Yongxi Zhang

Stochastic Collaborative Planning of Electric Vehicle Charging Stations and Power Distribution System

The increasing prevalence of electric vehicles (EVs) calls for the effective planning of the charging infrastructure. In this study, a multi-objective, multistage collaborative planning model is proposed for the coupled EV charging station infrastructure and power distribution network. The planning model aims to minimize the investment and operation costs of the distribution system while maximize the annually captured traffic flow. The uncertainties of EV charging loads are modeled for three different types of charging stations. The FISKs stochastic traffic assignment model is utilized to model realistic traffic flows. And a new class of volume-delay functions, conical congestion functions, is employed to overcome the shortcomings of the conventional Bureau of Public Roads function. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) algorithm is applied to find the nondominated solutions of the proposed collaborative planning model. Finally, simulations based on a 54-node distribution system are conducted to validate the  effectiveness of the proposed method.

The stochastic optimal dispatch model considering the uncertain line failures events under extreme weather disasters

Over the past few years several large scale failures of power system due to natural disasters has caused great loss to national economy. They also aroused the consideration of power network secure operations with the respect to natural disaster. Given that the line failure caused by natural disaster is a low probability event, in this paper, we used Poisson distribution theory to depict the uncertainty of line failure. A stochastic power system optimal dispatch model based on chance constrained programming method is also proposed in this paper as well. To solve the stochastic optimization problems with chance constraints programming effectively and numerically, we used sample average approximation to convert the uncertain stochastic optimal dispatch problems into non-continuous, non-differentiable optimal problems first, then through an improved differential evolution algorithm to calculate the total cost of shedding loads and generation. The results of IEEE 9-bus case study show that the dispatch model can effectively consider the uncertain effect of power components under nature disaster. The methods presented in this paper also provides more reasonable dispatch plan for power system disaster prevention and reduction.

Voltage regulation in distribution network using battery storage units via distributed optimization

This paper proposes a voltage regulation method by using battery energy storage (BES) units in distribution networks with abundant solar photovoltaic (PV) resources. The proposed method utilizes sparse optimization techniques to find the optimal actions of BES with minimal involved unit numbers and minimal active power output variation. Furthermore, a distributed Lagrangian primal-dual sub-gradient (DLPDS) algorithm is applied to solve the proposed method via local decision making with limited communication with neighbors. Finally, case studies are conducted on modified 15-bus and 43-bus radial distribution system to verify the performance of proposed method.

Stochastic Optimal Dispatch of Power System Considering the Correlation of Multiple Wind Farm Outputs

Abstract As an important way of addressing energy and environmental challenges, the market share of wind power generation has increased dramatically in the past decade and has introduced significant challenges to power system operation. In this article, the tail correlation between multiple wind farms is studied. The joint probability distribution of multiple wind farms is estimated by employing the Gumbel copula function. Based on the estimated joint probability distribution, a stochastic optimal dispatch model is proposed to take into account the chance constraints of energy utilization from multiple wind farms in the power system. The sample average approximation method is employed to handle the chance constraints in the proposed model, so as to transform stochastic optimal dispatch into a deterministic non-linear optimization problem. The quantum-inspired evolutionary algorithm is used to solve the proposed model. The proposed model and algorithm are tested with comprehensive case studies to demonstrate their effectiveness.

Energy internet risk assessment framework

Energy internet is a deep fusion product made up of electric power system, electrification traffic system and gas system linked by information technology. Using more complex technologies and covers larger geographical area as well as more diversity of users, it may be exposed to greater potential risks. The paper introduces the recent development status of energy internet globally. It proposes the typical energy internet structure and then set up a new framework for the risk assessment of energy internet. It analyzes the differences of risk assessment between energy internet and smart grid. It is a complicated system project for the future energy internet risk assessment research work. As it is still in the initial stage, there remains a lot of research work to be further developed.

Economic evaluation and state time-delayed feedback chaos control of dynamic supply function model in power market

The dynamic supply function model considering the decision-marking of market participants and transmission constrains of power network in power market is established. The different dynamic behaviors are analyzed, such as Nash equilibrium, period and chaos. With the average profit as the economic evaluation index, the economic performances of dynamic behaviors in the states of Nash equilibrium, period and chaos are analyzed and compared for three-node power market. Among them, the optimal performance appears in the state of Nash equilibrium. In view of the chaotic behaviors in power market, the state time-delayed feedback chaos control method is proposed. Finally, by means of the proposed control method, the chaotic state is controlled to the stable equilibrium point so that the economic performance of dynamic power market is effectively improved.

Flexible Operation Planning Scheme Considering Wind Power Generation Forecasting Uncertainties

Abstract Power system daily operational planning has become a difficult task with increasing wind penetrations into power system due to the variable and uncontrollable natures of wind resources. Although remarkable progress has been made in the development of wind power technology, wind speed prediction error still exists. In order to mitigate the negative effects caused by forecasting errors, several candidate daily operational plans should be scheduled ahead of time, with different wind power output scenarios taken into consideration. The key issue of this research work is to develop flexible operation framework that has the least cost adaption cost on the basis of receding horizon optimization. The selected operation plan may not be the cheapest one for one-time interval, but it takes the lowest cost considering the change process with most updated wind power information. Detailed case studies are performed to verify the feasibility of the proposed flexible operational planning framework.