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• Distinct DG Output Modes Validation: The simulation validates distinct DG output modes, impacting prediction. • Mode-Based State Estimation: The paper proposes an innovative mode-based state estimation using IPSO-DBSCAN clustering, overcoming k-means limitations. • Enhanced Data Recovery using DBSCAN: Enhanced data recovery employs DBSCAN with improved secondary clustering, guided by similarity and homogeneity, outperforming traditional methods. • BiGRU-PF State Estimation: BiGRU-PF enhances state estimation accuracy and robustness in distribution networks by detecting DG output modes, addressing k-means limitations, and improving data recovery. This paper proposes a new forecasting-aided state estimation (FASE) method for distribution systems that mitigates issues with uncertain distributed generation (DG) and lost measurements. We utilize an Improved Particle Swarm Optimization (IPSO)-optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for examination of historical DG output data. Based on identified DG output modes, it facilitates precise state prediction and data reconstruction,. The proposed method employs a Bidirectional Gated Recurrent Unit (BiGRU) neural network for state prediction and a particle filter (PF) for final state filtering. The method verification is provided through Python simulations of the Distribution Transformer Unit (DTU)7k distribution network system, demonstrating improved accuracy and robustness against sudden load change and bad data in measurements. [ABSTRACT FROM AUTHOR]

• A two-stage risk dispatching model for integrated transmission and distribution networks is proposed. The day-ahead phase focuses on the economics of the transmission and distribution network, while the intra-day phase focuses on operational safety. • The four dimensions of regulable resources, such as power side, grid side, load side and energy storage side, are coordinated to optimally dispatch the transmission and distribution network. • An improved particle swarm (PSO) algorithm solution model is proposed. In this paper, the dynamic adjustment of the inertia weight factor and learning factor can improve the ability to globally search for optimal values and increase the convergence speed of the algorithm. With the incorporation of more renewable energy sources, distribution networks are shifting from passive to active. The interconnection of the transmission and distribution networks will increase and the tidal currents will become bidirectional, affecting the stable operation of the transmission and distribution networks. This paper proposes a multi-timescale risk dispatch study for transmission and distribution networks that have a high proportion of distributed energy access to meet the rising demand for coupling between the two types of networks. First, a day-ahead scheduling model that takes into account various operational risks is developed with the goal of minimizing total operating costs, and it optimizes the source network load and storage of multiple adjustable resources of the transmission network and distribution network jointly under the risk limit constraint. Second, an intraday scheduling model is built with the goal of minimizing the risk cost of the transmission network and distribution network, and the day-ahead scheduling plan is modified by considering the day-ahead forecast error and the different operational risks of the transmission network and distribution network. Finally, the effectiveness of the proposed multi-time-scale risk dispatch model is verified through the analysis of arithmetic cases, and it is proved that the model can enable the interaction and coordination between transmission and distribution networks as well as the synergistic optimization of operating costs and operating risks. [ABSTRACT FROM AUTHOR]

• Frequency adaptive: multioscillatory control and repetitive control are studied. • Methods are applied for a grid-tie converter. • Memory and computational burden analysis of both methods are presented. • A linear prewarping for frequency adaptive multioscillatory control is proposed. • Practical implementation hints are provided. The paper describes two main approaches of periodic control under variable frequency conditions: frequency adaptive multioscillatory control (FAMOSC) and fractional delay repetitive control (FDRC). The methods are applied in the control system of a grid-tie converter to increase the quality of phase currents in case of distorted grid voltages. The paper compares structures, performance and implementation of FAMOSC, FDRC and the conventional approach of repetitive and multioscillatory control. Performances of the control systems are illustrated with experiments on a laboratory test bench. The article describes tuning process of FDRC and FAMOSC with the usage of Particle Swarm Optimization algorithm. A detailed analyse and research of the techniques in terms of computational complexity is presented. Practical hints for implementation and resource optimization of both techniques on DSP are included. For FAMOSC a fast implementation of prewarping is proposed. [ABSTRACT FROM AUTHOR]

Transmission reinforcement projects of a Transmission Expansion Plan can suffer implementation delays that can impact the short-term power system operation in unpredicted ways. These delays may lead project managers to paying particular care to the development of those plans proved to be most critical from this point of view. This paper proposes a multi-objective dynamic-programming based method to evaluate the impacts of such delays in a short-term transmission plan with respect to load shedding, reliability and losses. It uses the Binary Pareto-Optimal Particle Swarm Optimization as optimization tool. For the trade-off analysis and the final decision to be made, the paper proposes indices to rank the projects in the plan from most to least critical, should they be delayed, based on the severity of the impacts and the probability of occurrence, both on an annual basis and for the whole planning horizon. The proposed method has been tested on an actual Brazilian 40-bus subtransmission system for a 3-year and a 6-year planning horizon, and with the 24-bus IEEE RTS reliability test system for a 3-year planning horizon. To assess its accuracy and efficiency, the results are compared to those using the Non-dominated Sorting Genetic Algorithm II method (NSGA-II) and to the exhaustive search that considers every delay configuration, showing the same accuracy as the exhaustive search and better convergence properties than NSGA-II. The most critical projects are accurately ranked helping the project manager to decide which projects should receive higher attention to guarantee its construction on the planned date. The method obtained a reduction in computational time of 35% for the Brazilian system and of 42% for the IEEE RTS in relation to the exhaustive approach, demonstrating its efficiency. • Evaluates robustness of multi-year expansion plan with regards to project delays. • AC network, reliability, dynamic programming, multi-objective optimization. • Trade-off analysis with severity and probability indices, annually and whole horizon. • Rank critical projects with respect to their impacts in the power system operation. • Accurate classification of projects and considerable reduction in computational time. [ABSTRACT FROM AUTHOR]

• The PSO algorithm is used to solve the switch angles of MMC staircase modulation which overcomes the problem that the switch angles are difficult to solve. • The precise mathematical model of MMC is established and the output waveform expression of staircase modulation is given and simplified. • iSHE-PWM (improved selective harmonic elimination pulse width modulation) method can increase the number of switching angles and minimize THD. Combined with PSO algorithm it can reduce the MMC output waveform THD and output voltage error. Modular multilevel converter (MMC) is the first topology choice for flexible DC transmission systems. This paper presents a particle swarm optimization (PSO) algorithm aided modulation method to calculate the improved selective harmonic elimination pulse width modulation (iSHE-PWM) switching angles with the aim of reducing the total harmonic distortion (THD) and output voltage error of MMC. The SHE-PWM has more controllable variables compared with the staircase modulation so that it can be used to improve the output current waveform quality. This method retains the advantages of easy to control and low frequency from the staircase modulation. But amplitude of its high-order harmonic is high and this paper improves it. Firstly the mathematical model of MMC is set up and corresponding expressions of THD and voltage error are given. The joint function of THD and voltage error is considered as the fitness function. The PSO algorithm was used to minimize the joint function for solving the optimal switching angles. Finally the proposed method is verified through simulation results showing its potential to successfully solve the problem of difficulty in obtaining the iSHE-PWM switching angles while minimizing the THD and output voltage error of the MMC. [ABSTRACT FROM AUTHOR]

