Probabilistic curtailment analysis for transmission grid planning using Active Network Management

According to the EU Council in 2007, a target of 20% Renewable Energy Sources (RES) energy share was determined by the year 2020. Maximizing RES penetration, whilst simultaneously ensuring grid stability and security of electric supply, has become a major challenge for the grid operators. The aggregated effect of Distributed Generation (DG) units will affect increasingly the transmission grid operation and planning. More and more, the High Voltage (HV) grid has to export the excess of power produced at the Medium Voltage (MV) level, where DG units are connected. The energy flows become variable both in value and direction in substations at the interface with distribution networks, which is a complete change for the grid operator. Power flow congestions and voltage problems are particularly more likely to arise. Systematically reinforcing the network in order to absorb the last MWh produced by DG units located in unfavorable areas, while maintaining the traditional operation of the grid, is not efficient, i.e. neither economically viable for the community nor acceptable from the point of view of environmental impact. The intermittency of DG units makes it irrelevant to define the amount of connectable units on the basis of their installed power and the N-1 criterion. New paradigms to increase the grid capacity of accepting DG units before reinforcement are to be considered. And new methodologies for long-term and operational grid planning, giving allowance to this inherent variability in the generation, are therefore necessary.Active Network Management (ANM) allows to moving away from conventional grid operation towards a new approach, comprising (almost) real-time supervision and control of the DG units and network elements. Thanks to this new management of the system and accounting for the intermittent (i.e. weather-dependent) RES production, more DG units can be connected to an existing grid: the power produced by some DG units can be curtailed to eliminate possible congestions encountered for specific combinations of loads, generations and weather conditions. In others words, the use of an ANM scheme makes possible to maximize the grid utilization in enhancing the required flexibility of system operation to maintain power system security margins.A reasonable level of security in applying ANM is however required and it must be assessed before any possible application to the grid. This assessment can be performed based on a probabilistic approach: the uncertain parameters, i.e. each load and power produced by a DG unit, are modeled with probability density functions (pdf’s); the latter are then randomly sampled, to create so-called variants. These variants serve as input data for an Optimal Power Flow (OPF) module to find the possible redispatching or curtailment that could be necessary in each case. The state space is extremely vast, however, due to combinatorial explosion. Creating a sufficiently large sample of variants to cover all significant situations the grid can face appears intractable, and alternative approaches, combining a systematic search in the state space with an acceptable computation time, are to be developed.This research proposes a pragmatic methodology to handle the high dimensionality of the problem and estimate the impact of connecting a new DG unit, via the computation of several risk indices. A systematic approach guarantees searching all over the plausible congestion zones of the state space, while an on-target sampling drives the computational effort towards the direction of interest. This combined approach allows managing the computation time without falling into oversimplification or losing too much accuracy.Doctorat en Sciences de l'ingénieur et technologieinfo:eu-repo/semantics/nonPublishe

Probabilistic curtailment analysis for transmission grid planning using Active Network Management

According to the EU Council in 2007, a target of 20% Renewable Energy Sources (RES) energy share was determined by the year 2020. Maximizing RES penetration, whilst simultaneously ensuring grid stability and security of electric supply, has become a major challenge for the grid operators. The aggregated effect of Distributed Generation (DG) units will affect increasingly the transmission grid operation and planning. More and more, the High Voltage (HV) grid has to export the excess of power produced at the Medium Voltage (MV) level, where DG units are connected. The energy flows become variable both in value and direction in substations at the interface with distribution networks, which is a complete change for the grid operator. Power flow congestions and voltage problems are particularly more likely to arise. Systematically reinforcing the network in order to absorb the last MWh produced by DG units located in unfavorable areas, while maintaining the traditional operation of the grid, is not efficient, i.e. neither economically viable for the community nor acceptable from the point of view of environmental impact. The intermittency of DG units makes it irrelevant to define the amount of connectable units on the basis of their installed power and the N-1 criterion. New paradigms to increase the grid capacity of accepting DG units before reinforcement are to be considered. And new methodologies for long-term and operational grid planning, giving allowance to this inherent variability in the generation, are therefore necessary.Active Network Management (ANM) allows to moving away from conventional grid operation towards a new approach, comprising (almost) real-time supervision and control of the DG units and network elements. Thanks to this new management of the system and accounting for the intermittent (i.e. weather-dependent) RES production, more DG units can be connected to an existing grid: the power produced by some DG units can be curtailed to eliminate possible congestions encountered for specific combinations of loads, generations and weather conditions. In others words, the use of an ANM scheme makes possible to maximize the grid utilization in enhancing the required flexibility of system operation to maintain power system security margins.A reasonable level of security in applying ANM is however required and it must be assessed before any possible application to the grid. This assessment can be performed based on a probabilistic approach: the uncertain parameters, i.e. each load and power produced by a DG unit, are modeled with probability density functions (pdf’s); the latter are then randomly sampled, to create so-called variants. These variants serve as input data for an Optimal Power Flow (OPF) module to find the possible redispatching or curtailment that could be necessary in each case. The state space is extremely vast, however, due to combinatorial explosion. Creating a sufficiently large sample of variants to cover all significant situations the grid can face appears intractable, and alternative approaches, combining a systematic search in the state space with an acceptable computation time, are to be developed.This research proposes a pragmatic methodology to handle the high dimensionality of the problem and estimate the impact of connecting a new DG unit, via the computation of several risk indices. A systematic approach guarantees searching all over the plausible congestion zones of the state space, while an on-target sampling drives the computational effort towards the direction of interest. This combined approach allows managing the computation time without falling into oversimplification or losing too much accuracy.Doctorat en Sciences de l'ingénieur et technologieinfo:eu-repo/semantics/nonPublishe

