Research on Load Behavior Characteristics of Regional Distribution Network based on Data Mining

In order to meet the high permeability and the access of large scale distributed generation, at present and in the future, active distribution network becomes the trend of smart grid research. This paper introduces the influence of distribution network voltage distribution and stability of distributed energy access based on active distribution network; The reactive power and voltage optimization control method of active power distribution network is summarized from the aspects of optimal voltage regulation measure, voltage regulation measure of power network equipment, reactive power optimal dispatch of power system, and so on. Analysis of the current status of the research, and summarized the existing active distribution network with a distributed power pickup distribution network reactive power and voltage control of the existence of some problems and the insufficiency still some problems that must be solved in the study of reactive power and voltage optimization control of active distribution network are pointed out.

With the increasing penetration of distribution generations, distribution system is characterized by complex injection uncertainties, which brings formidable challenges for distribution network operation. As distributed renewable energy penetrating into power network, the operation mechanism and control strategy of distribution network become more complex, which makes it necessary to adjust the control measures to improve the operation quality and efficiency. In order to deal with the increasing uncertainties caused by renewable generations, a voltage aware synergetic dispatch method of active distribution network is proposed. By accounting for the voltage regulation characteristics of all kinds of generators and loads, flexible network topology and participation mechanism of regulation reserves of synchronous generators and connected transmission network in response to uncertainties, an optimization model of voltage aware synergetic dispatch of active distribution network is constructed. The proposed model is formulated as a mixed-integer nonlinear optimization problem and is resolved by the combination of firefly algorithm and nonlinear primal-dual interior-point method. Finally, case studies on the modified IEEE 33-bus distribution system are conducted to illustrate the effectiveness of the proposed method.

For the distribution network with high permeability distributed energy access, distribution network fault current distribution network operation mode, the influence of the distributed power control strategy, and the existing in ring network cabinet configuration does not meet the high sensitivity and high reliability requirements of the active power of fuse protection, in this paper, an adaptive protection system for active distribution network is proposed by using the passive protection terminal installed in the ring network cabinet and the protection host configured at the dispatching end. The protection host receives SCADA data of distribution network scheduling to obtain the topology of active distribution network, uses distributed power supply voltage and grid voltage to adaptively calculate the passive protection setting value, and sends the setting value to the passive protection terminal through communication to realize the adaptive protection function of active distribution network. Finally, the simulation model is established, and the fuse protection of active distribution network and the adaptive protection system with passive protection terminals are compared, and the sensitivity and reliability of the adaptive protection scheme are verified.

With the development of the economy and the improvement of people’s living standards, energy has become an increasingly indispensable substance. The large-scale development and utilization of fossil energy has caused problems of energy crisis and environmental pollution. In order to solve these two problems, it is necessary to develop and utilize renewable energy sources, and access distributed power sources in the distribution network to form an active distribution network. The active distribution network contains a large number of distributed power sources, energy storage units and reactive power compensation equipment. Although it provides more convenience for voltage and reactive power regulation of the distribution network, the intermittent and volatility of renewable energy sources will also be Its voltage has a big impact. In order to solve the voltage dip and overvoltage problem of active distribution network, a new active distribution network voltage control strategy is proposed by using model prediction control technology. Inaccurate system modeling is reduced by establishing active distribution bus voltages, distributed power supplies, and energy storage device prediction models. With the minimum adjustment cost as the control target, the second-order cone relaxation method is used to optimize the action behavior of each control device. Finally, the IEEE33 node simulation example is built on the matlab simulation platform. The results show that the proposed voltage control strategy has good control effect and robustness.

In view of the lack of discussion on the connection mode of double-loop network and the influence on the power supply capacity of distribution network in the current soft open point (SOP) planning method, this paper proposes a four-terminal SOP optimization configuration method for active distribution network considering the influence of power supply capacity. Firstly, the application scenario of four-terminal SOP and its control mode under normal and fault operation of distribution network are discussed. Secondly, a bi-level programming method for four-terminal SOP including planning and operation of active distribution network is proposed. On this basis, a planning model aiming at minimizing the sum of the annual investment and operation cost of the four-terminal SOP, the annual normal operation cost of the distribution network and the annual benefit cost taking into account the variation of power supply capacity is constructed. Thirdly, the hybrid integer nonlinear programming model is solved by combining particle swarm optimization, cone programming and power supply capability evaluation. Finally, an example is given to verify the validity and rationality of the proposed method.

