A novel mathematical model for integrating the hydrogen network of refinery with compressor allocation considered

The allocation and cost of compressors have significant influence on hydrogen network. A novel mixed integer nonlinear programming method is proposed for optimizing hydrogen network with the allocation of compressors considered. This model considers the streams compressed stage by stage with multiple compressors and all possible compression paths. The compression power cost of multiple compressors is deduced to optimize the number of compressors in each compression process. The trade-off between the power loss and capital cost of compression is analyzed for different compression paths. The superstructure and mathematical model are built to optimize the hydrogen network in terms of minimizing the total annual cost. The proposed model is flexible and efficient. Three literature cases are studied by the proposed method, and the optimal flowsheets are identified. Compared with previous methods, the computing time is significantly reduced and the total cost of compressors is reduced by 3.38%–8.46%.     

Optimal design of inter-plant hydrogen network with purification reuse/recycle

Hydrogen-rich gas from fertilizer plant and ethylene plant can be sent to refinery in a petrochemical complex, which can alleviate the deficit of hydrogen in refinery. Thus the optimization of inter-plant hydrogen network in a petrochemical complex is attractive. A novel superstructure for the optimal design of inter-plant hydrogen network with purification process is proposed and the corresponding mathematical programming models are presented for different scenarios. Two cases are utilized to illustrate the proposed approach. The flowrates of hydrogen utilities in refinery can be reduced via the inter-plant optimization. The number of inter-plant connections is optimized to simplify the network configuration. The shortcut model for pressure swing adsorption (PSA) considering the design conditions (pressure ratio and adsorbent selectivity) is also embedded into the mathematical programming model. The impacts of those design conditions of PSA on the performance of inter-plant hydrogen network are investigated. With the reduction of adsorbent selectivity and the increment of pressure ratio, the hydrogen recovery ratio will increase and the total annual cost will be reduced.     

Optimal design of sustainable hydrogen networks

Hydrogen is widely used in modern oil refineries to remove the sulfur, nitrogen and aromatic contents of fuels. The existence of such contents would aggravate the greenhouse gas (GHG) emission of petrol fuels. The ultimate goal of massive hydrogen consumption in refineries is to cut down the GHG emission. However, current researches on hydrogen networks are focusing on reducing the cost of hydrogen consumption. The environmental impact of hydrogen consumption, especially the GHG emission, has not been considered yet. If the hydrogen supply network itself discharges too much CO₂, then the significance of the hydrogen consumption will be discounted considerably. It is of great importance to design a sustainable hydrogen network. This paper presents a systematic mathematical modeling methodology for the optimal synthesis of sustainable refinery hydrogen networks. The proposed mixed integer nonlinear programming (MINLP) model accounts for both the economic and the environmental aspect of the hydrogen network. Total annual cost (TAC) is employed to evaluate the economic efficiency of the network, while the environmental performance is assessed by the total CO₂ emission of the network. Two types of fresh fuels are investigated in the case studies. A multi-objective optimization is carried out via the Pareto front generation, which is obtained by an adaptive weighted-sum method. The economic–environmental Pareto front will allow for determining the most promising options for the reuse, purification and combustion of hydrogen streams. The numerical example has shown the proposed approach to be efficient and powerful.     

An interval-parameter minimax regret programming approach for power management systems planning under uncertainty

In this study, an interval-parameter minimax regret programming (IMRP) method is developed for supporting the power management systems planning under uncertainty. This method incorporates techniques of interval linear programming (ILP) and minimax regret programming (MRP) within a general optimization framework. The developed IMRP could deal with multiple policy scenarios associated with different costs and risk levels without making any assumptions. It can analyze various economic consequences for all of the possible scenarios through minimizing the maximum cost regret values. The IMRP approach can successfully reduce the worst regrets incurred under the pre-regulated targets. Moreover, it can deal with uncertainties and complexities expressed as interval numbers. A case study of power management systems planning is then presented for demonstrating applicability of the developed approach. The results indicate that many decision alternatives are generated based on the interval solutions which can help decision makers identify the desired system designs with minimized economic cost loss and system-failure risk under uncertainty. The trade-off between system regret and security-failure risk can be handled effectively through this method. And the generated solutions can also provide multiple electric power generation patterns and capacity expansion schemes under the optimal strategy obtained through the developed IMRP method. It is indicated that the proposed method is efficient to provide the decision makers with available plans in actual operation of power management systems.     

