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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.

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.

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...

While dynamic regulating effects of pumped storage units have been more and more important in power systems integrated with large-scale wind power, outputs of the conventional reversible pumped storage units still cannot be dynamically adjusted in pumping mode due to the characteristics of pump-turbine. In order to expend the regulating range of reversible pumped storage unit, the variable speed solution is adopted in this paper to realize the dynamic regulating of wind power under the pumping mode. Based on the analysis of pump turbine characteristic curves, the dynamic model and corresponding control strategy of variable speed pumped storage unit are derived. And then, the IEEE 39-bus test system is used to verify wind power regulating performance of variable speed pumped storage unit in pumping mode. The results show that variable speed pumped storage unit has broadened the regulating range of wind power by dynamically adjusting pumping power and effectively reduce the frequency deviation of power system caused by the short-time wind power fluctuation.

The controller parameters of automatic generation control (AGC) system have a great impact on the dynamic performance of power system frequency control. The improper parameters of AGC controller may lead to a stability problem of grid frequency when a large load disturbance occurs. This will be even more serious in a high penetration of renewable energy power grid because of the lack of system inertia and regulation reserves. This paper presents a novel hybrid bacteria foraging optimization (BFO) and pattern search (PS) optimized fuzzy PID controller for AGC system of a two-area interconnected power system. The scaling factors of the fuzzy PID controller are optimized using the proposed hybrid BFO and PS (BFO-PS) optimization technique. The superiority of the proposed fuzzy PID controller has been shown by comparing the results with some recently published techniques for the same interconnected power system. Further, sensitivity analysis is carried out which demonstrates the ability of the proposed approach to wide changes in system parameters and operating load conditions.

The battery energy storage (BES) is very promising to be used for improving the performance of automatic generation control (AGC) in power system by offering fast active power compensation. However, the improper parameters of controller in AGC system may cause an unstable frequency problem. This paper presents an optimal controller design method based on bacteria foraging algorithm (BFA) for AGC system with BES. A two-area reheat thermal system is considered to be equipped with the proportional plus integral (PI) controllers. The BFA technique is employed to search for the optimum controller parameters by minimizing the integral of time multiply absolute error (ITAE) index. The performance of the proposed BFA tuning controller has been evaluated with that of the controllers tuned by particle swarm optimization (PSO) and genetic algorithm (GA). Simulation results emphasize the performance of AGC system with BES and demonstrate the superiority of the proposed BFA tuning controller compared to the optimized controller based on PSO and GA.

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.

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 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.

A new approach is presented to congestion analysis and optimal load shedding for aerospace power systems. Optimal powere ow,ananalytichierarchicalprocess,and theEverett optimization method (Everett,H., Operations Research, Vol. 11, 1963, pp. 399 ‐417) are employed for implementing this approach. Several system performance indices are developed to evaluate the degree of system congestion for different operation time stages. These performance indices include a circuit overload index and a system voltage problem index obtained through the optimal powere owcalculation.Thecongestedsystemisalleviatedby theavailablecontrolsuchasloadshedding.Therefore, themathematicalmodelofloadshedding,inwhichtheobjectiveisapayofffunction,isdevised.Thesolutionmethod of the model is the Everett method, generalized Lagrange multipliers. Detailed results are given in the paper.

This paper presents the harmonic state estimation (HSE) based on the Total Least Squares (TLS) through comprehensively considering the harmonic network parameter error and measurement system error. The proposed approach is tested on the IEEE 14–bus harmonic testing system. The satisfied results are obtained.

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.

This paper presents an approach to select the proper location of wind farm in power system. Two key factors for constructing wind farm are considered in this study. They are wind availability and wind speed. Because of the “undispatchable” nature of wind and the difficulty in forecasting, the wind availability and wind speed are random and uncertainty. Thus, the deterministic method is not appropriate to handle them. This paper uses fuzzy number to deal with the wind availability and wind speed. The term of average unavailable time (AUAT) is introduced in order to compute the probability of average wind availability. The proposed approach for selecting wind farm has been applied to the IEEE 30-bus test system.

The simultaneous feasibility test (SFT) performs the network sensitivity analysis under the base case and contingency cases in power systems. This paper presents the practical implementation of the simultaneous feasibility test in the energy market. SFT calculation includes loss sensitivities and constraint shift factors either for E-nodes or P-nodes and the used reference is the distributed load reference. Several SFT modes are discussed in the paper. The schematic of data flow between SFT and other market system applications in a day-ahead market clearing process is also analyzed. Especially, the dead bus logic is proposed to compute the sensitivities for the open / dead node such as open unit. The calculating results based on PJM system are shown in the paper.

