Optimal Management of Microgrids With External Agents Including Battery/Fuel Cell Electric Vehicles

García-Torres, Félix; Vilaplana, Daniel G.; Bordons, Carlos; Roncero‐Sánchez, Pedro; Ridao, Miguel A.

doi:10.1109/tsg.2018.2856524

Cited by 62 publications

(31 citation statements)

References 14 publications

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“…The monetary expenditures mainly comprise operation and maintenance costs of generation assets. For renewable-based generators, these costs are proportional to the energy generated [7], whereas, in the case of BES, they are quadratic functions of the energy exchanged with the grid [38]. The last element of the expenditures stands for the fuel cost of DEG, which can be calculated as a quadratic function of the energy generated, as follows [39]:…”

Section: Objective Functionmentioning

confidence: 99%

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DC Nanogrids for Integration of Demand Response and Electric Vehicle Charging Infrastructures: Appraisal, Optimal Scheduling and Analysis

et al. 2021

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With the development of electronic infrastructures and communication technologies and protocols, electric grids have evolved towards the concept of Smart Grids, which enable the communication of the different agents involved in their operation, thus notably increasing their efficiency. In this context, microgrids and nanogrids have emerged as invaluable frameworks for optimal integration of renewable sources, electric mobility, energy storage facilities and demand response programs. This paper discusses a DC isolated nanogrid layout for the integration of renewable generators, battery energy storage, demand response activities and electric vehicle charging infrastructures. Moreover, a stochastic optimal scheduling tool is developed for the studied nanogrid, suitable for operators integrated into local service entities along with the energy retailer. A stochastic model is developed for fast charging stations in particular. A case study serves to validate the developed tool and analyze the economical and operational implications of demand response programs and charging infrastructures. Results evidence the importance of demand response initiatives in the economic profit of the retailer.

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Section: Objective Functionmentioning

confidence: 99%

“…Using this distribution function, the scenario space can be generated for these parameters. For the model developed here in particular, the inflexible demand, solar irradiation, temperature and wind speed are assumed to be managed by this approach [38,49,50].…”

Section: Predictable Parametersmentioning

confidence: 99%

DC Nanogrids for Integration of Demand Response and Electric Vehicle Charging Infrastructures: Appraisal, Optimal Scheduling and Analysis

et al. 2021

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“…Hence, different works additionally allude to optimal generation for renewable micro-grids considering hybrid storage systems [42,43]. MPC was also used for energy management of micronetworks connected to charging stations for electric vehicles [44,45]. Thus, several papers have applied the MPC controller with satisfactory results in the hybridization of ESSs.…”

Section: B Literature Reviewmentioning

confidence: 99%

Impact of Incorporating Disturbance Prediction on the Performance of Energy Management Systems in Micro-Grid

Gbadega

Saha

2020

IEEE Access

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The design and implementation of appropriate advanced control strategies is a key factor for the effective integration of micro-grids into the electrical network. In view of this, the study proposes an Adaptive Model-Based Horizon Control technique in the bid to addressing issues related to the Energy Management System in micro-grid operations. The main objective of the energy management system is to balance energy generation and demand through energy storage, so as to optimize the operation of the microgrid with high penetrations of renewable energy sources. This paper further investigates the impacts of considering the prediction of disturbances on the performance of the Energy Management System based on the adaptive model predictive control algorithm in order to improve the operating costs of the micro-grid with hybrid-energy storage systems. The adaptive model predictive control algorithm solves the energy optimization problem in a renewable energy-based micro-grid with various types of energy storage systems that exchange energy with the host grid. More so, this optimization problem is resolved at each sampling period in order to determine the minimum running costs while satisfying demand and taking into account technical and physical constraints. The simulation results under different conditions have demonstrated how the use of an adaptive model predictive control based energy management system can enhance micro-grid operation, provided there is effective forecasting, and consequently minimized the running operating costs of micro-grid. More so, it is evident in the cost function, , obtained from the three scenarios conducted, that the perfect knowledge of the disturbance prediction is essential for effective micro-grid operations.

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“…This strategy considers energy balance, charging limits, State Of Charge (SOC), and power generation. Another approach presented by [14] investigated microgrid management considering the energy price, taking into account the energy exchange with external agents. Model Predictive Control (MPC) was employed in the optimization algorithm, which considered the operating cost and the battery degradation in the cost function specification.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids

et al. 2020

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Energy Storage Systems (ESS) and Distributed Generation (DG) are topics in a large number of recent research works. Moreover, given the increasing adoption of EVs, high capacity EV batteries can be used as ESS, as most vehicles remain idle for long periods during work or home parking. However, the high EV penetration introduces some issues related to the charging power requirements, thereby increasing the peak demand for microgrids where EV chargers are installed. In addition, photovoltaic distributed generation is becoming another issue to deal with in EV charging microgrids. Therefore, this new scenario requires an Energy Management System (EMS) able to deal with charging demand, as well as with generation intermittency. This paper presents an EMS strategy for Microgrids that contain an EV parking lot (EVM), Photovoltaic (PV) arrays, and dynamic loads connected to the grid considering a Point of Common Coupling (PCC). The EVM-EMS utilizes the projections of future PV generation and future demand to accomplish a dynamic programming technique that optimizes the EVs’ charging (G2V) or discharging (V2G) profiles. This algorithm attends to user preferences while reducing the demand grid dependences and improves the microgrid efficiency.

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Optimal Management of Microgrids With External Agents Including Battery/Fuel Cell Electric Vehicles

Cited by 62 publications

References 14 publications

DC Nanogrids for Integration of Demand Response and Electric Vehicle Charging Infrastructures: Appraisal, Optimal Scheduling and Analysis

DC Nanogrids for Integration of Demand Response and Electric Vehicle Charging Infrastructures: Appraisal, Optimal Scheduling and Analysis

Impact of Incorporating Disturbance Prediction on the Performance of Energy Management Systems in Micro-Grid

Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids

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