Optimal Power Flow Management for Grid Connected PV Systems With Batteries

Riffonneau, Yann; Bacha, Seddik; Barruel, Franck; Ploix, Stéphane

doi:10.1109/tste.2011.2114901

Cited by 865 publications

(467 citation statements)

References 27 publications

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“…Equation (35.7) defines the State of Health (SoH) of the battery as a ratio between currently available charge capacity (C re f ) and the nominal one. Equation (35.8) defines the charge capacity as a linear combination of the previous charge and a term that depends on the charge which is drained, where C nom is the nominal charge declared by manufacturer while Z b , linear aging coefficient, is a parameter depending on the battery technology [30]. The following two equations (Equation (35.9) and Equation (35.10)) allow to determine the State of Charge (SoC) and the equivalent battery current (I eq ), function of the current flowing from batteries (I), with respect to the nominal battery parameters: I re f the reference discharge current (provided by the manufacturer and used to compute the reference charge), the Peukert's coefficient k b and the charge actually used by the system, computed as current I eq times time slot (t slot ) length in seconds.…”

Section: Electrical Energy Storage Systemmentioning

confidence: 99%

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Joint Computing and Electric Systems Optimization for Green Datacenters

Pahlevan

Rossi

Valle

et al. 2017

Handbook of Hardware/Software Codesign

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This chapter presents an optimization framework to manage green datacenters using multi-level energy reduction techniques in a joint approach. A green datacenter exploits renewable energy sources and active Uninterruptible Power Supply (UPS) units to reduce the energy intake from the grid while improving its Quality of Service (QoS). At server level, the state-of-the-art correlation-aware Virtual Machines (VMs) consolidation technique allows to maximize server's energy efficiency. At system level, heterogeneous Energy Storage Systems (ESS) replace standard UPSs while a dedicated optimization strategy aims at maximizing the lifetime of the battery banks and to reduce the energy bill, considering the load of the servers. Results demonstrate, under different number of VMs in the system, up to 11.6% energy savings, 10.4% improvement of QoS compared to existing correlation-aware VM allocation schemes for datacenters and up to 96% electricity bill savings. IntroductionEver increasing demands for computing and growing number of clusters and servers in datacenters have ramped up the power consumption costs as an undesirable effect [21]. On the other hand, traditional fossil fuel concerns, carbon emissions and global warming impose the introduction of more sustainable energy sources and behavioural change of people [41], since 10% of the global consumption of electrical energy has been estimated to be consumed by Information Technology (IT) infrastructures [14].

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Section: Electrical Energy Storage Systemmentioning

confidence: 99%

“…The SoH of the battery decreases only during discharge, so it is calculated only during discharge, whereas the SoC is updated during both charge and discharge cycles. More details about the model and its utilization can be found in [29,30].…”

Section: Electrical Energy Storage Systemmentioning

confidence: 99%

Joint Computing and Electric Systems Optimization for Green Datacenters

Pahlevan

Rossi

Valle

et al. 2017

Handbook of Hardware/Software Codesign

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“…If it takes the battery minimum θ B hours to charge from zero to full capacity (or, to discharge from full capacity to zero), then charge and discharge rates will be limited according to Riffonneau et al [16] 0…”

Section: Technical Constraintsmentioning

confidence: 99%

“…Riffonneau et al [16] have developed a model for battery cyclic aging, which assumes that battery capacity degradation is linear with respect to battery depth of discharge (DOD) changes:…”

Section: Technical Constraintsmentioning

confidence: 99%

Distributed Energy Management of PV-Storage Systems for Voltage Rise Mitigation

Ansari

Simões

2017

Technol Econ Smart Grids Sustain Energy

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This paper develops a distributed consensusbased energy management scheme (EMS) for multiple photovoltaics+energy storage systems (PV+ESS) connected to a smart distribution network. First, each customer individually determines the optimal size and initial scheduling of ESS to be installed in parallel with PV over a dayahead planning horizon. The objective of installing ESS is to reduce the electricity bill while minimizing ESS aging effect. Then, after the day-ahead planning, customers manage their system in a near real-time fashion to account for voltage conditions in order to avoid voltage-rise-associated curtailments. Customers along the feeder communicate with each other and exchange information about voltage deviation at their nodes in order to reach a consensus about average voltage situation in the distribution network. Based on voltage situation, EMS updates the lower bound on net power exchange with the grid for the hour ahead. Also, an iterative method is proposed to determine the lifetime of ESS and the economic benefits gained over the lifetime. Simulation results for IEEE 33-bus test system prove the effectiveness of the proposed EMS and its financial viability. Keywords

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“…Riffonneau et al [6] have highlighted the importance of responsive optimisation close to real-time by adjusting the predictive strategy at each unpredicted disturbance according to actual measurements. Yuan et al [7] have addressed the economic optimisation of energy storage, in combination with large-scale wind energy, and have proposed a stochastic programming method to deal with the forecast error in order to minimise imbalance penalties.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical predictive control scheme for distributed energy storage integrated with residential demand and photovoltaic generation

Lampropoulos

Garoufalis

Bosch

et al. 2015

IET Generation, Transmission & Distribution

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A hierarchical control scheme is defined for the energy management of a battery energy storage system which is integrated in a low-voltage distribution grid with residential customers and photovoltaic installations. The scope is the economic optimisation of the integrated system by employing predictive control techniques. The approach is based on hierarchical decomposition of the optimisation problem in the time domain by composing a three-level scheduling and control scheme, that is, day-ahead, intra-hour, and real-time, where the initial and final states of each sub-problem are chosen as coordination parameters. The day-ahead and the intra-hour problems address the interactions with electricity markets during the scheduling phase. The real-time algorithm is able to adapt the operation of the battery system according to updated information about market conditions, residential demand, and local generation, and subject to the network capacity and other technical constraints. The simulation scenarios address the interactions with the day-ahead auction and the imbalance settlement system in the Netherlands.

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Optimal Power Flow Management for Grid Connected PV Systems With Batteries

Cited by 865 publications

References 27 publications

Joint Computing and Electric Systems Optimization for Green Datacenters

Joint Computing and Electric Systems Optimization for Green Datacenters

Distributed Energy Management of PV-Storage Systems for Voltage Rise Mitigation

Hierarchical predictive control scheme for distributed energy storage integrated with residential demand and photovoltaic generation

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