Energy Accommodation Research Articles

Assessment of machine learning models and conventional correlations for predicting heat transfer coefficient of liquid hydrogen during flow boiling

Liquid hydrogen has attracted widespread attention due to industrial decarbonization as an excellent carrier of renewable energy accommodation. Accurate prediction of the heat transfer coefficient during flow boiling has become a fundamental prerequisite for enhancing the design rationality and safety of hydrogen devices since a slight heat leak will trigger flow boiling. A novel selection standard of the dataset for liquid hydrogen flow boiling is proposed, and an experimental database of liquid hydrogen is compiled, including 923 data points from 5 sources. The prediction performance of eight conventional empirical correlations and six machine learning models for liquid hydrogen flow boiling is compared and evaluated. Results show that the prediction ability of machine learning models for the heat transfer coefficient of liquid hydrogen flow boiling is much higher than that of traditional correlations. Fang correlation is recommended to use as the calculation benchmark due to simplicity and reliability if the inlet condition is obscure. The Extra tree model shows the most excellent evaluating performance among the six machine learning models with a mean absolute deviation of 11.44% and a fairly superior R2 of 0.9543. The Froude number Fr exhibits an essential impact according to the feature importance analysis, which may need to be taken into account when developing new empirical correlations. An improved correlation for the flow boiling of liquid hydrogen is provided based on the Fang correlation, achieving a mean absolute deviation of 18.61% and a mean relative deviation of 16.49%. The results of this paper can offer some theoretical guidance for the design of a liquid hydrogen apparatus.

Evaluation of Renewable Energy Accommodation Capacity Considering Multiple Flexibility Resources of Source-Load-Storage

The utilization of multiple flexibility resources (MFR), such as source-load-storage (SLS-sides), plays a vital role in promoting the integration of large-scale grid-connected renewable energy (RE). Analyzing the renewable energy accommodation capacity (REAC) and assessing the impact of flexibility resources on enhancing REAC is vital for the coordinated development of SLS sides. This study focuses on the technical mechanisms of thermal power (TP) deep peak regulation, load demand response (DR), and energy storage (ES). Considering the operational constraints of these MFR, an evaluation model for REAC is established. The model is a multi-objective optimization framework that encompasses the maximized installed capacity of RE while minimizing the comprehensive expenses of the power system. A hierarchical decoupling solution method is used to solve the evaluation model, obtaining the Pareto optimal solution set for RE installed capacity and power system operating cost. Subsequently, a fuzzy membership function is applied to select a compromise solution, quantitatively characterizing the REAC. The case study results demonstrate that by effectively coordinating the “SLS-sides” MFR, the REAC of the IEEE-RTS96 system can be improved from 1, 758 MW to 2, 731 MW, indicating a remarkable enhancement of 55.35%.

Gas concentration in rarefied flows: Experiments and modeling

The relationship between gas-surface scattering dynamics and flow through conical systems in the rarefied regime has been explored with experimental and computational tools. Molecular beam-surface scattering experiments were used to characterize the inelastic scattering dynamics of Ar and N2 on a highly oriented pyrolytic graphite (HOPG) surface. These data were then used to develop a gas-surface scattering approach for kinetic fluid simulations, which combines the diffuse scattering and Cercignani-Lampis-Lord (CLL) models. The translational energy and angular flux distributions from the beam-surface scattering experiments were used to obtain the normal energy accommodation (αn) parameter as well as the effective surface mass (M) in the CLL model. Constant values of αn=0.9, M = 40 amu for Ar and αn=0.75, M = 28 amu for N2 were found to capture the experimental angular flux scattering distributions over a wide range of incident energies and angles. The gas-surface model was subsequently used in direct simulation Monte Carlo (DSMC) simulations of the flux of directed gas flow through a conical concentrator and compared with measurements of gas concentration with a prototype concentrator in separate molecular beam experiments. Comparisons of predicted concentration pressure ratios (pressure in an accommodation chamber with vs. without concentrator) with measured values for three free-stream velocities of Ar and N2 showed maximum deviations of 4.8% and 9.8% for Ar and N2, respectively. The simulations show that gas-surface collisions resulting in the loss of the normal component of the incident energy while preserving the tangential momentum of the impinging atom or molecule, resulting in super-specular scattering, lead to higher concentration of gases than purely specular, energy conserving gas-surface interactions. For conical concentrators, with inlet-to-outlet area ratios of 133 and 600, specular reflections result in concentration factors (flux through an orifice with vs. without concentrator) of only ∼8, while super-specular scattering results in concentration factors of 64 and 127, respectively.

