Yue Xiang

Optimal active distribution network planning:a review

Abstract The active distribution network is a new solution to the flexible utilization of distributed energy resources to suit the characteristics of the distribution network. Advanced “active” network management is to coordinate “generation, network, load” optimization and achieve the right balance between operational expenditure (OPEX) and capital expenditure (CAPEX). To demonstrate the advancement from introducing an active distribution network, the key features of distribution network planning mainly including traditional models is first introduced. Extensive literature in generic planning is then summarized and categorized in terms of objective functions, system modeling, solution algorithms, and tools. This is followed by an extended review and in-depth discussion of concepts and representative topic developments in optimal active distribution network planning. In contrast to traditional planning, it takes into account the effects from a range of active network interventions that are exercised at differing time scales while capturing the intrinsically stochastic behavior of renewable generation or demand response to produce strategic plans that are robust against a highly uncertain energy future. Finally, a multi-dimensional framework for optimal active distribution network planning is proposed to overcome the limitations of the current state of the art, and the challenges in each stage are also highlighted.

Investigation on Impacts of Alternative Generation Siting in Power Grids from the View of Complex Network Theory

Abstract In this article, the impacts of alternative generation integration in a power grid are discussed from the view of complex network theory. Using the improved complex network index, the structural performance of the system could be assessed in planning. Also, the distribution of load and generation are also considered in the modeling. Compared with the existing planning method, the proposed method can not only solve alternative generation units siting issues but also locate the corresponding conventional generation to be curtailed or replaced. Furthermore, as more information is obtained, e.g., related policy or cost parameters, a multi-objective comprehensive decision model is designed, the weight coefficient of which is determined by the two-tuple linguistic decision method. The proposed indices and models can effectively realize fast location and help improve the structural performance of the system with appropriate alternative generation integration. The models and methods are tested and verified by test cases.

A Chance-constrained Optimization Model for Determining Renewables Penetration Limit in Power Systems

Abstract In this article, an optimization model determining renewables penetration limit in power systems is presented. The penetration limit is defined as the enabling renewables output with quantified maximum capacity avoiding the violation of power system operation constraints. Thus, an optimal power flow (OPF)-based model with chance constraints is built and a framework including a Monte Carlo-based genetic algorithm is designed. Moreover, a transient stability verification and correction strategy based on trajectory sensitivity is proposed and modularized in the extended framework. The feasibility of the proposed methodology is verified using several test scenarios, and some related factors are investigated as well. The results indicate that renewables penetration limit can be increased by improving those studied factors.

Measurement-based power system frequency dynamic response estimation using geometric template matching and recurrent artificial neural network

Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications. Wide area measurement systems (WAMS) based on synchrophasors make power system dynamics visible to system operators, delivering an accurate picture of overall operating conditions. However, in actual field implementations, some measurements can be inaccessible for various reasons, e.g., most notably communication failure. To reconstruct these inaccessible measurements, in this paper, the radial basis function artificial neural network (RBF-ANN) is used to estimate the system dynamics. In order to find the best input features of the RBF-ANN model, geometric template matching (GeTeM) and quality-threshold (QT) clustering are employed from the time series analysis to compute the similarity of frequency dynamic responses in different locations of the power system. The proposed method is tested and verified on the Eastern Interconnection (EI) transmission system in the United States. The results obtained indicate that the proposed approach provides a compact and efficient RBF-ANN model that accurately estimates the inaccessible frequency dynamic responses under different operating conditions and with fewer inputs.

A traffic flow based planning strategy for optimal siting and sizing of charging stations

Planning and construction of charge stations are important aspects of promoting the development of electric vehicles (EVs). Integrated with the equilibrium traffic flow, a comprehensive optimization strategy aiming at achieving siting and sizing planning of charging stations on the road network is presented in this paper. The system optimization model is used for generating and assigning the equilibrium traffic flow in typical time periods based on the original-destination (OD) data. On this basis, queuing theory is used to deploy the capacity of each station in the alternative plan. Furthermore, an economic optimal planning model, including different types of cost functions, is proposed for the final decision, integrated with the proposed load capability constraint. Case studies have demonstrated the feasibility and effectiveness of the proposed planning strategy.

