Robust optimization of hydrogen network

Process integration is an effective way to reduce hydrogen utility consumption in refineries. A number of graphical and mathematical programming approaches have been proposed to synthesis the optimal network. However, as the operation of refineries encounters uncertainty with the rapidly changing market and deteriorating crude oil, existing approaches are inadequate to achieve robust hydrogen network distribution due to the uncertain factors. In this paper, robust optimization is introduced as a framework to optimize hydrogen network of refineries under uncertainty. In this framework, a number of scenarios representing possible future environments are considered. Both model robust and solution robust are explicitly incorporated into the objective function. A possible optimal network distribution which is less sensitive to the change of scenarios and has the minimum total annual cost is achieved by the tradeoff between the total annual cost and the expected error. Case studies indicate that this method is effective in dealing with hydrogen network design and planning under uncertainty in comparison to the deterministic approach and the stochastic programming method.     

Optimal operation of autonomous microgrid including wind turbines

This paper gives a novel hybrid optimization method to find optimal sitting and operation of an autonomous MG at the same time. The operation is optimized via finding the optimal droop gain parameters of DGs. The optimization problem is formulated as a multi-objective problem where the objectives are applied to minimize the fuel consumption of DGs and to improve the voltage profile and stability of MG subject to operational and security constraints. A hybrid algorithm, named HS-GA, is developed to solve the paper optimization problem. A new formulation of power flow is derived to run the proposed algorithm where the steady state frequency of system, reference frequency, reference voltage and droop coefficients of DGs, based on a droop controller, are considered as optimization variables. The performance of the paper approach is compared with other optimization and non-optimization methods in MG with 33and 69 buses using MATLAB. The performance of the proposed method is compared with a method that the parameters of DGs are pre-determined without conducting any optimization process. The results show, which optimized droop parameters improves the operation of the MG.     

济南炼化气体分馏装置流程模拟优化

中国石化济南分公司采用Aspen Plus流程模拟软件,建立两套气体分离装置模型。对其工艺流程进行了模拟,计算结果与装置现场实际参数十分吻合。在此基础上,分析各工艺参数在不同条件下对产品质量及能耗的影响。在保证产品质量达标前提下,根据现有生产条件,离线模拟最佳的操作参数。针对存在的问题,依据模拟结果对操作参数进行优化,达到了降低能耗、提高产品收率的目的。通过在环境温度较低情况下脱丙烷塔降压操作,降低装置能耗,优化脱丙烯塔进料位置,提高丙烯回收率,调整C4塔进料量,增产MTBE,优化脱乙烷塔,停运进料泵,实现装置挖潜增效319.25万元/a。该模型的应用表明,Aspen Plus软件能够根据生产条件的变化,及时分析装置运行状态,模拟最优参数,指导生产,实现装置效益的最大化。     

Multi-objective optimization of ANN-based PSA model for hydrogen purification from steam-methane reforming gas

A 4-bed-8-step pressure swing adsorption (PSA) process has been developed to produce high-purity hydrogen from the steam methane reforming (SMR) gas mixture. The Detailed models have been established for hydrogen purification based on the experimentally determined parameters. Two surrogate models are investigated to optimize the process performance using artificial neural networks (ANN), which have been well trained by the samples, obtaining from the Detailed models using Latin hypercube sampling strategy. The results indicate that ANNs could approximate the performance and dynamic behavior of PSA process with extremely high accuracy. Herein, a robust and fast multi-objective optimization approach of PSA process using genetic algorithm on the basis of different ANN-based surrogate models has also been proposed, in which Dual- and Tri-objective optimizations are taken into account. This research shows that the method can not only find out the optimal operating conditions of the PSA process for hydrogen production with higher than 99% accuracy, namely Pareto-Optimal Fronts, but also provide a reliable reference for operational enhancement.     

An optimization methodology for identifying robust process integration investments under uncertainty

Uncertainties in future energy prices and policies strongly affect decisions on investments in process integration measures in industry. In this paper, we present a five-step methodology for the identification of robust investment alternatives incorporating explicitly such uncertainties in the optimization model. Methods for optimization under uncertainty (or, stochastic programming) are thus combined with a deep understanding of process integration and process technology in order to achieve a framework for decision-making concerning the investment planning of process integration measures under uncertainty. The proposed methodology enables the optimization of investments in energy efficiency with respect to their net present value or an environmental objective. In particular, as a result of the optimization approach, complex investment alternatives, allowing for combinations of energy efficiency measures, can be analyzed. Uncertainties as well as time-dependent parameters, such as energy prices and policies, are modelled using a scenario-based approach, enabling the identification of robust investment solutions. The methodology is primarily an aid for decision-makers in industry, but it will also provide insight for policy-makers into how uncertainties regarding future price levels and policy instruments affect the decisions on investments in energy efficiency measures.     

