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The active distribution network (ADN) can provide the reactive power ancillary service (RPAS) to improve the operations of the transmission network operations (such as voltage control and network loss reduction) as distribution generation grows. In this context, an RPAS market is required to motivate the ADN to provide the RPAS to the transmission network since the transmission system operator (TSO) and the distribution system operator (DSO) are different entities. Hence, to obtain the TSO–DSO coordination in the RPAS market, this study proposes a two‐stage market framework on the basis of the successive clearing of the energy and RPAS markets. Additionally, a distributed market‐clearing mechanism based on an alternating direction method of multipliers (ADMM) is adopted to guarantee TSO's and DSO's information privacy. Furthermore, a binary expansion (BE) method is used to linearise the non‐convex bilinear terms in the market‐clearing model. The effectiveness of the proposed RPAS market framework and distributed market‐clearing mechanism is validated using two different test systems with different system scales.

This study proposes a distributed load restoration method to effectively model and solve the load restoration problem of integrated power distribution and natural gas systems (IDGSs) after faults for resilient operation. The IDGS load restoration problem is devised as a mixed-integer second-order cone programming (MISOCP) model to minimize the total load loss cost of IDGS. This model considers distribution network reconfiguration and the coupling effect between the power distribution system (PDS) and natural gas system (NGS). Moreover, a bidirectional gas flow model is used to offer more flexibility for load restoration when the NGS is faulty. By considering the independent management and information privacy of each energy subsystem, the proposed model is solved using an alternating direction method of multipliers (ADMM) algorithm in a distributed manner. Furthermore, an adaptive-ADMM algorithm is used for convergence acceleration. Finally, numerical studies are conducted to validate the proposed methods effectiveness.

This paper proposes a distributed energy management for active distribution network (ADN) considering aggregated office buildings. It aims to dispatch the flexible resources of office buildings to actively participate in the optimization of the ADN. Firstly, by considering the comfortable indoor temperature range and travel habits of workers, an optimal operation model for the office building with integrated heating ventilation and air conditioning (HVAC) systems and electric vehicle (EV) charging piles is constructed. Secondly, based on the piecewise linearization and second-order cone relaxation (SOCR), an ADN optimization model with aggregated office buildings is formulated and converted into a mixed-integer second-order cone programming (MISOCP) model. Then, considering the private information protection, the alternating direction method of multipliers (ADMM) is used to solve the ADN optimization model to realize the distributed energy management of the ADN with aggregated office buildings. Finally, the scheduling results of the ADN optimization model under different comfortable indoor temperatures and control methods of EVs are analyzed in the winter heating scenario. Numerical results show that the proposed method can increase the penetration of distributed photovoltaic (PV) generation and decrease losses of the distribution network while ensuring the temperature and travel demands of workers in office buildings. The information privacy of each district can also be protected with the proposed method.

Distribution-level electricity market provides a platform for trading energy and grid services from large-amount of small-scale distributed energy resources (DERs) located on distribution grids. The behavior of the DERs in the distribution electricity market may ultimately impact the market-clearing and locational marginal prices (LMPs) in the wholesale market. This paper proposes a bi-level optimization model for distribution market clearing and distribution locational marginal pricing (DLMP) considering the interactions between distribution and transmission wholesale markets. In the proposed model, the upper-level model represents the distribution system operator (DSO) market-clearing and the lower-level model represents the wholesale market-clearing by the independent system operator (ISO). The LMP at the substation will impact the DER dispatch and power demands of the DSO as well as the DLMP. In turn, the power demands of the DSO will further impact the ISO market and its LMPs. The equilibrium problem with equilibrium constraints (EPEC) approach is applied to find the equilibria of multiple DSOs and the ISO. The EPEC problem is transformed into a single-level mixed-integer convex problem in order to allow for efficient solving. The effectiveness of the proposed model and solution method are demonstrated through case studies.

In this paper, a regret-based risk-averse stochastic production task and energy management (PTEM) model for industrial microgrid is proposed, which is applied to a battery manufacturing plant (BMP) microgrid. We incorporate production task scheduling with production constraints in the energy management of industrial microgrid. The general PTEM of industrial microgrids is analyzed and the production processes and energy requirements of a BMP multi-energy microgrid prosumer are presented, in which thermostatically controlled loads (TCLs) in buildings are considered. Then, the regret-based risk-averse PTEM model is formulated as a mixed-integer linear programming (MILP) problem based on stochastic programming considering uncertainties of day-ahead electricity market price and photovoltaic (PV) power output. Conditional value-at-risk (CVaR) is introduced for risk control and utilized for minimizing the expected regret. Numerical studies are carried out to validate the proposed model by comparing with risk-neutral approach and deterministic model based on real market prices from PJM electricity market. The results show that the proposed PTEM approach is effectively risk-averse. In addition, the impacts of total target production task and risk-control parameter on expected cost and regret are analyzed.

