Search

Due to the strong coupling between power injections and bus voltages in DC distribution networks, it is impossible to achieve economic dispatch and recover the nominal voltages of all buses, simultaneously. In light of this, a multi-objective optimization-based control model is formulated for the multiple distributed energy resources (DERs) within the DC distribution network to be dispatched as a virtual power plant. A compromise can be made between the economic efficiency and voltage distribution quality by placing different weights on the multiple objectives. To this end, a distributed real-time multi-objective control strategy is proposed. Specifically, facilitated by sequential peer-to-peer communication, the dynamics of the cyber-physical DC distribution system imitate the primal-dual subgradient algorithm to the multi-objective optimization, thus can iteratively converge to its optimal solution. A dynamic weight adjustment mechanism is further designed to bound the bus voltages within a feasible range. This strategy is plug-and-play, privacy-preserving, robust to communication failures, and responsive to change of operating conditions compared to its centralized counterpart. Simulation studies of two typical DC distribution feeders verify the effectiveness of the proposed approach in various scenarios.

A comprehensive evaluation method of electric power prediction models using multiple accuracy indicators is proposed. To obtain the preferred models, this paper selects a number of accuracy indicators that can reflect the accuracy of single-point prediction and the correlation of predicted data, and carries out a comprehensive evaluation. First, according to Dempster-Shafer (D-S) evidence theory, a new accuracy indicator based on the relative error (RE) is proposed to solve the problem that RE is inconsistent with other indicators in the quantity of evaluation values and cannot be adopted at the same time. Next, a new dimensionless method is proposed, which combines the efficiency coefficient method with the extreme value method to unify the accuracy indicator into a dimensionless positive indicator, to avoid the conflict between pieces of evidence caused by the minimum value of zero. On this basis, the evidence fusion is used to obtain the comprehensive evaluation value of each model. Then, the principle and the process of consistency checking of the proposed method using the entropy method and the linear combination formula are described. Finally, the effectiveness and the superiority of the proposed method are validated by an illustrative instance.

Solving the energy crisis and environmental pollution requires large-scale access to distributed energy and the popularization of electric vehicles. However, distributed energy sources and loads are characterized by randomness, intermittence and difficulty in accurate prediction, which bring great challenges to the security, stability and economic operation of power system. Therefore, this paper explores an integrated energy system model that contains a large amount of new energy and combined cooling heating and power (CCHP) from the perspective of automatic generation control (AGC). Then, a gradient Q(σ,λ) [GQ (σ,λ)] algorithm for distributed multi-region interconnected power system is proposed to solve it. The proposed algorithm integrates unified mixed sampling parameter and linear function approximation on the basis of the Q(λ) algorithm with characteristics of interactive collaboration and self-learning. The GQ (σ,λ) algorithm avoids the disadvantages of large action spaces required by traditional reinforcement learning, so as to obtain multi-region optimal cooperative control. Under such control, the energy autonomy of each region can be achieved, and the strong stochastic disturbance caused by the large-scale access of distributed energy to grid can be resolved. In this paper, the improved IEEE two-area load frequency control (LFC) model and the integrated energy system model incorporating a large amount of new energy and CCHP are used for simulation analysis. Results show that compared with other algorithms, the proposed algorithm has optimal cooperative control performance, fast convergence speed and good robustness, which can solve the strong stochastic disturbance caused by the large-scale grid connection of distributed energy.

Asynchronous interconnection of two power systems by high voltage direct current transmissions (HVDCs) is a judicious choice to enhance the reliability of system operation and evade some risks. As one of the pioneers of asynchronous interconnection, the southern China power system encounters the challenge of coordinating generation of the sending-end and demand of the receiving-end. The day-ahead generation unit and tie-line scheduling problem is formulated as a two-stage mathematical optimization, in which the first-stage problem minimizes the variance of the remaining load series of receiving-end and generates a near-optimal interarea power transmission schedule, whereas the second-stage problem decides the production level of generation units and power flow of HVDC tie-lines with the objective of minimizing transmission losses. In the second-stage problem, discrete characteristics of HVDC power profile is explicitly modeled, and a quadratic loss function derived from historical data is used to calculate the overall loss of each HVDC, which is then piecewise linearized to form a tractable mixed integer linear programming. Numeric experiments based on realistic data of China Southern Power Grid are carried out. The feasibility and effectiveness of the proposed method is verified through comparing the optimized generation and transmission schedules with original ones which are generated based on expert knowledge.

