Optimal Operation Of Energy Hub Research Articles

Electric vehicles and the operation of multi-carrier energy systems in the smart grid: a review study

Smart grids have been connected to energy hubs (EHs) with a range of energy carriers and storage capacity to increase the efficiency, flexibility, and dependability of the energy system. One of the most challenging problems in smart grids nowadays is the scheduling of EHs. Although the interaction of several devices, such as EHs and electric vehicle charging stations (EVCSs), can help improve their performance, developing such intricately interwoven systems is difficult. In this paper, with the help of several papers, the impact of EVCSs on the optimal operation of EHs is investigated, despite the uncertainty surrounding electric vehicle charging behaviours. Additionally, the formulation concerning the energy hub's component parts, electric vehicles (EVs) in a single hub, and interconnected hubs is investigated. In addition, some simulation results from reviewed papers are presented and analyzed in this paper. The main goal of this paper is to analyze the effects of EVCS on the optimal management of EHs. Furthermore, some models of energy hubs integrated by EVCS are presented, and a numerical analysis is conducted to examine the function of EVCS in conjunction with electrical and thermal storage systems.

Optimal operation of energy hub considering reward-punishment ladder carbon trading and electrothermal demand coupling

As the massive carbon emissions from the power system, the shift of the power system to low-carbon becomes inevitable. In this context, this paper proposes a Stackelberg game approach to develop the operational strategy of energy hubs, which considers the reward and punishment ladder carbon trading mechanism and the coupling of electrothermal demand. To avoid the fixed carbon price in the past and the neglect of the incentive space for carbon trading, a ladder carbon trading mechanism of rewards and punishments is introduced to effectively depict carbon trading costs. The electrothermal coupling of the demand side is incorporated into the Stackelberg game model, and the flexible consumption model of users participating in the integrated demand response is established. The above model is solved by combining heuristic algorithm with solver CPLEX, which can solve the data privacy leakage problem of both parties. Simulation results show that the strategy can bring economic benefits to energy hub operators and users. Carbon emissions across the system were reduced by 14.4 %. Users with electric heating equipment can fully improve the electric load and heat load adjustment level, and improve their economic benefits by 2.4 % at the cost of a 3.2 % increase in carbon emissions.

Eco‐environmental operation of energy hub considering uncertainties of supply/demand‐side resources with introducing a novel optimization algorithm

AbstractIn this paper, the optimal operation of Energy hub (EH) based upon both demand and renewable energy resources (RESs) uncertainties is investigated. Here, an innovative scenario‐based energy hub energy management system (EH‐EMS) is proposed for simultaneous cost and emission reduction considering the abovementioned uncertainties. Moreover, a multi objective hybrid heuristic algorithm, the so‐called hBES‐MOGWO, is introduced for solving the proposed EH‐EMS, which is a combination of bald eagle search (BES) and multi objective gray wolf optimization (MOGWO) algorithms. The proposed EH consists of photovoltaic (PV), wind turbine (WT), boiler, heat storage (HS),combined heat and power (CHP) and multi‐carrier energy storage technology such as power to gas (PtG) technology, electric vehicles and heat storages. Flexible loads are also taken into account for reducing cost and emission by participation in demand response programs (DRPs). The optimization results demonstrate the effectiveness of the proposed hBES‐MOGWO algorithm. The simulation results illustrated that the total cost of EH‐EMS can be effectively reduced by installing energy storage systems and implementing DRPs. The results emphasize that considering uncertainties can lead to increase the operating costs and emission, while flexible loads participation in DRPs and PtG can reduce the aforementioned ones.

Optimal Operation of Energy Hub: An Integrated Model Combined Distributionally Robust Optimization Method With Stackelberg Game

This paper proposes a low-carbon operation model for an energy hub (EH) that combines the distributionally robust optimization (DRO) method with the Stackelberg game. Firstly, a bilevel single-leader-multi-follower Stackelberg game model is presented where the EH is the leader while users and electric vehicles (EVs) are regarded as two followers. Then, the Kullback-Leibler (KL) divergence-based DRO model is developed to deal with the uncertainty of renewable generation (RG) in the EH. Besides, Karush–Kuhn–Tucker (KKT) conditions, strong duality theory, and big- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> approach are combined to transform the bilevel model into a single-level model. The reformulated single-level operation model is incorporated into the KL-based DRO approach. Furthermore, since the crafted column and constraint generation (C&CG) algorithm can prevent possible numerical problems caused by the exponential function and accelerate the solution speed, the crafted C&CG algorithm with linearization for the upper-level slave problem is proposed to iteratively solve the KL-based DRO integrated with Stackelberg game. Finally, numerical case studies are conducted with all simulation results confirming the effectiveness of the proposed model and method.

