A dynamic optimization-based architecture for polygeneration microgrids with tri-generation, renewables, storage systems and electrical vehicles

Bracco, Stefano; Delfino, Federico; Pampararo, Fabio; Robba, Michela; Rossi, Mansueto

doi:10.1016/j.enconman.2015.03.013

Cited by 122 publications

(47 citation statements)

References 28 publications

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“…One example is the Smart Polygeneration Microgrid of the University of Genoa, composed by CHP units, natural gas boilers, thermal storage, absorbing chiller, a concentrated solar power unit coupled to Stirling engines, electrical storage, photovoltaic panels and recharging stations for electrical vehicles [115]. The optimal daily operation of the system is assured by an operation center that utilizes mathematical models.…”

Section: Towards a Multi-generation Scenariomentioning

confidence: 99%

Application of Wireless Sensor and Actuator Networks to Achieve Intelligent Microgrids: A Promising Approach towards a Global Smart Grid Deployment

et al. 2016

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Smart Grids (SGs) constitute the evolution of the traditional electrical grid towards a new paradigm, which should increase the reliability, the security and, at the same time, reduce the costs of energy generation, distribution and consumption. Electrical microgrids (MGs) can be considered the first stage of this evolution of the grid, because of the intelligent management techniques that must be applied to assure their correct operation. To accomplish this task, sensors and actuators will be necessary, along with wireless communication technologies to transmit the measured data and the command messages. Wireless Sensor and Actuator Networks (WSANs) are therefore a promising solution to achieve an intelligent management of MGs and, by extension, the SG. In this frame, this paper surveys several aspects concerning the application of WSANs to manage MGs and the electrical grid, as well as the communication protocols that could be applied. The main concerns regarding the SG deployment are also presented, including future scenarios where the interoperability of different generation technologies must be assured.

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Section: Towards a Multi-generation Scenariomentioning

confidence: 99%

Application of Wireless Sensor and Actuator Networks to Achieve Intelligent Microgrids: A Promising Approach towards a Global Smart Grid Deployment

et al. 2016

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“…In [13], a dynamic optimization model is proposed to minimize operating costs and CO 2 emissions, and is applied to the University of Genova Savona Campus test-bed facilities. A robust optimization approach for optimal microgrid management considering wind power uncertainty is presented in [14], in which a time-series based autoregressive integrated moving average model is used to characterize the wind power uncertainty through interval forecasting.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective energy management system for DC microgrids based on the maximum membership degree principle

Wang

Meng

et al. 2017

J. Mod. Power Syst. Clean Energy

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With the increasing quantity of DC electrical equipment, DC microgrids have been paid more and more attention. This paper proposes an approach to multi-objective optimisation of an energy management system (EMS) for a DC microgrid that includes a hybrid energy storage system (HESS). The operating and maintenance cost and the loss of power supply probability (LPSP) of the system are used as optimisation targets. The power flows of all distributed generators (DGs) in the DC microgrid during operating period are optimized. Based on the improved differential evolution (DE) algorithm, and by using the multi-objective non-dominated sorting method and the maximum membership degree principle (MMDP) of fuzzy control, the overall satisfaction degree of Pareto solutions to power flow optimization can be obtained. Simulation results verify the effectiveness of the proposed EMS optimization scheme, which is able to achieve an effective trade-off between the economy and the reliability of microgrid operation.

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“…2.3. Though the impact of the SOC limits on such capability has been extensively incorporated in [36,125,126,129,133], the impact of the SOC-dependent charging/discharging current [124] and voltage has not been thoroughly investigated in the literature.…”

Section: ) Modelling Ev Pv and Besmentioning

confidence: 99%

“…Therefore, the probabilistic charging strategies are incompatible with the charging facilities where frequent real-time control of charging is required. From historical solar irradiation [97,98,115,145] From historical data [125,167] By prediction [142,168] PV panel's tilt, azimuth, and seven other relevant weight coefficients [169] Estimated from a supplied solar intensity [56,98,123,125,145,168,170] Directly obtained from the historical data [97,118,167] Probabilistic PV output…”

