电-气-氢-热-风-光耦合综合能源系统分层级Energy Hub建模方法

随着多能融合的不断深入,综合能源系统可实现大规模可再生能源消纳。由于综合能源系统广泛涉及电、气、氢、热、风、光等多种能源形式,经典Energy Hub方法与图论Energy Hub方法在建模效率与复杂度等方面均存在局限性。文章提出了分层级Energy Hub建模方法,将综合能源系统从能量输入到输出的耦合过程建模为分配层、转换层、集成层、储能层、网络层5个环节,全面涵盖了综合能源系统复杂多元的多能耦合关系,为输入与输出搭建桥梁,确保能量供需平衡。最后,通过算例分析论证了所提方法的优势与可行性,以及与传统方法在可再生能源消纳量上的提升。

Multi-energy synergistic optimization in steelmaking process based on energy hub concept

The production process of iron and steel is accompanied by a large amount of energy production and consumption. Optimal scheduling and utilization of these energies within energy systems are crucial to realize a reduction in the cost, energy use, and CO_2 emissions.However, it is difficult to model and schedule energy usage within steel works because different types of energy and devices are involved. The energy hub(EH), as a universal modeling frame, is widely used in multi-energy systems to improve its efficiency, flexibility, and reliability.This paper proposed an efficient multi-layer model based on the EH concept, which is designed to systematically model the energy system and schedule energy within steelworks to meet the energy demand. Besides, to simulate the actual working conditions of the energy devices, the method of fitting the curve is used to describe the efficiency of the energy devices. Moreover, to evaluate the applicability of the proposed model, a case study is conducted to minimize both the economic operation cost and CO_2 emissions. The optimal results demonstrated that the model is suitable for energy systems within steel works. Further, the economic operation cost decreased by 3.41%, and CO_2 emissions decreased by approximately 3.67%.

Energy hub-based optimal planning for integrated energy systems considering part-load characteristics and synergistic effect of equipment

Integrated energy systems(lESs)represent a promising energy supply model within the energy internet.However,multi-energy flow coupling in the optimal configuration of IES results in a series of simplifications in the preliminary planning,affecting the cost,efficiency,and environmental performance of IES.A novel optimal planning method that considers the part-load characteristics and spatio-temporal synergistic effects of IES components is proposed to enable a rational design of the structure and size of IES.An extended energy hub model is introduced based on the“node of energy hub”concept by decomposing the IES into different types of energy equipment.Subsequently,a planning method is applied as a two-level optimization framework-the upper level is used to identify the type and size of the component,while the bottom level is used to optimize the operation strategy based on a typical day analysis method.The planning problem is solved using a two-stage evolutionary algorithm,combing the multiple-mutations adaptive genetic algorithm with an interior point optimization solver,to minimize the lifetime cost of the IES.Finally,the feasibility of the proposed planning method is demonstrated using a case study.The life cycle costs of the IES with and without consideration of the part-load characteristics of the components were$4.26 million and$4.15 million,respectively,in the case study.Moreover,ignoring the variation in component characteristics in the design stage resulted in an additional 11.57%expenditure due to an energy efficiency reduction under the off-design conditions.

Day-ahead P2P Energy Sharing Strategy Among Energy Hubs Considering Flexibility of Energy Storage and Loads

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.

Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm

To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources,has been carried out.This has been done using a new meta-heuristic algorithm,improved artificial rabbits optimization(IARO).In this study,the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method(TPEM).The IARO algorithm is applied to calculate the best capacity of hub energy equipment,such as solar and wind renewable energy sources,combined heat and power(CHP)systems,steamboilers,energy storage,and electric cars in the day-aheadmarket.The standard ARO algorithmis developed to mimic the foraging behavior of rabbits,and in this work,the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight technique.The proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO,particle swarm optimization(PSO),and salp swarm algorithm(SSA).The findings show that,in comparison to previous approaches,the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity,gas,and heating markets by satisfying the operational and energy hub limitations.Additionally,the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995%as compared to deterministic planning.

