Photovoltaic microgrids are a promising solution to improve electricity access in Africa, but the investment required for photovoltaic panels and batteries often remains substantial. Integrating a diesel generator can reduce the required photovoltaic and battery sizes, lowering the overall system cost. Here, a modeling and linear optimization framework is proposed to jointly optimize the sizing and operation of microgrid components to minimize the overall life-cycle cost while satisfying users' energy needs. Compared to existing research on the topic, the main contributions lie in a more detailed modeling of the diesel generator using a piece-wise linear function and in considering multiple possible rated powers for the diesel generator. The approach is applied to a rural village in Madagascar. The results indicate that integrating a diesel generator significantly lowers the life-cycle cost (from 267 to 134 k(sic)), while meeting the same energy demand. Future work will include environmental criteria in the optimization.

Heat pumps are crucial for decarbonizing the heating sector. Finding their optimal sizing and operation is beneficial for energy communities as it allows for cost minimization while ensuring thermal comfort and grid stability. However, a significant bottleneck is that such optimization problems are complex and very time-consuming to solve, particularly because sizing and operation are optimized at the same time. To address this challenge, in this work, we consider and implement a two-step optimization method to decouple sizing and operation to reduce computing times. We also provide results for a Belgian rural low-voltage feeder and show that the implemented two-step optimization method allows to reduce computing time by a factor 5 while only producing a 4% error on the value of the objective function. Our work can benefit energy communities by assisting them in sizing and operating heat pumps within significantly reduced computing times.

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 CO2 emissions. 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 CO2 emissions (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 CO2 emissions respectively. The remaining CO2 emissions 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.

Heat pumps are a great asset for decarbonizing the heating sector, which represents 14% of the CO2 emissions in Europe. In this article, we propose a methodology to formulate and solve a mixed-integer linear optimization problem to find the optimal sizing and operation of air-to-water heat pumps while accounting for the unbalanced grid constraints. The goal is to maximize heat pump economic and environmental benefits and ease their integration into the electricity grid. Novelties of the proposed optimization method compared to the literature include considering several buildings with their own thermal characteristics, formulating a bi-criteria objective function, and detailed modelling of the heat pump and the hydraulic circuit. We solve the optimization problem for a rural feeder located in Belgium. For the considered case study, we are able to integrate 100% of heat pumps while satisfying user thermal comfort and ensuring grid operational constraints. We achieve a 3.1-fold reduction in life-cycle CO2 emissions while increasing life-cycle costs by only 18% compared to gas boilers. Our proposed framework can be helpful to distribution system operators since it allows to mitigate heat pumps impact on the grid. Moreover, it can be useful to policymakers because it quantifies heat pumps decarbonization potential.

Air-source heat pumps (ASHP) have a significant potential for decarbonizing the heating sector. In this article, we compare the environmental impacts (climate change, particulate matter formation, human toxicity, and ozone depletion) of an ASHP and a natural gas boiler (NGB). The main originality is that we perform the life-cycle analysis (LCA) of the ASHP and the NGB for 18 European countries while sizing the ASHP according to the dwelling thermal demand. We highlight that using refrigerant R290 instead of R32 decreases the ASHP impact on climate change and ozone depletion. Moreover, the building stock is found to greatly influence the potential benefits of ASHP in several countries (e.g. the Czech Republic, Greece). In recent dwellings, ASHP reduces climate change in 17 out of 18 countries, with a 54 % average reduction. However, it often increases particulate matter formation mainly due to the electricity mix, and the use of copper for ASHP manufacturing. Our results can be helpful to European policy makers since they assess in which country ASHP should be installed to yield the highest reduction of environmental impacts. Countrywide, our results can help to deploy ASHP as they indicate which dwelling type should be given priority for ASHP installation.

