Minimum Network Loss Research Articles

Load Regulation Method of Interaction Between Electric Vehicle and Power Grid Based on LSTM

In response to the problem of disorderly charging of scaled new energy vehicles, which causes an increase in the intermittency and randomness of the overall load and then leads to the instability of the distribution network, an edge-control-based load regulation method for scaled vehicle-network interaction is proposed. Firstly, the sequence of influencing factors is constructed, and the data is processed by using weighted Marxist distance to realize the accurate extraction of electric vehicle load power data. Secondly, feature extraction of the data by using Long Short-Term Memory (LSTM) to realize fast and accurate prediction of regional load. Then, an edge-side regulation model is constructed with the objective function of minimizing network loss and minimizing peak-to-valley difference and solved by the wolf pack algorithm. Then, the edge controller is used to control the tide of each pile-end management unit to realize the effective cooperative regulation of each station-pile-net. Finally, using the edge controller physical device with an example of the distribution network in a region of Tianjin, it is verified that the method has good prediction accuracy and ensures the reasonable deployment as well as stability of the distribution network.

Active-reactive power collaborative optimization model of electrical interconnection system based on deep learning under the goal of “carbon neutrality”

With the goal of carbon neutralization put forward, new energy units have gradually become the main power supply, and its volatility has brought great challenges to the control and optimization of the system, especially the distribution network. In order to solve the reactive power problem caused by the high proportion of renewable energy connected to the distribution network, a source network load storage centralized optimal dispatching model considering reactive power optimization and multi energy collaborative interaction is proposed. The model aims at the optimal operation cost, minimum network loss and minimum carbon emission of the multi energy system, and considers reactive power compensation, energy storage regulation and energy conversion, so as to realize the safe and low-carbon economic dispatching of the electrical interconnection system. The switching capacity of the discrete reactive power compensation device is linearized, and the model is transformed into a mixed integer second-order cone programming problem for solution. The simulation results show that the proposed method can effectively compensate the reactive power required by the wind turbine parallel network, coordinate the energy interaction between electricity and gas, and improve the stability and flexibility of the distribution network after large-scale renewable energy access, According to the charging and discharging power and charge state, and the automatic adjustment of the gears of on load voltage regulating transformer and compensation capacitor bank, this paper shows that the system has safe and stable operation performance.

Optimal capacitor placement in a dominant induction motor loads power system

The challenge of optimal capacitor allocation is one of the complex problems in power systems, especially in large industries since they have many big capacity induction motors. In the literature, many works have been delivered for optimal capacitor placement, however, these works were simulated in a distribution network, not in an industry network with many large capacity induction motors. Therefore, this work proposes the optimal allocation of capacitors in a large industry with a significant amount of induction motor loads to minimize network losses. To determine the optimal capacitor location, this research uses the genetic algorithm (GA) method. This algorithm is interesting because of its simplicity and ability to discover optimal solutions comprehensively.

Recovery Analysis and Maintenance Priority of Metro Networks Based on Importance Measure

The metro network plays a vital role in the urban transportation system. However, the metro network is easily damaged by humans and natural disturbances. This can cause serious economic damage, such as the suspension of metro station operations and line disruptions. Therefore, we conducted this study in order to minimize the loss caused by the damage to the metro network and improve the performance of the network after recovery. Based on the cascading failures of metro networks, this paper proposes a recovery model for metro networks considering the value of time. Then, considering the time value, a new node importance measure is proposed using the determination of maintenance priorities. The maintenance priorities of nodes with different importance values are investigated to minimize network losses. Lastly, the applicability of the method is verified by a metro network in Zhengzhou city.

