Distribution Network Model Research Articles

A Hybrid Distribution Network Model for Cost, Quality and Distribution Optimization in Perishable Goods Supply Chains

The entrenched role of syndicates and intermediaries in the perishable goods sector inflates prices through artificial scarcity and hoarding, creating significant affordability challenges. Addressing these issues is critical amid rising global demand for fresh produce, which is highly vulnerable to quality degradation due to Supply Chain (SC) inefficiencies and inadequate cold storage practices. Consequently, formulating an SC Distribution Network (SCDN) becomes imperative to optimize distribution planning, mitigate quality deterioration, and ensure the sustainability of the SC. This research proposes an advanced SCDN architecture by developing a mixed-integer linear programming (MILP) model tailored for multi-echelon scenarios. The model aims to minimize overall SC costs, reduce cold storage expenses, and preserve the freshness of perishable goods through an efficient and hybrid distribution channel. The proposed model integrates competing objectives by addressing a multi-criteria problem via the weighted sum method (WSM) and is executed using the GUROBI optimizer in Python. Two case studies centred on the distribution of Mango and Jack fruits in Bangladesh validate the model's practicality. The findings highlight the strategic importance of optimal distribution center placement and a dual supply strategy, with plants meeting 63% of mango and 53% of jackfruit demand, reducing reliance on intermediaries by advocating direct shipping to markets while bypassing cold storage. This study further highlights the model's robustness and offers critical managerial insights, facilitating informed decision-making in the complex landscape of perishable product supply chains.

Optimization of Active Distribution Network Operation with SOP Considering Reverse Power Flow

As the penetration of distributed renewable energy increases, the phenomenon of bidirectional power flow in distribution networks becomes increasingly severe. Traditional regulation devices like OLTC (on-load tap changer) and CB (capacitor bank) cannot effectively mitigate reverse power flow in distribution networks due to their limitations. The transmission capacity of the distribution network under reverse power flow is approximately 50% of the rated capacity of the OLTC, leading to issues such as voltage limit violations and high wind and solar curtailment rates. This paper proposes a method for calculating the reverse power flow delivery capacity of distribution networks, quantitatively describing the distribution network’s delivery limits for reverse power flow. Based on this, a joint optimization model for multiple distribution networks with an SOP is established. The SOP is utilized to share reverse power flow delivery capacity among multiple distribution networks, enhancing operational economy and increasing the accommodation of the DG. Finally, the method’s effectiveness and correctness are verified in the IEEE 33-node system. The results validate that while joint operation does not enhance the reverse flow transmission capacity of a single distribution network, it can, through the shared reverse flow transmission capacity approach, elevate the reverse flow transmission capacity to approximately 70% during the majority of time periods.

Opinions on dynamic topology reconfiguration of distribution networks for PV hosting capacity enhancement

This paper investigates on dynamic topology reconfiguration of distribution networks to enhance PV hosting capacity. Firstly, a dynamic topology reconfiguration model of distribution networks considering N-1 security constraints is proposed to accommodate intermittent PV generation, and thus high penetration of renewable resources can be ensured to satisfy operational requirements under both normal and fault conditions with load transfer of feeders. Then, a hosting capacity enhancement strategy is presented to decrease risks of voltage and line overloading with reactive power flexibility of energy storage inverters, and is implemented to enhance the maximum hosting capacity of distributed renewable energy sources. Finally, simulation results have validated the effectiveness of the proposed method for PV hosting capacity enhancement of distribution networks.

Dynamic equivalent modelling for active distributed network considering adjustable loads charging characteristics

AbstractAs more renewable energy generators and adjustable loads such as electric vehicles are being connected to the power grids, load modelling of the distribution network becomes more complicated. Therefore, this paper explores a dynamic equivalent modelling method for active distribution network that takes into account electric vehicle charging. First of all the combination of integrated ZIP loads and motors is adopted as an equivalent model for active distribution networks. Subsequently, a four‐layer, tri‐stage deep reinforcement learning approach is used to solve the relevant key parameters of the proposed equivalent model. The method proposed in this paper fully utilizes the superiority of reinforcement learning in decision making, while the method combines the excellent feature extraction capability of deep learning. The method utilizes measurements obtained at boundary nodes to obtain an active distributed network equivalent model after a series of calculations. At the same time, adjustable loads are identified in detail. On the other hand, this method introduces a prioritized empirical playback mechanism, log‐cosh loss function, and adaptive operator to improve the computational efficiency of the method. From the simulation results, the present method is effective.