Optimization of long-distance and large-scale transmission of renewable hydrogen in China: Pipelines vs. UHV

The reverse distribution of renewable energy resources and load centers makes exploring the optimal transmission mode of long-distance and large-scale renewable hydrogen the key to solving the bottleneck of renewable hydrogen development. This study incorporates hydrogen pipeline (HGP), natural gas pipeline (NGP), and Ultra High Voltage (UHV) into an optimal planning model framework and analyzes the optimal transmission mode, quantity, network, and cost of large-scale renewable hydrogen in China. Constructing a sensitivity analysis framework, this study also investigates the optimal transmission mode changes under different scenarios. The results show that the optimal mode of large-scale renewable hydrogen transmission in the province is NGP, and 5.4% of supply level is the critical point to export renewable hydrogen inter-provincially. It switches to the combination of NGP and HGP when the unit transmission costs of these elements decrease to a certain proportion simultaneously or switches to HGP when the unit transmission cost of HGP decreases more than that of NGP. The complementary transmission mode of NGP and UHV is the optimal mode for inter-provincial transmission, and the HGP can be put into use for inter-provincial transmission only when the unit transmission cost of which is reduced to less than 25%. Jilin is the key node in the NGP network, and Tibet and Gansu are the key nodes in UHV network, and the participation or absence of which will have significant impact on the renewable hydrogen transmission system. Only minor adjustments to the transmission technical parameters of NGP or HGP can promote the qualitative overflight of the optimal transmission volume of them so as to achieve the target optimization at the minimum cost.     

Optimal design of inter-plant hydrogen networks with intermediate headers of purity and pressure

This paper addresses the optimal design of inter-plant hydrogen networks with intermediate headers of purity and pressure. A superstructure for inter-plant integration of hydrogen networks is presented, where all hydrogen sources and hydrogen sinks are indirectly matched with each other through intra-plant hydrogen headers and inter-plant hydrogen headers. The corresponding mathematical programming model is constructed and a tailored two-step strategy is proposed to solve the model. In this solving method, the topology of the inter-plant hydrogen network and the purity levels of the hydrogen headers are first determined to minimize the cost of hydrogen utilities. The placements of compressors and pressure levels of hydrogen headers are then optimized to reach the lowest total annual cost of the inter-plant hydrogen network. The application of the proposed method is illustrated via a practical industrial case of inter-plant hydrogen network in China.     

Two-stage robust energy management of a hybrid charging station integrated with the photovoltaic system

The optimal management of charging stations has become a critical issue in recent years. In this paper, the energy management of a hybrid charging station composed of an electrolyzer, fuel cell and hydrogen storage is analyzed that is integrated with a photovoltaic system. As well, the station is connected to the local power market to increase flexibility and it is assumed that the manager of the charging station is an intelligent decision-maker who tries to minimize the cost of vehicle. Due to the existence of uncertainties, generation of photovoltaic, market price and load demand are considered as uncertain parameters and two-stage stochastic programming is applied to model them. To achieve optimal management, a robust optimization approach is proposed for the uncertainty of day-ahead market price where the decision-maker adjusts the conservatism level. The presented method is linear risk-constrained programming that the results for risk-neutral and risk-averse strategies are compared. To validate the accuracy and robustness of the approach, interval-based stochastic programming is also implemented. According to the robust optimization, day-ahead market price uncertainty increases the total expected cost by about 8.9%. In return, the risk of scheduling is reduced significantly with the risk-averse strategy.     

Optimal design of advanced drop-in hydrocarbon biofuel supply chain integrating with existing petroleum refineries under uncertainty

This paper addresses the optimal design of an advanced hydrocarbon biofuel supply chain integrated with existing petroleum refineries. Three major insertion points from the biofuel supply chain to the petroleum refineries are investigated and analyzed, including bio-intermediates co-processed with crude oil, bio-intermediates co-processed with refinery intermediates, and finished biofuels blended with conventional petroleum products. A multiperiod, mixed-integer linear programming model is proposed that accounts for diverse conversion pathway, technology, and insertion point selections, biomass seasonality, geographical diversity, biomass degradation, demand distribution and government incentives. This model simultaneously optimizes the supply chain design, insertion point selection, and production planning. In addition, the conversion rate, operation cost associated with insertion points in petroleum refinery, as well as the biomass availability and product demand are modeled as fuzzy numbers to account for the data uncertainty. A fuzzy possibilistic programming approach is applied to this model, where possibility, necessity and credibility measures are adopted according to the decision makers' preference. This model is illustrated by the county level case study of Illinois. Compared to traditional biofuel supply chains, advanced hydrocarbon biofuel supply chain integrating with existing petroleum refinery infrastructure significantly reduces capital cost and total annualized cost.     