Adaptive Control Strategy of Multiemergency Power Supply Network System Connected to New Energy

In order to improve the adaptive control effect of the multiemergency power supply networking system connected to new energy (NE), this paper studies the adaptive control strategy (ACS) of the multiemergency power supply networking system connected to NE by combining intelligent algorithms and determines the appropriate installation node of the distributed power supply according to the voltage stability index. Moreover, this paper proposes a method to optimize the distribution network feeder reorganization and distributed power configuration problems based on the fireworks algorithm to reduce the network loss and improve the voltage distribution. In addition, distribution network reorganization and distributed power configuration optimization can more effectively minimize network losses and improve voltage distribution. Through the experimental research, it can be seen that the ACS of the multiemergency power supply network system connected to NE proposed in this paper has the established effect.

A hybrid approach for optimization of electric power distributed networks with photovoltaic sources

This paper presents a hybrid approach, which associates a mathematical programming technique with a metaheuristic, to optimize the electricity distribution network with penetration of photovoltaic (PV) generation. This optimization considers the distribution network reconfiguration (DNR) and the adjustment of the power factor of photovoltaic (PV) inverters about the reactive power, controlling the voltage profile and minimizing network losses. The methodology uses particle swarm optimization (PSO) for DNR and an optimal power flow (OPF) to adjust the value of the PV inverters and maintenance of network operating limits, this being a great difference from other methodologies. The objective of the hybrid approach is to obtain the best result for the problem, taking advantage of the best characteristics of both techniques. The hybrid approach was compared with the PSO, highlighting its superiority in terms of the solutions found. The tests were performed using the AMPL Modeling Language in the 33-node system with the addition of PV generation. The approach considers the variations in the powers generated by the PV systems and the variations in the loads over 24 h. The results indicate that the combination of PV inverter control and DNR can reduce the active power losses by more than 37% in 24 h.

Reactive Power and Voltage Optimization of New-Energy Grid Based on the Improved Flower Pollination Algorithm

In order to solve the reactive power and voltage control problem caused by the high proportion of new energy connected to the power grid, this paper takes the minimum voltage deviation, minimum network loss and maximum dynamic reactive power margin of the whole system as the comprehensive optimization objectives and establishes a reactive power and voltage optimization model by considering the reactive power regulation ability of SVC (Static Var Compensator) and new energy units. In view of the continuous and discrete variables in the model, the traditional continuous FPA (Flower Pollination Algorithm) is discretized to form an improved continuous-discrete hybrid FPA, and the tournament selection mechanism is adopted to speed up the convergence. Through the example analysis of the IEEE-39 bus system, the feasibility of the proposed reactive power and voltage optimal control method in the new energy grid is verified. Compared with GA (Genetic Algorithm), the results show that the improved FPA has high optimization accuracy, which is suitable for solving the reactive power and voltage optimization problem of the new energy grid.

Multi-objective distribution network planning method with distributed generation based on non dominated sorting differential evolution algorithm

Combined with the specific problems of distribution network planning with distributed generation, this paper constructs a multi-objective optimization model of distribution network planning with distributed generation. According to the distributed generation distribution network layout planning with distributed generation, under the condition of uncertain load prediction value of distributed generation distribution network, taking the minimum voltage stability index, minimum network loss and minimum investment cost of distributed generation as sub objectives, a multi-objective programming model is established, and the model is solved by non dominated sorting differential evolution (NSDE) algorithm.

Influence of electric vehicle distributed energy storage access on safety and stability of distribution network

This paper proposes a distributed energy storage control strategy for electric vehicles to improve the security and stability of distribution network when electric vehicles are connected. Firstly, the load characteristics of electric vehicles are investigated, and the optimal power flow model including energy storage power station, electric vehicle charging station considering V2G and distribution network is established. The objective function is established to minimize network loss, voltage deviation and load peak to peak. The problem is transformed into a mixed integer second-order cone optimization problem for solution, and based on the analysis of distributed energy storage model and constraints, the distributed energy storage control strategy of electric vehicle is established. Through the solution analysis in IEEE33 node system, it can be concluded that using this strategy can improve the power quality of distribution network and effectively improve the security and stability of distribution network.