Microgrids are of increasing interest because they can facilitate the integration of renewable energy sources. To make the most of microgrids, optimization problems are formulated and solved to determine their optimal planning (i.e. sizing and energy management). However, these problems are complex and time-consuming to solve. In this article, we focus on a temporal decomposition based on Benders’ algorithm to reduce computing time while still obtaining the optimal solution. The temporal decomposition divides the initial problem into subproblems with a smaller time interval. The first originality of this work is the proposition of a methodology to apply this temporal decomposition to mixed-integer linear problems for the optimal planning of microgrids. The second originality is the investigation of the influence of the following relevant parameters on the computing time of the temporal decomposition based on Benders’ algorithm: decomposition period, nature of the problem, overall time horizon and number of CPUs. In addition, contrary to previous literature, our proposed method exhibits computing time reductions. They are of up to 5.6 times for the considered case studies. Our results also highlight the existence of a decomposition period that maximizes the performances. Besides, we find that the temporal decomposition is particularly efficient for mixed-integer linear problems with large time horizons and when more than 16 CPUs can be used. The proposed generic methodology and our results can notably be useful to researchers and to microgrids project holders who aim at finding the optimal sizing and operation of their microgrid within reduced computing time.

Heat pumps are an important asset to decarbonize the heating sector. To maximize their economic and environmental benefits and ease their integration into the low-voltage grid, complex optimization problems are formulated. Solving these optimization problems can require very high computing times. In this work, we study clustering methods to select representative periods for heat pumps optimal operation in order to reduce computing times. Two clustering algorithms are considered: k-means and k-medoids. A challenge is to assess the quality of those algorithms since we cannot solve the problem over the whole time horizon, due to important computing times. We propose to solve a relaxed version of the initial problem. We compute the representative days for a case study: a rural Belgian district. Results indicate that k-means and k-medoids approximate well the values of grid stability indicators. For the considered case, k-means underestimates life-cycle costs associated to the heat pumps purchase and operation by 0.9%. K-medoids overestimates these costs by 3.3%.

Photovoltaic water pumping systems (PVWPS) are a promising solution to improve domestic water access in low-income rural areas. It is challenging, however, to make them more affordable for the local communities. We develop here a comparative methodology to assess relevant features of both widely employed PVWPS architecture with water tank storage, and hardly used PVWPS architecture with a battery bank instead of tank storage. The quantitative comparison is carried out through techno-economic optimization, with the goal of minimizing the life cycle cost of PVWPS with constraints on the satisfaction of the water demand of local inhabitants and on the groundwater resource sustainability. It is aimed to support decision-makers in selecting most appropriate storage for domestic water supply projects. We applied the methodology in the rural village of Gogma, Burkina Faso. Results indicate that the life-cycle cost of an optimized PVWPS with batteries is $24.1k while it is $31.1k if a tank is used instead. Moreover, reduced impact on groundwater resources and greater modularity to adapt to evolving water demand is noted if using batteries. However, as batteries must be replaced regularly and recycled adequately, PVWPS’ financial accessibility could increase only if sustainable and efficient operation, maintenance, and recycling facilities for batteries were present or developed locally.

Nowadays, urban energy projects are becoming more complex in order to meet operational, economic and environmental challenges. This requires using decision support tools in pre-study phases to easily design and adapt the energy system model several times in order to meet stakeholders requirements. This paper presents OMEGAlpes, a linear optimization tool designed to easily generate multi-carrier energy system models. Its purpose is to assist in developing district energy projects by integrating design and operation in pre-studies phases. OMEGAlpes is open-source and written in Python. A use example is described to illustrate the modeling and solving of optimization problems in OMEGAlpes.

State-of-the-art Modelica tools for modelling and simulating multi-physical systems have reached certain maturity among the building physics community. Hence, simulation is widely used for control, sizing and performance assessment of energy systems. However, serious efficiency issues arise for large-scale models. This article proposes a practical application of co-simulation methods on detailed district energy systems. The aim of this study is to assess performance and scalability of co-simulation through functional mock-up interfaces on a detailed and multi-physical district model. In particular, we propose a comparative analysis between classical simulation and co-simulation methods and a scalability analysis on a growing number of buildings. The models have been implemented using Modelica language and the OpenIDEAS library. A decomposition approach is taken for modelling the entire system, while stochasticity in the inputs is taken into account. Results are presented for various integration scenarios, including a classical integrated simulation for reference and co-simulations involving different master-algorithms within Dymola and DACCOSIM 2017. Scenarios are compared in terms of speed-up and accuracy of principal physical quantities representing key performance indicators such as indoor temperature, current and voltage at building's connection. The analysis shows that co-simulation can run up to 90 times faster than the integrated simulation for 24 buildings, while ensuring acceptable accuracy.

PurposeThis 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/approachA 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. FindingsResults show that both approaches are relevant for solving the energy management problem of the building MG. Originality/valueIntroduction 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.

