Regional Distribution Network Research Articles

Vehicle Routing Problem for Collaborative Multidepot Petrol Replenishment under Emergency Conditions

In recent years, emergency events have affected urban distribution with increasing frequency. For example, the 2019 novel coronavirus has caused a considerable impact on the supply guarantee of important urban production and living materials, such as petrol and daily necessities. On this basis, this study establishes a dual-objective mixed-integer linear programming model to formulate and solve the cooperative multidepot petrol emergency distribution vehicle routing optimization problem with multicompartment vehicle sharing and time window coordination. As a method to solve the model, genetic variation of multiobjective particle swarm optimization algorithm is considered. The effectiveness of the proposed method is analyzed and verified by first using a small-scale example and then investigating a regional multidepot petrol distribution network in Chongqing, China. Cooperation between petrol depots in the distribution network, customer clustering, multicompartment vehicle sharing, time window coordination, and vehicle routing optimization under partial road blocking conditions can significantly reduce the total operation cost and shorten the total delivery time. Meanwhile, usage of distribution trucks is optimized in the distribution network, that is, usage of single- and double-compartment trucks is reduced while that of three-compartment trucks is increased. This approach provides theoretical support for relevant government departments to improve the guarantee capability of important materials in emergencies and for relevant enterprises to improve the efficiency of emergency distribution.

Deep reinforcement learning assisted edge-terminal collaborative offloading algorithm of blockchain computing tasks for energy Internet

In the regional distribution network, microgrid is often used to build local energy system to realize regional autonomy in the process of power generation, transmission, and consumption. Applying blockchain technology in microgrid can meet the needs of security and privacy in energy transactions, and can conduct secure point-to-point transactions between anonymous entities. However, blockchain nodes will generate numerous computing-intensive tasks in the process of mining, and cause high delay in energy transaction. Therefore, we take advantage of mobile edge computing (MEC) technology and propose an edge-terminal collaborative mining task processing framework to increase the computing ability of the blockchain system. This framework includes three working modes: local computing, user collaboration and edge node collaboration. Particularly, the trust value of collaborative user nodes is considered to avoid security threats caused by malicious nodes. Furthermore, we establish a delay-and-throughput-based blockchain computing task offloading model, and use asynchronous advantage actor-critic (A3C) algorithm to jointly optimize offloading decision, transmission power allocation, block interval and size configuration. Simulation results show that, compared with Only-MEC and Fixed-BlockSize algorithms, the proposed algorithm can reduce the average delay by 1.7% and 2.5%, and improve the average transaction throughput by 12.1% and 28.5% respectively.

Source-Network-Load-Storage Coordinated Control System Based on Distribution IOT Cloud Platform and Virtual Power Plant

In view of the distribution network operation problems caused by many distributed generations integration to distribution network, and the increasingly serious peak valley imbalance in grid, this paper proposes a coordinated control system of source-network-load-storage based on virtual power plant technology, which is designed and implemented in the actual project. The system uses cloud platform technology and multi-energy complementary technology to realize coordination and optimization control mechanism between sources, network and loads in regional distribution network. The system is based on the distribution Internet of things cloud master platform. Through the virtual power plant technology, resources such as cogeneration, photovoltaic, wind, distributed energy storage, electric vehicles, flexible loads are aggregated to achieve coordinated and unified control, realize the optimal operation of multi-energy complementary. It can accept the control of the dispatching center, and participate in the power transaction of demand response. Based on the application scenario, this paper explains how to use virtual power plant technology to participate in demand response power transaction, and describes the transaction rules and processes. The system is beneficial to the safety and reliability of the distribution network. It can implement reasonable configuration and consumption of distribution generations. It can control controllable loads such as electric vehicle charging pile to participate in peak regulation of power grid, and provide strong technical support for the realization of demand response.

Research on Distribution Network Planning Strategy Considering New Energy and Volatile Multiple Loads

With the progress of new energy resources technology and distributed generation technique, using intelligent monitoring and integrated management technology to connect distributed power supply to regional distribution network to improve renewable energy utilization has become a hot research topic. This article summarizes and discusses the domestic and foreign research that considers the integration of new energy into the distribution network, analyses the impact of new energy output and the access of multiple volatile loads on the distribution network, and then studies the distribution network planning strategy of load access that considers new energy access and multiple volatility. Finally, an example proves the rationality and effectiveness of the method.

Research on charging strategy of electric vehicle considering user and load curve

With the increasing popularity of electric vehicles, the disordered charging of large-scale electric vehicles will have a great impact on the safe operation of regional distribution network. In order to solve the security problems that may occur in the power grid, this paper uses the time-sharing pricing time division method for EV charging to meet the needs of EV users. Based on this method, a multi-objective optimization model is established, which takes the electric vehicle charging capacity and power as the constraints, and based on the minimum user charging cost and the minimum load curve variance. Then, the model is solved by non-dominated sorting genetic algorithm (NSGA -Ⅱ), and the optimal compromise solution is extracted by using fuzzy set theory. Finally, the correctness of the proposed model is verified by the example.

