Energy Accommodation Research Articles

Operation mode and scheduling plan optimization approach for multiple balancing zones in a distribution system

Modern power systems are developing rapidly, with distributed energy, energy storage devices, adjustable loads, and other flexible resources consolidated through microgrids, virtual power plants, and integrated source–network–load–storage systems. This consolidation under various balancing zone models facilitates synergistic operations and has become critical to enhancing distributed power consumption and ensuring the reliability of electricity supply. Therefore, in light of the challenges of inadequate economic efficiency, reduced accommodation of renewable energy, and poorer operational reliability in distribution networks, this study proposes a category selection and flexibility resource scheduling method that considers the differences in multiple balancing zone models and modes. Firstly, the approach establishes a multi-dimensional characteristic evaluation index and multiple balancing zone operation models. The characteristic evaluation indicators are then utilized to assess the unique properties of the balancing zone system and eliminate unreasonable operating modes. Finally, through analyzing the effectiveness and differences of various balancing zone operation modes, an optimal operation mode is selected, and a scheduling plan is formulated. We conclude that the scheduling plan optimization method considering the operation mode can realize a reasonable choice of operation modes and achieve benefit optimization.

Aerothermodynamics of a sphere in a monatomic gas based on ab initio interatomic potentials over a wide range of gas rarefaction: subsonic flows

Aerothermodynamic characteristics of a sphere in a subsonic flow are calculated over a broad range of gas rarefaction by the direct simulation Monte Carlo method based on ab initio interatomic potentials and Cercignani–Lampis surface scattering kernel. Calculations of the drag and average energy transfer coefficients are performed for various noble gases in the range of Mach number from 0.1 to 1. The obtained results point out that the influence of the interatomic potential is weak in subsonic flows. A comparison of the present results with a linear theory shows that the numerical solutions at Mach number equal to 0.1 are close to those obtained from the linearized kinetic equation in the transitional and free-molecular regimes. In the near-continuum flow regime, the difference between the present solution and the linear theory is significant. To reveal the effects of the gas–surface accommodation, a few sets of the tangential momentum and normal energy accommodation coefficients are considered in simulations. It is shown that the effect of the accommodation coefficients on the sphere drag is not trivial, and, for non-diffuse scattering, the drag coefficient can be either larger or smaller than that for diffuse scattering. The effect of the sphere temperature is also investigated and the calculated values of the average energy transfer coefficient are used to find the Stanton number, recovery factor and adiabatic surface temperature. The numerical results for the sphere drag and energy transfer are compared with the semi-empirical fitting equations known from the literature.

Mitigating Risk and Emissions in Power Systems: A Two-Stage Robust Dispatch Model with Carbon Trading

The large-scale integration of renewable energy sources is crucial for reducing carbon emissions. Integrating carbon trading mechanisms into electricity markets can further maximize this potential. However, the inherent uncertainty in renewable power generation poses significant challenges to effective decarbonization, renewable energy accommodation, and the security and cost efficiency of power system operations. In response to these challenges, this paper proposes a two-stage robust power dispatch model that incorporates carbon trading. This model is designed to minimize system operating costs, risk costs, and carbon trading costs while fully accounting for uncertainties in renewable energy output and the effects of carbon trading mechanisms. This model is solved using the column-and-constraint generation algorithm. Validation of an improved IEEE 39-bus system demonstrates its effectiveness, ensuring that dispatch decisions are both robust and cost-efficient. Compared to traditional dispatch models, the proposed model significantly reduces system risk costs, enhances the utilization of renewable energy, and, through the introduction of a ladder carbon trading mechanism, achieves substantial reductions in carbon emissions during system operation.

An overview of grid-forming technology and its application in new-type power system

To address the global climate crisis, achieving energy transitions is imperative. Establishing a new-type power system is a key measure to achieve CO2 emissions peaking and carbon neutrality. The core goal is to transform renewable energy resources into primary power sources. The large-scale integration of high proportions of renewable energy sources and power electronic devices will dramatically change the operational mechanisms and control strategies of power systems. Existing wind and solar converters mostly adopt the grid-following control mode, which leads to significant challenges in system security and stability as it is insufficient to support the frequency and voltage of the grid. On the other hand, grid- forming control technology (GFM) can provide voltage and frequency support for the system, and thus becomes an effective measure to improve the inertia and damping characteristics of power systems. This paper illustrates the principles, control strategies, equipment types, application scenarios, and project implementation of grid-forming technology. The simulation and analysis based on a renewable-dominated real new-type power system show that GFM can significantly enhance the frequency and voltage support capacity of the power system, improve renewable energy accommodation capacity and grid transmission capacity under weak grid conditions, and play an important role in enhancing the stability and power supply reliability of renewable-dominated new-type power systems.

