Joint Control of Manufacturing and Onsite Microgrid System Via Novel Neural-Network Integrated Reinforcement Learning Algorithms

Microgrid is a promising technology of distributed energy supply system, which consists of storage devices, generation capacities including renewable sources, and controllable loads. It has been widely investigated and applied for residential and commercial end-use customers as well as critical facilities. In this paper, we propose a joint state-based dynamic control model on microgrids and manufacturing systems where optimal controls for both sides are implemented to coordinate the energy demand and supply so that the overall production cost can be minimized considering the constraint of production target. Markov Decision Process (MDP) is used to formulate the decision-making procedure. The main computing challenge to solve the formulated MDP lies in the co-existence of both discrete and continuous parts of the high-dimensional state/action space that are intertwined with constraints. A novel reinforcement learning algorithm that leverages both Temporal Difference (TD) and Deterministic Policy Gradient (DPG) algorithms is proposed to address the computation challenge. Experiments for a manufacturing system with an onsite microgrid system with renewable sources have been implemented to justify the effectiveness of the proposed method

Joint Manufacturing and Onsite Microgrid System Control using Markov Decision Process and Neural Network Integrated Reinforcement Learning

Onsite microgrid generation systems with renewable sources are considered a promising complementary energy supply system for manufacturing plant, especially when outage occurs during which the energy supplied from the grid is not available. Compared to the widely recognized benefits in terms of the resilience improvement when it is used as a backup energy system, the operation along with the electricity grid to support the manufacturing operations in non-emergent mode has been less investigated. In this paper, we propose a joint dynamic decision-making model for the optimal control for both manufacturing system and onsite generation system. Markov Decision Process (MDP) is used to formulate the decision-making model. A neural network integrated reinforcement learning algorithm is proposed to approximately estimate the value function given policy of MDP. A case study based on a manufacturing system as well as a typical onsite microgrid generation system is conducted to validate the proposed MDP model as well as the solution strategy

Simulation of solute transport in 3d porous media using random walk particle tracking method

Random walk particle tracking (RWPT) method provides a computationally effective way to characterize solute transport process in porous media. In this work, an object-oriented scientific software platform OpenGeoSys (OGS) was adopted for the simulation and visualization of the complex behavior of particles. Finite element method is used for the calculation of the velocity field which is necessary for the determination of the displacement of the particles through space. The RWPT method has been used in the simulation of the hydraulic process, diffusion and dispersion as it is proved to be well suitedfor such studies. In this work, efforts were taken to search for the solutionto simulate the retardation and decay processe in order to investigate the effects that appear in the contaminant plume evolution. Expressions for the effective coefficients governing the solute transport are derived for retardation model, based on a two-rate sorption-desorption approach. The RWPT model was first verified by a benchmark test of solute transport in a one-dimensional homogeneous media to analysis the accuracy of the method with comparison to the analytical solution. The analysis was the next ended to applications witht hree-dimensional homogeneous aquifer. This method can be used as a tool to elicit and discern the detailed structure of evolving contaminant plumes

Research on Flow Characteristics of Electronically Controlled Injection Device Developed for High-Power Natural Gas Engines

Accurate fuel supply is a key factor that influences the performance of high-power natural gas engines. The premixed and single-point natural gas supply system is the most commonly used method to ensure a large fuel supply but one of its shortcomings is the inaccuracy of the fuel supply. A new type of natural gas injection device with fungiform configuration and electronically controlled actuator was developed to achieve high efficiency and stable operation in high-power natural gas engines. Firstly, a computational fluid dynamics (CFD) model of the injection device was created. Based on this model, the key structure parameters that have a significant influence on the outlet flow were confirmed. A particle swarm optimization (PSO) model was developed to identify the optimal outflow structure. Then, a flow function for precise flow supply control was constructed based on a response surface model, according to the flow rates of the device under different control parameters. Finally, a flow-characteristic test bench and a high-power engine prototype were developed to verify the simulation and optimization results. The results indicate that the optimized outflow structure shows low pressure loss and a large flow rate, improving injection efficiency by 10.37% and mass flow by 11.78% under 0.4 Mpa pressure difference. More importantly, the cycle fuel supply could be controlled accurately for each cylinder owing to the developed flow function. Consequently, compared with the original engine using a single-point natural gas supply system, the cylinder performance imbalance was improved by 37.47%

