Network Active Power Research Articles

Design of ant colony planning algorithms for multi-stage grids of distribution networks considering network resilience

Network Destruction Resilience is the ability of a system to maintain good operation in the event of an external attack or internal failure. The resilience of distribution network is crucial to guarantee the reliability of power supply. In this paper, we design a planning algorithm considering network destructibility for the multi-stage ant colony planning problem of distribution networks. The method establishes the multi-stage network planning objective function of distribution network from the perspectives of total investment cost and annual operation cost of multi-node network frame, destruction resistance, active power and reactive network loss of distribution network, etc. Then based on the constraints of the model, the improved ant colony algorithm is used to solve the multi-stage network planning objective function of distribution network, and the results of the anti-termite colony planning of multi-stage network frame of distribution network are obtained. In order to verify the effectiveness of the algorithm, simulation experiments are carried out on real distribution network data. The results show that the proposed ant colony planning algorithm for multi-stage grid frames can effectively improve the destruction resistance of distribution networks, and reduce the total investment cost and annual operation cost of multi-stage grid frames, and reduce the network loss rate data of multi-stage grid frames of distribution networks after application. It provides an effective method for planning the distribution network.

Research on a Three-Stage Dynamic Reactive Power Optimization Decoupling Strategy for Active Distribution Networks with Carbon Emissions

The reactive power optimization of an active distribution network can effectively deal with the problem of voltage overflows at some nodes caused by the integration of a high proportion of distributed sources into the distribution network. Aiming to address the limitations in previous studies of dynamic reactive power optimization using the cluster partitioning method, a three-stage dynamic reactive power optimization decoupling strategy for active distribution networks considering carbon emissions is proposed in this paper. First, a carbon emission index is proposed based on the carbon emission intensity, and a dynamic reactive power optimization mathematical model of an active distribution network is established with the minimum active power network loss, voltage deviation, and carbon emissions as the satisfaction objective functions. Second, in order to satisfy the requirement for the all-day motion times of discrete devices, a three-stage dynamic reactive power optimization decoupling strategy based on the partitioning around a medoids clustering algorithm is proposed. Finally, taking the improved IEEE33 and PG&E69-node distribution network systems as examples, the proposed linear decreasing mutation particle swarm optimization algorithm was used to solve the mathematical model. The results show that all the indicators of the proposed strategy and algorithm throughout the day are lower than those of other methods, which verifies the effectiveness of the proposed strategy and algorithm.

A bi-level cooperating optimization for AC/DC power systems considering renewable energy integration

The integration of renewable energy on a large scale into the power system presents a significant challenge in ensuring the secure and cost-efficient operation of the system. Considering the uncertainty associated with wind and photovoltaic power, this paper develops a bi-level cooperating optimization strategy for AC-DC hybrid systems integrated with renewable energy generation. Firstly, to characterize the uncertainty of wind and photovoltaic power, the Latin hypercube sampling and the Kantorovich distance-based scenario reduction methods are employed to obtain the typical daily outputs of wind and photovoltaic power. Next, a bi-level multi-objective comprehensive optimization model is established to minimize the active power network loss and voltage deviation during system operation. To solve the bi-level optimization model, the proposed approach presents a multi-objective hybrid algorithm that combines the traditional particle swarm and whale algorithms, thereby enhancing the optimization capability of the algorithm through the incorporation of a whale enveloping search strategy into particles. Finally, the simulation results explain the effectiveness and practicability of the proposed strategy and methods, as well as the superiority of the proposed algorithm.

Multi Objective Optimization Framework to Provide Reactive Power Support to the Local Distribution Grid Using a Heuristic Approach.

Reactive power support to the local distribution grid becomes mandatory as it highly impacts the network's voltage profile and active power loss. Reactive power support improves the performance and efficiency of the grid. However, higher compensation to the line increases losses, deteriorates the voltage profile, and occupies the investment cost. The location of these reactive power compensators (RPC) also influences the network's performance. This paper presents a multi-objective strategy for optimal reactive power compensation to get techno-economic benefits. An objective function, which includes technical and economic criteria, has been formulated. The proposed MOF includes weighted distributed objectives that are minimized to get a trade-off solution. The proposed MOF has been minimized using three different heuristic algorithms, i.e. TLBO, GA and PSO. The proposed strategy has been tested on 56 buses 11 KV Gurukul distribution network located at Bhavnagar, Gujarat, India. NR load flow has been utilized to investigate losses and bus voltages.

