon emissions by 742.28 kg. The latter emphasized that the proposed model achieves the purpose of reducing the system cost, saving energy and reducing emissions.

Hot dry rock (HDR) is regarded as a promising resource of geothermal energy and becomes an important field for future geothermal development due to its advantages of high temperature, wide distribution and huge reserves. At present, HDR research is mainly focused on the modeling and efficiency evaluation of power generation cycle, but its relationship with the source side of the system has not been considered in the field of integrated energy systems. Therefore, this paper proposes a day‐ahead scheduling method for regional integrated energy systems (RIES) with HDR based on information gap decision theory (IGDT). First, the heat transfer system model of HDR is established according to the energy flow model and basic structure of the HDR enhanced geothermal system (EGS). Second, a comprehensive geothermal energy system scheduling model is established from HDR based on the energy hub modeling structure. Then, the IGDT is introduced to analyze the renewable energy output uncertainty in the model. Finally, through a real RIES analysis, the simulation results verified the correctness and effectiveness of the proposed model. The scheduling cost was ¥47,073 when EGS participated in the scheduling. Access to EGS reduced the system's total 24‐h energy purchase by 8305 kW, natural gas consumption by 3051.9 m3, and total carbon emissions by 742.28 kg. The latter emphasized that the proposed model achieves the purpose of reducing the system cost, saving energy and reducing emissions. [ABSTRACT FROM AUTHOR]

Energy hubs (EHs) represent a pivotal paradigm in achieving optimal resource utilization across various energy domains. This paper presents an advanced framework for the optimal management of a smart island, leveraging the synergies within the water-electricity nexus. By integrating diverse resources, including electricity, water, heat, and hydrogen, the proposed EH model aims to meet the multifaceted demands of consumers on the island. To enhance operational efficiency, this study delves into the nuanced impacts of key EH components, elucidating their roles in meeting demand profiles and minimizing operational costs. Formulated as a mixed integer linear programming (MILP) model, the EH optimization problem is addressed using the GAMS optimization tool. The overarching objective is to fulfill consumer demand while concurrently optimizing resource utilization, considering factors such as storage degradation costs and emissions from fossil-fuel-based units. In addition to strategic optimization, this study pioneers a novel approach to stochastic parameter forecasting, integrating convolutional neural networks (CNNs) and long-short-term memory networks (LSTMs). By harnessing the capabilities of these advanced forecasting techniques, the EH model can anticipate dynamic changes in demand patterns with heightened accuracy and precision. The empirical results underscore the transformative potential of the proposed EH framework, showcasing significant reductions—up to 30%—in emission costs. Moreover, the study underscores the pivotal role of EHs as enablers for scaling up renewable energy penetration, offering a robust foundation for sustainable energy transitions in island communities and beyond. Additionally, implementing a load-shifting demand response program can lower total costs by approximately $257 per day, offering significant savings for EHs over extended periods. [Display omitted] • Modeling a mixed integer linear programming for energy management of an energy hub. • Comprehensive study on hydrogen and electric storage systems. • Uncertainty modeling using convolutional and long short-term memory networks. • Modeling water–electricity nexus to meet water and electricity demand. [ABSTRACT FROM AUTHOR]

Copyright of Mugla Journal of Science & Technology is the property of Mugla Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces a comprehensive and resilient multi-energy system (MES) designed for independent planning and real-time implementation. A robust daily coordinated planning model is proposed, incorporating adjustable optimization with fundamental operational and uncertainty constraints. The model integrates various energy sources and systems, including photovoltaics, wind turbines, combined heat and power (CHP) units, energy storage system (ESS), electric vehicle (EV), electric boilers, and power-to-gas (P2G) facilities, to manage electricity, natural gas, and heat demands. The objective is to minimize MES operational costs while meeting electricity and heat requirements, considering renewable energy uncertainties. It includes the development of a two-stage flexible robust optimization model that accounts for energy equilibrium, capacity constraints, and demand response mechanisms. The model incorporates price-based demand response with both switchable and interruptible loads, enhancing system controllability and flexibility. Additionally, a scenario generation and reduction technique based on the Kantorovich distance is employed to effectively manage forecast errors and uncertainties. A novel modified Slime Mold Algorithm (SMA) is utilized to solve the optimization problem, demonstrating superior convergence and computational efficiency compared to traditional meta-heuristics. The slime mold algorithm is further enhanced with chaos theory, using a sine map to introduce dynamic exploration capabilities. The findings indicate that the proposed multi-energy system model effectively balances electricity, natural gas, and heat loads while accommodating renewable energy fluctuations. The enhanced slime mold algorithm provides optimal solutions swiftly, ensuring reliable and cost-effective multi-energy system operation.

