In this article, a robust machine-learning based computational framework that couples multi-layer neural network (MLNN) proxies and multi-objective particle swarm optimizer (MOPSO) to design wateralternative-CO2 injection (CO2-WAG) projects is presented. The proposed optimization protocol considers various objective functions including oil recovery and CO2 storage volume. Expert MLNN systems are trained and employed as surrogate models of the high-fidelity compositional simulator in the optimization workflow. A large volume of blind testing applications is employed to confirm the validities of the proxies. When multiple objective functions are considered, two approaches are employed to treat the objectives: the weighted sum method and Pareto-front-based scheme. A field scale implementation to Morrow-B formation at Farnsworth Unit (FWU) to optimize the tertiary recovery strategy is discussed. In this work, investigations will focus on comparing the optimum solution found by the aggregative objective function and the solution repository covered by the Pareto front, which considers the physical and operational constraints and reduces uncertainties involved by the multi-objective optimization process. Necessary trade-offs need to be decided using the solution repository to balance the project economics and CO2 storage amount.
High penetration of intermittent renewable energy creates challenges to system integration. Large solar production during the day shifts peak grid demand to evening hours. These along with random nature of EV charging require distribution scale testbed for thorough analysis. In this paper a larger testbed system that is developed and deployed at the University of California, Riverside (UCR) is described. The technical challenges to implement this testbed and make it operational are discussed. A multiobjective problem is formulated and strategies based on that are developed for the implementation of three microgrids. Operational results and environmental benefits are presented including a zero net energy building (ZNE), battery energy storage, peak shaving to help the local utility on their historic highest demand day.
The understanding of wind turbine wake interactions in large wind farms contributes to control power losses and turbulence increases, which is crucial to optimize the design of a wind farm. The wake effect in complexterrain wind farms is much more complicated, and the related problems are still not investigated in depth. This study tries to fill in this research gap from the experimental aspect. This paper based on the wind field measurement in a typical complex-terrain wind farm in north China. The wind turbines are built in mountainous positions and the maximum height difference between wind turbines is 171.3m. A vertical-wind-mast-type lidar and a threedimensional-scanning-wind lidar were used to measure wind turbine wakes. The multiple wake effect downstream of four aligned wind turbines are investigated. The numerical experimental data and results are demonstrated in this paper. Huge experimental difficulties exist in deciding proper lidar scan strategies and adopting effective integration of measured data from various remote sensing platforms. This study also summarizes the difficulties and gives out the experience from the measurements, which is a guidance for the future measurements in the complex-terrain wind farms.
In recent years, as a multifunctional application of photovoltaic technologies, building-integrated photovoltaic (BIPV) glazing is used to generate power while natural lighting is provided as part of building façade. Unlike those PV windows made by crystalline silicon solar cells, the semi-transparent cadmium telluride (CdTe) photovoltaic (STPV) windows can admit natural daylight with a certain degree of transmittance without any shading. Therefore, it can provide better visual comfort to occupants. Adopting STPV windows will also affect the overall building energy consumption due to the low solar heat gain coefficient (SHGC). The thermal feature of common STPV windows can be beneficial for reducing the cooling load in the summer. However, when adopting the STPV windows in the heating-dominated regions, it will increase the heating load as most of the solar heat gain will be blocked by the solar cells in the PV windows. To improve the thermal insulation performance of STPV glazing, a novel vacuum photovoltaic insulated glass unit (VPV IGU) is proposed. This paper investigated the overall energy performance of a typical office in Harbin mounted with the innovative vacuum PV glazing systems. Two different configurations of the vacuum PV glazing were compared by the simulation work conducted by EnergyPlus. The results show that the first configuration, which the vacuum glazing is the internal layer, has better thermal performance from April to October, while the second configuration, the one with the vacuum glazing as an external layer, has superior thermal performance in winter as solar heat gain through PV glazing can contribute to indoor heating. The combination of STPV glazing and vacuum glazing may provide a significant energy saving potential in cold regions of China
Batteries are the bottleneck technology of electric vehicles (EVs), which hosts complex and hardly observable internal chemical reactions. This paper presents a big data-driven battery management method utilizing the deep learning algorithm, with the ability to work stably under dynamic conditions and whole battery life cycle. First, a Deep Belief Network-Extreme Learning Machine (DBN-ELM) algorithm-based battery model is established to extract the deep structure features of battery data, and in which the rain-flow cycle counting algorithm is used to reflect the battery degradation phenomenon. Next, to improve real-time performance of Battery Management System (BMS), a conjunction working mode between the Cloud-based BMS (C-BMS) and BMS in vehicles (V-BMS) is proposed, and a battery State of Charge (SoC) estimation method based on the interaction between C-BMS and V-BMS is also presented. Using the battery data to verify the model effectiveness and accuracy, the error of the battery SoC estimation is within 3%.