Risk management for hydrogen networks across refineries

Stricter environmental regulations and policies are leading to increasing requirements for the quality of the oil product. Hydrogen fluctuation in the hydrofining process of refineries remains a considerable problem that may affect the quality of refinery products and increase operating costs. As hydrogen supply across multiple refineries is common in the process industry, the optimal design of interplant hydrogen networks (multi-refinery hydrogen networks) is inevitable in improving the stability of hydrogen supply and reducing the utility consumption of the entire system. This paper introduces the Worst-Case Conditional Value at Risk (WCVaR) concept for the synthesis of the interplant hydrogen network system with hydrogen source flowrate fluctuations. WCVaR is adopted to indicate the expected value of the pure hydrogen content in hydrogen networks in the worst case. Considering the adjustability of hydrogen utility in the realistic production process, this paper studies the stability of interplant hydrogen networks under constant and adjustable hydrogen utilities. The Monte Carlo simulation verifies that the multi-refinery hydrogen network structures obtained by this method are more robust.     

Robust optimal operation of continuous catalytic reforming process under feedstock uncertainty

The continuous catalytic regenerative (CCR) reforming process is one of the most significant sources of hydrogen production in the petroleum refining process. However, the fluctuations in feedstock composition and flow rate could significantly affect both product distribution and energy consumption. In this study, a robust deviation criterion based multi-objective optimization approach is proposed to perform the optimal operation of CCR reformer under feedstock uncertainty, with simultaneous maximization of product yields and minimization of energy consumption. Minimax approach is adopted to handle these uncertain objectives, and the Latin hypercube sampling method is then used to calculate these robust deviation criteria. Multi-objective surrogate-based optimization methods are next introduced to effectively solve the robust operational problem with high computational cost. The level diagram method is finally utilized to assist in multi-criteria decision-making. Two robust operational optimization problems with different objectives are solved to demonstrate the effectiveness of the proposed method for robust optimal operation of the CCR reforming process under feedstock uncertainty.     

A mathematical programming framework for energy planning in services’ sector buildings under uncertainty in load demand: The case of a hospital in Athens

The aim of this paper is to provide an integrated modeling and optimization framework for energy planning in large consumers of the services’ sector based on mathematical programming. The power demand is vaguely known and the underlying uncertainty is modeled using elements from fuzzy set theory. The defined fuzzy programming model is subsequently transformed to an equivalent multi-objective problem, where the minimization of cost and the maximization of demand satisfaction are the objective functions. The Pareto optimal solutions of this problem are obtained using a novel version of the ε-constraint method and represent the possibly optimal solutions of the original problem under uncertainty. In the present case, in order to select the most preferred Pareto optimal solution, the minimax regret criterion is properly used to indicate the preferred configuration of the system (i.e. the size of the installed units) given the load uncertainty. Furthermore, the paper proposes a model reduction technique that can be used in similar cases and further examines its effect in the final results. The above methodology is applied to the energy rehabilitation of a hospital in the Athens area. The technologies under consideration include a combined heat and power unit for providing power and heat, an absorption unit and/or a compression unit for providing cooling load. The obtained results demonstrate that, increasing the degree of demand satisfaction, the total annual cost increases almost linearly. Although data compression allows obtaining realistic results, the size of the proposed units might be slightly changed.     