Aggregate power flexibility of multi-energy systems supported by dynamic networks

The multi-energy system, encompassing electricity networks, district heating networks (DHNs), and hydrogen-enriched compressed natural gas (HCNG) networks, provides an alternative for promoting intermittent renewable energy accommodation and enhancing operational flexibility. This paper investigates the aggregate flexibility of the multi-energy system based on dynamic network models and admissible power fluctuation regions of decomposed subsystems. The dynamic processes within HCNG networks/DHNs are analyzed, and the inner-box method is employed to approximately quantify the individual flexibility of the dynamic networks. Furthermore, the optimal dispatchable regions of decomposed subsystems, accounting for internal gas/heating load uncertainties, are evaluated based on distributionally robust optimization. Through distribution-level power aggregation, the flexibility of the multi-energy system is quantified utilizing geometric methods. Numerical results on two test systems verify the effectiveness of the proposed methodology.

Flexible energy utilization potential of demand response oriented photovoltaic direct-driven air-conditioning system with energy storage

The surge in air conditioning electricity consumption exacerbates grid peak load. To counteract grid peaking pressures and accommodate a high penetration rate of renewable energy, a photovoltaic direct-driven air-conditioning system (PVACS) integrated with energy storage was suggested. The power response characteristics of the air conditioner based on indoor temperature set-point regulation were clarified with an on-site test. The test results indicated a significant flexible energy utilization potential through intelligent temperature management. Whereas, its flexibility is limited by high ambient temperature or inadequate air conditioner capacity. Then, the effects of demand response were simulated, aiming to achieve the synchronization between air conditioner power and photovoltaic (PV) power. Simulation results showed that the demand response strategies effectively reduced peak power by 45.2% and increased self sufficiency ratio and self consumption ratio to 0.83 and 0.91, respectively. Moreover, the role of short-term electricity energy storage was considered for further promoting solar energy accommodation and grid peak shaving. Optimized energy storage further reduced peak power by more than 23.4%, while mitigating the temporal supple-demand mismatch.

Low carbon collaborative planning of integrated hydrogen-ammonia system and power distribution network

The conversion of renewable energy into chemical energy carriers, such as ammonia, enhances the accommodation of renewable energy for power distribution networks (PDN) and supports the decarbonization for ammonia production systems (APS). This paper addresses the integration of PDN with APS through a collaborative planning considering a carbon emission flow (CEF) model. A collaborative planning model with carbon emissions constraints is designed, and a sensitivity analysis is performed to evaluate the impact of varying renewable energy costs on the system. To further reduce carbon emissions from green ammonia production and stabilize renewable energy volatility, a nodal CEF model that incorporates energy storage systems (ESS), wind turbines (WT), and photovoltaics (PV) is proposed. The economic feasibility of the proposed model is verified via comprehensive experiments. Numerical results show that collaborative scheduling reduces both costs and carbon emissions in PDN and APS. Implementing a carbon tax encourages the transition from grey to blue ammonia production, while a decrease in renewable energy costs further promotes the shift from blue to green ammonia. Besides this, the analysis indicates that integrating long-term operation of WT and PV, together with low-cost renewable electricity and ESS, boosts the economic efficiency of the green ammonia production system.

