Scheduling Of Distribution Networks Research Articles

Bi-Level Optimal Scheduling of Distribution Networks with the Photovoltaic Prosumer Groups Based on Contract Theory

Bi-Level Optimal Scheduling of Distribution Networks with the Photovoltaic Prosumer Groups Based on Contract Theory

Multi-Time Scale Cloud-Edge Collaborative Scheduling Strategy for Distribution Network Considering Spatiotemporal Characteristics of Demand Response

The increasing penetration rate of distributed resources in the distribution network has brought about significant volatility and uncertainty problems. Demand response (DR) can flexibly change the energy consumption method of the user to balance supply and demand. This paper first considers the spatial distribution characteristics of DR resources to schedule DR resources to construct a distributed resource cloud-edge collaborative scheduling framework. Based on this, the distribution network scheduling requirements are combined with the multi-time scale characteristics of DR. A three-stage cloud-edge collaborative optimization scheduling strategy for distributed resources in the distribution network is proposed, which allocates the multi-time scale scheduling tasks of DR resources to the cloud and edge. Secondly, taking the cloud and edge as the optimization platform, a three-stage optimization decision-making model of the distribution network is established. In the day-ahead stage, the global optimization decision is made by combining cloud-centralized optimization with edge-independent optimization. In the intraday stage, edge-rolling optimization is carried out. In the real-time stage, the edge-distributed calculation is based on the consensus algorithm. Finally, the effectiveness and economy of the proposed model and strategy are verified by an example analysis.

Multivariate low-carbon scheduling of distribution network based on improved dynamic carbon emission factor

The “load-following” characteristic of the power system makes the electricity consumption behavior on the load side crucial for the low-carbon operation of the distribution network. To address this, this paper proposes an improved dynamic carbon emission factor for the distribution network, taking into account the spatiotemporal characteristics of carbon emission intensity and the integration capacity of photovoltaics (PV). Based on this, a calculation method for the carbon emissions of the distribution network load is provided. Subsequently, for commercial and industrial user scenarios, demand response models are separately constructed for commercial and industrial loads based on different driving mechanisms. Using time-of-use electricity prices as decision variables, optimization scheduling of the distribution network is carried out with the objectives of minimizing scheduling costs and carbon emissions. At the same time, a case study is conducted in an improved IEEE-33 node distribution network. The results indicate that, under the guidance of the improved dynamic carbon emission factor, load transfer can be achieved through fluctuating electricity prices, effectively reducing the scheduling costs of the distribution network, decreasing carbon emissions, and enhancing the PV integration capacity of the distribution network in different user scenarios. Finally, it is hoped that in the future, this optimization method can be widely applied, and further research can explore coordinated strategies among generation, network, load, and storage to advance the development of the power industry.

Online Distribution Network Scheduling via Provably Robust Learning Approach

Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this paper, whose key lies in the transformation of the MIP-based DNS into the simple linear program problem with a much faster solving time. It formulates the parametric DNS model to construct the offline training dataset and then proposes the provably robust learning approach to learn the integer variables of MIP. The proposed learning approach is adversarial to minor perturbation of input scenario. After training, the learning model can predict the integer variables to achieve online scheduling. Case study verifies the acceleration effectiveness for online DNS.

Markov decision process based value chain calculation of water distribution network scheduling

Precise value of scheduling decisions forms the cornerstone of water distribution network (WDN) scheduling optimization, which aims at conserving energy and enhancing network operational efficiency. This article proposes a computational methodology for evaluating the value chain of scheduling decisions in WDN. The scheduling process is modeled as a Markov decision process with immediate reward function, action and state space. Due to the periodicity of water supply and sequential nature of scheduling, the calculation quantifies cumulative value of scheduling decisions by incorporating state transition probability with expected value. The effectiveness and applicability of the proposed evaluation method are demonstrated using scheduling data from a real world WDN. The method provides rational values on scheduling period and strategies, offering practical feedback for scheduling decisions.

Improving the Flexibility of the Distribution Network Based on the Robust Chance Constraint Method

While the large-scale access to distributed new energy brings economic and environmental benefits, its inherent uncertainty characteristics also trigger problems such as voltage overruns and fluctuations. In order to effectively solve the adverse effects of large-scale grid connection of distributed new energy, this paper proposes the optimization method of regional autonomy capacity based on the robust chance constraint model. First, the regional autonomy capacity measurement method based on distribution robust optimization is studied, and the regional autonomy robust chance constraint model containing distributed energy is constructed. Second, the active distribution network scheduling model is studied to achieve the optimization of regional self-energy capacity. Finally, simulation examples show that the proposed approach can fully utilize the flexible resources within the distribution network and effectively improve the autonomy of the distribution network park.

