Optimal Economic Schedule for a Network of Microgrids With Hybrid Energy Storage System Using Distributed Model Predictive Control

Artículo Open Access en el sitio web el editor. Pago por publicar en abierto.In this paper, an optimal procedure for the economic schedule of a network of interconnected microgrids with hybrid energy storage system is carried out through a control algorithm based on distributed model predictive control (DMPC). The algorithm is specifically designed according to the criterion of improving the cost function of each microgrid acting as a single system through the network mode operation. The algorithm allows maximum economical benefit of the microgrids, minimizing the degradation causes of each storage system, and fulfilling the different system constraints. In order to capture both continuous/discrete dynamics and switching between different operating conditions, the plant is modeled with the framework of mixed logic dynamic. The DMPC problem is solved with the use of mixed integer linear programming using a piecewise formulation, in order to linearize a mixed integer quadratic programming problem.Ministerio de Economía, Industria y Competitivadad DPI2016-78338-RComisión Europea 0076-AGERAR-6-

Binary Search Algorithm for Mixed Integer Optimization: Application to energy management in a microgrid

This paper presents a binary search algorithm to deal with binary variables in mixed integer optimization problems. One example of this kind of problem is the optimal operation of hydrogen storage and energy sale and purchase into a microgrids context. In this work was studied a system composed by a microgrid that has a connection with the external electrical network and a charging station for electric cars. The system modeling was carried out by the Energy Hubs methodology. The proposed algorithm transforms the MIQP (Mixed Integer Quadratic Program) problem into a QP (Quadratic Program) that is easier to solve. In this way the overall control task is carried out the electricity purchase and sale to the power grid, maximizes the use of renewable energy sources, manages the use of energy storages and supplies the charge of the parked vehicles.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-RUniversidad de Sevilla CNPq401126/2014-5Universidad de Sevilla CNPq303702/2011-

Energy Management Strategies in hydrogen Smart-Grids: A laboratory experience

As microgrids gain reputation, nations are making decisions towards a new energetic paradigm where the centralized model is being abandoned in favor of a more sophisticated, reliable, environmentally friendly and decentralized one. The implementation of such sophisticated systems drive to find out new control techniques that make the system “smart”, bringing the Smart-Grid concept. This paper studies the role of Energy Management Strategies (EMSs) in hydrogen microgrids, covering both theoretical and experimental sides. It first describes the commissioning of a new labscale microgrid system to analyze a set of different EMS performance in real-life. This is followed by a summary of the approach used towards obtaining dynamic models to study and refine the different controllers implemented within this work. Then the implementation and validation of the developed EMSs using the new labscale microgrid are discussed. Experimental results are shown comparing the response of simple strategies (hysteresis band) against complex on-line optimization techniques, such as the Model Predictive Control. The difference between both approaches is extensively discussed. Results evidence how different control techniques can greatly influence the plant performance and finally we provide a set of guidelines for designing and operating Smart Grids.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-

An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes

In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and uncertainty. In this framework, microgrids constitute an effective solution to deal with the coordination and operation of these distributed energy resources. This paper proposes a Genetic Algorithm (GA) to address the combined problem of Unit Commitment (UC) and Economic Dispatch (ED). With this end, a model of a microgrid is introduced together with all the control variables and physical constraints. To optimally operate the microgrid, three operation modes are introduced. The first two attend to optimize economical and environmental factors, while the last operation mode considers the errors induced by the uncertainties in the demand forecasting. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm was applied to an example scenario to illustrate its performance. The achieved simulation results demonstrate the validity of the proposed approach.Ministerio de Ciencia, Innovación y Universidades TEC2016-80242-PMinisterio de Economía y Competitividad PCIN-2015-043Universidad de Sevilla Programa propio de I+D+

Holistic approach for microgrid planning and operation for e-mobility infrastructure under consideration of multi-type uncertainties

