Regional Distribution Network Research Articles

Construction and Optimization of Dynamic Assessment Indicators for Distribution Grid Carrying Capacity with High Proportion of Distributed Resource Connections

Abstract In this study, we evaluate the enhanced carrying capacity of the modified IEEE 33-node distribution system, which incorporates a high percentage of distributed resource (DR) integration, utilizing the whale optimization algorithm. The evaluation employs a fuzzy comprehensive evaluation (FCE) method, wherein we construct FCE indices for factors influencing the distribution network’s carrying capacity. These indices involve calculating weight coefficients and establishing an affiliation function, culminating in the output of the final fuzzy evaluation result vector. The analysis identifies three nodes—11, 18, and 31—within the IEEE 33-node system for decentralized DR installation. Based on initial scenario data, the maximum carrying capacity of the distribution network is determined to be 40.68 MW. The analysis further reveals that the utilization rate of distributed resources reaches 100% at specific times, specifically at 2:00 AM and 6:00 AM. During the 3:00 to 5:00 AM interval, both the system voltage excursion index and the composite score for distribution network losses are calculated to be low. These findings suggest the feasibility of integrating a single regional distribution network with other energy networks, thereby facilitating multi-regional resource interconnection and enhancing the flexibility of system operations. This approach offers significant implications for improving the robustness and efficiency of electrical distribution systems.

Real-time Strategy for Voltage Regulation of Distribution Networks Considering the Incomplete Response of Distributed Resources

To solve the problem of nodes whose voltage exceeds the security constraint in the scenario of massive distributed generation access, a real-time strategy for voltage regulation of the distribution network considering the incomplete response of distributed resources is proposed. The strategy includes two parts: a real-time strategy for voltage regulation of the distribution network and the improvement of the strategy to deal with the incomplete response of distributed resources. Firstly, the real-time voltage autonomy of the distribution network is realized through the distributed resource strategy for voltage regulation; Then, aiming at the incomplete response of distributed resources, the strategy for voltage regulation is improved, and the margin iteration and feedback regulation are added; Finally, based on the IEEE33 node model, we verify the strategy for voltage regulation which is proposed in this paper. The results of the experiment show that the strategy for voltage regulation can effectively solve the problem of distribution network voltage out of limit, and has good anti-interference ability for the incomplete response of distributed resources.

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.

Research on Optimal Voltage Control of Distribution Network with the Participation of EVs and PVs

With the accelerating penetration of photovoltaics (PVs) and electric vehicles (EVs), distribution networks face the risks of voltage violations and fluctuations. On the one hand, conventional voltage regulation resources like OLTC transformers and capacitor banks feature slow response and limited lifetime duration, making them incapable of quickly responding to the temporary voltage issues created by PVs and EVs. On the other hand, EVs and PVs interact with the power grid via fully controllable power electronic converters capable of real-time adjusting their operating settings, making them ideal voltage support resources. To exploit the voltage support capability of PVs and EVs, this paper proposes a two-stage control scheme for the voltage regulation of distribution networks, consisting of the day-ahead and intraday control stages. The day-ahead control mitigates potential voltage violations via day-ahead scheduling of the operation settings for OLTC transformers and capacitor banks. The intraday control further alleviates voltage deviations and voltage fluctuations based on the reactive power support of PV systems and the rational EV charging/discharging scheduling. A rolling optimization-based control technique is proposed in the intraday control stage to achieve real-time control of EVs and PVs with the stochastic nature of EV charging behaviors inherently considered. The proposed two-stage voltage regulation scheme is validated via case studies performed on the IEEE 123-node test feeder integrated with PVs and EVs.

Assessment method of distribution network health level based on multivariate information

In order to enhance self-monitoring and self-diagnosis capabilities in smart distribution networks, this paper proposes a method for assessing the health level of the network based on multivariate information. First, we construct an evaluation indicator system for the health of the smart distribution network by integrating the smart distribution network information system. Next, we utilize the improved back propagation (BP) neural network and multivariate indicator information to calculate the health indexes of both the grid layer and equipment. We then solve the health index of the equipment layer based on network topology and goal-oriented methodology. Furthermore, by utilizing the health information of both the equipment and grid layer, we apply fuzzy evaluation and Dempster-Shafer (D-S) evidence theory to obtain the health level of the distribution network. We provide a comprehensive evaluation of the overall health status of the smart distribution network. Finally, the proposed method is validated using data from a regional distribution network. The results demonstrate its effectiveness in improving the smart distribution networks’ overall health and stability by enabling more effective self-monitoring and self-diagnosis.