This paper studies the voltage stability problem in microgrid with multi-induction motor (MIM) loads. Firstly, the dynamic coupling relationship between grid voltage and each motor load slip in microgrid with MIM loads is deduced based on the power balance theory of power system. Then, a multi-source and master slave control based microgrid model is built, where Matlab/Simulink software is used to analyze microgrid voltage stability under circumstances of MIMs superimposed starting and microgrid fault. Through simulation calculation and analysis, the measures to improve the voltage stability of microgrid with MIM loads are proposed. According to the simulation results, the interaction between MIM loads has great influence on microgrid voltage stability. In addition, the critical cleaning time (CCT) of series connected master slave control based microgrid is affected by the distance between three phase short circuit fault location and the induction motor load location, as well as the distance betwe...

Battery swapping is an effective way for the energy fast recharge of electric vehicles (EVs). The batteries stored in the battery swapping stations (BSSs) might be an emerging form of energy storage that can be used in the future power systems. Unlike traditional battery storage, the application of BSSs storage needs to consider the behaviour of EV users and the charging process in the BSSs. The calculation of controllable capacity (CC) plays a key role in the application of BSSs storage. This study proposes a Monte Carlo stochastic simulation method (MCSS) to estimate the CC of BSSs. The queuing problems in EV batteries swapping are simulated by the proposed MCSS method. The CC then can be obtained from the queues analysis. Simulation results demonstrate the availability of the proposed method.

Clean energies such as wind energy and solar energy have increased very fast in order to meet the environmental requirements. However, due to the uncertainties of wind and solar energy, the large-scale integration of new energy presents a great challenge to the power system economic dispatch. Thus, a multi-time-scale robust economic dispatch strategy of a multi-source hybrid power system based on the variable confidence level is proposed. The deterministic constraints of each time scale are transformed into robust constraints that take the uncertainty into account. Meanwhile, the robust level whose confidence level increases with the shortening of time scale is set to improve the scheduling conservative degree step by step. The selection principle of the robust level of each time scale is also presented. The proposed approach is applied to an IEEE 9-bus system. The calculation results are compared with those from the traditional multi-time scheduling method and show the effectiveness of the paper, which can reduce the uncertainty impact of wind, solar, and load forecast, as well as achieve a great balance of security, economic, and environmental benefits.

Strong winds, ice storms, lightning, and other extreme weather events predominantly affect the security of overhead transmission lines (OHTLs). Thus, it is important to use the weather forecast information to predict the possible fault events and make an early warning to improve the operational reliability of the power system. Wind swing discharge is a common failure mode under strong winds or downbursts. Wind swing discharge early warning can be treated as a typical problem of comparison and prediction with uncertain inputs as a consequence of fluctuations. This paper proposes a probabilistic early warning method of wind swing discharge based on wind forecast. First, the influences of wind forecast accuracy on the prediction of the swing angle are analyzed. Then, the probability distributions of forecast wind direction angle and wind speed are established based on the prior distribution of wind forecast accuracy. The wind swing angle is calculated based on wind direction and wind speed samples through Monte Carlo simulation. The calculated swing angle is then compared with the designed maximum swing angle of the suspension insulator string to realize the probabilistic early warning of wind swing discharge for OHTLs under short-range wind forecasts. Finally, the proposed method is verified with a practical example.

An enhanced cascading failure model integrating data mining technique is proposed in this paper. In order to better simulate the process of cascading failure propagation and further analyze the relationship between failure chains, in view of a basic framework of cascading failure described in this paper, some significant improvements in emerging prevention and control measures, the subsequent failure search strategy as well as the statistical analysis for the failure chains are made elaborately. Especially, a sequential pattern mining model is employed to find out the association pertinent to the obtained failure chains. In addition, a cluster analysis model is applied to evaluate the relationship between the intermediate data and the consequence of obtained failure chain, which can provide the prediction in potential propagation path of cascading failure to reduce the risk of catastrophic events. Finally, the case studies are conducted on the IEEE 10-machine-39-bus test system as benchmark to demonstrate the validity and effectiveness of the proposed enhanced cascading failure model. Some preliminary concluding remarks and comments are drawn.

Squirrel-cage induction generators (IGs) are widely used in distributed generation (DG). When the voltage at the point of common coupling is fluctuant, the embedded IG will show the impedance characteristic with dynamic changes under the different fluctuation frequencies. In addition, the drive train of IG set has great impact on the voltage flicker attenuation. This paper observes the dynamic response of IG to the voltage flicker through the experiments and further defines the flicker attenuation factor and transfer coefficient. A linearization model of IG with two-mass equivalent drive train is constructed through comparing the impacts of different drive trains (such as diesel engine, wind turbine) on the voltage flicker attenuation. Then an analytical method is proposed to determine the dynamic impedance, attenuation factor, transfer coefficient and flicker limit for IG integrated into distribution network. The correctness of the proposed method is verified by the experimental tests and the dynamic simulation using the detailed model of IG set. The parameters sensitivities of drive train and generator to the voltage flicker attenuation effect are analyzed and discussed in the paper.

