• Energy Research

The measurement and model errors always exist in the sample or data analysis part of Microgrids (MGs) system, bringing oscillation and inaccuracy to the controller and energy management system (EMS). This paper proposed a Kalmanfilter (KF)-based control strategy for the battery energy storage system (BESS) in MGs with integrated renewable energy sources. The control scheme aims at minimizing the active and reactive power purchased from the main grid by regulating the output current of BESS. A detailed model of a wind-PV-batteryMG is built, and the sampling and measurement inaccuracy is considered. The control strategy is tested under rapid power supply oscillation and load changes, and the results show good performance.

The proportion of wind energy in global energy structure is growing rapidly, promoting the development of wind power forecasting (WPF) technologies to solve the uncertainty and intermittence of wind power generation. However, the nonlinear and stochastic features of wind power time series restrain the accuracy of multi-step prediction performance. A multi-step WPF (MS-WPF) approach based on a time series bi-level empirical mode decomposition (BLEMD) method and BiLSTM neural network is proposed in this paper to improve the WPF accuracy of regional wind power generators. Since the uncertainty is always generated through coupled factors from both wind and weather-to-power conversion, the linearity feature is first introduced as an aspect apart from the frequency in the proposed approach to decompose the wind power time sequence data. The proposed BLEMD introduces Pearson product-moment correlation coefficient to evaluate the linearity of time series and a linearity-based decomposition algorithm is designed accordingly. To further enhance the precision and release computation burdens, a DL-based prediction strategy, including a BiLSTM network, a CNN-BiLSTM network, and a mean weight estimation method are implemented to predict the components separately. The proposed method only relies on local data, greatly reducing the data acquisition and computation cost. The precision of the proposed MS-WPF is verified by a 2.5 kW wind turbine with horizons from 5 s to 30 s, a 1.5 MW wind turbine with horizons from 10 min to 1 h, and a 51 MW wind farm with horizons from 1 h to 6 h. The comparative experimental results with other cutting-edge methods indicated that the proposed MS-WPF has superior prediction accuracy and stable performance for multi-step prediction. Peer Reviewed

The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid hydrogen–electricity Storage System (HSS) in a residential microgrid (RMG). The HEMS dispatches an electrolyzer-fuel cell-based hydrogen energy storage (ES) unit for seasonal energy shifting and an on-site battery stack for daily energy allocation against the uncertainty from the renewable energy source (RES) and demand side. The online decision-making of the proposed HEMS is realized through two parallel fuzzy logic (FL)-based controllers which are decoupled by different operating frequencies. An original local energy estimation model (LEEM) is specifically designed for the decision process of FL controllers to comprehensively evaluate the system status and quantify the electricity price expectation for the HEMS. The proposed HEMS is independent of RES prediction or load forecasting, and gives the optimal operation for HSS in separated resolutions: the hydrogen ES unit is dispatched hourly and the battery is operated every minute. The performance of the proposed method is verified by numerical experiments fed by real-world datasets. The superiority of the HEMS in expense-saving manner is validated through comparison with PSO-based day-ahead optimization methods, fuzzy logic EMS, and rule-based online EMS.

A fuzzy logic based online energy management system (FLEMS) is designed in this paper to achieve the optimal electricity cost in a residential Microgrid (MG). The proposed FLEMS is combined by a local energy price model (LEPM) and a fuzzy-logic strategy. The LEPM will preprocess the sampling data to estimate the electricity market and local MG status. The fuzzy-logic mimics the artificial intelligent assessment to economic issues and make decision for the charging and discharging operation for energy storage system (ESS). In the FLEMS, not only electricity price and supply-demand balance, but also ESS state of charge are considered for the efficient and stable operations. The proposed method does not relay on the accurate prediction of renewable energy source and local loads. Historical experience of the system is involved by the LEPM and guides the ESS operation in the fuzzy-logic. A real-world data based household-level residential MG model is established to validate the performance of the FLEMS. A hourly-resolution-Particle swarm optimization (PSO) with perfect day-ahead prediction is implemented as the baseline to verify the superiority of the proposed method.

Generally running with frequent braking over short distances, the urban rail transit train generates great quantities of regenerative braking energy (RBE). The RBE feedback system can effectively recycle RBE and give it back to the AC grid. However, the lack of damp and inertia of generators makes conventional PWM RBE feedback system more sensitive to power fluctuations. To address this issue, a synchronverter-based RBE feedback system of urban rail transit is designed in this paper. First, the structure of the feedback system is presented. Then, the synchronverter-based control strategy with greater flexibility and higher stability is fully discussed. Furthermore, the parameter design of the system is analyzed in detail. Finally, simulation results and experimental results are provided to show the good dynamic performance of the system. Using this synchronverter-based approach, the system supplies traction power to the traction network when the train accelerates and gives the RBE back to the AC grid when the train brakes, in light of the variation of the DC bus voltage. Moreover, the system can be self-synchronized with the AC grid and make corresponding power management on the basis of changes in the voltage amplitude as well as the frequency of the grid. In this sense, the RBE feedback system becomes more flexible, effective and robust.

The application of hydrogen (H2) energy in Microgrids (MGs) is suppressed by the limited energy conversion efficiency between H2 and electricity, even though the energy density of H2 outperforms the electricity storage. This paper proposed a multi-objective energy management system (EMS) to economically operate a hybrid H2-electricity system under the flexible electricity market, considering the energy loss in not only power-to-gas (P2G) and gas-to-power (G2P) conversion but also the energy consumption of H2 pressurization. Particle swarm optimization (PSO) is deployed to perform a day-ahead schedule for the electricity storage system and H2 storage system separately. The global optimal operation is achieved by the proposed method to allocate renewable energy and shift the cheap electricity. The performance of the proposed EMS is verified through numerical experiments based on real-world data. The influence of prediction methods on the proposed MOEMS is further tested and analyzed to improve the reproducibility of the approach. The results indicate that the proposed MOEMS could effectively schedule the operation of the hybrid storage system.