An efficient power plant model of electric vehicles for unit commitment of large scale wind farms

AbstractAn efficient power plant model of electric vehicles (E-EPP) considering the travelling comfort levels of EV users is developed to investigate the contribution of EVs on the unit commitment (UC) of large scale wind farms. Firstly, a generic EV battery model (GEBM) is established considering the uncertainties of battery parameters. Then, a Monte Carlo Simulation (MCS) method is implemented within the E-EPP to obtain the available response capacity of EV charging load over time. And a UC strategy using the E-EPP based on power flow tracing is developed. Finally, a modified IEEE 118-bus system integrated with wind farms is used to verify the effectiveness of the E-EPP for the UC of large scale wind farms

Improved Multiobjective Harmony Search Algorithm with Application to Placement and Sizing of Distributed Generation

To solve the comprehensive multiobjective optimization problem, this study proposes an improved metaheuristic searching algorithm with combination of harmony search and the fast nondominated sorting approach. This is a kind of the novel intelligent optimization algorithm for multiobjective harmony search (MOHS). The detailed description and the algorithm formulating are discussed. Taking the optimal placement and sizing issue of distributed generation (DG) in distributed power system as one example, the solving procedure of the proposed method is given. Simulation result on modified IEEE 33-bus test system and comparison with NSGA-II algorithm has proved that the proposed MOHS can get promising results for engineering application

A Cooperative Control Scheme for AC/DC Hybrid Autonomous Microgrids

The AC/DC hybrid microgrid (MG) has been widely promoted due to its high flexibility. The capability to operate in islanding mode is an appealing advantage of the MG, and also sets higher requirements for its control system. A droop control strategy is proposed on account of its distinguishing feature of automatic power sharing between distributed generations (DGs), but it introduces some drawbacks. Therefore, distributed cooperative secondary control is introduced as an improvement. In order to optimize the active power sharing in AC/DC hybrid microgrids, a number of cooperative control strategies have been proposed. However, most studies of AC/DC hybrid microgrids have mainly focused on the control of the bidirectional converter, ignoring the effects of secondary control within subnets, which may make a difference to the droop characteristic. This paper extends the cooperative control to AC/DC hybrid microgrids based on normalizing and synthesizing the droop equations, and proposes a global cooperative control scheme for AC/DC autonomous hybrid microgrids, realizing voltage restoration within AC and DC subnets as well as accurate global power sharing. Ultimately, the simulation results demonstrate that the proposed control scheme has a favorable performance in the test AC/DC hybrid system

A Fast Reactive Power Optimization in Distribution Network Based on Large Random Matrix Theory and Data Analysis

In this paper, a reactive power optimization method based on historical data is investigated to solve the dynamic reactive power optimization problem in distribution network. In order to reflect the variation of loads, network loads are represented in a form of random matrix. Load similarity (LS) is defined to measure the degree of similarity between the loads in different days and the calculation method of the load similarity of load random matrix (LRM) is presented. By calculating the load similarity between the forecasting random matrix and the random matrix of historical load, the historical reactive power optimization dispatching scheme that most matches the forecasting load can be found for reactive power control usage. The differences of daily load curves between working days and weekends in different seasons are considered in the proposed method. The proposed method is tested on a standard 14 nodes distribution network with three different types of load. The computational result demonstrates that the proposed method for reactive power optimization is fast, feasible and effective in distribution network

A Distribution Network State Estimation Method With Non-Gaussian Noise Based on Parallel Particle Filter

The particle filter (PF) algorithm is a powerful method for tackling non-Gaussian noise interference in distribution network state measurement. However, this algorithm suffers from slow solving speed and lengthy calculation time. To overcome this, a state estimation method based on parallel particle filter (PPF) is proposed, which leverages the independent computation features of each particle in the PF model to improve computational efficiency. This study utilizes the parallel architecture of Compute Unified Device Architecture (CUDA) and General Purpose Graphics Processing Units (GPGPU) to establish a one-to-one correspondence between particles and computing threads. An improved rejecting-resampling method is introduced to solve the problem of low execution efficiency caused by unmerged access to GPGPU memory. In addition, according to the relationship between the particle number and estimation accuracy of state variable of the PPF, the optimal particle number suitable for parallel computation is solved. Ultimately, the simulation results indicate that the proposed method can be used to effectively filter the non-Gaussian-colored noises from the collected data, which meets the requirements of the distribution network state estimation for the accuracy and real-time performance

Distributed photovoltaic short‐term power forecasting using hybrid competitive particle swarm optimization support vector machines based on spatial correlation analysis

Abstract In order to further improve the accuracy of distributed photovoltaic (DPV) power prediction, this paper proposes a support vector machine (SVM) model based on hybrid competitive particle swarm optimization (HCPSO) with consideration of spatial correlation (SC), for realizing short‐term PV power prediction tasks. Firstly, the spatial correlation analysis is conducted on the distributed PV stations. The k‐means clustering method based on morphological similarity distance improvement and mutual information function is used to select the best reference station and the best delay, which generates strongly correlated PV sequences. Then, a hybrid algorithm of particle swarm optimization (PSO) and sine cosine algorithm (SCA) in a competitive framework (HCPSO) is proposed, aiming to fuse the fast convergence capability of PSO algorithm with the global search capability of SCA algorithm, while enabling the algorithm to effectively handle high‐dimensional optimization problems based on a competitive mechanism. Finally, the HCPSO algorithm is combined with SVM algorithm, which expands the applicable scenarios of the SVM model and effectively improves the accuracy of PV short‐term prediction

Risk-based Security Assessment in Distribution Network with the Integration of Photovoltaic

With the development of distribution network, distributed generation such as wind and photovoltaic (PV) power will become increasingly prominent in the near future. PV is widely constructed because of advantages it has. However, the volatility and randomness of PV makes it more complex than traditional energy in the security assessment of distribution network. Based on risk theory, considering the randomness of PV, node low voltage risk index and line overload risk index are established in this paper. Also, K (N - 1 + 1) principle for distribution network which is developed from traditional (N-1) deterministic principle is applied to reflect the flexible structure of distribution network. IEEE three-feeder example system is utilized to investigate the influence of PV power on the security assessment of distribution network

Risk-based Security Assessment in Distribution Network with the Integration of Photovoltaic

With the development of distribution network, distributed generation such as wind and photovoltaic (PV) power will become increasingly prominent in the near future. PV is widely constructed because of advantages it has. However, the volatility and randomness of PV makes it more complex than traditional energy in the security assessment of distribution network. Based on risk theory, considering the randomness of PV, node low voltage risk index and line overload risk index are established in this paper. Also, K (N - 1 + 1) principle for distribution network which is developed from traditional (N-1) deterministic principle is applied to reflect the flexible structure of distribution network. IEEE three-feeder example system is utilized to investigate the influence of PV power on the security assessment of distribution network

Reliability Evaluation of Distribution System Considering Sequential Characteristics of Distributed Generation

In allusion to the randomness of output power of distributed generation (DG), a reliability evaluation model based on sequential Monte Carlo simulation (SMCS) for distribution system with DG is proposed. Operating states of the distribution system can be sampled by SMCS in chronological order thus the corresponding output power of DG can be generated. The proposed method has been tested on feeder F4 of IEEE-RBTS Bus 6. The results show that reliability evaluation of distribution system considering the uncertainty of output power of DG can be effectively implemented by SMCS