In the last few years, most of the western countries, alerted by the increasing of the populations and energy consumption with the consequent increment of fossil fuel emissions, started to promote and adopt several strategies aimed to an intelligent, clean and sustainable production and utilization of the energy, especially in the transportation, the industrial and the domestic field.
One of the most relevant issue in this context is the efficient integration of the Renewable Energy Sources(RES) and Electric Vehicle(EV) charge stations infrastructures both characterized by intermittent stochastic behavior.
The spreading of such power systems, if are not intelligently managed, would increase the risk of outages, the waste of energy, local faults and accelerate the deterioration of electric infrastructure.
Obviously all these circumstances are reflected on the cost of the dispatched kWh.
The need of an electric grid supported by an ICT infrastructure and distributed Energy Storage Systems(ESSs) able to manage such kind of energy production and assuring the power demand, the safety and stability of the electric grid is growing more and more.
Grid connected-Microgrids(MGs) are the most affordable solution for the development of smart grid infrastructures.
Grid connected MG are local electric grids able to promote the local energy consumption and to support the stability of the power grid which is connected to.
In this project it is proposed a new model based on graph theory to represent and visualize the MG energy flows. It consists in focusing how much MG energy demand and energy production must be managed in real time with respect to shiftable loads or controllable energy sources, that can be managed with a certain degree of flexibility in a given time horizon. The analysis of such model can drive the synthesis of suitable MG Energy Management Systems, based on soft Computing techniques and optimized through evolutionary algorithms, following a data driven approach.
The adoption of soft computing techniques can support the design phase and real time control strategy (i.e. MG EMS design) for this complex system affected by stochastic and non-linear dynamics, reducing the computational efforts as well. Fuzzy Logic, Neural Networks and Clustering techniques can be employed to design a robust and efficient real time decision making inference system also applying a hybridization of these (e.g. in literature are often mentioned as Adaptive Neural Fuzzy Inference System and Fuzzy K-means techniques). These systems once implemented in a specified problem can be optimized both off-line either online through a data driven approach that can rely for example on Evolutionary Algorithms such as Genetic Algorithm and Particle Swarm Optimization. These Evolutionary Algorithms can be applied on such kind of decision making inference systems in order to realize and/or efficiently tune their parameters even in case of MO optimization problems.
The EMS must be designed considering the minimization of MG operational costs without stress the connected power grid, namely reducing the power transients with the grids.
Deterministic methods like Linear Programming, Mixed Integer Linear Programming and Dynamic Programming as can be seen in literature are often used as EMS solution, although they must be supported by a robust and highly efficient prediction algorithm. Besides, these solutions are affected by high computational costs that can compromise the design of MG EMS decision making systems and their tuning can result extremely difficult in case of MO problems.
In this work such methods will be used as benchmark solution in order to build and test the proposed EMS.
Once set the EMS, the investigation of 2^nd life battery for EV FS will be considered in order to observe additional MG operational costs reductions.
The use of Neural Networks (e.g. Recurrent Neural Networks or Echo State Networks) for energy production and energy load prediction will be investigated as well in order to improve the EMS performance.
A graph model will be developed to represent and study the MG energy flows and the EMS performances, respectively.
[1] Scholer Richard, SAE International, ¿DC Charging and Standards for Plug-in Electric Vehicles¿, 2013.
[2] R. Deng, Z. Yang, M. Y. Chow and J. Chen, "A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches," in IEEE Transactions on Industrial Informatics, June 2015.
[3] S. Leonori and E. De Santis and A. Rizzi and F. M. F. Mascioli, IEEE Congress on Evolutionary Computation (CEC), ¿Multi objective optimization of a fuzzy logic controller for energy management in microgrids¿, July 2016.