Real-time state estimation of the EPFL-campus medium-voltage grid by using PMUs

Pignati, Marco; Popović, Miroslav; Barreto, Sergio; Cherkaoui, Rachid; Flores, German Dario; Boudec, Jean-Yves Le; Mohiuddin, Maaz; Paolone, Mario; Romano, Paolo; Sarri, Styliani; Tesfay, Teklemariam Tsegay; Tomozei, Dan-Cristian; Zanni, Lorenzo

doi:10.1109/isgt.2015.7131877

Cited by 114 publications

(98 citation statements)

References 16 publications

(18 reference statements)

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“…With our implementation, we have shown that iPRP can support several sessions between hosts without any significant delay or processing overhead. We have made our implementation publicly available and are currently installing it in our campus smart-grid [11].…”

Section: Implementation and The Diagnostic Toolkitmentioning

confidence: 99%

iPRP: Parallel redundancy protocol for IP networks

Popović

Mohiuddin

Tomozei

et al. 2015

2015 IEEE World Conference on Factory Communication Systems (WFCS)

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Abstract-Reliable packet delivery within stringent delay constraints is of primal importance to industrial processes with hard real-time constraints, such as electrical grid monitoring. Because retransmission and coding techniques counteract the delay requirements, reliability is achieved through replication over multiple fail-independent paths. Existing solutions such as parallel redundancy protocol (PRP) replicate all packets at the MAC layer over parallel paths. PRP works best in local area networks, e.g., sub-station networks. However, it is not viable for IP layer wide area networks which are a part of emerging smart grids. Such a limitation on scalability, coupled with lack of security, and diagnostic inability, renders it unsuitable for reliable data delivery in smart grids. To address this issue, we present a transport-layer design: IP parallel redundancy protocol (iPRP). Designing iPRP poses non-trivial challenges in the form of selective packet replication, soft-state and multicast support. Besides unicast, iPRP supports multicast, which is widely using in smart grid networks. It duplicates only time-critical UDP traffic. iPRP only requires a simple software installation on the end-devices. No other modification to the existing monitoring application, end-device operating system or intermediate network devices is needed. iPRP has a set of diagnostic tools for network debugging. With our implementation of iPRP in Linux, we show that iPRP supports multiple flows with minimal processing and delay overhead. It is being installed in our campus smart grid network and is publicly available. I. INTRODUCTIONSpecific time-critical applications (found for example in electrical networks) have such strict communication-delay constraints that retransmissions following packet loss can be both detrimental and superfluous. In smart grids, critical control applications require reliable information about the network state in quasi-real time, within hard delay constraints of the order of approximately 10 ms. Measurements are streamed periodically (every 20 ms for 50 Hz systems) by phasor measurement units (PMUs) to phasor data concentrators (PDCs). In such settings, retransmissions can introduce delays for successive, more recent data that in any case supersede older ones. Also, IP multicast is typically used for delivering the measurements to several PDCs. Hence, UDP is preferred over TCP, despite its best-effort delivery approach. Increasing the reliability of such unidirectional (multicast) UDP flows is a major challenge.The parallel redundancy protocol (PRP, IEC standard [1]) was proposed as a solution for deployments inside a local area network (LAN) where there are no routers. Communicating devices need to be connected to two cloned (disjoint) bridged networks. The sender tags MAC frames with a sequence number and replicates it over its two interfaces. The receiver discards redundant frames based on sequence numbers.PRP works well in controlled environments, like a substation LAN, where network setup is entirely up ...

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Section: Implementation and The Diagnostic Toolkitmentioning

confidence: 99%

iPRP: Parallel redundancy protocol for IP networks

Popović

Mohiuddin

Tomozei

et al. 2015

2015 IEEE World Conference on Factory Communication Systems (WFCS)

Self Cite

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“…Our implementation is publicly available and is currently being installed in our campus smart-grid [6]. In the future, we intend to do extensive measurements on our smart-grid and study the performance of iPRP in real networks.…”

Section: B Processing Overhead Caused By Iprpmentioning

confidence: 99%

“…Failure to satisfy these constraints can result in economic losses or, even worse, human lives can be endangered in cases when these failures affect safety mechanisms. Notable examples of such applications (often built on top of cyber-physical systems) are process-control and emergencymanagement applications in the oil and gas industry [1], realtime detection of pollutants in the water/wastewater industry [2], vehicle-collision avoidance in car industry [3], automatic train control [4], process control in chemical industry [5], state estimation in smart grid [6], high-frequency trading [7] and distributed online gaming [8].…”

mentioning

confidence: 99%

iPRP—The Parallel Redundancy Protocol for IP Networks: Protocol Design and Operation

Popović

Mohiuddin

Tomozei

et al. 2016

IEEE Trans. Ind. Inf.