Robust optimal operation of energy hub incorporating integrated thermal and electrical demand response programs under various electric vehicle charging modes

Although the energy hub (EH) concept results in different benefits, the system uncertainties might affect the optimized decisions. Several studies have studied EH’s optimal operation, considering the system uncertainties. However, there is a research gap in proposing a novel EH’s robust optimal operation under various charging modes of electric vehicles (EVs), besides integrated electrical and thermal demand response programs (DRPs). The main purpose of this research is to fill such a knowledge gap. In this paper, the uncertainties of renewable energy distributed resources and electricity price have been concerned using the robust optimization method. The integrated charging load of EVs under coordinated and uncoordinated charging modes has been distinguished using the Monte Carlo simulation (MCS). Another contribution is to study different schemes of hybrid electrical, heat, and cooling energy storage systems in the introduced robust framework. Simulation results infer that a 15.87% decrement in EH’s operation cost is achievable by applying the integrated thermal and electrical DRPs, using the electric heat pump (EHP) and hybrid storage system consisting of the ice storage system. The comparative test results show that the advantages of integrated thermal and electricity DRPs and charging management of EVs are highlighted by using EHPs. The sensitivity analyses of the EH’s operation cost against the robustness efficiency parameters corresponding to the uncertainty level show that the advantages of the proposed method are highlighted while the EH’s uncertainties increase.

Optimal operation of energy hubs including parking lots for hydrogen vehicles and responsive demands

Energy hubs (EHs) are units that enable the simultaneous supply of different types of energy demands by converting energy carriers, and using energy storage systems. Energy storage systems can significantly help maintain the balance between energy production and energy demand, while enabling the use of renewable energy resources, and improve the flexibility of energy hubs through the efficient management of energy supply. In this study, a stochastic model is designed for unit commitment (UC) in Energy hubs, which include hydrogen vehicle (HV) parking lot, electric heat pump (EHP), absorption chiller (AC), photovoltaic (PV) module, boiler, hydrogen electrolyzer (HE) and electric, thermal, cooling and hydrogen storage systems. Here, natural gas (NG) and electricity are the input of the EH and are used to supply electric, hydrogen, heat, cooling and NG demands. In this work, uncertainties of demands, the initial power of hydrogen vehicle tanks and PV power are modeled, and the impact of storage systems, parking lot and demand response on EH operation are also investigated. The proposed mixed integer linear programming (MILP) model is solved for unit commitment in EH using the CPLEX solver in the GAMS software. The results show that the EH operation cost is reduced by 27.58% in the presence of demand response, energy storage systems by 12.68%, and hydrogen vehicles by 2.9%. In addition, according to the results, it can be found that the cooling storage system by 6.19% has the significant impact on reducing EH operation costs compared to electrical, hydrogen and thermal storage systems, while electric demand response by 15.89% reduction in operation costs is more effective than others. Moreover, the impact of different contingencies on the EH operation is evaluated. The results indicate that the hydrogen demand is fully supplied despite the exit of the power grid. This is particularly due to the presence of hydrogen vehicles (HV tanks) in the model. Also, simulations show that the outage of the power grid leads to 1288.64 kW of energy not served.