A new model of energy carriers (micro-refinery output products) in the concept of an energy hub is presented. In addition, in the presented model, the effect of different models of parking lot in an energy hub is analyzed. In this study, the uncertainty of the number of electric vehicles was modeled using the Monte Carlo method, and then considering the same conditions, the uncertainty of the number of electric vehicles was calculated using the Probability-Possibility hybrid method. In addition, the study uses a scenario-based approach to address uncertainties related to multi-carrier energy demand and multi-carrier energy prices. In the present paper, ensuring water demand is of great importance, which is why the proposed energy hub structure of a sea desalination unit and sour water treatment that is extracted from the micro refinery output was analyzed. The optimization model presented in this paper for the energy hub is a complex integer linear programming (MILP) that is solved using a CPLEX solver in the GAMS environment. The results show that the use of the Probability-Possibility method resulted in an 8 % reduction in the final energy supply cost in the energy hub compared to the Monte Carlo method.

This manuscript proposes a hybrid system for an integrated solar and natural gas hybrid model with photovoltaic/thermal (PV/T) collectors and gas turbines (GT). The proposed optimization method combines both Dynamic Differential Annealed Optimizations (DDAO) and Radial Basis Function Neural Network (RBFNN), so it is called the DDAO-RBFNN approach. Natural gas, solar energy, coal, and geothermal energy are the sources of the system, while electricity, cooling, and heating are the energy needs of the system. A multi-objective stochastic model incorporates load and renewable energy uncertainty to obtain more efficient, economic, and environmental decisions. By utilizing an existing data set, the proposed approach is to forecast a new set. Some characteristics, such as historical weather data, building loads, and market information are required for modelling the energy hub and unpredictable factors. The proposed RBFNN method is used to model the energy hub through data collection, analysis, and initial sampling of uncertainty factors. To provide optimal outcomes with great computational time, the accuracy of DDAO approach is used. Smart operation managements consider both the energy supply and demand sides to achieve optimal state functioning of the hybrid CCHP scheme. Finally, the proposed system is performed in MATLAB/Simulink site and the performance of the proposed method is compared with existing methods. [ABSTRACT FROM AUTHOR]

Energy hubs are a complex system designed to enable the efficient transmission, transformation, and retention of diverse energy sources within consumption networks. This research presents an innovative model designed to optimize energy distribution across three hierarchical layers: production, distribution, and ultimate consumption. This model incorporates an interconnected system of varied energy hubs equipped with various technologies and situated within residential structures. Given the potential for equipment failures over time and the varying stochastic demand from customers in different seasons, the proposed model incorporates backup equipment and storage systems to enhance reliability and fulfilling customer requirements. The objectives of the model encompass minimizing operational costs and mitigating pollution arising from energy consumption ensuring overall reliability in the event of potential energy hub equipment failure. To solve this problem, a mixed-integer linear programming (MILP) approach is employed. Specifically, the CPLEX solver within the GAMS software is utilized to identify an optimal solution. The results of the model demonstrate that incorporating backup and storage systems reduces costs and enhances overall efficiency of the system. Additionally, a case study is undertaken to evaluate the applicability of the proposed model in Omid Town, a mixed-use space in Tehran. The case study showcased the benefits of energy hubs, including reducing power outages to zero percent and saving an average of 15% energy during non-peak months for income generation, while effectively managing energy supply, facilitating storage, and enabling exchange between residential and other units. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Electricity consumption is increasing rapidly and many countries are looking for ways to cope with the energy crisis. Morever, the world is facing the problem of global warming caused by emissions. Therefore, it is of great importance to operate power systems efficiently. Energy Hub (EH) represents a versatile energy system capable of providing efficient and optimal solutions for the operation of power systems across multiple carriers. This paper examines the optimization of an EH encompassing renewable energy systems (RES) like wind and photovoltaic, combined heat and power (CHP), transformer, absorption chiller, energy storage system (ESS) and furnace with aiming at minimizing the cost. Demand response is an energy sector strategy that entails modifying electricity consumption patterns in reaction to fluctuations in electricity supply or pricing. The objective of demand response programs (DRP) is to curtail or shift electricity consumption during periods of elevated electricity prices. Therefore, Electrical Demand Response Program (EDRP) for electrical demand and the Thermal Demand Response Program (TDRP) for heating demand are incorporated into the EH. The optimization problem is formulated as Mixed Integer Linear Programming (MILP) and solved with CPLEX solver in GAMS. The outcomes of various case studies are compared to ascertain the model's efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study proposes a cooperative fuzzy optimal scheduling method for multi-energy hubs considering carbon trading. The traditional integrated energy system has difficulty integrating system economy and environment, as well as allocating benefits to multiple subjects. To address the problem, a multi-energy hub cooperative fuzzy optimization dispatching model, considering carbon trading, was developed to promote low carbon emissions and high renewable energy penetration. The model optimizes the dispatch of the integrated energy system while reducing CO2 emissions, also reducing the impact of renewable energy output uncertainty. Moreover, decision-makers can adjust the risk tolerance of the system according to their own risk appetite. To ensure a fair method of benefit distribution to achieve overall optimum through cooperation, this study introduced and examined three methods of secondary benefit distribution in cooperative games: Shapley value method, Kernel method, and Equal DP (Disruption Propensity) method. Finally, the model's economic, environmental protection, and stability were verified by an example of cooperative optimal scheduling and collaborative residual benefit secondary allocation of three energy hubs. Moreover it was found that in the secondary allocation,the Equal DP method was more stable and acceptable to all subjects compared to the other two methods. [ABSTRACT FROM AUTHOR]

