Distribution Network Model Research Articles

Line loss calculation model of low-voltage distribution network under harmonic and three-phase unbalance disturbances and its experimental validation

With the grid integration of distributed power sources and traction loads, the power quality problem of the power system has become more and more serious, aggravating the distribution network line losses, so it is important to study the calculation model of distribution network line loss under power quality disturbance in order to find loss reduction measures. This paper first theoretically derives the calculation model of line loss of low-voltage distribution network under separate disturbance of 2 common power quality problems, namely, harmonics and three-phase unbalance. Then, using the composite power quality disturbance decoupling method, the calculation model of line loss of low-voltage distribution network under the disturbance of harmonics and three-phase unbalance is derived, which improves the accuracy of line loss calculations. Finally, experiments are conducted for each part by Matlab/Simulink simulation tool to verify the correctness of the theoretical derivation.

The multilevel coordinated optimal control method of a photovoltaic distribution network based on an improved FPA algorithm

The control method of the photovoltaic distribution network has problems of low control accuracy and high line loss of the photovoltaic distribution network. Therefore, this paper proposes a multilevel coordinated optimization control method of a photovoltaic distribution network based on an improved FPA algorithm. First, we analyze the FCM algorithm, use the Euclidean distance to calculate the number of clusters, analyze the basic principle of the flower pollination algorithm, build the coordinated optimization control model of the photovoltaic distribution network, calculate the power flow result of the photovoltaic distribution network by improving the FPA algorithm, determine the global pollination behavior according to the flower pollination algorithm, and use the greedy strategy to obtain the optimal candidate solution. At this time, the obtained solution is the multi-level coordinated optimization control result of the photovoltaic distribution network. The experimental results show that this method can effectively improve the multi-level coordination and optimization control effect of the photovoltaic distribution network and reduce the line loss of the photovoltaic distribution network.

Second-Order Cone Programming (SOCP) Model for Three Phase Optimal Power Flow (OPF) in Active Distribution Networks

With the high penetration of distributed generations (DGs), modern distribution systems require novel and efficient optimal power flow (OPF) models. This paper proposes a second-order cone programming (SOCP) based AC-OPF model for three-phase radial power distribution networks. Mutual coupling effects are generally ignored in the existing multi-phase SOCP AC-OPF models. To overcome this critical issue, the proposed SOCP-OPF model introduces a coupling coefficient for the mutual coupling effects on the three-phase unbalanced lines. The derivation of the coupling coefficients has been illustrated with the required proof that the relaxation is tight and the solution from the proposed OPF model is optimal for an unbalanced multi-phase distribution network. The SOCP-OPF model is evaluated on several IEEE standard distribution networks without and with high penetration of Distributed Energy Resources (DERs). It has been shown that the proposed OPF model provides an optimal global solution for convex objective functions. Also, this OPF model is scalable and computationally efficient compared to Nonlinear Programming (NLP) and other convex OPF models.

A single-phase ground fault intelligent wire selection method based on an ant colony algorithm

For the single-phase ground fault, which has the highest probability of occurrence in a 10 kV distribution network, the ant colony algorithm is combined with a neural network for fault line selection, and the algorithm is optimized. The study builds a 10 kV distribution network model in MATLAB and simulates the neutral ground fault. It compares the results of the optimized line selection algorithm, and the traditional line selection algorithm proves that the accuracy of the method in identifying faulty lines is significantly better than that of the traditional method.

A two‐stage adaptive‐robust optimization model for active distribution network with high penetration wind power generation

AbstractHigh penetration of uncertain wind power generation brings challenges to power system operational security and economy. Here, an adjustable box uncertainty set is first presented to characterize the spatiotemporal correlation of multiple wind power generations. Afterward, a two‐stage risk‐adjusted robust energy dispatching model is established as a min‐max‐min optimization problem for active distribution networks operating with high penetration wind power generation. Then a three‐layer Nest Column and Constraint Generation (NCCG) decomposition approach is designed to efficiently solve the proposed model. Finally, the proposed two‐stage adaptive robust optimization model and NCCG approach are validated by a distribution network with uncertain wind power generations, and the simulation results indicate the distribution network operation economy could be compromised with robustness by adjusting the conservative regulation parameter.