Energy hub modeling to minimize residential energy costs considering solar energy and BESS

This paper aims to optimize total energy costs in an operational model of a novel energy hub(EH) in a residential area. The optimization problem is set up based on daily load demand(such as electricity, heat, and cooling) and time-of-use(TOU) energy prices. The extended EH model considers the involvement of solar photovoltaic(PV) generation, solar heat exchanger(SHE), and a battery energy storage system(BESS). A mathematical model is constructed with the objective of optimizing total energy cost during the day, including some constraints such as input-output energy balance of the EH, electricity price,capacity limitation of the system, and charge/discharge power of BESS. Four operational cases based on different EH structures are compared to assess the effect of solar energy applications and BESS on the operational efficiency. The results show that the proposed model predicts significant changes to the characteristics of electricity and gas power bought from utilities, leading to reduced total energy cost compared to other cases. They also indicate that the model is appropriate for the characteristics of residential loads.

Emission Trading Based Optimal Scheduling Strategy of Energy Hub with Energy Storage and Integrated Electric Vehicles

This paper focuses on the optimal scheduling of the district energy system with multiple energy supply modes and flexible loads.For multi-energy system(MES),the energy hub(EH)model including energy storage system and integrated electric vehicle(EV)is established.Based on the model,the influence of pollutant trading market on total operation cost is analyzed,and the optimal scheduling strategy is further put forward to realize the minimum purchase cost and emission tax cost of the MES.Finally,this paper compares the economic benefit of the fixed mode and the response mode,and discusses the contribution of the energy storage device and the multi-energy complementary mode to energy utilization efficiency.The simulation results indicate that optimal scheduling strategy of the EH can coordinate various energy complementary modes reasonably.Meanwhile,the proposed strategy is able to improve the operation economy of the EH,and ensure the better response effect of the demand side.The sensitivity analysis demonstrates the impact of pollutant emission price change on emission reduction.

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.

Optimization of Distributed Integrated Multi-energy System Considering Industrial Process Based on Energy Hub

As a typical scenario of distributed integrated multi-energy system(DIMS),industrial park contains complex production constraints and strong associations between industrial productions and energy demands.The industrial production process(IPP)consists of controllable subtasks and strict timing constraints.Taking IPP as a control variable of optimal scheduling,it is an available approach that models the IPP as material flow into an extension energy hub(EH)to achieve the optimization of industrial park.In this paper,considering the coupling between the production process and energy demands,a model of IPP is proposed by dividing the process into different adjustable steps,including continuous subtask,discrete subtask,and storage subtask.Then,a transport model of material flow is used to describe the IPP in an industrial park DIMS.Based on the concept of EH,a universal extension EH model is proposed considering the coupling among electricity,heat,cooling,and material.Furthermore,an optimal scheduling method for industrial park DIMS is proposed to improve the energy efficiency and operation economy.Finally,a case study of a typical battery factory is shown to illustrate the proposed method.The simulation results demonstrate that such a method reduces the operation cost and accurately reflects the operation state of the industrial factory.

Comparison and Error Analysis of Off-design and Design Models of Energy Hubs

The accuracy of the simulation model has a pro-found 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.More-over,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.

Optimization for Micro-energy Grid Dispatch Based on Non-supplementary Fired Compressed Air Energy Storage Aided Energy Hub and Hybrid Hyper-spherical Search

Micro-energy grids have shown superiorities over traditional electricity and heating management systems.This paper presents a hybrid optimization strategy for micro-energy grid dispatch with three salient features.First,to enhance the ability to support new storage equipment,an energy hub model is proposed using the non-supplementary fired compressed air energy storage(NSF-CAES).This provides flexible dispatch for cooling,heating and electricity.Second,considering the unique characteristics of the NSF-CAES,a sliding time window(STW)method is designed for simple but effective energy dispatch.Third,for the optimization of energy dispatch,we blend the differential evolution(DE)with the hyper-spherical search(HSS)to formulate a hybrid DE-HSS algorithm,which enhances the global search ability and accuracy.Comparative case studies are performed using real data of scenarios to demonstrate the superiorities of the proposed scheme.