• Establishing a realistic unbalanced harmonic power flow calculation model. • Establishing DG line loss optimization model with harmonic constraint. • Analyzing several DG line loss optimization schemes in distribution network with multi-type harmonic loads. • The harmonic contents have been decreased but also increased the line loss. • Under the same line loss, the optimal capacity of double DG is less than that of single DG, • The optimization of double DG is better without considering the cost. With the large penetration of power electronic devices in distribution networks, power quality problems such as line loss and harmonics have become the main factors affecting the access of distributed generation (DG). At present, the research on DG integration, such as the optimization of the DG capacity using particle swarm optimization algorithm and ant colony algorithm, is mainly focused on the traditional distribution network. In contrast, the research on the power electronic distribution networks, which include a large number of harmonic sources, is not sufficient. This paper presents a novel optimization method for connecting DG to power electronic distribution network to reduce line loss. Firstly, based on the frequency domain model of the distribution network, this paper establishes the harmonic model of distribution network components, including generators, transformers, lines, harmonic sources and non-harmonic sources. Then the location and capacity of DG are optimized using genetic algorithm with network loss reduction as a target and voltage and harmonic as constraints. Finally, the power electronic distribution network is modelled based on the IEEE 34 - node standard model. The obtained results confirmed that the optimization model with harmonic constraints can effectively reduce the line loss by 108.26 kW and the line loss rate by 4.67 % using single DG. Also, when using the double DG, the line loss and the line loss rate are decreased by 118.53 kW and 5.14 %, respectively. In addition, the optimization effect of dual DG is better than that of single DG without considering the cost. [ABSTRACT FROM AUTHOR]

• A pricing method of Time-of-Use transmission price is proposed based on ATC curve. • A market allocation model of power generation is built based on Prospect Theory. • A bi-level model is solved by combining heuristic algorithms with Cplex tool. • The proposed transmission price promotes the utilization for certain project. The electricity delivered by inter-regional line projects is impacted by the operation of spot market, the previously approved fixed transmission pricing method based on planned electricity may not be fully applicable. To solve the above problem, the Time-of-Use transmission pricing method for inter-regional line with spot market at both ends is proposed in this paper. Firstly, it analyses the impact of transmission price on the electricity distribution of the power producers in the spot market at both ends, and the clearing price and total power generation cost in the spot market at the receiving-end. Then, the calculation method of daily available transmission capacity is introduced. Further, based on the incentive of transmission price, a Bi-Level optimization model is established by considering spot market clearing at both ends and the cost recovery of inter-regional line. On the transmission side, the optimal model of transmission price to maximize the benefits of the inter-regional line is built. On the spot market side, consider transmission prices, the power distribution model in the sending-end market and a unit commitment optimization model in the receiving-end market are established, and the relationship between transmission price and transmission electricity is found for the inter-regional line. The two models are interacted and restricted by each other, so a Bi-Level model of the cooperation between the inter-regional line and the spot market is formed. Finally, an example is simulated based on the Particle swarm optimization algorithm and Cplex optimization tool, and the effectiveness of the method is verified. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

Abstract Having continuous load structure and composition information of substations has a great relevance in power system analysis such as load modeling, load forecasting and demand-side management. In this paper, a parsimonious approach for load composition estimation using non-intrusive load disaggregation techniques for low voltage substations is presented with a concept of using ZIP load model characteristics of the aggregate active and reactive powers as predictor features. The disaggregation system uses machine learning algorithms such as Function Fitting Multi-Layer Perceptron Artificial Neural Network (MLP-ANN), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). During the study, a simulation dataset was generated using Monte Carlo simulation. Moreover, a comparative analysis with a benchmarked paper has been assessed and the proposed approach significantly outperforms. [ABSTRACT FROM AUTHOR]

This paper proposes a combined model of Multi-Objective Dynamic Economic and Emission Dispatch (MODEED) with Demand Side Management (DSM) to investigate the benefits of DSM on generation side. This model considers a day ahead based load shifting DSM approach. In order to analyse the effect of DSM on the generation side, the objectives of dynamic economic and emission dispatch problem were minimized individually and simultaneously with and without DSM. A test system with six thermal generating units was considered for the validation of the proposed method. In this paper, authors used Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to minimize the objectives of MODEED problem simultaneously. The simulation results of the MOPSO algorithm have also been compared with the non-dominated sorting genetic algorithm (NSGA-II). It is clear from the results that the proposed combined model is able to give benefits to both utilities and generating companies. [ABSTRACT FROM AUTHOR]

• A two-step scheme is proposed coordinating the distribution grid, aggregator, and EV. • Temporal-spatial features are exploited to achieve EV charging orderly. • Modified multi-objective particle swarm optimization algorithm is proposed. • Load variance and aggregator revenue are improved when meeting charging demands. • A higher proportion of private charging facilities enhance comprehensive benefits. Burdens of increasing penetration of electric vehicles (EVs) on distribution systems have attracted wide attention to the research of EV charging coordination. Nevertheless, existing coordination lacks the joint consideration of the temporal-spatial flexibility of EVs, which could reduce load fluctuations when meeting the EV charging demands. Moreover, a thorough investigation is required for the coordination of the benefits to the distribution system operator (DSO), EV aggregator (EVA), and EVs. In this paper, a novel EV charging scheme considering the temporal-spatial features of EVs is proposed, achieving the coordination of DSO, EVA, and EVs in two steps. First, a mixed-integer second-order cone programming (MISOCP)-based EV charging schedule is developed for DSO. Second, the temporal-spatial features of EVs are exploited by EVA to track the charging schedule. Specifically, a dynamic charging price mechanism based on active power margin is proposed for public charging facilities and a specific load regulation is designed for private charging facilities. On this basis, the modified adaptive multi-objective particle swarm optimization (AMOPSO) algorithm is proposed, including adaptive flight parameter adjustment and termination mechanisms. Case studies demonstrate the proposed strategy can attenuate load variance and raise EVA revenue. Further, the impact analysis of penalty price and the price elasticity of electricity demand can provide references for stable distribution network operation, higher EVA revenue, and charging cost reduction. [ABSTRACT FROM AUTHOR]

• It is applicable to large power systems in a reasonable computing time. • The exploited formulation is a bilevel programming. Due to the inherent nonconvexity and nonlinearity of the problem, efficient solution such as BPSO is required to be explored. • Numerical results on test systems and a real large size power grid demonstrate the effectiveness of the proposed approach. Recent large blackouts in power systems showed that common reliability criteria are not efficient anymore. Since cascading outages are the prevalent cause of more blackouts, and there is enormous interaction among different elements, determining the sequence of events before occurring blackouts is very challenging. Contingency screening methods are used to overcome some difficulties and identification of bottlenecks in power systems. However, risky contingencies are those with high probability, high consequences, or both. In this paper, a challenging work of seeking multiple events which potentially may result in cascades is addressed. Severe contingency identification, which is known as N-k problem in the literature, is difficult to deal with even for small values of k. A modified Binary Particle Swarm Optimization (BPSO), which proposed in this paper, could help power system planners and operators to upgrade the network resiliency by finding critical contingences that may initiate cascading outages. Severe contingencies are detected by computer simulations in the IEEE 39-bus test system and a real-sized network. The results are compared to the results of the IEEE 24-bus test system, which shows the method is more effective. [ABSTRACT FROM AUTHOR]

This paper proposes the application of fictitious reference iterative tuning (FRIT) method to optimize the gains of dc voltage controller of grid connected photo-voltaic (PV) system. It may be difficult to achieve good control of dc voltage using conventional PI controller having only one-degree-of-freedom (1-DOF) due to the trade-off between overshoot (in step response) and disturbance response. In this paper, the optimal control of dc voltage is proposed with improved disturbance response by implementing 2-DOF PI controller structure. FRIT method has been programmed in MATLAB based upon the particle swarm optimization (PSO) algorithm. The fundamental idea related to FRIT method is the extraction of input and output data, reference model setting and range of controller gains. The performance of dc voltage control for the optimized 2-DOF PI controller is also compared with the fuzzy logic controller (FLC) response. [ABSTRACT FROM AUTHOR]

