System Network Loss Research Articles

Multi-Objective Optimization and Reconstruction of Distribution Networks with Distributed Power Sources Based on an Improved BPSO Algorithm

The continuous integration of distributed power into the distribution network has increased the complexity of the distribution network and created challenges in distribution-network reconfiguration. In order to make the distribution network operate in the optimal mode, this paper establishes a multi-objective reconfiguration-optimization model that takes into account active network loss, voltage offset, number of switching actions and distributed power output. For a distribution network with a distributed power supply, it is easy for the traditional binary particle swarm optimization algorithm to fall into a local optimum. In order to improve the convergence speed of the algorithm and avoid premature convergence, this paper adopts an improved binary particle swarm optimization algorithm to solve the problem. The IEEE33 node system is used as an example for simulation verification. The experimental results show that the algorithm improves the convergence speed and global search ability, effectively reduces the system network loss, and greatly improves the voltage level of each node. It improves the stability and economy of distribution-network operation and can effectively solve the problem of multi-objective reconfiguration.

Network partition and distributed voltage coordination control strategy of active distribution network system considering photovoltaic uncertainty

Aiming at the voltage exceeding problem caused by the introduction of Photovoltaic (PV) in Active distribution network (ADN), a comprehensive electrical distance index was established considering active and reactive voltage sensitivity, and the uncertainty of traditional PV probability density function (PDF) was optimized by random simulation method. The electrical distance expectation matrix between nodes was established by discretization of PV output probability distribution characteristics, and the ADN system was partitioned by affinity propagation (AP) algorithm. Taking active power loss as the objective function, the non-convex reactive power optimization control problem is transformed into a convex quadratic programming problem by LinDistFlow equation. An improved alternating direction method of multipliers (ADMM) which adaptively adjusts the penalty parameters is used to convert the finite boundaries of adjacent regions into data. By coordinating on-load tap-changer (OLTC), capacitor banks (CBs), and PV inverters on different time scales, the fast optimal control of global voltage in ADN is realized. The proposed method is tested on IEEE 33-bus and IEEE 123-bus distribution systems. After the optimization control algorithm described in this paper, the system network loss was reduced by 3.88%. Compared with the semi-definite programming relaxation, the calculation speed of LinDistFlow equation is increased by 72.2%. The results fully verify the feasibility and high efficiency of the control strategy described in this paper.

Research on Hazard Analysis and Control Strategy of Power System Dispatching Operation Based on Information Fusion Technology

Abstract In this paper, a decision model for grid fault diagnosis based on multi-source information fusion is established, and the probability of component fault is obtained by using the change of switching quantity characteristic information and electrical quantity characteristic information during grid fault, and the static fault degree obtained by switching quantity information and the voltage and current energy distortion degree and fault degree obtained by electrical quantity information is used as independent evidence bodies, which are fused by improved D-S evidence fusion technique, and the fused The results are decided by improved fuzzy C-mean decision model to finally determine the fault components. Then, the optimal control of the grid is studied, and the corresponding self-healing control model is established according to the different operating conditions of the system so as to propose a self-healing control strategy of the microgrid based on the improved particle swarm algorithm. After analysis and verification, the accuracy of the grid fault diagnosis method proposed in this paper reaches 0.9681, and the diagnosis results are consistent with the pre-defined fault elements compared with FCM. The system network loss decreases from 0.146 kW to 0.031 kW, and the maximum power supply capacity increases from 1.574 to 2.468 after using the improved particle swarm algorithm-based microgrid self-healing control strategy. Therefore, the method in this paper can improve the reliability of grid operation and resist the risk of accidents.