Low-carbon optimal scheduling for distribution networks under supply and demand uncertainty

This paper presents a low-carbon optimal scheduling model for distribution networks with wind and photovoltaic (PV), accounting for supply and demand uncertainties. The model optimizes thermal generation costs, wind and PV maintenance costs, and carbon emissions using a chance-constrained approach with fuzzy variables. The clear equivalent class method simplifies these constraints for easier problem-solving. Validation on the IEEE-30 node system shows the model reduces costs by 32.9% and carbon emissions by 19.2% compared to traditional scheduling, effectively lowering both costs and the carbon footprint. This real-world optimization approach tackles uncertainty in renewable energy supply and improves system efficiency and sustainability.

A tri-level model for optimal management of active distribution networks enabling two-layer local markets

The deregulation of distribution systems enable local energy trading under the umbrella of local market strategies, which are launched and cleared by local entities like distribution system operators. The emergence of active players connected to distribution networks such as microgrids path the way to two-layer market structures, within which different local markets must coexist and coordinate. This new paradigm advocates for developing new management and market models suitable for multi-level local markets. This paper focuses on this issue. In particular, a new management model for active distribution networks enabling two-layer local markets is developed, which casts as an original tri-level optimization approach. This paper applies the developed methodology to the case in which different microgrids connected to a distribution market launch local markets for energy trading within the microgrid, but it could be applied to similar frameworks such as energy communities or virtual power plants. The new approach stablishes a game-oriented market coordination which preserves the hierarchical feature of the coordinated market strategy. A case study on a 33-bus radial distribution network serves to validate the model and illustrates how active players such as distributed generators and flexible demands perform under local market rules. A number of scenarios are studied under different distributed generation and microgrid penetration, showing that incrementing the number of both leads to improve the economy of the distribution system operator and microgrids. Finally, a sensitivity analysis regarding the renewable generation potential is studied.

Static voltage bilayer optimization for distribution networks based on Sobol’ method

Abstract In active distribution networks, the uncertainty of a large number of distributed power sources makes the voltage stability problem of distribution networks more and more prominent. The existing optimization methods tend to deal with the economy and voltage stability of distribution networks as independent objectives, with less consideration of their connection, making it difficult to realize the efficient use of flexible resources. The paper introduces upper and lower connection factors and proposes a two-layer optimization model for distribution network voltage using the Sobol’ method. First, taking into account the uncertainty of distributed power sources and loads in active distribution networks, a probabilistic tidal current calculation model for active distribution networks is constructed, based on which the impact of load fluctuations on the L index is quantitatively analyzed by the global sensitivity of the Sobol’ method. Secondly, the upper and lower connection factors are calculated based on the sensitivity analysis results. The upper economic objective and the lower stability objective are connected through the connection factors to establish a two-layer optimization model to solve the flexible resources in the system optimally. Finally, simulations on the modified IEEE33 node system validate that the proposed optimization model enhances distribution network security while considering economic factors.