• A two-step optimal scheduling method based on power classification is proposed for MG. • Demand response and renewable energy uncertainty are developed to handle stability. • The approach manages to optimize the system dispatch by considering FM power supply. • A frequency modulation strategy based on BP neural network is proposed. The grid connection of wind power and photovoltaics significantly increases the uncertainty of the operation and scheduling of power systems. The maximum power that can be transmitted in a particular time slot often fluctuates randomly around a certain value according to a certain probability distribution, and a prediction error occurs between the predicted and actual power. When the actual maximum power that wind power and photovoltaics can generate is less than the power arranged in the scheduling plan, a significant increase in operating costs will occur, as well as an increase in the frequency fluctuation of the microgrid. This may harm the system. To study the effects of the uncertainty of renewable energy output on microgrid dispatching, this paper divides power sources into basic load and frequency modulated (FM) power sources according to the FM characteristics and establishes a multi-objective optimal dispatching model of island microgrids considering the uncertainty of renewable energy output, using the economic benefit and frequency fluctuation rate of the microgrids as the optimization target. Additionally, this paper proposes a two-step optimal scheduling method based on power classification: First, the Monte Carlo method is used to estimate the expectation and variance of the objective function, and a certain number of samples are obtained through sampling. Second, the particle swarm optimization algorithm is used to determine the installation plan of the FM and base load power sources to minimize the average sample cost. Finally, a backpropagation neural network is used to construct the frequency modulation strategy of FM power sources. An analysis with examples shows that, in the context of many samples, the model and proposed two-step scheduling method are reasonable and effective. [ABSTRACT FROM AUTHOR]

• The optimal scheduling model for VPP to participate in carbon trading and green certificate trading is established. • The reverse search strategy and dimension mutation strategy of the proposed SCV-PSO algorithm can significantly improve the performance of the algorithm. • The proposed optimal scheduling model can significantly improve the economy and environmental friendliness of VPP operation. The grid connection of large-scale clean energy provides the possibility for the establishment of a clean energy system. The urgent problem that needs to be solved is how to improve the utilization efficiency of clean energy to reduce carbon emissions. First, the virtual power plant (VPP) operation mode under the carbon trading and green certificate trading mechanism is analyzed. Secondly, this paper incorporates carbon trading mechanism and green certificate trading mechanism into the optimal dispatch model of VPP including wind power generation, photovoltaic power generation, gas turbines and energy storage device. Taking the net profit of VPP as the optimization goal, which can take into account both economic and environmental protection. Based on whether VPP participates in carbon trading and green certificate trading, three schemes are established and compared and analyzed. In addition, to cope with the volatility of renewable energy, the utilization rates of the renewable energy output scenarios for four typical days under the three schemes were compared and analyzed. To solve this problem, this paper proposes the Self-Conclusion and Variational Particle Swarm Optimization (SCV-PSO) algorithm. The simulation results show that the VPP optimal scheduling model and solving algorithm proposed can effectively improve the utilization rate of renewable energy and reduce the carbon emission under the premise of ensuring economic efficiency. It can provide a useful reference for the low-carbon economic operation of the power system in the future. [ABSTRACT FROM AUTHOR]

• Using the Mixture Correntropy with Laplace kernel function and Gaussian kernel function, which is good at dealing with the complex characteristics in kernel space, a Mixture Correntropy Long Short-term Memory network (MC-LSTM) is developed and used as a short-term wind power predictor. • Combined with IVMD-SE data preprocessing strategy, a combined wind power prediction model based on IVMD-SE method and MC-LSTM network is designed and recorded as IVMD-SE-PMC-LSTM prediction model. With the development of emerging technology, wind power forecasting hybrid with artificial intelligence methods has become a research hotspot. Most of these methods are based on Mean Square Error (MSE) loss. However, when conducting the forecasting studies, the forecasting models built based on the traditional MSE loss have a poor effect, and the wind power data also lack the sensitivity to the nuclear parameters, make it difficult to achieve satisfactory results. Therefore, a wind power forecasting method based on Mixture Correntropy (MC) Long Short-term Memory (LSTM) neural network and Improved Variational Mode Decomposition (IVMD) is proposed in this paper. Aiming at the fact that the mixing coefficient and kernel parameters in Maximum Mixture Correntropy Criterion (MMCC) loss have an impact on its performance, Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters, and PMC(PSO-MC)-LSTM model is constructed. Meanwhile, an IVMD-SE data preprocessing strategy combining Sample Entropy (SE) and IVMD is proposed. The IVMD-SE-PMC-LSTM hybrid forecasting model is constructed. Finally, four groups original data from a wind farm are simulated to verify the forecasting performance of the proposed method. The results show that the hybrid forecasting method proposed in this paper can be better applied to the forecasting with higher data complexity. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose a simple transmission congestion pricing scheme based on tracing principle by considering generator fixed cost, cost for incurring loss and transmission congestion cost. Restructuring has brought about considerable changes by the virtue of which electricity is now a commodity and has converted into deregulated type. Such a competitive market has paved way for innumerable participants. This concept of restructuring has led to overloading of transmission lines. In this paper, power flow tracing has been employed by using suitable optimization algorithm, where the real power generation has been maximized. Congestion in the transmission line has been produced in a new fashion by maximizing the real power demand. The power flow under normal operating condition and congestion is determined and hence the difference in power flow is estimated. Based on the estimated power flow difference, the transmission line congestion cost is computed. Pool model and bilateral model has been considered in simulation study to introduce the concept of deregulation. The proposed method is tested and validated on Modified IEEE 30 bus test system and Indian utility 69 bus test system. [ABSTRACT FROM AUTHOR]

Maintenance scheduling of power generating units is very essential for the economical and reliable operation of a power system. The objective of Generator Maintenance Scheduling (GMS) problem is to find the exact time interval for preventive maintenance of power generating units in order to minimize the operating cost, maximize the system reliability and to extend the life time of the generating units. In this paper, the problem of scheduling of generating units for maintenance is formulated as a mixed integer optimization problem by considering minimizing the operating cost. Since generator maintenance scheduling is a mixed integer problem, differential evolution algorithm is suitably modified to handle the integer variables. The control variables in differential evolution algorithm are integers which denote the starting period of each generating unit for carrying out maintenance work. The lambda iteration method is used to determine the optimal generation schedule of committed generating units. This paper presents a mathematical approach assisted differential evolution (MADE) to solve maintenance scheduling problem in a power system. The performance of the proposed algorithm is validated by considering two test systems. The result obtained by the proposed MADE method is compared with mathematical approach assisted particle swarm optimization. The test results reveal the capability of the proposed MADE algorithm in finding optimal maintenance schedule for the GMS problem. [ABSTRACT FROM AUTHOR]