Research on Dynamic Reactive Power Cost Optimization in Power Systems with DFIG Wind Farms

As the power market system gradually perfects, the increasingly fierce competition not only drives industry development but also brings new challenges. Reactive power optimization is crucial for maintaining stable power grid operation and improving energy efficiency. However, the implementation of plant–grid separation policies has kept optimization costs high, affecting the profit distribution between power generation companies and grid companies. Therefore, researching how to effectively reduce reactive power optimization costs, both technically and strategically, is not only vital for the economic operation of the power system but also key to balancing interests among all parties and promoting the healthy development of the power market. Initially, the study analyzes and compares the characteristic curves of synchronous generators and DFIGs, establishes a reactive power pricing model for generators, and considering the randomness and volatility of wind energy, establishes a DFIG reactive power pricing model. The objective functions aimed to minimize the cost of reactive power purchased by generators, the price of active power network losses, the total deviation of node voltages, and the depreciation costs of discrete variable actions, thereby establishing a dynamic reactive power optimization model for power systems including doubly-fed wind farms. By introducing Logistic chaotic mapping, the CSA is improved by using the highly stochastic characteristics of chaotic systems, which is known as the Chaotic Cuckooing Algorithm. Meanwhile, the basic cuckoo search algorithm was improved in terms of adaptive adjustment strategies and global convergence guidance strategies, resulting in an enhanced cuckoo search algorithm to solve the established dynamic reactive power optimization model, improving global search capability and convergence speed. Finally, using the IEEE 30-bus system as an example and applying the improved chaotic cuckoo search algorithm for solution, simulation results show that the proposed reactive power optimization model and method can reduce reactive power costs and the number of discrete device actions, demonstrating effectiveness and adaptability. When the improved chaotic cuckoo algorithm is applied to optimize the objective function, the optimization result is better than 7.26% compared to the standard cuckoo search algorithm, and it is also improved compared to both the PSO algorithm and the GWO algorithm.

Adaptive Reactive Power Optimization in Offshore Wind Farms Based on an Improved Particle Swarm Algorithm

To address the reactive power optimization control problem in offshore wind farms (OWFs), this paper proposes an adaptive reactive power optimization control strategy based on an improved Particle Swarm Optimization (PSO) algorithm. Firstly, an OWF multi-objective optimization control model is established, with the total sum of voltage deviations at wind turbine (WT) terminals, active power network losses, and reactive power margin of WTs as comprehensive optimization objectives. Innovatively, adaptive weighting coefficients are introduced for the three sub-objectives, enabling the weights of each optimization objective to be adaptively adjusted based on real-time operating conditions, thus enhancing the adaptability of the reactive power optimization model to changes in operating conditions. Secondly, a Uniform Adaptive Particle Swarm Optimization (UAPSO) algorithm is proposed. On one hand, the algorithm initializes the particle swarm using a uniform initialization method; on the other hand, it improves the particle velocity update formula, allowing the inertia coefficient to adaptively adjust based on the number of iterations and the fitness ranking of particles. Simulation results demonstrate the following: (1) Under various operating conditions, the proposed adaptive multi-objective reactive power optimization strategy can ensure the stability of node voltages in offshore wind farms, reduce active power losses, and simultaneously improve reactive power margins. (2) Compared with the traditional PSO algorithm, UAPSO exhibits an approximately 10% improvement in solution speed and enhanced solution accuracy.

The optimization and operation of multi-energy-coupled microgrids by the improved fireworks algorithm-shuffled frog-leaping algorithm.