Research on Reactive Power Optimization Control Method for Distribution Network with DGs Based on Improved Second-Order Oscillating PSO Algorithm

With the increasing penetration of distributed generation (DG) in the distribution network, the original network structure of the distribution network has been changed. In addition, the randomness and intermittency of renewable power generation will also have an impact on the voltage and power flow of the distribution network. To solve this problem, this paper proposes a reactive power optimization control method for distribution network with DGs based on second-order oscillating particle swarm optimization (PSO) algorithm with a constriction factor. Considering the economic operation of the distribution network, the proposed control method realizes the coordinated operation of the DGs and battery group with the conventional static reactive power compensation device, so as to improve the voltage quality of the distribution network and reduce the system network loss. At the same time, an improved second-order oscillating PSO algorithm is proposed to improve the speed and convergence of the multiobjective algorithm. Finally, the effectiveness of the proposed control method is verified by using MATLAB/Simulink on IEEE 33 bus distribution network with DGs in both static and dynamic situations.

Study of Loss Reduction and Network Planning of a High Proportion Distributed Generation System

In order to realize a “double carbon” goal, it is necessary to further conserve energy and reduce emissions on the power grid. The high proportion of distributed generation dramatically impacts the power system network loss. Distributed generation connected to the distribution network is dependent on its location and capacity. In this work, distributed generation location and capacity are optimized by considering investment and operation costs, active power loss, and voltage security. Based on the particle swarm optimization algorithm, the 9-bus system is optimized and analyzed. It is found that (1) for the radial distribution network system, the closer the access location of the distributed generation is to the end of the load, the smaller the line loss of the system; (2) with the increase of DG load capacity ratio, the overall network loss of the system will first decrease and then increase. Furthermore, the IEEE standard 33 bus distribution network system is further optimized and simulated in this study. It is demonstrated that the proposed method reduces network losses and improves voltage quality.

Coordinated optimization of source–storage–load in distribution network based on edge computation

A large number of distributed photovoltaics are linked to the distribution network, which may cause serious power quality problems. Based on edge computing, this article put forward a strategy that aggregates multiple distributed resources, such as distributed photovoltaics, energy storage, and controllable load to solve this problem, emphasizing the coordination and optimization between distributed resources. Firstly, an edge computing architecture that can be fully applicable to the coordination of source–storage–loads is constructed. Based on this architecture, the distributed photovoltaic, energy storage and interruptible loads are optimized with the minimum operating cost of edge computation nodes as the objective function. Combined with the operation requirements of the distribution system, lessen the system network loss and make sure the stable and safe operation of the net; Finally, the model is changed with a second-order cone model and verified in an improved IEEE 33 node example. The results show that this method can effectively reduce the loss cost of the system, and can effectively solve the problem of system voltage exceeding the limit, reduce the voltage fluctuation, and maintain the safe and stable operation of the system.

Comprehensive Benefit Evaluation Analysis And Application Research of Energy Storage

In recent years, the penetration rate of renewable energy in the power system has increased year by year, and the allocation of energy storage is an important development trend to improve the operation stability of the grid-connected power system containing renewable energy. This paper first analyzes the basic concept and operation principle of energy storage devices, and then explains the costs and benefits of energy storage devices. Finally, the industrial park and energy storage power station are used as practical application scenarios to verify the correctness of the proposed method. The example results show that energy storage should be installed in a place where the system network loss is minimal and the reliability of power supply can be maximized, and the capacity of the energy storage system should match the load capacity of the bus bar of the installation site, so as to achieve the greatest comprehensive benefits.

Distribution Network Reactive Power Optimization Method Based on Filter Coordinated Quantum State Transfer Algorithm

To solve the problem of mixed integer nonlinear programming in dynamic reactive power optimization of distribution networks, a two-stage dynamic reactive power optimization model is used to reduce the size of the reactive power optimization calculation.The first stage is nonlinear planning, with the objective of minimizing the combined system network loss and node voltage offset. The second stage is a mixed-integer planning, with the objective of minimizing the incremental power system network loss on the basis of satisfying the constraints such as the number of equipment regulation throughout the day. In view of the fact that the conventional penalty function method to deal with the constraints will result in improper selection of penalty factors, an improved quantum state transfer algorithm with mixed filter technique is proposed. The algorithm adopts a two-population parallel search approach, and the individual adaptation degree and constraint violation degree are constituted as filter pairs as evaluation metrics to replace the penalty functions. Finally, the proposed algorithm, the traditional state transfer algorithm and Colony Algorithm are solved separately for the model using the IEEE 14 system as examples, and the correctness of the proposed model and algorithm is verified by comparing and analyzing the simulation results.

