With the increasing use of renewable energy resources in power systems, it is necessary to overcome the limitations of these resources in terms of the supply and demand balance in high-voltage power systems. A viable solution is employing zero net energy community microgrids, which can manage the demand locally and minimize the impact to the power grid. To this end, this paper proposes an optimal scheduling method for a zero net energy community microgrid with customer-owned energy storage systems (CES). It is assumed that the microgrid operator operates in the CES market based on a bilateral contract. The CES aggregators constitute the CES and participate in the market. The aggregated CES (ACES) is considered as a single energy storage system in the proposed scheduling method. Numerical examples are presented to demonstrate the effectiveness of the proposed method.

Recently, interest in microgrids, which are composed of distributed generation (DG), distributed storage (DS), and loads, has been growing as a potentially effective clean energy system to mitigate against climate change. The microgrid is operated in the grid-connected mode and the islanded mode according to the conditions of the upstream power grid. The role of the energy storage system (ESS) is especially important to maintain constant the frequency and voltage of an islanded microgrid. For this reason, various approaches for ESS control have been put forth. In this paper, a fuzzy PID controller is proposed to improve the frequency control performance of the ESS. This fuzzy PID controller consists of a fuzzy logic controller and a conventional PI controller, connected in series. The fuzzy logic controller has two input signals, and then the output signal of the fuzzy logic controller is the input signal of the conventional PI controller. For comparison of control performance, gains of each PI controller and fuzzy PID controller are tuned by the particle swam optimization (PSO) algorithm. In the simulation study, the control performance of the fuzzy PID was also tested under various operating conditions using the PSCAD/EMTDC simulation platform.

This paper presents an online optimal operation framework for multiple battery energy storage systems (BESSs) of a large-scale customer under time-based pricing. Many publications have been reported on optimal battery operation techniques but most of them were analyzed in a simulation environment or a specifically designed test bed. However, this paper focuses on implementing the proposed scheme into actual multiple battery storage units and investigating the performance during long-term field operation. The operation framework consists of two levels: optimal scheduling and real-time dispatch. The optimal scheduling is calculated every hour, using a model predictive control based nonlinear optimization model, to minimize the daily electricity usage cost while regulating the peak. The real-time dispatch determines final commands to multiple battery systems by monitoring the system state and checking for any violations of the operation constraints. The two-level control scheme was designed to handle uncertainty in forecast load and estimated state-of-charge levels of batteries. The operation method was applied into the energy management system supervising one lithium-polymer BESS and two lead-acid BESSs of an industrial site. Comprehensive field operation results prove the reliability and effectiveness of the optimal operation framework.

Home appliances connected to Internet-of-Things (IoT) platforms have been extensively installed in smart homes. In this context, home energy management systems (HEMSs) have emerged as a viable solution for reducing energy costs. Although several studies have analyzed the implementation of HEMSs, a majority of these studies were based on the installation of numerous sensors. Owing to the complexity and costs associated with the installation of multiple sensors, implementation of HEMSs in smart homes is challenging. This paper presents an energy management scheme for an HEMS that minimizes the energy cost in a smart home with data obtained from IoT-based appliances typically used in a smart home. Case studies were conducted to demonstrate the effectiveness of the proposed method. Prototype software (version 11) based on the proposed method for a HEMS and a test house with IoT-based appliances were also implemented in the case studies.

In recent years, battery fires have become more common owing to the increased use of lithium-ion batteries. Therefore, monitoring technology is required to detect battery anomalies because battery fires cause significant damage to systems. We used Mahalanobis distance (MD) and independent component analysis (ICA) to detect early battery faults in a real-world energy storage system (ESS). The fault types included historical data of battery overvoltage and humidity anomaly alarms generated by the system management program. These are typical preliminary symptoms of thermal runaway, the leading cause of lithium-ion battery fires. The alarms were generated by the system management program based on thresholds. If a fire occurs in an ESS, the humidity inside the ESS will increase very quickly, which means that threshold-based alarm generation methods can be risky. In addition, industrial datasets contain many outliers for various reasons, including measurement and communication errors in sensors. These outliers can lead to biased training results for models. Therefore, we used MD to remove outliers and performed fault detection based on ICA. The proposed method determines confidence limits based on statistics derived from normal samples with outliers removed, resulting in well-defined thresholds compared to existing fault detection methods. Moreover, it demonstrated the ability to detect faults earlier than the point at which alarms were generated by the system management program: 15 min earlier for battery overvoltage and 26 min earlier for humidity anomalies.