With the development of demand-side management in the smart grid, load-serving entity (LSE) plays a more important role for consumers, which purchases energy from the electricity market and sells it to consumers. Moreover, aggregated thermostatically controlled loads (TCLs) in smart buildings can provide additional demand response capacities and require efficient energy management methods. This article proposes a stochastic optimal energy management and pricing model for LSE with aggregated TCLs and energy storage based on the Stackelberg game and stochastic programming. The energy management and pricing problem are formulated as a bilevel optimization model. The upper level model aims to maximize LSE's expected profit under market price uncertainties and determines the offering prices to consumers. According to the offering price from upper level model, the lower level model optimizes the power purchasing pattern for consumers of two types of buildings with TCLs: factory and office buildings. The nonlinear bilevel model is reformulated and converted into mixed-integer linear programming using a strong duality theory. The proposed model is validated by numerical studies based on real market prices from the PJM electricity market. In addition, the impacts of energy storage, number of buildings, comfortable indoor temperature limits, and offering price limits on LSE's profit are analyzed.

Bean curd dreg (BCD) is a by-product of bean products, which can be used as a great ingredient for composting, but it combined with cow manure and corn stalk composting was rarely reported. In this study, the effect of BCD on the maturity and the micro-ecological environment was investigated under a lab-scale composting experiment and found that BCD was conducive to improve the maturity of compost during the BCD application. The excitation-emission matrix (EEM) showed that the final humus content in BCD treatments was richer than that in CK treatment. High-throughput sequencing results showed that BCD-applied better ameliorated the bacteria community structure with higher Actinomycetes abundance and lower denitrifying bacteria abundance in the late stage of composting. PICRUSt results showed that BCD-added decreased the abundance of microbial metabolic genes in the high temperature period (> 70 °C), but the metabolic abundance increased rapidly as the temperature cooled down. Compared with CK, the metabolic abundance decreased significantly on day 24, which was consistent with the conclusion of composting maturity. Redundancy analysis (RDA) results indicated that there were significant discrepancies in the microbial community structure of samples at different composting periods and the change of the dominant population in the BCD-treated compost samples were more outstanding than that in the CK treatment. Hence, BCD is a very good composting modifier that compensates for the disadvantages of composting and enhances the fertility of the compost product.

In this letter, we propose a market-based bi-level conic optimal energy flow (OEF) model of integrated electricity and natural gas systems (IENGSs). Conic alternating current optimal power flow (ACOPF) is formulated in the upper-level model, and the generation cost of natural gas fired generation units (NGFGUs) is calculated based on natural gas locational marginal prices (NG-LMPs). The market clearing process of natural gas system is modeled in the lower-level model. The bilevel model is then transferred into a mixed-integer second-order cone programming (MISOCP) problem. Simulation results demonstrate the effectiveness of the proposed conic OEF model.

Power-to-gas (P2G) and gas-fired generation units (GFGUs) enable the bidirectional energy flow of integrated energy systems (IESs). The static security of IES should be analysed considering the interdependence between energy systems. In this study, the static security of IES is analysed, and a novel static security control strategy to improve the security of IES after N−1 contingency is proposed considering P2G. First, electricity–gas IES model is presented, and the multi-energy flow of IES is solved by the Newton–Raphson method. Then, N − 1 static security analysis is carried out for the electricity system and natural gas system. Based on the obtained Jacobian matrix during the energy flow calculation process after N − 1 contingency, variable sensitivity matrixes of voltage, gas pressure, branch power flow and pipeline flow to control variables are derived. Furthermore, a static security control strategy for IES based on the sensitivity matrices is developed to regulate operation statuses of P2G and GFGU and compressor outlet pressure to mitigate the violation of security constraints. The performance of the proposed analysis method and control strategy is evaluated by IES 4-12 and IES 118-48 test systems. The results demonstrate that the proposed control strategy can mitigate security risks of IES after N − 1 contingencies.

Coordinated operation of integrated electricity and district heating system (IEDHS) has great potential to enhance the flexibility of the power system to cope with the wind power curtailment. This study proposes an interval optimal scheduling algorithm for IEDHSs, considering the dynamic characteristics of the heating network. The model of the district heating system with dynamic characteristics including transmission delay and heat losses, is established in detail, and the uncertainties of both wind power and electricity and heating loads are described with interval numbers. Then an interval optimal scheduling model of the IEDHS is formulated to minimise the IEDHS operation cost. The impacts of the transmission delay and heat losses of the heating network on the scheduling of the IEDHS are analysed. Case studies are performed on the PJM 5-bus electricity system with a 6-node district heating system and IEEE 39-bus electricity system with a 12-node district heating system to evaluate the effectiveness of the proposed model. The results demonstrate that the dynamic characteristics of the heating network can integrate more wind power and enlarge the width of the cost interval.

