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In recent years, increased awareness on global warming effects led to a renewed interest in all kinds of green technologies. Among them, some attention has been devoted to hybrid-electric aircraft – aircraft where the propulsion system contains power systems driven by electricity and power systems driven by hydrocarbon-based fuel. Examples of these systems include electric motors and gas turbines, respectively. Despite the fact that several research groups have tried to design such aircraft, in a way, it can actually save fuel with respect to conventional designs, the results hardly approach the required fuel savings to justify a new design. One possible path to improve these designs is to optimize the onboard energy management, in other words, when to use fuel and when to use stored electricity during a mission. The purpose of this paper is to address the topic of energy management applied to hybrid-electric aircraft, including its relevance for the conceptual design of aircraft and present a practical example of optimal energy management.

To address this problem the dynamic programming (DP) method for optimal control problems was used and, together with an aircraft performance model, an optimal energy management was obtained for a given aircraft flying a given trajectory.

The results show how the energy onboard a hybrid fuel-battery aircraft can be optimally managed during the mission. The optimal results were compared with non-optimal result, and small differences were found. A large sensitivity of the results to the battery charging efficiency was also found.

The novelty of this work comes from the application of DP for energy management to a variable weight system which includes energy recovery via a propeller.

The use of energy storage devices helps the consumers to utilize the benefits and flexibilities brought by smart networks. One of the major energy storage solutions is using electric vehicle batteries. The purpose of this paper is to develop an optimal energy management strategy for a consumer connected to the power grid equipped with Vehicle-to-Home (V2H) power supply and renewable power generation unit (PV).

The problem of energy flow management is formulated and solved as an optimization problem using a linear programming model. The total energy cost of the consumer is optimized. The optimal values of decision variables are found using CPLEX solver.

The simulation results demonstrated that if the optimal decisions are made regarding the V2H operation and managing the produced power by solar panels then the total energy payments are significantly reduced.

The gap that the proposed model is trying to fill is the holistic determination of an optimal energy procurement portfolio by using various embedded resources in an optimal way. The contributions of this paper are in threefold as: first, the introduction of mobile storage devices with a periodical availability depending on driving schedules; second, offering a new business model for managing the generation of PV modules by considering the possibility of grid injection or self-consumption; third, considering Real Time Pricing in the suggested formulation.

To foster the grid integration of both electric vehicles (EV) and renewable generators, the purpose of this paper is to investigate the possible synergies between these players so as to jointly improve the production predictability while ensuring a green mobility. It is here achieved by the mean of a grid commitment over the overall power produced by a collaborative system which here gathers a photovoltaic (PV) plant with an EV fleet. The scope of the present contribution is to investigate the conditions to make the most of such an association, mainly regarding to the management strategies and optimal sizing, taking into account forecast errors on PV production.

To evaluate the collaboration added value, several concerns are aggregated into a primary energy criterion: the commitment compliance, the power spillage, the vehicle charging, the user mobility and the battery aging. Variations of these costs are computed over a range of EV fleet size. Moreover, the influence of the charging strategy is specifically investigated throughout the comparison of three managements: a simple rule of thumb, a perfect knowledge deterministic case and a charging strategy computed by stochastic dynamic programming. The latter is based on an original modeling of the production forecast error. This methodology is carried out to assess the collaboration added value for two operators’ points of view: a virtual power plant (VPP) and a balance responsible party (BRP).

From the perspective of a BRP, the added value of PV-EV collaboration for the energy system has been evidenced in any situation even when the charging strategy is very simple. On the other hand, for the case of a VPP operator, the coupling between the optimal sizing and the management strategy is highlighted.

A co-optimization of the sizing and the management of a PV-EV collaborative system is introduced and the influence of the management strategy on the collaboration added value has been investigated. This gave rise to the presentation and implementation of an original modeling tool of the PV production forecast error. Finally, to widen the scope of application, two different business models have been tackled and compared.

The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power isolated microgrid. The microgrid investigated combines renewable and conventional power generation.

Five bio-inspired optimization methods include an advanced proposed multi-objective particle swarm optimization (MOPSO) approach which is comparatively applied for OEM of the implemented microgrid with other bio-inspired optimization approaches via their comparative simulation results.

Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization methods. Moreover, the proposed MOPSO is successfully applied to perform 24-h OEM microgrid. The simulation results also display the merits of the real time optimization along with the arbitrary of users’ selection as to satisfy their power requirement.

This paper focuses on the OEM of a designed microgrid using a newly proposed modified MOPSO algorithm. Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization approaches.

Microgrids are inclined to use renewable energy resources within the availability limits. In conventional studies, energy interchange among microgrids was not considered because of one-directional power flows. Hence, this paper aims to study the optimal day-ahead energy scheduling of a centralized networked multi-carrier microgrid (NMCMG). The energy scheduling faces new challenges by inclusion of responsive loads, integration of renewable sources (wind and solar) and interaction of multi-carrier microgrids (MCMGs).

The optimization model is formulated as a mixed integer nonlinear programing and is solved using GAMS software. Numerical simulations are performed on a system with three MCMGs, including combined heat and power, photovoltaic arrays, wind turbines and energy storages to fulfill the required electrical and thermal load demands. In the proposed system, the MCMGs are in grid-connected mode to exchange power when required.

The proposed model is capable of minimizing the system costs by using a novel demand side management model and integrating the multiple-energy infrastructure, as well as handling the energy management of the network. Furthermore, the novel demand side management model gives more accurate optimal results. The operational performance and total cost of the NMCMG in simultaneous operation of multiple carriers has been effectively improved.