计及分布式电源不确定性的多微网鲁棒博弈研究

多微网系统可以提高分布式电源消纳率和系统供电稳定性,分布式电源出力的不确定性却给多微网的日前调度带来挑战。文章综合考虑多微网系统中分布式电源出力不确定性和各微网间的非合作博弈行为,提出了一种针对多主体决策的非合作鲁棒博弈优化模型,并采用列约束生成算法对所构建模型进行求解,从而得到各微网在非合作博弈模式下的鲁棒Nash均衡解以及调度方案。仿真结果表明,与确定性调度方案相比,所构建的鲁棒博弈优化调度方案可以在保证微网运行经济性的同时,还具备一定的鲁棒性。     

含分布式发电并网虚拟发电厂鲁棒优化经济调度方法

文章基于鲁棒优化理论建立了虚拟发电厂最优经济调度模型。首先以虚拟发电厂发电净收益最大为目标函数,计及出力计划约束、机组运行约束、机组启停约束、储能运行约束等必要约束条件建立系统优化运行模型;然后考虑风光出力区间不确定性,以风光出力为自然决策者,以虚拟发电厂为系统决策者,分别制定博弈策略和支付,建立虚拟发电厂最优经济调度鲁棒优化模型,并对其Nash均衡点进行分析;基于两阶段松弛法将所建立的鲁棒优化模型转化为有限可解的步骤;最后通过一个算例验证了所建立的模型在制定虚拟发电厂运行计划方面的经济优势。     

Risk-constrained scheduling of a CHP-based microgrid including hydrogen energy storage using robust optimization approach

Recently, the integration of various energy resources, including renewable generation and combined heat and power (CHP) units in microgrids, has created the opportunity of off-grid operation with a suitable range of reliability. This paper presents an optimization model to schedule an islanded MG with various resources, including CHP, photovoltaic (PV), and boiler, as the primary energy provision sources besides electric battery storage, thermal storage and hydrogen energy system (HES). The HES has the power-to-hydrogen (P2H) and hydrogen-to-power (H2P) modes, which increases the flexibility of the scheduling. The uncertainty management is the most essential task in the CHP-based MGs scheduling problem, since the power and heat productions are interrelated and can result in economic losses without enough deliberations. Hence, this paper proposes the robust optimization approach (ROA) to cope with the uncertainties associated with the PV production and electric and heat load demands. The robust counterparts are applied to the deterministic problem to create a tractable adjustable robust framework. The problem is structured as a mixed-integer linear programming (MILP) handled by the General Algebraic Modeling System (GAMS) using CPLEX solver. The results verified the effectiveness of the proposed robust counterparts in managing the associated risk. The results illustrated a conscious scheduling strategy under robust conditions. However, the more preserved decisions are taken, the higher operational cost is realized. In this regard, the increment of robustness level from the lowest value (deterministic condition) to the highest value (conservatism condition) increased the operation cost by about 43.29%.     

Multi-objective optimization of a joule cycle for re-liquefaction of the Liquefied Natural Gas

A LNG re-liquefaction plant is optimized with a multi-objective approach which simultaneously considers exergetic and exergoeconomic objectives. In this regard, optimization is performed in order to maximize the exergetic efficiency of plant and minimize the unit cost of the system product (refrigeration effect), simultaneously. Thermodynamic modeling is performed based on energy and exergy analyses, while an exergoeconomic model based on the total revenue requirement (TRR) are developed. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms namely NSGA-II. This approach which is based on the Genetic Algorithm is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained and a final optimal solution is selected in a decision-making process. An example of decision-making process for selection of the final solution from the available optimal points of the Pareto frontier is presented here. The feature of selected final optimal system is compared with corresponding features of the base case and exergoeconomic single-objective optimized systems and discussed.     

Thermodynamic analysis of hydrogen production via sorption-enhanced steam methane reforming in a new class of variable volume batch-membrane reactor

Combined reaction–separation processes are a widely explored method to produce hydrogen from endothermic steam reforming of hydrocarbon feedstock at a reduced reaction temperature and with fewer unit operation steps, both of which are key requirements for energy efficient, distributed hydrogen production. This work introduces a new class of variable volume batch reactors for production of hydrogen from catalytic steam reforming of methane that operates in a cycle similar to that of an internal combustion engine. It incorporates a CO₂ adsorbent and a selectively permeable hydrogen membrane for in situ removal of the two major products of the reversible steam methane reforming reaction. Thermodynamic analysis is employed to define an envelope of ideal reactor performance and to explore the tradeoff between thermal efficiency and hydrogen yield density with respect to critical operating parameters, including sorbent mass, steam to methane ratio and fraction of product gas recycled. Particular attention is paid to contrasting the variable volume batch-membrane reactor approach to a conventional fixed bed reaction–separation approach. The results indicates that the proposed reactor is a viable option for low temperature distributed production of hydrogen from methane, the primary component of natural gas feedstock, motivating a detailed study of reaction/adsorption kinetics and heat/mass transfer effects.     