A stochastic hierarchical optimization and revenue allocation approach for multi-regional integrated energy systems based on cooperative games

The interconnection of multiple regional integrated energy systems (RIES) can effectively enhance the low-carbon and flexible operation capabilities of RIES, but the uncertainty and multi-energy interaction of the system pose challenges to the stable operation of RIES. Therefore, a stochastic hierarchical optimization and revenue allocation approach is proposed to optimize the operational strategy of multi-RIES with multi-operator participation, multi-energy interactions, and multiple uncertainties. Firstly, agent-based modeling, Latin hypercube sampling and simultaneous backward reduction based on the Wasserstein metric are proposed to capture the power-side and load-side uncertainties. Secondly, based on the Nash bargaining game and the alternating direction method of multipliers algorithm, multi-RIES peer-to-peer transactions of electricity, thermal, and natural gas are optimized through energy cooperation and scheduling strategies. Subsequently, a Nash-Harsanyi bargaining game revenue allocation method that considers both fairness and renewable energy accommodation is proposed to ensure the stable operation of the alliance. Finally, simulations are conducted on a multi-RIES in northern China, demonstrating that the proposed model and approach can achieve inter-grid flexible resource complementarity, improve RIES economics, increase local renewable energy accommodation rate, and reduce carbon emissions.

Multi-stage risk-based assessment for wind energy accommodation capability: A robust and non-anticipative method

The volatility and uncertainty of wind energy has brought great challenges to its accommodation in electric power systems. To determine a wind power trading strategy in the real-time power market, the accurate assessment of wind energy accommodation capability (WAC) is of great significance. However, most current studies investigate the wind energy accommodation potential from the perspective of dispatch, but rarely provide quantitative assessment results. Additionally, the non-anticipativity of decisions has not been considered in the assessment yet. This paper proposes a novel quantitative assessment method for WAC based on the multi-stage robust optimization (RO), which addresses the anticipativity issues in the traditional two-stage method while maintaining the decision-making process' robustness. In the assessment method, the operational risk is integrated via a tractable scheme to obtain dynamic admissible WAC boundaries. To obtain the optimal solution of the proposed multi-stage RO problem, a fast robust dual dynamic programming (FRDDP) algorithm is employed, where the adaptive techniques are developed to accelerate the computation. Numerical studies on the modified IEEE 14-Bus system and a real-world system in Zhejiang Province of China validate the effectiveness of the proposed assessment model and adaptive acceleration techniques. The simulation results demonstrate the presented method brings a 18.87% reduction in operational cost, and reduces 29.34% curtailment of wind energy. Compared with the original FRDDP, the adaptive technique significantly reduces the computational consumption by 73.34% on the real-world test system.

Impact of surface physisorption on gas scattering dynamics

Engineering flow systems operating under low pressures and/or at the micro/nano scale generally include a physically adsorbed gas layer next to the surface. In this paper, we develop a scattering kernel that accounts for the effect of adsorption, arising from van der Waals interactions, on the dynamics of molecules impinging on solid smooth surfaces. In the limit of low bulk density, surface adsorption becomes negligible and the scattering kernel recovers consistently the Cercignani–Lampis model, which best describes molecular collisions with a clean, smooth surface. In the limit of high bulk density, a dense adsorbed molecular layer forms next to the surface and its presence is picked up by the Maxwell model with complete diffuse reflection, which better captures the multiple collisions suffered by molecules. A weight coefficient based on the Langmuir adsorption isotherm is incorporated into the modelling to handle the transition between these two limiting conditions of low and high densities. The proposed model is validated against high-fidelity molecular dynamics simulations that are performed for a variety of gas–surface combinations and adsorbed molecular layers with different densities. It is shown that the proposed model very well captures the scattering patterns of beams of gas molecules at different velocities impinging on surfaces, as well as momentum and energy accommodation coefficients in the entire range of explored conditions.