This paper presents the placement and sizing of energy hubs (EHs) in electricity, gas, and heating networks. EH is a coordinator framework for various power sources, storage devices, and responsive loads. For simultaneous modeling of economic, operation, reliability, and flexibility indices, the proposed scheme is expressed as a three-objective optimization in the form of Pareto optimization based on the sum of weighted functions. The objective functions of this problem respectively minimize the planning cost of EHs (equal to the total cost of construction of hubs and their expected operating cost), the expected energy loss of the mentioned networks, and the expected energy not-supplied (EENS) of these networks in the case of an N − 1 event. The problem is constrained by power flow equations and operation and reliability constraints of these network together with the EH planning and operation model, and flexibility constraints of the EHs. Then, to achieve unique optimal solution in the shortest possible time, a linear approximation model is extracted for the proposed scheme. Moreover, scenario-based stochastic programming (SBSP) is employed to model uncertainties of load, energy cost, renewable power, and accessibility of the mentioned network equipment. Finally, the obtained numerical results indicate the capability of the proposed scheme in enhancing the economic and flexibility situation of EHs and improving the reliability and operation status of energy networks along with achieving optimal planning and operation for EHs.

The contemporary energy sector has witnessed a notable surge in integrating renewable energy sources (RESs) within energy hubs (EHs). The intermittent characteristics inherent to RESs present a consequential challenge, notably compromising the operational flexibility of EHs. This paper proposes a two-stage adaptive robust optimization (ARO)-based interconnected energy and gas system (IPGS) with optimally sized and allocated components and energy hubs (EHs). The IPGS components and EHs are optimally sized and allocated in the first stage for optimal performance before the uncertainty realization while minimizing the investment costs. The model’s second stage incorporates optimizing operations and managing RES output uncertainty. In the second stage, the RES output uncertainty is maximized after realization while minimizing operation cost. The integration of battery energy storage systems (BESSs), thermal energy storage systems (TESSs), hydrogen storage (HSs), power-to-gas (P2G), and gas-to-power (G2P) technologies collectively serve as flexible resources that also handle uncertainty. The IPGS also incorporates a carbon capture system (CCS) to support the decarbonization of power systems. To further minimize operation costs, methane is synthesized and sold, and fuel cells are added to convert hydrogen to electricity sold to the grid. A column and constraint generation (CC&G) algorithm decomposes the ARO model using the duality theory and dual cuts. The ARO model minimizes the investment and operation costs under the worst-case realization of the uncertainties, namely, PV output, electricity demand, heat demand, and electricity and gas prices. Simulation results have demonstrated the effectiveness of the proposed method through reduced energy consumption from the grid and increased RES penetration while handling RES output intermittency. The ARO model eliminated load shedding from 31.5 MW for IEEE 24-bus and 60.3 MW for IEEE 118-bus to zero.

The study aims to develop a methodology for selecting and justifying a multi-energy hub based on renewable energy sources for agricultural complex. The methodology has an international dimension and was tested for the pilot case of the study in an agricultural site in Kazakhstan. The methodology consists of two parts. With the help of the EnergyPro software package, simulation of technical and economic analysis and optimization of energy hub operation for several different energy generation units was carried out. During the simulation, four different scenarios of an energy hub based on solar and wind energy, biomass and heat pump as well as coal-based fossil energy sources were considered. The second part of the methodology was based on the economic justification of climate-neutral technological solutions using multi-energy hubs in the agriculture sector. Climate neutrality diagram was created by use of GHG emission trading experience for a detailed technical and economic analysis and selection of the best renewable sources from various installation sites. Results show that the most promising and cost-effective option is the scenario with an integrated wind park, heat pump and biomass boiler.