• A combination model for controlling APD-RPD of TGS was designed based on controlling the generator frequency and its terminal voltage simultaneously. • Mathematical model derivation proposed for tracking the RPD and preserving the APD of TGS at the infinite bus. • Creation of control gain parameters (G P 1 , G P 2 , G P 3 and G P 4 ) to meet the APD and RPD based mathematical model. • Performance assessment of proposed system compared with the relevant previous works. This paper presents a new control strategy for tracking the reactive power demand (RPD) from the turbo generator system at the infinite bus, while maintaining constant active power demand (APD). The positive and negative disturbances of RPD are applied by ±10 % of 0.6 pu while APD is applied at 0.8 pu. The tracking of RPD and APD depends on a new mathematical model to control simultaneously both of the governor valve position and the exciter field voltage by four gain parameters (G P 1 , G P 2 , G P 3 and G P 4 ). These parameters will be found applying the bus admittance matrix model. To improve the system dynamics, 10th parameters of two fractional order PID (FOPID) controllers are optimized (OFOPID) by particle swarm optimization (PSO). The simulations revealed that the settling time, rise time, overshoot and undershoot could be reduced to a smaller value than the recently published researches. The voltage level has been regulated at the generator terminals by ±1.3 % while its settling time is 0.15 s and preserving the generator frequency at the nominal value with an overshoot of ±0.0046 Hz. So, the proposed method is significantly improved and highly effective as compared to previous related works. [ABSTRACT FROM AUTHOR]

• To propose sooty-tern optimization (STO) for designing PSS parameters of multi-machine power system. • The performance of STO is evaluated using eigenvalue analysis, simulation results and performance indices. • The results are matched with other powerful optimization techniques. • Robustness is verified on hidden operating cases also under critical disturbances. This paper reports a comparative study among four bio-inspired meta-heuristic techniques i.e. Sooty-Tern Optimization (STO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to tune the robust Power System Stabilizer (PSS) parameters of the multi-machine power system. These approaches are successfully tested on two bench-mark systems: sixteen-machine, sixty-eight-bus New England Extended Power Grid (NEEPG) and three-machine, nine-bus Western System Coordinating Council (WSCC). The efficacy of planned PSS via STO and GWO is validated by extensive non-linear simulations, eigenvalue analysis, and performance indices for numerous operating conditions under decisive perturbations, and outcomes are matched with those of GA and PSO techniques. In addition, the robustness is also tested for these algorithms. The results indicate that the PSS design using STO and GWO improves the small-signal stability and damping performance for mitigating inter-area and local area modes of low-frequency oscillations compared to GA and PSO. [ABSTRACT FROM AUTHOR]

• A virtual damping stabilizer for BESS inertia capability enhancement is proposed. • Frequency nadir, zenith, and oscillations are mitigated, and damping is improved. • BESS size is reduced, and lifetime is maintained by smoothing the power profile. • Verified via real-time experiment, sensitivity analysis, and small signal stability. • The balance between frequency stability and BESS initial size and cost is achieved. The Inverter-based generator lacks natural inertia, which leads to instability problems in Microgrid frequency. Virtual inertia is a promising control concept that rapidly injects/absorbs active power to/from the power system to ensure the frequency stability of low-inertia systems. This paper proposes an innovative virtual damping stabilizer (VDS) integrated into battery energy storage system (BESS) based virtual inertia and damping control loops. The proposed VDS adds supplementary damping and stabilizing capability to the BESS by properly correcting and controlling its inertia pumped/drawn power. The VDS aims to enhance frequency stability, decrease the necessary BESS large capacity, and lower the initial expense. Furthermore, the proposed VDS helps operate the BESS efficiently by smoothing the output power profile during transient disturbances, consequently elongating the lifetime. The Particle Swarm Optimization (PSO) method is utilized to get the optimal parameters of the proposed VDS. A frequency control model of an isolated microgrid with the proposed controller is presented, considering various operating conditions. Time-domain simulations and experimental validation using OPAL-RT verify the efficacy of the proposed VDS scheme. Sensitivity and eigenvalues analyses assess the VDS's dynamic stability enhancement and robustness against inherent uncertainties in Microgrid parameters. The results demonstrate a superior frequency response and a reduction in the BESS size compared to PSO and model predictive controller (MPC) based virtual inertia and damping strategies. [ABSTRACT FROM AUTHOR]

• Growing Importance of Power Quality (PQ) Issues. • Simultaneous Allocation of Capacitors and Active Power Filters. • Synergistic Approach for Harmonic Pollution Reduction. • Application of a Modified Particle Swarm Optimization Algorithm. • Enhanced Power Quality and Harmonic Mitigation. • Optimization of Network Losses and Voltage Profile. The importance of Power Quality (PQ) issues in modern power systems has been growing in the last years. Capacitors are employed for optimizing network losses and increasing the voltage profile. However, in harmonic polluted networks, the placement of capacitors at their economic optimal locations may not be feasible due to harmonic constraints. Under such conditions, harmonic filters can mitigate harmonic pollution and enable capacitors to be optimally placed. This paper presents a novel approach for simultaneously optimizing the allocation of Active Power Filters (APFs) and capacitors, to improve the harmonic condition, network losses, and voltage profile of distribution networks. The method models APFs as harmonic sources in the Harmonic Power Flow (HPF) procedure, while capacitors are modelled as their corresponding harmonic admittance. Furthermore, a modified Particle Swarm Optimization (PSO) algorithm is presented as the optimization tool. Three case studies were conducted on the IEEE 18-bus test system. In the first study, optimal capacitor placement was performed with no regard to harmonic constraints, reducing network losses by 326 kW. The harmonic limits were then considered and satisfied by optimal APF placement. The total cost resulted in $241,983. The second study considered harmonic limits in the procedure of optimal capacitor placement, improving voltage profiles and network losses, but exceeding some harmonic constraints, which were subsequently satisfied by APF placement. The solution yielded the total cost of $264,942. The third study introduced simultaneous allocation of capacitors and APFs, proving to be the most cost-effective strategy ($225,417 total cost), promising enhanced network performance and efficiency, and adding valuable insights for future power system optimization. [ABSTRACT FROM AUTHOR]

Multi-region combined heat and power economic dispatch (MRCHPED) is a brand new optimization issue in power system operation. The MRCHPED uses the multiple regional structure to integrate power-only units, CHP units and heat-only units to achieve optimal economic dispatch while ensuring the supply of heat and power within each region. In this paper, an educational achievement guided group teaching optimization algorithm (EAGTOA) is developed and applied to solve the MRCHPED problem. In EAGTOA, aiming to improve the search efficiency, two historical archives are introduced to collect the valuable information of educational achievements from students and teachers, and are used in the update process of the EAGTOA. The EAGTOA algorithm is applied to solve three MRCHPED problems, including a small-scale MRCHPED with 2 regions and 9 units, a medium-scale MRCHPED with 3 regions and 17 units, and a large-scale MRCHPED with 3 regions and 96 units. The performance of EAGTOA is compared with several classical and recently-developed optimization algorithms. The simulation results show that the proposed EAGTOA can achieve better results in terms of solution accuracy and convergence speed for the MRCHPED problems. • Mathematical model of multi-region CHP economic dispatch (MRCHPED) is provided. • New group teaching optimization algorithm called EAGTOA is developed. • The EAGTOA is utilized to solve three MRCHPED with 9,17 and 96 units. • Simulation results show that EAGTOA achieves excellent results for the MRCHPED. [ABSTRACT FROM AUTHOR]

