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%.

For effective integrity management of marine renewable energy systems in the dynamic and uncertain ocean environments, understanding the failure dynamics is crucial. The cost of investment in marine/offshore renewable energy infrastructures and the associated cost due to failure and loss of energy production necessitate a predictive monitoring methodology that is dynamic and adaptive. This paper presents an integrated multi‐state pure‐birth‐pure‐death Markovian‐net profit value model for the offshore turbine subsystem failure analysis and its cost‐based consequences. The integrated model captures the offshore turbine subsystem's dynamic failure states and its economic implications due to the cost of energy loss and downtime for the period under consideration. The model applies a phase‐type exponential distribution to describe the monotonic state of failure. The methodology is demonstrated with an offshore wind turbine gearbox, and it captures the dynamic state of the system and its failure mechanisms. The cumulative effect of the subelements deterioration decreases the gearbox performance by over 35% within the first 2 years of operation.

This paper presents a Short-term load forecast using Artificial Neural Network (ANN) method and applied it to Secretariat's 33KV feeder in Port Harcourt Metropolis electric power system. This predicts future load one hour in advance for 24 hours using past load readings data over the years , obtained from Port Harcourt Electricity Distribution Company (PHEDC) zonal office for the month of March and June 2015.The main stages include pre-processing of past data sets, network training and forecasting. The proposed Artificial neural network is composed of 3 layers: an input, a hidden, and an output layer consisting of six (6) and one (1) neuron in the input and output layers respectively, while that of the hidden layer vary for the different performance of the network, Levenberg-Marquardt (TRAINLM) training algorithm was used for the training process and a Mean squared error and Root mean squared error of 0.39% and 0.63% respectively were obtained when the trained neural network was tested on a week's data. This depicts a high degree of reliability of the proposed neural network to forecast load in PortHarcourt metropolis.

Grid-connected microgrids consisting of renewable energy sources, battery storage, and load require an appropriate energy management system that controls the battery operation. Traditionally, the operation of the battery is optimised using 24 h of forecasted data of load demand and renewable energy sources (RES) generation using offline optimisation techniques, where the battery actions (charge/discharge/idle) are determined before the start of the day. Reinforcement Learning (RL) has recently been suggested as an alternative to these traditional techniques due to its ability to learn optimal policy online using real data. Two approaches of RL have been suggested in the literature viz. offline and online. In offline RL, the agent learns the optimum policy using predicted generation and load data. Once convergence is achieved, battery commands are dispatched in real time. This method is similar to traditional methods because it relies on forecasted data. In online RL, on the other hand, the agent learns the optimum policy by interacting with the system in real time using real data. This paper investigates the effectiveness of both the approaches. White Gaussian noise with different standard deviations was added to real data to create synthetic predicted data to validate the method. In the first approach, the predicted data were used by an offline RL algorithm. In the second approach, the online RL algorithm interacted with real streaming data in real time, and the agent was trained using real data. When the energy costs of the two approaches were compared, it was found that the online RL provides better results than the offline approach if the difference between real and predicted data is greater than 1.6%.

The use of combined heat and power (CHP) systems has recently increased due to their high combined efficiency and low emissions. Using CHP systems in behind-the-meter applications, however, can introduce some challenges. Firstly, the CHP system must operate in load-following mode to prevent power export to the grid. Secondly, if the load drops below a predefined threshold, the engine will operate at a lower temperature and hence lower efficiency, as the fuel is only half-burnt, creating significant emissions. The aforementioned issues may be solved by combining CHP with a battery energy storage system (BESS); however, the dispatch of CHP and BESS must be optimised. Offline optimisation methods based on load prediction will not prevent power export to the grid due to prediction errors. Therefore, this paper proposes a real-time Energy Management System (EMS) using a combination of Long Short-Term Memory (LSTM) neural networks, Mixed Integer Linear Programming (MILP), and Receding Horizon (RH) control strategy. The RH control strategy is suggested to reduce the impact of prediction errors and enable real-time implementation of the EMS exploiting actual generation and demand data on the day. Simulation results show that the proposed method can prevent power export to the grid and reduce the operational cost by 8.75% compared to the offline method.