This chapter selects several classic optimal power flow (OPF) algorithms and describes their implementation details. These algorithms include traditional methods such as Newton method, gradient method, linear programming, as well as the latest methods such as modified interior point (IP) method, analytic hierarchy process (AHP), and particle swarm optimization (PSO) method. The goal of OPF is to find the optimal settings of a given power system network that optimizes the system objective functions such as total generation cost, system loss, bus voltage deviation, emission of generating units, number of control actions, and load shedding while satisfying its power flow equations, system security, and equipment operating limits. The phase shifters are adjusted sequentially and their direction of adjustments are governed by the impact on the primary objective function of minimal line overload, in the search technique. This chapter focuses on applying PSO methods to solve the OPF problem.

This chapter deals with the power flow problem. The power flow algorithms include the Newton-Raphson method in both polar and rectangular forms, the Gauss-Seidel method, the DC power flow method, and all kinds of decoupled power flow methods such as fast decoupled power flow, simplified BX and XB methods, as well as decoupled power flow without major approximation. Power system state estimation derives a real-time model through the received data from a redundant measurement set. Different kinds of methods about state estimation are introduced in the chapter. Among them, the weighted least squares (WLS) state estimation methods are widely used. There are three most commonly used measurement types in power system state estimation. They are the bus power injections, the line power flows, and the bus voltage magnitudes.

This paper presents an integrated approach to study the optimal power flow (OPF) with phase shifter for alleviating line overloads. It is noted that the general OPF calculations are hourly based and the control variables of OPF are continuous. However, the calculations of phase shifter are daily based, and the variable of phase shift is discrete as well as the numbers of allowable adjustments for each phase shifter per day are also discrete. The proposed scheme uses the rule-based method to bridge the problems. The hourly based OPF solution will be sent to rule based system to check the phase shifter constraints. Meanwhile, the rule based system will transfer the available phase shifter control as input to OPF. The OPF hourly based is solved by the extended quadratic interior point method. The proposed approach is tested on the IEEE 30-bus system with 4 phase shifters.

This paper studies aerospace power system automation using the optimal power flow (OPF) and Everett method. Several system performance indices are developed to evaluate the degree of system congestion for different operation time stages. These performance indices include a circuit overload index and a system voltage problem index and obtained through the OPF calculation. The congested system is alleviated by the available control such as load shedding. In order to reach this objective, the mathematical model of load shedding, in which the objective is payoff function, is devised. The Everett method—Generalized Lagrange Multipliers is employed to solve the problem. The results on NASA power system and IEEE-30 bus test system are given in the paper.

This paper presents an integrated optimal power flow (OPF) with phase shifter approach to enhance power system security. The general OPF calculations are hourly based and the control variables of OPF are continuous. However, the calculations of phase shifter are daily based, and the variables related to phase shifter are discrete. Therefore, the general OPF cannot be directly used to solve this problem. The paper develops the rule-based OPF with phase shifter scheme to bridge the problems. In order to effectively alleviate the line overloads, the ranking of phase shifter locations is conducted based on contingency analysis and sensitivity analysis. The best phase shifter sites are identified and selected into a rule-based system accordingly. The handshaking procedure between the adjustment of the selected phase shifters and OPF calculation is proposed using a rule-based method. The hourly-based OPF problem is solved by the extended quadratic interior point method. The IEEE 30-bus system is used to test the proposed scheme.

A new idea using an analytic hierarchical process (AHP) is proposed for Var placement compensation. AHP uses parallel analytical criteria for selecting and ranking placement of Var compensation. Due to their independent nature these criteria are not necessarily the same although both aim to identify weak nodes in maintaining system voltages. AHP also considers quantitative criteria. It is especially suitable for problems that are difficult to analyse. AHP provides a simple, convenient and comprehensive means of selection and ranking. The proposed algorithms have been tested on the IEEE 14-bus and 30-bus systems with satisfactory results. The paper also proposes a new nonlinear optimisation neural network approach to study the reactive power problem. The approach is applied to Var control optimisation, in which the objective is to minimise the system voltage profile. The approach is a penalty-minimisation method with weights based on optimisation theory and neural optimisation methods. It is used to solve the nonlinear programming problem with equality and inequality constraints. The paper demonstrates that the energy function used is a Lyapunov function, and the equilibrium point of the proposed neural network corresponds to the optimal solution of a constrained optimisation problem.

A fully coupled active and reactive dispatch using linear programming and an analytic hierarchical process is developed. The purpose of combined active and reactive dispatch (CARD) is to achieve the overall objective of minimum generation cost and to improve the distribution of reactive power and voltage, subject to constraints which ensure system security. Security is defined as the maintenance of individual circuit flows, generator reactive power output and system voltages within limits under normal system conditions and contingency cases. The weighting factors of different kinds of objective functions are determined by an analytic hierarchical process (AHP), considering the relative importance of these objective functions. The developed method is demonstrated through its application to determine the combined active and reactive dispatch of a 713-node network representing a practical power system, provided by the National Grid Company plc (NGC). The calculation results show that a good balance can be achieved among the various objectives and a satisfactory generation pattern is obtained from the enhanced CARD program.