Dynamic probabilistic risk analysis of the fast cascade phase of large disturbances in power system

Power systems have experienced wide-area disturbances in the last decades, including large blackouts in the U.S. and Europe that impacted millions of customers. According to previous blackout analysis, the development of a cascading event leading to a blackout can be split in two phases. In an initial "slow cascade" phase, an initiating contingency (e.g. a line trip), though not supposed to challenge the electrical stability of the grid because of the N-1 security criterion, triggers a thermal transient developing on characteristic times much longer than the electrical time constants. This transient increases significantly the likelihood of additional contingencies. The loss of additional elements can then trigger an electrical instability. This is at the origin of the subsequent "fast cascade" phase, where a rapid succession of events can possibly lead the system to blackout. This paper is devoted to the study of the fast cascade phase of power system large disturbances. Dynamic Probabilistic risk assessment (PRA) has been developed, mostly in nuclear engineering, for identifying dangerous accident scenarios, while capturing the interaction between the dynamic evolution of a system in transient conditions and the occurrence of events along an accident sequence. Discrete dynamic event trees (DDET) is the core of the scheme used in this research. Misoperation of distance protection systems, involved in the propagation of disturbances, is integrated into the approach to provide more trustworthy results. The objective of this paper is the identification of dangerous scenarios leading to blackout in the fast cascade phase and the estimation of their frequency, using dynamic PRA. The methodology is applied to a test grid and results are analyzed.info:eu-repo/semantics/publishe

A Comparative Analysis of Dynamic vs. Quasi-static Approaches for Resilience Assessment of a Bulk Power System Against Severe Wind Events

Severe weather-related events are one of the main causes of large-scale electric power outages worldwide. Although the probability of occurrence of these events is low, they are considered into the high-risk category due to their significant consequences. The intensity and frequency of these events have gradually increased in the last decades and are expected to keep increasing in the future due to climate change. To this end, power grid resilience is critical to reduce the risk and vulnerability to these events. In the context of resilience assessment, an important step is the simulation of the performance of power systems during these highly impactful events, which can be performed either by quasi-static or dynamic approaches. In this work, both approaches are applied for the assessment of the resilience of a bulk power system against severe wind events, and a comparative analysis of the results is provided. The main advantage of using dynamic simulation is to detect the outages of the system assets related to electrical instability during the events. Eurostag software is used to perform the dynamic simulation with a variable time step to increase the efficiency of the computational module. The results show that the analysis of the resilience of power system by a quasi-static approach leads to a considerable underestimation of the resilience metrics, mainly related to ignoring the intervention of protection systems during severe wind events.info:eu-repo/semantics/publishe

An efficient probabilistic approach to dynamic resilience assessment of power systems with large-scale integration of distributed generation

info:eu-repo/semantics/publishe

A Probabilistic Approach to Dynamic Resilience Assessment of Power Systems

info:eu-repo/semantics/publishe

Towards a 3-level blackout probabilistic risk assessment: achievements and challenges

info:eu-repo/semantics/publishe

Two-level integrated probabilistic analysis of the blackout risk in transmission power systems

info:eu-repo/semantics/publishe

Two-level blackout probabilistic risk analysis: application to a test system

info:eu-repo/semantics/publishe

Determination and analysis of pesticide residues in fieldgrown and greenhouse-grown tomatoes using liquid chromatography-mass spectrometry

The present study aimed to extract pesticide residues in the field and greenhouse-grown tomatoes and homemade paste based on the quick, easy, cheap, effective, rugged, and safe sample preparation method (QuEChERS) before determined by the liquid chromatography-mass spectrometry (LC-MS). The mean difference in percentage reduction of deltamethrin (DLM) and acetamiprid (ACT) in raw tomatoes of greenhouse-grown was obtained at 91.42 and 90.00%, respectively, which was insignificantly more than filed condition (84.91% and 86.34%). Maximum reduction percentages of the DLM in paste under greenhouse and field tomato conditions were achieved by more than 95.86% and 93.11%, respectively. The residual concentration of both DLM (91.42%) and ACT (90.00%) in the greenhouse decreased more than the field (84.91% and 86.34%), respectively. Abamectin(ABA) reached below the MRL in a shorter time after spraying (2 days). Considering the pre-harvest interval (PHI) period of deltamethrin and abamectin can reach their residual concentration to the MRL in both conditions, which were determined by LC-MS. According to the results of the current study, 7 and 5 days can be suggested as the PHI period of the acetamiprid for field and greenhouse-grown tomatoes, respectively. Therefore, using pesticides in the proper dosage, considering appropriate PHI, and harvesting can reduce their residues in agricultural product