Multi-Objective Optimal Scheduling of Distribution Network with Electric Vehicle Charging Load Considering Time-Varying Road Impedance

Multi-Objective Optimal Scheduling of Distribution Network with Electric Vehicle Charging Load Considering Time-Varying Road Impedance

Optimal Scheduling of Distribution Network With Autonomous Microgrids: Frequency Security Constraints and Uncertainties

Widespread integration of autonomous microgrids (MGs) into the distribution network (DN) makes the scheduling of distribution systems challenging. Especially, the uncertainties of renewable energy sources (RESs) and load demand put the MG frequency at the risk of violation for its low inertia and the high penetration of RESs. To address the schedule problems associated with uncertainties and frequency security, a day-ahead scheduling optimization model is proposed based on the chance-constrained programming (CCP) theory. The frequency security constraints and uncertainties of RESs/load demand associated with MGs are considered during the development of the proposed model. The total operation cost of the distribution system consisting of DN and MGs is minimized, while the MG frequency deviations caused by uncertainties are restricted in the predefined safe range by the proposed CCP-based model. Besides, a linearization method is presented and used to transform the proposed model into a mixed-integer linear program to determine the efficiency of the CCP-based model. Finally, numerous case studies are conducted to illustrate the effectiveness and good scalability of the proposed model and understand the advantages of using the model. The RT-LAB hardware-in-loop tests are carried out to study the real-time performance under the scheduling solved by the proposed model. Simulation and experimental results validate that the proposed model can be used to minimize the total operation cost of the DN system integrated with MGs while ensuring that the MG frequency deviations are operated within an acceptable range.

Optimization for Transformer District Operation Considering Carbon Emission and Differentiated Demand Response

With the promotion of the “dual carbon” goal, a large number of distributed photovoltaic power are connected to the distribution network. Since the current operation optimization of the low-voltage transformer district is based on single objectives such as the economy and power reliability, the model is relatively simple and difficult to adapt to the large-scale access of photovoltaics. Therefore, this article comprehensively considers carbon emissions, different load characteristics, and differentiated demand response of the district. An optimization method for low-voltage transformer district operation under the dual-carbon background is proposed. First, the typical structure of a low-voltage transformer district is introduced. Second, the load types and characteristics of the low-voltage transformer district are analyzed, and differentiated demand response models are established for different types of loads. Finally, taking the minimum economic cost and carbon emission as the objective, the low-voltage transformer district operation optimization model considering carbon emission and differentiated demand response is established by considering the voltage overrun of the photovoltaic access point, substation capacity constraint, and carbon emission constraint. The simulation results show that the model can effectively reduce the economic cost and carbon emissions of the low-voltage transformer district, achieve more than 95% reasonable utilization rate of new energy in the low-voltage transformer district, improve the lateral time distribution of load in the low-voltage transformer district, and provide an effective means for low-carbon scheduling of distribution networks.

High-Efficiency Power Generation Device of Magnetic Declination Thermoelectric Material and Multisource Coordination Optimization of Distribution Network

The first-order phase transition compounds have attracted widespread attention from refrigeration enterprises and scientists due to their giant magnetocaloric effect and refrigeration temperature region spanning room temperature. Magnetic substances are crystal structures composed of magnetic ions or atoms with a certain thermal motion or vibration. This research mainly discusses the multisource coordination optimization of magnetic declination thermoelectric material efficient power generation device and distribution network. In this study, large doses of MnFe(P,Si) compounds with different ratios were prepared. The reason for choosing a large dose is that the properties and structures of the samples prepared in a large amount and a small amount will be more or less the same for the samples fired in the same proportion and in the same process. At the same time, mass production also prepares for future mass production. Thermomagnetic power generation described in this study is a form of power generation that converts thermal energy directly into electrical energy. The conversion of thermal energy to electric energy is realized by the change of magnetization in the closed coil by the thermomagnetic material in the magnetic field environment, and then, the change of the magnetic flux in the closed coil is realized. The distribution network refers to the power network that receives electrical energy from the transmission network or regional power plants and distributes it locally through distribution facilities or distributes it to various users step by step according to the voltage. It is composed of overhead lines, cables, towers, distribution transformers, isolating switches, reactive power compensators, and some ancillary facilities. A network that plays an important role in distributing electrical energy in the power grid. This research starts with the active distribution network scheduling priority and the distribution network scheduling coordination control framework. Combined with the scheduling and operation characteristics of each component in the distribution network, the coordinated optimization scheme of the distribution network and the source-network load-storage coordinated control framework are investigated and analyzed. The scheduling resources of the model include the power of the substation flowing into the distribution network, the output of the adjustable distributed power source, the output value of the energy storage element, the closing status of the tie switch, and the section switch. In the study, it was found that when the Fe content was 0.63, the Curie temperature of the compound could reach 273 K at the highest. Scientifically designing a thermomagnetic power generation demonstration device is an important part of the realization of thermomagnetic power generation. The magnetic declination thermoelectric materials designed in this study will help to improve the power generation efficiency.

