Distribution Network Operators Research Articles

Hybrid Optimization-Based Sequential Placement of DES in Unbalanced Active Distribution Networks Considering Multi-Scenario Operation

The increasing penetration of distributed generation (DG) brings about great economic and environmental benefits, while also negatively affecting the operation of distribution networks due to its high intermittency. Although distributed energy storage (DES) can effectively deal with the problems caused by massive DG penetrations by decoupling the generation and consumption of electricity, the placement of DES significantly determines the effectiveness of its capabilities. Unfortunately, existing DES placement studies are commonly based on a balanced network model, whereas practical distribution networks are unbalanced. In addition, existing DES placement studies are mostly based on an extreme scenario and rarely consider the operational complexity resulting from the uncertainties of DGs and loads. To address the aforementioned challenges, this paper proposes a hierarchical and sequential DES placement strategy in distribution networks by considering multi-scenario operations. Specifically, the proposed hierarchical framework for DES placement includes three sequential layers: outer, inter, and inner. In the outer layer, a multi-scenario comprehensive loss sensitivity index (MSCLSI) is first introduced to search for the most effective DES placement location. Subsequently, the sizing and scheduling of DES for the selected location are conducted through coordinated optimization across the inter and inner layers, which can be solved using a hybrid method combining particle swarm optimization and second-order cone programming (PSO-SOCP). Finally, a series of detailed simulations are carried out over the IEEE-33 test system and the experimental results demonstrate that the proposed scheme can provide significant effectiveness and superiority compared to the state-of-the-art schemes.

Research on Typical Process Mini Programs for Overhead Distribution Lines based on Non Power Outage Operation Technology in Distribution Network

To address the challenges and risks associated with the complex design of distribution network architecture, uneven skill levels of construction personnel, and inadequate training of construction personnel, this article proposes a typical process mini program for overhead distribution lines based on uninterrupted operation of distribution networks. Through requirement analysis, the functions of the mini program were determined, and the front-end and system back-end functions of a typical process mini program for overhead distribution lines based on uninterrupted operation of the distribution network were designed. Based on the development architecture of the mini program and combined with cloud services, a typical process mini program for overhead distribution lines based on uninterrupted operation of the distribution network has been developed. The establishment of a typical process library and knowledge base for overhead distribution lines based on uninterrupted operation of the distribution network has been achieved, making it convenient for users to search, learn, and collect typical processes in a timely manner, and to learn and apply them to maintenance sites anytime, anywhere. The typical process mini program for overhead distribution lines based on uninterrupted operation of distribution network lays the foundation for better leveraging the technical advantages of uninterrupted operation of distribution network, further improving the reliability of distribution network power supply, and ensuring the accuracy of uninterrupted operation of distribution network.

Co-Optimization Operation of Distribution Network-Containing Shared Energy Storage Multi-Microgrids Based on Multi-Body Game.

Under the carbon peaking and carbon neutrality target background, efficient collaborative scheduling between distribution networks and multi-microgrids is of great significance for enhancing renewable energy accommodation and ensuring stable system operation. Therefore, this paper proposes a collaborative optimization method for the operation of distribution networks and multi-microgrids with shared energy storage based on a multi-body game. The method is modeled and solved in two stages. In the first stage, a multi-objective optimization configuration model for shared energy storage among multi-microgrids is established, with optimization objectives balancing the randomness of renewable energy fluctuations and the economics of each microgrid undertaking shared energy storage. The charging and discharging interactive power of energy storage and each microgrid at various time periods are obtained and passed to the second stage. In the second stage, with the distribution network as the leader and shared energy storage and multi-microgrids as followers, a game optimization model with one leader and 2 followers is established. The model is solved based on an outer-layer genetic algorithm nested with an inner-layer solver to determine the electricity purchase and sale prices among the distribution network, multi-microgrids, and shared energy storage at various time periods, thereby minimizing operational costs. Finally, based on the power interaction of microgrids to measure their contributions, an improved Shapley value cost allocation method is proposed, effectively achieving a balanced distribution of benefits among the distribution network, shared energy storage, and multi-microgrids, thereby improving overall operational revenue. Meanwhile, a new method for calculating the shared energy storage capacity and the upper limit of charging and discharging power based on a game framework was proposed, which can save 37.23% of the power upper limit and 44.89% of the capacity upper limit, effectively saving the power upper limit and capacity upper limit.