• Proposing a hierarchical battery sizing approach to improve dynamic performance of MG. • Optimal sizing of BESS based on stochastic models to minimize frequency fluctuations. • Secondary BESS optimal sizing and placement to improve static and dynamic stability. • Proposing a dynamic objective function based on system eigenvalues. Integration of energy storage systems (ESSs) to microgrids (MGs) due to the growing penetration of intermittent energy resources is a great motivation to develop studies in the context of optimal sizing and placement of these systems. In this paper, a multi-objective bi-level optimization problem is addressed based on the stochastic nature and dynamic model of power system elements. As a preliminary step, the optimal size and capacity of the battery energy storage system (BESS) are determined to minimize frequency fluctuations. This level has been performed based on three approaches considering the daily intra-hour stochastic data. In the secondary level, the dynamic behaviour of all MG elements has been considered and correspondingly a dynamic objective function based on system eigenvalues and voltage sensitivity index (VSI) has been developed. According to the proposed objective function, the secondary step for sizing and placement of the battery units has been performed by adaptive particularly tuneable fuzzy particle swarm optimization (APT-FPSO) algorithm to reduce power losses, improve the voltage profile/ stability, and mitigate low-frequency fluctuations. Finally, the effectiveness of the proposed approach is evaluated using time-domain simulations, voltage sensitivity studies, and eigenvalue analysis. [ABSTRACT FROM AUTHOR]

In this paper, an optimal power dispatch formulation is proposed in the operating horizon using real time measurements to enhance the performance of online energy scheduling in microgrid considering renewable energy, grid power trade and demand response. A stochastic weight tradeoff particle swarm optimization (SWT-PSO) based backward–forward sweep (BFS) optimal power flow (OPF) method is used by the microgrid central controller (MGCC) to obtain the online optimal schedules of distributed energy resources (DERs). The effectiveness of the suggested approach is investigated on the residential feeder of the CIGRE LV benchmark microgrid in terms of reduction in fuel & emission costs and voltage deviation. In addition, settling time in overall implementation is drastically reduced. Also, the paper analytically justifies the use of (1) renewable energy sources, grid trade and prioritized load curtailment, (2) SWT-PSO over priority listing method and (3) online OPF over online economic dispatch (ED). [ABSTRACT FROM AUTHOR]

In this paper a novel approach regarding the optimal penetration of Distributed Generation (DG) in Distribution Networks (DNs) towards loss minimization is proposed. More specific, a Local Particle Swarm Optimization (PSO) variant algorithm is developed in order to define the optimal active and reactive power generation and/or consumption requirements for the optimal number and location of nodes that yield loss minimization. Thus, the proposed approach provides the optimal number, siting and sizing of DGs altogether. In addition, based on the optimal power requirements of the resulted nodes, a combination of potential DG types to be installed is recommended. The proposed objective function in this paper is also innovative since it embeds the constraint of reverse power flow to the slack bus by the formation of a new penalty term. The proposed methodology is applied to 30 and 33 bus systems. The results indicate the optimal number, locations, and capacity of DG units, which were calculated simultaneously. Finally, the impact of the predefined amount of permissible reverse power flow to the optimal solution is also examined through two scenarios: the first considers zero reverse power flow and the second unlimited reverse power flow. [ABSTRACT FROM AUTHOR]

Distribution network expansion planning (DNEP) is one of the most important tools to deal with the demand growth in a system. DNEP is usually carried out through reinforcement or installation of new components. In this paper, a new and combined methodology is used to consider several practical aspects in DNEP such as uncertainty, distributed generation (DG), load growth, electricity market and multi stage dynamic expansion are included in the planning. So that DNEP is addressed in the presence of distributed generation (DG), considering load and price uncertainties under electricity market environment. The proposed planning aims at minimizing investment and operational costs simultaneously. Since DNEP in coordination with DG planning leads to reduce planning cost; therefore, the coordinated DNEP and DG planning are presented in this paper. The proposed planning is implemented by the particle swarm optimization (PSO) technique. Besides, the uncertainties are modeled as the probability distribution function (PDF) and Monte-Carlo simulation (MCS) is used to insert the uncertainties into the programming. The proposed planning is carried out based on the 9-bus as well as Kianpars–Ahvaz test systems (Kianpars–Ahvaz is a practical network in Ahvaz province, Iran). The simulation results demonstrate the ability and effectiveness of the proposed planning to deal with uncertainties under electricity market environment. [ABSTRACT FROM AUTHOR]

This paper considers a price-taker generation station producer that participates in a day-ahead market. The producer behaves as a price-taker participant in the day-ahead electricity market. In electricity market, the price-taker producer could develop bidding strategies to maximize own profits. While making optimal bidding strategy, the market price uncertainty needs to be considered as they have direct impact on the expected profit and bidding curves. In this paper, a hybrid approach based on information gap decision theory (IGDT) and modified particle swarm optimization (MPSO) is used to develop the optimal bidding strategy. Information gap decision theory is used to model the optimal bidding strategy problem. It assesses the robustness/opportunity of optimal bidding strategy in the face of the market price uncertainty while price-taker producer considers whether a decision risk-averse or risk-taking. The optimization problems to delivering IGDT approach are solved using MPSO. It is shown that risk-averse or risk-taking decisions might affect the expected profit and bidding curve to day-ahead electricity market. The IGDT–MPSO method is illustrated through a case study and compared to IGDT–MINLP method. [ABSTRACT FROM AUTHOR]

This paper presents modified particle swarm optimization to solve economic dispatch problems with non-smooth/non-convex cost functions. Particle swarm optimization performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes Gaussian random variables in velocity term which improves search efficiency and guarantees a high probability of obtaining the global optimum without considerably worsening the speed of convergence and the simplicity of the structure of particle swarm optimization. The efficacy of the proposed method has been demonstrated on four test problems and four different non-convex economic dispatch problems with valve-point effects, prohibited operating zones with transmission losses, multiple fuels with valve point effects and the large-scale Korean power system with valve-point effects and prohibited operating zones. The results of the proposed approach are compared with those obtained by other evolutionary methods reported in the literature. It is found that the proposed modified particle swarm optimization based approach is able to provide better solution. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of reactive power control of distributed generation (DG) units in the medium voltage (MV) distribution systems to maintain the system voltages within the predefined limits. An efficient approach for the load flow calculation is used here which is based on the topological structure of the network. It has been formulated for the radial distribution systems. A direct voltage sensitivity analysis method is developed in this paper which is also based on the topological structure of the network and independent of the network operating points. Thus, the sensitivity matrix is calculated once with the load flow program and it is used in all the system working conditions. The problem of DGs reactive power control is formulated as an optimization problem which uses the sensitivity analysis for linearizing the system around its operating points. The objective of the optimization problem is to return the system voltages inside the permitted range by using the reactive power of DGs in an optimal way. The optimal solutions are obtained by implementing particle swarm optimization (PSO) algorithm. Then, the results are verified by running a load flow considering new values of DGs reactive power. The procedure is repeated as long as a voltage violation is observed. Simulation results reveal that the proposed algorithm is capable of keeping the system voltages within the permitted limits. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid computational framework based on Sequential Quadratic Programming (SQP) and Particle Swarm Optimization (PSO) to address the Combined Unit Commitment and Emission (CUCE) problem. By considering a model which includes both thermal generators and wind farms, the proposed hybrid computational framework can minimize the scheduling cost and greenhouse gases emission cost. The viability of the proposed hybrid technique is demonstrated using a set of numerical case studies. Moreover, comparisons are performed with other optimization algorithms. The simulation results show that our hybrid method is better in terms of the speed and accuracy. The main contribution of this paper is the development of a emission unit commitment model integrating with wind energy and combining the SQP and PSO methods to achieve faster and better performance optimization [ABSTRACT FROM AUTHOR]