Bi-Level Decentralized Optimal Economic Dispatch for Urban Regional Integrated Energy System Under Carbon Emission Constraints

Based on the real-time environmental constraints in urban regional construction, this paper constructed a bi-level decentralized low-carbon optimal dispatching model of the urban regional integrated energy system (RIES), including the park integrated energy systems (IESs). In this model, a bi-level optimal allocation model of carbon emission constraints between the urban and the park is proposed for the first time. The upper urban will formulate the real-time carbon emission constraints based on real-time environmental monitoring, decomposing the historical carbon emissions to the lower park IESs; the lower park will meet the real-time carbon emission constraints during optimization. We through the upper urban with the lower park between the bi-level decentralized optimization to ensure that the objective function’s upper urban power, natural gas, and heat distribution network system is minimum total network loss. In addition, it is necessary to ensure the minimum operating cost of each park IESs and focus on how to meet the requirements of the overall environment of urban RIES. Furthermore, we study the influence of optimal allocation strategy of carbon emission constraints on network loss, and operating cost of urban RIES under different scenarios. Then, an improved analytical target cascading (ATC) method is applied to solve the bi-level decentralized optimal dispatching model of urban RIES. Finally, an example under three different scenarios is given to verify the superiority and effectiveness of the proposed model and the improved method.

A Hybrid Approach for Optimization of Distribution Networks with Photovoltaic Sources

This paper presents a hybrid approach, which associates a mathematical programming technique with a metaheuristic, to optimize the electricity distribution network with penetration of photovoltaic (PV) generation. This optimization considers the distribution network reconfiguration (DNR) and the adjustment of the power factor of photovoltaic (PV) inverters in relation to the reactive power, controlling the voltage profile and minimizing network losses. The methodology uses particle swarm optimization (PSO) for DNR and an optimal power flow (OPF) to adjust the value of the PV inverters and maintenance of network operating limits. The tests were performed using the AMPL Modeling Language in the 33-node system with the addition of PV generation. The approach considers the variations in the powers generated by the PV systems and the variations in the loads over a 24 h period. The results indicate that the combination of PV inverter control and DNR can reduce the active power losses by more than 37% in a 24-hour period.

Recovery strategy of multilayer network against cascading failure

Cascading failure phenomena widely exist in real-life circumstances, such as the blackouts in power networks and the collapse in computer networks. In this paper, we construct a cascading failure model on the multilayer network, taking into account the number of invalid neighbors of nodes, the failure frequency of nodes, the effect between layers, and the percolation process. To minimize network losses caused by the cascading process, we propose a recovery strategy, i.e. repairing some certain clusters formed by ineffective nodes and links. The recovery strategy is discussed in detail, like whether to add links to the network, how many links are needed at least to add, how many layers are demanded to restore, and how to choose the values of [Formula: see text] and restorable threshold [Formula: see text] to improve the network performance. Besides, we theoretically analyze the cascading failure model with recovery strategy by virtue of mean-field approximation and generating function techniques. The theoretical solutions are found to be consistent with experimental results simulated on the ER as well as BA networks. In addition, we also investigate the affecting factors of network robustness. The effects of failure threshold [Formula: see text], base number [Formula: see text], and threshold [Formula: see text] between layers on network behaviors depend on the values of average degree [Formula: see text] and recovery proportion [Formula: see text]. These results may provide particular reference significance for maintaining system security, adjusting the network performance, and enhancing network robustness.

A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization

Electric vehicles’ (EVs) technology is currently emerging as an alternative of traditional Internal Combustion Engine (ICE) vehicles. EVs have been treated as an efficient way for decreasing the production of harmful greenhouse gasses and saving the depleting natural oil reserve. The modern power system tends to be more sustainable with the support of electric vehicles (EVs). However, there have been serious concerns about the network’s safe and reliable operation due to the increasing penetration of EVs into the electric grid. Random or uncoordinated charging activities cause performance degradations and overloading of the network asset. This paper proposes an Optimal Charging Starting Time (OCST)-based coordinated charging algorithm for unplanned EVs’ arrival in a low voltage residential distribution network to minimize the network power losses. A time-of-use (ToU) tariff scheme is used to make the charging course more cost effective. The concept of OCST takes the departure time of EVs into account and schedules the overnight charging event in such a way that minimum network losses are obtained, and EV customers take more advantages of cost-effective tariff zones of ToU scheme. An optimal solution is obtained by employing Binary Evolutionary Programming (BEP). The proposed algorithm is tested on IEEE-31 bus distribution system connected to numerous low voltage residential feeders populated with different EVs’ penetration levels. The results obtained from the coordinated EV charging without OCST are compared with those employing the concept of OCST. The results verify that incorporation of OCST can significantly reduce network power losses, improve system voltage profile and can give more benefits to the EV customers by accommodating them into low-tariff zones.