Mechanism-specific chemical energy accommodation with finite-rate surface chemistry in non-equilibrium flow

During atmospheric reentry, the vehicle surface is exposed to highly non-equilibrium flow. The vehicle surface can experience heterogeneous recombination of reactive atoms, which contributes to its aerothermodynamic heating. This process is followed by chemical energy accommodation (CEA), where the released energy is either transferred to the surface or the internal energy modes of the recombined molecule. Heterogeneous recombination can be categorized into Eley–Rideal (ER) and Langmuir–Hinshelwood mechanisms, which differ in their methods of molecule formation and degrees of CEA. The complete CEA assumption may not consider the dependency of CEA on the mechanisms of heterogeneous recombination. This study aims to consider the mechanism-specific CEA for a more accurate prediction of surface heat flux. The authors implement mechanism-specific CEA within the direct simulation Monte Carlo framework using the finite-rate surface chemistry model, resolving elementary surface reactions and assigning a CEA coefficient, β, to each mechanism. The model is verified through comparisons with analytical solutions of surface coverage and validated against benchmark references. A parametric investigation of rarefied hypersonic flow over a two-dimensional cylinder is conducted under different freestream Mach and Knudsen numbers. Results show a reduction in total heat flux of up to 14.44% using mechanism-specific CEA compared to the complete CEA assumption. The reduction is attributed to the relative contribution of the ER mechanism, which can be a function of atomic partial pressure at the boundary layer.

Two-stage robust optimization for optimal operation of hybrid hydrogen–electricity storage system considering ladder-type carbon trading

The prominent problems of renewable energy curtailment and its uncertainty have become a hot topic. To the end, with consideration of environmental friendliness, energy utilization efficiency and operation cost, this paper proposes a hybrid hydrogen–electricity storage system (HHES) operation framework comprising assorted types of coupling devices and carbon capture system (CCS) under ladder-type carbon trading (LCT) mechanism. By considering energy balance constraints, equipments’ operation constraints and erratic rates of RESs and multiple loads, a scheduling model with source-load uncertainties is developed with the goal of minimizing the system’s operation cost and carbon emission. Then, a two-stage tri-level robust model is built to find the optimal scheduling plan under multiple uncertainties given the various sources and loads. The first level is to make an optimal dispatch plan and the second level is to find the worst scenario under the given uncertainty set. The third level aims at enhancing renewable energy accommodation rate and reducing load shedding rate in the worst case. Since the model is formed as a tri-level mixed integer optimization problem, the Column and Constraint Generation (CCG) method is adopted to achieve the optimal result. Finally, numerical analyses are performed. The results show that the proposed approach realizes a flexible dispatch of multiple energy and a high utilization level of multiple storages. Furthermore, this paper studies the influence of uncertainty and uncertainty budget parameters with the comparison of the deterministic model and robust models with different uncertainties. The results show that the proposed HHES has strong robustness and can provide a good reference for energy dispatch.

Optimal operation of energy-intensive load considering electricity carbon market

Energy-intensive load benefits from low electricity tariff and carbon emission, since they occupy certain amounts in the total cost of the product. This paper considers energy-intensive load participation in the electricity as well as carbon trading to reduce the cost. Firstly, an electricity-carbon model is established based on the correlation value method to calculate the carbon emissions of energy-intensive load based on their electricity consumption to realize the carbon amount. Afterwards, the baseline method is used to allocate free carbon emission quotas to energy-intensive load and a reward-penalty carbon trading price mechanism considering offset is proposed. Next, the objective function to achieve maximum benefits, and to reduce output fluctuation, and to improve new energy accommodation is proposed. The case studies show that, by comparing multi-objective function optimization, the optimization target proposed in this paper can effectively reduce wind power output fluctuations and improve wind power accommodation. Through the total participation in carbon trading and electricity market income, multi-objective optimization can increase the system income while ensuring that energy-intensive load meets production requirements under the premise of reducing carbon emissions, verifying the effectiveness of the low-carbon optimal operation model proposed in this paper.

The effects of energy accommodation of reflected gas molecules on flow structures during expired spacecraft reentry

To study the influence of energy accommodation of scattering gas molecules on flow fields during large expired spacecraft reentry, a more elaborated gas-surface interaction model, compared with full Maxwellian diffuse model, is employed in implicit algorithms based on Boltzmann model equation. The characteristic distributions around cylinder at different fluid regimes are accordingly obtained by implicit algorithms, Navier-Stokes solver and DSMC ((Direct Simulation Monte Carlo) method. And the consistency of these results is verified. It is confirmed that present algorithms are capable of solving external flow problems covering various fluid regimes. Then the simulation results see that under current conditions set in the paper, pressure and temperature are proportional to wall activation (ω=Tw/T∞, Tw is surface temperature, T∞ denotes as free stream temperature), but their amplitudes alter with ω at different fluid regimes. As for the effects of energy accommodation coefficients (αe), both pressure and temperature profiles vary in a linear way with αe. However, the variation ranges of these parameters are diverse with regard to different fluid regimes. These observations are favor to the construction of efficient forecasting software, which could predict the flight path of large defunct spacecraft. In this forecasting software, the external ballistics computations and aerothermodynamic simulations are synchronously carried out.