In this paper, an efficient approach of combining Takagi–Sugeno–Kang fuzzy system with wavelet based neural network is presented. The model replaces the constant or a linear function of inputs in conclusion part of traditional TSK fuzzy model with wavelet neural network (WNN), thus each rule uses fuzzy set to separate the input space into subspaces spanned by different wavelet functions. For finding the optimal values for parameters of our proposed fuzzy wavelet neural network (proposed-FWNN), a hybrid learning algorithm integrating an improved particle swarm optimization (PSO) and gradient descent algorithm is employed. The two-layer inline-PSO process is proposed in this paper, whose adjustment scheme is more fitting the consequent pattern learning based gradient descent optimization and will locate a good region in the search space. Simulation examples are given to test the efficiency of proposed-FWNN model for identification of the dynamic plants. It is seen that our modeling and optimization approach results in a better performance. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) problem is an optimization tool through which secure and economic operating conditions of power system is obtained. In this paper, particle swarm optimization (PSO) with an aging leader and challengers (ALC-PSO) is applied for the solution of the OPF problem of power system equipped with flexible ac transmission systems (FACTS). The two FACTS devices, namely, thyristor controlled series capacitor and thyristor controlled phase shifter are considered for this study. This study is implemented on modified IEEE 30-bus and IEEE 57-bus test power systems with four different objectives. The results presented in this paper demonstrate the potential of ALC-PSO algorithm and show its effectiveness for solving the OPF problem with FACTS devices over the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature. [ABSTRACT FROM AUTHOR]

It is often necessary to investigate the output power against load demand in a system having distributed generation (DG) resources connected to the existing conventional power system. In this paper, the load frequency control (LFC) problem is presented using different optimization algorithms for two types of power system configurations: (i) hybrid configuration of thermal power system (TPS) integrated with DG, comprising wind turbine generators (WTGs), diesel engine generators (DEGs), fuel cells (FCs), aqua-electrolyzer (AE) and battery energy storage system (BESS); (ii) two area interconnected power system with DG connected in area-1. The inclusion of wind energy system in DG, having high variability in its output power, results into a challenging task for the realization of an effective controller design. This difficulty is further enhanced with random variation of load demand. The control scheme proposed in this paper is based on linear matrix inequalities (LMI) with its parameters tuned by particle swarm optimization (PSO), as a new contribution to earlier studies. The robustness of this controller is thoroughly demonstrated in the above hybrid power systems with different conditions of load disturbances, wind power and parameter variations. [ABSTRACT FROM AUTHOR]

• A systematic planning method to allocate dynamic VAR sources is proposed. • The concept of voltage control area is proposed to partition a bulk power system. • A zone-based voltage supporting index is defined in the site selection process. • A zone-based distributed optimization framework is put forward. In this paper, a practical planning method to allocate dynamic VAR sources (DVSs) using the concept of voltage control areas (VCAs) is proposed to enhance short-term voltage stability of power systems. Firstly, based on the defined transient distance index, a spectral clustering method is employed to partition a system into VCAs. Then, candidate DVS sites in each zone are determined by the proposed voltage supporting index. In each VCA, the capacity of candidate sites is preliminarily estimated by use of the dichotomy method to decide the most effective control bus and the initial capacity guess, based on which further system-wide optimization is implemented using particle swarm optimization (PSO) to obtain the final allocation scheme. Case study on the New England 39-bus and 68-bus test systems along with the comparisons with the existing methods confirm the effectiveness and advantages of the proposed methodology. [ABSTRACT FROM AUTHOR]

• A hybrid prediction method is designed to improve the prediction accuracy of data. • An energy management strategy based on demand-side response is proposed. • A neighbor prediction method is designed to solve data interruption problem. • An improved particle swarm optimization algorithm is proposed to solve the optimization model. Data interruption may cause the centralized energy management system of a microgrid (MG) to collapse. To solve this problem, a hybrid prediction-based energy management strategy is proposed in this paper to predict interrupted data for the centralized dispatching process of an MG. This hybrid prediction method is designed following a combination of model predictive control and extreme learning machine techniques. Based on the predicted data of distributed energy resources and three types of loads from demand-side response, an optimization model is formulated to minimize the operational cost. If some predicted data are interrupted during transmission, then a prospect vulnerability assessment method is applied to select a neighboring device to predict the interrupted data. In the end, an improved particle swarm optimization algorithm is proposed with the help of genetic algorithms to accelerate convergence to global optimal solutions for the proposed MG energy management problem. The effectiveness of the proposed models and solution methods is also verified by a case study. [ABSTRACT FROM AUTHOR]

In this paper, a projection-based distributed algorithm is proposed to solve the economic dispatch problem of power system with communication delays under switching topologies. The algorithm is suitable for economic dispatch problem with strongly convex cost function, and ensures that the output power of each generator is always kept within its output limit. In addition, the topology is only required to be jointly connected. Firstly, the relationship between the equilibrium point and the optimal solution of the algorithm is analyzed, and then a common Lyapunov function is introduced for a set of switching topologies. According to the Lyapunov stability theory and Cauchy convergence criterion, LMIs-based sufficient conditions for the convergence of the algorithm are obtained. Finally, the effectiveness of the algorithm under different cases are verified by MATLAB simulation results. • The economic dispatch under switching topology with communication delay is studied. • A projection-based distributed algorithm with is proposed to solve the problem. • The communication topology is only required to be jointly connected. • The delay-dependent LMI conditions for the convergence of the algorithm are obtained. • The effects of parameters on the delay bound and convergence rate are analyzed. [ABSTRACT FROM AUTHOR]

• A method of period partitioning based on the silhouette coefficient is proposed. • A TOU price optimization model considering benefits and reliability is established. • A multi-objective simulated annealing particle swarm optimization algorithm is developed. • A bi-level TOU price optimization model for high reliability demand system is presented. The reliability of power system will vary with different demand response strategies, and it is significant to make a decision on demand response for satisfying differential reliability demand of power system. First, a peak-valley period partition model with an objective of maximizing silhouette coefficient was established based on the fuzzy clustering and step-wise iteration technology. Second, this paper proposed a multi-objective time of use electricity price optimization model considering the interests of both supply and demand sides, as well as reliability demand, and a multi-objective simulated annealing particle swarm optimization algorithm was developed to obtain solution. Third, the Pareto front curve of the dual-objective function was fitted by using a third-order Hermite interpolation algorithm, and the time of use prices corresponding to the reliability demand was obtained by using the back propagation neural network algorithm. Finally, the proposed model and algorithm were verified by the RBTS system, and the results indicate good rationality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Taking the minimization of annualized cost as the objective function, as well as introducing environmental penalty cost, a bi-level optimal configuration model of integrated energy station is established to realize the collaborative optimization in the planning and operation stages. • Two different programming methods, the column-and-constraint generation (C&CG) and particle swarm optimization coordination with mixed-linear integer programming (PSO-MILP), are proposed to solve the model. The comparison and analysis of the programming results are illustrated in solving the two-stage robust planning problem. • Three operation modes of self-adaption, FEL and FTL are comprehensively considered to optimize the configuration of integrated energy station. On this basis, the sensitivity of heat-to-electric ratio (HPR) of CHP units and electric storage to the planning results are analyzed. • The planning results of integrated energy station are evaluated based on system dynamics (SD), which has certain guidance for the actual project. Operation modes of combined heat and power (CHP) units are closely related to the economic benefits of energy application in integrated energy station. In this paper, a novel bi-level optimal configuration method is proposed considering different operation modes of CHP, and a life-cycle assessment system on economic and environmental benefits is constructed using system dynamics (SD) method. Firstly, the basic structure and operation issues of a typical integrated energy station employing CHP are addressed. Secondly, the bi-level collaborative optimization model of the integrated energy station is constructed to minimize the annualized cost considering three operating modes, namely following the thermal load (FEL), following the electric load (FTL) and switching adaptively between FEL and FTL. Then, the model is solved by the column-and-constraint generation and particle swarm optimization coordination with mixed-linear integer programming respectively. Subsequently, the sensitivity of CHP units heat-to-electric ratio and electric storage to the planning results are analyzed in detail. Finally, case studies are carried out based on an actual integrated energy station. The optimization results show that, compared with FEL and FTL operating modes of the CHP units, the annualized cost of the system under adaptive operation mode is reduced by 26.5% and 42.8%, respectively. Besides, the assessment of the optimized system indicates that the total profit and carbon dioxide emission reduction of the station during its life-cycle are expected to reach 28.53 million CNY and 25.33 ten thousand tons respectively, which provides a reference for decision-making and a basis for flexible operation. [ABSTRACT FROM AUTHOR]