Introduction and modeling of the multiple energy demands within the MCMG. A novel time- and incentive-based demand side management, characterized by shifting techniques, is applied to reshape the load curve, as well as for preventing the excessive use of energy in peak hours. This paper analyzes the need to study how inclusion of multiple energy infrastructure integration and responsive load can impact the future distribution network costs.

The purpose of this paper is to introduce a problematic phenomenon that can occur when managing multi electrical sources systems by optimization.

The energy management problem is formulated as a linear optimisation problem. Two approaches are developed and applied to detect the possible existence of equivalents solutions. The first is based on Dulmage‐Mendelsohn (DM) decomposition. With this method the structure of the optimisation problem is analysed. The second approach is a numeric approach; the detection of equivalents solutions is made by the formulation of new optimisation problem and the objective function of this problem is to maximise the distance between two equivalents solutions.

The numeric approach is more efficient than the structural approach. In some cases, applying DM decomposition may not be sufficient to detect the risk of W effect. This is because DM decomposition does not take the value of variable's coefficient into consideration, which is important to determine the degrees of freedom in the set of variables.

Multi sources systems are widely used, especially in buildings where renewable energies have good potential application. The linear formulation of the management problem may induce an existence of equivalent command strategies. The detection approach presented in this paper shows that some solutions are better than others from an applicabability point of view. They will not exhaust rapidly the storage system. This approach can be implemented in virtual sources plant to avoid solutions with this so‐called W effect.

The purpose of this study is to examine state-of-the-art in hybrid-electric propulsion system modeling and suggest new methodologies for sizing such advanced concepts. Many entities are involved in the modelling and design of hybrid electric aircraft; however, the highly multidisciplinary nature of the problem means that most tools focus heavily on one discipline and over simplify others to keep the analysis reasonable in scope. Correctly sizing a hybrid-electric system requires knowledge of aircraft and engine performance along with a working knowledge of electrical and energy storage systems. The difficulty is compounded by the multi-timescale dynamic nature of the problem. Furthermore, the choice of energy management in a hybrid electric system presents multiple degrees of freedom, which means the aircraft sizing problem now becomes not just a root-finding exercise, but also a constrained optimization problem.

The hybrid electric vehicle sizing problem can be sub-divided into three areas: modelling methods/fidelity, energy management and optimization technique. The literature is reviewed to find desirable characteristics and features of each area. Subsequently, a new process for sizing a new hybrid electric aircraft is proposed by synthesizing techniques from model predictive control and detailed conceptual design modelling. Elements from model predictive control and concurrent optimization are combined to formulate a new structure for the optimization of the sizing and energy management of future aircraft.

While the example optimization formulation provided is specific to a hybrid electric concept, the proposed structure is general enough to be adapted to any vehicle concept which contains multiple degrees of control freedom that can be optimized continuously throughout a mission.

The proposed technique is novel in its application of model predictive control to the conceptual design phase.

In modern cloud services, resource provisioning and allocation are significant for assigning the available resources in efficient way. Resource management in cloud becomes challenging due to high energy consumption at data center (DC), virtual machine (VM) migration, high operational cost and overhead on DC.

In this paper, the authors proposed software-defined networking (SDN)-enabled cloud for resource management to reduce energy consumption in DC. SDN-cloud comprises four phases: (1) user authentication, (2) service-level agreement (SLA) constraints, (3) cloud interceder and (4) SDN-controller.

Resource management is significant for reducing power consumption in CDs that is based on scheduling, VM placement, with Quality of Service (QoS) requirements.

The main goal is to utilize the resources energy effectively for reducing power consumption in cloud environment. This method effectively increases the user service rate and reduces the unnecessary migration process.

As a result, the authors show a significant reduction in energy consumption by 20 KWh as well as over 60% power consumption in the presence of 500 VMs. In future, the authors have planned to concentrate the issues on resource failure and also SLA violation rate with respect to number of resources will be decreased.

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.

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.

Results show that both approaches are relevant for solving the energy management problem of the building MG.

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.

The purpose of this paper is to demonstrate that using advanced powertrain technologies can help outperform the state of the art in F1 and LeMans motor racing. By a careful choice and sizing of powertrain components coupled with an optimal energy management strategy, the conflicting requirements of high-performance and high-energy savings can be achieved.

Five main steps were performed. First, definition of requirements: basic performance requirements were defined based on research on the capabilities of Formula 1 race cars. Second, drive cycle generation: a drive cycle was created using these performance requirements as well as other necessary inputs such as the track layout of Circuit de la Sarthe, the drag coefficient, the tire specifications, and the mass of the vehicle. Third, selection of technology: the drive cycle was used to model the power requirements from the powertrain components of the series-hybrid topology. Fourth, lap time sensitivity analysis: the impact of certain design decisions on lap time was determined by the lap time sensitivity analysis. Fifth, modeling and optimization: the design involved building the optimal energy management strategy and comparing the performance of different powertrain component sizings.

Five different powertrain configurations were presented, and several tradeoffs between lap time and different parameters were discussed. The results showed that the fastest achievable lap time using the proposed configurations was 3 min 9 s. It was concluded that several car and component parameters have to be improved to decrease this lap time to the required 2 min 45 s, which is required to outperform F1 on LeMans.

This research shows the capabilities of advanced hybrid powertrain components and energy management strategies in motorsports, both in terms of performance and energy savings. The important factors affecting the performance of such a hybrid race car have been highlighted.