Aiming to achieve the flexible operation of distribution network (ADN), an energy management strategy of ADN with integrated distributed wind power and smart buildings is proposed in this study. First, based on the thermal storage characteristics of buildings, the resistor–capacitor model is used to develop an energy consumption prediction model of smart buildings. Second, the smart buildings are modelled as flexible resources of ADN. A multi-objective ADN model is further formulated based on analytic hierarchy process (AHP) by comprehensively considering constraints of power grid and smart buildings. Then, the unified model of ADN is transformed by second-order conic relaxation to guarantee the global optimal solution. Finally, the scheduling results for the unified model of ADN under different control methods for heating ventilation and air-conditioning (HVAC) systems are compared in the winter heating scenario. In addition, the impact of the demand response capability for smart buildings on the economic and secure operation of the ADN is further analysed. Numerical studies demonstrate that the proposed method can increase the penetration of local wind power, reduce peak-valley load difference and network loss of the grid while ensuring the comfort temperature of customers, and further achieve the flexible operation of ADN.

The application of boron in fuel-rich solid propellant is limited by its long combustion time, ignition delay and low combustion efficiency. To overcome these problems, B/Nitrocellulose (NC)/Fe particles were fabricated by electrospraying. The reactivity and combustion behavior of B/NC/Fe particles were characterized by TG/DSC, oxygen bomb calorimeter and laser ignition. Compared with pure boron, lower oxidation temperature was observed in the thermal analysis of prepared particles. Meanwhile, the prepared particles released more heat and ignite more readily during the combustion. Furthermore, B/NC/Fe particles were added into Hydroxyl-terminated polybutadiene (HTPB)/Ammonium perchlorate (AP) propellants as additives to test their combustion performance. The burning rate of propellants in air and closed bomb were both improved by 8.20% and 117%, respectively. Overall, B/NC/Fe particles not only show the superior reaction performance, but also contribute to the tunable burning rate of propellants.

This paper proposes an adjustable robust power dispatch (ARPD) model under low-carbon economy to accommodate the uncertainty of wind power with combined wind-storage system (CWSS) and carbon capture power plants (CCPPs). First, battery energy storage (BES), which is considered as adjustable under worst-case scenarios, is introduced to wind power plants to form a combined wind storage system structure to improve operation flexibility. Then, a dispatching model of CCPPs is presented and analysed. The operating characteristics of CCPPs make it possible to control net power outputs and carbon emissions independently and flexibly. The integration of BES and carbon capture and storage (CCS) improves the operation flexibility of both wind power plants and conventional power plants. The objective function of the proposed model under interval-uncertain wind power is to minimize the total cost under the worst wind power output case and the context of low-carbon economy. The proposed model is implemented on the modified PJM 5-bus system with one wind farm and the IEEE-118 bus system with five wind farms. Numerical results demonstrate that the proposed model is effective at reducing carbon emissions and wind power curtailment, and verify that as the number of CCPPs increases, the overall costs come down.

To provide a theoretical basis for improving the thermal safety of spent acid, its decomposition behaviour was studied. A mixed solution of n-propyl nitrate (NPN) and nitric acid was analysed using isoperibolic experiments and differential scanning calorimetry. An accurate kinetic model based on the analysis was proposed to describe the decomposition of the spent acid by applying an isoconversional method and nonlinear regression to the DSC data. We found that the thermal decomposition in the mixed solution involved two competing reactions: the decomposition of NPN in the nitric acid solution and the decomposition of nitric acid. The decomposition of the mixed solution followed a 0.9082th Prout–Tompkins equation with autocatalysis, with a differential kinetic mechanism function of f(α) = (1 − α)0.9082·α0.1291. Lastly, the thermal safety evaluation and thermodynamic parameters were studied. The self-accelerating decomposition temperature and the critical temperature of thermal runway were 338.70 K and 346.76 K, respectively, in an approximately closed system, and 347.25 K and 355.73 K, respectively, in an open system. These results show that the spent acid was more hazardous than the NPN and nitric acid solution. In addition, the thermal stability of the mixed solution in an open system was higher than that of the mixed solution in an approximately closed system.