• Improved version of Binary Particle Swarm Optimization (BPSO) to solve the security-constrained transmission network expansion planning (SCTNEP) problem using the DC network model. • Parallel implementation of the improved BPSO reduces execution times. • Transmission losses are included using a quadratic function and piecewise linear approximation approach. • Simulation results are compared to those obtained from classic BPSO to verify the efficiency of the improved version of the BPSO. • The proposal improves the BPSO by using superior population configurations to solve the SCTNEP. - This paper proposes an improved version of Binary Particle Swarm Optimization (BPSO) to solve the security-constrained transmission network expansion planning (TNEP) problem using the DC network model. Some modifications, such as superior population configurations, are made to improve the efficiency of the BPSO by adapting it to the needs of the TNEP to obtain better results. A parallel implementation of the improved BPSO reduces execution times. Transmission losses are included using a quadratic function and piecewise linear approximation approach. Considering transmission losses in the long-term planning horizon can avoid underinvestment in network capacity and result in a different network expansion plan. The proposed approach is validated with numerical simulations of the classic Garver system, and its performance is tested on the IEEE 24-bus and 118-bus systems for a ten-year planning horizon. The simulation results are compared to those obtained from classic BPSO to verify the efficiency of the improved version of the BPSO. [ABSTRACT FROM AUTHOR]

The large-scale grid integration of renewable energy source via a voltage source inverter (VSI)-based DC system results in a continuous reduction in system kinetic inertia, deteriorating grid frequency stability. To mitigate this problem, this paper proposes a generic security-constrained inertia emulation (SIE) scheme, which integrates an appropriately-sized supercapacitor (SC) bank with a VSI-based DC system via a bidirectional DC/DC converter. Inertial response provision is enabled by an SIE control loop developed for the DC/DC converter controller to adjust the SC voltage along with the grid frequency. It is further underpinned by adding an inertia adjustment algorithm to cope with potential security risks of VSI overcurrent and DC system overvoltage. The SIE scheme features a plug-and-play advantage without modifying the original system. The SIE controller parameter optimization is also carried out through the small-signal stability analysis. The effectiveness of the SIE scheme is evaluated using a modified IEEE 39-bus test system in the presence of generator outage and grid fault. • RES inertia emulation is enabled by varying a supercapacitor voltage with frequency. • Supercapacitance for inertia emulation is reduced by a bidirectional DC/DC converter. • VSI overcurrent during inertia emulation is avoided by adaptively adjusting inertia. • The scheme features plug-and-play advantage without modifying original RES system. [ABSTRACT FROM AUTHOR]

• A new combination forecasting model is proposed to improve the ultra-short-term forecast accuracy of traction load. • The design concept of the combined model proved to be reasonable. • This model can get the changing trend of the predicted load in different time scales. The ultra-short-term prediction of traction load is a key aspect of electric power control in electrified railroads. In order to provide more accurate traction load prediction data for the control process, this paper designs a combined prediction method combining Discrete Wavelet Transform(DWT), Temporal Convolutional Network (TCN) and Support Vector Regression based on Particle Swarm Optimization(PSO_SVR) for the characteristics of traction load with strong random fluctuation, large jump amplitude and frequent no-load. This method will use DWT model to decompose the traction load with strong random fluctuation and difficult to predict into sub-series with single fluctuation frequency and easy to predict; then, according to the frequency difference of different series, TCN model is selected to predict medium and low frequency series. The SVR model is selected to predict the high frequency sequences. Finally, the prediction results of each sub-sequence are summed to obtain the final prediction. The combined prediction model designed in this paper is used to predict the traction load of traction power supply stations. The experimental results show that the prediction method proposed in this paper has higher prediction accuracy than the existing prediction methods in the current prediction field. [ABSTRACT FROM AUTHOR]

• Combined with the error reconstruction rate, an adaptive VMD technology is developed and used for data preprocessing. • Two mainstream deep learning methods (LSTM, DBN) are used as sub-series predictors, and the classic PSO algorithm is utilized to determine the number of neurons in the DBN network. • A novel hybrid prediction model based on the PSO-DBN nonlinear combination mechanism is proposed. Wind power forecasting plays a vital role in enhancing the efficiency of power grid operation and increasing the competitiveness of power market. In this paper, a novel hybrid forecasting model is developed by using the decomposition strategy, nonlinear weighted combination, and two deep learning models to overcome the drawbacks of the linear weighted combination and further enhance wind power forecasting accuracy and stability. Firstly, the variational mode decomposition (VMD) technique is employed to decompose the original wind power series to extract local features. Then, the long short-term memory neural networks (LSTM) and deep belief networks based on particle swarm optimization (PSO-DBN) are utilized to construct sub-series prediction models. Finally, the multiple sub-series forecasting models are integrated by a nonlinear weighted combination method based on PSO-DBN to construct a hybrid model for short-term wind power forecasting. To verify the performance of the developed forecasting model, wind speed data of 10 min from a wind farm in Shaanxi Dingbian, China are selected as case studies. The results show that the proposed method in this paper is more effective than other existing methods. [ABSTRACT FROM AUTHOR]

• The electrical equipment was divided into time-shifting and non-time-shifting power. • The starting time of time-shifting load was selected as the variable in PSO algorithm. • The battery capacity was selected to meet the demand of the power peak at night. With the continuous expansion of the development scale, photovoltaic integrated greenhouses are becoming an important type of smart grid prosumer. The demand side management (DSM) of energy is becoming increasingly important in terms of satisfying the energy consumption requirements of production and increasing the economic benefits of the same piece of land. By analyzing greenhouse production activities and the operation of electrical equipment, a particle swarm optimization (PSO) scheme is proposed in this paper, which selects the starting time of time-shifting powers as optimization factors and maximizes the income of power generation as the objective function. In addition, this paper analyzes the problem of energy consumption scheduling in greenhouse production. The results show that this scheme helps to optimally schedule the greenhouse energy consumption and achieves maximum profit by ensuring the reasonable operation of the system. The optimization scheme is beneficial to guide the production activities in greenhouses and the construction of photovoltaic integrated greenhouse systems. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a new, an Improved Particle Swarm Optimization (IPSO) is proposed for optimizing the power system performance. Recently, the Particle Swarm Optimization (PSO) technique has been applied to solve power engineering optimization problems giving better results than classical methods. Due to slow convergence and local minima, particle swarm optimization fails to give global results. To overcome these drawbacks, in this paper presents the application of improved particle swarm optimization for optimal sizing and allocation of a Static Compensator (STATCOM) and minimize the voltage deviations at all the buses in a power system. This algorithm finds an optimal settings for present infrastructure as well as optimal locations, sizes and control settings for Static Compensator (STATCOM) units. A 30 bus system is used as an example to illustrate the technique. Results show that the Improved Particle Swarm Optimization (IPSO) is able to find the best solution with statistical significance and a high degree of convergence. The simulation results are presented to show a significant improvement of the power system reliability and feasibility and potential of this new approach. [Copyright &y& Elsevier]