Study on Optimal Operation of AC-DC Hybrid Distribution Network

With the increase of power load and the access of distributed generation, AC/DC hybrid distribution network will become the development trend of urban distribution network in the future. The core of AC/DC distribution and distributed generation are voltage source converters based on full control switches. This paper simply analyzes the operation principle and control mode of voltage source converters, a power flow calculation method of AC/DC hybrid distribution system is proposed, and the optimization model of AC/DC hybrid distribution network with minimum network loss and minimum voltage deviation of DC network is established. The particle swarm swarm (PSO) algorithm is used to solve the calculation. Considering the load disturbance, the droop control is set for each VSC, at the same time this paper compares the advantages and disadvantages of the whole time optimal power calculation and droop control combined with the optimal power calculation method. Then the control mode of the distribution network is proposed. Finally, an example of PG&E 69-node distribution network in the United States is given to verify the correctness of the proposed model and algorithm.

Techno-economic approach towards reactive power planning ensuring system security on energy transmission network

Abstract This research work proposes a planning strategy pertaining to the techno-economic operation of Indian power systems. The proposed strategy focused on the reactive power (VAr) planning (RPP) subject to the system operating cost minimization ensuring system security. To mitigate the RPP issue, unique meta-heuristic hybridized techniques are adopted to size the optimal parameter settings such as alternator’s reactive power, tap settings of transformers etc. instigated by power flow analysis satisfying all equality and inequality constraints. The controlling variables are optimally determined to solve this non-linear RPP problem. The bottleneck for the installation of static VAr compensators at weak buses is also wiped out by different analytical techniques viz. loss sensitivity, power flow and modal analysis. Two different inter-regional transmission networks prevailing in India are considered to measure the adaptability, efficacy and efficiency of the proposed approach. The results obtained by applying the proposed approach have confirmed that network loss-minimization procedures produce an acceptable solution and reduce the operating cost which is especially important in RPP. The responses of bench mark functions and statistical analysis of the outcomes from both systems allow us to assess the overall efficiency and efficacy of the proposed techno-economic planning approach.

Loss-Voltage Sensitivity Analysis Based Battery Energy Storage Systems Allocation and Distributed Generation Capacity Upgrade

Distributed Generation (DG) has become a key component in modern power industry due its significant advantages over the traditional power generation methods. Nevertheless, best expected outcomes can only be achieved with optimal allocation of DG resources where inappropriate allocation may impose problems in power system stability, protection and quality. This paper presents analytical approaches for optimizing the DG size, BESS (Battery Energy Storage Systems) capacities and power dispatch in Medium Voltage (MV) networks. Since most of the existing analytical approaches related to optimizing DG sizes for minimizing network losses and voltage deviations have considered individual objective function separately, both parameters may not be minimized simultaneously. Thus, in this paper, an analytical methodology was formulated based on an objective function built on new parameter Loss-Voltage Sensitivity Index (LVSI) that evaluates both minimum impact of network loss and voltage variations for optimizing the DG size. The results obtained from this approach were compared with a conventional Genetic Algorithm (GA) formulated by the authors. The BESS capacities are determined considering the effects of Load Proportionality Factor (LPF), State of Charge limits (SOC) of battery storages, number of load areas and the portion of daily off-peak solar generation period energy consumption expected to be served by each BESS unit. The significance of this BESS capacity determination methodology is highlighted as the BESS capacities can be numerically calculated whereas in existing work, they are heavily relying on conventional optimization techniques which do not give an idea about the internal behavior of parameters which determine the capacities of BESS units. Moreover, an optimal BESS dispatch algorithm is also presented in this paper for minimizing the energy losses and voltage deviations. The applicability of the proposed methodologies are verified using the standard IEEE-33 and IEEE-69 test bus systems. Simulations carried out in MATLAB is used to illustrate the accuracy and the appropriateness of the proposed approaches.