Multivariate stochastic modeling of plugin electric vehicles charging profile and grid impact analysis

The electrification of the transportation sector presents a significant challenge to the power grid. The accurate modeling of the plug-in electric vehicle (PEV) charging load is essential since it can guide policymakers and grid operators to make informed decisions regarding grid reinforcement to facilitate the additional charging demand of PEVs. This research paper introduces a hybrid technique for estimating the aggregated PEV charging load based on a stochastic model and real-world dynamics. The proposed methodology includes multivariate stochastic modeling of PEV driving behaviors which is combined with the powertrain simulations performed on multiple models of PEVs operating under two different driving cycles to determine critical parameters such as energy consumption, the initial state of charge (SOC), and grid charging energy requirements. These parameters are used to estimate daily PEV charging demand based on real-world dynamics. The grid impact analysis is performed on the model of an actual distribution network, located at the National University of Sciences and Technology (NUST), through load flow analysis to identify the weakest nodes in the grid and the percentage of transformer loading due to additional PEV charging load. In addition, the research paper proposes a techno-economic grid reinforcement solution, performed on the Homer Pro, to minimize the detrimental impact of the additional PEV charging load on grid voltage stability. The proposed solution includes measures such as optimal placement of distributed generations (DGs), including photovoltaic (PV) panels, storage systems, and diesel generators, to ensure reliable and efficient charging infrastructure for PEVs.

Evaluation Method of the Incremental Power Supply Capability Brought by Distributed Generation

More and more distributed generation (DG) and energy storage (ES) devices are being connected to the distribution network (DN). They have the potential of maintaining a stable supply load during failure periods when using islanding operations. Therefore, DG and ES have capacity value, i.e., improving the power supply capability of the system. However, there are strong fluctuations in DG outputs, and the operations of ES devices have sequential characteristics. The same capacity of DG has different load-bearing capabilities compared to conventional thermal or hydroelectric units. This paper proposes a method for evaluation of power supply capability improvement in DNs. First, the temporal fluctuation in both power source and load demand during fault periods is considered. A DN island partition model considering the secondary power outage constraint is established. Then, a modified genetic algorithm is designed. The complex island partition model is solved to achieve accurate power supply reliability evaluation. And the incremental power supply capability associated to DG and ES devices is calculated. Finally, a case study is conducted on the PG&E 69-bus system to verify the effectiveness of the proposed method. It is found that with a 20% configuration ratio of ES devices, the power supply capability improvement brought about by 6 MW DG can reach about 773 kW.

Distributed new energy information acquisition model of distribution network based on Beidou communication

Abstract Aiming at the low efficiency of distributed new energy information collection in distribution network and the poor encryption of information transmission process, a distributed new energy information collection model based on Beidou communication is proposed. The new energy information acquisition terminal, data acquisition and transmission equipment, Beidou satellite communication module and dispatching master terminal are designed respectively to form the hardware module of the distribution network distributed new energy information acquisition model. Use Beidou communication to collect new energy information, realize the encrypted transmission of new energy information based on Beidou short message, and complete the construction of distribution network distributed new energy information collection model based on Beidou communication. The experimental results show that the bandwidth allocation effect of the Beidou benchmark station of the model is good, the Beidou communication distance is long, the data encryption can be completed in a small space by using the model, the transmission efficiency of new energy data is high, and the data integrity can be effectively guaranteed.

Spatial optimization of integrated energy system including hydrogen electrolysis center based on mixed integer second‐order cone programming

AbstractHydrogen energy is playing an increasingly important role in integrated energy systems (IES) due to its multiple uses. Green hydrogen can be produced from electricity generated from renewable energy, reducing carbon emissions and environmental pollution. Previous work usually only considers electric vehicles (EVs) as flexible loads, and there are few studies on the production and application process of green hydrogen. In this paper, hydrogen electrolysis centers (HECs) and EVs are simultaneously schedulable loads, integrated into IES, and optimized. The distribution network model is established, and the optimization goal is to minimize network loss while ensuring the safe operation of the power grid. Through the power flow optimization method of mixed‐integer second‐order cone programming, the space optimization scheduling of EVs in the IES is carried out, and the optimal location of the HEC is reasonably planned and set. A simulation instance of IEEE 33 nodes is used to verify the proposed method. The final results show that this method can minimize network loss in the range of safe operation of the system for the spatial scheduling of electrolytic hydrogen centers and EVs. At the same time, it can also provide a reference for HEC addressing.

