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Active Network Management for Electrical Distribution Systems

Active Network Management for Electrical Distribution Systems

the net energy flow may be reversed, from the distribution network to the transmission network, and flows within the distribution network may be very different from those observed historically. In order to host a larger share of distributed generation [ 2 ] and avoid poten- tially prohibitive reinforcement costs [ 3 ], active network management (ANM) strategies have recently been proposed as alternatives to the fit and forget approach. The principle of ANM is to address congestion and voltage issues via short-term decision-making policies [ 4 ]. Frequently, ANM schemes maintain the system within operational limits in quasi real-time by relying on the cur- tailment of wind or solar generation [ 5 , 6 , 7 ]. Curtailment of renewable energy may, however, be very controversial from an environmental point of view and should probably be considered as a last resort. With that mindset, it is worth investigating ANM schemes that could also exploit the flexibility of the loads, so as to decrease the reliance on generation curtailment. Exploiting flexible loads within an ANM scheme comes with several challenges. One such challenge is that modulating a flexible load at one instant will often influence its modulation range at subsequent instants. This is because flexible loads (e.g. heat pumps)
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Active network management for electrical distribution systems: problem formulation and benchmark

Active network management for electrical distribution systems: problem formulation and benchmark

Index terms— Active network management, electric distribution network, flexibility services, renewable energy, optimal sequential decision-making under uncertainty, large system 1 Introduction In Europe, the 20/20/20 objectives of the European Commission and the consequent finan- cial incentives established by local governments are currently driving the growth of electricity generation from renewable energy sources [1]. A substantial part of the investments lies in the distribution networks (DNs) and consists of the installation of units that depend on wind or sun as a primary energy source. The significant increase of the number of these distributed genera- tors (DGs) undermines the fit and forget doctrine, which has dominated the planning and the operation of DNs up to this point. This doctrine was developed when DNs had the sole mission of delivering the energy coming from the transmission network (TN) to the consumers. With this approach, adequate investments in network components (i.e., lines, cables, transformers, etc.) must constantly be made to avoid congestion and voltage problems, without requiring con- tinuous monitoring and control of the power flows or voltages. To that end, network planning is done with respect to a set of critical scenarios consisting of production and demand levels, in
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Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution

Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution

A first objective of this work is to facilitate the comparison of solution techniques that have been developed in the research community. To that end, we first propose a generic formulation of ANM related decision-making problems. More specifically, we detail a procedure to state these problems as Markov Decision Processes (MDP), where the system dynamics describes the evolution of the electrical network and devices, while the action space encompasses the control actions that are available to the DSO. Af- terwards, we instantiate this procedure on networks of 5, 33, and 77 buses, and use the elements of the resulting MDPs to build a simulator of these systems, which is available at http://www.montefiore.ulg.ac.be/~anm/. As a second contribution, we provide quantitative results for the resolution of the ANM problem cast as a stochastic mixed-integer nonlinear program (MINLP), as well as a mixed-integer second-order cone programming (MISOCP) relaxation and a mixed-integer linear programming (MILP) ap- proximation, using state of the art open source and commercial solvers. We then perform a sensitivity analysis over the size of the distribution system, the amount of flexibility available in the system, and the number of scenarios considered in the deterministic equivalent of the stochastic program. Finally, a last contribution lies in the features modeled in this work. Compared to the work of [9] and [10], we explicitly account for uncertainty, and for discrete variables stemming from the activation of flexibility ser- vices. Compared to our work, [9] relies only on a continuous nonlinear programming formulation, and thus does not analyze linear or second order cone programming formu- lations, but models a storage system, and [10] also models discrete decisions variables, but they are related to capacitor banks switching and storage system operation modes. The latter reference also uses MISOCP and MILP formulations.
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Real-time Corrective Control in Active Distribution Networks

Real-time Corrective Control in Active Distribution Networks

Centralized control scheme for voltage control and congestion management In this chapter the application of the MPC principle, introduced in Section 1.3 , to corrective con- trol of active DNs is presented. It is shown how the MPC principle is used to predict and correct the network operating conditions. The control problem formulation is followed by a short dis- cussion of techniques used to reach a feasible solution and guarantee the stability of this control. Furthermore, a centralized scheme using the so introduced formulation is detailed. It aims at cor- recting abnormal voltages and/or managing thermal congestions using two different objectives: (i) voltage correction with minimum control effort, or (ii) minimum deviation of control variables from their references for both voltage correction and congestion management. It is shown that Ob- jective (ii) can accommodate various contexts of applications and information transfers between entities acting on the DGUs, in accordance with the regulatory policy. The proposed method effec- tiveness is demonstrated on a 32-bus and a 75-bus test system followed by further tests to evaluate the robustness of the scheme in case of communication failures and slow dynamics of the system components. In addition, some discussions are provided on practical issues of controlling flexible loads. The chapter ends with an extension of the controller to make use of the automatic LTC of the transformer connecting the MV DN to the transmission grid.
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Active network management: planning under uncertainty for exploiting load modulation

