Canbing Li

An Optimized EV Charging Model Considering TOU Price and SOC Curve

Large-scale deployment of electric vehicles (EVs) is anticipated in the foreseeable future. Heavy intermittent charging load of EVs will create bottlenecks in supplying capacity and expose power system to severe security risks. In this paper, we propose an intelligent method to control EV charging loads in response to time-of-use (TOU) price in a regulated market. First, an optimized charging model is formulated to minimize the charging cost. Then, a heuristic method is implemented to minimize the charging cost considering the relation between the acceptable charging power of EV battery and the state of charge (SOC). Finally, the charging cost and energy demand in different time intervals are compared for both typical charging pattern and optimized charging pattern. Results show that the optimized charging pattern has great benefit in reducing cost and flatting the load curve if the peak and valley time periods are partitioned appropriately.

Chance-Constrained Optimization-Based Unbalanced Optimal Power Flow for Radial Distribution Networks

Optimal power flow (OPF) is an important tool for active management of distribution networks with renewable energy generation (REG). It is better to treat REG as stochastic variables in the distribution network OPF. In addition, distribution networks are unbalanced in nature. Thus, in this paper, a chance constrained optimization-based multiobjective OPF model is formulated to consider the forecast errors of REG in the short-term operation of radial unbalanced distribution networks. In the model, expected total active power losses of distribution lines, expected overload risk and voltage violation risk with respect to N-1 contingencies are minimized, and inequality constraints in the normal state are satisfied with a predefined probability level. Thus, the profitability and security can be balanced in the presence of stochastic REG. The proposed multiobjective OPF problem is solved by the multiobjective group search optimization and the two-point estimate method. Simulation results show that distribution network economy and postcontingency performance deteriorate with increased penetration level of REG, and the penetration level has a greater impact than the forecast errors of REG.

A New Stepwise Power Tariff Model and Its Application for Residential Consumers in Regulated Electricity Markets

Stepwise power tariff (SPT), which has been put into practice, is a crucial way for energy saving and environment protecting. In this paper, a new optimal model of SPT based on residential demand response model is presented. The optimal decision is proposed to restrain high electricity consumption as well as safeguard benefits of both supply and demand sides. As a result, the objective is designed to minimize electricity consumption and constraints are taken into consideration thoroughly, including acceptable index of consumers, average price, sales profit of power providers and basic electricity demand, which serves as a foundation for smooth implement of SPT. To solve the constrained optimal problem, genetic algorithm (GA) is employed. The effectiveness of the model and algorithm is investigated and demonstrated based on real data of 300 residents by a numerical example. The study shows that the method can reduce power consumption obviously with little sacrifice of the benefits of consumers and power providers.

Improved group search optimization method for optimal power flow problem considering valve-point loading effects

Abstract Optimal power flow (OPF) is an important tool for smart grid optimal dispatch. In this paper, an improved group search optimization (IGSO) method is proposed to solve the OPF problem with the valve-point loading effects, which is an optimization problem with many local optima. In the IGSO algorithm, the search space of the scroungers is enlarged to give a more sufficient exploitation around the producer and to help the scroungers explore other areas to avoid falling into a local optimum. A strategy is also introduced to help scroungers to learn from other members to improve the global searching ability. Moreover, scroungers and rangers update their positions only when better positions are found. Good performances of the IGSO algorithm on the OPF problem with the valve-point loading effects are verified by tests on a 26-bus system, and the IEEE 30-bus and IEEE 118-bus systems.

A Two-Stage Stochastic Programming Approach Considering Risk Level for Distribution Networks Operation With Wind Power

Wind power is a challenge for active operation of distribution networks because of its stochastic nature. In this paper, a two-stage stochastic-programming-based optimal power flow model is proposed for the short-term operation (e.g., 1 h ahead) of distribution networks with wind power. In this model, the wind power is dispatched in the first stage, and then, in the second stage, not only the additional cost caused by the difference between dispatched wind power and actual wind power is considered into the recourse cost but also the operational risk is introduced to characterize the system security level. Both the number and severities of violations and near violations can be reflected by the risk index. Thus, the impacts of the wind power dispatch decision on the system operation regarding the realizations of wind power can be comprehensively evaluated. The 69-bus system is used to verify the effectiveness of the proposed approach. Simulation results show that the proposed approach can give a much lower risk level with a small increase in operational cost.