A Dual-Path Neural Network for High-Impedance Fault Detection

High-impedance fault detection poses significant challenges for distribution network maintenance and operation. We propose a dual-path neural network for high-impedance fault detection. To enhance feature extraction, we use a Gramian Angular Field algorithm to transform 1D zero-sequence voltage signals into 2D images. Our dual-branch network simultaneously processes both representations: the CNN extracts spatial features from the transformed images, while the GRU captures temporal features from the raw signals. To optimize model performance, we integrate the Crested Porcupine Optimizer (CPO) algorithm for the adaptive optimization of key network hyperparameters. The experimental results demonstrate that our method achieves a 99.70% recognition accuracy on a dataset comprising high-impedance faults, capacitor switching, and load connections. Furthermore, it maintains robust performance under various test conditions, including different noise levels and network topology changes.

Two-stage multi-objective framework for optimal operation of modern distribution network considering demand response program.

To improve the inadequate reliability of the grid that has led to a worsening energy crisis and environmental issues, comprehensive research on new clean renewable energy and efficient, cost-effective, and eco-friendly energy management technologies is essential. This requires the creation of advanced energy management systems to enhance system reliability and optimize efficiency. Demand-side energy management systems are a superior solution for multiple reasons. Firstly, they empower consumers to actively oversee and regulate their energy consumption, resulting in substantial cost savings by minimizing usage during peak hours and enhancing overall efficiency. The Demand Response Program (DRP) and optimal power sharing have gained significant attention to provide technical and economic benefits, while they require an efficient operation framework. Therefore, a two-stage framework is proposed for multi-objective operation of a distribution network with several generation resources. The first stage applies DRP to maximize the distribution network operator's (DNO) profit by optimizing common incentive rate for all consumers participate in DRP and an individual curtailed power for each consumer. In addition to an individual incentive rate for each consumer participates in DRP which is a new solution in the field of demand side management. The second stage achieves optimal power sharing among generation resources, while considering multiple objectives and incorporating the modified load of the first stage. The multi-objective problem is formulated to reduce energy losses, voltage deviation, total operational cost, gas emissions, and maximize the voltage stability index. The problem is optimized using a combination of the technique for order of preference by similarity to ideal solution (TOPSIS) and the elephant herding optimization (EHO) technique. The framework is validated using a modified IEEE 33-bus that incorporates photovoltaic system, diesel generators, and wind generation system. The proposed framework based on an individual incentive rate DRP provides superior response compared to common incentive rate DRP which reduces the total energy losses by 38.13%, reduces the total generation cost by 9.468%, and reduces the emission by 5.9%.

An information gap decision theory and improved gradient-based optimizer for robust optimization of renewable energy systems in distribution network

In this paper, a robust fuzzy multi-objective framework is performed to optimize the dispersed and hybrid renewable photovoltaic-wind energy resources in a radial distribution network considering uncertainties of renewable generation and network demand. A novel multi-objective improved gradient-based optimizer (MOIGBO) enhanced with Rosenbrock’s direct rotational technique to overcome premature convergence is proposed to determine the problem optimal decision variables. The deterministic optimization framework without uncertainty minimizes active energy loss, unmet customer energy, and renewable generation costs. The study also examines the impact of dispersed and hybrid renewable resources on solving the problem. In the robust optimization framework considering the deterministic obtained results, the focus is on determining the maximum uncertainty radius (MUR) of renewable resource generation and network demand based on the uncertainty risk. The MURs and system robustness are optimally determined using information gap decision theory (IGDT) and the MOIGBO, considering various uncertainty budgets under worst-case scenarios. The deterministic results indicate that the MOIGBO effectively balances the objectives and identifies the final solution within the Pareto front, according to fuzzy decision-making. The results also reveal that the dispersed case yields better objective values than the hybrid case. Furthermore, the MOIGBO outperforms MOGBO and multi-objective particle swarm optimization (MOPSO) in improving distribution network operations. The robust results show that maximum system robustness is achieved at 30% uncertainty risk due to forecasting errors, with MUR values of 0.54% for resource production and 12.56% for load demand.

A Cloud-Edge Intelligence-Based Optimization Method for Distribution Network Partitioning and Operation Considering Simulation Inaccuracy

A Cloud-Edge Intelligence-Based Optimization Method for Distribution Network Partitioning and Operation Considering Simulation Inaccuracy

The relevance of the issue of increasing the energy efficiency of the electricity conversion process in the current conditions of operation of the electricity distribution networks of Ukraine