In order to understand better thermal safety of the nitrolysis solution from the nitration of hexamethylenetetramine, its decomposition behavior was studied. The nitrolysis solution prepared was analyzed using isoperibolic experiments, differential scanning calorimetry (DSC), differential thermal analysis-thermogravimetry, and fourier transform infrared spectroscopy. An accurate kinetic model was proposed to describe the decomposition in the nitrolysis solution by applying an ordinary differential equation fitting method to isothermal DSC data and non-isothermal DTA data. We found that the thermal decomposition in the mixed solution is attributed to pyrolysis of organic nitrates in acid solution. The process is coupled with rapid reaction rate, great heat release, and huge amounts of reddish-brown gas produced. The decomposition in the nitrolysis solution follows a 1.162th Prout-Tompkins equation with autocatalysis, with a differential kinetic mechanism function of f(α) = (1 − α)1.162·α0.4189. The thermal safety evaluation and safety parameters were finally studied based on four criteria and isoperibolic experiment data. The thermal safety margin is less than 164.57 K and the bursting point of pyrolysis is 338.43 K at 5 min of delay time. These results show that the nitrolysis solution is hazardous and the thermal sensitivity is higher than that of 2-ethylhexyl nitrate and hexogen.

Transient stability assessment (TSA) of power system based on enhanced projection twin support vector machine (SVM) is proposed. The high dimensional binomial optimization problem based on traditional SVM for TSA was transformed into two low dimensional binomial optimization problems, and the improved optimization method of particle swarm was used to optimize the parameters of projection twin SVM to improve the TSA accuracy. Firstly, an initial sample set composed of system features and projection energy function features is constructed to obtain the optimal feature collection through feature compression. Then, the efficiency of transient stability evaluation is improved by solving two low-dimensional binomial optimizations after dimensionality reduction, and the optimal projection axes of the transient stability state are obtained, such as stable and unstable classes. At the same time, improved particle swarm optimization is used for parameter selection to improve accuracy. Finally, the validity and accuracy are verified by the IEEE-39 node system.

Power-to-gas (P2G) facilities and natural gas fired power units provide flexibility to integrated electricity and natural gas systems (IENGS) for wind power accommodation and ramp deployment. This paper proposes a stochastic coordinated scheduling model for IENGS considering ramping costs with P2G storage and wind power. The operation model of natural gas system with P2G is presented, and the benefits of P2G integration are analyzed. To address the uncertainties of wind power and energy loads, multiple representative scenarios are generated. The flexible ramping requirements and costs are incorporated and analyzed, and flexible ramp can be provided by P2G in this work. The coordinated scheduling model for IENGS is formulated as a two-stage stochastic programming problem, in which day-ahead scheduling for electricity systems is modeled in the first stage model and scheduling of natural gas systems is carried out in the second stage model. Numerical case studies on a modified PJM 5-bus electricity system with a 7-node natural gas system and the IEEE 118-bus system with a 20-node Belgian natural gas system verify that P2G can help accommodate wind power, provide additional flexible ramping capacities, and reduce the gas supply from gas suppliers and gas load shedding.

In traditional integrated electricity and district heating systems, the inflexible operation of combined heat and power units leads to a large amount of wind power curtailments during winter. The thermal inertia of aggregated buildings can provide additional operational flexibility to promote wind power accommodation. In this study, a day-ahead scheduling model for integrated electricity and district heating system considering the thermal inertia of buildings is proposed. In this work, the operation model of the district heating network under constant mass flow and variable temperature operation strategy is presented, and the aggregated model of buildings based on the detailed thermal model of buildings is established. Then, the scheduling framework is analysed and the day-ahead scheduling model is formulated as quadratic programming problem to minimise the operation cost of integrated electricity and district heating system. The validity of the proposed model is verified by the case studies performed on a 6-bus power system with a 6-node heating system and IEEE 39-bus electricity system with a 16-node heating system. The results demonstrate that the thermal inertia of buildings can provide additional operational flexibility and effectively help reduce wind power curtailment and operation costs.

During the actual operation of micro grid, the power fluctuation of tie line caused by the Load power fluctuation of renewable energy or load prediction deviation needs to be controlled in real time to ensure the economy of micro grid operation. This paper aims at a power system model based on the framework of "distribution network - industrial park micro grid - enterprise load user", and studies a power control method of micro grid tie line based on user reward scheme. Firstly, the objective economic function of the micro-grid in the park is established, and then the optimal reward scheme for internal load users of the micro-grid in the park is derived according to two typical load response models. The power fluctuation can be stabilized by encouraging load users to respond to demand, thus the internal load users can benefit while reducing their own economic losses and the day-ahead scheduling results of tie lines can be effectively tracked. Taking a production industrial park as an example, the effectiveness of the proposed optimization model and algorithm is verified by an example analysis.