EV Dispatch Control for Supplementary Frequency Regulation Considering the Expectation of EV Owners

Electric vehicles (EVs) are promising to provide frequency regulation services due to their fast regulating characteristics. However, when EVs participate in supplementary frequency regulation, it is challenging to simultaneously achieve the dispatch of the control center and the expected state of charge (SOC) levels of EV batteries. To solve this problem, in this paper we propose a vehicle-to-grid (V2G) control strategy, in which an uncertain dispatch is implemented in the control center without detailed EV charging/discharging information. The regulation from the control center is achieved by allocating the regulation task within the frequency regulation capacity of EVs. The expected SOC levels of EV batteries are guaranteed by a real-time correction of their scheduled V2G power in EV charging stations. Simulations on an interconnected two-area power system validate the effectiveness of the proposed V2G control in achieving both the frequency regulation and the expected SOC levels of EVs.

A Multi-Stage Restoration Method for Medium-Voltage Distribution System With DGs

A fast and effective service restoration method is proposed for medium-voltage distribution system with distributed generators by combining intentional islanding of distributed generators with network reconfiguration to maximize restoration of out-of-service loads. The whole process is divided into four stages: 1) matching islanding schemes; 2) restoration of network connectivity and distributed generators; 3) network reconfiguration; and 4) optimization of load shedding. Whether the last three stages are executed or not depends on violations of operational constraints. In accordance with the topological feature of distribution system, a new concept of fundamental loop matrix is presented and applied in detecting out-of-service areas, restoration of network connectivity, and network reconfiguration. Simulation studies on the PG&E 69-bus system shows the effectiveness of the proposed approach.

Assessment Method and Indexes of Operating States Classification for Distribution System With Distributed Generations

Self-healing is a key characteristic and goal of smart grid, which is based on the classification of the operating states of the power system. With views to the differences between the distribution and transmission system in terms of the operation model and structural features, an essential difference can appear in the classification of operating states between these two systems. According to the characteristics of distribution system with distributed generations (DGs), a new method based on hierarchical classification is proposed to classify the operating states of distribution system with DGs. In this method, several important performances reflecting the operating conditions of distribution system are regarded as critical attributes, including external stability, reliability, integrity, and economy. Moreover, different transition paths and control targets in different states are proposed to demonstrate the effectiveness of the classification method, which aim at the safety and reliability of the distribution system operation. Finally, a control strategy based on state classification is presented to support the decision-making for the self-healing distribution system. A case study demonstrates the feasibility and effectiveness of the classification method for the operating states of distribution system.

Forecasting of load model based on typical daily load profile and BP neural network

Load modeling is recognized as a difficult issue in field of power system digital simulation. The reliability of the simulation results depends on the veracity of the load model which will further affect power system planning and aid decision making. In order to increase the accuracy of the load model, the composite loads of power consuming-industries were classified by their industry attributes and the components of them were also analyzed in this paper. Then, the mathematical model of load composition is established on the basic of typical daily load profile and the identification algorithm developed by C language is used to identify the parameters of composite loads by choosing the data collected during the corresponding characteristic time period of the typical day. Based on the model vector machine theory and the parameters identified, the parameters of composite load model of power consuming-industries can be calculated by using the way of least square approximation. And the BP neural network was used to forecast the parameters of composite loads of power consuming-industries. Finally, an example shows the validity of the proposed scheme.

Synergistic and priority control for electric vehicles power allocation in participating in AGC

Large scale integration of electric vehicles (EVs) provides new ideas for power system stability and control. Connected to power grid, EVs can be taken as distributed energy storage and thus cooperate with traditional automatic generation control (AGC) units to help stabilize the system frequency and achieve energy saving. On the basis of previous researches, taking into account the influence of battery state of charge (SOC) on the maximum charging/discharging power and the charging demand of EVs users, this paper firstly expounds the AGC control system framework with the contribution of EVs. Then, coordination and priority control scheme for multiple EVs power allocation during the charging/discharging processes is discussed and the main constraints are formulated. Finally, simulations for EVs participating in a two-area interconnected power system AGC are performed. The results show that rational power allocation and effective utilization of EVs resources can be fully achieved by implementing the proposed method, so as to better improve power system stability and achieve energy saving.