Multi‐objective design optimization of microreactor arrays for distributed renewable energy conversion: A case study

AbstractRenewable energy accommodation entails extensive use of modular devices, whose performance degrades remarkably from the lab scale to the industrial scale. This work presents a multi‐objective design optimization approach for modular devices scale‐up as exemplified by hierarchical numbering‐up of reverse water‐gas shift microreactors. The optimization objectives cover a wide span of competing system performance metrics, including the reaction performance, energy efficiency, space occupation, environmental impact, and costs. The decision variables involve the hierarchical geometry, branching, and inter‐stack connection schemes of the microreactor stacks. The obtained Pareto‐optimal designs suggest that multiple short stacks with small intra‐stack distribution channels and large inter‐stack connection tubes can well balance cost and performance. Meanwhile, single‐stack structures with large distribution channels are more suitable for portable applications. The methodology not only can be extended to the numbering‐up of various types of microreactors but also has essential guidance for the integration design of other modular devices.

Opinions on power grid infrastructure investments for renewable energy accommodation in China

Opinions on power grid infrastructure investments for renewable energy accommodation in China

Study on the Dynamic Optimal Control Strategy of an Electric-Hydrogen Hybrid Energy Storage System for a Direct Drive Wave Power Generation System

A direct drive wave power generation system (DDWPGS) has the advantages of a simple structure and easy deployment, and is the first choice to provide electricity for islands and operation platforms in the deep sea. However, due to the off-grid, the source and load cannot be matched, so accommodation is an important issue. Hydrogen storage is the optimal choice for offshore wave energy accommodation. Therefore, aiming at the source-load mismatch problem of the DDWPGS, an electric-hydrogen hybrid energy storage system (HESS) for the DDWPGS is designed in this paper. Based on the characteristics of the devices in the electric-hydrogen HESS, a new dynamic power allocation strategy and its control strategy are proposed. Firstly, empirical mode decomposition (EMD) is utilized to allocate the power fluctuations that need to be stabilized. Secondly, with the state of charge (SOC) of the battery and the operating characteristics of the alkaline electrolyzer being considered, the power assignments of the battery and the electrolyzer are determined using the rule-based method. In addition, model predictive control (MPC) with good tracking performance is used to adjust the output power of the battery and electrolyzer. Finally, the supercapacitor (SC) is controlled to maintain the DC bus voltage while also balancing the system’s power. A simulation was established to verify the feasibility of the designed system. The results show that the electric-hydrogen HESS can stabilize the power fluctuations dynamically when the DDWPGS captures instantaneous power. Moreover, its control strategy can not only reduce the start-stop times of the alkaline electrolyzer but also help the energy storage devices to maintain a good state and extend the service life.

Distributed Conditional-Distributionally robust coordination for an electrical power and flexibility-enhanced district heating system

In an integrated electricity and heating system (IEHS), building flexibility-enhanced district heating systems (DHSs) that use advanced flexibility-enhanced technologies are effective for promoting renewable energy accommodation. However, it is challenging to comprehensively utilize these flexibility-enhanced technologies in IEHS dispatch. This paper proposes a distributed conditional-distributionally robust coordination method for an electrical power and flexibility-enhanced DHS. The DHS is retrofitted with bypass compensation technology and uses a variable mass flow operation strategy to enhance its flexibility and promote renewable energy accommodation. Moreover, conditional-distributionally robust optimization is used to consider the inherent dependency between the forecasting error of a wind farm and the forecast output information, which goes beyond the state-of-the-art models. Additionally, a novel reformulation method is adopted to transform the problem with random variables and bilinear constraints into a more tractable programming problem, yielding a conservative approximative model of the original model that guarantees feasibility. Finally, to protect the privacy of electrical power system and DHS operators, this paper proposes a double-layer distributed algorithm to solve the problem in a distributed fashion while guaranteeing feasibility. Case studies demonstrate that the proposed model with flexibility-enhanced DHSs significantly promotes renewable energy accommodation and reduces dispatch costs, and the proposed double-layer distributed algorithm effectively solves the dispatch problem in a distributed fashion and guarantees the feasibility of dispatch results.