Modeling and optimization of power distribution network using response surface methodology for power integrity analysis

Modeling and optimization of power distribution network using response surface methodology for power integrity analysis

Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms

ProblemWith the rapid development of social economy, the problem of line losses in distribution networks gradually becomes prominent, which directly affects the efficiency and economy of power systems.MethodologyIn order to reduce line losses, a loss optimization model for low and medium voltage distribution networks based on an improved Gray Wolf optimization support vector machine is proposed. The optimization model introduces a dimensional learning strategy based on the original model to enhance the adaptability and robustness of the model.ResultsThe experimental results show that the Mean Absolute Percent Error (MAPE) of the proposed algorithm is 8.62%, the Mean Absolute Error (MAE) is 1.30% and the Root Mean Square Error (RMSE) is 2.26%. Compared with the traditional Gray Wolf Optimized Support Vector Machine, the errors of the improved model are reduced by 15.27%, 3.33% and 4.70%, respectively.ContributionsOur study demonstrates that extracellular vesicles secreted by the gut microbiota can influence the nervous system via the microbial-gut-brain axis. Furthermore, we found that the extracellular vesicles secreted by the gut microbiota from the probiotic group exert a beneficial therapeutic effect on anxiety and hippocampal neuroinflammation.

Short-term water demand prediction using stacking regression–based machine learning

ABSTRACT Forecasting of water demand and equitable allocation of local water resources are used to reduce and eliminate water shortages and waste. The key emphasis of this research article is to estimate water demand using the prediction model for the Peroorkada urban water distribution network. The characteristics, such as head, pressure, and base demand, related to the water demand were the features of the prediction model. The prediction model has been developed using python. The water distribution network consists of 99 nodes. The demand graph for a time interval of 6 h has been plotted and predicted for all the nodes, and 24-h interval demand has been plotted for vulnerable nodes, which were determined by the sensor placement toolkit. This study included 13 machine learning algorithms, including three hybrid/stacked regression techniques. The least absolute shrinkage and selection operator-based stacking regressor model performs the best at demand prediction. Single prediction models were outperformed by stacking regressor models.

Optimization Research on the Impact of Charging Load and Energy Efficiency of Pure Electric Vehicles

In this paper, the negative impact of the charging load generated by the disorderly charging scheme of large-scale pure electric vehicles on the operation performance of the power grid system and the problem of reducing its charging energy efficiency are studied and analyzed. First, based on Matlab 2022a simulation software and the Monte Carlo random sampling method, the probability density model of the factors affecting the charging load is constructed, and the total charging load of different quantities is simulated. Second, the IEEE33-node distribution network model is introduced to simulate the influence of charging load on the grid under different permeability schemes. Finally, the multi-objective genetic algorithm is used to optimize the charging cost and battery life. Taking the 20% permeability scheme as an example, the research results show that, compared with the disorderly charging scheme, the multi-objective optimization scheme reduces the peaking valley difference rate by 24.34%, the charging load power generation cost by 29.5%, and the charging cost by 23.9%. The power grid profit increased by 45.8%, and the research conclusion has practical significance for the energy efficiency optimization of pure electric vehicles.

Nash Bargaining-Based Coordinated Frequency-Constrained Dispatch for Distribution Networks and Microgrids

As the penetration of distributed renewable energy continues to increase in distribution networks, the traditional scheduling model that the inertia and primary frequency support of distribution networks are completely dependent on the transmission grid will place enormous regulatory pressure on the transmission grid and hinder the active regulation capabilities of distribution networks. To address this issue, this paper proposes a coordinated optimization method for distribution networks and microgrid clusters considering frequency constraints. First, the confidence interval of disturbances was determined based on historical forecast deviation data. On this basis, a second-order cone programming model for distribution networks with embedded frequency security constraints was established. Then, microgrids performed economic dispatch considering the reserves requirement to provide inertia and primary frequency support, and a stochastic optimization model with conditional value-at-risk was built to address uncertainties. Finally, a cooperative game between the distribution network and microgrid clusters was established, determining the reserve capacity provided by each microgrid and the corresponding prices through Nash bargaining. The model was further transformed into two sub-problems, which were solved in a distributed manner using the ADMM algorithm. The effectiveness of the proposed method in enhancing the operational security and economic efficiency of the distribution networks is validated through simulation analysis.