An improved discrete particle swarm optimization approach for a multi-objective optimization model of an urban logistics distribution network considering traffic congestion

To optimize urban logistics networks, this paper proposes a multi-objective optimization model for urban logistics distribution networks (ULDN). The model optimizes vehicle usage costs, transportation costs, penalty costs for failing to meet time windows, and carbon emission costs, while also considering the impact of urban road traffic congestion on total costs. To solve the model, a DPSO (Discrete Particle Swarm Optimization) algorithm based on the basic principle of PSO (Particle Swarm Optimization) is proposed. The DPSO introduces multiple populations to handle multiple targets and uses a variable neighbourhood search strategy to improve the search ability of particles, which helps to improve the local search ability of the algorithm. Simulation results demonstrate the effectiveness of the proposed model in avoiding traffic congestion, reducing carbon emissions costs, and time penalty costs. The optimization comparison results between DPSO and PSO also verify the superiority of the DPSO algorithm. The proposed model can be applied to real-world urban logistics networks to improve their efficiency, reduce costs, and minimize environmental impact.

Novel Faulted-Section Location Method for Active Distribution Networks of New-Type Power Systems

This paper puts forward a fault location method combining the improved matrix algorithm and the genetic tabu algorithm based on multi-source information in view of the limitation of existing fault location methods in active distribution networks, such as long-time consumption, low accuracy, and low tolerance performance. Firstly, the fault region location method is proposed to simplify the active distribution network model and reduce the matrix calculation dimension. Secondly, the improved matrix algorithm is proposed to locate the fault section, and a positioning result verification method is proposed to improve the tolerance performance. Finally, the genetic tabu algorithm is proposed to optimize suspicious fault sections when the verification is incorrect and obtain the positioning result. The simulation results show that the fault location method proposed in this paper locates faster and performs better in both accuracy and tolerance in different fault conditions than others.

Autonomous topology planning for distribution network expansion: A learning-based decoupled optimization method

The distribution of urban electricity load is highly uneven in both space and time, posing significant challenges to the problem of distribution network expansion planning (DNEP). An autonomous topology planning method for distribution network expansion is proposed to address that, driven by a learning-based decoupled optimization approach. The planning problem is decoupled from the impact of distribution network operation and used as reinforcement cost prediction for autonomous planning after the generation of topology schemes. First, a reinforcement model of the original distribution network is built that considers the actual distance calculation method of load nodes. Then an effective method for generating historical operation samples is designed, followed by the development of a learning-based reinforcement cost prediction model for the original distribution network based on operation state learning using XGBoost, which enables the prediction of said costs under different scenarios. Utilizing a distribution network topology generating method driven by the adjustable weight minimum spanning tree (AWMST) algorithm, an autonomous topology expansion planning strategy generation method employing the learning-based decoupled optimization approach is established. The feasibility and superior analysis of the proposed method is verified and analyzed on an actual urban distribution network case with simulation scenarios. The results show that the proposed method is faster and more flexible to deal with the distribution network planning problem brought about by rapid load growth in urban distribution networks.