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Abstract-Reliable packet delivery within stringent delayconstraints is of paramount importance to mission-critical computer applications with hard real-time constraints. Because retransmission and coding techniques counteract the delay requirements, reliability may be achieved through replication over multiple fail-independent paths. Existing solutions, such as the parallel redundancy protocol (PRP), replicate all packets at the MAC layer over parallel paths. PRP works best in local area networks. However, it is not viable for IP networks that are a key element of emerging mission-critical systems. This limitation, coupled with diagnostic inability and lack of security, renders PRP unsuitable for reliable data-delivery in these IP networks. To address this issue, we present a transport-layer solution: the IP parallel redundancy protocol (iPRP). Designing iPRP poses non-trivial challenges in the form of selective packetreplication, soft-state and multicast support. iPRP replicates only time-critical unicast or multicast UDP traffic. iPRP requires no modifications to the existing monitoring application, end-device operating system or to the intermediate network devices. It only requires a simple software installation on the end-devices. iPRP has a set of diagnostic tools for network debugging. With our implementation of iPRP in Linux, we show that iPRP supports multiple flows with minimal processing-and-delay overhead. It is being installed in our campus smart-grid network and is publicly available. I. INTRODUCTIONSpecific mission-critical computer applications have hard delay-constraints. Failure to satisfy these constraints can result in economic losses or, even worse, human lives can be endangered in cases when these failures affect safety mechanisms. Notable examples of such applications (often built on top of cyber-physical systems) are process-control and emergencymanagement applications in the oil and gas industry Reliable and timely packet delivery, even in the order of 10 ms, is of utmost importance in satisfying the hard-delay constraints. The classic approaches to reliable communication through coding and retransmission are not compatible with the hard delay-constraints. An alternative is to achieve reliability through replication over multiple fail-independent paths, which is the focus of this paper. More precisely, we present a solution for packet-replication over multiple paths in IP networks. Indeed, as we discuss next, existing solutions apply to MAClayer networks and cannot be transposed to IP networks that are a requirement for many of the aforementioned applications.

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“…RESULTS AND DISCUSSION The framework is tested in simulation to verify its ability to dispatch the operation of a monitored distribution feeder of the EPFL campus [11]. The available power consumption measurements are split into daily sequences and used to determine the day-ahead power consumption forecast and the advertised power consumption profile.…”

Section: A Day-ahead Of Operationmentioning

confidence: 99%

Control of a battery energy storage system accounting for the charge redistribution effect to dispatch the operation of a medium voltage feeder

Sossan

Torregrossa

Namor

et al. 2015

2015 IEEE Eindhoven PowerTech

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Abstract-We describe a process to dispatch the operation of a medium voltage distribution feeder and improve its qualityof-service using a battery energy storage system (BESS). It is organized according to a day-ahead and intra-day structure to allow the integration with electricity markets. In the former stage, the average power transit on a 5-minute resolution is determined for the next day of operation using adaptive blackbox forecasting. In the latter stage, the feeder operation is dispatched according to the previously determined trajectory. The resulting tracking problem is accomplished by using the BESS to compensate for deviations from the dispatched plan, which are likely to occur due to forecasting errors. For the first time in the literature, the control strategy is realized using model predictive control (MPC) accounting for the battery charge redistribution effect. This phenomena is described using a stateof-the-art lithium battery model. A simulated proof-of-concept is given.

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Real-time state estimation of the EPFL-campus medium-voltage grid by using PMUs

Cited by 114 publications

References 16 publications

iPRP: Parallel redundancy protocol for IP networks

iPRP: Parallel redundancy protocol for IP networks

iPRP—The Parallel Redundancy Protocol for IP Networks: Protocol Design and Operation

Control of a battery energy storage system accounting for the charge redistribution effect to dispatch the operation of a medium voltage feeder

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