Integrating large-capacity wind power into a power system has a great impact on the voltage stability of a power system. Especially, a fault in such a system can result in the tripping of a large amount of wind power generation or system collapse. This article studies the effect of large-capacity wind power generation on power system voltage stability under a fault condition. The adopted wind power model consists mainly of a wind turbine, drive device, asynchronous generator, and control system. The proposed analysis approach is applied to a practical power system with wind power, which is located in Gansu province in China. Through simulation and analysis, the characteristic and stability principle of an asynchronous wind generator group are recognized. A speed increase in a wind generator group is closely related to voltage drop after fault, which may cause voltage instability if they both impact each other after fault. The detailed results are presented in the article.

This paper presents an application of a coordinated static VAr compensator (SVC) device as additional control for the reactive power (VAr) optimization problem and analyzes the impact on system loss and voltage profile. The coordinated SVC model controls internal, local, and remote devices simultaneously. It has two modes of operation: 1) voltage-control mode; and 2) Q control mode. This paper analyzes the functions of the coordinated SVC and implements them in the practical power systems. The impact of VAr optimization with coordinated SVC model on loss minimization and voltage enhancement are also discussed by comparing it with VAr optimization with general SVC model as well as VAr optimization without the SVC model.

This paper proposes a rule based comprehensive approach to study distribution network reconfiguration (DNRC). The DNRC model with line power constraints is set up, in which the objective is to minimize the system power loss. In order to get the precise branch current and system power loss, a power summation based radiation distribution network load flow (PSRDNLF) method is applied in the study. The rules that are used to select the optimal reconfiguration of distribution network are formed based on the system operation experiences. The proposed rule based comprehensive approach is implemented in distribution network in Guiyang South Power Supply Bureau. For the purpose of illustrating the proposed approach, two distribution network systems are tested and analyzed in the paper.

Network losses are being considered in the standard market design. The calculation of loss sensitivity that is based on the distributed generator slack is commonly used for economic dispatch in energy control center. In order to obtain the accurate location-based marginal prices, an efficient and robust method to calculate loss sensitivity for an arbitrary slack bus within the real-time period (1–10 min) is needed in the real-time energy markets. This paper presents a fast and useful approach to calculate loss sensitivity for any slack bus. This method is based on the results taken from the distributed slack buses without repeating a traditional power flow analysis. The proposed approach is tested and verified.

This paper presents a new approach to study the optimal reactive power (VAR) control problem using a modified interior point (MIP) method. Basically, a two-step procedure is employed: computing a unified ranking process for optimal reactive power support sites using an analytic hierarchical process (AHP), and solving a optimal VAR control model for determining the values at the selected reactive power support sites. The AHP and sensitivity analysis methods are used to determine the sites of optimal VAR support service. The objective used in the optimal VAR control model is to minimize system real power losses and to penalize any new VAR utilization, so that the overall system VAR control options are optimized. The proposed approach is tested on the IEEE 14-bus and 30-bus systems, from which satisfactory results have been obtained.

This paper presents a new nonlinear optimization neural network approach to study the problem of security-constrained interconnected multiarea economic dispatch. It is a penalty-minimizing neural network approach with weights based on optimization theory and neural optimization method. In the paper the model of security-constrained multiarea economic dispatch is set up, in which tie-line security and transfer constraints in each area are considered. In addition, a simple analysis of buying and selling contract in a multiarea environment is made. The proposed method is tested on a system of three interconnected areas. The detailed results and analysis are given in the paper.

This paper presents an approach to calculate the price of reactive power (VAR) support service in a multi-area power system. The methods are cost-benefit analysis (CBA) and nonlinear convex network flow programming (NLCNFP). By using multiple cost-benefit indices, the VAR support benefits with respect to power delivery increases of tie lines are computed. These benefits are used to determine the VAR pricing in multi-areas environment. The multi-area optimization model with VAR pricing is solved by NLCNFP algorithm, which is a combined method of quadratic programming (QP) and network flow programming (NFP). The proposed VAR pricing scheme was tested on IEEE 118-bus system, which is divided into three areas.