This study aims to address optimization and operational challenges in multi-energy coupled microgrids to enhance system stability and reliability. After analyzing the requirements of such systems within comprehensive energy systems, an improved fireworks algorithm (IFWA) is proposed. This algorithm combines an adaptive resource allocation strategy with a community genetic strategy, automatically adjusting explosion range and spark quantity based on individual optimization status to meet actual needs. Additionally, a multi-objective optimization model considering active power network losses and static voltage is constructed, utilizing the shuffled frog-leaping algorithm (SFLA) to solve constrained multi-objective optimization problems. Through simulation experiments on a typical northern comprehensive energy system, conducted with a scheduling period of T = 24, the feasibility and superiority of IFWA-SFLA are validated. Results indicate that IFWA-SFLA performs well in optimizing microgrid stability, managing electrical energy flow effectively within the microgrid, and reducing voltage fluctuations. Furthermore, the circuit structure and control strategy of microgrid energy storage bidirectional inverters based on IFWA are discussed, along with relevant simulation results.

Research on Optimal Scheduling Strategy of Microgrid Considering Electric Vehicle Access

The random output of renewable energy and the disorderly grid connection of electric vehicles (EV) will pose challenges to the safe and stable operation of the power system. In order to ensure the reliability and symmetry of the microgrid operation, this paper proposes a microgrid optimization scheduling strategy considering the access of EVs. Firstly, in order to reduce the impact of random access to EVs on power system operation, a schedulable model of an EV cluster is constructed based on the Minkowski sum. Then, based on the wavelet neural network (WNN), the renewable energy output is predicted to reduce the influence of its output fluctuation on the operation of the power system. Considering the operation constraints of each unit in the microgrid, the network active power loss and node voltage deviation are taken as the optimization objectives, and the established microgrid model is equivalently transformed via second-order cone relaxation to improve its solution efficiency. Based on network reconfiguration and flexible load participation in demand response, the economy and reliability of system operation are improved. Finally, the feasibility and effectiveness of the proposed method are verified based on the simulation examples.

Research on Reactive Power Optimization Based on Hybrid Osprey Optimization Algorithm

This paper presents an improved osprey optimization algorithm (IOOA) to solve the problems of slow convergence and local optimality. First, the osprey population is initialized based on the Sobol sequence to increase the initial population’s diversity. Second, the step factor, based on Weibull distribution, is introduced in the osprey position updating process to balance the explorative and developmental ability of the algorithm. Lastly, a disturbance based on the Firefly Algorithm is introduced to adjust the position of the osprey to enhance its ability to jump out of the local optimal. By mixing three improvement strategies, the performance of the original algorithm has been comprehensively improved. We compared multiple algorithms on a suite of CEC2017 test functions and performed Wilcoxon statistical tests to verify the validity of the proposed IOOA method. The experimental results show that the proposed IOOA has a faster convergence speed, a more robust ability to jump out of the local optimal, and higher robustness. In addition, we also applied IOOA to the reactive power optimization problem of IEEE33 and IEEE69 node, and the active power network loss was reduced by 48.7% and 42.1%, after IOOA optimization, respectively, which verifies the feasibility and effectiveness of IOOA in solving practical problems.

Multi-objective random-fuzzy optimal power flow of transmission-distribution interaction considering security region constraints

In order to deal with the static voltage stability region problem of the power system caused by the penetration of distributed energy resources, this paper presents an interactive random-fuzzy optimal power flow method for transmission-distribution network considering security region constraints. Firstly, based on the random-fuzzy theory, the output of wind power and multi-energy coupling energy hub connected to transmission-distribution network are obtained. On this basis, the security region model of transmission-distribution network is established. As the interaction and cooperation between multiple distribution networks and transmission network throughout the system are described, the global random-fuzzy model of transmission-distribution interaction is established. Then, the security domain opportunity constraints are incorporated into the multi-objective optimization model of transmission-distribution network. Finally, based on the improved IEEE30 bus transmission system and IEEE33 bus distribution system, the power flow of transmission-distribution interactive network under the proposed model is calculated. The simulation results validate the effectiveness of the proposed model and algorithm. The safe operation distance of the power grid system can be improved and the active power network loss and power generation cost can be reduced.