Optimal substation capacity planning method in high-density load areas considering renewable energy

With the increasing penetration of renewable energy, the adaptability of the existing substation planning model in terms of capacity and quantity of transformer needs to be further studied when preferring large-capacity substations. Considering the variations of renewable energy penetration rate and load, this paper proposes a method to optimize the total capacity of substations in distribution networks. This paper introduces the influence of renewable energy access on power supply reliability and introduces the idea of partitioning for economic analysis of reliability. An economic analysis model for simultaneously optimizing the capacity and quantity of substation transformer in the distribution network is constructed, taking into account the effects of reducing net load and enhancing the reliability of the distribution feeders resulting from renewable energy access to the medium and low voltage side of the substation. Various wiring means of the distribution network are retained and the impact of renewable energy access on the reliability of the network power supply is quantified. The optimization model is solved by the multivariate universe optimizer(MVO) algorithm with stronger optimality finding capability and short solving time. Finally, the case study results of a regional distribution network are employed to demonstrate and verify the validity and rationality of the method.

Isotopic characterisation and mobile detection of methane emissions in a heterogeneous UK landscape

Characterising methane (CH4) sources and their stable isotope values at the regional level is important for taking effective mitigation actions as well as more accurately constraining global atmospheric CH4 budgets. We performed dual stable isotope (13C, 2H) analysis of CH4 emission sources as well as mobile 13C measurements in North-West England, in a region with a mix of natural and anthropogenic emission sources as well as potentially exploitable shale gas deposits. Dual isotope analysis was performed for enteric fermentation, animal waste, landfill gas, wetlands, and natural gas from the regional distribution network. Microbial emission sources’ δ13C values ranged from −72.1 ± 0.31‰ to −53.1 ± 1.17‰ with agricultural sources and landfills showing partially overlapping values (−65.3 ± 0.41‰ to −72.1 ± 0.31‰ and −59.2 ± 0.26‰ to −70.4‰, respectively). However, the use of a dual isotope approach with δ2H provided additional separation between agricultural (−340 ± 0.8‰ to −322 ± 19.5‰) and landfill (−312 ± 0.3‰ to −282‰) CH4. All microbial sources were clearly distinct from natural gas with mean values of −39.5 ± 1.38‰ and −184 ± 4.9‰ for δ13C and δ2H, respectively. Mobile measurements conducted over a distance of 557 km detected emissions from two out of four surveyed managed landfills in the region. Multiple gas leaks were detected, which may confound emissions from other thermogenic sources. When separating the surveyed area by land-use into agricultural and urban, we found that background levels of CH4 were more depleted by around 1‰ in areas with agricultural land use compared to urban areas, but emissions from gas leaks and landfills are present in both categories. Our findings highlight the complexity of isoscapes in regions with multiple types of emission sources and the value of dual-isotope measurements in source attribution.

A Gaussian Process Based Fleet Lifetime Predictor Model for Unmonitored Power Network Assets

This paper proposes the use of Gaussian Process Regression to automatically identify relevant predictor variables in a formulation of a remaining useful life model for unmonitored, low value power network assets. Reclosers are used as a proxy for evaluating the efficacy of this method. Distribution network reclosers are typically high-volume assets without on-line monitoring, leading to an insufficient understanding of which factors drive their failures. The ubiquity of reclosers, and their lack of monitoring, prevents the tracking of their individual remaining life, and, confirms their use in validating the proposed process. As an alternative to monitoring, periodic inspection data is used to evaluate asset risk level, which is then used in a predictive model of remaining useful life. Inspection data is often variable in quality with a number of features missing from records. Accordingly, missing inputs are imputed by the proposed process using samples drawn from an advanced form of joint distribution learned from test records and reduced to its conditional form. This work is validated on operational data provided by a regional distribution network operator, but conceptually is applicable to unmonitored fleets of assets of any power network.

