On the basis of velocity-flow-density relationship and traffic-energy consumption relationship, this paper proposes a prediction method of the spatial and temporal characteristic of electric vehicle charging load using the traffic data. By analyzing the residential travel data, a probability model was built to generate trip chains of a day, which contain destination and start time. Then vehicle transfer model was used to simulate driving vehicles on the roads and SOC could be calculated by the road condition and temperature. Drivers would charge vehicles when SOC is below the charging threshold. Finally, by using the Monte Carlo method, charging load of a real traffic model was calculated according to charging demand from all electric vehicles at different time and location during the area.

Due to the unbalanced three-phase loads, the single-phase distributed photovoltaic (PV) integration, the long feeders, and the heavy loads in a three-phase four-wire low voltage distribution network (LVDN), the voltage unbalance factor (VUF), the network loss and the voltage deviation are relatively high. Considering the uncertain fluctuation of the PV output and the load power, a robust optimal allocation of decentralized reactive power compensation (RPC) devices model for a three-phase four-wire LVDN is proposed. In this model, the uncertain variables are described as box uncertain sets, the three-phase simultaneous switching capacity and single-phase independent switching capacity of the RPC devices are taken as decision variables, and the objective is to minimize the total power loss of the LVDN under the extreme scenarios of uncertain variables. The bi-level optimization method is used to transform the robust optimization model with uncertain variables into bi-level deterministic optimization models, which could be solved alternately. The nonlinear programming solver IPOPT in the mature commercial software GAMS is adopted to solve the upper and lower deterministic optimization models to obtain a robust optimal allocation scheme of decentralized RPC devices. Finally, the simulation results for an actual LVDN show that the obtained decentralized RPC scheme can ensure that the voltage deviation and the VUF of each bus satisfied the secure operation requirement no matter how the PV output and load power changed within their own uncertain sets, and the network loss could be effectively reduced.

Energy transmission in cooling/heating and gas pipelines has time-delay, which impacts the power balance between various sources and loads in the optimal dispatch of an integrated energy campus microgrid (IECM). This paper proposed an optimal dispatch model for IECMs, which considered the time-delay of energy transmission in cooling/heating and gas pipelines. In this model, the electricity and natural gas purchase cost of the IECM was made into an objective function. Partial differential equations (PDEs) were used to describe the time-delay in pipelines, and the discrete variables that described the switching number of absorption chillers and heat exchange units were included in the constraints. The proposed model was essentially a mixed-integer nonlinear programming (MINLP) model with PDE constraints. Orthogonal collocation on finite elements (OCFE) in the two-dimensional domain was used to transform PDEs into algebraic equations (AEs). Several linearization methods, including piecewise linearization and the big M method, were used to transform the initial MINLP model into a mixed-integer linear programming (MILP) model to reduce computational complexity. OCFE was compared with the first-order finite difference method, and simulation results were used to demonstrate the accuracy and efficiency of the proposed algorithm. The impact of the time-delay of pipelines on IECM was analyzed through comparison with a steady-state model. INDEX TERMS Integrated energy campus microgrids, optimal dispatch, time-delay, orthogonal collocation on finite elements,

Lignin degradation restricts corn stover anaerobic fermentation efficiency. The vacuum negative pressure aerobic hydrolysis pretreatment of corn stover was tested, and the optimal combined pretreatment conditions were presented in this paper. Because of the physical characteristics of light weight and large specific porosity of stover, it led to the formation of a scum layer during the fermentation process and thus reduced the gas production rate. In the pretreatment design, the vacuum conditions (0.02-0.08 MPa) and dwell time (5-20 min) were selected to see the changes of volumetric weight, swelling and specific porosity of corn stover, resulting in an increase of the volumetric weight by 7.18%-28.72%, an increase of the swelling by 3.18%-58.59%, and a decrease of the specific porosity by 9.34%-38.59%, as compared with the CK group. Continuous vacuum negative pressure treatment could discharge the air inside the stover destroy the microstructure, and cause the stover to settle more easily during the aerobic hydrolysis process. The optimal aerobic hydrolysis temperature and time were determined to be 39°C and 12.65 h, respectively. With the optimal pretreatment, the corn stover anaerobic fermentation test realized a cumulative methane yield of 260.44 mL/g VS, 22.71% higher than CK group; meanwhile, the hydraulic retention time was shortened by 32.39%.

Due to the absence of historical data and the errors of measurement instruments, there may be uncertainties in the distribution parameters of the random variables describing the uncertain fluctuations of node power including renewable energy station output and load power in the combined cooling heating and power (CCHP) campus microgrid. In this paper, intervals are used to describe the uncertainties of distribution parameters of the random variables, and an interval probabilistic energy flow (IPEF) calculation model of the CCHP campus microgrid is established. Introducing the interval arithmetic (IA) into the cumulant method, an IA-based IPEF algorithm is proposed to obtain the analytical expressions of probability density function or cumulative distribution function intervals of the state variables. Moreover, affine arithmetic (AA) is introduced to address the interval extension problem in the calculation, and an AA&IA-based IPEF algorithm is proposed. By constructing the correlation transformation matrixes, the correlation among different node power is considered in the IPEF calculation. A case study on a CCHP campus microgrid demonstrates that the results of the AA&IA-based IPEF algorithm are more accurate than those of the IA-based IPEF algorithm by using the results of the double-layer Monte Carlo method as a reference. Moreover, the proposed algorithms are more efficient than the double-layer Monte Carlo method. INDEX TERMS CCHP campus micro-grid, interval probabilistic energy flow calculation, higher-order uncertainty, cumulant method, interval arithmetic, affine arithmetic, correlation NOMENCLATURE A． ACRONYMS