Dispatching System of New Energy Connected to Grid under Smart Grid

The introduction of smart loads in the dispatching of the distribution network helps to make full use of new energy generation and reduce the dispatch of standby units. Summarize the characteristics of distributed energy generation and load, introduce intelligent load and its application in distribution network dispatching. In view of the distributed characteristics of new energy, neural network is used to predict in the distribution network dispatching with distributed energy access. The coordination of the multi-agent system can achieve good distribution network scheduling effects, and a single agent can also use its own autonomy and learning experience to participate in the distribution network scheduling.

Research on the Application of Distributed Energy in Power System

With the expansion of the installed capacity of distributed power generation in China, especially the increase in the proportion of the installed capacity of distributed power generation in some areas to the system load, some potential technical and management problems, and contradictions in the development of large-scale centralized distributed power generation have become increasingly apparent, mainly including distributed power generation, consumption, and safety. Aiming at the distributed characteristics of new energy, this article adopts a multi-agent system in the distribution network dispatching with distributed energy access. The coordination of the multi-agent system can achieve good distribution network scheduling effects, and a single agent can also use its own autonomy and learning experience to participate in the distribution network scheduling. Designed a distribution network dispatching control system based on multi-agent technology and its implementation process. A distribution network example with distributed energy access is used to verify the effectiveness of smart loads in distribution network dispatching.

Research on power exchange flexibility optimization of urban AC/DC distribution network

After large-scale access to distributed generation of new energy generation in urban AC/DC distribution network, its own randomness and uncertainty affect the supply-demand balance mechanism of distribution network. Therefore, this paper studies the power exchange flexibility optimization method of urban AC/DC distribution network to provide reference for the operation and scheduling of distribution network and main network. First, the active power exchange model of distribution network is established, the extreme value of power exchange of distribution network is obtained, and the flexible range of exchange power is calculated. Next, the flexibility evaluation index is established, and the operation characteristics of energy storage are transformed to complete the research on power exchange flexibility optimization of urban AC/DC distribution network. Through the actual example analysis, the results show that after the optimization of the design method, the net load curve is smoother, the effect of reducing the peak valley difference is more significant, and the overall curve is relatively smooth, which shows that the design method has certain effectiveness.

Multi-timescale Active Distribution Network Scheduling Considering Demand Response and User Comprehensive Satisfaction

A large number of distributed energy resources (DERs) integrate into the distribution network, which changes the power flow, increases the power fluctuations, and complicates the scheduling of the distribution network. To cope with that, a multitimescale scheduling method, which considers the demand response as well as user satisfaction, is proposed in this paper. First, in the day-ahead stage, both the generation-side and demand-side are combined to minimize the operating costs and reduce the impact of DERs. Second, in the real-time stage, the model predictive control method is introduced, smoothing the power fluctuations and maximizing the consumed renewable energy. Finally, the user comprehensive satisfaction is considered, ensuring the users’ benefit and improving the flexibility of users to participate in scheduling while shifting electricity demands. By optimizing the generation-side and demand-side on both day-ahead and real-time timescales, the proposed method can improve the operation status for the distribution network effectively while ensuring the interests of users. Simulation on an improved IEEE-33 bus distribution system verifies the effectiveness of the proposed method.

Optimal allocation of distributed energy storage in active distribution network via hybrid teaching learning and multi-objective particle swarm optimization algorithm

This work aims at solving complex problems of the optimal scheduling model of active distribution network, teaching strategies are proposed to improve the global search ability of particle swarm optimization. Moreover, based on the improved Euclidean distance cyclic crowding sorting strategy, the convergence ability of Li Zhiquan algorithm is improved. With the cost and voltage indexes of the energy storage system of the distribution network as the goal, different optimized configuration schemes are constructed, and the improved HTL-MOPSO algorithm is adopted to find the solution. The results show that compared with the traditional TV-MOPSO algorithm, the proposed algorithm has better convergence performance and optimization ability, and has a lower economic cost. In short, the algorithm proposed can provide a basis for improving the optimization of active distribution network scheduling strategies.