Abstract: This paper presents an optimal power flow controller for a utility connected microgrid based on a real-time self-tuning method. The purpose is to control the flow of the active and reactive power between the main grid and the microgrid composed of Distributed Generation (DG) units. Sharing power at the desired ratio by the DG units is the main performance parameter which is considered during the load change. This paper also shows the response of the controller in situations, where the load is either higher or greatly lower than the rated power of the DG unit. In this work, the controller scheme is composed of an inner current control loop and an outer power control loop based on a synchronous reference frame and the conventional PI regulators. The power controller is designed for active–reactive power (PQ) control strategy. Particle Swarm Optimization (PSO) is an intelligent searching algorithm that is applied for real-time self-tuning of the power control parameters. In this paper, the proposed strategy is that the required load power is shared equally between the microgrid and the utility based on the PSO algorithm during the load change. The utility supplies the difference power when the load is more than the power generated by the microgrid, while it injects the extra power to the grid when the load is less than the power generated by the microgrid. The results show that the proposed controller offers an excellent response and proves the validity of the proposed strategy. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to the solution of optimal power generation for economic dispatch (ED) using improved particle swarm optimization (IPSO) technique. In this paper an improved PSO technique is suggested that deals with equality and inequality constraints in ED problems. A constraint treatment mechanism called dynamic search space squeezing strategy is devised to accelerate the optimization process and simultaneously the dynamic process inherent in the conventional PSO algorithm is preserved. The application and statistical performance of various intelligent algorithms such as differential evolution (DE), particle swarm optimization (PSO) and improved particle swarm optimization (IPSO) are considered on economic dispatch problems with non-smooth cost functions considering valve point effects and multiple fuel options. To determine the efficiency and effectiveness of various intelligent algorithms, three experiments are conducted considering only multiple fuel options, considering both valve-point and multiple fuel options and also taking into account the valve point loadings, ramp rate limits and prohibited operating zones. The simulation results reveal that the proposed IPSO has provided the better solution with a very high probability to demonstrate its robustness over other intelligent techniques such as DE, PSO and improved genetic algorithm with multiplier updating (IGA_MU), ant colony optimization (ACO), artificial bee colony algorithm (ABC), hybrid swarm intelligent based harmony search algorithm (HHS) and fuzzy adaptive chaotic ant swarm optimization (FCASO). The proposed IPSO ensures convergence within least execution time and provides quality solutions as compared to earlier reported best results. [Copyright &y& Elsevier]

Abstract: In this paper, a new method is proposed to optimize the placement and size of multiple Active Power Line Conditioners (APLCs). The objective is to minimize the investment cost, while the voltage total harmonic distortion and the individual harmonic distortion as constraints are maintained within the standard level. Since the APLC size is a discrete value, the objective function has a number of local minima. To deal appropriately with this, a Modified Discrete Particle Swarm Optimization (MDPSO) has been proposed. In this optimization method, DPSO has been developed by Genetic Algorithm (GA) operators in order to increase the diversity of the optimizing variables and decrease the risk of trapping in local minima. To evaluate the proposed method, two cases are studied. In case 1, the robustness and accuracy of the MDPSO are compared with other methods, DPSO, GA, Simulated Annealing (SA), and Discrete Nonlinear Programming (DNLP). The IEEE 14-bus distribution system is modified and used in this case. In case 2, a comparison is performed between the results obtained when the APLC cost function is assumed to be proportional with its rating, as done in almost all previously published papers, and when a more realistic APLC cost is considered. For this case, the IEEE 18-bus distribution system is employed as the test system. It is illustrated that the proposed method is more accurate and robust than other methods. This is also highlighted that the lowest cost planning is achieved when the realistic APLC cost function is used. [Copyright &y& Elsevier]

Abstract: This paper aims to select the optimal location and setting parameters of SVC (Static Var Compensator) and TCSC (Thyristor Controlled Series Compensator) controllers using PSO (Particle Swarm Optimization) to mitigate small signal oscillations in a multimachine power system. Though Power System Stabilizers (PSSs) associated with generators are mandatory requirements for damping of oscillations in the power system, its performance still gets affected by changes in network configurations, load variations, etc. Hence installations of FACTS devices have been suggested in this paper to achieve appreciable damping of system oscillations. However the performance of FACTS devices highly depends upon its parameters and suitable location in the power network. In this paper the PSO based technique is used to investigate this problem in order to improve the small signal stability. An attempt has also been made to compare the performance of the TCSC controller with SVC in mitigating the small signal stability problem. To show the validity of the proposed techniques, simulations are carried out in a multimachine system for two common contingencies, e.g., load increase and transmission line outage. The results of small signal stability analysis have been represented employing eigenvalue as well as time domain response. It has been observed that the TCSC controller is more effective than SVC even during higher loading in mitigating the small signal stability problem. [Copyright &y& Elsevier]

Abstract: This paper presents an optimal power control strategy for an autonomous microgrid operation based on a real-time self-tuning method. The purpose of this work is to improve the quality of power supply of the microgrid where some Distributed Generation (DG) units are connected to the grid. Voltage and frequency regulation, and power sharing are the main performance parameters which are considered in this work, particularly during the transition from grid-connected to islanding operation mode and also during load change. In this work, two typical DG units are connected in parallel to configure the microgrid. The controller scheme is composed of an inner current control loop and an outer power control loop based on a synchronous reference frame and the conventional PI regulators. The power controller employs two typical strategies: active–reactive power (PQ) control strategy and voltage–frequency (Vf) control strategy. Particle Swarm Optimization (PSO) is an intelligent searching algorithm that is applied for real-time self-tuning of the power control parameters. The proposed strategy in this paper is that both DG units adopt the Vf control mode once the microgrid is islanded in order to regulate the microgrid voltage and frequency, whereas during the load change, only the second DG unit invokes the PQ control mode to ensure maximum power exportation. The results show that the proposed controller offers an excellent response to satisfy the power quality requirements and proves the validity of the proposed strategy. [Copyright &y& Elsevier]

Abstract: In this paper a new algorithm is proposed to determine the optimal sizes of fixed and switched capacitors together with their optimal locations in a radial distribution system so that net savings are maximized and improvement in the voltage profile is achieved. The algorithm searches for all possible locations in the system for a particular size of capacitor and places the capacitor at the bus which gives maximum reduction in active power loss. The optimal sizes are chosen to be standard sizes that are available in the market i.e., discrete sizes of capacitors are considered. The algorithm is tested on standard 69 bus system, 85 bus system and practical 22 bus systems. And the results are compared with results of other methods like particle swarm optimization and genetic algorithm, as available in the literature. The loss reduction obtained in this paper for the two standard test systems is highest compared to the other technique as reported in the literature. Cost analysis is also presented with and without capacitor placement on the three bus systems considering three different loading levels. [Copyright &y& Elsevier]