Yunfei Mu

Optimal distributed generation planning in active distribution networks considering integration of energy storage

A two-stage optimization method is proposed for optimal distributed generation (DG) planning considering the integration of energy storage in this paper. The first stage determines the installation locations and the initial capacity of DGs using the well-known loss sensitivity factor (LSF) approach, and the second stage identifies the optimal installation capacities of DGs to maximize the investment benefits and system voltage stability and to minimize line losses. In the second stage, the multi-objective ant lion optimizer (MOALO) is first applied to obtain the Pareto-optimal solutions, and then the ‘best’ compromise solution is identified by calculating the priority memberships of each solution via grey relation projection (GRP) method, while finally, in order to address the uncertain outputs of DGs, energy storage devices are installed whose maximum outputs are determined with the use of chance-constrained programming. The test results on the PG&E 69-bus distribution system demonstrate that the proposed method is superior to other current state-of-the-art approaches, and that the integration of energy storage makes the DGs operate at their pre-designed rated capacities with the probability of at least 60%.

Planning of Fast EV Charging Stations on a Round Freeway

A novel planning method of fast electric vehicle (EV) charging stations on a round freeway was developed, considering the spatial and temporal transportation behaviors. A spatial and temporal model based on the origin-destination (OD) analysis was developed to obtain all the EV charging points (the location on the round freeway that an EV needs recharging due to the low battery capacity). Based on a shared nearest neighbor (SNN) clustering algorithm, a location determination model was developed to obtain the specific locations for EV charging stations with their service EV clusters. A capacity determination model based on the queuing theory was proposed to determine the capacity of each EV charging station. The round-island freeway in Hainan Island of China was employed as a test system to illustrate the planning method. Simulation results show that the developed planning method can not only accurately determine the most suitable locations for EV fast charging stations considering the travelling convenience of EV users, but also minimize the sum of waiting cost and charger cost.

An efficient power plant model of electric vehicles considering the travel behaviors of EV users

Under the vehicle-to-grid (V2G) concept, an efficient power plant model of the E V aggregation (E-EPP) considering the travel behaviors of EV users is developed to determine the system V2G capability along a whole day. Firstly, based on the market survey data of various EV batteries, a nonlinear generic battery model (GBM) is established, which can describe the charging/discharging characteristics and the variation of state-of-charge (SOC) of EV batteries. Then, considering different traveling behaviors of EV users and combined with the proposed GBM, a Monte Carlo Simulation (MCS) method is implemented to build the E-EPP model. Finally, three charging strategies (dumb charging, smart charging and hybrid charging) are considered to evaluate and compare the V2G capability boundary of the E-EPP by using a typical test example. Simulation results show that the developed E-EPP can determine the V2G capability of EVs along a typical day in order to keep the traveling comfort levels of EV users.

A reactive power evaluation model for EV chargers considering travelling behaviors

A reactive power evaluation model is proposed to estimate the available V2G capacity of reactive power provided by EV chargers during a day considering the travelling behaviors of EV users. Firstly, a generic EV charger model (GECM) is developed to describe the reactive power response characteristic of EV charger. Then, considering the travelling behaviors of EV users, a Monte Carlo simulation (MCS) method is implemented to build the reactive power evaluation model. Finally, the available reactive power from EV chargers is analyzed and compared under two different charging strategies (dumb charging and smart charging). Simulation results show that the reactive power evaluation model can determine the available reactive power capacity for V2G along a day, which can support the operation of the power system.

A multi-level service restoration strategy of distribution network considering microgrids and electric vehicles

The increasingly serious energy crisis and environmental pollution problems promote the large-scale application of microgrids (MGs) and electric vehicles (EVs). As the main carrier of MGs and EVs, distribution network is gradually presenting multi-source and active characteristics. A multi-level service restoration strategy of distribution network with MGs and EVs is proposed in this paper, which makes full use of the power supply capability from MGs and EVs. An optimal power flow (OPF) model is constructed to minimize the network loss after the service restoration. The proposed service restoration method is verified by applying a single fault on a modified IEEE distribution system with three-feeder and eighteen nodes containing MGs and EVs, which shows its feasibility and effectiveness.

Dynamic frequency control of autonomous microgrid based on family-friendly controllable loads

Autonomous microgrid (AMG) develops fast in recent years due to the capability of supplying power to remote areas and avoiding high investment on transmission facilities. Frequency is a key issue for the stable operation of AMG with a large penetration of intermittent renewable energy resources, such as the wind turbines (WT), photovoltaic generators (PV), etc. The fast development of demand response technology provides a new opportunity for the frequency control of AMG. In this paper, a novel decentralized demand-side control (DDC) strategy for family-friendly controllable refrigerators considering customer comfort levels is proposed to regulate the frequency of AMG in coordination with energy storage system (ESS). The refrigerators under DDC respond to the local frequency signals and dynamically adjust their operation cycles. Meanwhile, a customer participation degree which is proportional to the frequency deviation is introduced to evaluate the customer comforts when participate in the frequency regulation. Finally, a benchmark low voltage AMG is established as the test system to verify the effectiveness of the DDC strategy. Simulation results show that the DDC strategy not only can improve the frequency control effect of AMG effectively, but also can reduce the capacity of ESS to a certain extent.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines (MTs) are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes, which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of MTs. Considering the time-scale difference of various dynamic processes occurring within MTs, the electromechanical subsystem is treated as a fast quasi-linear process, while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 MT show that the proposed modeling method can well capture the system dynamics, and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

A multi-level control strategy for transmission congestion relief based on the capability from active distribution network

In this paper, a multi-level control strategy for transmission congestion is proposed based on the active reconfiguration and islanding capability from active distribution network. The multi-level control strategy includes an active reconfiguration scheme pursuing the minimum tie line power between the transmission and distribution network, an active islanding scheme based on the multi-source characteristic of active distribution network and a demand response scheme. Case studies are carried out on a typical IEEE 30-bus system with a three-feeder active distribution network to verify the effectiveness of the proposed multi-level control strategy. Simulation results show that the strategy can contribute to congestion relief of transmission network to some extent, and guarantee the reliability index of the distribution network at the same time, which has a good engineering application prospect.

An Approach to Propose Optimal Energy Storage System in Real-Time Electricity Pricing Environments

Based on the fact that the penetration of renewable energies is increasing dramatically, almost all the energy markets have changed and taken action to present the strategy of real-time pricing over the last decade. However, the research on how these renewables, which is going to become the vital part in the integrated energy system, coordinate with other energy sources under the real-time pricing. Moreover, with the development of energy storage system, issues about how to evaluate the participation of them in the integrated energy system and how to provide an optimal capability for them in a given settings should be given more consideration. This paper will focus on introducing an approach to coordinate the participation of all the energy resources in the integrated energy systems within and without energy storage system.

Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing

Abstract In order to coordinate the scheduling problem between an isolated microgrid (IMG) and electric vehicle battery swapping stations (BSSs) in multi-stakeholder scenarios, a new bi-level optimal scheduling model is proposed for promoting the participation of BSSs in regulating the IMG economic operation. In this model, the upper-level sub-problem is formulated to minimize the IMG net costs, while the lower-level aims to maximize the profits of the BSS under real-time pricing environments determined by demand responses in the upper-level decision. To solve the model, a hybrid algorithm, called JAYA-BBA, is put forward by combining a real/integer-coded JAYA algorithm and the branch and bound algorithm (BBA), in which the JAYA and BBA are respectively employed to address the upper- and lower- level sub-problems, and the bi-level model is eventually solved through alternate iterations between the two levels. The simulation results on a microgrid test system verify the effectiveness and superiority of the presented approach.