A scenario-based approach for optimal operation of energy hub under different schemes and structures

Because of economic and environmental issues' importance in the energy field, moving toward smart energy systems and energy hubs (EHs) has accelerated. The impacts of uncertainties, e.g., stochastic behaviors of renewable distributed generations (DGs), on EHs are fundamental challenges that should be considered carefully. Although several studies have been done in the area of EHs, a knowledge gap exists about developing an approach considering uncertainties under different EHs' structures and topologies. This research purposes of responding to such a research gap. In this research, a scenario-based approach for EHs' optimal operation considering wind turbine (WT) and photovoltaic (PV) uncertainties is proposed. The proposed approach is applied to EH under different schemes. Using the k-means clustering algorithm decreases the computational burden, while the appropriate accuracy is achievable. The proposed stochastic optimization problem is solved using the genetic algorithm (GA). The comparative view is considered to investigate the impacts of cooling and heating components like the heat pump (HP), absorption chiller (AC), and heat storage (HS) on EH's optimal operation and energy cost. According to this research findings, the EH's daily energy cost under a scheme using the AC, HP, and HS is approximately 6.5% less than a scheme only using HP. Also, using the HS and HP alongside the AC leads to 5.6% and 6.4% cost-saving, respectively. But for a better comparison, the investment and operation and maintenance (O&M) cost are considered, in which case Structure 3 (AC + HP) is more efficient both in terms of energy consumption and investment costs.

Optimal operation of energy hubs integrated with electric vehicles, load management, combined heat and power unit and renewable energy sources

As an integral part of multi-energy systems, the energy hub acts a major task in developing the flexibility, efficiency, and reliability. Due to the increasing progress of science and human communities and the rise in air pollutants and Earth temperature, the need for renewable energies and electric vehicles has increased. The only challenge to the use of new energies is the uncertainty in their production due to the lack of continuous solar irradiation and wind in different hours of the day. Accordingly, this paper addresses an optimal load dispatch form for an energy hub to decrease the total costs of the energy hub, such as exploitation costs and CO2 emission costs. This energy hub includes a heat storage unit, combined heat and power (CHP) unit, photovoltaic (PV) arrays, gas boiler, wind turbine (WT), and electric vehicles (EV). EV uncertainty is modeled via Monte Carlo simulation and a developed algorithm based on grasshopper search is adopted for dealing with future uncertainties in electricity price. Moreover, the proposed model considers the electric and thermal demand response (DR) methods comprehensively. Herein, three scheduling scenarios are discussed with different charge/discharge and DR settings. The numerical and graphical results demonstrate that, by choosing a coordinate charge/discharge mode for the EVs, the final costs are successfully reduced. Compared to Scenario 1, the total costs of Scenario 2 are reduced by 12%. Consequently, it can be obvious that the EVs’ matched charge/discharge is successfully decreased the energy costs for the consumers. Compared to Scenario 2, the total costs of Scenario 3 are decreased by 5.76%. The results also indicate that by implementing the DR programs, total consumer costs can be further decreased.

Optimal Operation of Energy Hub Based Micro-energy Network with Integration of Renewables and Energy Storages

This study proposes an optimized model of a micro-energy network (MEN) that includes electricity and natural gas with integrated solar, wind, and energy storage systems (ESSs). The proposed model is based on energy hubs (EHs) and it aims to minimize operation costs and greenhouse emissions. The research is motivated by the increasing use of renewable energies and ESSs for secure energy supply while reducing operation costs and environment effects. A general algebraic modeling system (GAMS) is used to solve the optimal operation problem in the MEN. The results demonstrate that an optimal MEN formed by multiple EHs can provide appropriate and flexible responses to fluctuations in electricity prices and adjustments between time periods and seasons. It also yields significant reductions in operation costs and emissions. The proposed model can contribute to future research by providing a more efficient network model (as compared with the traditional electricity supply system) to scale down the environmental and economic impacts of electricity storage and supply systems on MEN operation.

An optimal schedule model of multi-energy hubs network integrating solar energy

Recently, multi-energy systems based on energy hub are introduced because of significant benefits in reducing energy and emission cost. This paper proposed an optimal schedule model of multi-energy hubs networks consisting of energy hubs, renewable sources, and energy storage which are connected by electrical and natural gas distribution networks. In the proposed mixed-integer nonlinear programming model, the objective is to minimize the operation, energy, and emission costs of energy hubs with both renewable sources and storage and energy distribution networks. The proposed schedule framework allows simultaneously selections of optimal operation structure of EHs together with the optimal operation parameters of energy distribution networks and therefore this model can maximize the profit of the entire large-scale multi-energy hubs network. Besides, the operation parameters and energy loss of both electrical and natural gas distribution networks are considered in conjunction with optimal operation of energy hubs and thus guarantee the operation and optimization of the network in all operational scenarios. The IEEE 5-bus test system is utilized to demonstrate the applicability of the proposed model. The simulation results show the feasibility of the proposed model, and demonstrate that the energy hubs, renewable sources, and energy storage in the proposed structure significantly enhance the efficiency of the multi-energy hubs network by reducing not only energy and operation costs but also emission.