Theoretical modelling of non-equilibrium reaction–diffusion of rarefied gas on a wall with microscale roughness

Heat and mass transports through a rough surface are among the most fundamental and important phenomena in either natural or engineering problems. In this paper, theoretical modelling and direct simulation Monte Carlo method are employed to study the heterogeneous reaction–diffusion features induced by microscale roughness which is comparable to the molecular mean free path of the ambient gas. A quasi-one-dimensional homogeneous model is proposed, and it consists of an external diffusion region outside the roughness elements and an internal reaction–diffusion region which could be equivalent to a smooth surface with an effective chemical property. The external macroscopic diffusion can be characterized by a non-equilibrium criterion – the Damköhler number. The internal diffusion in micro-cavities must be analysed by considering the rarefied gas effects on the diffusivity, and another non-equilibrium criterion, the Thiele number, is introduced to evaluate the effective boundary condition imposed on the external region. Analytical formulae based on these criteria are derived to predict the equivalent surface reaction–diffusion performance, and the predictions compare well with the numerical results of different types of surface reaction, even on the three-dimensional roughness. This reveals that the roughness could either enhance or weaken the apparent reaction rate depending on the non-equilibrium degree. This study could enrich our understanding of the gas–surface interactions on a rough wall, such as the oxidation, catalysis and energy accommodation, and also preliminarily provides a practical method for evaluation of the aerothermochemical performance of coating materials of hypersonic vehicles.

A data-driven frequency criteria assessment by determining system minimum level of inertia

The generation of inverter-based renewable energy affects system frequency and is limited by frequency criteria. In a power system with two or more criteria, it is important to understand whether the frequency criteria are appropriate because these should not differ in terms of renewable energy generation for consistent power operation. This study proposes a method to evaluate the frequency criteria applied to the system operation data by determining the minimum level of inertia required for the latter according to the effect of renewable energy generation. The minimum level of inertia can be determined as the minimum inertia value that satisfies the frequency criteria when inertia and frequency regulating reserve change with the operation of the generator. The minimum level of inertia was determined by conducting a frequency simulation by applying two frequency criteria in Korea. The proposed methodology has been applied to 7080 operation data, when two maximum output generators are operating more than 1400 MW simultaneously, out of 23003 time-based data extracted from the Energy Management System (EMS). Further, from the results, the minimum level of inertia for the N-1 frequency criterion was higher than the generator N-2 frequency criterion. This result indicates that the N-1 frequency criterion is more conservative in terms of the accommodation of renewable energy than the N-2 frequency criterion and that renewable energy will be limited by the N-1 frequency criterion. An important finding of this study is that the two frequency criteria in South Korea are applied differently in terms of the minimum level of inertia.

Two-Stage Robust Optimization for Prosumers Considering Uncertainties from Sustainable Energy of Wind Power Generation and Load Demand Based on Nested C&CG Algorithm

This paper develops a two-stage robust optimization (TSRO) model for prosumers considering multiple uncertainties from the sustainable energy of wind power generation and load demand and extends the existing nested column-and-constraint generation (C&CG) algorithm to solve the corresponding optimization problem. First, considering the impact of these uncertainties on market trading strategies of prosumers, a box uncertainty set is introduced to characterize the multiple uncertainties; a TSRO model for prosumers considering multiple uncertainties is then constructed. Second, the existing nested C&CG algorithm is extended to solve the corresponding optimization problem of which the second-stage optimization is a bi-level one and the inner level is a non-convex optimization problem containing 0–1 decision variables. Finally, a case study is solved. The optimized final overall operating cost of prosumers under the proposed model is CNY 3201.03; the extended algorithm requires only four iterations to converge to the final solution. If a convergence accuracy of 10−6 is used, the final solution time of the extended algorithm is only 9.75 s. The case study result shows that prosumers dispatch the ESS to store surplus wind power generated during the nighttime period and release the stored electricity when the wind power generation is insufficient during the daytime period. It can contribute to promoting the local accommodation of renewable energy and improving the efficiency of renewable energy utilization. The market trading strategy and scheduling results of the energy storage system (ESS) are affected by multiple uncertainties. Moreover, the extended nested C&CG algorithm has a high convergence accuracy and a fast convergence speed.

