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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.

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.

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.

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.

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.

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.

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.

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.

An optimal scheduling model for a hybrid energy microgrid considering the building based virtual energy storage system (VSS) is developed in this paper. The VSS model is developed by utilizing the building thermal equilibrium equation taking the heat storage characteristics of building into consideration. Firstly, mathematical models of various energy systems and VSS in the hybrid energy microgrid are developed. Then, an optimal scheduling model is developed to minimize the operation costs of the microgrid. Numerical studies demonstrate that the proposed optimal scheduling model can provide the microgrid with an effective and economical scheduling scheme and reduce the operation costs.

This paper proposes a security-constrained economic dispatch (ED) model for integrated natural gas and electricity systems. Natural gas system is modeled and its security constraints are integrated into the ED model. The Gas Shift Factor (GSF gas ) matrix is defined to reflect the impact of gas supply and load of each node on pipeline flow. The objective function for ED model is to minimize power system operating costs considering coal-fired generating units operating costs and natural gas-fired generating units (NGFGU) operating costs respectively. Numerical case studies with the IEEE 30-bus system integrated with a seven-node gas system demonstrate the effectiveness of the proposed ED model.

An economic dispatch (ED) model is proposed in this study for accommodating high penetrations of wind power with the integration of battery energy storage (BES) in power systems. In the proposed ED model, a wind-storage combined system (WSCS) model is studied to collectively mitigate the output fluctuations and improve the wind power utilisation. In addition, the proposed concept of composite operating costs include the unit operation cost, environmental cost, reserve cost, compensation cost for wind power curtailment, and energy loss cost of ES. With the minimisation of the composite operating costs as the objective function of the proposed ED model with WSCS, a modified bacterial foraging algorithm (MBFA) is proposed to solve the optimisation problem based on a combination of the bacterial foraging algorithm (BFA) and the Particle Swarm Optimisation (PSO). Furthermore, the case study on the IEEE 30-bus system has been used to demonstrate the feasibility and effectiveness of the proposed ED model and the performance improvement of the MBFA algorithm over BFA or PSO alone. The comparison between different scenarios shows that the integration of ES in the proposed model effectively improves the wind power utilisation and the system efficiency.

The interconnection and coupling of integrated energy systems (IES) including electricity system, natural gas system and district heating system become increasingly tight. It brings opportunities for improving energy consumption efficiency as well as challenges on security interactions. Thus, the concepts of the IES security region (SR), which considers all the essential operational constraints of IES (including voltage magnitude constraint, thermal constraint, gas pressure constraint, gas transmission constraint, and temperature constraint), are discussed in this paper. These concepts stem from the ideas of power system security regions and are expanded to the multi-dimensional IES involving the limits and parameters of electricity, natural gas, and district heating networks. A set of optimization models are developed to explore the SR boundaries (SRBs) in the IES. Also, a piecewise approximation is proposed to represent the SRB of IES in a multi-dimensional space with a minimum set of hyperplanes. After that, the potential implementations of SR for security assessment and optimal control in the IES are presented and analyzed. Numerical results from the IES 30-20-2 × 6 system demonstrates the feasibility and effectiveness of the proposed method and verifies that it can significantly enhance the situation awareness ability of the IES.

Summary In wind-integrated power systems, the stochastic and random wind power brings more uncertainties to power system operation. To address multiple uncertain factors including wind power forecast error, generator failure, and load forecast error, a conditional value-at-risk (CVaR)-based optimal spinning reserve determination method for wind-integrated power system is proposed in this paper with consideration of both reliability and economy of system operation under a certain level of risk. The CVaR model of spinning reserve considering different uncertain factors is built and then incorporated into the coordinated scheduling model of power generation and spinning reserve in wind integrated power system. The optimization model is solved by mix integer linear programming, and the CVaR-based spinning reserve allocation and the power output of each unit are optimally determined at a certain confidence level. The proposed method can provide a trade-off between the economy and reliability of system operation with respect to the confidence level of risk. Simulation results on 10-unit system and 100-unit system demonstrate the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

This paper proposes a stochastic day-ahead scheduling framework for integrated energy systems (IES) considering uncertain wind power generation and multienergy loads. The structure and mode...

In the traditional scheduling of integrated electricity and district heating systems (IEDHS) with high penetrations of wind power, large amounts of wind power curtailments can still occur. Exploring the underlying flexibilities within the existing infrastructure can be beneficial to the further integration of wind power. The thermal inertia of district heating network and aggregated buildings can provide such flexibility. This paper proposes a collaborative scheduling model of integrated electricity and district heating systems considering the thermal inertia of district heating network and aggregated buildings and a flexibility assessment method of the integrated electricity and district heating systems. With the detailed thermal model of the aggregated buildings and the transmission time delay characteristics of heating network pipelines, a thermal inertia model of the district heating systems is proposed. Then, the scheduling model considering thermal inertia of district heating network and the aggregated buildings is formulated as a quadratic programming problem, the objective function of which is to minimize the operating cost of integrated electricity and district heating systems. Four scheduling cases based on whether to consider the transmission time delay characteristics of heating network pipelines or the adjustable indoor temperature of aggregated buildings are established, and a flexibility assessment method of different cases for the electricity system and coupling component is proposed. The validity of the model is verified by cases studies. Numerical results show that the proposed optimization scheduling model can make use of the heat storage capacity of district heating network and aggregated buildings, reduce operating costs, increase the flexibility of electricity system and effectively promote wind power integration.

The molecular and crystal structure of a cocrystal energetic material (ethylenediamine triethylenediamine tetraperchlorate) is determined by means of the x-ray diffraction analysis. The compound crystallizes in the orthorhombic system of the Cmc21 space group with cell dimensions a = 8.1030±0.0016 A, b = 24.725±0.005 A, and c = 10.195±0.002 A. The thermal decomposition mechanism of the title compound is studied by applying the Kissinger’s and Ozawa’s methods. Sensitivity tests reveal that the title compound has a sensitive nature.

The process of non-plasma ignition of lead styphnate by a silicon-based semiconductor bridge is studied. Discharge characteristics with plasma and non-plasma ignition are analyzed. It is demonstrated that non-plasma ignition is characterized by a low energy and can be applied in cases with fine-grain energy materials.

In this account, our recent progress in highly chemoand stereo-selective asymmetric hydrogenation of some polyfunctionalized carbonyl substrates is briefly reviewed. The furtherance of the study in this field and their applications is prospected.