The electricity network has reached its transport capacity limits in various areas in the Netherlands and the challenge of granting connections became critical. The concept of energy hubs, where neighboring prosumers collaborate to optimize the available capacity, poses itself as a short-term alternative for grid reinforcement. This study presents a coordinated, MPC-based combined with the partheno-genetic algorithm, optimization approach enabling a smooth transition into an electric fleet for logistics companies, taking part of an energy hub, while respecting the grid’s limited capacity and taking into account uncertainties arising from load and generation profiles. The last-mile deliveries of the logistic company are depicted by the Electric Vehicle Routing Problem formulation, and the partheno-genetic algorithm is implemented to solve it, where the parameters of interest are fed to the energy management system that minimizes the overall energy costs at the energy hub while incorporating day-ahead market prices. The intermittency of renewable generation and load demand is tackled by adopting the model predictive control (MPC) framework, providing a corrective mechanism that ensures that the overarching objective of the system is met while respecting the grid’s limitation. Three charging strategies at the hub are investigated: dynamic charging, where charging power varies by magnitude and time, direct charging, having a fixed charging power and time, and delayed overnight charging, where the charging power is spread over a scheduled horizon. The results demonstrate the mitigation of the grid’s limited connection capacity while attaining cost savings with the proposed dynamic charging strategy, ranging between 11.5% and 52% compared to the delayed overnight charging and direct charging strategies.

The Energy Internet is expected to transform the landscape of electricity generation portfolio, distribution, and consumption through the integration of advanced sensing, communication, and control technologies in daily grid operations. This paper explores the profound impact of various smart grid concepts, such as dynamic pricing, distributed generation, and demand management, on information and communication technologies services within the Energy Internet. The synergy between smart grid principles and the Energy Internet has introduced a new dimension to efforts aimed at enhancing energy efficiency and reducing operational costs in communication networks and data centers. This survey provides a comprehensive overview of the Energy Internet Concept, strategies for achieving energy-efficient communications and data centers, and the dynamic interplay between the Energy Internet and information and communication infrastructures. While previous studies have individually examined the Energy Internet, energy-efficient communications, and green data centers, a critical need exists to systematically categorize and survey research at the intersection of these fields. To bridge this gap, our survey commences by elucidating the energy Internet concept and its architectural framework. Subsequently, an exploration of energy-routing devices and algorithms employed in prior studies is undertaken. Finally, the challenges encountered within the Energy Internet domain are explained. This comprehensive survey aims to offer a panoramic perspective on the Energy Internet, illustrating its conceptual intricacies and challenges, along with an exploration of how previous studies have endeavored to address them.

The development of the Energy Internet has improved the efficiency of energy utilization and promoted sustainable development of power and energy systems. The multi-energy system modeling considering the dynamic process of transmission line is one of the key research points of Energy Internet operation control. Through the energy circuit theory, the lumped parameter model of natural gas pipelines is built and the dynamic characteristic parameters under the control instruction are extracted. Combined with dynamic characteristic parameters, the long short-term memory (LSTM) neural network is designed to fit the natural gas pipeline dynamic process into discrete linear time-varying (LTV) equations. Combined with the equations, an energy hub method is used to build a control model of industrial parks with multi-energy distribution system. Using the rolling optimal control strategy given in this paper, the model is solved by the Matlab-Yalmip solver and rolling control instructions of each energy conversion unit are obtained. Finally, the case study demonstrates that the LSTM neural network-based modeling method presented in this paper can accurately fit the dynamic process of a natural gas pipeline system. The rolling control model of the multi-energy system can improve the efficiency of energy utilization, exhibit the transmission line status constraints during the optimization control process and improve reliability of the multi-energy system operation.

Multi-energy systems are one of the key technologies to tackle energy crisis and environmental pollution. An energy hub (EH) is a minimum multi-energy system. Interconnection of multiple EHs through energy routers (ERs) can realize mutual energy assistance. This paper proposes a peer-to-peer (P2P) energy sharing strategy between EHs including ERs in an interconnected system, which is divided into two levels. In the lower level, a method of determining the charging/discharging constraints of energy storage devices is proposed. Based on the Lyapunov optimization method, virtual queues are used to model the energy storage devices and flexible loads in the system. The objective is to minimize the overall operating cost of the interconnected system. In the upper level, a non-cooperative game model is introduced to minimize the cost of purchasing power from other EHs for each EH. A best response-based method is adapted to find the Nash equilibrium. The simulation outcomes demonstrate that application of the proposed strategy can reduce operating costs of an interconnected system and each EH. On basis of a real-world dataset of interconnected EHs, both analytical and numerical results show the effectiveness of the proposed strategy.