Active network management: planning under uncertainty for exploiting load modulation

University of Liège, Liège 4000, Belgium {qgemine, e.karangelos, dernst, bertrand.cornelusse}@ulg.ac.be Abstract This paper addresses the problem faced by a distribution system operator (DSO) when planning the operation of a network in the short-term. The problem is formulated in the context of high penetration of renewable energy sources (RES) and distributed generation (DG), and when flexible demand is available. The problem is expressed as a sequential decision- making problem under uncertainty, where, in the first stage, the DSO has to decide whether or not to reserve the availability of flexible demand, and, in the subsequent stages, can curtail the generation and modulate the available flexible loads. We analyze the relevance of this formulation on a small test system, discuss the assumptions made, compare our approach to related work, and indicate further research directions.
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Understanding active network management in 30 minutes

Understanding active network management in 30 minutes

i s. This entity does not necessarily need to be the Distribution Network Operator (DNO). 2. Compensation mechanism for the owners of PV installations who have to curtail their power. If no compensation mechanisms are implemented, fair curtailment strategies need to be developed so as to ensure that it is not always the same PV installations which have to curtail power.

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Short-term active distribution network operation under uncertainty

Short-term active distribution network operation under uncertainty

I. I NTRODUCTION Distribution System Operators (DSO) aim for active net- work management strategies to address congestion problems using short-term decision-making policies [1]. Many actions can be taken beforehand such as network reconfiguration, tap-changer adjustments, etc. Despite all these actions, DSOs struggle to integrate the increasing amount of distributed generation. This increased production creates congestion prob- lems in critical parts of distribution networks that need to be addressed. To alleviate congestion problems, the European regulatory policy allows DSOs to limit the access of generators to their networks. This access is traditionally formalized by firm-access contracts defining access limits re-evaluated yearly at best. Computing these long-term limits is not straightfor- ward. The paper [2] provides a pragmatic approach to compute such limits which relies on tools that are routinely used in distribution system planning and operation. Firm access limits to the network are however inefficient since they are computed on worst-case scenarios over large time periods. The paper [3] shows, via a test case, that setting dynamic access limits can increase the production of generators by 55%, considering uncertainty on both production and consumption. This result is obtained with a technical optimum curtailment policy which does not consider regulatory constraints. Regulators often im- pose “last-in, first-out” (LIFO) policies, i.e. the last generator installed is the first to be limited. Alternative regulatory poli- cies are investigated in the literature, see paper [4]. Examining
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Distribution of some active elements in primary graphite precipitates

Distribution of some active elements in primary graphite precipitates

Keywords: Ductile iron, Graphite growth, Auger analysis, Cerium, oxygen Introduction As does magnesium, cerium and other rare earth el ements are known to be graphite spheroidizers in cast irons, meaning that added at minute level in the melt before casting they lead to graphite precipitating as disconnected spheres or nodules instead of interconnected lamellae. Distribution of these elements in graphite has long been studied but most generally with analytical means having poor spatial and/or analytical resolution. In an attempt to describe cerium distri- bution within graphite nodules, Hillert and Lindlblom 1 used
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Dynamic Equivalents of Active Distribution Networks: A short Review

Dynamic Equivalents of Active Distribution Networks: A short Review

Overall, the approach of [21, 22, 23] results in a seventh-order non-linear quasi state space format. Reference [24] offers an attractive and engineering sound solution, in practical use by the utilities across WECC (formerly WSCC) in the USA. The solution involves following steps: • Represent transmission system at connection points as large machine (an infinite bus) as illustrated in Fig. 5, • Make step changes in voltages and frequencies at the connection points and record active and reactive powers

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Distribution of some active elements in primary graphite precipitates

Distribution of some active elements in primary graphite precipitates

spectra were taken in points 1, 2 and 3 in Fig. 3 c across the bottom extremity of the Fe-rich inclusion. The results are reported in Fig. 3 d and reveal a Ce peak at 84 eV in all three points, as well as oxygen peaks around 509 eV. The spectrum corresponding to point 3 shows slight Fe-peaks at 610, 650 and 703 eV, suggesting that this element is also present within graphite at point 3, while it is not in point 1. This suggests that point 1 corresponds to a cerium oxide which may have formed %*3&A0*#7K#7HC5*,#&,8#3*'4A$#'*S*3+47,#8A'4,5#076484/3&+47,# of graphite and/or the iron inclusion, while point 3 would most probably be associated to an even distribution of oxygen, iron and cerium in graphite. Further, a cerium signal is detected 4,#174,+#O#B)43)#0)7B0#+)40#*6*$*,+#40#8*/,4+*6C#1'*0*,+#4,# the iron-rich inclusion, though no quantitative conclusion could be made, in particular concerning the relative amount of cerium in graphite and the iron-rich inclusion.
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Energy Efficient Content Distribution in an ISP Network