With a large number of distributed power sources connected to the distribution network, power supply reliability has always been one of the most concerned indicators for power supply companies. Fault mode and effect analysis (FMEA) can predict the impact of fault isolation and load switching diversity in active intelligent distribution networks. In this paper, a standardized model of reliability system evaluation is constructed by FEMA table, and the reliability index is solved by combining Monte Carlo analysis method. The simulation with IEEE-RBTS BUS6 as an example shows that the method can quantify the effect of distributed power supply on the reliability of active distribution network, which proves that the proposed method is effective and feasible to solve the index.

With the extensive grid connection of distributed generation (DG), due to the safe and stable operation restrictions of the distribution network, the abandoning of wind and solar energy become more serious. As a new type of power distribution device, soft open point (SOP) is reliable and efficient in power control, which not only effectively solve a series of problems caused by distributed power supply access, but also greatly increase the flexibility and economy of active distribution network(ADN). This paper considers the distributed power supply characteristics and demand response (DR), and establishes the planning model of SOP in the active distribution network combining planning and operation. The network layer uses the minimum integrated cost of the power distribution company as the objective function to perform SOP location and capacity. The load layer adjusts the load with the minimum cost of participating DR users. The operation optimization layer determines the power at both ends of the SOP with the minimum cost of the distribution network in each scenario as the objective function. In order to account for the timing characteristics and uncertainties of distributed power output and load, this paper analyzes the distributed power output and load curve in typical scenarios. The time-of-use electricity price mechanism is introduced to reflect the demand side characteristics of the active distribution network. In this paper, an improved multi-objective particle swarm optimization (IMOPSO) is used to solve the above model. Finally, the IEEE-33 node power distribution system is selected as an example to simulate. Through comparative analysis, it is found that the planning model of this paper can maximize economic benefits and has higher feasibility.

The access of distributed generation has grant influences on the power distribution system network structure and as a consequence, changes its failure risk. This article presents a power failure risk evaluation model of power distribution network, which including two aspects of device level and feeder level. In the first place, the risk of the power distribution network containing distributed generation will be evaluated from two aspects, device failure possibility and device failure severity. Basing on varies devices' evaluation, through the comprehensive evaluation method to quantify the feeder's risk of failure. When the risk exceeds the threshold value, the system would start its safety precaution. Numerical example results show that, the evaluation method that this article established can realize the risk evaluation and early warning of the power distribution network containing distributed generation, the results have guiding significance for construction retrofitting, and hastens the optimal operation for active power distribution network.

With increasing connection of distributed generations (DGs), the power flow in electric distribution network is no longer unidirectional and the network has become active distribution network. Thus, the connection of DGs in electric distribution networks has created a challenge for distribution network operators due to bidirectional power flow. One of the main technical challenges of an active distribution network is to maintain an acceptable voltage level in the system. This has initiated efforts in using various voltage control strategies to regulate the network voltage profile so that the voltage is maintained at its allowable voltage limits. A number of voltage control methods have been applied to solve voltage control problems associated with the connection of DGs in a distribution system. These voltage control strategies may be classified as decentralized or distributed and centralized or coordinated voltage control. From the literature, a number of coordinated voltage control strategies have been implemented to provide better and faster control to the system. This chapter presents an improved coordinated voltage control method in active electric distribution network by coordinating the three voltage control methods, namely, power factor control, on load tap changer control and generation curtailment control. These voltage control methods are coordinated using fuzzy logic by considering the load bus voltages and DG power as inputs and the voltage control actions as the outputs. The fuzzy logic if-then control rules which are generated to build the fuzzy logic control system are based on the simulations results as well as from the previous works. Results obtained using the fuzzy logic based coordinated voltage control has shown that the voltages are able to be kept within its permissible limits.

With the gradual increase of distributed energy penetration, the traditional optimization model of distribution network can no longer guarantee the stable and efficient operation of the distribution network. In order to deal with the inevitable uncertainty of distributed energy, a new robust optimal operation method is proposed for active distribution network (ADN) based on the minimum confidence interval of distributed energy Beta distribution in this paper. First, an ADN model is established with second-order cone to include the energy storage device, capacitor bank, static var compensator, on-load tap changer, wind turbine and photovoltaic. Then, the historical data of related distributed energy are analyzed and described by the probability density function, and the minimum confidence interval is obtained by interval searching. Furthermore, via taking this minimum confidence interval as the uncertain interval, a less conservative two-stage robust optimization model is established and solved for ADN. The simulation results for the IEEE 33-bus distribution network have verified that the proposed method can realize a more stable and efficient operation of the distribution network compared with the traditional robust optimization method.

