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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 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.

This paper aims to consider both the greenhouse gas (GHG) emissions and behavioural response in the optimal sizing of solar photovoltaic systems (PV modules and batteries) for energy communities. The objective is to achieve a high self-sufficiency rate whilst taking into account the grid carbon intensity and the global warming potential of system components.

Operation and sizing of energy communities leads to optimization problems spanning across multiple timescales. To compute the optimisation in a reasonable time, the authors first apply a simulation periods reduction using a clustering approach, before solving a linear programming problem.

The results show that the minimum GHG emissions is achieved for self-sufficiency rates of 19% in France and 50% in Germany.

The analysis is restricted to specific residential profiles: further work will focus on exploring different types of consumption profiles.

This paper provides relevant self-sufficiency orders of magnitude for energy communities.

This paper combines various approaches in a single use case: environmental considerations, behavioural response as well as multi-year energy system sizing.