Resilience improvement model of distribution network based on two-stage robust optimization

Frequent natural disasters have a serious impact on the power system. It is urgent to ensure the normal operation of the distribution network under disaster events. This paper divides the stages of improving the resilience of the distribution network into two aspects: long-term investment planning and short-term response. We combine the decision-making process with the chronological order of natural disasters, and a N-k uncertainty set is applied to describe the uncertainty of distribution network damage. We propose a line hardening and distributed generations configuration strategy for the purpose of enhancing the resilience of distribution networks and countering extreme natural disasters. The problem is expressed as a tri-level robust optimization formulation in order to minimize the load shedding under the worst line damaged scheme. The first level corresponds to the long-term investment planning and preparation of pre-disaster stage. The second level represents the stage of line damage, and the third level matches the optimal scheduling during the post-disaster period. On the basis, this mixed integer linear programming problem is transformed into a solvable form, which is calculated by a modified Column and Constraint Generation (C&CG) algorithm. Case studies show that our proposed model can reduce load shedding to 70% of the original level, and the running time of the algorithm is less than 60 s, which verifies the model can effectively enhance the resilience of the distribution network and enhance its ability to respond to natural disasters.

Robust optimization model of flexible distribution network considering source-load uncertainty

The increase in renewable energy permeability and the electrical load uncertainty aggravates the fluctuation of power flow and bus voltage magnitudes of the distribution network (DN). Then, the fluctuation threatens system security and makes increasing difficulties in DN regulation. However, the widespread utilities of power electronic technology promote rapid developments in flexible distribution networks, thereby providing certain opportunities to treat the open question. Therefore, this study proposes a robust optimization model of flexible distribution network allowing for system security and economy. Firstly, a optimal power flow (OPF) model of flexible distribution network with various active management strategies is derived and constructed by taking the minimum loss and voltage offset as the objective function. The model is linearized and relaxed to convex problem by a second-order cone. Then, the robust OPF model of flexible distribution network is established considering the uncertainty of load and renewable energy. Finally, a relaxation-based co-evolution algorithm is proposed to solve the model. The modified IEEE 33 bus system and PG&E 69 bus system are involved for case studies, in which the calculation results under various scenarios are compared. Consequently, the effectiveness and practicability of proposed model to locate and cope with the worst situation is demonstrated.

Artificial Neural Networks-Based Fault Diagnosis Model for Distribution Network

With many branch lines in radiant distribution networks, diagnosing faults in a distribution network is very difficult. It is of great significance to identify different types of faults quickly and accurately for the stable operation of the power grid. This research presents a fault identification model for a distribution network based on artificial neural networks. The principal component analysis first extracts features from transitory data in a distribution network. The resulting low-dimensional data is subsequently used to update the artificial neural network model. The artificial neural network may also identify the type of fault. The proposed model’s fault detection accuracy is improved over the traditional approach by examining distribution network fault data during the simulation test.

Multi-objective Collaborative Planning Method for Micro-energy Systems Considering Thermoelectric Coupling Clusters

As the continuous development of integrated energy system and distributed power supply, operation economy and regional energy Internet reliability, especially micro-energy system, are increasing. Therefore, it is necessary to build multi-energy complementary micro-energy system, innovate energy supply mode, realize collaborative and efficient utilization among multi-energy systems, improve energy utilization efficiency and absorb renewable energy. In this paper, the decision model of distribution network planning scheme including distributed generator supply is established from four aspects: investment and operation cost, extra reserve capacity, energy conservation, reliability cost. The decision model involves a lot of parameter calculation and selection judgment, so after the decision goal is determined, an decision framework based on DS-MAS is established, that is, parameters are automatically calculated and selected based on different scenarios. Model validity is proved via a practical decision project.

Optimization of integrated energy system considering transmission and distribution network interconnection and energy transmission dynamic characteristics

The traditional hierarchical scheduling and operational model of transmission and distribution networks has failed to fully utilize the adjustable resources of the entire grid, consequently limiting the integration of renewable energy. To fully excavate the potential of transmission and distribution (T&D) coordination in enhancing the level of renewable energy assimilation and operational efficiency, in this paper, transmission grid and distribution network in the framework of synergetic unit commitment (TDS-UC) considering the dynamic characteristics of the electricity-gas-heat integrated energy system is proposed. The framework developed in this paper allows for the integration of multiple energy sources, including electricity, gas, and heat, into the dispatch strategy of the TG. To improve scheduling flexibility, the dynamic characteristics of the natural gas and heating networks are also considered in the TDS-UC model. A coordinated interactive optimization model for T&D is formulated, taking into account the multi-energy coupling relationship of the IES. To solve the TDS-UC model, a decentralized analytical target cascading algorithm is used to obtain an independent TG optimization problem and multiple ADNs local optimization problems that can be solved in parallel. The proposed model is transformed into an easy-to-solve mixed-integer linear programming (MILP) problem using an improved one-dimensional approximation piecewise linearization method to deal with the non-convexity nonlinear natural gas Weymouth equation. Numerical experiments are conducted on both a T6D2 and a T118D10 test system to demonstrate the efficiency and practicality of the proposed methodology.© 2017 Elsevier Inc. All rights reserved.