Comprehensive Evaluation of Interval Equalization of Power Quality in Active Distribution Network Based on CVAE-TS

AbstractWhen distributed generation is connected to the distribution network, there are many uncertainties and various loads, and the power quality is difficult to judge. Therefore, a comprehensive evaluation method for power quality interval balance of active distribution network based on CVAE-TS (coordination Vocational Academic Education-Takagi–Sugeno) is proposed. The method of change vector analysis (CVA) is adopted to generate power data samples of active distribution network. The category labels of each data are added to the original power data as training sets for CVAE, and the number of samples generated within the interval is increased. TS modeling is adopted, fuzzy set theory is combined with least square method, incomplete electric energy data in data sample is converted into complete electric energy data by filling method, and the applicability of data is enhanced. An interval evaluation model of power quality in active power network is established by using analytic hierarchy process. The experimental results show that this method can effectively realize the sampling and filling of power data in active distribution network, the evaluation results are accurate and reliable, and the horizontal distribution of power quality can be intuitively known.

Reactive power optimization for distribution network system with wind power based on improved multi-objective particle swarm optimization algorithm

• In the multi-objective particle swarm optimization algorithm, the adaptive mesh is introduced to reflect the density of particles, and according to the density information, the global optimal particles are selected by roulette mechanism and the scale of external repository is maintained, which effectively ensures the uniformity and diversity of Pareto frontier distribution. • The proposed IMOPSOA has faster convergence speed and shorter average computing time than NSGA-II algorithm, can get Pareto frontier with better distribution and better results, which makes the voltage stability of the distribution network system with wind power higher. Aiming at the uncertainty of the grid-connected output of wind turbines, a scenario analysis method based on probability occurrence is used to transform the uncertainty model into multi scenario problems with different occurrence probabilities, a reactive power optimization model is established with the goal of minimizing the active power network loss and voltage deviation. Aiming at the poor diversity of Pareto frontiers obtained by traditional methods, an improved multi-objective particle swarm optimization algorithm is proposed. The algorithm uses adaptive grids to obtain the density of particles in external archives, selects the global optimal particle and maintains the scale of the external repository according to the density information using a roulette mechanism, effectively ensuring the uniformity and diversity of the Pareto frontier distribution. The algorithm is used to calculate reactive power optimization of the IEEE 33-bus system with wind power, and compared with the existing NSGA-Ⅱ algorithm. The results show that the Pareto frontier obtained by the proposed algorithm is better, the voltage stability and active power loss reduction rate of the distribution network system with wind power is higher.

Bi-Level Volt/VAR Optimization in Distribution Networks With Smart PV Inverters

Optimal Volt/VAR control (VVC) in distribution networks relies on an effective coordination between the conventional utility-owned mechanical devices and the smart residential photovoltaic (PV) inverters. Typically, a central controller carries out a periodic optimization and sends setpoints to the local controller of each device. However, instead of tracking centrally dispatched setpoints, smart PV inverters can cooperate on a much faster timescale to reach optimality within a PV inverter group. To accommodate such PV inverter groups in the VVC architecture, this paper proposes a bi-level optimization framework. The upper-level determines the setpoints of the mechanical devices to minimize the network active power losses, while the lower-level represents the coordinated actions that the inverters take for their own objectives. The interactions between these two levels are captured in the bi-level optimization, which is solved using the Karush-Kuhn-Tucker (KKT) conditions. This framework fully exploits the capabilities of the different types of voltage regulation devices and enables them to cooperatively optimize their goals. Case studies on typical distribution networks with field-recorded data demonstrate the effectiveness and advantages of the proposed approach.