Reducing the hygroscopicity and thermal decomposition temperature of ammonium perchlorate is of great significance for its application in solid rocket propellants. In this work, an in situ chemical precipitation reaction of stearic acid with copper acetate or cobalt acetate was adopted to prepare hydrophobic coatings of AP. The prepared samples were characterized by scanning electron microscopy, infrared spectroscopy, X-ray diffraction, contact angle measurement, differential thermal analysis and thermogravimetric analysis. The results show that a compact and uniform hydrophobic layer of copper stearate (CuSt2) or cobalt stearate (CoSt2) is successfully coated on the surface of AP particles. Compared with the raw AP, the contact angles of AP/CuSt2 and AP/CoSt2 with a coating content of 10 wt% are increased by 136.2° and 133.6°, respectively. Meanwhile, their moisture contents after being placed at 79 wt% relative humidity for 66 h are only 7.42% and 7.68% of the raw AP case. In addition, the high-temperature decomposition peak temperatures of AP/CuSt2 and AP/CoSt2 at the optimum coating content (6 wt%) are lowered by 113.3 °C and 75.8 °C, respectively, revealing that the hydrophobic coatings have high catalytic activity in the decomposition of AP. This research provides a promising strategy for reducing the hygroscopicity and simultaneously improving the thermal performance of AP, and this is also a reference for the modification of other materials.

Energetic thin films have been widely used in the field of microscale energy-demanding systems. In this work, a novel energetic metal–organic frameworks [Cd5(Mtta)9]n film is fabricated on the copper substrate through an in situ synthesis method for the first time. A 5-methyl tetrazole/copper complex is deposited to modify the copper substrate through an electrochemical-assisted strategy. The [Cd5(Mtta)9]n film is then integrated on the surface of modified copper substrate by an in situ reaction of acetonitrile with sodium azide and cadmium nitrate. The morphological, structural and compositional information of the [Cd5(Mtta)9]n film is characterized by field emission scanning electron microscopy, X-ray diffraction and infrared spectrum. The thermostabilities of the [Cd5(Mtta)9]n film are studied by differential scanning calorimetry and thermogravimetry. In addition, the preliminary laser ignition test is conducted by using pulsed Nd:YAG laser. The results show that the as-prepared [Cd5(Mtta)9]n film possesses a rod-like topography. The flame height and ignition duration of [Cd5(Mtta)9]n film reach 9.0 mm and 220 µs, respectively, revealing that [Cd5(Mtta)9]n film is an excellent ignition material. This research opens up a new avenue for the preparation of novel energetic films, which provides potential applications on microelectromechanical systems to achieve functional nanoenergetics-on-a-chip.

Aiming to utilize the flexibility of smart buildings for flexible operation of active distribution network, a combined modeling and optimal scheduling method for the active distribution network with integrated smart buildings is proposed in this paper. Based on the heat storage characteristics of a building, the energy consumption prediction model of the building considering different heating zones with different orientations is developed using the Resistor-Capacitor thermal network model. Then, different optimal control methods of the heating, ventilation and air-conditioning system in the building are developed. The energy consumption management of the heating, ventilation and air-conditioning system is achieved by adjusting the room temperature within the suitable temperature comfort range. In order to further consider the impact of the integration of smart buildings on the economic and security operation of the active distribution network, the optimal scheduling method of the active distribution network with integrated smart buildings is developed considering the load factor of the aggregation of the smart buildings. Finally, the optimal scheduling results of the aggregation of the smart buildings under different heating, ventilation and air-conditioning control methods in the winter heating scenario are analyzed. In addition, based on the branch flow model, the optimal power flow model of active distribution network with on-load tap changer is constructed by piecewise linearization and second-order cone relaxation to achieve flexible and optimal operation of the active distribution network. Thus, the impacts of the optimal schedules of the aggregation of smart buildings on the economic and security operation of the active distribution network are further evaluated. Numerical studies demonstrate that the proposed optimal scheduling method can make full use of the demand response potential of the smart buildings and further contribute to the operating costs reduction of the smart buildings. Meanwhile, the optimization of the active distribution network with the load factor of the aggregation of buildings can reduce the power loss and increase the minimum voltage magnitude of the active distribution network utilizing the flexibility of the smart buildings.

The integration of large-scale wind power brings challenges to the operation of integrated energy systems (IES). In this paper, a day-ahead scheduling model for IES with wind power and multi-type energy storage is proposed in a scenario-based stochastic programming framework. The structure of the IES consists of electricity, natural gas, and heating networks which are all included in the model. Operational constraints for IES incorporating multi-type energy storage devices are also considered. The constraints of the electricity network, natural gas network and heating network are formulated, and nonlinear constraints are linearized. The calculation method for the correlation of wind speed between wind farms based on historical data is proposed. Uncertainties of correlated wind power were represented by creating multiple representative scenarios with different probabilities, and this was done using the Latin hypercube sampling (LHS) method. The stochastic scheduling model is formulated as a mixed integer linear programming (MILP) problem with the objective function of minimizing the total expected operation cost. Numerical results on a modified PJM 5-bus electricity system with a seven-node natural gas system and a six-node heating system validate the proposed model. The results demonstrate that multi-type energy storage devices can help reduce wind power curtailments and improve the operational flexibility of IES.