Detection and isolation of faulty line in an active distribution network using intelligent numerical relay

Aiming at the problem that the global optimization results of distribution network can not satisfy the needs of multiple parties, a cooperative development strategy of active distribution network and integrated energy system which based on autonomous state space is proposed. Firstly, the satisfactory solution group of the optimal operation of the distribution network is calculated, and, the interaction power between the distribution network and the integrated energy system is taken as the base point of the autonomous state space, and the shortest radius of the transient security region under the base point range is calculated, thereby determining the autonomous state space of the interaction between the integrated energy system and the active distribution network. Secondly, optimize the integrated energy system in the autonomous state space to determine the output and cost of dispatching equipment in the integrated energy system. The calculation example shows that the collaborative optimization strategy proposed in this paper can give consideration to the interests of active distribution network safety optimization operation and integrated energy system operator cost optimization at the stage of power market reform not yet fully mature.

An active distribution network comprises renewable generation sources like solar and wind. Also, an active distribution network feeds electrical loads and plug-in hybrid electric vehicles. An optimal active and reactive power management strategy is imperative for a technically feasible and financially viable operation. The renewable sources are interfaced via power electronic voltage source converters, which can control the reactive power injection into the network. In this paper, a joint optimal active and reactive power control strategy is proposed, which utilizes the reactive power handling capability of the voltage source converters. Uncertainties of renewable generation, load demand, grid energy price, and charging load demand of the plug-in hybrid electric vehicles are modeled and incorporated using the probabilistic "Hong's point estimate method". A group of measures like optimal procurement of active and reactive power from available resources, optimal battery energy storage scheduling, demand response, smart transformer scheduling, and network reconfiguration are carried out to maximize the expected profit of the distribution network operator. The proposed method is validated by simulation studies on a thirty-three bus active distribution network.

The quantity of wind and photovoltaic power-based distributed generators (DGs) is continually rising within the distribution network, presenting obstacles to its safe, steady, and cost-effective functioning. Active distribution network dynamic reconfiguration (ADNDR) improves the consumption rate of renewable energy, reduces line losses, and optimizes voltage quality by optimizing the distribution network structure. Despite being formulated as a highly dimensional and combinatorial nonconvex stochastic programming task, conventional model-based solvers often suffer from computational inefficiency and approximation errors, whereas population-based search methods frequently exhibit premature convergence to suboptimal solutions. Moreover, when dealing with high-dimensional ADNDR problems, these algorithms often face modeling difficulties due to their large scale. Deep reinforcement learning algorithms can effectively solve the problems above. Therefore, by combining the graph attention network (GAT) with the deep deterministic policy gradient (DDPG) algorithm, a method based on the graph attention network deep deterministic policy gradient (GATDDPG) algorithm is proposed to online solve the ADNDR problem with the uncertain outputs of DGs and loads. Firstly, considering the uncertainty in distributed power generation outputs and loads, a nonlinear stochastic optimization mathematical model for ADNDR is constructed. Secondly, to mitigate the dimensionality of the decision space in ADNDR, a cyclic topology encoding mechanism is implemented, which leverages graph-theoretic principles to reformulate the grid infrastructure as an adaptive structural mapping characterized by time-varying node–edge interactions Furthermore, the GATDDPG method proposed in this paper is used to solve the ADNDR problem. The GAT is employed to extract characteristics pertaining to the distribution network state, while the DDPG serves the purpose of enhancing the process of reconfiguration decision-making. This collaboration aims to ensure the safe, stable, and cost-effective operation of the distribution network. Finally, we verified the effectiveness of our method using an enhanced IEEE 33-bus power system model. The outcomes of the simulations demonstrate its capacity to significantly enhance the economic performance and stability of the distribution network, thereby affirming the proposed method’s effectiveness in this study.

With continuous improvement of the retention rate of household electrical appliances, the peak valley difference of power distribution network, especially the rural distribution network is expanding. A reasonable management method of the demand side is needed to realize load shifting and economic operation of distribution network. At present, the cost of energy storage(ES) equipment is decreasing, which provides a new idea for the optimization of distribution network operation. This paper studied the charge/discharge model and strategy of ES, and constructed a multi-period optimal operation strategy model of distribution network with ES. Sparse coding automatic differentiation technology was applied to reduce coding workload, and constraint reduction of ES was proposed to reduce differential calculation, which enhanced the feasibility and efficiency of the model solving. Finally, the simulation results of an actual distribution network with ES demonstrated the accuracy of the proposed model, the efficiency of the proposed algorithm and the feasibility of the proposed solving method.