A Complex Affine Distribution Power Flow Considering the Power Correlation of Distributed Photovoltaic Generations

As the development of distributed photovoltaic (PV), the power flow distribution and the voltage level of the distribution network change dramatically, which increases the difficulty and complexity of the distribution network regulation. In this paper, a complex affine distribution power flow considering the power correlation of distributed photovoltaic generations is proposed, which improves the accuracy and effectiveness of power flow calculation of the distribution network. Firstly, the k-means clustering algorithm is used to divide the distributed photovoltaic into several clusters, and the complex affine model of distributed generations (PVs) and energy storage system (ESS) is established. The forward-backward sweep power flow based on the complex affine arithmetic is used to solve the problem. Compared with other algorithms, the completeness and accuracy of the proposed algorithm are verified. Two different scenarios are formed by establishing the complex affine power model of the energy storage element before and after accessing ESS. By comparison, the effect of energy storage on the reduction of distributed PV fluctuations is verified.

Multi-objective optimal peak load shaving strategy using coordinated scheduling of EVs and BESS with adoption of MORBHPSO

With increase of electrical vehicles (EVs), carbon emissions and dependence on fossil energy would be reduced. However, uncoordinated charging may further raise the peak load and magnify the load imbalance which will pose a key challenge to the system reliability. Hence, the peak load shaving whereby coordinated optimal scheduling of EVs and energy storage systems (ESS) has attracted more and more attention. And challenges arise in terms of multiple objectives, algorithms with better global searching performance and constraints handling methods. In this paper, the proposed multi-objective optimal peak load shaving strategy aims to achieve the best peak load shaving effect with the minimum electricity cost whereby coordinated scheduling of EVs and battery energy storage systems (BESS). Especially, load balance constraint, charging/discharging power limits considering state of charge (SOC) as well as capacity limits of EVs and BESS, vehicles to grid (V2G), time-of-use (TOU) price and driving behavior of EVs with different types are all considered. The multi-objective random black-hole particle swarm optimization algorithm (MORBHPSO) with adjustable power redundancy method is adopted. Four case studies have been carried out on a regional distribution network with 130 EVs and 20 BESS. And satisfactory results were obtained in terms of better peak load shaving effect (a 70.6 % decrease in load fluctuation level) and better economic benefit (a 40.56 % reduction in electricity cost). Moreover, MORBHPSO performs better than multi-objective particle swarm optimization algorithm (MOPSO) in terms of a 41.47 % decline in load fluctuation level and a 5.44 % decrease in electricity cost with only half the iterations. Furthermore, it is also found that different from the impact of EVs' driving behavior, TOU price is conducive to obtaining smoother load curve and lower electricity cost.

Interaction Strategy Between the Vehicle-station-grid Considering Privacy Protection

The number of electric vehicles (EVs) is increasing daily, and the charging demand for large-scale EVs has a greater impact on the distribution network, so it is urgent to study the cooperative interaction optimization strategy of the “EV-charging station-distribution grid.” In this paper, we take the charging load of EVs in the regional distribution network as the research object. We establish a multi-intelligence reinforcement learning-based optimization strategy for the interaction between EVs and the grid to obtain the optimal charging decision to minimize the peak-to-valley difference between the user-side economy and the grid-side load. The results show that the proposed EV-grid interaction optimization strategy can effectively reduce the peak-to-valley difference in charging station load and realize the efficient cooperative interaction of the “vehicle-station-grid.”

Research on the Planning of Electric Vehicle Fast Charging Stations Considering User Selection Preferences

The global energy and environmental crisis promotes the development of electric vehicles (EVs), and the rational planning of EV fast charging stations is an important influencing factor for their development. In this paper, for the EV fast charging station capacity planning problem, a joint-optimization model for optimal planning of EV fast charging stations and the economic operation of a distribution network is constructed, considering the impact of user preference selection and EV access on the regional distribution network. To address the problems of low efficiency and local convergence found in traditional heuristic optimization algorithms, an improved krill swarm optimization algorithm (CKHA) that introduces chaotic optimization parameters to make the initial population as uniformly distributed as possible is proposed to find the optimal planning scheme for EV fast charging stations. The case results show that the optimal planning model and its solution method are effective.