Optimal Operation of Energy Hubs With Large-Scale Distributed Energy Resources for Distribution Network Congestion Management

Congestion problems might occur in distribution networks as the penetration of distributed energy resources (DERs) progresses. This study focuses on the complementarity of multiple energy resources in energy hubs (EHs) to solve possible distribution network congestions. First, we consider an EH in which combined cooling, heating and power (CCHP) units and heat pumps are integrated with renewable energy resources. The EH models the intrinsic coupling relationship among various energy carriers, forming a flexible operation where electricity, natural gas, cooling and heat can complement each other. Next, an optimal operation strategy of multiple energy hubs enables gas-to-electricity to provide local energy supply for EH during electricity peak periods, and consume renewable energy generation by the complementarity of electricity, heat and cooling. In addition, the uncertainty of renewable energy resources is taken into account via scenario-based stochastic programming. Numerical results show that the proposed operation strategy explores EHs’ operation flexibility to mitigate distribution network congestion when we consider high renewable energy penetration in power systems.

A Scenario-Based Approach for Optimal Operation of Energy Hub Under Different Schemes and Structures

A Scenario-Based Approach for Optimal Operation of Energy Hub Under Different Schemes and Structures

Optimal Operation of Energy Hubs Considering Uncertainties and Different Time Resolutions

This article presents a robust chance-constrained optimization framework for the optimal operation management of an energy hub (EH) in the presence of electrical, heating, and cooling demands, and renewable power generation. The proposed strategy can be used for optimal decision making of operators of EHs or energy providers. The electrical energy storage device in the studied EH can handle the fluctuations in operating points raised by such uncertainties. In order to model the hourly demands and renewable power generation uncertainties, a robust chance-constrained close-to-real-time model is adopted in this article. The considered EH in this study follows a centralized framework and the EH operator is responsible for the optimal operation of the hub assets based on the day-ahead scheduling. A thorough analysis of energy flows with different carriers is presented. In addition, a numerical stability test regarding the selection of the time step size is performed to guarantee the solution's time resolution independence, occurring in previous studies.

Matrix modeling of energy hub with variable energy efficiencies

The modeling of multi-energy systems (MES) is the basic task of analyzing energy systems integration. The variable energy efficiencies of the energy conversion and storage components in MES introduce nonlinearity to the model and thus complicate the analysis and optimization of MES. In this paper, a standardized matrix modeling approach is proposed to automatically model MES with variable energy efficiencies based on the energy hub (EH) modeling framework. Piecewise linearization is used to approximate the variable energy efficiencies; as a result, a component with variable efficiency is equivalent to several parallel components with constant efficiencies. Splitters and concentrators are proposed as standardized components to facilitate the split and merge of energy flows imposed by piecewise linearization. The nonlinear energy conversion and storage relationship in EH can thus be further modeled under a linear modeling framework using matrices. Such matrix modeling approach makes the modeling of an arbitrary EH with nonlinear energy components highly automated by computers. The proposed modeling approach can further facilitate the operation and planning optimization of EH with variable efficiencies. Case studies are executed in MATLAB and presented to show how the nonlinear approximation accuracy and calculation efficiency can be balanced using the proposed model in the optimal operation of EH. For the optimal operation of a five-component EH with energy storage, the proposed approach reduces the approximation error from 13.7% to 0.1% with only 0.2 s computation time increase compared to the exiting constant efficiency model.