This letter proposes a novel phase-angle measurement algorithm through establishing a mathematical model of virtual αβ stationary coordinate that utilizes two expression forms of a positive-sequence component in two axes stationary reference frame. This method could greatly eliminate the sinusoidal error of the conventional discrete Fourier transform (DFT) algorithm. The accuracy and performance of the proposed algorithm are proved by relevant simulation results.

This paper proposes an improved method to study distribution network reconfiguration (DNRC) based on a refined genetic algorithm (GA). The DNRC model, in which the objective is to minimize the system power loss, is set up. In order to get the precise branch current and system power loss, a radiation distribution network load flow (RDNLF) method is presented in the study. The refined genetic algorithm is also set up, in which some improvements are made on chromosome coding, fitness function and mutation pattern. As a result, premature convergence is avoided. The proposed approach is tested on 16-bus and 33-bus distribution networks. Study results are given in the paper.

This paper reports an application of the Everett optimization technique for value-based load shedding in naval-ship power system with network constraints and application of reliability constraints. A new congestion analysis index—expected contingency margin (ECM), which considers the probability of disturbance, and reliability indices loss of load probability (LOLP) and expected unserved energy (EUE) are presented to filter the critical outage and congestion cases. The optimal load shedding scheme carried out using the Everett optimization technique uses a utility function, which addresses the cost of allocating pay-off while satisfying the network and reliability constraints and generation capacity limits. The algorithm search for the optimal λ to form a fair pricing mechanism, which produces maximum pay-off and minimum load shedding while, satisfying system constraints. The proposed approach is examined on naval-ship power system and detailed results are shown.

This paper presents a new nonlinear convex network flow programming (NLCNFP) model and algorithm for solving the security-constrained multi-area economic dispatch (MAED) problem. The tie-line security and transfer constraints in each area are considered in the MAED model. A simple analysis of a buying and selling contract in a multi-area environment is also made. The NLCNFP model of security-constrained multi-area economic dispatch was set up and solved by using a combined method of quadratic programming (QP) and network flow programming (NFP). The concept of maximum basis in the network flow graph was introduced to change the constrained model into an unconstrained QP model, which was easily solved by the reduced gradient method. The proposed approach is tested on a system of four interconnected areas with satisfactory results.

Since conventional optimal power flow (OPF) algorithms are limited and too slow for operational planning, power system operation and planning engineers have been seeking efficient OPF algorithms for three decades. The interior point method is one of the most efficient algorithms. In this paper, an improved quadratic interior point (IQIP) method is used to solve comprehensive OPF problems with a variety of objective functions, including economic dispatch, VAr planning and loss minimization. The IQIP method features a general starting point (rather than selected good point as in the general interior point method) and fast convergence. Consequently, the OPF package described in this paper offers huge improvements in speed, accuracy and convergence in solving multi-objective and multi-constraint optimization problems. It will be a very useful tool for the power industry.

This paper proposes a new method to study reactive power pricing and new VAr source selection using the analytic hierarchical process (AHP), and presents a price structure for providing reactive power service and control based on the OPF approach. The cost of reactive power support service is determined based on capability and contribution to improvement of system performance such as security, reliability and economics, by calculating sensitivity of objective function with respect to reactive power support. For VAr planning purpose, an analytic hierarchical process model is devised which comprehensively considers the network topology and all types of benefits including operating cost, system voltage and loss for each candidate VAr source site. The proposed approach for calculating VAr pricing and selecting the new VAr sources was tested on an IEEE 30-bus system. The calculation results are analyzed by the cost-benefit analysis (CBA) method.

This paper presents a new nonlinear convex network flow programming (NLCNFP) model and algorithm for solving the on-line economic power dispatch and automatic generation control (AGC) with security and economy. Two AGC functions—load frequency control (LFC) and economic dispatch control (EDC) are modeled on the same timescale as EDC. This new AGC modeling and solution algorithm meets not only the control requirement of frequency and tie-line power as in the conventional load frequency control, but also the line security and system economy. The proposed approach is tested on an IEEE 30-bus system with satisfactory results.

This paper proposes a new approach to construct the maximal steady-state security regions of power systems using optimization. The method is based on DC load flow. For the first time, the maximal steady-state security region—hyperbox is directly computed through a linear programming (LP) model, in which the upper and lower limits of each component forming hyperbox are taken as unknown variables, and the objective is to maximise the sum of each generator's power adjustment range. The proposed approach is tested on the IEEE six-bus and 30-bus systems. The calculation results and comparison show that the new method is superior to the extending method which is the conventional means for constructing maximal steady-state security regions.