Research on redundancy margin analysis method of 10kV regional distribution network based on demand-side response

The current status of the regional 10kV distribution network has problems such as the unreasonable capacity declaration, capacity redundancy, and line margin reduction due to a large number of distributed PV and electric vehicle access. In this regard, a redundancy margin analysis method of a 10kV regional distribution network based on demand-side response is proposed. The customer distribution substation power consumption data and operation data are analyzed to establish a 10kV regional distribution network redundancy margin analysis model, and the model is constrained. In the experiments, the proposed method is verified for analysis accuracy. The analysis of the experimental results shows that the proposed method has a high analytical accuracy for analyzing the redundancy margin of the 10kV regional distribution network.

Deep reinforcement learning based topology-aware voltage regulation of distribution networks with distributed energy storage

Both the high penetration of clean energy with strong fluctuation and the complicated variable operation condition bring great challenges to the voltage regulation of the distribution network. To deal with the problem, a topology-aware voltage regulation multi-agent deep reinforcement learning (MADRL) algorithm is proposed. The distributed energy storages (DESs) are modeled as agents to regulate voltage autonomously in real-time, which could fast adapt to dynamic topological scenarios. Firstly, taking the minimization of voltage fluctuation and maximization of reserve capacity as the target, the optimal voltage regulation model is established. Secondly, a topology extraction method considering voltage sensitivity is proposed for dynamic topology clustering, and the obtained typical topology is added to the observation set of agents. Then, the optimal voltage regulation model is formulated to the decentralized partially observable Markov decision process (Dec-POMDP) framework, in which only local information is required for the agent during the test process to decision-making to realize the hierarchical and partitioned control of voltage. Finally, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is used to solve the Dec-POMDP model. The feasibility and superiority of the proposed algorithm are verified and analyzed in the simulation under different scenarios.

Erratum: Over-voltage regulation of distribution networks by coordinated operation of PV inverters and demand side management program

An erratum on: Over-Voltage Regulation of Distribution Networks by Coordinated Operation of PV Inverters and Demand Side Management Program.By Heidari Yazdi SS, Rahimi T, Khadem Haghighian S, Bagheri M and B. Gharehpetian G (2022) Front. Energy Res. 10:920654. doi: 10.3389/fenrg.2022.920654 Due to a production error, there was a mistake in the author order. The correct author order should be: Seyed Saeid Heidari Yazdi, Tohid Rahimi, Saeideh Khadem Haghighian, Gevork B. Gharehpetian, and Mehdi Bagheri. The publisher apologizes for this mistake. The original version of this article has been updated.

A charging plan generation method for EV clusters considering the operation of regional distribution network

The continuous development of large-scale electric vehicles (EVs) has provided abundant dispatchable resources for the power grid. However, the existing control methods are not enough to support the effective interaction of the EVs and the grid. This paper proposes a method for generating a charging plan of EV clusters considering the stable operation of the regional distribution network, which fully considers the load regulation requirements of the grid and the charging economy of the EV clusters. Combined with the improved Particle Swarm Optimization based on Genetic Algorithm, a day-ahead charging plan for multiple EV clusters can be figured out. It can be proved by the case analysis that, compared with the disordered charging behavior and the participation of demand response with peak and valley electricity price, the charging plan generated by the method proposed can reduce the comprehensive cost of EV charging while optimizing the operation parameters of the distribution network, and it can reduce the calculation dimension in the process of plan generation.

A hybrid group-making decision framework for regional distribution network outage loss assessment based on fuzzy best-worst and MARCOS methods