Distributed optimal scheduling for virtual power plant with high penetration of renewable energy

The virtual power plants are aggregations of distributed generators, grid-connected devices in user side. The operation of virtual power plants affects the economic benefits and environmental benefits. However, due to the stochastic and fluctuating nature of power generation from renewable energy sources, the optimal scheduling problem for the virtual power plant containing high-dimensional random variables is difficult to solve. The conic power flow constraints are considered in the virtual power plant day-ahead optimal scheduling models to maximize the revenue by virtue of optimizing the flexible resources such as energy storage system and interruptible load. To quantify the uncertainties in the optimization problem, this paper proposes a network state-based power scenario reduction strategy for renewable energy generation, where typical scenarios are selected by the state of the grid voltage. The proposed day-ahead scheduling model is a mixed-integer, nonlinear, large-scale, stochastic optimization problem with high dimensional random variables, which is difficult to be solved directly by traditional centralized method. By temporally decoupling the charging and discharging model of energy storage systems, a distributed optimization algorithm is proposed based on multi-temporal power flow decoupling optimization model. The simulation results show that the proposed distributed optimization algorithm has high accuracy and good convergence, the virtual power plant can achieve day-ahead optimal scheduling and effectively promote renewable energy accommodation under the security and reliability of the grid.

Efficiency improvement and flexibility enhancement by molten salt heat storage for integrated gasification chemical-looping combustion combined cycle under partial loads

Chemical-looping combustion offers a promising carbon capture technology for coal-fired power generation. Enhancing the operational flexibility and energy efficiency of such plants is crucial for achieving primary energy savings and renewable energy accommodation. In this study, the operating characteristics of an integrated gasification chemical-looping combustion combined cycle (IGCLCCC) under various loads are studied, and the deterioration mechanism of its thermodynamic performance is revealed. Further, an IGCLCCC scheme coupled with molten salt heat storage is proposed and evaluated from thermodynamic performance and operational flexibility. Study results indicate that the net efficiency of the IGCLCCC reaches 37.7% under 30% load, which is superior to those of conventional CO2 capture power plants under full load. Adopting the heat storage system significantly improves the operational flexibility and energy efficiency of the IGCLCCC by expanding its operating range from 30.0∼100.0% to 25.7∼105.3%. The equivalent round-trip efficiency of the heat storage system reaches 134.6% due to the upgrading of the thermal energy from the exhaust gas of the air turbine from original low levels to high levels driven by the exergy saving during the oxygen carrier-air reaction because of the air preheating to a high temperature.

Optimal Scheduling of a Cascade Hydropower Energy Storage System for Solar and Wind Energy Accommodation

The massive grid integration of renewable energy necessitates frequent and rapid response of hydropower output, which has brought enormous challenges to the hydropower operation and new opportunities for hydropower development. To investigate feasible solutions for complementary systems to cope with the energy transition in the context of the constantly changing role of the hydropower plant and the rapid evolution of wind and solar power, the short-term coordinated scheduling model is developed for the wind–solar–hydro hybrid pumped storage (WSHPS) system with peak shaving operation. The effects of different reservoir inflow conditions, different wind and solar power forecast output, and installed capacity of pumping station on the performance of WSHPS system are analyzed. The results show that compared with the wind–solar–hydro hybrid (WSH) system, the total power generation of the WSHPS system in the dry, normal, and wet year increased by 10.69%, 11.40%, and 11.27% respectively. The solar curtailment decreased by 68.97%, 61.61%, and 48.43%, respectively, and the wind curtailment decreased by 76.14%, 58.48%, and 50.91%, respectively. The high proportion of wind and solar energy connected to the grid in summer leads to large net load fluctuations and serious energy curtailment. The increase in the installed capacity of the pumping station will promote the consumption of wind and solar energy in the WSHPS system. The model proposed in this paper can improve the operational flexibility of hydropower station and promote the consumption of wind and solar energy, which provides a reference for the research of cascade hydropower energy storage system.