Energy Efficient Content Distribution in an ISP Network

To give more insight, Fig. 1(a) reports the best level h k for each class k for the two scenarios. k ranges between 1 and V C . The levels on the left are the most popular ones and hence, to minimize the costs of moving the information frequently from the cache to users, it is better to store these classes in the closest level to users, i.e., the access part of the network. Moving from left to right, the popularity decreases, and therefore the classes are stored in the inner levels of the topology (metro and core). At last, very unpopular classes are assigned to level 0, i.e., they are not cached at all. Interestingly, the percentage of the total number of stored classes is around 1.7% and 0.5% for the Moroccan and the FT networks, respectively. Thus, we can conclude that with the considered power and popularity models, the ISP needs to store a little amount of content information to achieve energy and bandwidth savings. This is an encouraging result showing that caching not only has benefits on QoS and customer experience, but it can also lead to a better management of the ISP power consumption.
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Security Exposures with Simple Network Management Protocol

Security Exposures with Simple Network Management Protocol

The IP header of the SNMP packet includes the source address (Figure 2)[13]. It is easy to spoof the network manager source address using raw sockets in the construction of the SNMP packet (Masquerading via Message Stream Modification). By monitoring network traffic, one can determine a network management host address (Traffic Analysis and Disclosure). Network Management software typically use the ICMP ping command and SNMP GET-REQUESTS to discover the network. Periodically a network discovery daemon polls each node in its management domain to determine the connection and the identification of network elements. By simply monitoring and examining ICMP ping and SNMP GET-REQUESTS, the intruder may determine the address of the management workstation. With such an attack, the spoofing station will not receive a response from the agent. This of course is of little consequence to the attacker. If the intent of the attacker is to disable a router or other network element, all the attacker needs to do is send a SET-REQUEST command to the target agent. A response from the agent is irrelevant.
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Network lifetime management in wireless sensor networks

Network lifetime management in wireless sensor networks

V. S IMULATION R ESULTS In order to present the efficiency of our approach, the simu- lation results were compared to four other methods which are namely, the Optimal Beacon and Superframe Orders in WSNs (OBSO), [21], an Adaptive Algorithm to Optimize the Dynamics of IEEE 802.15.4 Network (AAOD), [22], the Battery Aware Beacon Enabled IEEE 802.15.4, as an adaptive and Cross-Layer Approach (BARBEI) [23], as well as the IEEE 802.15.4 with (BO, SO)=(7, 5) [24]. For its good evaluation, the INETMANET/OMNET++ simulator is chosen to check the approach proposed. The OMNET++ is well known as a reliable, extensible and a modular simulator [25] in addition to its affectivity in constructing a realistic environ- ment. It is very flexible simulator which able to accept many modules such as the INETMANET framework.
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A network management framework using mobile agents

A network management framework using mobile agents

it was important to know how our framework rates against an equivalent remote approach. To achieve this, we built a test setup that enabled the mobile agent to be the closest possible to the device to manage. It is obvious that being closer to devices should lower the total traffic on the network while load balancing the charge on many devices. What is less obvious is calculating the penalty of moving network management code from one place to another [20] . The second test network uses a restrained version of the first test network. The result of this subset is a network with only one route from one host to another. The mobile agent can manage each device from the closest management station. This test network makes it easier to test the performance of mobile agents against stationary agents. The last test (Test 4) was limited to failures that imply at least one migration for the mobile diagnostic agent. It does not use the search agent to provide a fair comparison. The mobile agent returns to the source to show its diagnostic, even if it has the ability to do its diagnostic at the destination. The stationary agent uses the same diagnostic algorithm, but is limited to no mobility at all.
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Active Social Media Management: The Case of Health Care

Active Social Media Management: The Case of Health Care

Figure 2: Proportion of Each Type of Posting of employee numbers rather than client numbers. In other words, hospitals are able to avoid this outcome if they consciously and exclusively gear their communications toward patients. Column (2) of Table 10 examines how the proportion of staff-targeted postings affects the results. Strikingly, the more content that hospitals target at staff, the larger is the previously documented effect. That is, hospitals with a higher proportion of staff-targeted postings are more likely to see their user-generated content become a function of their number of employees than number of clients. This result emphasizes that the results presented before need not necessarily be considered to be detrimental to all hospital aims. Presumably there are a subset of hospitals who have, for various strategic reasons, decided to use their social media presence to engage with employees rather than clients. The earlier results suggest that their strategy of active management is successful at increasing employee social media activity.
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Securing Wireless LAN Access : A Network Management Approach