Rolling dispatch framework of integrated electricity-gas system considering power-to-gas facilities and natural gas network dynamics: Modelling and case studies

Rolling dispatch strategy is widely used in power systems to handle the uncertainty and volatility of renewable energy. Meanwhile, flexible gas-fired units and emerging power-to-gas (P2G) facilities integrate the power system and natural gas system as an integrated electricity-gas system (IEGS). Hence, rolling dispatch should be applied to the whole IEGS. This paper designs the rolling dispatch framework in the IEGS and proposes the partial-differential-equations-involved (PDEs) mathematical models that accurately formulate the natural gas network dynamics and the operation properties of P2G facilities to retain the facticity of the IEGS in the rolling dispatch process (e.g., the linepack characteristics of natural gas networks and the physical limits of the P2G facilities). The impact of uncertain wind power on the IEGS operation is also considered. In order to reduce the computational burdens, this paper employs a combination of implicit difference method and the technique of linearization to transform the original rolling dispatch model with PDEs and chance constraints into mixed-integer linear programming which is solvable by off-the-shelf solvers. The results of the case studies demonstrate that the proposed rolling dispatch strategy promotes renewable energy accommodation, enhances the flexibility, security, and efficiency of the IEGS, and mitigates the impact of renewable energy's uncertainty and volatility on the IEGS.

Optimal Dispatch of Integrated Energy System under “Double Carbon” Target

With the aggravation of environmental problems, countries are more and more aware of the importance of protecting the environment. In 2020, China proposed the goal of “double carbon”. As the main carbon emission industry, the energy industry will become the main battlefield. Therefore, whether the power industry can achieve low-carbon electricity and promote the accommodation of new energy will be the key to achieving carbon neutrality. Under the new background, power grid dispatching should not only consider a single economic benefit, which will lead to a large number of wind and solar curtailments, but also take into account environmental benefits and reduce CO2 emissions. In this paper, an optimal dispatching model of an Integrated Energy System(IES) is established, and CCS is added to CHP units to capture CO2 produced by CHP and send it to P2G. Aiming at the total cost optimization, carbon trading mechanism, wind curtailment cost, and solar curtailment cost are added in the model to ensure the environmental benefits in operation.

A data-driven approach towards fast economic dispatch in integrated electricity and natural gas system

Effective economic dispatch for the integrated energy system (IES) can improve energy efficiency and promote renewable energy accommodation. Tradition IES economic dispatch are based on model-based methods that rely on accurate system parameters and uncertainty prediction. This paper proposes a data-driven fast economic dispatch method based on deep reinforcement learning (DRL) in an integrated electricity and natural gas system (IEGS). Unlike other DRL-based studies that spend a lot of computation time to calculate power flow, we employ DNNs to learn the complex nonlinear relationship existing in the IEGS, namely IEGSNet. Then, the economic dispatch problem is formulated as a Markov decision process, and solved by the soft actor–critic​ (SAC) algorithm. Simulation results illustrate that the proposed method achieves the similar operation cost to the tradition DRL-based dispatch method, but takes almost one tenth of the training time. Additionally, the computation time of the proposed method for 10-day dataset is at least two orders of magnitudes shorter that the model-based optimization algorithm.

Chronological operation simulation for power system with peak regulation ancillary service market in China: Formulation, solving, and validation

Peak regulation ancillary service market (PRSM) is an emerging and effective mechanism for renewable energy accommodation in China, especially under Carbon Neutral. To comprehensively analyze its long-term impact on the power system, a chronological operation simulation (COS) model is proposed. Besides, the computational-burden balancing technique based on the time-domain decomposition is developed to improve the computational efficiency of COS. The real-world northeast China power system with PRSM has been analyzed. Case studies validate the simulation with published statistical data and demonstrate the accuracy. Besides, 52.16% computation time is reduced compared to the widely used rolling horizon method. The necessity of PRSM for renewable energy accommodation is verified through chronological simulation. The relationship between peak regulation costs and renewable energy penetration is also given, which demonstrates its applicability.