A Framework of Integrating Manufacturing Plants in Smart Grid Operation: Manufacturing Flexible Load Identification

In the deregulated electricity markets run by Independent System Operator (ISO), a two-settlement (day-ahead and real-time) process is typically used to determine the electricity price to the end-use customers at different buses. In the day-ahead settlement, the demand is predicted at each bus based on the previous consumption behavior of the consumers and thus, Locational Marginal Price (LMP) can be determined and shared to the consumers. A significant gap is usually observed between the planned and real-time demands due to the uncertainties of the weather (temperature, wind-speed etc.), the intensity of business, and everyday activities. Therefore, a large price variation may occur in the real-time market and the dispatching plan needs to be adjusted to respond to the variation. To reduce the gap between the day-ahead and real-time dispatching plans, a modified framework, i.e., a three-settlement process considering the integration of the manufacturing plants into the existing two-settlement process is proposed in this study. The manufacturing end-use customers report the flexibility of their loads to the ISO so that the ISO can update the day-ahead price through an updated dispatching plan that utilizes the feedback of the load flexibility from the manufacturers. A mathematical model is developed to identify the flexible and non-flexible loads of the manufacturers. Particle Swarm Optimization (PSO) is used to solve this mathematical model and a case study is conducted to illustrate the effectiveness of the model

Capacity Constrained Influence Maximization in Social Networks

Influence maximization (IM) aims to identify a small number of influential individuals to maximize the information spread and finds applications in various fields. It was first introduced in the context of viral marketing, where a company pays a few influencers to promote the product. However, apart from the cost factor, the capacity of individuals to consume content poses challenges for implementing IM in real-world scenarios. For example, players on online gaming platforms can only interact with a limited number of friends. In addition, we observe that in these scenarios, (i) the initial adopters of promotion are likely to be the friends of influencers rather than the influencers themselves, and (ii) existing IM solutions produce sub-par results with high computational demands. Motivated by these observations, we propose a new IM variant called capacity constrained influence maximization (CIM), which aims to select a limited number of influential friends for each initial adopter such that the promotion can reach more users. To solve CIM effectively, we design two greedy algorithms, MG-Greedy and RR-Greedy, ensuring the $1/2$-approximation ratio. To improve the efficiency, we devise the scalable implementation named RR-OPIM+ with $(1/2-\epsilon)$-approximation and near-linear running time. We extensively evaluate the performance of 9 approaches on 6 real-world networks, and our solutions outperform all competitors in terms of result quality and running time. Additionally, we deploy RR-OPIM+ to online game scenarios, which improves the baseline considerably.Comment: The technical report of the paper entitled 'Capacity Constrained Influence Maximization in Social Networks' in SIGKDD'2

Characteristics and sources of water-soluble organic aerosol in a heavily polluted environment in Northern China

Water-soluble organic aerosol (WSOA) in fine particles (PM2.5) collected during wintertime in a polluted city (Handan) in Northern China was characterized using a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (AMS). Through comparing with real-time measurements from a collocated Aerosol Chemical Speciation Monitor (ACSM), we determined that WSOA on average accounts for 29% of total organic aerosol (OA) mass and correlates tightly with secondary organic aerosol (SOA; Pearson's r = 0.95). The mass spectra of WSOA closely resemble those of ambient SOA, but also show obvious influences from coal combustion and biomass burning. Positive matrix factorization (PMF) analysis of the WSOA mass spectra resolved a water-soluble coal combustion OA (WS-CCOA; O/C = 0.17), a water-soluble biomass burning OA (WS-BBOA; O/C = 0.32), and a water-soluble oxygenated OA (WS-OOA; O/C = 0.89), which account for 10.3%, 29.3% and 60.4% of the total WSOA mass, respectively. The water-solubility of the OA factors was estimated by comparing the offline AMS analysis results with the ambient ACSM measurements. OOA has the highest water-solubility of 49%, consistent with increased hygroscopicity of oxidized organics induced by atmospheric aging processes. In contrast, CCOA is the leastwater soluble, containing 17% WS-CCOA. The distinct characteristics of WSOA from different sources extend our knowledge of the complex aerosol chemistry in the polluted atmosphere of Northern China and the water-solubility analysis may help us to understand better aerosol hygroscopicity and its effects on radiative forcing in this region. (C) 2020 Published by Elsevier B.V.Peer reviewe