Integrating renewable energys ources in sectors such as electricity, heat, and transportation must be structured in an economic, technological, and emission- efficient manner to address global environmental issues.Microgrids appear to be the solution for large-scale renewable energy integration in these sectors.The microgrid components must be optimally planned and operated to prevent high costs, technical issues, and emissions. Existing approaches for optimal microgrid planning and operation in the literature do not include a solution for e-mobility infrastructure. As a consequence, a compact e-mobility infrastructure metho- dology is provided.The development of e-mobility infrastructure has as sociated uncertainties (short and long-term). As a result, a new stochastic method re- ferred to as IGDM-DRO is proposed in this dissertation.The proposed method provides a risk-averse strategy for microgrid planning and operation by including long-term and short-term uncertainty related to e-mobility.The multi-cut ben- der decomposition is applied for IGDM-DRO to prevent the suggested method’s intractability.Finally, the deterministic and stochastic methodologies are com bined in an ovelholistic approach for microgrid design and operation in terms of cost and robustness.The proposed method ist ested on a new settlement area in Magdeburg, Germany, under three different EV development scenarios (nega- tive, trend, andpositive).The share for the number of electric vehicles reached 31 percent of conventional vehicles by the end of the planned horizon. As a result, the microgrid’s overall cost has been increased by 2.3 to 2.9 percent per electric vehicle.Three public electric vehicle charging stations will be required in the investigated settlement are a intrend 2031.The investigated settlement area will require a total cost of 127,029 € in the trend scenario.To achieve full robustness against long-term uncertainties,the cost of the microgrid needs to be increased by 80 percent

Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG(Lab)(2)) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications

MILITARY VEHICLE OPTIMIZATION AND CONTROL

It is remarkable that there are no deployed military hybrid vehicles since battlefield fuel is approximately 100 times the cost of civilian fuel. In the commercial marketplace, where fuel prices are much lower, electric hybrid vehicles have become increasingly common due to their increased fuel efficiency and the associated operating cost benefit. An absence of military hybrid vehicles is not due to a lack of investment in research and development, but rather because applying hybrid vehicle architectures to a military application has unique challenges. These challenges include inconsistent duty cycles for propulsion requirements and the absence of methods to look at vehicle energy in a holistic sense. This dissertation provides a remedy to these challenges by presenting a method to quantify the benefits of a military hybrid vehicle by regarding that vehicle as a microgrid. This innovative concept allowed for the creation of an expandable multiple input numerical optimization method that was implemented for both real-time control and system design optimization. An example of each of these implementations was presented. Optimization in the loop using this new method was compared to a traditional closed loop control system and proved to be more fuel efficient. System design optimization using this method successfully illustrated battery size optimization by iterating through various electric duty cycles. By utilizing this new multiple input numerical optimization method, a holistic view of duty cycle synthesis, vehicle energy use, and vehicle design optimization can be achieved

Operational Cost Minimization of Grid Connected Microgrid System Using Fire Fly Technique

oai:oai.jieee.a2zjournals.com:article/1Present time, green energy sources interfacing to the utility grid by utilizing microgrid system is very vital to satisfy the ever increasing energy demand. Optimal operation of the microgrid system improved the generation from the distributed renewable energy sources at the lowest operational cost. Large amount of constraints and variables are associated with the microgrid economic operation problem. Thus, this problem is very complex and required efficient technique for handing the problem adequately. This research utilized the fire fly optimization technique for solving the formulated microgrid operation problem. Fire fly algorithm is based on the behaviour and nature of the fire flies. A microgrid system modelling which incorporated various distributed energy sources such as solar photo voltaic, wind turbine, micro tur-bine, fuel cell, diesel generator, electric vehicle technology, etc.. Energy storage system is utilized in this research for supporting renewable energy sources’ integration in more reliable and qualitative way. Further, the electric vehicle technology i.e. battery electric vehicle, plug-in hybrid electric vehicle and fuel cell electric vehicle are utilized to support the microgrid and utility grid systems with respect to variable demands. Optimal operational cost-minimization problem of the developed microgrid system is solved by fire fly algorithm and compared with the grey wolf opti-mization and particle swarm optimization techniques. By comparative analysis it is clear that the fire fly algorithm provides the minimum operational cost of microgrid system as compared to the GWO and PSO. MATLAB software is utilized to model the microgrid system and implementation of the optimization techniques