Optimization of Intelligent Distribution of Distribution Network in the Presence of Distributed Sources

In order to improve the intelligent distribution effect of the distribution network in the presence of distributed generation, this study combines intelligent algorithms to analyze the intelligent distribution of the distribution network in the presence of distributed generation. Moreover, this study establishes an objective function composed of system network loss expectation, voltage stability index, and reactive power compensation equipment investment. In addition, this study improves the artificial bee colony algorithm through tabu search algorithm and genetic algorithm and obtains an improved TS-IABC algorithm. Finally, this study compares and tests the performance of different algorithms in the form of functions and simulations. The simulation results show that the optimal intelligent distribution method of the distribution network in the presence of distributed power generation proposed in this study has a good effect and can promote the intelligent distribution of the distribution network.

Optimal planning of Cross-regional hydrogen energy storage systems considering the uncertainty

The cross-regional consumption of renewable energy can effectively solve the problem of the uneven spatial distribution of renewable energy. To explore the application of hydrogen energy storage systems (HESS) for cross-regional consumption of renewable energy, optimal planning of cross-regional HESS considering the uncertainty is researched in this study. Firstly, a two-layer planning model is proposed to consider investment and operation costs. The upper layer of the model aims to determine a feasible planning strategy for HESS based on existing conditions. The purpose of the lower layer is then to determine the optimal scheduling strategy and minimum operating cost of the system considering uncertainty based on distributionally robust optimization. Furthermore, the solution method that combines the modified Backtracking Search Algorithm (MBSA) and Yalmip/Cplex is utilized to determine the optimal placement and sizing of the HESS. Finally, the simulation experiment is performed with the case of Southwest China to verify the effectiveness of the proposed planning model and corresponding solution method. The simulation results show that the MBSA has a better convergence speed and stronger global search capability performance than the traditional BSA. MBSA converges at 44th, which is quicker than the 60th of BSA; the optimal objective value obtained by MBSA is $1,038,929,953.66, which is better than that of BSA of $1,051,512,303.18. In addition, the resulting HESS planning scheme can effectively reduce the network loss of the system, reduce the peak-to-valley load difference, and improve renewable energy consumption. Specifically, the system network loss is reduced from 1.56 MWh to 1.47 MWh, the peak-to-valley difference is reduced from 245.61 MW to 225.88 WM, and the total renewable energy consumption of four typical days is increased from 6582.07 MW to 7626.03 MW.

Research on Power System Joint Optimal Generation Scheduling Based on Improved Balance Optimizer

This article presents a power system joint optimization generation regulation method based on the improved balance optimizer, which takes the five factors of power system network loss, voltage offset, generation cost, fuel cost, and comprehensive pollution emission as the objective function and takes the internal power balance of the system, each generator set, generation capacity, generation flow, and up and down climbing as the constraints. Fully considering the current energy-saving development objectives and the impact of economic dispatching, taking stable and safe operation as the core, the power generation dispatching model is established by improving the balance optimizer. The model realizes the maximum power generation with the lowest energy consumption parameters and transitions from the original power generation energy consumption of the power system to the best energy-saving power generation energy consumption so that the power value of the system reaches the target balance and completes efficient dispatching. Simulation experiments show that the proposed method can ensure the most reasonable power load in both summer and winter. The average load in summer and winter is reduced from 254.78/mw to 205.36/mw, down about 19.39%, which can ensure the power generation stability of the power system. The average power generation cost after dispatching is 129,920 $/h, which is significantly improved by comparing with 131,225 $/h before dispatching and can realize certain environmental benefits.