Green energy is driving the evolutions of energy industry and carbon emission is becoming an important concern. Considering the increasing couplings among various energy sectors, this paper investigates multi-period optimal energy flow and energy pricing in carbon-emission embedded integrated energy systems, including electricity, natural gas, and district heating networks. Firstly, an optimal scheduling model of integrated energy systems was proposed in this paper. The models of DC power flow, natural gas pipeline flow and heating network energy flow are presented and linearized for the optimization problem. Natural gas-fired generators and combined heat and power (CHP) units are modeled as coupling components of electricity-gas and electricity-heating networks. Then, based on the optimal scheduling model, the locational marginal prices (LMP) for electricity, natural gas and heating network are determined. Moreover, the carbon emission caused by energy production has been taken into account in the optimal scheduling and energy pricing process. Finally, case studies on a combined network consisting of IEEE 39-bus system, Belgium 20-node natural gas system and 6-node heating system demonstrate the effectiveness of the proposed model and the impacts of carbon emission on system scheduling and LMP.

Intermolecular energetic materials (IEMs) have become the focus of attention due to their excellent performance in combustion, ignition, energy release and sensitivity. In this paper, two IEMs were successfully designed and prepared by the self-assembly of 5-aminotetrazole (5-ATZ) and transition metal ions on the surface of ammonium perchlorate (AP) particles. The scanning electron microscope, infrared spectroscopy, powder X-ray diffraction, thermogravimetric analyses, and differential thermal analyses were employed to characterize the prepared samples. The results show that the surface of AP particles is completely covered by a uniform and dense M(5-ATZ)4Cl2 (M = Cu, Co) self-assembled layer. Thermal analyses show that the high-temperature decomposition peak temperatures of AP/Cu(5-ATZ)4Cl2 (10 wt%) and AP/Co(5-ATZ)4Cl2 (10 wt%) are decreased by 93.3 °C and 127.1 °C compared to the case of pure AP, while the heat releases of them are increased by 539 J∙g−1 and 580 J∙g−1. In addition, the burning rates of AP/Cu(5-ATZ)4Cl2/Al (10 wt%) and AP/Co(5-ATZ)4Cl2/Al (10 wt%) are increased by 6.8 mm∙s−1 and 7.8 mm∙s−1 compared with the case of AP/Al. Moreover, the hygroscopicity tests show that the prepared samples do not agglomerate aftter being placed in the atmosphere with a relative humidity of 79% for 66 h. This research shows considerable application prospects in improving the combustion and energy performance of solid propellants, it is also a reference for the design and preparation of other novel IEMs.

Ammonium perchlorate is an important oxidizer in solid rocket propellants, underwater explosives and pyrotechnics. The crystal quality of ammonium perchlorate has an important impact on its application in these fields. In this work, a dropwise cooling crystallization method was proposed to crystallize ammonium perchlorate in gas–liquid two-phase suspension systems. Compared with the typical cooling crystallization method, dropwise cooling crystallization is favorable to control supersaturation and then control the shape and size of ammonium perchlorate crystals. The prepared samples were characterized by scanning electron microscopy, optical microscopy, laser particle distribution measurement, thermogravimetric analysis and powder X-ray diffraction. It is clearly shown that the as-prepared ammonium perchlorate crystals exhibit a high-index faced polyhedron morphology with a uniform shape and a narrow particle size distribution, the size of which can be controlled within the range of 13.64 μm to 50.33 μm. In addition, the gas-phase medium plays a good role in mixing, and it is found that higher gas-phase medium flow rates and higher feed rates of concentrated ammonium perchlorate solution result in a smaller average size of ammonium perchlorate crystals. Moreover, the crystallization method proposed in this study also has a good effect on the crystallization of sodium nitrate and ammonium chloride. This research provides a reference for the crystallization of other crystalline materials in crystal engineering.

The integration of high-penetration distributed generators (DGs) with smart inverters and the emerging power electronics technology of soft open points provide increased controllability and flexibility to the operation of active distribution networks. Existing works on distributed energy storage planning have not fully considered the coordinated operation of these new power electronic devices with distributed energy storage systems, leading to less economic investment decisions. This paper proposes an optimal planning model of distributed energy storage systems in active distribution networks incorporating soft open points and reactive power capability of DGs. The reactive power capability of DG inverters and on load tap changers are considered in the Volt/VAR control. Moreover, soft open points are modeled to provide flexible active and reactive power control on the associated feeders. Hourly network reconfiguration is conducted to optimize the power flow by changing the network topology. A mixed-integer second-order cone programming model is formulated to optimally determine the locations and energy/power capacities of distributed energy storage systems. Finally, the effectiveness of the proposed model is validated on a modified IEEE 33-node distribution network. Considering soft open points, DG reactive power capability, and network reconfiguration, the results demonstrate the optimal distributed energy storage systems planning obtained by the proposed model achieves better economic solution.