International PV trade is an emerging but prosperous market, which contains high uncertainty and complexity. Functional trade patterns (FTPs) are the basic structural cooperation blocks that better contribute to the evolution trend of global PV trade pattern. In this paper, we aim at identifying functional trade patterns in the international photovoltaic trade with trade interpretations. Based on the trade data of PV commodities from 2007 to 2016, complex networks with countries as nodes and trade flows as links are modeled. FTPs are identified from PV trade networks, their roles on both global and local trade patterns are studied respectively, and countries roles are further measured by FTPs. We find that, 1) FTPs exist with certain structures and trade interpretation; 2) FTPs promote network scale and countries’ centrality; 3) Countries roles measured by FTPs provide fresh perspective of countries’ trade contribution. We suggest policy makers making cooperation with consideration of forming FTPs locally, and understand their countries’ roles by measuring with FTPs.
Energy monitoring system is developed for a microgrid consisting of Utility Electricity supply, DG sets, solar generation etc. Smart metering with developed data logger and software is adopted to monitor, measure, and control the electrical loads. This is used to control devices employed in HVAC and lighting systems across multiple locations in the campus considered as microgrid. Energy Management System (EMS) using SCADA is implemented at the Institute to monitor, control, and optimize the performance of the entire electrical network. The usage of the system would be for viewing the real time energy data for monitoring and control of electrical system performance like dash board, device communication status, Meter status, Line diagrams, and historical data for analyzing the past performance of the electrical system. Based on the analysis, preventive measures can be taken to avoid fault occurrence, addition of loads, phase balancing, planning for future loads etc in the network.
Global warming and water scarcity are two serious cases that the whole world is facing. The electric power system is a carbon-intensive and water-intensive department emitting and consuming a large amount of carbon dioxide and water respectively. Emerging renewable technology to the power generation system is a solving method and tendency for reducing carbon emissions and the water consumption but taking time to be realized. In this case, deploying an optimal power generation mix under the existing condition should be considered to mitigate those cases. In this paper, an optimal power generation redistribution model is utilized to minimize the total amount of national carbon emissions, and water consumption of arid regions. Case study for 30 provinces of China in 2015 is selected. Five power generation types, namely, thermal power, hydropower, nuclear power, wind power and solar power are discussed. The upper and lower boundary of provincial electricity generation with different energy resources are quantified to limit the adjustment range. The results show that the total carbon emissions can be reduced 1.3% after optimization; the water consumption in provinces like Shandong, Hebei, Tianjin, Beijing and Shanxi facing water scarcity is decreased, which can mitigate the water scarcity to some extent. The optimization results preliminarily revealed the optimization model is effective and could provide a new perspective to optimize the power generation of regions in China.
The integration of intermittent and volatile renewable energy resources requires increased flexibility badly in the operation and planning of the grid. Energy storage together with storage-like loads, which are collectively called generalized energy storage, performed well in terms of provide flexibility. This paper provides classification of various generalized energy storage in terms of different level. More importantly, key technologies of optimal configuration are proposed, including characteristic analysis, modeling, uncertainty analysis, optimization, economic analysis, and value promotion. Besides, the current research status, technical difficulties, and future research directions are involved in each part. Finally, through conclusion and prospect, helpful suggestions are put forward for the related research in the future.
With the large scale access of distributed generation to distribution networks, the increase of distributed generation permeability has brought a series of impacts on voltage, power quality, dispatching and operation of distribution networks. Optimal configuration of energy storage systems can effectively solve these issues brought by the increased penetration of distribute generation. In this study an interactive bi-level optimal energy storage planning approach has been proposed, which takes the average annual net cost optimization into consideration. In the proposed approach, the capacity configuration and the charging/discharging power of energy storage systems are carefully analyzed while life-cycle cost including investment cost, operation and maintenance cost, replacement cost, recovery value and disposal cost, as well as energy storage arbitrage income, government’s incentives and environmental benefits are synthetically deliberated. Finally, the feasibility and effectiveness of the proposed optimal configuration strategy has been simulated on a real UK distribution feeder model.