Optimal Sizing of Energy Storage System in Active Distribution Networks Using Fourier–Legendre Series Based State of Energy Function

A novel method for optimal sizing of energy storage system (ESS) in active distribution networks using state of energy (SOE) function is proposed in this letter. The SOE of ESS in an operational cycle is characterized by an approximately continuous function based on Fourier–Legendre series expansion. Then, the ESS capacity can be determined by the optimized SOE function extreme. The effectiveness and advantages of the proposed method are verified by test cases.

Scale Evolution of Electric Vehicles: A System Dynamics Approach

Electrification of transportation has drawn increasing attention for the sake of low-pollution emission and high-fuel economy. Scientific scale forecasting of electric vehicles (EVs) is fundamental to promote EVs’ integration for transportation and electric power industries. To capture the evolution pattern, a system dynamics (SD) approach is proposed to simulate and forecast the scale of the EVs. The proposed SD model can integrate various factors and quantify their relationship by comprehensive reasoning. Causal loop diagrams are designed to describe the relationship between factors and variables, and their quantifications are formulated by different business models and surveys. The main procedure for simulation includes survey, problem analysis, variable definition, feedback analysis, and model building. The effectiveness of the SD approach is verified by case studies. Furthermore, sensitivity analysis of the key factors, such as fuel price, subsidy policy, and so on, is also conducted.

Two-stage Control Strategy for Structure Optimization of Faulted Distribution System with Distributed Generation

Abstract In this article, a structure optimization strategy is designed for the faulted distribution system with distributed generation. The strategy is composed of two stages: the island partition and the remaining network reconfiguration. In the first stage, single-source electrical betweenness is proposed, and a partition model is built on the basis of variable network structure characteristics and flexible load features. Then binary decision method has been introduced to transform the original partition problem into a “satisfiability problem,” which can be solved in two steps, and the initial partition results are obtained using the implicit enumeration method, followed by static survivability check, interruptible load modification, and reactive compensation calculation. In this manner, the final scheme for the island partition is obtained. After that, based on the ant colony algorithm, the remaining network is reconstructed in the second stage. The effectiveness of the proposed strategy is verified by case studies in two test systems.

Agent-Based Modeling for Scale Evolution of Plug-in Electric Vehicles and Charging Demand

Scale evolution pattern recognition of plug-in electric vehicles (PEVs) and charging demand modeling are essential for various involved sectors to promote PEV proliferation and integration into power systems. Considering that the market penetration development of PEVs will drive the evolution of charging demand, an integrated dynamic method based on an agent-based modeling technology is proposed in this paper by combining scale evolution model with charging demand model to jointly detect the possible PEVs evolution patterns and long-term charging demand profiles. Heterogeneous consumers presenting different preferences in making vehicle purchase decisions and the interactions with other consumers via social dynamics are taken into consideration in the scale evolution model. After obtain the scale of PEVs by aggregating individual consumer agents purchase behavior, the driving patterns, charging behavior habits, and charging strategies are systematically incorporated into the charging demand model. Case studies demonstrate the feasibility and effectiveness of the proposed methodology by taking an urban area as an example. Furthermore, the factors that affect the market evolution of PEVs and the charging demand are also simulated and analyzed.

A coordinated charging strategy for electric vehicle considering three-phase load unbalance

The electric vehicle charging load bring serious problems to the operation of power system. In this paper, a coordinated electric vehicle charging strategy is proposed to settle problem of load unbalance in three-phase distribution system. By monitoring the real-time power flow via the current sensor in each phase, the charging power is adjusted to implement the coordinated charging. Numerical results demonstrate that, the proposed strategy is able to reduce the current in the neutral wire and make the customers charging arrangement unchanged.