Intelligent fault diagnosis in power distribution networks using LSTM-DenseNet network

This paper introduces a novel fault diagnosis method that combines DenseNet and Long Short-Term Memory (LSTM) networks. The DenseNet utilizes its unique dense block structure to detect subtle variations in three-phase voltage and zero-sequence current signals. In addition, the Squeeze-and-Excitation (SE) module is introduced in DenseNet. The SE module enhances DenseNet's feature representation by adapting the importance of each channel in the feature map. Furthermore, integrating the LSTM model enables capturing time-domain features of fault signals, enhancing the analysis of waveform changes and trends. These extracted features are subsequently fused in a cascaded manner, leveraging the strengths of both approaches to obtain a more comprehensive information representation. To better explain the capability of feature extraction in each part of the model, t-distributed Stochastic Neighbor Embedding (t-SNE) method is used for visual analysis. The proposed method is evaluated using two distribution network models, namely the 10 kV and IEEE34 networks, in simulation. The verification results indicate that the proposed method achieves exceptionally high accuracy in fault identification for both tested distribution network models, with rates of 99.87 % and 99.82 %, respectively, while also demonstrating robust performance in noisy environments. This performance surpasses that of other related methods, underscoring the enhanced effectiveness of our approach.

Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory

In the context of integrating renewable energy sources such as wind and solar energy sources into distribution networks, this paper proposes a proactive low-carbon dispatch model for active distribution networks based on carbon flow calculation theory. This model aims to achieve accurate carbon measurement across all operational aspects of distribution networks, reduce their carbon emissions through controlling unit operations, and ensure stable and safe operation. First, we propose a method for measuring carbon emission intensity on the source and network sides of active distribution networks with network losses, allowing for the calculation of total carbon emissions throughout the operation of networks and their equipment. Next, based on the carbon flow distribution of distribution networks, we construct a low-carbon dispatch model and formulate its optimization problem within a Markov Decision Process framework. We improve the Soft Actor–Critic (SAC) algorithm by adopting a Gaussian-distribution-based reward function to train and deploy agents for optimal low-carbon dispatch. Finally, the effectiveness of the proposed model and the superiority of the improved algorithm are demonstrated using a modified IEEE 33-bus distribution network test case.

An Attentional Graph Neural Network-based Fault Point Positioning Model For Low-voltage Distribution Networks

An Attentional Graph Neural Network-based Fault Point Positioning Model For Low-voltage Distribution Networks

Chance-constrained bi-level optimal scheduling model for distribution network with thermal controllable load aggregators

An increasing amount of distributed renewable energy is being integrated into distribution networks to achieve decarbonization. It is essential to exploit demand-side flexible resources further to enhance system flexibility in response to the intermittency and unpredictability of renewable energy sources. This paper introduces a polytope-based aggregation method for thermostatically controlled loads (TCLs), aggregating numerous individual TCLs into a unified virtual battery via the aggregator (AGG). This approach avoids the dimensionality curse faced by the distribution system operator when directly controlling each TCL, while efficiently utilizing TCL flexibility. Subsequently, a bi-level optimization model is established, where AGGs are treated as independent stakeholders participating in the distribution network scheduling optimization through the local energy market. This model incorporates chance constraints to address the uncertainty of renewable energy sources. Finally, the distributionally robust chance constraint (DRCC) method is used to convert chance constraints into a linear form, and strong duality theory and Karush–Kuhn–Tucker conditions are applied to transform the bi-level model into a single-level model with equilibrium solutions. Case studies on the IEEE 33-bus network demonstrate that the proposed polytope-based aggregation method substantially improves computational efficiency with minimal optimality loss. Additionally, the DRCC method offers superior economic performance compared to robust and deterministic optimization approaches, while maintaining robustness.

