Real time OpenDSS framework for distribution systems simulation and analysis

Montenegro, Davis; Hernandez, Miguel; Ramos, Gustavo

doi:10.1109/tdc-la.2012.6319069

Cited by 74 publications

(47 citation statements)

References 10 publications

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“…In general, when all loads are regarded as PQ constant, Newton-Raphson method can be applied either by its complete formulation [22] or decoupled formulation [23]. Other Gauss-based methods suitable to be applied at distribution level can be used instead [24,25].…”

Section: Evaluation Of Power Losses To Be Allocated Among the Networkmentioning

confidence: 99%

Energy Loss Allocation in Smart Distribution Systems with Electric Vehicle Integration

2018

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This paper presents a three-phase loss allocation procedure for distribution networks. The key contribution of the paper is the computation of specific marginal loss coefficients (MLCs) per bus and per phase expressly considering non-linear load models for Electric Vehicles (EV).The method was applied in a unbalanced 12.47 kV feeder with 12,780 households and 1000 EVs under peak and off-peak load conditions. Results obtained were also compared with the traditional roll-in embedded allocation procedure (pro rata) using non-linear and standard constant power models. Results show the influence of the non-linear load model in the energy losses allocated. This result highlights the importance of considering an appropriate EV load model to appraise the overall losses encouraging the use and further development of the methodology

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Section: Evaluation Of Power Losses To Be Allocated Among the Networkmentioning

confidence: 99%

Energy Loss Allocation in Smart Distribution Systems with Electric Vehicle Integration

2018

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“…Load profiles and EV charging profiles are generated by MATLAB by calling either the CREST tool or codes based on the model aforementioned. Load flow analysis function is conducted by Open Distribution System Simulator (OpenDSS), which is an open source software for electric distribution systems simulation [12]. Fig.1 shows an overall structure of the proposed simulation platform.…”

Section: A Network Simulation Platformmentioning

confidence: 99%

Electric Vehicle charging management algorithm for a UK low-voltage residential distribution network

Qiao

Yang

2016

2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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Abstract-The integration of Electric Vehicles (EVs) into lowvoltage (LV) residential distribution networks inevitably increases the overall demand, especially peak demand, which may cause thermal or voltage issues. In this paper, a 400V practical residential distribution network is modelled and used to quantify these impacts due to the growing penetration of EVs. Residential load profiles in 1-minute resolution and EV charging profiles with recorded State of Charge (SOC) are randomly and statistically created. Then, a simple charging management algorithm with locally made decision is suggested at EV users' charging points. Results prove that this approach can mitigate the negative impacts of EV charging on network assets. Moreover, it can reduce EV users' electricity cost for charging based on existing UK electricity price scheme 'Economy 7,' without compromising EV usage or substantial network infrastructure reinforcement or installation of extensive monitor, control and communication system. The simulation models and analysis are implemented in MATLAB/OpenDSS as an LV distribution network simulation platform.

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“…Use network performance modeling and simulation tool, such as the Open Distribution System Simulator (OpenDSS) [18], allows quick changes to the network design and its model for dynamic operation optimization and for the design optimization of the ADN with different network configurations. Firstly, the automatic and quick generation of optimal operation schedule for a given ADN based on the modeling tool's accurate prediction of network performance is needed for the real-time optimal operation and control of the ADN.…”

Section: Introductionmentioning

confidence: 99%

Application and Comparison of Metaheuristic and New Metamodel Based Global Optimization Methods to the Optimal Operation of Active Distribution Networks

et al. 2018

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As an imperative part of smart grids (SG) technology, the optimal operation of active distribution networks (ADNs) is critical to the best utilization of renewable energy and minimization of network power losses. However, the increasing penetration of distributed renewable energy sources with uncertain power generation and growing demands for higher quality power distribution are turning the optimal operation scheduling of ADN into complex and global optimization problems with non-unimodal, discontinuous and computation intensive objective functions that are difficult to solve, constituting a critical obstacle to the further advance of SG and ADN technology. In this work, power generation from renewable energy sources and network load demands are estimated using probability distribution models to capture the variation trends of load fluctuation, solar radiation and wind speed, and probability scenario generation and reduction methods are introduced to capture uncertainties and to reduce computation. The Open Distribution System Simulator (OpenDSS) is used in modeling the ADNs to support quick changes to network designs and configurations. The optimal operation of the ADN, is achieved by minimizing both network voltage deviation and power loss under the probability-based varying power supplies and loads. In solving the computation intensive ADN operation scheduling optimization problem, several novel metamodel-based global optimization (MBGO) methods have been introduced and applied. A comparative study has been carried out to compare the conventional metaheuristic global optimization (GO) and MBGO methods to better understand their advantages, drawbacks and limitations, and to provide guidelines for subsequent ADN and smart grid scheduling optimizations. Simulation studies have been carried out on the modified IEEE 13, 33 and 123 node networks to represent ADN test cases. The MBGO methods were found to be more suitable for small- and medium-scale ADN optimal operation scheduling problems, while the metaheuristic GO algorithms are more effective in the optimal operation scheduling of large-scale ADNs with relatively straightforward objective functions that require limited computational time. This research provides solution for ADN optimal operations, and forms the foundation for ADN design optimization.

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Real time OpenDSS framework for distribution systems simulation and analysis

Cited by 74 publications

References 10 publications

Energy Loss Allocation in Smart Distribution Systems with Electric Vehicle Integration

Energy Loss Allocation in Smart Distribution Systems with Electric Vehicle Integration

Electric Vehicle charging management algorithm for a UK low-voltage residential distribution network

Application and Comparison of Metaheuristic and New Metamodel Based Global Optimization Methods to the Optimal Operation of Active Distribution Networks

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