This paper presents a robust framework for optimally coordinating integrated electricity and gas networks incorporating energy hubs and active loads. The energy hubs contain different sources, converters, and storage units to supply a variety of consumers. The designed system internally exchanges energy and gas using interface equipment and it simultaneously can participate in the day-ahead markets. This study also investigates the influence of various components such as renewable resources and energy storage systems on reactive power management. According to the high penetration of renewable energies and their significant impact on the network operation, a robust method consisting of information gap decision theory and fuzzy approach is applied to handle the uncertainty of wind and solar units. In addition, the role of flexible loads and prosumers is investigated by a time-based strategy, and eventually, a sensitivity analysis is implemented to evaluate the behavior of critical parameters. The presented model is a non-linear type that is linearized utilizing appropriate procedures to guarantee the problem convergence. The results show that the robust optimization with the maximum confidence level increases pollution and cost functions by 14.73% and 49.68%, respectively. In return, active loads decrease the mentioned values by 7.67% and 16.04%, respectively which validates their notable effects. The reactive power control of renewable resources and storage systems reduces the reactive power transferring through tie-lines and consequently improves technical specifications.

Energy hubs (EHs) enable all types of energy customers to participate in demand response programs (DRPs), such as inelastic loads, by combining electricity, heat, natural gas, and other types of energy. Integrated demand response (IDR) is the result of this new vision. From a global warming perspective, environmental emissions are a significant issue to be considered. Furthermore, hydrogen has been recognized as an attractive fuel for decarbonizing sectors that contribute to global warming. Thus, this paper provides a solution to global environmental problems through the utilization of renewable energy sources (RESs) and green hydrogen. In addition, electric vehicles (EVs) are expected to contribute significantly to this scenario due to their rapid expansion. Considering the uncertainty of electricity prices, this paper focuses on coordinating EV parking with hydrogen storage systems (HSS) and IDR with the aim of increasing flexibility, where a robust optimization (RO) method has been implemented to solve the problem. The results demonstrate that in the case of a deterministic solution to the problem and where uncertainty is at the highest level, the proposed scheme reduces the total operating costs by 13.89% and 8.67%, respectively. This indicates that the proposed scheme could avoid overinvestment and cost-effectively achieve the given carbon emission target.

The transition to a near-zero-emission power and energy industry for facing up to global warming issues is dominated by the use of renewable energy resources such as bioenergy and solar energy. When these resources are coordinated within an energy hub framework, the system’s flexibility is increased and dispatchable energy is provided by enhancing the share of renewable-dominated power. This paper proposes a dynamic scheduling framework for an energy hub with a biomass-solar hybrid renewable system. A hybrid forecasting model based on convolutional neural networks (CNNs) and Gated recurrent units (GRUs) is developed first to capture solar-related uncertainty sensibly, which will provide a great opportunity for the learning-based controller to determine an effective operation strategy in an optimal manner, especially on a cloudy-weather day. Then, a supervised federated neural architecture search (SFNAS) technique has been presented to eliminate the need for manual engineering of deep neural network models and the unnecessary computational burden associated with them. Finally, the deep deterministic policy gradient (DDPG), as an actor-critic deep reinforcement learning (DRL) methodology, enables the biomass-based energy hub to achieve cost-effective dynamic control strategies by addressing the decision-making problem as a highly dynamic continuous state-action model. The major conclusions of the numerical results show the effectiveness of the proposed SFNAS-DDPG method from average operating cost reduction up to 7.31% compared to the conventional DDPG model.

This paper investigates a multi-objective optimal energy planning strategy for a hub, incorporating renewable and non-renewable resources, like PV, tidal turbine, fuel-cell, CHP, boiler, micro-turbine, reactor, reformer, electrolyzer, and energy storage by utilizing the time of use program (TOU). In this strategy, tidal turbine, fuel-cell, and reformer technologies are considered novel technologies that simultaneously reduce the proposed hub’s cost and pollution. The hub’s total cost and pollution are considered objective functions. To make the results more realistic, characteristics of the tidal turbine are investigated by utilizing the manufactory’s company information. The problem is then modeled as real mixed integer programming (RMIP) and is solved in GAMS software using a CPLEX solver. Epsilon constraints method and fuzzy satisfying approach are used to select the optimal solution based on the proposed model. Finally, a sensitivity analysis is performed to assess the effective parameters that affect the planning’s results. The results show that the overall pollution is reduced by about 9% by assuming the proposed planning, and the total profit is increased by about 30%.