Global warming caused by excessive CO2 emissions has make it urgent to develop low-carbon economy. The carbon capture system is effective to reduce the carbon footprint of coal-fired power plants. Meanwhile, using more renewable energies such as wind power will require less generations from traditional power plants and hence limit the overall carbon emission. However, the wind power is intermittent by nature and hence may fluctuate and present a stochastic feature. In order to effectively reduce the overall carbon emission, a stochastic day-ahead scheduling optimization model with wind power integration incorporating carbon capture power plant (CCPP) and coupon-based demand response (CDR) is proposed in this work. Firstly, the formulation of CDR and the operating mechanism of CCPPs are clarified. Then the flexible operation mechanism aiming at reducing wind power curtailment and CO2 emissions is analyzed. The random forecasting errors of the day-ahead hourly wind power output and loads are considered. Monte Carlo method is applied to simulate stochastic scenarios and a scenario reduction method is applied to ease the computational burden. Simulation results with on the PJM 5-bus system and the IEEE 118-bus system demonstrate the effectiveness of the proposed method in carbon emission reduction and wind curtailments decrease.

Aiming to further achieve the flexible operation of the active distribution network by utilizing heating, ventilation and air-conditioning systems and electric vehicles of office buildings, a hierarchical scheduling method of active distribution network considering flexible loads in office buildings is proposed in this paper. In stage I, based on the heat storage property of office buildings and the behavioral property of office workers, an energy optimal scheduling model of the office building incorporating heating, ventilation and air-conditioning systems and electric vehicles is established. In stage II, an optimal scheduling method of the active distribution network considering on-load tap changer and flexible loads (i.e., heating, ventilation and air-conditioning systems and electric vehicles) in office buildings is developed by using the second-order conic relaxation and piecewise linearization. Finally, the scheduling results for the heating, ventilation and air-conditioning systems and electric vehicles in office buildings under different cases are compared in a winter scenario, and the security and economy of the active distribution network considering flexible loads in office buildings are further analyzed. Numerical studies demonstrate that the proposed method can contribute to the office building cost reduction and the load factor increment while guaranteeing the heating and the transportation demands of office workers. It can further improve the security and economic operation of the distribution network.

Evaluation of available transfer capability (ATC) is a complicated process involving the determination of the total transfer capability (TTC) and the existing transfer commitments (ETC). Considering the uncertain renewable generation such as wind power will bring more challenge to this process. Previously the uncertainty of wind power is considered either in the ED model or the following TTC calculation, but not included in two process simultaneously. Therefore, the ATC output may not be accurate since the uncertainty impact two problem simultaneously. To consider the uncertainty and correlation of wind power in both ISO’s ED and ATC evaluation, this paper proposes a bi-level optimization framework in which the ATC evaluation is formulated as the upper level problem and the ED is the lower level. The bi-level model is first converted to a mathematic program with equilibrium constraints (MPEC) by recasting the lower level problem as its Karush-Kuhn-Tucker (KKT) optimality conditions, and then transformed to a mixed-integer linear programming (MILP) problem which can be solved by existing optimization tools. Case studies on the PJM 5-bus and IEEE 118-bus systems are presented to verify the proposed methodology and the impacts of correlation of wind power on ATC are analysed.

Nanometal fuels have a great potential in energetic field for their high reactivity and their low ignition temperature. However, their high surface energy makes them aggregate. In this work, the electrospinning was employed to fabricate the nanoboron/nitrocellulose nanofibers expecting to enhance the reactivity of nanoboron. The results of SEM and TEM show that nanoboron is well dispersed in the nitrocellulose fiber matrix. TG–DSC confirms that the nanoboron in the fibers reacted more drastically than pure nanoboron and nanoboron/nitrocellulose powders resulted from the thermal analysis. Combustion propagation velocity shows that the presence of nanoboron in nitrocellulose fibers causes the faster and more intensive burning than the mechanical mixture of nanoboron and nitrocellulose.

This work proposes a measurement-based voltage stability index, namely wide-area measurement-based voltage stability sensitivity based on L index. The sensitivities of L index with respect to nodal reactive power (Q) and real power (P) injections are first derived. The derived L-Q and L-P sensitivities analyze the impact of nodal injection to nodal voltage stability and can help determine the reactive power compensation and emergency load shedding amount for voltage stability control. To improve the computational efficiency, a simplified L index, L′, along with its sensitivities with respect to nodal reactive and real power injection (L′-Q and L′-P sensitivities) are derived which makes the proposed approach suitable for the practical large-scale systems. Moreover, a control strategy for voltage stability is proposed based on the L-Q, L-P, L′-Q, and L′-P sensitivities. The proposed sensitivities and control strategy are tested on the New England 39 bus system and the IEEE 118 system. Test results on both systems verify the proposed sensitivities and the control strategy by demonstrating their accuracy and robustness in voltage stability assessment and control. In conclusion, the proposed measurement-based sensitivities can be applied to voltage stability assessment and control by using the wide-area measurements.