Two-Stage Optimal Scheduling Strategy of Microgrid Distribution Network Considering Multi-Source Agricultural Load Aggregation

With the proposed “double carbon” target for the power system, large-scale distributed energy access poses a major challenge to the way the distribution grid operates. The rural distribution network (DN) will transform into a new local power system primarily driven by distributed renewable energy sources and energy storage, while also being interconnected with the larger power grid. The development of the rural DN will heavily rely on the construction and efficient planning of the microgrid (MG) within the agricultural park. Based on this, this paper proposes a two-stage optimal scheduling model and solution strategy for the microgrid distribution network with multi-source agricultural load aggregation. First, in the first stage, considering the flexible agricultural load and the market time-of-use electricity price, the economic optimization is realized by optimizing the operation of the schedulable resources of the park. The linear model in this stage is solved by the Lingo algorithm with fast solution speed and high accuracy. In the second stage, the power interaction between the MG and the DN in the agricultural park is considered. By optimising the output of the reactive power compensation device, the operating state of the DN is optimised. At this stage, the non-linear and convex optimization problems are solved by the particle swarm optimization algorithm. Finally, the example analysis shows that the proposed method can effectively improve the feasible region of safe operation of the distribution network in rural areas and improve the operating income of a multi-source agricultural load aggregation agricultural park.

Multi-Stage Rolling Grid Expansion Planning for Distribution Networks Considering Conditional Value at Risk

Existing single-stage planning and multi-stage non-rolling planning methods for distribution networks have problems such as low equipment utilization efficiency and poor investment benefits. In order to solve the above problems, this paper firstly proposes a multi-stage rolling planning method for distribution networks based on analyzing the limitations of the existing planning methods, which divides the planning cycle of the distribution network into multiple planning stages, and makes rolling amendments to the planning scheme of each stage according to the latest information during the planning cycle. Then, a multi-stage rolling planning model of distribution network taking into account conditional value at risk is established with the objective of minimizing the total investment and operation cost of the distribution network. On the one hand, the users’ electricity bill is taken into account in the objective function, and the necessity of this part of the benefits is demonstrated. On the other hand, the conditional value at risk is used to quantify the uncertainty of the operation cost in the process of the expansion planning of the distribution network, which reduces the operation cost risk of the distribution network. Next, this paper uses the rainflow counting method to characterize the capacity decay characteristics of energy storage in the distribution network, and proposes an iterative solution framework that considers energy storage capacity decay to solve the proposed model. Finally, the proposed method is applied to an 18-node distribution network planning case. This confirms that the multi-stage rolling planning method could improve the investment benefits and reduce the investment cost by approximately 27.27%. Besides, it will increase the total cost by approximately 2750 USD in the case if the users’ electricity bill is not taken into account. And the maximum capacity of energy storage may decay to 87.6% of the initial capacity or even lower during operation, which may cause the line current to exceed the limit if it is not taken into account.

A Post-Disaster Fault Recovery Model for Distribution Networks Considering Road Damage and Dual Repair Teams

A Post-Disaster Fault Recovery Model for Distribution Networks Considering Road Damage and Dual Repair Teams

A two-layer economic resilience model for distribution network restoration after natural disasters

Improving the resilience of energy networks and minimizing outages is crucial, making the development of self-healing strategies essential for ensuring a continuous power supply and boosting resilience. As decentralized prosumers become more integrated into smart grids, innovative coordination methods are required to fully leverage their potential and improve the system's self-healing capabilities. Hence, in this paper, we present a decentralized two-layer model for active distribution networks (ADNs) incorporating commercial energy hubs, Parking lots (PLs), hydrogen refueling stations, and mobile units (MUs). This model aims to maximize the flexible capacities of flexible prosumers under emergencies and includes a dynamic mechanism for transporting hydrogen to refueling stations via trucks. In the first layer of the model, the AND operator provides the required services to flexible prosumers, including commercial energy hubs, PLs and hydrogen refueling stations. During this layer, the movement paths of mobile units, such as battery-based mobile storage units (MSUs) and mobile hydrogen units (MHUs), are determined. In the second layer, prosumers independently plan and decide the extent of their service contributions. Subsequently, with the actual service capacities established, the AND operator re-optimizes the initial planning to finalize the system schedule and precise movement paths of the mobile units. The proposed model was tested on a modified 83-bus distribution network using the CPLEX solver in GAMS. Simulation results demonstrate a 54.9 % reduction in forced load shedding (FLS) due to the dispatch of mobile units and a further 84.4 %% reduction in FLS from leveraging the flexible capacities of commercial energy hubs and PLs.