Optimal Allocation of Electric Vehicles Parking Lots and Optimal Charging and Discharging Scheduling using Hybrid Metaheuristic Algorithms

The issue of simultaneous planning of electric vehicles and distributed generation resources has received more attention from energy researchers in recent years. Scattered renewable sources do not have a certain amount of production and, according to research, follow possible mathematical functions. Renewable energy sources are modeled on wind and solar production, both of which are moderately generated per hour. In this study, using, the optimal allocation problem of the electric vehicles parking lots and the optimal operation scheduling of the electric vehicles in a smart distribution network are studied as a novel optimization problem. In the proposed problem, the different factors including the technical and the economic issues are considered for achieving a realistic solution. In terms of technical issues, minimizing network losses, and minimizing voltage drop in feeders, as well as supplying all network demand are considered. Also, the total cost of the charging and discharge at the electric vehicles parking lots, and the total cost paid for purchasing power from upstream network are given as economic issues in the proposed problem. Moreover, the price-based DRP is considered due to the implementation of the demand side management program. To obtain the optimal solution, a hybrid metaheuristic algorithms (HMA) has been used. The proposed problem is simulated on the standard IEEE 69-bus. It is solved by the proposed HMA and is compared with another heuristic method. The obtained results confirm the accuracy and efficiency of the proposed problem. The obtained results show increased to an acceptable level, the voltage profile was improved and network losses were reduced. Finally, the results curves and tables show the efficiency of the proposed method.

Dynamic reactive power optimization of distribution network based on variable step size beetle antennae search algorithm

To solve the dynamic reactive power optimization problem in the distribution network, a novel optimization algorithm (variable step size beetle antennae search algorithm, VBAS) is introduced. Meanwhile, from the economic point of view, the minimum network loss and capacitor switching cost are taken as the objective functions, and the dynamic reactive power optimization model is established. In contrast with the majority of existing traditional algorithms that are intricate in process, the conducted algorithm is of less calculate and easier achieved. Besides, the variable step size strategy is introduced to improve optimization accuracy. The effectiveness of this algorithm is verified by simulation analysis on the IEEE 33- bus system, and the results show that VBAS obtains the optimum operation point and outperforms traditional algorithms significantly.

Incorporación de Generación Renovable y Segmentación de Redes Eléctricas

En el presente trabajo se desarrolla una metodología que facilita la transición de una red eléctrica centralizada tradicional a una configuración de red eléctrica basada en microredes. Se incorporan generadores inyectando potencia en posiciones estratégicas de la red. Además, partiendo de la topología de una red radial centralizada, se delimitan microredes con el propósito de otorgarles la mayor capacidad para que puedan funcionar de forma aislada. Esta optimización se realiza considerando la minimización de las pérdidas de la red y maximizando la autonomía potencial de las microredes obtenidas. Para desarrollar la metodología propuesta, se emplea recocido simulado como algoritmo metaheurístico de optimización.

Operation optimization of AC/DC hybrid distribution network considering uncertainty

In order to solve a series of problems such as the increase of AC/DC hybrid distribution network loss caused by the uncertainty of wind and solar output. Based on the uncertainty theory, wind power plants and photovoltaic power generation are modeled, and a power flow calculation method for AC/DC distribution networks is proposed. Considering that when consuming fixed distributed generation, the minimum network loss is the objective function, and a particle swarm optimization-based AC/DC operation optimization method is proposed. Finally, the validity and feasibility of the proposed method are verified by simulation examples.