Xingying Chen

A hybrid particle swarm optimization approach for distribution network reconfiguration problem

A hybrid particle swarm optimization (PSO) approach is proposed here to solve the distribution network reconfiguration (DNR) problem. This approach is a combination of the binary PSO algorithm and the discrete PSO algorithm. In the problem-solving process, the distribution network is simplified through grouping the branches, and then each group of branches is represented by one dimensional coding. Based on the DNR necessary condition summarized in this paper, particles are evolving regularly, and this improves the efficiency of the search process. Each cycle of iteration within the optimization process is accomplished through two steps. The first step is using the roulette bet method to choose the optimal group of branches that should be opened, and this method is based on the sigmoid ( ) function value in the binary PSO algorithm. The second step adopts the discrete PSO algorithm proposed in this paper to further select the optimal branch that should be disconnected in the group selected in the first step. Simulation on a typical 69-node case is conducted, the result of which has indicated both a rapid convergence and a good robustness of this hybrid approach.

A novel approach for dynamic reconfiguration of the distribution network via multi-agent system

A novel systematic algorithm for the dynamic reconfiguration of the distribution network was proposed based on multi-agent system in this paper. In which, one day was divided into several time intervals and each was managed by a work agent. An improved hybrid particle swarm optimization algorithm was applied in each time interval to produce a solution set which included some enough good solutions. Then the coordination agent would select a solution from each work agents solution set, and coordinate them until the number constraint of switching operations was satisfied. The whole architecture of the multi-agent system was presented, and the function modules of the work agent and coordination agent were also discussed. Computer simulations were conducted to show the effectiveness of the proposed algorithm.

A hybrid algorithm of ordinal optimization and Tabu Search for reactive power optimization in distribution system

Reactive power optimization is an important issue in the distribution system, which can reduce the power loss and improve the voltage condition. In this paper, a hybrid algorithm named OOTS is proposed for reactive power optimization in the distribution system, which is based on Ordinal Optimization (OO) and Tabu Search (TS). The proposed algorithm can produce a good initial solution for TS because of the powerful global search ability of OO, and the convergence speed of TS can be promoted largely. The optimization process of the mathematical model consists of two steps, the first is to obtain a good initial solution for TS via OO, and the second is to find the global optimal solution using TS. Finally, a case study is conducted on a 28-bus distribution system. The optimization results are compared with OO and TS respectively, which show that the proposed hybrid algorithm has better convergence performance and stronger global optimization ability.

Three-phase short circuit analysis of DFIG-based wind generation and the crowbar biggest resistance setting

The short-circuit current contribution of wind turbines has not received much attention so far. This paper establishes the doubly fed induction generator mathematical model and carried on corresponding short circuit analysis. Regarding the sudden short circuit process as a superimpose of steady state operation and transient state operation provoked by reverse voltage, stator three-phase short circuit current expression is obtained by carrying on the Laplace transformation and the inverse transformation to the state space equation. And the expression is confirmed by simulation. In addition, the character and attenuation rule of stator short circuit current component are analyzed, and maximum resistance of short circuit protective device is set.

Robust control of variable-speed adjustable-pitch wind energy conversion system based on hamiltonian energy theory

Based on Hamiltonian energy theory, this paper proposes a pitch angle and excitation torque controller for the wind energy conversion systems (WECS), such that the closed-loop system can achieve its stability. Furthermore, in the presence of disturbances, the closed-loop system is finite-gain L2 stable by the Hamiltonian controller. The Hamiltonian energy approach provides us a physical insight and gives a new way to the controller design. The simulations are illustrated to show that the proposed method is effective.

Optimal bidding strategies with risks for LSEs in competitive electricity markets

In competitive electricity markets which are open for both generation and distribution sides, load serving entities (LSEs) are required to participate in the market competition with risks, since their profits usually reduced because of the shortage of power supply, and their purchasing prices were fluctuant with uncertainties due to the unforeseen bidding. This paper develops the optimal bidding strategies with risks for LSEs to participate in competitive electricity markets, in which stepwise bidding functions and pay-as-bid settlement protocols are utilized. A normal probability distribution function is used to describe the bidding behaviors of rivals, and the issue of constructing optimal bidding strategies for LSEs is formulated as a multi-objective stochastic optimization model, then Monte-Carlo approach is applied to get the corresponding solutions. The proposed bidding strategies have been tested on an electricity market with 3 power companies (Gencos) and 4 LSEs, and relevant analyses have been presented.

Short-term load forecasting based on time series reconstruction and support vector regression

The load curve presents certain randomness for reasons such as human social activities, the load of electric vehicle charging and discharging and so on, which covers up the regularity of load sequence. This paper proposes an approach which can restore the original feature of the loads. In this approach, firstly, the bad data is excluded; Secondly, the characteristics of the time series are extracted by the time series reconstruction; At last, the loads are forecasted by support vector regression and the forecasting sequences are taken as training samples of the next prediction. After repeat predictions, the final series are obtained then restored, and precise prediction result is obtained. This paper uses the load data of a city in Jiang Su. Compared with traditional support vector regression method, this approach ensures a higher prediction accuracy.

Combined approach for short-term wind power prediction: A case study of the east coast of China

Power systems with high wind power experience increased variability and uncertainty. Therefore accurate forecast technique is essential to cope with the uncertainty in day-ahead electricity market. Statistical forecast model is considered as a powerful technique for wind-power forecast, however, the forecast accuracy significantly drops as the forecast horizon grows. Focusing on wind speed persistence, a hybrid multi-step-ahead (HMS) method is proposed, which composed of direct multi-step-ahead prediction (DMS) approach and indirect multi-step-ahead prediction (IMS) approach in time-series forecasting. To validate the effectiveness of the proposed method, one-month period of real data, which is collected from five operating wind farms in the east coast of China, is used for test. The persistence model (PM) is used as a benchmark with the new forecast method. Test results show that the proposed method is accurate and effective.

The variable weight combination load forecasting based on grey model and semi-parametric Regression Model

Grey model using dimensional information update technology always contains the latest information from sample data, it guarantees the accuracy of mid-long term load forecasting. Semi-parametric regression model which combines the advantages of parametric model and nonparametric model, it can fully reflect the complexity and uncertainties of the load change. This paper put forward an improved method which combines grey model with semi-parametric regression model by time-varying weight for load forecasting, the proposed method would make full use of data information and consider its inherent regularity completely, which makes prediction more realistic. At last, a comparison of the error has been made between the single model and the combination model. The test example results show that this method has higher precision.

The Real-Time Assessment of Electric Distribution Network Load Capability

To improve the security and reliability of a distri bution network, several issues, such as influences of operation constrains, real-time load margin calculation, and onl ine security level evaluation, are with great signi ficance. In this paper, a mathematical model for load capability onlin e assessment of a distribution network is establish ed, and a repetitive power flow calculation algorithm is proposed t o solve the problem as well. With assessment on thr ee levels: the entire distribution network, a sub-area of the netw ork and a load bus, the security level of current o peration mode and load transfer capability during outage are thus obt ained. The results can provide guidelines for preve ntion control, as well as restoration control. Simulation results sho w that the method is simple, fast and can be applie d to distribution networks belonged to any voltage level while taking into account all of the operation constraints.