360×104t/a加氢裂化装置运行分析

大连石化360×104t/a加氢裂化装置是目前国内最大的生产中油型加氧裂化装置,采用美国UOP公司的工艺技术,催化剂为UOP公司HC-115LT型催化剂.截至2010年末,装置已累计运行649d,催化剂设计运行寿命1050d.针对该装置2010年的运行情况,从物料平衡、能耗、原料情况、产品质量、催化剂性能等方面进行考察,结果显示:2010年,装置总能耗为26.19kg标油/t原料,远低于设计能耗35.5kg标油/t原料,但在换热网络优化和新氢机的变频控制方面,尚有很大节能潜力可挖;2010年,该装置变动费用累计完成53.86元/t,指标为不大于58元/t,完成指标;生产的航煤为合格,优质品,柴油产品各项指标均符合设计要求,硫含量只有0.32～0.58μg/g,满足欧Ⅳ柴油标准要求.由于原料质量较好,金属含量、硫含量、残炭值等指标均优于设计值,推断其催化剂失活速率较小.装置新氢纯度较低,进入运行末期时,较低的新氢纯度可能影响产品质量与催化剂活性.     

Optimum design and operation under uncertainty of power systems using renewable energy sources and hydrogen storage

The presented work addresses the design and optimization under uncertainty of power generation systems using renewable energy sources and hydrogen storage. A systematic design approach is proposed that enables the simultaneous consideration of synergies developed among numerous sub-systems within an integrated power generation system and the uncertainty involved in the system operation. The Stochastic Annealing optimization algorithm is utilized to handle the increased combinatorial complexity and to enable the consideration of different types of uncertainty in the performed optimization. A parallel adaptation of this algorithm is proposed to address the associated computational requirements through execution in a Grid computing environment. The proposed developments are implemented in a system that consists of photovoltaic panels, wind generators, accumulators, an electrolyzer, storage tanks, a compressor, a fuel cell and a diesel generator. Numerous design and operating parameters are considered as decision variables, while uncertain parameters are associated with weather fluctuations and operating efficiency of the employed sub-systems. The obtained results indicate robust performance under realizable system designs, in response to external or internal operating variations.     

Economy and CO2 emissions trade-off: A systematic approach for optimizing investments in process integration measures under uncertainty

In this paper we present a systematic approach for taking into account the resulting CO₂ emissions reductions from investments in process integration measures in industry when optimizing those investments under economic uncertainty. The fact that many of the uncertainties affecting investment decisions are related to future CO₂ emissions targets and policies implies that a method for optimizing not only economic criteria, but also greenhouse gas reductions, will provide better information to base the decisions on, and possibly also result in a more robust solution. In the proposed approach we apply a model for optimization of decisions on energy efficiency investments under uncertainty and regard the decision problem as a multiobjective programming problem. The method is applied to a case of energy efficiency investments at a chemical pulp mill. The case study is used to illustrate that the proposed method provides a good framework for decision-making about energy efficiency measures when considerations regarding greenhouse gas reductions influence the decisions. We show that by setting up the problem as a multiobjective programming model and at the same time incorporating uncertainties, the trade-off between economic and environmental criteria is clearly illustrated.     

Hydrogen production from glycerol steam reforming for low- and high-temperature PEMFCs

This paper presents a thermodynamic study of a glycerol steam reforming process, with the aim of determining the optimal hydrogen production conditions for low- and high-temperature proton exchange membrane fuel cells (LT-PEMFCs and HT-PEMFCs). The results show that for LT-PEMFCs, the optimal temperature and steam to glycerol molar ratio of the glycerol reforming process (consisting of a steam reformer and a water gas shift reactor) are 1000 K and 6, respectively; under these conditions, the maximum hydrogen yield was obtained. Increasing the steam to glycerol ratio over its optimal value insignificantly enhanced the performance of the fuel processor. For HT-PEMFCs, to keep the CO content of the reformate gas within a desired range, the steam reformer can be operated at lower temperatures; however, a high steam to glycerol ratio is required. This requirement results in an increase in the energy consumption for steam generation. To determine the optimal conditions of glycerol steam reforming for HT-PEMFC, both the hydrogen yield and energy requirements were taken into consideration. The operational boundary of the glycerol steam reformer was also explored as a basic tool to design the reforming process for HT-PEMFC.     