Low-carbon scheduling research of integrated energy system based on Stackelberg game under sharing mode

To achieve a win-win situation among multiple stakeholders within the integrated energy system, this paper proposes a low-carbon scheduling research of integrated energy system based on Stackelberg game under sharing mode. Firstly, an operation framework of integrated energy system based on leader-follower game was established, and a model of integrated energy system operator and user coalition was established considering carbon trading mechanism. Secondly, bi-directional long short-term memory is used to predict the travel data of electric vehicles, and a schedulable capacity model for electric vehicle cluster as shared energy storage was established. Then, an energy trading optimization model based on leader-follower game was constructed for integrated energy system operator and user coalition, and an improved Shapley value is adopted to realize cost allocation within user coalition. Finally, based on the demonstration project of Sino-Singapore Tianjin Eco-City is selected as the research object to make example analysis. The calculation results show this paper can achieve a reasonable benefit allocation among multiple participants, reduce carbon emissions of integrated energy system, and increase renewable energy accommodation capacity. The relevant achievements are a preliminary exploration for an effective mode of sharing economy in integrated energy system.

Energy saving and flexibility analysis of combined heat and power systems integrating heat-power decoupling technologies in renewable energy accommodation

World energy is undergoing a decarbonization transformation with the rapid growth of renewable energy. Combined heat and power, with its abundant stock and efficient characteristics, can support decarbonization. This study focuses on enhancing the energy saving and flexibility of combined heat and power systems decoupled by the vapor compression heat pump and electric boiler. The characteristics of the governing valves and the governing stage are confirmed as key factors influencing energy saving. A general modeling method is proposed to accurately determine the characteristics of the governing valves and the governing stage. The valve-point effect is obtained from the formation mechanism. With a reference case, applying the heat-power decoupling technologies results in a maximum reduction of power load of up to 70 MW, greatly enhancing the operation flexibility. The fuel saving per unit peak shaving ranges from 0.1355 kg·kWh−1 to 0.1382 kg·kWh−1 in the system decoupled by the electric boiler and from 0.2284 kg·kWh−1 to 0.2481 kg·kWh−1 in the system decoupled by the vapor compression heat pump. The maximal variation ranges of fuel saving per unit peak shaving, with and without the valve-point effect, are 0.0197 kg·kWh−1 and 0.0055 kg·kWh−1, respectively. After considering the valve-point effect, the range of fuel saving per unit peak shaving is tripled. Additionally, there are higher energy-saving regions near valve points. It is recommended to operate the decoupling systems under these working conditions, with governing valves opened at an appropriate opening degree. The energy-saving potential of these systems can be further exploited by accommodating the valve-point effect in the integration of renewable energy.

The planning method of new energy distribution network in plateau area based on local accommodation

Abstract The planning method of new energy distribution network in plateau area based on local accommodation is studied to improve the local accommodation capacity of new energy distribution network in plateau area. The framework of new energy distribution network planning in the plateau area is constructed, and the distribution network equipment suitable for the plateau environment is selected based on the harsh environment in the plateau area; in the framework of new energy distribution network planning in plateau area, a bi-level planning model considering multi-flexible resource coordinated scheduling and energy storage is established. The upper level planning model takes the maximum accommodation of new energy as the objective, and the lower level planning model takes the optimal daily operation benefit of energy storage under the given configuration as the optimization objective; the probabilistic power flow method and C-PSO are used to solve the bi-level model. The experiment shows that this method ensures the stability of the new energy distribution network in the plateau area, reduces the amount of abandoned wind energy and the operating cost, and improves the local accommodation capacity of the core higher than the distribution network.

