Reliability Analyses of Transmission and Substation Facilities and Electricity Supply to Urban Consumers in 2007

Reliabilities of transmission and substation facilities with voltage levelof220 kVoraboveIn 2007, there were totally 356 power supply enterprises, including 28 EHV transmission and substation enterprises, and 245 power generation enterprises submitting reliability performances of electric facilities at a voltage level of 220 kV or above to the Electric Power Reliability Management Center. The reliability indices of transmission and

DEVELOPMENT OF THE ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC URBAN BUSES

A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy combining a logic threshold approach and an instantaneous optimization algorithm is proposed for the investigated PHEUB. The objective of the energy management strategy is to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing the engine fuel consumption and maintaining the battery state of charge in its operation range at all times. Under the environment of Matlab/Simulink, a computer simulation model for the PHEUB is constructed by using the model building method combining theoretical analysis and bench test data. Simulation and experiment results for China Typical Bus Driving Schedule at Urban District （CTBDS_UD） are obtained, and the results indicate that the proposed control strategy not only controls the hybrid system efficiently but also improves the fuel economy significantly.

Estimating the Spatial Variation of Electricity Consumption Anomalies and the Influencing Factors

Effective detection of abnormal electricity users and analysis of the spatial distribution and influencing factors of abnormal electricity consumption in urban areas have positive effects on the quality of electricity consumption by customers,safe operation of power grids,and sustainable development of cities.However,current abnormal electricity consumption detection models do not consider the time dependence of time-series data and rely on a large number of training samples,and no study has analyzed the spatial distribution and influencing factors of abnormal electricity consumption in urban areas.In this study,we use the Seasonal-Trend decomposition procedure based on Loess(STL)based time series decomposition and outlier detection to detect abnormal electricity consumption in the central city of Pingxiang,and analyze the relationship between spatial variation and urban functions through Geodetector.The results show that the degree of abnormal electricity consumption in urban areas is related to geographic location and has spatial heterogeneity,and the abnormal electricity users are mainly located in areas with highly mixed residential,commercial and entertainment functions in the city.The results obtained from this study can provide a reference basis and a theoretical foundation for the detection of abnormal electricity consumption by users and the arming of electricity theft devices in the power grid.

Energy Sources Driven Electricity Production: A Global Tactical and Strategical Paradigm

Energy forecasting for electricity productivity is the process of applying statistics with possible Quantum or Classical Computing with help from new innovative techniques offered by artificial intelligence to make predictions about consumption levels.This kind of computation presents corresponding utility costs in both the tactical and strategical or short term and long term.Energy forecasting models take into account historical data,trends,weather inputs,tariff structures,and occupancy schedules in the urban city due to population growth,etc.to make predictions.Additionally,energy forecasting as future paradigm is driven by electricity production demand and it is a cost-effective technique to predict future energy needs,which is a paradigm to achieve demand and supply chain equilibrium based on available energy both renewable and non-renewable sources.

Chance-constrained Coordinated Optimization for Urban Electricity and Heat Networks

Urban electricity and heat networks(UEHN)consist of the coupling and interactions between electric power systems and district heating systems,in which the geographical and functional features of integrated energy systems are demonstrated.UEHN have been expected to provide an effective way to accommodate the intermittent and unpredictable renewable energy sources,in which the application of stochastic optimization approaches to UEHN analysis is highly desired.In this paper,we propose a chance-constrained coordinated optimization approach for UEHN considering the uncertainties in electricity loads,heat loads,and photovoltaic outputs,as well as the correlations between these uncertain sources.A solution strategy,which combines the Latin Hypercube Sampling Monte Carlo Simulation(LHSMCS)approach and a heuristic algorithm,is specifically designed to deal with the proposed chance-constrained coordinated optimization.Finally,test results on an UEHN comprised of a modified IEEE 33-bus system and a 32-node district heating system at Barry Island have verified the feasibility and effectiveness of the proposed framework.

Electricity consumption patterns within cities:application of a data-driven settlement characterization method

Urban areas presently consume around 75%of global primary energy supply,which is expected to significantly increase in the future due to urban growth.Having sustainable,universal energy access is a pressing challenge for most parts of the globe.Understanding urban energy consumption patterns may help to address the challenges to urban sustainability and energy security.However,urban energy analyses are severely limited by the lack of urban energy data.Such datasets are virtually non-existent for the developing countries.As per current projections,most of the new urban growth is bound to occur in these data-starved regions.Hence,there is an urgent need of research methods for monitoring and quantifying urban energy utilization patterns.Here,we apply a data-driven approach to characterize urban settlements based on their formality,which is then used to assess intraurban urban energy consumption in Johannesburg,South Africa;Sana’a,Yemen;and Ndola,Zambia.Electricity is the fastest growing energy fuel.By analyzing the relationship between the settlement types and the corresponding nighttime light emission,a proxy of electricity consumption,we assess the differential electricity consumption patterns.Our study presents a simple and scalable solution to fill the present data void to understand intra-city electricity consumption patterns.