The work sets out the basic conditions for the functioning of Ukrainian electric networks, including: the need to comply with the requirements of European integration; functioning in conditions of a deficit in generating capacity and market relations; changing the nature of electricity consumption; wear and tear of electric network equipment. The tasks that are associated with the conditions for functioning and require solutions for the reliable functioning of electric networks and further activities of Ukraine as a member of the European Union are indicated. The main tasks set out in the work include: improving the reliability of electricity supply; the need to increase the level of informatization, automation and energy efficiency. It is noted that on the way to solving the set tasks, the implementation of work on the reconstruction of electric networks is carried out, the basis for financing which is investment programs. The issue of improving the energy efficiency of the process of transforming electric energy under the current conditions of the functioning of the Ukrainian electric networks is revealed and analyzed. Based on the results of the analysis, it is established that the issue of improving the energy efficiency of the process of transforming electric energy is relevant at the time of reconstructing electric networks. It is indicated that the analysis of the energy efficiency of transforming electric energy can be carried out on the basis of a technical and economic model of the costs of transforming electric energy and the parameters that affect the size of the costs. It is noted that in the case of presenting the parameters included in the technical and economic models of costs for the transformation of electrical energy in the form of separate components, a competing effect is observed between the components. It is noted that the advisability of the formulation of general calculation methods for calculating the economic component during the review of the nominal power of transformers.

Fair charging management of PHEVs in radial distribution networks with DG resources-a case study.

Considering the widespread use of PHEVs in advanced societies and the issues ahead, researchers' thinking has focused more on this issue. The important issue is that the use of EVs is increasing due to the advantages, but the necessary infrastructure for their charging stations in the distribution networks does not exist. The high penetration level of EVs can create a potential risk for the existing distribution network; the fair charging of EVs has a special value. This paper presents a new model for the fair charging management of EVs at the medium voltage level of a distribution network equipped with dispatchable and non-dispatchable distributed generation (DG) resources. A fuzzy controller is used to adjust the charging rate of EVs within the permissible periods for charging stations, At the same time, the voltage control and reactive power management tool is also available for the distribution network operator through DG resources that can be dispatched, such as diesel generators. Numerical studies are used on a 25-bus IEEE test distribution system in the presence and absence of DG resources. The simulation results show that the presence of DG resources and voltage control and reactive power management at the different buses along the feeder causes a larger number of electric vehicles in different charging stations of the distribution network can be provided their consumption energy from network. In addition, the time difference for EV charging is minimized, and only the number of EVs that can be charged at the various stations will be different. Volt/Var control tools through DSO cause an increase in the number of charged EVs at various stations.

THE INFLUENCE OF THE RESISTANCE OF THE NEUTRAL GROUNDING RESISTOR ON THE 20 KV VOLTAGE DISTRIBUTION MODES OF OPERATION

The article examines the impact of different modes of neutral grounding on the operating parameters of 20 kV distribution networks, in particular, the selection of the optimal resistor resistance for resistive neutral grounding. One of the main tasks is to improve the efficiency of distribution networks of the UES of Ukraine by switching from a voltage of 10 kV to 20 kV. This solution is aimed at reducing power losses, increasing the amount of transmitted energy and improving the operation of relay protection. In the context of such a transition, the choice of the method of grounding the neutral of the power transformer is particularly relevant, which significantly affects the reliability and safety of operation of electrical networks. In the study, an analysis of the effect of resistive grounding of the neutral of the transformer on the operating characteristics of the 20 kV network was performed, as well as determination of the optimal value of the resistance of the resistor. The article presents the results of a comparison of a network with an isolated neutral and resistive grounding. The main operating parameters, such as short-circuit currents, overvoltages on undamaged phases, power losses and operation of relay protection, which have a significant impact on the stability and safety of network operation, are analyzed. Modeling is performed using the PowerFactory software complex, which provides the ability to accurately reproduce the real operating conditions of electrical systems. To conduct the research, a simulation model of the distribution network was created based on a two-transformer 110/20 kV substation, which includes two sections with outgoing lines of different lengths and loads. Special attention was paid to the selection of the resistance of the neutral grounding resistor and its influence on network parameters. Four options were considered in the study: isolated neutral and resistive grounding with a resistance of 10 Ohm, 15 Ohm and 20 Ohm. It was established that in order to ensure stable and safe operation of the distribution network, it is important to correctly choose the neutral grounding method. The best option is resistive grounding with a resistance of 15 Ohms, which allows you to reduce power losses, provides a sufficient level of currents for the effective operation of relay protection, and minimizes the risks of overvoltages on undamaged phases. This study is important for increasing the reliability of the operation of distribution networks and the security of electricity supply, especially in the context of the modernization of Ukraine's UES networks.

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.

Effective Energy Saving in Rural Networks of 0.38 kV

Discusses the principle of operation of rural distribution networks with a voltage of 0.38 kV, as well as the analysis and selection of a particularly relevant technical solution that allows balancing the asymmetric modes of operation of a network with a voltage of 0.38 kV. Statistical studies have been conducted to improve the efficiency of electricity quality and the power loss coefficient in the networks.