An efficient capuchin search algorithm for allocating the renewable based biomass distributed generators in radial distribution network

It is essential to keep the stability of electric distribution network especially during abnormal operating conditions, this guarantees service continuity to the customers. Installing small-scale generating units named distributed generators (DGs) can enhance the stability of the grid via minimizing the system losses and improving the network voltage profile. Many approaches, conventional and metaheuristic, have been conducted in installing the DGs in distribution network, however some shortages like slow convergence and stuck in local optima represent obstacles in identifying the appropriate locations and sizes of DGs. Therefore, this paper proposes an efficient methodology of capuchin search algorithm (CapSA) which is presented for the first time to determine the optimum locations, sizes, and power factors of biomass-based DGs (BDGs) in the radial distribution network. The proposed methodology is implemented via adapting the first rows of CapSA population to integer numbers assigned to the BDGs’ locations. The target is minimizing the network active power loss with keeping the power flow, bus voltage, and transmission line in their normal ranges. Two distribution networks of IEEE 33-bus and IEEE 69-bus are analyzed, the CapSA results are compared to chimp optimizer (CO), heap-based optimizer (HBO), seagull optimization algorithm (SOA), differential evolution (DE), and sine cosine algorithm (SCA). Moreover, installing different numbers of BDGs are presented to validate the proposed approach. Furthermore, statistical tests of Friedman and ANOVA are conducted to assess the CapSA performance. In IEEE 33-bus network, the proposed approach minimized the active and reactive power losses to 7.9745 kW and 10.4134 kVAr respectively via installing four BDGs. While for IEEE 69-bus system, the active and reactive losses are minimized to 2.1092 kW and 1.5065 kVAr respectively via the proposed CapSA. The obtained results confirmed the preference of the proposed CapSA in installing the BDGs in radial networks.

A novel artificial hummingbird algorithm for integrating renewable based biomass distributed generators in radial distribution systems

Improving the performance of the electric distribution network is essential to meet the needs of the customer and guarantee the service continuity. Installing generators with small sizes known as distributed generators (DGs) can contribute to enhance the network operation by mitigating the network loss and improving the voltage profile. Integrating these generators in inappropriate places can cause serious consequences to the network operation. Therefore, this paper proposes a novel metaheuristic approach of artificial hummingbirdalgorithm (AHA) to identify the best locations and sizes of biomass-based DGs in radial distribution network. The proposed approach has enriched exploration and exploitation phases that enhancing its search capability and avoiding stuck in local optima. The network active power loss and the voltage deviation are selected as the targets to be minimized. Moreover, a new version of AHA is programmed to solve multi-objective problem with the purpose of mitigating both targets. The analysis is conducted on three radial distribution networks of IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus. Three scenarios are implemented in each network, the first one is minimizing the active power loss, the second one is mitigating the voltage deviation, and the last one is multi-objective problem. Also, biomass-based DGs with unity, fixed, and optimal power factors are analyzed. Excessive comparison to fractal search algorithm, particle swarm optimizer, genetic algorithm, the whale optimization algorithm, sperm swarm optimization, tunicate swarm algorithm, pathfinder algorithm, seagull optimization algorithm, and sine cosine algorithm, multi-objective water cycle algorithm, multi-objective grey wolf optimizer, and multi-objective sparrow search algorithm is conducted. Moreover, statistical tests of Wilcoxon, Friedman, ANOVA, and Kruskal Wallis are performed to assess the performance of the proposed approach. The gotten results confirmed the preference and competence of the proposed approach in integrating the biomass-based DGs in radial distribution networks.

Active distribution network active and reactive power coordinated dispatching method based on discrete monkey algorithm

The operation and dispatching pressure of active distribution network is gradually increasing with the integration of distributed generators into urban distribution network unevenly. It is necessary to carry out research on active and reactive power coordinated dispatching. In order to obtain the coordinated dispatching scheme rapidly, the active and reactive power coordinated dispatching is decoupled in this paper. Reactive power optimization and active power dispatching are carried out respectively. The final coordination dispatching scheme is formulated through the cross iteration of active power dispatching and reactive power optimization. Firstly, the reactive power optimization model is set by mainly considering adjusting transformer tap and changing capacitor output capacity. The model is solved by a two-stage method to obtain the dynamic dispatching scheme. Secondly, active power dispatching model of active distribution network is set considering network reconfiguration. To obtain the solution quickly, the discrete monkey algorithm is used to solve the model, among which particle swarm optimization algorithm is used in the climb process to solve the sequential coupling problem of switch state variables. Finally, the node voltage quality can be effectively improved and grid loss of distribution transformer cluster can be significantly reduced by a few of adjustments of transformer taps, shunt capacitors and switch. The practicability of dynamic coordination dispatching method is proved.