Assessment of using hydrogen in gas distribution grids

Substantial changes in the energy system are necessary to achieve greenhouse gas neutrality. Green hydrogen is a key to defossilisation. Politicians frequently mention the use of hydrogen in the building sector to supply decentrally produced heat as a potential field of application. An advantage repeatedly mentioned is that the existing gas distribution network infrastructure is an important asset that could still be used in the future. However, there is a lack of analyses of the conversion of gas distribution networks to hydrogen focussing on the economic implications on the costs of the distribution network infrastructure. The paper provides insights using a techno-economic model network analysis (MNA) tool called gas Distribution grId modelliNg tOol (DINO). The analysis is carried out for Germany and considers hydrogen use in all counties. The results are compared to a synthetic methane and electrification scenario. In the hydrogen scenario, the total need for distribution grids is decreasing until 2050 by at least 130,000 km. The network length of the synthetic methane scenario is slightly lower and that of the electrification scenario drops to zero. The annual operation costs are lower in all scenarios as gas demand and infrastructure are reduced. Nevertheless, the total annual cost in the hydrogen scenario is potentially two times higher than in the case of the synthetic methane scenario and more than four times higher than in the electrification scenario. Based on the present results, it is questionable whether an advantage of the continued use of the existing gas distribution grid infrastructure in case of synthetic gas or hydrogen scenarios exists.     

A P-Graph approach for the synthesis of hydrogen networks with pressure and impurity constraints

The increasing demand for hydrogen in refineries and petrochemical plants is challenging these facilities to minimise their hydrogen utility without incurring high capital and operating costs. As environment-related fuel specifications become more stringent, the demand for hydrogen increases, especially for the operation of hydrodesulphurisation in refineries. A P-graph model is developed in this paper for the synthesis of hydrogen networks. The model is capable of generating optimal and near-optimal solutions for the hydrogen network. The proposed methodology is computationally efficient and require minimal understanding of programming language. The developed model includes both direct recycle/reuse and regeneration schemes; and accounts for pressure and impurity constraints in the hydrogen network. In addition, the application of hydrogen header can also be handled by the P-graph model. The methodology is illustrated with three literature examples and the results obtained match those reported in literature.     

考虑电转气消纳风电的电-气综合能源系统两阶段鲁棒协同调度

针对传统随机规划方法和区间优化方法处理风电出力不确定性的不足之处,该文提出含电转气设备的电力-天然气综合能源系统两阶段鲁棒协同调度模型,并考虑天然气网络运行约束对燃气轮机和电转气设备调度出力及备用配置的影响。模型以风电基准场景下系统的日前调度运行成本及最劣风电场景下实时调度成本之和为目标函数,建立具max-min结构的双层优化模型,并在主/子问题求解框架下采用列约束生成（C&CG）方法进行求解。最后,在Matlab平台下构建仿真算例验证所提鲁棒协同调度模型的有效性。     

Multi-objective and multi-period hydrogen refueling station location problem

Creating a distribution network and establishing refueling stations arises as an important problem in order to meet the refueling needs of hydrogen fuel cell vehicles. In this study, a multi-objective and multi-period hydrogen refueling station location problem that can take into account long-term planning decisions is proposed. Firstly, single objective mathematical models are proposed for the problem by addressing the cost, risk and population convergence objectives. Afterwards, a goal programming model is proposed and the results that will arise when three objectives are taken into consideration at the same time are examined. A risk analysis approach applied for each location alternative is considered in order to handle risk concerns about the hydrogen refueling station settlement. A case study is conducted in Adana, one of the crowded cities in Turkey, to determine the long-term location network plan. Covered population, operational risk and earthquake risks are used as input of the risk analysis method. The case study results show that the goal programming model covers the area with 77 hydrogen refueling stations by different types and capacities during the years from 2020 to 2030. In addition, a computational study is carried out with different alternative scenarios (different number of consumption nodes and all parameters in the model). The computational study results show that the highest deviations from the optimal solution on the model are observed in the distances between consumption nodes and targeted service area parameters which affect about 50% of absolute deviations on average. According to results, the proposed approach selects the station location suitable for the expected changes over the years.     

Fair electricity transfer price and unit capacity selection for microgrids

Microgrids are defined as an area of electricity distribution network that can operate autonomously from the rest of the network. In order to achieve the best economic outcomes, the participants in a microgrid can benefit from cooperation in microgrid design and operation. In this paper, a mathematical programming formulation is presented for fair, optimised cost distribution amongst participants in a general microgrid. The proposed formulation is based on the Game-theory Nash bargaining solution approach for finding optimal multi-partner cost levels subject to given upper bounds on the equivalent annual costs. The microgrid planning problem concerning the fair electricity transfer price and unit capacity selection is first formulated as a mixed integer non-linear programming model. Then, a separable programming approach is applied to reform the resulting mixed integer non-linear programming model to a mixed integer linear programming form. The model is applied to a case study with a microgrid involving five participants.     

