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This paper considers off-grid microgrids (MGs) from the Philippine archipelago and analyses their energy generation in differents aspects. Seven different energy clusters are used, representing realistic configurations and renewable energy shares. A Robust Model Predictive Control (MPC) framework is used for the energy management and coordination task of these island MGs. The MPC is based on a min./max. optimization procedure, which takes into account the whole uncertainty set. The reliability of the MG operations are analysed with respect to the different clusters; this evaluation is conducted using μ -analysis, performed with respect to the baseline model and the uncertainty set. The demand-side compliance of the MG is also investigated, with respect to stochastic behaviours of the demands and of the renewable sources (wind and solar). Numerical simulation results are presented in order to demonstrate that reliable power outlets are produced despite variation in renewables and of the demands. This paper offers a thorough analysis of simple energy system coordinated via MPC, showing how this method can indeed be used for renewable MG management, offering robustness and ensuring reliability. • A generalized model is presented for Off-Grid Microgrids with multiple carriers. • The Philippine Off-grid islands are considered, with diesel, solar, and wind energy. • A Robust Model Predictive Control scheme is proposed to manage these Microgrids. • Demand compliance and operation reliability are assessed through robustness analysis. • Numerical simulation illustrate reliability under multiple scenarios. [ABSTRACT FROM AUTHOR]

This paper thoroughly analyses energy, economic and environmental (3E) performance of using different battery (BAT) energy storage system like lead acid battery (LAB), lithium-ion battery (LIB), vanadium redox flow (VRF) battery and mechanical energy storage (MES) like flywheel and pumped hydro storage (PHS) using three different dispatch strategies (DS) such as cycle charging (CC), load following (LF) and generator order (GO), respectively. Andaman and Nicobar(A&N) Island of India have no main grid. Hybrid Optimization Model for Electric Renewable (HOMER) Pro software is used to study feasibility of power generation using various sources. Study incorporating tidal plant with wind turbine and Bio-DG in micro grid is considered first time for an Indian scenario as per authors' knowledge with application of various DS and energy storage technologies. The superiority of LIB over LAB and MES is proved in this study. Wind/Bio-DG /tidal/LIB have relatively least net present cost(NPC), cost of energy (COE) of $1.30M and $0.263, respectively among five energy storage devices using CC dispatch strategy compared to other two strategies. It is investigated that LIB uses only 250 units of batteries, which is 46% less than total utilized LAB units for catering same load. In terms of expected lifespan, energy losses, energy production and state of charge (SOC), performance of LIB are superior to LAB. SWOT and sensitivity analysis have been performed considering fuel price and wind speed. Different techno economic indicators like cost summary, cash flow, fuel summary, economics, electrical parameters, renewable fraction (RF), return of investment (ROI), lowest simple payback period, internal rate of return (IRR), pollutant emission are considered and compared with each other for LAB and LIB combinations. [ABSTRACT FROM AUTHOR]

This paper introduces the probabilistic roadmap (PRM) concept in maritime transportation for solving the ship weather routing (SWR) problem. SWR is a path finding problem that seeks for the optimum route (path) for a ship moving from an initial port to a final desired port considering the weather conditions (winds, waves, sea currents). PRM is an old effective algorithm for robot motion planning based on a systematic use of a graph-based map which represents the robot's workspace. Using this map PRM explores feasible paths around the obstacles until finding a collision-free path between the starting and goal configurations of the robot. Considering the digital map of the ship's navigation area as well as forecasting data (updated every 3 h) concerning the weather conditions, a modified PRM algorithm is presented for tackling SWR. The proposed algorithm starts by taking random samples across the ship's navigation area which follow a particular distribution. Then, it uses a local planner to construct a large undirected graph (a roadmap) with nodes all the generated sample points connected with feasible (collision-free) edges (paths). Each path is related with a weight estimated by considering the weather conditions in the corresponding sea's region. Finally, given the departure and desired arrival ports, a modified Dijkstra's algorithm is applied to the resulting graph to search for the optimal path in terms of both fuel consumption and travel time that connects the two ports in the roadmap. The proposed algorithm operates in real-time determining the best solution within less than 1 min. Simulation simulations over the Aegean archipelagos as well as the Mediterranean Sea demonstrate the efficiency of the developed algorithm. • This paper introduces the probabilistic roadmap (PRM) concept in maritime transportation. • The forecasting data updated every 3 h. • The proposed algorithm operates in real-time determining the best solution within less than 1 min. [ABSTRACT FROM AUTHOR]

• The concept of SPEV sharing is explored in the energy scheduling of clustering islands. • The space-temporal coupled characteristics of SPEV and battery packs are analyzed. • The energy scheduling and passenger scheduling constraints of SPEV are described. • An optimal energy scheduling model for clustering islands is established by considering the operational flexibility of SPEV. • Simulation results validate the effectiveness of the proposed model for the diversified operation demands of clustering islands. Pelagic clustering islands are rich sources of renewable energy. However, they are isolated by natural sea areas, making it difficult to realize the interconnectivity of energy generation and utilization among different types of clustering islands. This paper proposes the strategy of coordinated and optimal energy scheduling for pelagic clustering islands based on shared power exchanging vessels (SPEV). First, the energy interconnection model among different types of islands is analyzed based on the concept of SPEV sharing. Second, the space-temporal coupling of SPEV navigation is established by considering SPEV sharing transportation capacity and the charging-discharging state of mobile battery packs. On this basis, a clustering islands energy scheduling model is established to minimize the comprehensive operation costs of clustering islands, considering the battery pack charge–discharge constraints, SPEV navigation route constraints, and passenger traveling constraints. The established energy scheduling model fully explores the space-temporal energy transfer flexibility of SPEV considering meeting the travel demands of passengers. Finally, clustering islands in the South China Sea are considered in the case study, and the simulation results indicate that the proposed SPEV-based clustering islands energy scheduling strategy can effectively improve the renewable energy consumption rate and the operating economy of clustering islands. [ABSTRACT FROM AUTHOR]