Multi-objective distributionally robust optimization for hydrogen-involved total renewable energy CCHP planning under source-load uncertainties

Hydrogen is one type of clean renewable energy. By introducing hydrogen production, storage and conversion devices into the combined cooling, heating and power (CCHP) system to replace its fossil fuel components, the hydrogen-involved total renewable energy CCHP (H-RE-CCHP) system is formed, thus bringing an effective way to promote energy transition towards low carbon. However, the conflict between economy and environment, and interference of source-load uncertainties hinder the low-cost construction as well as economic, stable, and low-carbon operation of H-RE-CCHP. In view of this, this paper proposes a multi-objective distributionally robust optimization (DRO) model for H-RE-CCHP planning. Firstly, a moment-based ambiguity set is constructed to define the possible range for the joint distribution of solar irradiation and multiple loads. Secondly, considering the “worst-case” distribution within the ambiguity set, the economic and environmental objective functions are presented for minimizing system construction, maintenance and operation costs while reducing carbon emission. Furthermore, the sizing- and operation-related constraints are formulated according to such principles as maintaining multi-energy balance when the forecasting errors of uncertainties occur. Finally, by applying strong duality and augmented ε-constraint for model solving, Pareto optimal solution set is obtained, and from which final planning and operation scheme is selected according to decision preference and interactions between objectives. Simulations mainly verify: 1) due to considering multiple objectives, H-RE-CCHP planning and operation reach the reasonable trade-off between economy and environment, that is, carbon emission/economic cost can be reduced by 99.81 %/66.70 % comparing with single economic/environmental objective; 2) with applying DRO in resisting uncertainties, 100 % renewable energy accommodation is achieved while meeting stable H-RE-CCHP operation; etc. Accordingly, this paper provides an effective approach for renewable energy system planning, thus contributing to the low carbon development of economic society.

Research on internal external collaborative optimization strategy for multi microgrids interconnection system

Microgrid is one of the most effective solutions to integrate renewable generation into power system. However, microgrids also have some limitations, such as increasing renewable energy accommodation, promoting multi-energy complementarity, and improving energy efficiency. Through autonomous management and energy interaction among microgrids. The establishment of multi microgrids provides ideas for solving the above problems. Multi microgrids has obvious advantages in promoting renewable energy accommodation, improving the economy and energy utilization of power grid operation, and reducing the impact on the distribution network. Therefore, from the perspective of energy scheduling, this paper proposes a internal external collaborative optimization strategy for multi microgrids. Firstly, a cogeneration microgrid model based on graph theory is established, which can not only represent the energy transmission process inside the microgrid, but also the energy interaction process between microgrids. Secondly, a microgrid interconnection pipeline transmission model based on the loss function is established. This model can not only reflect the energy transmission state in the pipeline, but also facilitate the linear solution of the optimization model. Finally, by setting new decision variables and constraints, the linear solution of the internal external collaborative optimization of the multi microgrids is realized, and its rationality and feasibility are verified by a case.

Parameter optimization decision framework for transient process of a pumped storage hydropower system

The construction of Pumped Storage Hydropower Systems (PSHSs) has significance for improving the renewable energy accommodation capacity and meeting the peaking demand for future power systems. During the rapid construction of PSHS, whether the transient process meets the safety requirements is a core issue for decision-makers. The traditional methods to ensure the safety of PSHS transient process are based on Code for Design of PSHS (NB/T 10072–2018), which lacks a systematic theoretical framework to overcome the disadvantages of traditional methods, such as a large amount of calculation and strong subjectivity. Therefore, this study proposes a novel decision framework to determine the optimization parameters for PSHS transient process. The approach is based on the characteristics of PSHS design and transient process and an optimized evaluation system consisting of eleven influencing factors and five evaluation indexes are proposed and validated against experimental data from an actual PSHS. A comprehensive ranking of the influencing factors is also realized, which avoids the deviation caused by subjective experience. The results show that the change in pipe diameter has a greater influence on the surge tank than other factors. Besides, the available optimization parameters and priorities are different from various transient indexes. Finally, the feasibility of the optimization framework decision is verified through the case study in China and provides theoretical guidance to optimize the transient characteristics of PSHS according to the priorities.