Worldwide natural gas consumption has increased significantly, especially for power generation in electricity systems with the gas-to-power (G2P) process of natural gas fired units. Supply for both natural gas and electricity systems should be dispatched economically and simultaneously due to their firm interconnection. This paper proposes a security-constrained bi-level economic dispatch (ED) model for integrated natural gas and electricity systems considering wind power and power-to-gas (P2G) process. The upper level is formulated as an ED optimization model for electricity system, while the lower level is an optimal allocation problem for natural gas system. Natural gas system is modeled in detail. In addition, the security constraints and coupling constraints for the integrated energy systems are considered. The objective function is to minimize the total production cost of electricity and natural gas. The lower model is converted and added into the upper model as Karush-Kuhn-Tucher (KKT) optimality conditions, thus the bi-level optimization model is transformed into a mix-integer linear programming (MILP) formulation. Numerical case studies on the PJM-5bus system integrated with a seven-node gas system and IEEE 118-bus system integrated with a modified Belgian high-calorific gas system demonstrate the effectiveness of the proposed bi-level ED model.

With the increasing installed capacity of wind power and the interdependencies among multiple energy sectors, optimal operation of integrated energy systems (IES) with combined cooling, heating and power (CCHP) is becoming more important. This paper proposes an optimal dispatch strategy for IES with CCHP and wind power. Natural gas system is modeled and its security constraints are integrated into the optimal dispatch model. The gas shift factor (GSFgas) matrix for natural gas system is derived to quantify the impact of gas supply and load at each node on the gas flow through the pipelines so that the pipeline flow equation is linearized. The objective function of the optimization model is to minimize the total operation cost of IES. Then the model is transformed into mixed integer linear programming (MILP) formulation to improve the computation efficiency. Numerical case studies conducted demonstrate the lower operation cost of the proposed model facilitating wind power integration.

Voltage security region (VSR) is a powerful tool for monitoring the voltage security in bulk power grids with high penetration of renewables. It can prevent cascading failures in wind power integration areas caused by serious over or low voltage problems. The bottlenecks of VSR for practical applications are computational efficiency and accuracy. To bridge these gaps, a general optimization model for tracking voltage security region boundary (VSRB) in bulk power grids is developed in this paper in accordance with the topological characteristics of the VSRB. First, the initial VSRB point on the VSRB is explored with the traditional OPF by using the base case parameters as initial values. Then, the rest VSRB points on the VSRB are tracked one after another with the proposed optimization model by using the parameters of the tracked VSRB point as the initial values to explore its adjacent VSRB point. The proposed approach can significantly improve the computational efficiency of the VSRB tracking over the existing algorithms, and case studies in the WECC 9-bus and the Polish 2736-bus test systems demonstrate the high accuracy and efficiency of the proposed approach on exploring the VSRB.

Penicillin fermentation dreg (PFD) is a solid waste discharged by pharmaceutical enterprises in the fermentation production process. Due to the residual antibiotic of PFD, the risk of antibiotic resistance bacteria (ARB) generation should be considered in the disposal process. High-throughput quantitative PCR (HT-qPCR) and 16S rRNA gene sequencing were performed to investigate the effect of PFD on the dynamics of antibiotic resistance genes (ARGs) and bacterial community during a lab-scale soil experiment. After the application of PFD, the bacterial number and diversity showed an obvious decrease in the initial days. The abundances of Streptomyces and Bacillus, which are the most widespread predicted source phyla of ARGs, increased remarkably from 4.42% to 2.59%–22.97% and 21.35%. The increase of ARGs was observed during the PFD application and the ARGs carried by PFD itself contributed to the initiation of soil ARGs. The results of redundancy analysis (RDA) show that the shift in bacterial community induced by variation of penicillin content is the primary driver shaping ARGs compositions.

This paper proposes a bi-level optimal energy storage system (ESS) siting and sizing algorithm to mitigate the voltage deviation in distribution networks. In the upper level, a multi-objective optimization model, which minimizes the ESS investment cost, line losses, power purchasing cost, and voltage deviation, is presented. Then analytic hierarchy process (AHP) is introduced to transform the multi-objective model into a single-objective model that can be easily solved. In the lower level, the ESS operation strategy is optimized to smooth the load curve and lower the power purchasing cost. The proposed methodology is tested on the IEEE 33 bus system to evaluate its performance. Simulation results demonstrate that the proposed approach can improve the voltage quality, maximize the network economy, and reduce the real power loss without changing the distribution network topology.