Research on Investment Scale Calculation and Accurate Management of Power Grid Projects Based on Three-Level Strategy

In order to improve the investment accuracy of power grid projects, this paper takes distribution network investment as the research object, and proposes a distribution network precision investment method based on a three-level strategy of region, power supply grid, and power supply unit. Firstly, building a precise investment index system for the investment scale of the regional distribution network, and calculate the investment situation through the precise investment calculation formula of the regional distribution network. Secondly, building a time sequence evaluation system for the overall construction of the power supply grid of the distribution network, and then study the prioritization method based on investment projects. The time sequence method for the overall construction of the power supply unit of the distribution network and the multi-objective optimization precision investment method based on the target grid framework. Finally, the effectiveness and operability of the above precision investment method are verified through empirical application.

Research on Multi-level Distribution Strategy Based on Energy Internet

Smart city energy Internet is to comply with the trend of sustainable energy development, urban power energy as the center, comprehensive utilization of all kinds of energy, especially renewable energy, to achieve interconnection and common optimization. Based on the background of distribution network in Disney Park, this paper studies the regulation, operation and management strategy of distribution network of smart city energy Internet. This paper describes the characteristics of the energy Internet, and then analyzes the key technologies that affect power grid regulation, operation and management. The paper investigated the distribution network construction in Shanghai Disney Park under the background of urban energy Internet pilot region to understand the allocation of distributed energy in the park grid. Through simulation calculation and analysis, this paper puts forward a distribution network regulation and management scheme suitable for the characteristics of energy Internet. In this paper, the study found that with the rapid development of power grid, especially all kinds of distributed energy access, the new lines are becoming more. In a wide range of power grid structure is becoming more and more complex, in order to ensure the reliability of power supply and power supply quality, scientific regulation under the background of urban energy Internet development mode, enhancing the management level of power grid is the inevitable developing trend of power grid.

Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method

The efficient spatial load forecasting (SLF) is of high interest for the planning of power distribution networks, mainly in areas with high rates of urbanization. The ever-present spatial error of SLF arises the need for probabilistic assessment of the long-term point forecasts. This paper introduces a probabilistic SLF framework with prediction intervals, which is based on a hierarchical trending method. More specifically, the proposed hierarchical trending method predicts the magnitude of future electric loads, while the planners’ knowledge is used to improve the allocation of future electric loads, as well as to define the year of introduction of new loads. Subsequently, the spatial error is calculated by means of root-mean-squared error along the service territory, based on which the construction of the prediction intervals of the probabilistic forecasting part takes place. The proposed probabilistic SLF is introduced to serve as a decision-making tool for regional planners and distribution network operators. The proposed method is tested on a real-world distribution network located in the region of Attica, Athens, Greece. The findings prove that the proposed method shows high spatial accuracy and reduces the spatial error compared to a business-as-usual approach.

Large‐Scale Desalination and the External Impact on Irrigation‐Water Salinity: Economic Analysis for the Case of Israel

AbstractRecent agroeconomic studies in water‐scarce countries such as Spain, Australia, Saudi Arabia, and Israel have revealed the economic viability of irrigating high‐value crops with desalinated water. However, the worldwide growth of large‐scale desalination capacities is primarily designed to resolve urban‐water scarcity, disregarding the impact of desalination on irrigation‐water salinity. We develop a dynamic hydroeconomic programming model where infrastructure capacities and allocations of water quantities and salinities in a regional water distribution network are endogenous. We show that subsidizing desalination is socially warranted because the associated reduction in irrigation‐water salinity is an external effect of water consumption by all water users. Empirical application to the entire state of Israel indicates that large‐scale desalination of seawater and treated wastewater for irrigation is optimal. This result stems from the large share of irrigation‐intensive salinity‐sensitive high‐value crops, motivated by Israel's policies to support local agriculture. Ignoring salinity results in a 45% reduction in desalinated irrigation water, a 29% reduction in farming profits, a 250% increase in water suppliers' profits, and an average deadweight loss of $1,200 a year per hectare of arable land.