In this paper, the formulation of the energy market in two wholesale and retail models for different energy networks such as electric, gas and thermal networks in the presence of energy hubs according to the two-layer energy management system is presented. In the first layer of EMS, the coordination of resources and ALs with the operator of EHs is considered, and in the second layer of EMS, the coordination of the operator of EHs with ENOs is considered. In the proposed design, the mentioned networks have participated in the wholesale market as private distribution companies or DisCo and then buy energy from it. These companies have shared the purchased energy in the retail energy market environment between consumers and EHs connected to itself. This design is expressed in the form of two-level optimization, the upper level of which is the minimization of the expected energy cost of ENs in the mentioned markets, and the other level is the minimization of the expected energy losses of ENs in the retail market. In the following, the Karush–Kuhn–Tucker (KKT) method and Pareto optimization technique based on epsilon constraint method were used to derive the single-level and single-objective problem. Then, the unscented transformation (UT) method was used to model the uncertainties of load, energy price, renewable power and EV energy demand. Finally, based on the numerical results, it was observed that the proposed plan achieves the highest profit for EHs in proportion to the time-varying energy price. Also, with the optimal energy management of EHs, it has been able to reduce the energy cost of ENs by about 12% compared to load flow studies.

The combination of different energy vectors like electrical energy, hydrogen, methane, and water is a crucial aspect to deal with in integrated local energy communities (ILECs). The ILEC stands for a set of active energy users that maximise benefits and minimise costs using optimisation procedures in producing and sharing energy. In particular, the proper management of different energy vectors is fundamental for achieving the best operating conditions of ILECs in terms of both energy and economic perspectives. To this end, different solutions have been developed, including advanced control and monitoring systems, distributed energy resources, and storage. Energy management planning software plays a pivotal role in developing ILECs in terms of performance evaluation and optimisation within a multi-carrier concept. In this paper, the state-of-the-art of ILECs is further enhanced by providing important details on the critical aspects related to the overall value chain for constituting an ILEC (e.g., conceptualisation, connecting technologies, barriers/limitations, control, and monitoring systems, and modelling tools for planning phases). By providing a clear understanding of the technical solutions and energy planning software, this paper can support the energy system transition towards cleaner systems by identifying the most suitable solutions and fostering the advancement of ILECs.

A promising energy carrier and storage solution for integrating renewable energies into the power grid currently being investigated is hydrogen produced via electrolysis. It already serves various purposes, but it might also enable the development of hydrogen-based electricity storage systems made up of electrolyzers, hydrogen storage systems, and generators (fuel cells or engines). The adoption of hydrogen-based technologies is strictly linked to the electrification of end uses and to multicarrier energy grids. This study introduces a generic method to integrate and optimize the sizing and operation phases of hydrogen-based power systems using an energy hub optimization model, which can manage and coordinate multiple energy carriers and equipment. Furthermore, the uncertainty related to renewables and final demands was carefully assessed. A case study on an urban microgrid with high hydrogen demand for mobility demonstrates the method’s applicability, showing how the multi-objective optimization of hydrogen-based power systems can reduce total costs, primary energy demand, and carbon equivalent emissions for both power grids and mobility down to −145%. Furthermore, the adoption of the uncertainty assessment can give additional benefits, allowing a downsizing of the equipment.

In this paper, a user thermal comfort criterion based on predicted mean vote (PMV) values is introduced to realize the optimal operation of an improved energy hub (IEH) while considering thermal inertia and user thermal behavior. A three-layer optimization model based on user thermal comfort is constructed which fully considers user thermal comfort demand, IEH operating costs, and energy network constraints. Moreover, since IEH optimization considering user thermal comfort is a multi-objective bilevel optimization (MNBO) problem, this paper proposes an improved multilayer nested quantum genetic algorithm (IMNQGA) to solve it. Finally, the effectiveness of the proposed optimization model and algorithm is verified through the analysis of the four modes. The examples show that the proposed optimal control method can reduce the system's operating costs and improve energy efficiency while satisfying user thermal comfort demand. [ABSTRACT FROM AUTHOR]

