Energy Management and Economic Operation Optimization of Microgrid under Uncertainty

Microgrid provides an effective means to promote renewable energy utilization via deploying multiple distributed generations (DGs) with energy storage systems (ESSs), loads, control devices and protect devices, which can operate in either islanded mode or grid-connected mode. In order to coordinate the output of different DGs and realize the potential of renewable energy, energy management and economic dispatch of microgrid is needed. Both distributed energy resources (DERs) and user loads in microgrid have uncertainty characteristics; so the randomness of the wind speed and solar radiation intensity are modeled by interval mathematics and the interval output of the wind turbine and photovoltaic (PV) generation system are obtained. Then, a microgrid economic optimization model based on interval optimization method is proposed. Next, combined with the time-of-use characteristic, issue of the power exchange with the external grid has been considered. Finally, Considering the effect of ESS, this chapter discusses the impacts of uncertainty of renewable energy power and load power on optimization results, as well as the effects of the degree of load uncertainty or load fluctuation on scheduling results. The results verify the robustness and effectiveness of the proposed method in dealing with uncertainty optimization problem of microgrid

Transformer Volume Reduction: A New Analysis and Design of an SSSA Control Based 20kW High Power Density Wide Range Resonant Converter

Isolated DC/DC converters play a pivotal rolein the realm of power electronics, particularly in the contextof electric vehicle (EV) fast charging. These converters areresponsible for delivering high-voltage direct current toEVs, sourced from a 3-phase power factor correction (PFC)converter, and exhibit compatibility with both low-voltageand high-voltage vehicle batteries. However, in manyinstances, the demand for constant power charging invarious applications results in a significant portion of thetransformer volume, thereby leading to a decrease inconverter power density. This paper presents a newanalysis and design for a converter based on secondaryside semi-active (SSSA) control. This analysis providestheoretical support for transformer volume reduction andpower density increase. It employs SSSA control totransfer stored energy from the transformer to the resonantnetwork during boost operation, even when fs > fr, with theexcitation inductance participating in resonance. Based onthis analysis, the design of a 20kW 650-850V input to300-900V with 66.7A max output prototype is discussed.The objective is to achieve the highest feasible converterpower density. The designed results confirm that the2*PQ6535 (or 1*PQ6549) core can effectively serve the20kW transformer, resulting in an ultra-high power densityof 14.36 kW/L (235 W/in3)

Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion Laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework.Comment: Accepted by IEEE Transactions on Intelligent Vehicle

Improved adaptive gray wolf genetic algorithm for photovoltaic intelligent edge terminal optimal configuration

Photovoltaic (PV) intelligent edge terminals (IETs) integrate data acquisition, processing, storage and upload functions for intelligent operations of PV power stations. However, the cost of installing a PV IET at one PV station is relatively high. In order to achieve the goal of multiple distributed PV stations sharing one PV IET on the premise of ensuring reliability, the paper proposes a method for the optimal configuration of PV IETs. First of all, considering the economy and reliability of optimizing configuration of PV IET, a two-layer optimization model is established. After that, to solve the nonlinearity of the proposed model, an improved adaptive genetic algorithm and gray wolf optimization (IAGA-GWO) is proposed. Finally, through two application cases of PV IETs, it is proved that the optimized configuration method in this paper can reduce the cost under the premise of ensuring the reliability

Enhancing Localization of Mobile Robots in Distributed Sensor Environments for Reliable Proximity Service Applications

Mobile robots can effectively coordinate information among sensor nodes in a distributed physical proximity. Accurately locating the mobile robots in such a distributed scenario is an essential requirement, such that the mobile robots can be instructed to coordinate with the appropriate sensor nodes. Packet loss is one of the prevailing issues on such wireless sensor network-based mobile robot localization applications. The packet loss might result from node failure, data transmission delay, and communication channel instability, which could significantly affect the transmission quality of the wireless signals. Such issues affect the localization accuracy of the mobile robot applications to an overwhelming margin, causing localization failures. To this end, this paper proposes an improved Unscented Kalman Filter-based localization algorithm to reduce the impacts of packet loss in the localization process. Rather than ignoring the missing measurements caused by packet loss, the proposed algorithm exploits the calculated measurement errors to estimate and compensate for the missing measurements. Some simulation experiments are conducted by subjecting the proposed algorithm with various packet loss rates, to evaluate its localization accuracy. The simulations demonstrate that the average localization error of the robot is 0.39 m when the packet loss rate is less than 90%, and the average running time of each iteration is 0.295 ms. The achieved results show that the proposed algorithm exhibits significant tolerance to packet loss while locating mobile robots in real-time, to achieve reliable localization accuracy and outperforms the existing UKF algorithm

Guest editorial : Dynamic analysis, control, and situation awareness of power systems with high penetrations of power electronic converters

In recent decades, global power grids have evolved with a rapid and extensive development of power electronic converters (PEC), including renewable energy systems (RES), high-voltage DC (HVDC) transmission, flexible AC transmission system (FACTS), energy storages, and microgrids. The distinct characteristics of power electronic devices traditional synchronous generators, especially their rapid control speed, wide-band performance and lack of inertia response and spinning reserve, are altering grid dynamics, and inducing new stability challenges. Continuation of such trends could further exacerbate the risk to the stability of power grids because of factors such as low inertias, lack of spinning reserve to quickly nullify active power mismatch between demand and supply. Therefore, scientific investigations on novel dynamic modelling and stability analysis methods, data-driven monitoring and situation awareness on grid inertia-power-frequency evolution, grid dynamic frequency forecast methodologies in consideration of novel PEC control schemes, and advanced PEC grid integration control schemes to minimise frequency management risks become increasingly crucial for the secured operations of power systems with high PEC penetrations. In this Special Issue, namely ‘Dynamic Analysis, Control, and Situation Awareness of Power Systems with High Penetrations of Power Electronic Converters’, we have presented eight original papers of sufficient quality and innovation. The 10 eventually accepted papers can be clustered into three two categories, namely novel control design, stability and fault analysis

Steady-State Power Quality Synthetic Evaluation Based on the Triangular Fuzzy BW Method and Interval VIKOR Method

With the increasing consumption of fossil fuels, renewable sources power generation has attracted more and more attention. However, with the integration of renewable energy and a large number of non-linear loads in power systems, several power quality problems are attracting the attention of researchers. At present, only national standards for an individual power quality index have been set in China. When evaluating power quality in practice, the individual standard cannot reflect a comprehensive level of power quality. In this paper, a synthetic evaluation method for steady-state power quality is proposed. Firstly, the traditional BW (best-worst) method is improved based on the triangular fuzzy number, to obtain the interval weight of each evaluation index. Then, the interval VIKOR method is used to evaluate the steady-state power quality monitoring data, and the final evaluation results are obtained. The validity of the proposed method is verified by the experimental data from the dynamic simulation laboratory of Tianjin University