Fault Section Identification of the Power Cables in Urban Distribution Networks by Amplitude Differences Between the Zero-Sequence Currents and Those Flowing in Cable Sheaths and Armors

The power supply reliability of the urban distribution networks is being challenged by cable faults. Owing to the complex factors, such as system neutral, fault condition, and DG integration, it is difficult to detect and locate the fault cable. In this paper, the equivalent circuit model of the distribution cable is firstly created by considering the multiconductor couplings. The mathematical relation of the amplitude differences between zero-sequence currents and those flowing in cable sheaths and armors at the local end of the healthy and fault cables are then derived in theory. Finally, according to the network topology and measurement locations, a novel method for identifying the fault cable section is proposed by using the defined space-measurement matrix. The simulation model of a real urban distribution network is established in PSCAD/EMTDC. The effectiveness and robustness of the proposed method are validated. The results demonstrate that the method is barely affected by the neutral grounding mode, fault initial condition, and distributed generation (DG) penetration level.

Peer-to-peer energy trading among multiple microgrids considering risks over uncertainty and distribution network reconfiguration: A fully distributed optimization method

This paper proposes a two-layer optimization framework to co-optimize the P2P energy trading among multiple microgrids (MMGs) under uncertainty and optimal topology planning of the distribution networks (DNs). At the upper layer, the traditional verification optimal power flow model of DNs is transformed into a prosumer-focused and transaction-oriented dynamic network reconfiguration model. At the lower layer, uncertainty from wind power generations is integrated into the operating model of individual MGs and addressed by the stochastic programming (SP) method. Meanwhile, the conditional value at risk technique is introduced to find a trade-off between cost minimization and risk aversion flexibly. To establish the global negotiation mechanism among all participants (not only between distribution system operators and MGs, but also among MMGs), a fully distributed method is developed by combining an analytical target cascading algorithm and an alternating direction multiplier method. Furthermore, a diagonal quadratic approximation method is utilized to linearize the quadratic penalty term so that achieving parallel computing for all independent optimization subproblems. Simulations of different strategies, models, and distributed algorithms are implemented to verify the rationality and validity of the proposed method. The results of these case studies demonstrate that the proposed risk-averse SP approach can avoid over-optimistic solutions, the obtained P2P trading strategies are immune to uncertainty and P2P trading behaviors among MMGs can help reduce network losses of DNs. In addition, comparisons with other distributed algorithms verify the high performance of the proposed fully distributed method.

Shall we always use hydraulic models? A graph neural network metamodel for water system calibration and uncertainty assessment

Representing reality in a numerical model is complex. Conventionally, hydraulic models of water distribution networks are a tool for replicating water supply system behaviour through simulation by means of approximation of physical equations. A calibration process is mandatory to achieve plausible simulation results. However, calibration is affected by a set of intrinsic uncertainty sources, mainly related to the lack of system knowledge. This paper proposes a breakthrough approach for calibrating hydraulic models through a graph machine learning approach. The main idea is to create a graph neural network metamodel to estimate the network behaviour based on a limited number of monitoring sensors. Once the flows and pressures of the entire network have been estimated, a calibration is carried out to obtain the set of hydraulic parameters that best approximates the metamodel. Through this process, it is possible to estimate the uncertainty that is transferred from the few available measurements to the final hydraulic model. The paper sparks a discussion to assess under what circumstances a graph-based metamodel might be a solution for water network analysis.