In recent decades, climate change has severely increased the concerns over the resilience of power systems. Extreme events may lead to the failure of a large number of system components and result in interruption of supply to lots of consumers. In this research, the main objective is to use and assess the potential of large industrial energy hubs (EHs) in resilience improvement of power systems. Mobile energy storage and demand response programs are also used to decrease involuntary demand shed in system and enhance system resilience. A stochastic mixed-integer linear programming model is developed considering the uncertainties in damaged transmission lines, hurricane time and repair time. Case study is a modified IEEE 24-bus power system with EHs. The studied EH receives gas from a gas network, exchanges power with power system, includes combined heat and power (CHP), generator, electrolyzer, boiler, thermal storage and hydrogen storage system and feeds electric, heat and hydrogen demands. Expected load not supplied (ELNS) is used as resilience metric. The results approve significant impact of EH in improvement of power system resilience. In scenarios in which the bus connected to EH is isolated, EH supplies the power system demand located at that bus and thereby improves system resilience; on the other hand, in other scenarios, EH typically behaves as an electricity consumer of power system. According to the results, mobile energy storage causes a 2.4% improvement in ELNS. The results also show that demand response program improves ELNS by 7.1%. • Energy hub and mobile storage are used for resilience enhancement of power systems. • Uncertainties in damaged lines, hurricane time and repair time are considered. • The effect of responsive demands on power system resilience is evaluated. • The effect of EH generator size on power system resilience is assessed. [ABSTRACT FROM AUTHOR]

With the development of the economy, people's demand for green energy has increased significantly. However, the traditional single fossil energy supply system cannot meet the needs of low-carbon. Therefore, this study employs energy hub to establish a multi-energy flow network that enables the integration of carbon flow within the network. Additionally, by utilizing the multi-energy flow trend, a carbon flow tracking method is adopted to achieve real-time carbon flow calculation. Results show that this network calculates the electricity cost of 20043 yuan, gas cost of 67253 yuan, and carbon emission cost of 3152 yuan. Compared with the traditional energy flow system, gas cost is reduced by 4.3% and 1.7%, electricity cost by 21.3% and 15.0%, and carbon emission cost by 8.7% and 6.6%. The two-way sharing carbon flow calculation model calculates that the user side and power supply side of the node each bear half of the network loss, proving two-way sharing effectiveness. Test results on IEEE5 machine 14-node system show that the calculation method can accurately find high-emission and low-emission areas, making the carbon emission allocation between power generation and user more fair and reasonable. This research can effectively reduce emissions cost, accurately calculate emissions flow in real time, and facilitate reasonable emission reduction planning. [ABSTRACT FROM AUTHOR]

To support the energy transition in the area of defence, we developed a tool and conducted a feasibility study to transform a military site from being a conventional energy consumer to becoming an energy-positive hub (or prosumer). Coupling a green energy source (e.g., photovoltaic, wind) with fuel cells and hydrogen storage satisfied the dynamic energy consumption and dynamic hydrogen demand for both the civilian and military mobility sectors. To make the military sector independent of its civilian counterpart, a military site was connected to a renewable energy hub. This made it possible to develop a stand-alone green-energy system, transform the military site into a positive energy hub, and achieve autonomous energy operation for several days or weeks. An environmental and economic assessment was conducted to determine the carbon footprint and the economic viability. The combined installed capacity of the solar power plant and the wind turbine was 2.5 times the combined peak consumption, with about 19% of the total electricity and 7% of the hydrogen produced still available to external consumers. • Military sites can become near zero or even zero RES energy hubs. • RES power generation components must be oversized to meet dynamic consumption. • RES energy hubs enable economic feasibility of large-scale hydrogen technologies. • The mathematical model developed allows environmental assessment and economic viability. • RES energy hub produces surplus green electricity and supports hydrogen mobility. [ABSTRACT FROM AUTHOR]

Use of different energy carriers together, known as an energy hub, has been a hot topic of research. The hub manager manages the energy in the hub and supplies the energy demanded by the consumers. Herein, a multi-carrier energy storage system comprising a thermal energy storage system, ice storage system, and hydrogen storage system is considered for a multi-carrier energy system to exploit their economic benefits. The proposed combined cooling, heating and electricity microgrid not only participates in the electricity, gas and heat market to meet the needs of electricity, heating and cooling, but also can use the new hydrogen power technology used in the hydrogen storage system to increase the efficiency of the whole system participate in the market. In addition, a multi-energy demand response model is used as a new concept of demand response on electrical and thermal loads, which provides more options for multi-energy end users in energy management policies. To cover the uncertainty of the existence of different energy sources in the used model and the price of electricity, a new method has been used. The LHS sampling is used to develop diverse scenarios, and the backward scenario reduction method is adopted to decrease the number of scenarios. To solve the objective function and optimization, the metaheuristic whale algorithm improved by a local search algorithm is utilized. The main goal is to reduce the operation costs of the multi-energy carrier system. The obtained results show that the hydrogen storage utilization and multi-energy demand response can reduce the system operation cost by 6% for the studied experimental system. Furthermore, as the uncertainty budget increased, the total cost of operations increased by 10%. This is because the level of planning conservatism increases with each increase in the uncertainty budget. • Suggesting an integrated energy communications based on power to hydrogen equipment. • Suggesting thermal and electrical energy storages and integrated demand response. • Applying a new developed optimization approach to solve problem and manage uncertainties. [ABSTRACT FROM AUTHOR]