Day-ahead Chance-constrained Energy Management of Energy Hubs:A Distributionally Robust Approach

The day-ahead management schedules of hybrid energy hubs are intricate and usually exposed to various uncertainties with the penetration of renewable sources and different demands.Furthermore,it is difficult to access to precise probability distribution functions and exact moment information of uncertain variables.To cope with these issues,an energy management scheme based on the distributionally robust optimization approach is developed for the energy hub.It makes no assumptions of certain probability distributions and can be implemented with limited empirical data and partial information of underlying uncertainties.The operational strategy can provide decision makers with a preliminary and robust optimal solution in the day-ahead market.Numerical results illustrate the economical benefit of the energy model,and the effectiveness of the proposed approach in chance-constrained energy management is demonstrated by comparing with other cases.Index Terms-Chance constraint,distributionally robust optimization,energy hub,energy management.

Optimal operation of cold–heat–electricity multi-energy collaborative system based on price demand response

In a multi-energy collaboration system, cooling, heating, electricity, and other energy components are coupled to complement each other. Through multi-energy coordination and cooperation, they can significantly improve their individual operating efficiency and overall economic benefits. Demand response, as a multi-energy supply and demand balance method, can further improve system flexibility and economy. Therefore, a multi-energy cooperative system optimization model has been proposed, which is driven by price-based demand response to determine the impact of power-demand response on the optimal operating mode of a multi-energy cooperative system. The main components of the multi-energy collaborative system have been analyzed. The multi-energy coupling characteristics have been identified based on the energy hub model. Using market elasticity as a basis, a price-based demand response model has been built. The model has been optimized to minimize daily operating cost of the multi-energy collaborative system. Using data from an actual situation, the model has been verified, and we have shown that the adoption of price-based demand response measures can significantly improve the economy of multi-energy collaborative systems.

Modeling Method of Multi-Energy Systems Based on LSTM Algorithm

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.

Distributed Robust Optimal Dispatch of Regional Integrated Energy Systems Based on ADMM Algorithm with Adaptive Step Size

This paper proposes a distributed robust optimal dispatch model to enhance information security and interaction among the operators in the regional integrated energy system(RIES).Our model regards the distribution network and each energy hub(EH)as independent operators and employs robust optimization to improve operational security caused by wind and photovoltaic(PV)power output uncertainties,with only deterministic information exchanged across boundaries.This paper also adopts the alternating direction method of multipliers(ADMM)algorithm to facilitate secure information interaction among multiple RIES operators,maximizing the benefit for each subject.Furthermore,the traditional ADMM algorithm with fixed step size is modified to be adaptive,addressing issues of redundant interactions caused by suboptimal initial step size settings.A case study validates the effectiveness of the proposed model,demonstrating the superiority of the ADMM algorithm with adaptive step size and the economic benefits of the distributed robust optimal dispatch model over the distributed stochastic optimal dispatch model.

Optimal Expansion Planning Model for Integrated Energy System Considering Integrated Demand Response and Bidirectional Energy Exchange

The challenges of energy shortage and environmen-tal protection motivate people to take various measures to use energy wisely,and integrated energy systems are such a measure to tackle this challenge.In this paper,an optimal expansion planning model for an integrated energy system consisting of power grid,gas network and multiple energy hubs is proposed,where the planning objective is to minimize operational fuel cost and capital investment cost covering carbon capture equipment and energy hubs among others.To demonstrate the advantage of the proposed planning model,six case studies are investigated,and 13.47%annual cost savings can be achieved compared with the baseline planning scenario,which does not consider bidirectional energy exchange and integrated demand response program.Index Terms-Bidirectional energy exchange,energy hubs,integrated energy system,integrated demand response.