Криза прав власності у розподільчому сегменті ринку природного газу України

During the time of independent Ukraine's existence, the authorized state bodies have not developed effective mechanisms for the use of state-owned gas distribution networks by private operators. Despite the state being the de facto owner of these networks, it was unable to receive appropriate compensation for their operation, and no incentives were created for the modernization of the networks. Meanwhile, private operators were motivated to consistently report a decrease in the value of such assets. Another complex issue, in terms of the efficiency and fairness of managing the gas distribution system, is the ownership of networks built with consumer funds and transferred to gas distribution network operators for operation. The lack of clear understanding regarding the ownership of gas transportation networks results in significant wear and tear of the networks and substantial production and technical losses. Gas transmission operators are not interested in making significant investments in the renewal and development of networks, as they do not own them, and there are no other obligations that could incentivize such activities. The state does not receive any income from the use of state property by economic entities. In the context of the Russian-Ukrainian war, the issue of property rights and further investments in the development of gas distribution networks has once again come to the forefront. The need to adapt the energy system to the new challenges of the war necessitates the formation of a decentralized model of the national energy system. In the gas sector, this means that at the local level, local self-government bodies will need to take the initiative in implementing small energy projects to meet the energy supply demands of the local population, such as the construction of various types of gas cogeneration plants. The construction of such projects will require the adaptation and development of the existing gas distribution networks to meet new requirements. The formation of a new decentralized model of the national energy network is impossible without resolving the old problems related to the ownership rights of gas distribution networks and establishing a market-based tariff setting for the operation of these networks. The article analyzes the theoretical prerequisites for the emergence of the property rights crisis in the distribution segment of the natural gas market based on the theory of property rights. Through retrospective analysis, the main flaws in the state energy policy in the management of state-owned gas distribution networks are identified, which led to the current property rights crisis and increased transaction costs among entities in Ukraine’s natural gas market. Based on the obtained results and taking into account foreign experience in the privatization and regulation of the economic activities of gas distribution operators, the author proposes various ways to address the problems of managing property rights for the distribution gas networks.

Voltage regulation in distribution networks by electrical vehicles with online parameter estimation

The integration of a large number of electric vehicles (EVs) offers a new perspective for providing voltage regulation services for the operation of distribution networks. The flexible charging and discharging capabilities of EVs can help mitigate voltage fluctuations and improve grid stability. In this paper, we utilize EV clusters by controlling the discharging power to realize voltage regulation of distribution networks. We formulate a feedback-based optimization problem with the objectives of minimizing voltage mismatch as well as reducing the cost of voltage regulation services provided by EV clusters. Then we propose an algorithm with online resistance estimation to find the optimal solution without requiring complete information about distribution networks. The convergence of the proposed algorithm is guaranteed by the oretical proof. Numerical results in 33-bus system validate the performance of the algorithm. The results validate the applicability of the proposed approach in distribution networks, highlighting the potential of EV clusters as a flexible and cost-effective solution for voltage regulation.

The Evaluation Method of the Power Supply Capability of an Active Distribution Network Considering Demand Response

The accurate quantification of power supply capability (PSC) is crucial for the planning and operation of active distribution networks. To address the issue of PSC quantification, this paper presents a PSC evaluation method of active distribution networks while considering demand response (DR). Firstly, an incentive-based DR model is introduced, followed by the development of a PSC evaluation model that incorporates DR. This model completely describes the PSC of active distribution networks while considering N-1 security constraints. Secondly, a PSC evaluation algorithm based on uniform state-space sampling is presented, enabling the quantification of the complete PSC of active distribution networks with DR. Then, the influence of the load reduction coefficient in DR on the PSC is studied. It is found that the maximum PSC increases with the load reduction coefficient initially and then stabilizes. Furthermore, measures such as appropriately increasing the load reduction coefficient and expanding the bottleneck component capacity are proposed to enhance the PSC. Finally, the effectiveness of the proposed PSC evaluation method is verified by two active distribution networks, CASE1 and CASE2. The proposed method visualizes the complete PSC of active distribution networks considering DR as a curve and quantifies it as an interval value. For CASE1, the complete PSC is quantified within the range of [4.0, 8.4] MVA, and for CASE2, it is quantified within the range of [17.0, 33.0] MVA. The proposed measures effectively enhance the PSC, facilitating the efficient, safe, and low-carbon operation of smart distribution networks.