Simulation-based modeling and optimization for refinery hydrogen network integration with light hydrocarbon recovery

Purge gases from hydrocrackers and hydrotreaters and refinery off-gases are important hydrogen sources. Some of these hydrogen sources are also rich in light hydrocarbons that are valuable energy resources and chemical materials. In this work, a systematic method is proposed to integrate hydrogen networks considering light hydrocarbon recovery. This work first develops a hydrogen network superstructure with light hydrocarbon recovery. Aspen HYSYS is employed for rigorous process and thermodynamic modeling of the light hydrocarbon recovery process, and a simulation-optimization model is then developed. To solve the simulation-optimization model efficiently, the genetic algorithm is used as the global solver to determine the feed to light hydrocarbon recovery unit, and the linprog and fmincon solvers are combined to determine the optimal hydrogen network design. The application and effectiveness of the proposed method is validated through a case study. The results show that fresh hydrogen consumption decreases by 13% and the total annualized cost reduces to 72% because of light hydrocarbon recovery. This method could provide useful guides for the management of hydrogen and light hydrocarbons in refineries.     

Modelling and control of hydrogen and energy flows in a network of green hydrogen refuelling stations powered by mixed renewable energy systems

The planning of a hydrogen infrastructure with production facilities, distribution chains, and refuelling stations is a hard task. Difficulties may rise essentially in the choice of the optimal configurations. An innovative design of hydrogen network has been proposed in this paper. It consists of a network of green hydrogen refuelling stations (GHRSs) and several production nodes. The proposed model has been formulated as a mathematical programming, where the main decisions are the selection of GHRSs that are powered by the production nodes based on distance and population density criteria, as well the energy and hydrogen flows exchanged among the system components from the production nodes to the demand points. The approaches and methodologies developed can be taken as a support to decision makers, stakeholders and local authorities in the implementation of new hydrogen infrastructures. Optimal configurations have been reported taking into account the presence of an additional hydrogen industrial market demand and a connection with the electrical network. The main challenge that has been treated within the paper is the technical feasibility of the hydrogen supply chain, that is mainly driven by uncertain, but clean solar and wind energy resources. Using a Northern Italian case study, the clean hydrogen produced can be technically considered feasible to supply a network of hydrogen refuelling stations. Results show that the demands are satisfied for each time period and for the market penetration scenarios adopted.     

Optimization-based planning of a biomass to hydrogen (B2H2) system using dedicated energy crops and waste biomass

The utilization of biomass for hydrogen production is one of the promising options for a sustainable energy system. In this paper, we develop a new optimization-based approach for design and analysis of the B2H2 system including production, storage, and distribution using dedicated energy crops as well as various resources of waste biomass. To achieve this goal, we first develop an optimization model using mixed-integer linear programming technique that includes practical variables and constraints for decision-making about the usage of dedicated energy crops. We then conduct a case study of the B2H2 system for the road transportation sector of future Korea. As a result, we identify an optimal system configuration that includes the utilized biomass types, occupied land sizes, the number and location of facilities, and the biomass and hydrogen flows between regions. We also analyze the cost distributions and the sensitivity of the main cost drivers on the total annual cost (TAC). The results reveal that the proposed B2H2 system is economically competitive with some of the other renewable-based hydrogen supply systems (wind and solar) in Korea.     

A decision problem under uncertainty for nuclear power system development

Decision making under uncertainty is a further step in comparison with decision under risk in the more realistic approach to decision problems concerning, for instance, nuclear power system development. In this paper the theory developed is, however, based in a great measure on that of risk preference. The theory of decision making under uncertainty is applied to a nuclear power system NPS consisting of PHWRs and PWRs integrated with LMFBRs. Nine development alternatives of the system which evolves for a period of 40 years are considered. The fast reactor integration is accomplished beginning in year 15 with a variable time delay so that for every alternative, six final states are possible. An econometric model of the system offers the cost price of annual energy generated by the system at the end of the given time interval for every possible state of any alternative. Further, the complete ignorance case is considered, resulting from the principle of insufficient reason, and the risk preference theory is applied. Then the partial ignorance case is taken into account and finally it it shown how we can infer a plausible a priori optimal probability distribution to have an optimal decision characterized by an optimal selected development alternative, for which a minimum certain equivalent of cost price of annual energy is realized with an accepted level of risk and a determined value of risk averter.