Scientifically assessing the outage loss of regional distribution networks is conducive to the supplying-power enterprises and government to formulate development plans. The purpose of this study is to develop a systematic group decision-making framework to effectively solve the problem of outage loss assessment. Firstly, a multi-dimensional index system containing qualitative and quantitative attributes is established. Then, a relative entropy-based (RE-based) approach that incorporates fuzzy proximity and fuzzy similarity entropy weights is employed to determine experts’ dynamic weights. Subsequently, the fuzzy best-worst method (BWM) is presented to identify optimal criteria weights, thereby retaining the originality of weight information. Finally, the alternatives are rank-ordered based on the fuzzy MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) method by determining utility functions in relation to ideal and anti-ideal solutions. A case study is conducted based on the developed framework. The sufficient sensitivity analysis illustrates that the method adopted in this paper provides good robustness to fluctuations in criteria weights, and comparative analysis verifies the applicability and effectiveness of the framework. This study offers the scientific basis and decision support for the deployment of reducing regional distribution network outage loss and enriches the research methods and practical applications of the decision-making field. • A hybrid framework is given for regional distribution network outage loss assessment. • A comprehensive index system with qualitative and quantitative attributes is built. • The dynamic weights of experts are obtained by combining fuzzy PEW and fuzzy SEW. • Fuzzy best-worst method is adopted to determine the weights of evaluation attributes. • The fuzzy MARCOS enables a more flexible and effective decision-making.

Over-Voltage Regulation of Distribution Networks by Coordinated Operation of PV Inverters and Demand Side Management Program

The increase of Photovoltaics (PV) units’ penetration factor in the power grids might create overvoltage over the network buses. The active power curtailment (APC) and the reactive power provision methods use inverters to regulate their output active and reactive powers for high PV-penetrated grids. However, the mentioned solutions would reduce the maximum injectable active solar power to the grid, not financially acceptable. Continuous employment of the maximum apparent power capacity of the inverters will practically decrease the inverters’ lifetime, require special design considerations, and make the control system complex. To overcome those issues, a feasible solution would be increasing the load consumption within the time intervals in which the grid faces the over-voltage problem. In this research, the demand response (DR) program is employed. Load shifting techniques are exerted to move a portion of loads from the peak hours to when further power consumption is expected for voltage level reduction purposes. A new long-term strategy based on the coordinated operation of the PV inverters and load shifting techniques is proposed to resolve the over-voltage issue in the network. Consequently, the PV inverter’s contribution to voltage control is reduced; a new sight of DR potential is implemented, and also the under-voltage level in peak times is decreased significantly.

Multi-Source Data Processing and Fusion Method for Power Distribution Internet of Things Based on Edge Intelligence

With the rapid advancement of the Energy Internet strategy, the number of sensors within the Power Distribution Internet of Things (PD-IoT) has increased dramatically. In this study, an edge intelligence-based PD-IoT multi-source data processing and fusion method is proposed to solve the problems of confusing storage and insufficient fusion computing performance of multi-source heterogeneous distribution data. First, a PD-IoT multi-source data processing and fusion architecture based on edge smart terminals is designed. Second, the multi-source sensor data in the distribution network is unified in dimension and magnitude. By introducing the Box–Cox transform to improve the data offset problem in the Z-score normalization process, a multi-source heterogeneous data processing method for distribution networks based on the Box–Cox transform Z-score is proposed. Then, the conflicting phenomena of DS inference methods in data source fusion are optimally handled based on the PCA algorithm. A multi-source data fusion model based on DS inference with conflict optimization is constructed to ensure the effective fusion of distribution data sources from different domains. Finally, the effectiveness of the proposed method is verified by an experimental analysis of an IEEE39 node system in a regional distribution network in China.

Research on influencing factors of line loss rate of regional distribution network based on apriori-interpretative structural model

The power industry involves people’s livelihood. While ensuring the normal operation of the power system, power companies also need to consider strengthening line loss management to promote grid efficiency. Through grounded theory, 19 factors affecting regional distribution network line loss are extracted from the management and technical perspectives. The Apriori algorithm is used to analyze the data of Chuzhou distribution network line loss rate, and the connection between different influencing factors and line loss rate is analyzed. Association rules are used to filter out the key influencing factors. Finally, the calculation results of the Apriori algorithm and the interpretation structure model (ISM) are combined to make up for the shortcomings of ISM’s strong subjectivity and draw the influence path diagram of the regional distribution network line loss rate based on Apriori-ISM. The results show that measurement errors, the number of strain-resistant sections, the total number of special transformers, the cross-sectional area of conductors, the number of aging transformers, load switching, power theft, and operating time are the direct factors affecting the line loss rate of the regional distribution network, whose confidence levels are 100%, 81.06%, 91.76%, 89.36%, 89.38%, 83.23%, 84.39% and 90.53% respectively, and the correlation is strong.