Enhancing Renewable Energy Accommodation by Coupling Integration Optimization with Flexible Retrofitted Thermal Power Plant

Thermal power units (TPUs) play a crucial role in accommodating the high penetration of renewable energy sources (RESs) like wind turbines (WTs) and photovoltaics (PVs). This paper proposes an evaluation framework to quantitatively analyze the flexibility potential of retrofitted TPUs in enhancing the accommodate capability of RESs through coupling integration and optimal scheduling. Firstly, the coordination framework for coupling TPUs with RESs is outlined, including a comprehensive analysis of benefits and implementation strategies. Secondly, an annual optimal scheduling model for TPUs and RESs is developed, incorporating deep peak regulation services, ladder-type constraints for retrofitted TPUs, and their operational characteristics before and after the coupling integration. Thirdly, indices to evaluate RES accommodation levels and TPU regulation capacities are proposed to quantify the performance of power sources. Finally, a real-world case study is conducted to demonstrate that integrating retrofitted TPUs with RESs through coupling significantly enhances RES utilization by 3.6% and boosts TPUs’ downward regulation capabilities by 32%.

Investigation of solar accommodation via storage configuration in district heating system based on granularity analysis

In the pursuit of achieving carbon neutrality, it is essential to advance the broad accommodation of renewable energy for the realization of a low-carbon district heating system (DHS). The heat storage configuration serves as an effective assurance for facilitating the accommodation of renewable energy in DHS. This paper analyzes the impact of heat storage configuration on the accommodation of solar energy in DHS, employing a granularity analysis method. This study proposes an optimization method for determining the maximum solar energy accommodation rate in solar-assisted DHSs using the granularity analysis method. Furthermore, this study optimizes the heat storage capacity configuration to facilitate varying ratios of solar energy accommodation. Finally, a technical-economic analysis of the solar-assisted DHS is performed in the context of rising energy cost. The case study reveals that greater granularity leads to a higher solar energy accommodation rate. Specifically, when the granularity increases from 0.43% to 22.22%, the solar energy accommodation rate increases by 8.01%. Moreover, the heat storage capacity configuration increases with the increase of the solar energy accommodation rate, while the heat storage capacity required to accommodate per unit of solar energy decreases. Additionally, as the granularity increases from 0.43% to 22.22%, the heat storage capacity configuration under the same solar energy accommodation ratio increases gradually. This study explores the potential of granularity-based heat storage configuration optimization in promoting solar energy accommodation and achieving the economy of DHS, which is of great significance for the decarbonization of DHS.

Grey-box model-based demand side management for rooftop PV and air conditioning systems in public buildings using PSO algorithm

As the major power consumer in buildings, Heating, ventilation and air conditioning (HVAC) systems can significantly contribute to the economical operation of the power system during the peak periods in summer or winter, by flexibly responding to the grid demands. Indoor air temperature set-point resetting is a typical passive demand response (DR) control strategy that have potentials to be widely implemented by inverter air conditioners in residential buildings. However, in the existing small and medium-size public buildings, the compressor motor of HVAC systems mostly does not allow for stepless speed changes. This means that the indoor temperature set-point resetting strategy may results in frequent on/off switching of the chiller plant, leading to a compromised lifespan. Although rooftop photovoltaic systems (PVs) are expected to be widely adopted in new buildings, their potential for peak load shaving or valley load filling has received limited investigation. This study proposes a grey-box model-based demand side management (DSM) method for variable air volume systems (VAVs) and rooftop PVs in public buildings. The DSM method utilizes the particle swarm optimization (PSO) algorithm to determine optimal indoor air temperature and/or evaporator outlet water temperature set-point schedules for the VAVs, as well as optimal charging/discharging schedules for the storage battery of rooftop PVs. This approach aims to reduce the peak power demands of the integrated system without causing severe thermal discomfort, increasing chiller mechanical costs (refers to frequent on/off switching of the chiller plant in this paper), or overcharging/over-discharging of storage batteries. The results of case studies demonstrate that the strategy of co-resetting indoor air temperature – evaporator outlet water temperature set-points outperforms the indoor air temperature set-point resetting strategy in terms of both peak power demands reduction and mechanical cost saving when applied to the VAVs in low performance buildings. In comparison, for the VAVs installed in normal buildings, both the strategies yield significant decreases in peak power demands and mechanical cost by fully utilizing the thermal capacity of the building internal thermal mass. Furthermore, by implementing the DSM method for rooftop PVs, the integrated system can achieve zero or near-zero net power demands during the DR periods. The proposed DSM method can also be utilized in valley load filling programs to facilitate the accommodation of renewable energy.