This paper proposes an energy management system (EMS) for battery storage systems in grid-connected microgrids. The battery charging/discharging power is determined such that the overall energy consumption cost is minimized, considering the variation in grid tariff, renewable power generation and load demand. The system is modeled as an economic load dispatch optimization problem over a 24 h horizon and solved using mixed integer linear programming (MILP). This formulation, therefore, requires knowledge of the expected renewable energy power production and load demand over the next 24 h. To achieve this, a long short-term memory (LSTM) network is proposed. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the EMS that benefits from using actual generation and demand data on the day. At each hour, the LSTM predicts generation and load data for the next 24 h, the dispatch problem is then solved and the battery charging or discharging command for only the first hour is applied in real-time. Real data are then used to update the LSTM input, and the process is repeated. Simulation results show that the proposed real-time strategy outperforms the offline optimization strategy, reducing the operating cost by 3.3%.

The levellised cost of energy of wave energy converters (WECs) is not competitive with fossil fuel-powered stations yet. To improve the feasibility of wave energy, it is necessary to develop effective control strategies that maximise energy absorption in mild sea states, whilst limiting motions in high waves. Due to their model-based nature, state-of-the-art control schemes struggle to deal with model uncertainties, adapt to changes in the system dynamics with time, and provide real-time centralised control for large arrays of WECs. Here, an alternative solution is introduced to address these challenges, applying deep reinforcement learning (DRL) to the control of WECs for the first time. A DRL agent is initialised from data collected in multiple sea states under linear model predictive control in a linear simulation environment. The agent outperforms model predictive control for high wave heights and periods, but suffers close to the resonant period of the WEC. The computational cost at deployment time of DRL is also much lower by diverting the computational effort from deployment time to training. This provides confidence in the application of DRL to large arrays of WECs, enabling economies of scale. Additionally, model-free reinforcement learning can autonomously adapt to changes in the system dynamics, enabling fault-tolerant control.

In islanded ac microgrids consisting of renewable energy sources (RES), battery-based energy storage system (BESS), and loads, the BESS balances the difference between the RES power and loads by delivering/absorbing that difference. However, the state of charge and charging/discharging power of the battery should be kept within their design limits regardless of variations in the load demand or the intermittent power of the RES. In this paper, a supervisory controller-based on fuzzy logic is proposed to assure that the battery power and energy do not exceed their design limits and maintaining a stable power flow. The microgrid considered in this paper consists of a PV, battery, load and auxiliary supplementary unit. The fuzzy logic controller alters the ac-bus frequency, which is used by the local controllers of the parallel units to curtail the power generated by the PV or to supplement the power from the auxiliary unit. The proposed FLC performance is verified by simulation and experimental results.

Grid-connected microgrids comprising renewable energy, energy storage systems and local load, play a vital role in decreasing the energy consumption of fossil diesel and greenhouse gas emissions. A distribution power network connecting several microgrids can promote more potent and reliable operations to enhance the security and privacy of the power system. However, the operation control for a multi-microgrid system is a big challenge. To design a multi-microgrid power system, an intelligent multi-microgrids energy management method is proposed based on the preference-based multi-objective reinforcement learning (PMORL) techniques. The power system model can be divided into three layers: the consumer layer, the independent system operator layer, and the power grid layer. Each layer intends to maximize its benefit. The PMORL is proposed to lead to a Pareto optimal set for each object to achieve these objectives. A non-dominated solution is decided to execute a balanced plan not to favor any particular participant. The preference-based results show that the proposed method can effectively learn different preferences. The simulation outcomes confirm the performance of the PMORL and verify the viability of the proposed method.

This study is concerned with control of grid connected inverters using odd‐harmonic repetitive scheme. Owing to the inherent long convergence period of repetitive control, a feedforward loop of the fundamental component of the grid voltage is used to make sure that the inverter's output current is bounded during the convergence period. The well‐known deficiency of repetitive control in rejecting harmonics when used with varying grid frequency has been solved by using an adaptive scheme in which the switching and sampling frequencies vary with respect to the grid frequency. The main qualities of the proposed controller are its effectiveness, simplicity and straightforwardness of implementation using a digital signal processor. Simulation and practical results are presented to demonstrate the controller capabilities.

Grid operators face challenges with the increasing integration of wind energy into electric grids, necessitating uninterrupted wind power generation during outages to maintain system stability. Due to voltage dips there is a significantly impact on grid-connected doubly fed induction generators (DFIGs). Hence, integrating DFIG with grid battery storage system (GBSS) is to provide essential active and reactive power support at the point common coupling (PCC), aligning requirement of low voltage ride through (LVRT), and other services to the electric grid. The article proposes and experimentally validates a coordinated control scheme between the grid side converter (GSC) of the DFIG and the GBSS. In this dynamic adjustment of power set points within the outer control loop responds to voltage and frequency changes at the PCC. The control scheme facilitates multiple services to grid by seamless plug-and-play functionality between DFIG and GBSS converters. The proposed method, utilizing GBSS, achieves significant voltage improvement and enhanced reactive power injection, while effectively managing stator voltage, rotor-side converter current, and dc link voltage oscillations. The study incorporates hardware-in-the-loop (HIL) testing using real-time digital simulator (RTDS) with hardware setup of 2.5 kW dc motor emulating a wind turbine interconnected via power amplifier (PA). Simulation and experimental results demonstrate the effectiveness of proposed coordinated control scheme indicating its potential significance in grid conditions by withstanding voltage dips and load variations.