Economic Dispatch of Distribution Network With Dispersed Wind Power Considering Network Reconfiguration

With the large-scale development of renewable energy sources such as wind and solar energy, the grid connection of renewable energy sources poses a certain threat to the security and stability of the power system, and also brings a great challenge to the economic dispatch of the distribution network. Traditional and single dispatching methods such as load demand response or network reconfiguration alone cannot meet the needs of a safe and economic operation of the distribution network. This article proposes an economic dispatching method for distribution networks with dispersed wind power considering network reconfiguration, and establishes an economic dispatching model with the objective function of minimizing distribution network operation cost, reconfiguration cost, and total system network loss. Based on the optimal scheduling of energy storage and reactive power compensation devices in the distribution network and the full utilization of demand response, a mixed integer second-order cone programming (MISOCP) method with multi-objective collaborative optimization is proposed. The results of the tests using the IEEE33 node system verify the feasibility and applicability of the proposed method in this article.

Optimal Scheduling for Flexible Regional Integrated Energy System with Soft Open Point

The Regional Integrated Energy System (RIES) has brought new modes of development, utilization, conversion, storage of energy. The introduction of Soft Open Point (SOP) and the application of Power to Gas (P2G) technology will greatly deepen the coupling of the electricity-gas integrated energy system, improve the flexibility and safety of the operation of the power system, and bring a deal of benefits to the power system. On this background, an optimal dispatch model of RIES combined cold, heat, gas and electricity with SOP is proposed. Firstly, RIES architecture with SOP and P2G is designed and its mathematical model also is built. Secondly, on the basis of considering the optimal scheduling of combined cold, heat, gas and electricity, the optimal scheduling model for RIES was established. After that, the original model is transformed into a mixed-integer second-order cone programming model by using linearization and second-order cone relaxation techniques, and the CPLEX solver is invoked to solve the optimization problem. Finally, the modified IEEE 33-bus system is used to analyze the benefits of SOP, P2G technology and lithium bromide absorption chillers in reducing system network loss and cost, as well as improving the system’s ability to absorb wind and solar and operating safety.

Research on Active Coordination Control Method of Distribution Network Power Supply Based on Improved Firefly Algorithm

The location and capacity optimization configuration of distributed power supplies ensures that they. Make better technical and economic use. When the distributed power supply is connected to the distribution network, it will cause the trend size and direction of each branch to change, so that the system loss is not only related to the load size, but also related to the location and capacity of DG. Therefore, it is important to study the rational planning of DG in depth. In order to improve the system network loss effectively, a distributed power distribution power distribution network reactive optimization model with the minimum active loss of the system is established, and an improved multi-target firefly algorithm is proposed for the problem that the traditional firefly algorithm will oscillate repeatedly near the extreme point later in the iteration. In view of the shortcomings of the traditional firefly algorithm, which is easy to precocious and over-dependent on control parameters, the chaotic search strategy and global idea are integrated into the firefly algorithm, and an improved firefly algorithm is proposed, which is applied to solve the planning problem of distributed power supply. The example shows that the proposed algorithm has good practicability and adaptability, and also verifies the practical significance of the proposed model[1].

Active and reactive power joint optimization of active distribution network under new energy access

The access of large-scale distributed generation (DG) to the active distribution network has a great influence on the power flow, node voltage and network loss of the distribution network. In this paper, in order to improve the operation level and economic performance of the active distribution network, the distributed power supply reactive power output and load reduction are used as control variables to establish a multi-objective optimization model with the total network loss, voltage deviation and load reduction cost as the objective function. The voltage of each node, system network loss, load reduction cost and their corresponding control variables are calculated by improved simulated annealing multi-objective particle swarm optimization algorithm. Finally, the IEEE33 node system is used for example verification, the results show that the mathematical model can achieve coordinated control of voltage, reduce network loss and meet the requirements of actual power grid operation.