Abstract Nowadays, researchers are studying the energy discharging of electric vehicles (EVs) in charging stations. Furthermore, these stations can help maintain the balance of electric energy and reduce costs in the energy hub in the discharging mode. In this regard, this article proposes a probability and possibility approach by the Z‐number method to investigate the uncertainty of the EV owners’ behaviour at charging stations during the discharge of electric energy to the energy hub. Also, here, the uncertainty of electric energy demand, thermal energy, drinking water, and the price of electric energy, thermal energy, drinking water, and electric energy production from a solar power plant has been done by the scenario‐base method. The seawater desalination unit has been modelled to meet the drinking water needs. To check the final results of the proposed method, the mixed integer linear programming (MILP) model has been selected for energy management in the energy hub in the CPLEX optimization solver. The final results show that modelling the behaviour of EV owners in the charging stations using the Z‐number method leads to reduced consumption of some energy carriers in comparison with the fuzzy method in the proposed energy hub.

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Nowadays, researchers are studying the energy discharging of electric vehicles (EVs) in charging stations. Furthermore, these stations can help maintain the balance of electric energy and reduce costs in the energy hub in the discharging mode. In this regard, this article proposes a probability and possibility approach by the Z‐number method to investigate the uncertainty of the EV owners' behaviour at charging stations during the discharge of electric energy to the energy hub. Also, here, the uncertainty of electric energy demand, thermal energy, drinking water, and the price of electric energy, thermal energy, drinking water, and electric energy production from a solar power plant has been done by the scenario‐base method. The seawater desalination unit has been modelled to meet the drinking water needs. To check the final results of the proposed method, the mixed integer linear programming (MILP) model has been selected for energy management in the energy hub in the CPLEX optimization solver. The final results show that modelling the behaviour of EV owners in the charging stations using the Z‐number method leads to reduced consumption of some energy carriers in comparison with the fuzzy method in the proposed energy hub. [ABSTRACT FROM AUTHOR]

Although the severity of the COVID-19 pandemic has appears to have subsided in most parts of the world, nevertheless, in addition to six million deaths, it has yielded unprecedented challenges in the economy, energy, education, urban services, and healthcare sectors. Meanwhile, based on some reports, smart solutions and technologies have had significant success in achieving pandemic-resilient cities. This paper reviews smart city initiatives and contributions to the prevention and treatment of coronavirus disease, as well as reducing its destructive impact, leading towards pandemic-resilient economic and health systems. Furthermore, the situational awareness contributions are reviewed in pandemic-resilient governance. The main contribution of this study is to describe the construction of social capital in smart cities as a facilitator in creating a pandemic-resilient society in crisis through two analyses. Moreover, this research describes smart cities' energy as interconnection of energy hubs (EHs) that leads to a high level of resiliency in dealing with the main challenges of the electricity industry during the pandemic. Energy-hub-based smart cities can contribute to designing pandemic-resilient energy infrastructure, which can significantly affect resilience in economic and health infrastructure. In brief, this paper describes a smart city as a pandemic-resilient city in the economic, energy, and health infrastructural, social, and governmental areas. [ABSTRACT FROM AUTHOR]

The energy hub (EH) integrates various types of energy supply equipment and loads, improving operational efficiency but bringing uncertainty. For dealing with the uncertainty factors that EH faces, the multi-EHs operation mode has more advantages than the single-EH operation mode. Therefore, a cooperative game-based coordinated model for multiple EHs with different risk preferences is built. The expected cost of all EHs is served as the objective function. Also, all EH’s conditional value at risk (CVaR) does not exceed its acceptable upper limit. The upper limit of CVaR can be obtained by solving a bi-objective optimization model for the independent operation of EH. Especially each EH has a different risk preference in the independent operation model, i.e., the weighting factor between costs and risks. Furthermore, by using scenario-based stochastic programming, the above models are transformed into linear models. Then, a novel two-factor cost allocation method for multi-EH with different risk preferences is proposed. The fixed and proportional allocation coefficient of each EH is obtained by solving a set of allocation equations based on the Shapley value. Finally, numerical studies with three EHs demonstrate the effectiveness of the proposed method. In the case study, the overall expected cost and CVaR of multiple EHs decreased by 4.99 % and 15.88 %, respectively.