Hybrid Energy Sharing Mechanism for Integrated Energy Systems Based on the Stackelberg Game

Integrating the sharing economy and the power industry is of positive significance for the development of the energy market.With the energy market transforming from a traditional vertical structure to an interactive and competitive structure,users'roles need to change,along with supply and demand interacting more frequently.Thus,the traditional centralized optimization method for a single energy source can hardly reveal the complex multi-entity behavior of multi-energy coupling.Therefore,this paper establishes a distributed electrical-gas-thermal energy sharing mechanism centered on an energy hub that can converse energy,and build a more applicable integrated energy system body.First,the supply-demand interaction and the energy conversion process is constructed with reference to the operationa丨mode of’a sharing economy and the dual role of prosumers.A Stackelberg model is established with the integrated energy system operator as the leader and prosumers as the followers,to simultaneously optimize the profit of the leader in the upper level and the comfort of the fo!lowers,energy use and utility in the lower level.Furthermore,for protecting the participants’privacy,a distributed algorithm is used to find the optimal solution to equilibrate the model,and the existence and uniqueness of the solution is proved.Finally,a case study validates the effectiveness of the hybrid energy sharing mechanism and provides a reference for the integration of the energy sharing economy with the integrated energy system.

Economic Scheduling of Gaseous-liquid Hydrogen Generation and Storage Plants Considering Complementarity of Multiple Products

The accessible and convenient hydrogen supply is the foundation of successful materialization for hydrogen-powered vehicles(HVs).This paper proposes a novel optimal scheduling model for gaseous-liquid hydrogen generation and storage plants powered by renewable energy to enhance the economic feasibility of investment.The gaseous-liquid hydrogen generation and storage plant can be regarded as an energy hub to supply concurrent service to both the transportation sector and ancillary market.In the proposed model,the power to multi-state hydrogen(P2MH)process is analyzed in detail to model the branched hydrogen flow constraints and the corresponding energy conversion relationship during hydrogen generation,processing,and storage.To model the coupling and interaction of diverse modules in the system,the multi-energy coupling matrix is developed,which can exhibit the mapping of power from the input to the output.Based on this,a multi-product optimal scheduling(MPOS)algorithm considering complementarity of different hydrogen products is further formulated to optimize dispatch factors of the energy hub system to maximize the profit within limited resources.The demand response signals are incorporated in the algorithm to further enhance the operation revenue and the scenario-based method is deployed to consider the uncertainty.The proposed methodology has been fully tested and the results demonstrate that the proposed MPOS can lead to a higher rate of return for the gaseous-liquid hydrogen generation and storage plant.

Integration of power-to-hydrogen in day-ahead security-constrained unit commitment with high wind penetration

The increasing integration of variable wind generation has aggravated the imbalance between electricity supply and demand. Power-to-hydrogen(P2H) is a promising solution to balance supply and demand in a variable power grid, in which excess wind power is converted into hydrogen via electrolysis and stored for later use. In this study, an energy hub(EH) with both a P2H facility(electrolyzer) and a gas-to-power(G2P) facility(hydrogen gas turbine) is proposed to accommodate a high penetration of wind power. The EH is modeled and integrated into a security-constrained unit commitment(SCUC) problem, and this optimization problem is solved by a mixed-integer linear programming(MILP) method with the Benders decomposition technique. Case studies are presented to validate the proposed model and elaborate on the technological potential of integrating P2H into a power system with a high level of wind penetration(HWP).

From demand response to integrated demand response: review and prospect of research and application

In the traditional power system demand response, customers respond to electricity price or incentive and change their original power consumption pattern accordingly to gain additional benefits. With the development of multi-energy systems (MES) in which electricity, heat, natural gas and other forms of energy are coupled with each other, all types of energy customers are able to participate in demand response, leading to the concept of integrated demand response (IDR). In IDR, energy consumers can response not only by reducing energy consumption or opting for off-peak energy consumption but also by changing the type of the consumed energy. Taking the traditional demand response in power system as a starting point, the studies of the fundamental theory, framework design and potential estimation of demand response in power system are reviewed, and the practical cases and software development of demand response are introduced. Finally, the current theoretical research and application of IDR are assessed.