Probabilistic–proactive distribution network scheduling against a hurricane as a high impact–low probability event considering chaos theory

High impact–low probability (HILP) incidents, such as hurricanes, usually and gravely damage electric distribution networks. A resilient distribution network must have an ability to recover itself with a fast restoration methodology against the effects of HILP events. Due to improve the electric distribution network resiliency against HILP incidents, this paper suggests a probabilistic–proactive distribution network operation model based upon the chaos theory (P-PDNOMC(. Here, a resilience index based upon operational cost and load shedding cost is employed to decrease the effects of a hurricane as an HILP incident. P-PDNOMC is formulated as a framework that consists of a novel hurricane modelling and operation scheduling in normal and emergency conditions with uncertainties considering. It should be noted that a prediction approach based on the chaos theory and the least-squares support vector machine (LS-SVM) is also designed to consider the provisional and spatial behaviour of the hurricane. Furthermore, this paper proposes a novel optimization framework for damaged lines determination based upon the P-PDNOM C (DLsP-PDNOM C) by considering multi-zone and multi-period damaged equipment budget constraints. Here, the load shedding cost is applied to present the efficiency of the proposed model. The numerical results indicate the resiliency enhancement of the electric distribution network in the face of HILP incidents.

Applying multiple types of demand response to optimal day‐ahead stochastic scheduling in the distribution network

With the increasing power demand of end-users, demand side management has been an important resource to been applied to the optimal day-ahead scheduling of distribution network. This work deeply studies the mechanism of multi-type demand response projects participating in load management. The authors particularly design the novel compensation mechanisms of interruptible load (IL) and transferable load (TL). The step-wise price-quantity offer package including basic response stage and elastic stages is devised for IL while the two-dimensional alternating function based on transferable time and transferable capacity is set up to quantify the load shifting cost for TL. To model uncertainties of distributed generation output power and response behaviour of end-users, a large number of stochastic scenarios are generated by the Monte Carlo method and Latin Hypercube Sampling and clustered into some typical scenarios by K-means method. Then, the optimal day-ahead stochastic scheduling model of the distribution network is proposed based on the obtained typical scenarios. In addition, three indexes including the contribution degree, confidence degree and strategy confidence degree are put forward to evaluate the rationality of the scheduling decision. Finally, the numerical results on the IEEE 33-bus network verify the effectiveness of the proposed model.

Optimal Scheduling and Real-Time Operation of Distribution Networks With High Penetration of Plug-In Electric Vehicles

In this article, a method for real-time control and optimal operation scheduling of distribution networks with high penetration of plug-in electric vehicles (PEVs) is proposed. The main targets of the method are to minimize PEV charging cost at aggregator level, satisfy all distribution network and PEV constraints using accurate models of low computational requirements. An innovative definition of the flexibility of a PEV to change its active power is used to achieve efficient optimal active power dispatch to each PEV of a cluster. Real-time optimal dispatch of the reactive power to each PEV is also achieved. A hierarchical multiagent system is designed in order to deal with the increased complexity of the controlled system and collect the necessary information from the network. It is shown that the proposed method requires very small computation and communication times that do not depend on the number of PEVs. The convergence of the method is ensured with only one round of agent negotiations. The efficiency of the method is proved by detailed simulation results of a realistic distribution network and based on processed real data sets of the activity of the drivers.

The Load Forecasting Method Based on Adaptive Neural Network and TLBO Algorithm

Load forecasting is of great significance for the arrangement and optimization of the distribution network scheduling. This paper proposes a load forecasting method based on an improved TLBO optimized adaptive neural network. Firstly, the ‘teaching’ phase in the basic TLBO algorithm is improved, and the average value of all search individuals is changed while adopting adaptive teaching factors, so that the performance of TLBO in the entire search space can be adaptively improved. Then, the ‘learning’ phase of the TLBO is improved. The Gaussian mutation operator is introduced in the learning phase to maintain the diversity of the population and avoid the TLBO algorithm’s premature convergence and local optimization. Finally, using the improved TLBO algorithm to optimize the adaptive neural network forecast model. In the end, the actual load data of Tianjin Power Grid was used to verify the accuracy of the simulation results.

Optimal scheduling of distribution network incorporating topology reconfiguration, BES and load response: A MILP model

Distributed generation (DG) is becoming increasingly important due to the serious environmental pollution caused by conventional fossil-energy-based generation and the depletion of non-renewable energy. As the flexible resources in the active distribution network (ADN), battery energy system (BES) and responsive load (RL) are all able to assist renewable DG integration in day-ahead dispatch. In addition, the security and economic level can be well improved by adjusting network topology. Therefore, in this paper, a coordinated day-ahead scheduling method incorporating topology reconfiguration, BES optimization and load response is presented to minimize the total day-ahead operation cost in the ADN. Linearized current injection models are presented for renewable DG, RL and BES based on the linear power flow model, and an extensible linear switching operations calculation (ELSOC) method is proposed to address the network reconfiguration. Thus, a mixed integer linear programming (MILP) model is proposed to optimal coordinated operation of ADN. The correctness and effectiveness of the proposed method are demonstrated by the simulations on the modified test system. In addition, the combined scenario and Monte-Carlo method is used to handle the uncertainties of loads and DGs, and the results of different uncertainties can further verify the feasibility of the proposed model.