停车场选择行为影响因素研究

以小雁塔为中心周边区域的停车场为研究对象,采用问卷调查的方式对出行者停车场选择行为进行调查。运用logistic模型,对影响停车场选择行为的出行者性别、出行目的、停车费用和停车时间进行了研究分析,确定影响停车场选择行为的主要因素为停车费用和停车目的。结果表明,停车时间和停车目的对停车场的选择行为影响较为显著;出行者性别和停车费用对停车场选择的影响不显著。

The Online Assessment of Electric Distribution Network Load Capability

To improve the security and reliability of a distribution network, several issues, such as influences of operation con-strains, real-time load margin calculation, and online security level evaluation, are with great significance. In this pa-per, a mathematical model for load capability online assessment of a distribution network is established, and a repeti-tive power flow calculation algorithm is proposed to solve the problem as well. With assessment on three levels: the entire distribution network, a sub-area of the network and a load bus, the security level of current operation mode and load transfer capability during outage are thus obtained. The results can provide guidelines for prevention control, as well as restoration control. Simulation results show that the method is simple, fast and can be applied to distribution networks belonged to any voltage level while taking into account all of the operation constraints.

Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm

Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy,the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy.To improve forecasting accuracy,this paper focuses on two aspects:①proposing a novel hybrid method using Boosting algorithm and a multistep forecast approach to improve the forecasting capacity of traditional ARMA model;②calculating the existing error bounds of the proposed method.To validate the effectiveness of the novel hybrid method,one-year period of real data are used for test,which were collected from three operating wind farms in the east coast of Jiangsu Province,China.Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared.Test results show that the proposed method achieves a more accurate forecast.

Agent-based Modeling and Simulation for the Electricity Market with Residential Demand Response

Currently,critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response(RDR)programs.In typical RDR programs,residential customers react to the price or incentive-based signals,but the actions can fall behind flexible market situations.For those residential customers equipped with smart meters,they may contribute more DR loads if they can participate in DR events in a proactive way.In this paper,we propose a comprehensive market framework in which residential customers can provide proactive RDR actions in a day-ahead market(DAM).We model and evaluate the interactions between generation companies(GenCos),retailers,residential customers,and the independent system operator(ISO)via an agent-based modeling and simulation(ABMS)approach.The simulation framework contains two main procedures—the bottom-up modeling procedure and the reinforcement learning(RL)procedure.The bottom-up modeling procedure models the residential load profiles separately by household types to capture the RDR potential differences in advance so that residential customers may rationally provide automatic DR actions.Retailers and GenCos optimize their bidding strategies via the RL procedure.The modified optimization approach in this procedure can prevent the training results from falling into local optimum solutions.The ISO clears the DAM to maximize social welfare via Karush-Kuhn-Tucker(KKT)conditions.Based on realistic residential data in China,the proposed models and methods are verified and compared in a large multi-scenario test case with 30,000 residential households.Results show that proactive RDR programs and interactions between market entities may yield significant benefits for both the supply and demand sides.The models and methods in this paper may be used by utility companies,electricity retailers,market operators,and policy makers to evaluate the consequences of a proactive RDR and the interactions among multi-entities.

Control Strategies of Large-scale Residential Air Conditioning Loads Participating in Demand Response Programs

Residential air conditioning(RAC)loads have great potential to be included in demand response(DR)programs.This paper studies large-scale RAC loads participating in DR programs,such as modeling,parameters identification,DR characteristics and control strategies.First,an aggregate model of large-scale RAC loads are established based on the buildings’performance with heat storage and insulation,avoiding the calculation of a single RAC model.Then,parameters of the aggregate model are identified based on the RACs’power and outdoor temperatures.Based on the aggregate model,DR characteristics of RAC loads are analyzed,including the dynamic relationship between power,outdoor and indoor temperature,and the potential of DR combined with the users’comfort.Next,the DR control strategies adapted for large-scale RAC loads are established by adjusting the temperature set-points.The DR strategies consider users’comfort and calculate the control signals of each RAC load according to the DR power,including adjustment temperature and adjustment time,which are sent to each RAC load for execution.In the DR process,the control center does not need to obtain the users’indoor temperature,which is conducive to protecting the users’privacy.DR strategies of RAC loads when the control degree within/beyond the DR potential are both proposed,and a load recovery control strategy is also introduced.Finally,the effectiveness and accuracy of the proposed model and DR control strategies are verified by simulation results.

Optimization of Urban Multi-energy Flow Systems Considering Seasonal Peak Shaving of Natural Gas

In order to alleviate the shortage of natural gas supply in winter,relevant policies have been issued to promote the construction of gas peak-shaving and storage facilities.Largescale gas storage can transfer the supply-demand relationship of natural gas in time sequence,which has great potential in improving the economy and reliabillity of urban multi-energy flow systems.Addressing this issue,this paper proposes a mid-and long-term energy optimization method for urban multi-energy flow system that considers seasonal peak shaving of natural gas.First,the energy supply and demand features of different energy subsystems are analyzed.Then,a network model of the electricity-gas-heat multi-energy flow system is established.Considering the time-of-use electricity price mechanism and the seasonal fluctuations of the natural gas price,a mid-and long-term energy optimization model maximizing the annual economic revenue is established.The alternative direction multiplier method with Gaussian back substitution(ADMM-GBS)algorithm is used to solve the optimal dispatch model.Finally,the proposed method is verified by employing the actual data of the demonstration zone in Yangzhong City,China.The simulation results show that the proposed method is effective.