Risk-based approach for safe terminal operation and route planning of on-road hydrogen distribution network

Risk-based approach for safe terminal operation and route planning of on-road hydrogen distribution network

Multi mode coordinated control algorithm for DC near-field photovoltaic based on adaptive mutation particle swarm optimization

Excessive photovoltaic power in a distributed photovoltaic system may cause problems such as overvoltage and reverse power flow in the distribution network, and the safe and stable operation of distribution networks faces potential challenges or risks. To ensure stable operation,this article proposes an innovative multi-mode coordinated control strategy for DC near-field photovoltaic systems that integrates adaptive mutation particle swarm optimization technology to achieve more efficient control performance. The operation of distributed photovoltaic system is divided into five modes: single-machine reactive power regulation, multi-machine reactive power coordination, active power reduction mode in multi machine systems, Existing power recovery models and reactive power recovery mode; So the mathematical model of the inverter main controller is constructed, using adaptive mutation particle swarm optimization algorithm to solve the model, in order to improve solving efficiency and accuracy Committed to overcoming the limitations of slow convergence speed and susceptibility to local optima in particle swarm optimization algorithms, in order to optimize algorithm performance, when optimizing the particle swarm optimization algorithm, synchronously tuning the learning factor and inertia weight parameters is aimed at accelerating the convergence process and improving the accuracy of the algorithm. By introducing a mutation mechanism, the search domain of the particles is expanded, thereby enhancing the global optimization efficiency of the algorithm. The experimental data shows that the optimized control parameters of the algorithm significantly enhance the dynamic response characteristics of the system, and its convergence speed is faster and its steady-state accuracy is higher. After 60 iterations, the control accuracy reached 98.15%, and the feature value near the virtual axis of the system was optimized from −1328 to −1.647. The fluctuation of each electric quantity of the system was smaller than that of the original parameter, the stability could be reached faster after troubleshooting, and the coordinated control effect is better.

A framework for demand response load forecasting based on federated learning

Abstract Addressing the scenario where distribution network operators predict load curves based on user data to participate in demand response, and to cope with issues such as user data privacy protection and the operators’ lack of permission to access metering data, this paper proposes a framework and method for demand response load forecasting based on federated learning. Focusing on features including weather, time, and users’ participation status in demand response, this approach utilizes horizontal federated learning technology to integrate data from various sources while safeguarding user data privacy. Furthermore, a balanced scheduling scheme for participating users and samples, along with data preprocessing, is integrated to enable effective discrimination of load curves. Case study analysis demonstrates that the proposed load forecasting method can maintain superior prediction performance while ensuring the privacy of user data.

Network-aware electric vehicle charging/discharging scheduling for grid load management in a hierarchical framework

The increasing adoption of electric vehicles (EVs) poses significant challenges for power system operations, requiring scalable coordination to mitigate their negative impacts and leverage their potential to enhance grid conditions. This paper introduces a scalable, three-layer hierarchical framework for optimal EV charge and discharge scheduling (EVCDS) that coordinates key agents: EVs, EV aggregators (EVAs), and the distribution network operator (DNO). The optimization problem is developed as an exchange problem and solved using the alternating direction method of multipliers (ADMM) in a decentralized approach. The proposed EVCDS addresses economic factors by minimizing battery degradation costs at the EV level and charging costs at the EVA level, while managing technical aspects at the DNO level by minimizing load curve variance and limiting power capacity. Moreover,voltages at network nodes are calculated using the DistFlow model to simplify the optimization and ensure compliance with standard operational limits. Compared to uncoordinated EV charging, EVCDS reduces load profile deviations by 85% and total costs by 91%, while also improving bus voltage profiles.

A comprehensive review of optimal placement of EVCS and sizing in active distribution network

ABSTRACT The transition from Internal Combustion (IC) engines to Electric Vehicles (EVs), the electric load on the distribution system has increased gradually in last few decades. There are various challenges in the development of EVCS infrastructure to give cost effective charging. In this article smart charging system (Grid to Vehicle (G2V), Vehicle to Grid (V2G), Vehicle to Vehicle (V2V) and Mobile Charging Station (MCS)), EV load modeling and the latest optimization method used by the researchers are discussed. EVCS as compensation devices to reduce the effect of EV load on the distribution network along with the safety considerations is also discussed in this article. Further, a Smart Energy management system is discussed to increase the EVCS owner profit. Also, the EVCS have to be sited at optimal place in the grid to reduce the system power losses, ensure stability, improve reliability, etc. and also ensure the EV demands. Suitable integration of (EVCS) at the proper places is challenging and essential to overcome the above issues. In addition, there are some other parameters like cost of EVCS and the behavior of EV which are also important for Distribution network operators and the charging station owners are also discussed in this article.