Smoothing control of solar photovoltaic generation using building thermal loads

Distributed energy resources such as solar photovoltaic (PV) systems are seeing significant expansion in power grids worldwide. However, serious system integration issues have arisen including voltage fluctuations and accelerated aging of voltage regulation devices, when adopting mass amounts of volatile energy resources onto legacy distribution networks originally designed for unidirectional power flow. Building heating, ventilation and air-conditioning (HVAC) systems, collocated with the distributed generation, can be considered as flexible loads due to the inherent building thermal inertia. HVAC systems can be proactively controlled to improve voltage regulation of distribution networks with high PV penetrations. This paper presents a smoothing solution that modulates HVAC power in response to volatile PV generation as a means to mitigate fluctuations in the net demand and generation. To demonstrate the effectiveness and evaluate performance gains, hardware-in-the-loop (HIL) tests were carried out using a 3-ton variable-speed heat pump. The HIL tests leveraged a building thermal dynamic model and a steady-state power flow model for a 33-bus distribution network to capture realistic indoor thermal responses and distribution voltage variations during PV smoothing control. Test results showed that the developed strategy was effective in reducing variations of net demand and generation with negligible impact on indoor comfort. More than 55% reductions of voltage fluctuation were achieved and tap operations of voltage regulators could be fully or partially eliminated with proactive PV smoothing.

Research on Multi-Scenario Distributed Wind and Photovoltaic Power Accommodation Capacity of Distribution Network Based on Time Series Production Simulation

With the large-scale access of distributed new energy into the distribution network, the evaluation on the accommodation capacity of distributed new energy in the distribution network has important guiding significance for the scientific planning of distributed generation and grid connection. Considering the parked and regionalized development trend of distributed power generation mode, this paper proposes a method for evaluating the distributed wind and photovoltaic power accommodation capacity of regional distribution networks based on time series production simulation from the perspective of practical applications. Based on the timing difference and complementarity of the load characteristics of substations on the distribution network side, we conducted an evaluation on the distributed wind and photovoltaic power accommodation capacity of the regional distribution network, and through taking the flexible operation mode of the distribution network side into consideration, realized the mutual assistance from time domain and space dimension of distributed new energy consumption and carrying capacity of various substations. Through establishing a mathematical model for the consumption and carrying capacity analysis in the multi-scenario modes such as local node consumption, local station consumption and allowing certain percentage of abandoned electricity, we came out the scale of the maximum consumption of distributed new energy in different scenarios as well as the overall grid connection layout scheme. In the end, we used such evaluation method to conduct an empirical analysis on the distribution network in certain region, verified the correctness and effectiveness of the model, and provided theoretical supports for guiding the regional distributed new energy overall planning and scientific grid connection.

A GWO-GRNN based model for short-term load forecasting of regional distribution network

Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems, which not only requires high accuracy and fast calculation speed, but also has a diversity of influential factors and strong randomness. This paper proposes a short-term load forecasting model for regional distribution network combining the maximum information coefficient, factor analysis, gray wolf optimization, and generalized regression neural network (MIC-FA-GWO-GRNN). To screen and decrease the dimension of the multiple-input features of the short-term load forecasting model, MIC is first used to quantify the non-linear correlation between the load and input features, and to eliminate the ineffective features, and then FA is used to reduce the dimension of the screened input features on the premise of preserving the main information of input features. After that the high-precision short-term load forecasting based on GWO-GRNN model is realized. GRNN is used to regressively analyze the input features after screening and dimension reduction, and the parameter of GRNN is optimized by using the GWO, which has strong global searching ability and fast convergence. Finally a case study of a regional distribution network in Tianjin, China verifies the accuracy and applicability of the proposed forecasting model.

A New Harmonic Mitigation System With Double Balanced Impedance Filtering Power Transformer for Multistage Distribution Network

In this article, a new harmonic mitigation system using double balanced impedance filtering power transformer (DBI-FPT) is proposed for improving the power quality (PQ). Designed for multistage distribution network with severe harmonic pollution, the proposed system enhances multiport filtering performance, mitigates resonance along with reduction of construction and maintenance costs compared to other alternative system. The simplified model of the proposed system is established by combining the short-circuit impedance calculation method and the characteristics of dual-balanced winding. By analyzing the influence of manufacturing errors on the dual-balanced winding in detail, the optimization method for filtering performance is given, which in turn guides the practical application of DBI-FPT effectively. After that, through comprehensively comparative analysis, the multiport harmonic mitigation characteristics, resonance avoidance, and amplification weaken mechanism of the proposed method are revealed. The experimental results validate that the proposed system is suitable for multiterminal PQ improvement and industrial application in multistage regional distribution network.

The 2020 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2020 (Power TAC 2020). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. Costs include fees for publication and withdrawal of tariffs, for rectifying supply-demand imbalances, for contributions to peak demand, and for customer connections. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we approximate the effects of locational-marginal pricing through manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of whom have production capacity such as solar panels or wind turbines. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. A distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure. Real-time balancing of supply and demand is managed by a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. Changes for 2020 are focused on stability and on making customer evaluation of regulation rates more realistic, and are highlighted by change bars in the margins. See Section 4.1.1 for details.