Optimal operation of energy hubs integrated with renewable energy sources and storage devices considering CO2 emissions

Electricity production, heat, and cooling requirements are one of the main problems for energy distribution network businesses. Different energy grids like gas and electricity run concurrently as a particular solution for overcoming the issue. “Energy Hub” (EH) is a vague approach for an energy center composed of electricity, gas, heat and at times cooling networks. Energy hub systems can be considered as a powerful alternative to improve the use of existing energy resources. This paper proposes a novel technique for optimum operation and configuration of multiple energy hubs. The proposed EH contains different types of energy sources and generation with multi-type energy storage devices to feed electrical, heating and gas demands. The EH contains renewable energy resources (wind and PV), combined heat and power cogeneration, power-to-gas units and gas-fired generator to provide interdependencies of infrastructures for these energy sources. Uncertainties of both wind and PV energy resources are considered in this study. A multi-objective optimization problem is formulated to maximize social welfare and minimize CO2 emissions. Genetic Algorithm (GA) is used to solve the overall optimization problem. The impact of the hub configuration on the EHs stability and security are examined. The proposed model is tested on IEEE 69 bus through several case studies to examine its effectiveness and applicability. A comparative analysis is conducted between different configurations of the EH. The comparison proved that the best performance in terms of configuration, operational, emissions, and stability is obtained with a combination of PV generator, wind turbine, electrical storage, thermal storage, natural gas storage, and power to gas unit.

Comparison and error analysis of off-design and design models of energy hubs

The accuracy of the simulation model has a profound impact on the optimal operation of the energy hubs (EHs). However, in many articles, the constant model of the efficiency of equipment is adopted to formulate the operation system, which would probably lead to a simplification of the simulation models. But, EHs are typically operated under off-design condition due to the fluctuations in cooling, heating, electricity requirement. Moreover, even though the off-design characteristics are considered, few studies have suggested comparing the differences between those two models by considering the operation cost. In order to assess the effect of the off-design characteristics of EH on the optimal operation accuracy in this paper, two test cases are performed on the fixed and variable load conditions, respectively. In addition, the individual effect of off-design characteristics of each equipment on the optimal operation cost of the EH is also investigated through four optimization runs. It is worth mentioning that the optimal operation problem of the EH considering the off-design characteristics and on-off status of the equipment is a mixed integer non-linear programming problem (MINLP). By testing the design and off-design models on the two cases, the results of simulation demonstrate that the optimal operation cost for the off-design model is larger than that for the design model. Nonetheless, in the aspect of the authenticity of the system operation strategy, the off-design model performs better than the design model. Furthermore, a larger relative error of the system operation cost between the two models can be observed when the EH is operated under a relatively lower load condition, revealing that the influence of off-design characteristic on the optimal operation of EHs is too significant to be neglected.

Optimal operation of energy hub in competitive electricity market considering uncertainties

This paper proposes a novel energy hub model for areas using both heat and cold demands that arise due to the major changes in environmental temperature in different periods of the year. The energy demand and the electrical price in a competitive electricity market are uncertain with stochastic values which are usually performed by a probability distribution function. Therefore, a stochastic mathematical model representing an optimal operation of energy hub is based on the objective function of minimization of energy costs (including electricity and gas). Several constraints such as energy balance, limited capacity of the transformer, air conditioners, gas boilers, absorption chillers, combined heat, and power and battery energy storage system are also incorporated into the model to guarantee the required specifications. The high-level algebraic modeling software, general algebraic modeling system has been employed to undertake calculations. Finally, numerical results have illustrated the efficiency and capability of the proposed models.

Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response

Renewable energy based Distributed Energy Resources (DERs) are essential of coping with greenhouse gases emission and growing energy needs. Combined Heat and Power (CHP), Wind, Energy Storage technologies (ES) and Demand Response programs (DR) have been confirmed the valuable resources. In this paper, Energy Hub (EH) as a super node in electrical distribution network receives various energy carriers; gas, electricity and wind in its input, and then after conversion, storage, direct connection or shifting demands provides hub required demands; electricity, heat, gas and water. The hub is optimally operated based on objective function considering economic, greenhouse gases emission, reliability and efficiency terms in predicted and stochastic environment of wind, electricity demand and Real Time Pricing (RTP) market. A Monte Carlo simulation is employed to generate scenarios tree based predicted RTP, wind and electricity demand. GAMS; high level algebraic modeling software is employed to reduce and read scenarios for Stochastic Programming in Mixed Integer Linear Programming (MILP) model of proposed approach to endorse when and what technologies should be optimally operated to achieve minimum operation costs and maximum reliability improvement with comparison of nine different cases (wind, price and electricity demand certainties and uncertainties) to satisfy a commercial load. Impact of DERs and effect of wind, price and demand uncertainties are investigated on total hub operation costs and hub reliability and also on which technology most be operated.