Section: Real-time Ev Load Dispatching Addressing Pv Output Variabilimentioning

confidence: 99%

“…Deploy BES [124,[127][128][129] Reduce losses [36,128] Deploy both [30,36,[125][126][127] Reduce unbalance [36,119] Reduce loading [36] Reduce costs [36,86,118,123,126] Reduce GHG emissions [86,117] Reduce transformer, feeder capacities required [36,129] Improve voltages [36,75,116,127] Improve QoS [36] Even though these papers form a comprehensive realm of studies regarding whether to augment grid or deploy PV and BES, the following limitations can be pointed out,  The papers above have assumed that either augmenting/reconfiguring the grid/phase or deploying onsite PV and BES is the best solution for reducing the impact, costs, GHG and increasing QoS. However, in practice, their various combinations (e.g., grid augmentation and PV; grid augmentation, PV, and BES; etc.)…”

Section: 34mentioning

confidence: 99%

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PV-based EV charging station with vehicle-to-grid services for business premises

Islam¹

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The transportation sector is the second major contributor to the global greenhouse gas (GHG) emissions, next to electricity generation sector. GHG emissions are projected to grow in the future due to the over-reliance on fossil fuels for both electricity generation and transportation sectors.However, the growth can be stalled by gradually introducing electric vehicles (EVs) as they produce less GHG compared to their conventional counterparts. Despite that, their proliferation has been stunted by a completely new set of challenges in addition to a very high capital cost. These challenges include low driving ranges, scarcity of electric vehicle charging stations (EVCS), long charging time and inability to provide charging service readily. Nevertheless, some of these challenges can be addressed by installing EVCS at business premises (i.e., offices, universities, etc.) and augmenting EVCS with onsite PV and battery energy storage (BES). In that case, the impacts of EV charging on the grid will be less significant; charging services will be available readily, and GHG emissions growth can be reduced significantly. Moreover, EVCS, under suitable conditions, would be able to provide vehicle-to-grid (V2G) services, which makes EVCS more attractive.Despite such prospective opportunities, offhand planning and operational planning of EVCS would deem them unachievable. In addition, the inherent intermittency of PV, and EV and grids loads will pose considerable hindrance on availing those benefits.Hence, this research focusses on an EVCS with onsite PV and BES, in the perspective of planning and operational planning including V2G services. The cornerstone of the planning and operational planning is an appropriate EV load model that captures the uncertainties of EV charging. In addition, the EV load model reflects the grid voltage and EV battery's state of charge (SOC)-dependency of EV charging. Moreover, it accounts for the diversity of the EV population embodied by market shares, battery sizes, charging levels, and charging voltages, currents, and power factors. Furthermore, it is scalable to facilitate seamless assimilation of a large EV population. Therefore, a new EV load model is first proposed with MATLAB/Simulink validation reflecting the factors above along with the recommendations by the standard BS EN 61851-23:2014. This EV model is then incorporated into the planning activities, which include four chronological exercises. Firstly, the impact (i.e., grid voltage, current, losses, etc.) of the EV charging on the grid at different locations, namely home, office, public charging, is investigated considering the uncertainties. Secondly, the optimal location of the PV and BES based EVCS is divulged regarding the reduction of the impact and costs involved while enhancing the charging quality of service (QoS). Thirdly, a suitability analysis is performed for the obtained optimal location to find the most suitable combination of the grid upgrading, PV deployment, and BES iii deployment to satisfy the QoS, ...

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A dynamic optimization-based architecture for polygeneration microgrids with tri-generation, renewables, storage systems and electrical vehicles

Cited by 122 publications

References 28 publications

Application of Wireless Sensor and Actuator Networks to Achieve Intelligent Microgrids: A Promising Approach towards a Global Smart Grid Deployment

Application of Wireless Sensor and Actuator Networks to Achieve Intelligent Microgrids: A Promising Approach towards a Global Smart Grid Deployment

Multi-objective energy management system for DC microgrids based on the maximum membership degree principle

PV-based EV charging station with vehicle-to-grid services for business premises

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