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Regulations will soon require seaports to provide Cold Ironing (CI) for ships at berth to reduce greenhouse gas emissions. Implementing CI usually involves the design of a renewable-based seaport microgrid. In this article, we propose a methodology for optimizing size and energy management of seaport microgrids, including CI, to minimize costs and CO2emissions. The methodology is applied to design the seaport microgrid of Martinique island. Novel contributions of this work are the use of solely linear programming for optimization, the load curve determination of single container ships at berth, and the consideration of a microgrid including storage in an island seaport. The results of the case study highlight that the microgrid would significantly reduce CO2emissions (up to 1.58 times) but may lead to higher costs compared to the current design where ship diesel generators are used. However, in future years, it should be noted that the current design will become prohibited in most ports due to stricter environmental regulations. Results also provide a comprehensive overview of three microgrid designs, each representing a distinct compromise between economic and environmental considerations while satisfying the power demand of the ships at berth and of the quayside loads. In particular, the least carbon-emitting solution maximizes the PV surface area at 10,600 m2. The storage capacity is set at 9.5 MWh. These components represent 7 and 2% of the life-cycle CO2emissions respectively. The remaining CO2emissions are due to the electricity imported from the main grid of Martinique. Even though the proposed methodology has been applied to a specific case study in this work, it is generic and transferrable to other cases and can serve as a valuable tool for port authorities.

Purpose This paper aims to present two mathematical models to solve the Energy Management problem of a building microgrid (MG). In particular, it proposes a deterministic mixed integer linear programming (MILP) and non-linear programming (NLP) formulations. This paper focuses on the modelling process and the optimization performances for both approaches regarding optimal operation of near-zero energy buildings connected to an electric MG with a 24-h time horizon.Design/methodology/approach A general architecture of a MG is detailed, involving energy storage systems, distributed generation and a thermal reduced model of the grid-connected building. A continuous non-linear model is detailed along with linearizations for the mixed-integer liner formulation. Multi-physic, non-linear and non-convex phenomena are detailed, such as ventilation and air quality models.Findings Results show that both approaches are relevant for solving the energy management problem of the building MG.Originality/value Introduction and modelling of the thermal loads within the MG. The resulting linear program handles the mutli-objective trade-off between discomfort and the cost of use taking into account air quality criterion. Linearization and modelling of the ventilation system behaviour, which is generally non-linear and non-convex equality constraints, involving air quality model, heat transfer and ventilation power. Comparison of both MILP and NLP methods on a general use case provides a solution that can be interpreted for implementation. [ABSTRACT FROM AUTHOR]

Purpose – Multiphysical models are often useful for the design of electrical devices such as electrical machines. In this way, the modelling of thermal, magnetic and electrical phenomena by using an equivalent circuit approach is often used in sizing problems. The coupling of such models with other models is difficult to take into account, partly because it adds complexity to the process. The purpose of this paper is to propose an automatic modelling of thermal and magnetic aspects from an equivalent circuit approach, with its computation of gradients, using selectivity on the variables. Then, it discusses the coupling of various physical models, for the sizing by optimization algorithms. Sensibility analyses are discussed and the multiphysical approach is applied on a permanent magnet synchronous machine. Design/methodology/approach – The paper allows one to describe thermal and magnetic models by equivalent circuits. Magnetic aspects are represented by reluctance networks and thermal aspects by thermal equivalent circuits. From circuit modelling and analytical equations, models are generated, coupled and translated into computational codes (Java, C), including the computation of their Jacobians. To do so, model generators are used: CADES, Reluctool, Thermotool. The paper illustrates the modelling and automatic programming aspects with Thermotool. The generated codes are directly available for optimization algorithms. Then, the formulation of the coupling with other models is studied in the case of a multiphysical sizing by optimization of the Toyota PRIUS electrical motor. Findings – A main specificity of the approach is the ability to easily deal with the selectivity of the inputs and outputs of the generated model according to the problem specifications, thus reducing drastically the size of the Jacobian matrix and the computational complexity. Another specificity is the coupling of the models using analytical equations, possibly implicit equations. Research limitations/implications – At the present time, the multiphysical modelling is considered only for static phenomena. However, this limit is not important for numerous sizing applications. Originality/value – The analytical approach with the selectivity gives fast models, well adapted for optimization. The use of model generators allows robust programming of the models and their Jacobians. The automatic calculation of the gradients allows the use of determinist algorithms, such as sequential quadratic programming, well adapted to deal with numerous constraints. [ABSTRACT FROM AUTHOR]