Development and demonstration plant operation of an opposed multi-burner coal-water slurry gasification technology

The features of the opposed multi-burner (OMB) gasification technology, the method and process of the research, and the operation results of a pilot plant and demonstration plants have been introduced. The operation results of the demonstration plants show that when Beisu coal was used as feedstock, the OMB CWS gasification process at Yankuang Cathy Coal Co. Ltd had a higher carbon conversion of 3%, a lower specific oxygen consumption of about 8%, and a lower specific carbon consumption of 2%–3% than that of Texaco CWS gasification at the Lunan Fertilizer Plant. When Shenfu coal was used as feedstock, the OMB CWS gasification process at Hua-lu Heng-sheng Chemical Co. Ltd had a higher carbon conversion of more than 3%, a lower specific oxygen consumption of about 2%, and a lower specific coal consumption of about 8% than that of the Texaco CWS gasification process at Shanghai Coking & Chemical Corporation. The OMB CWS gasification technology is proven by industrial experience to have a high product yield, low oxygen and coal consumption and robust and safe operation.     

Integration of hydrogen storage system and wind generation in power systems under demand response program: A novel p-robust stochastic programming

Penetration of renewable energy sources (RESs) in power systems increase all over the world to overcome current challenges, most importantly environmental issues. Beside advantages of RESs, their integration into the power systems have imposed various challenges considering uncertain and intermitted power output. To cope with these challenges, utilizing energy storage systems with renewable energy sources alongside the demand response (DR) programs are considered as reliable solutions. On the other hand, in an uncertain environment, minimizing worst-case cost or regret is counted as an important criterion to evaluate operation of any system under uncertain parameters. Therefore, in this paper, optimal operation of power systems is solved under penetration of wind turbines, hydrogen storage system, and DR programs in an uncertain environment. To guarantee robust operation of the system under the worst-case scenario, a novel stochastic p-robust optimization method (SPROM) is proposed which combines both stochastic programming and robust optimization approaches where minimizes the worst-case cost or regret level. The performance of the developed model is evaluated considering a six-bus test system under two cases as stochastic optimization (SO) and SPROM. Obtained results show that the maximum regret level is reduced considerably using the proposed SPROM comparing with pure stochastic method.     

Robust multi-objective optimization of methanol steam reforming for boosting hydrogen production

Methanol steam reforming (MSR) has been considered as a promising method for producing pure hydrogen in recent decades. A comprehensive two-dimensional steady-state mathematical model was developed to analyze the MSR reactor. To improving high purity hydrogen production, a triple-objective optimization of the MSR reactor is performed. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is employed as a robust optimization approach to maximize the three objectives, termed as, methanol conversion, CO selectivity, and H₂ selectivity. The Pareto optimal frontier has also been provided and the ultimate solution of the Pareto front has been found by the three decision-making methods (TOPSIS, LINMAP, and Shannon's Entropy). Among the three distinct decision-making approaches, LINMAP presents better results according to the deviation index parameter. It has been shown that a perfect agreement is available between the plant and simulation data. Operating under the optimum values based on the LINMAP method confirms an almost 47.04% enhancement of H₂ mass fraction compared to the conventional industrial MSR reactor. The predicted results advocate that the key superiority of the optimized-industrial reactor is the remarkable higher production rate of hydrogen compared to the conventional MSR reactor which makes optimized-industrial reactor both feasible and beneficial.     

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.     

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。     

A practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plants

In this paper a novel Multi-objective fuzzy self adaptive hybrid particle swarm optimization (MFSAHPSO) evolutionary algorithm to solve the Multi-objective optimal operation management (MOOM) is presented. The purposes of the MOOM problem are to decrease the total electrical energy losses, the total electrical energy cost and the total pollutant emission produced by fuel cells and substation bus. Conventional algorithms used to solve the multi-objective optimization problems convert the multiple objectives into a single objective, using a vector of the user-predefined weights. In this conversion several deficiencies can be detected. For instance, the optimal solution of the algorithms depends greatly on the values of the weights and also some of the information may be lost in the conversion process and so this strategy is not expected to provide a robust solution. This paper presents a new MFSAHPSO algorithm for the MOOM problem. The proposed algorithm maintains a finite-sized repository of non-dominated solutions which gets iteratively updated in the presence of new solutions. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository, within the limits. The proposed algorithm is tested on a distribution test feeder and the results demonstrate the capabilities of the proposed approach, to generate true and well-distributed Pareto-optimal non-dominated solutions of the MOOM problem.