Research on energy transaction mode of regional microgrid cluster and distribution network considering wind power uncertainty

In order to reduce the unnecessary energy interaction between the microgrid and the distribution network, while promoting the local accommodation of wind power, this paper presents an energy transaction mode of regional microgrid cluster and distribution network. Firstly, the mode integrates the load demand and wind power output in the area based on regional multi-microgrid operator. And then, the transaction mode uses the load transfer rate to describe the user response of time of use (TOU) price. Meanwhile, the mode also establishes a model for the TOU price of regional microgrid cluster system considering the uncertainty of wind power. Finally, an NSGA-II algorithm is used to solve the multi-objective optimization problem in the model. Based on the experimental results, the transaction mode achieves cost reduction of microgrid cluster, while reducing unnecessary interaction between the regional microgrid cluster system and the distribution network.

Research on Influencing Factors of Equipment Asset Performance Evaluation Level of Regional Distribution Network Based on Interpretive Structure Model

The evaluation of equipment asset performance level of regional distribution network is an important link. As the equipment assets of the regional distribution network are affected by many factors, the performance level is low. Based on this, this paper considers 11 influencing factors from the two dimensions of cost input impact and performance output impact, establishes a model of the interpretation structure of the equipment asset performance level of the regional distribution network, and conducts detailed analysis and discussion. The results show that more attention needs to be paid to key factors such as the emission reduction rate of coal-to-electricity projects, annual conventional power grid construction investment costs, coal-to-electricity project investment, annual conventional grid operation and maintenance costs, and electricity sales revenue, which will help improve the performance level of equipment assets in the regional distribution network.

Multi-attribute evaluation and deviation analysis of regional distribution network investment effect

As the input ratio of regional distribution network increases year by year, higher requirements are put forward for high quality and high reliability of power consumption. Under this background, this paper involves 11 evaluation indexes from three dimensions of reliability, economy and environmental protection, and designs the evaluation index system of investment effect of regional distribution network. The multiattribute AHP-TOPSIS-entropy weight combination evaluation method for the investment effect of regional distribution networks is constructed. And select distribution network A, B, C area for example analysis, the results show that the n-1 pass rate, power supply reliability, power consumption growth and emission reduction of clean energy generation are positively correlated with the investment effect of regional distribution network.

Research and Application of Integrated Energy Coordination Optimization Control in Distribution Network

In recent years, China’s new energy industry has developed rapidly, and photovoltaic and wind power installed capacity has ranked first in the world. However, at the same time of rapid development of new energy sources, the problem of consumption has become increasingly prominent, and the phenomenon of abandoning wind and abandoning light is serious. This paper will start from the power generation principle of distributed power supply and the charge and discharge mechanism of energy storage. Through the analysis of the characteristics of distributed power supply, the new energy consumption is the most, the thermal power input is the minimum, and the distributed power supply is established. The cooperative optimization model of distribution network is constructed, and the coordinated optimization control model based on distribution network automation technology is constructed, and the experimental simulation is carried out to obtain the optimal coordinated energy optimization control model for distribution network to realize the safety, high efficiency and high degree of regional distribution network.

Inverter-Based Voltage Control of Distribution Networks: A Three-Level Coordinated Method and Power Hardware-in-the-Loop Validation

The reactive power of the photovoltaic (PV) inverters has great potential for voltage regulation of distribution networks. In this paper, a new three-level coordinated control method for PV inverters is proposed to address network voltage fluctuation and violation issues. In Level I, a ramp-rate control is designed to smooth the network voltage fluctuations, while in Level II, a droop control is designed to alleviate the network voltage deviations. If the local compensation provided by Level I and II is not enough to regulate the network voltages within the required limits, the Level III control based on dynamic average consensus can respond and share the reactive power requirement among other inverters in a distributed way. The proposed control method can smooth the voltage profiles, restrain the voltage rise/drop problem, and coordinate all PV inverters in real-time when there is no feasible local solution. The stability analysis of the proposed three-level coordinated control for network voltage regulation is provided. The power hardware-in-the-loop (PHIL) experiment has been conducted for validating the proposed control method under various scenarios.

Reliability modelling and analysis of water distribution network based on backpropagation recursive processes with real field data

Any water distribution network (WDN) is a key element in critical infrastructure. As time series data mining and modelling are developing apace, these provide promising tools for the research of water resources management. The aim of this article is to explain how incomplete information on the failures in water mains can be used and processed effectively. The data available and under investigation are the truncated failure field data of a regional water distribution network recorded during the last 15 years. We introduce the application of novel, non-trivial dynamic backpropagation recursive time-series models which are later successfully validated by the recorded data. Real field data of a WDN were used while these data were elaborated with a backpropagation Kalman recursor. The results can be used for predicting the reliability of a WDN, or as inputs into a water management system to optimise maintenance, crisis management and emergency planning.