In recent years, co-simulation has become an increasingly industrial tool to simulate Cyber Physical Systems including multi-physics and control, like smart electric grids, since it allows to involve different modeling tools within the same temporal simulation. The challenge now is to integrate in a single calculation scheme very numerous and intensely inter-connected models, and to do it without any loss in model accuracy. This will avoid neglecting fine phenomena or moving away from the basic principle of equation-based modeling.

The development of complex multi-domain and multiphysic systems, such as Smart Electric Grids, have given rise to new challenges in the simulation domain.These challenges concern the capability to couple multiple domain-specific simulators, and the FMI standard is an answer to this.But they also concern the scalability and the accuracy of the simulation within an heterogenous system.We propose and implement here the concept of a Matryoshka FMU, i.e. a first of its kind FMU compliant with the version 2.0 of the FMI standard.It encapsulates DACCOSIM -our distributed and parallel master architecture -and the FMUs it controls.The Matryoshka automatically adapts its internal time steps to ensure the required accuracy while it is controlled by an external FMUcompliant simulator.We present the JavaFMI tools and the DACCOSIM middleware used in the automatic building process of such Matryoshka FMUs.This approach is then applied on a real-life Distributed Energy System scenario.Regarding the Modelica system simulated in Dymola, improvements up to 250% in terms of computational performance are achieved while preserving the simulation accuracy and enhancing its integration capability.

Hybrid electrical vehicles involve two sources of energy, usually gasoline and electricity. The energy management determines the power sharing between the internal combustion engine and the electrical machine (EM). It is highly dependent on the driving cycle (i.e. the use of the vehicle). In this context, the optimal sizing of the EM is determined by: the driving cycle, the power-train characteristics (i.e. ratios and physical limitations e.g. maximum torque available) and the energy management. The key idea of this work is to involve the driving cycle and the environment of the electrical machine in a global multi-objective optimization process taking into account an optimal energy management and an accurate model of the EM based on magnetic circuit equivalent model.

A way to improve existing hybrid vehicles is by globally optimizing the design of their components in relation to their actual use. This paper proposes a global optimal design method for power-split hybrid electric vehicles (PS-HEVs) equipped with an electric variable transmission (EVT). A genetic algorithm is used to optimize system parameters. The process includes discrete dynamic programming (DDP) for optimal energy management. The optimization focuses on minimizing fuel consumption and the number of battery cells. Pareto fronts are depicted and compared for different drive cycles, and they show the existence of a possible tradeoff between fuel consumption and battery size.

This paper presents a mathematical model for load d emand, batteries and thermal envelop of the building to the Energy Management (EM) problem of a Microgrid (MG) by means of a deterministic Mixed Integer Linear Programming (MILP) approach. T is way, the thermal envelop of the building can be seen as a load demand and thermal storage. I n the EM problem, the objective is to determine a generation and consumption policy that minimises, o ver a planning horizon, the operation cost and the thermal comfort subject to economic and technical c onstraints. We propose a detail modelling of different load demands and Li-ion batteries. To ana lyse the proposed modelling, a didactic MG and simple thermal envelop of a building is used, conne cted to the main grid, although, the proposed models could also be used in the island operation. The results indicate that the models are adequate f or the MG EM, analyses of the impacts and energies pol ices.

Purpose Multiphysical models are often useful for the design of electrical devices such as electrical machines. In this way, the modeling 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 paper proposes 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 modeling 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 SQP, well adapted to deal with numerous constraints.

Numerous researches about hybrid electrical vehicles (HEVs) deal with topologies, technologies, sizing and control. These aspects allow reducing transportation costs and environmental impacts. This study focuses on the sizing of the electrical machine (EM) of the HEV, taking into account its surroundings: the hybrid system, the driving cycle and an optimal energy management. In this study, the parallel HEV is the study case. In a classical HEV design process, a scaling factor is usually applied on an efficiency map model to fix the standard power of the EM. The efficiency and the maximum torque power are scaled using a linear dependency on the rated maximum power. However, this method has some disadvantages. This study proposes two formulations of a scaling model based on a magnetic circuit model (MCM) with one or ten parameters. Then, the MCM is involved in a multi-objective optimisation process of the HEV. This process is a global sizing process using dynamic programming as an optimal energy management. Optimal sizings of the hybrid vehicle are then proposed for various driving conditions.