Real-time congestion management in active distribution network based on dynamic thermal overloading cost

Haque, A.N.M.M.; Shafiullah, D.S.; Nguyen, Phuong H.; Bliek, Frits

doi:10.1109/pscc.2016.7540985

Cited by 25 publications

(34 citation statements)

References 14 publications

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“…Essentially the ToU tariff is predetermined by the utility and is independent of the load including the EV. Authors in [28] have considered an agent-based approach where real time price signal is used to minimize the congestion problem at MV/LV transformer. Although this paper looks into the thermal loading of the MV/LV transformer, it does not consider the influence of ambient temperature on the thermal loading of the transformer.…”

Section: Related Workmentioning

confidence: 99%

Reducing the Impacts of Electric Vehicle Charging on Power Distribution Transformers

et al. 2020

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This paper investigates the effects of high penetration levels of Electric Vehicle (EV) charging on power distribution transformers and proposes a new solution to minimize its negative impacts. There has been growing concern over Greenhouse Gas (GHG) emissions within the transportation sector, which accounts for about 23% of total energy-related carbon-dioxide emissions. The main solution to this problem is the electrification of vehicles. However, large scale integration of EVs into existing grid systems poses some challenges. One major challenge is the accelerated aging of expensive grid assets such as transformers. In this paper, a demand response mechanism based on the thermal loading of transformers, is proposed. The proposed solution is modeled as an optimization problem, where a new time of use (ToU) tariff is used to shift the EV load considering the thermal loading of transformers, thereby minimizing their accelerated aging. The simulation results show that the accelerated aging of transformers can be reduced without augmenting the existing grid.

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Section: Related Workmentioning

confidence: 99%

Reducing the Impacts of Electric Vehicle Charging on Power Distribution Transformers

et al. 2020

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“…where is the number of time intervals, the duration of each is ∆ hours and normal insulation life of the transformer is hours. According to IEEE standards, the normal insulation life of a well dried, oxygen free distribution transformer is 180,000 h or 20.55 years [2,16]. Step 2: The expected load is converted to the resulting hottest-spot temperature, θ H , of the transformer.…”

Section: Overloading Cost Of the Transformermentioning

confidence: 99%

“…Congestion or thermal overloading result from power flows through a network asset (lines, cables, transformers) exceeding its transfer capability. Even though the network assets are usually designed to withstand power flows beyond a defined margin, continuous overloading leads to ageing and subsequent failure of the distribution cables and transformer windings [2,3]. The conventional approach of reinforcing the network assets to tackle such issues necessitates a huge investment, and it is also deemed redundant, as the frequency of such issues is unpredictable [4].…”

Section: Introductionmentioning

confidence: 99%

“…However, for a market-based DR mechanism, a sound methodology for congestion management is important to reflect the realized ageing of the network assets to a representative financial loss. An integrated congestion management mechanism has been proposed in [2,16] for the residential networks that involve the dynamic loading model of a distribution transformer. In reality, procurement of flexibility can be a more complex problem involving multiple involved actors in the same network.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Tariff for Day-Ahead Congestion Management in Agent-Based LV Distribution Networks

et al. 2020

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Capacity challenges are becoming more frequent phenomena in residential distribution networks with new forms of loads, distributed renewable energy resources (RES) and price-responsive applications. Different types of demand response programs have been introduced to tackle these challenges through iterative changes in price and/or contractual participations based on incentives. In this research, a dynamic network tariff-based demand response program is proposed to address congestion problems in low-voltage (LV) networks. The formulation takes advantage of the scalable architecture of the agent-based systems that allows local decision making with limited communication. Energy consumption schedules are developed on a day-ahead basis depending on the expected cost of overloading for a number of probable scenarios. The performance of the proposed approach has been tested through simulations in the unbalanced IEEE European LV test feeder. Simulation results reveal up to 82% reduction in congestion on a monthly basis, while maintaining the quality of supply in the network.

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“…In literature, approaches exist describing the prediction of curtailment for long term scenarios in the context of expansion planning [7]- [10] and during RE integration planning [11] or for congestion management in real time grid operation [12], [13]. Short-term predictions of congestions are essentially required for participants of energy markets in the context of redispatch [2] and locational marginal prices [14].…”

mentioning

confidence: 99%

Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties

Memmel¹,

Schlüters²,

Völker³

et al. 2021

IEEE Access

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Renewable energies curtailment induced by grid congestions increase due to grown renewable energies integration and the resulting mismatch of grid expansion. Short-term predictions for curtailment can help to increase the efficiency of its management. This paper proposes a novel, holistic approach of a short-term curtailment prediction for distribution grids. The load flow calculations for congestion detection are realized by taking different operational security criteria into account, whereas the models for the node-injections are adjusted to the characteristic of each grid node specifically. The determination of required curtailment based on the resulting congestions considers uncertainties of component loading and its corresponding probability. The forecast model is validated using an actual 110 kV distribution grid located in Germany. In order to meet the requirements of a forecast model designed for operational business, prediction accuracy, and its greatest source of error are analyzed. Furthermore, a suitable length of training data is investigated. Results indicate that a six month time period for maintenance gains the highest accuracy. Curtailment prediction accuracy is better for transmission system operator components than for distribution system operator components, but the Sørensen Dice factor for the aggregated grid shows a high match of historic and predicted curtailment with a value of 0.84 and a low error for curtailed energy, which makes 2.23% of the historic curtailed energy. The model is a promising approach, which can contribute to improvement of curtailment strategies and enable valuable insight into distribution grids.

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Real-time congestion management in active distribution network based on dynamic thermal overloading cost

Cited by 25 publications

References 14 publications

Reducing the Impacts of Electric Vehicle Charging on Power Distribution Transformers

Reducing the Impacts of Electric Vehicle Charging on Power Distribution Transformers

Dynamic Tariff for Day-Ahead Congestion Management in Agent-Based LV Distribution Networks

Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties

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