Multi-objective coordinated scheduling of anomalous control domain with electric/thermal energy storage and translatable load

Difficulties in the consumption of new energy such as wind and light are one of the main reasons for the low penetration rate of distributed power. The multi-objective coordinated scheduling model of anomalous control domain with electric/thermal energy storage and translatable load is constructed from the aspects of energy utilization, system network loss and environmental pollution cost. A hybrid random black hole algorithm is proposed to improve the algorithm by using global search mechanism. Optimization ability. The example analysis is carried out by IEEE30 node power distribution system. The simulation results show that the coordinated optimization of energy storage and translatable load can effectively improve energy efficiency, reduce system network loss and environmental pollution cost, and verify the rationality of the proposed model

An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach

The planning problem of distributed generators (DG) accessing the AC/DC distribution network is a hot research topic at present. In this paper, a location and volume model of DG is established that considers DG operation and maintenance costs, DG investment costs, system network loss costs, fuel costs, pollution compensation costs, and environmental protection subsidies. Furthermore, voltage and power constraints are also considered in the model. To solve the proposed model, a hybrid algorithm called the GA-ACO algorithm is presented that combines the ant colony algorithm (ACO) and the genetic algorithm (GA). On one hand GA has good robustness, good adaptability, and quick global searching ability but it also has some disadvantages such as premature convergence and low convergence speed. On the other hand, ACO has the ability of parallel processing and global searching but its convergence speed is very low at the beginning. The IEEE-33 node distribution network is taken as a basic network to verify the rationale of the proposed model and the effectiveness of the proposed hybrid algorithm. Simulation results show that the proposed model is very in line with reality, the hybrid algorithm is very effective in solving the model and it has advantages in both convergence speed and convergence results compared to ACO and GA.

Multi-objective Optimization of Distribution Network Based on Model Predictive Control

With the increasing penetration of distributed generation(DG) in distribution network and the rising demand of electricity users, the uncertainty of distributed generation output and randomness of load will lead to over-voltage and power backward in distribution network. This paper proposes a multiobjective active and reactive power coordination optimal control strategy, which based on multi-time scale model predictive control (MPC). The objectives of proposed strategy consists of increasing distributed energy consumption, decreasing grid voltage deviation and decreasing system network loss, which are implemented in long time scale. And in short time scale, the energy storage active power is adjusted according to the prediction errors of load and new energy power generation. The effectiveness of the proposed optimization scheme is verified in simulation analysis.

Planning of distributed generation in distribution network based on improved particle swarm optimization algorithm

Large-scale access of distributed power can improve the current environmental pressure, at the same time, increasing the complexity and uncertainty of overall distribution system. Rational planning of distributed power can effectively improve the system voltage level. To this point, the specific impact on distribution network power quality caused by the access of typical distributed power was analyzed and from the point of improving the learning factor and the inertia weight, an improved particle swarm optimization algorithm (IPSO) was proposed which could solve distributed generation planning for distribution network to improve the local and global search performance of the algorithm. Results show that the proposed method can well reduce the system network loss and improve the economic performance of system operation with distributed generation.

Voltage and reactive power optimisation of offshore wind farms based on terminal voltage control mode of DFIG

In order to solve the problem that the voltage of the collector system rises and the loss increases in the offshore wind farm, the reactive power adjustment capability of the doubly-fed induction generator (DFIG) is studied. This study presents a method to optimise the system voltage and network loss with the terminal voltage of DFIG as the control target. The immune genetic algorithm is used to solve the voltage and reactive power optimisation problem of offshore wind farm in ensuring the voltage is within the allowable range when the active power out of DFIG changes. So that the reactive power adjustment capability of DFIG is fully used, both the system network loss and the reactive power compensation capacity are reduced, which verifies the correctness of the method.