Dynamic reconfiguration of distribution network based on dynamic optimal period division and multi-group flight slime mould algorithm

This paper suggests a multi-period dynamic reconfiguration method for the distribution network. The reconfiguration period is preliminarily divided based on the load monotonicity and amplitude change with taking the length of the actual load as the reconfiguration time section. A multi-objective dynamic reconfiguration is modeled based on the time-varying load distribution network considering network active power loss, static voltage stability, and load balance. The optimal period partition function is dynamically adjusted by the time-division method under the constraint of switching actions. The paper also proposes a multi-group flight slime mould algorithm (MFSMA) by applying a multi-group optimization strategy and adaptive Levy flight for the dynamic distribution network reconfiguration problem. Through experiments on IEEE-33 and IEEE-118 bus distribution networks, the experimental results show that the MFSMA for the multi-period dynamic reconfiguration effectively simulates the actual reconfiguration of the distribution network and provides a new scheme for the economic, stable, and safe operation of the distribution network.

A power loss minimization strategy based on optimal placement and sizing of distributed energy resources

AbstractDue to the enhanced price of electricity, the gradual depletion of fossil fuels, and the global warming concerns, power loss minimization through deployment of distributed generators (DGs) has attracted significant attention in recent decades. This paper proposes a genetic algorithm (GA) based strategy for minimization of active and reactive power losses through optimal location and size of DGs. It also quantifies and tallies the total network power losses for the cases with random as well as optimal allocation of DGs. To validate the accuracy of the obtained results from GA, another nature‐inspired optimization algorithm, cuckoo search, is also deployed. The simulation results on IEEE 30 and 118 bus systems indicate that the proposed strategy not only can effectively reduce the total network active and reactive power losses but also lead to the improvement of network voltage profile.

Dynamic regulation method of distributed generation in distribution network under 5G network

Aiming at the problem of uneven power flow distribution caused by distributed power supply access, which leads to high voltage deviation and active network loss, a dynamic control method of distributed power supply in 5G network is proposed. The distributed topology structure of the distribution network under 5G network is generated, and the static topology information is extracted to establish the power partition. The distribution network in each area is connected to its own bus through different feeders. The distribution network reconstruction period is divided in the partition, and the network reconstruction period and the corresponding switch action scheme are determined. The state of distributed power supply was estimated according to reconstruction period and active/reactive power balance index, and the stability of dynamic control was determined. Based on the stability control, the dynamic control model of distributed power supply is established, and the network voltage can reach the preset target by adjusting the power of power switch. The simulation results show that the average voltage deviation of the proposed control method is 1.976, which is 4.431 and 3.783 lower than those based on data drive and flexible load regulation, respectively. The active power network loss of the control method in this paper is 0.342 MW, which is 0.385 MW and 0.346 MW lower than that of the control method based on data drive and flexible load regulation, respectively. Therefore, this method can improve the absorption capacity of distributed power supply and promote the power flow reciprocity of distribution network.

Stability of inverter-interfaced power systems with multi-scale-free properties

Scale-free networks are proven to have many distinct properties and numerous statistical studies have shown that most large power systems have the scale-free topology. However, the overall system behavior is also constrained by node dynamics, of which the interaction effects with network topology remain unknown. Here, we investigate the individual and mutual effects of network degree distribution, with two system dynamic parameters, on system small-disturbance angle stability. We show that network degree distribution and active power injection distribution are critical in determining system small-disturbance angle stability while frequency coefficient shows relatively less significant influence. Further, the scale-free level for degree distribution positively interacts with active power injection distribution on the overall system behavior. Consequently, not only network topology but also system dynamic parameters and, importantly, their synergistic effects matter in determining system behavior. Based on the results, we further provide guidance for the coordination of the various parameters to improve system small-disturbance angle stability.