Securing Wireless LAN Access : A Network Management Approach

3. The system secures ISM (802.11 compliant) as well as 915 MHz spread spectrum radio modems. 4. Security Policy Management may be automated. A network of intercommunicating software agents furnishes this functionality. Currently being implemented in the Java programming language, the system provides a web-based user interface. Each software agent has specific functions within a hierarchical, inter-agent relationship. A secure server authenticates: wireless LAN users, system managers and system agents for access to network resources. Node management agents (NMA) protect WLAN access points against unauthorized accesses. NMAs also monitor WLAN modem connections to the WLAN Access Points via the SNMP agent of the access points to challenge newly-connected users. The NMA restricts unauthorized access to the wired network by changing filter objects of the WLAN access point. Users may be certified on the basis of authentication and MAC identification of WLAN or network interface cards. Depending on organizational security policy, access may only be allowed to wireless modems which have been previously registered with the system. An alarm message notifies system administrators of any unauthorized access via the WLAN or to the security system.
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Two-echelon distribution with city hub capacity management

Two-echelon distribution with city hub capacity management

These parameters act as key performance indicators for decision makers aiming at implementing an efficient two-echelon distribution system with limited storage capacity. Acknowledgements This work received funding from the Austrian Federal Ministry for Transport, Innova- tion and Technology (BMVIT) in the framework of the research programme ”Stadt der Zukunft” and the Austrian Federal Ministry of Science, Research and Economy (BMWFW) under grant agreement no. 854921 (CIVIC), as well as from the European Union’s Hori- zon 2020 research and innovation program, and from the Austrian Research Promotion Agency (FFG) under project no. 845100 (EMILIA).
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Novel paradigms for advanced distribution grid energy management

Novel paradigms for advanced distribution grid energy management

4.4.3 Real-time process In the real-time process, controllable loads need to fulfill the transactions they applied for in day-ahead and intraday, and the aggregators/BRE need eventu- ally to execute balancing orders transmitted by the TSO in response to accepted bids on the balancing mechanism. In a similar way to previous processes, we will try to eliminate imbalances or to trade them into the market, but in this case the imbalance is measured with respect to an objective that depends on the mechanism to which resources were allocated. For example, if resources were allocated to an aggre- gator participating to the adjustment reserves, the objective will be to reduce/augment the energy con- sumed with respect to the last half an hour; while if resources were allocated to a distribution system re- serve the objective may be to follow a curve provided by the DSO. The real time mechanism is not to be confused with Primary Control, which is autonomous frequency and voltage control, and the fastest re- sponse to stabilize the grid under system dynam- ics. In this work we assume the physical resources attributed to the VDG are grid-connected and thus
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Combined Local and Centralized Voltage Control in Active Distribution Networks

Combined Local and Centralized Voltage Control in Active Distribution Networks

powers. The latter are communicated to the next layer, an MPC-based centralized controller, which handles the operation constraints. A combined scheme was proposed in [17] in which local controllers provide fast responses and a centralized controller uses power injection predictions for the next hours to update the droop parameters of the local controllers, ensuring that the voltages are kept within their limits in the forecast time interval. Reference [18] proposes a two-stage strategy for distributed energy storage management. In day-ahead, the optimized battery charge/discharge schedules are calculated centrally. Then, they are communicated to local controllers for further short-term adjustments, when approaching real-time.
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Influence of Active Queue Management Parameters on Aggregate Traffic Performance

Influence of Active Queue Management Parameters on Aggregate Traffic Performance

T HE E VOLUTION OF THE RED DROPPING FUNCTIONS of the few published measurement study is limited in scope because it only considers the router performance (as opposed to the end-to-end performance) and it does not clearly describe the mea- surement settings and the exact information being measured [7]. In other publications, the authors use simulations to evaluate RED and examine the impact of the parameter choice on the end-to-end performance [8], [9], [10], [11], [12], [13]. But, realistic simulation settings are hard to come up with , and the traffic generated by most simulators is generally different from real network traffic. Simulations typically use infinite greedy TCP sources, connections with constant round trip delays, or a small number of connections. A couple of papers have been published that propose extensions or changes to the RED algorithms to make it more robust or adaptive like FRED [14], or SRED [15], however these papers do not question or evaluate the performance and suitability of the RED algorithm. Consequently, it is not yet clear how to choose AQM parameters. The only parameter values can be found in [5] and [6]. These values also rely on simple experiments as well as on the authors’ intuition.
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