Browsing by Author "Zhang, Xin"
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Item Open Access Agricultural Load Modeling Based on Crop Evapotranspiration and Light Integration for Economic Operation of Greenhouse Power Systems(Chinese Society for Electrical Engineering (CSEE) in collaboration with CEPRI (China Electric Power Research Institute) and IEEE, 2019-12-07) Li, Zeming; Liu, Junyong; Xiang, Yue; Zhang, Xin; Chai, YanxinThe threat of environmental degradation attracts great attention to clean energy production and transportation. However, the limited scope of energy consumption causes large-scale of clean energy sources to be abandoned in Sichuan province. In the meantime, the development of modern greenhouse cultivation has transformed the agriculture industry to have a brand-new type of electrical load in the grid. Consequently, the agricultural load can be used to consume the clean energy while facilitating plant growth. In this paper, an innovative agricultural load model is proposed based on crop evapotranspiration and daily light integration. Furthermore, the proposed agricultural load model is also applied to investigate the electricity consumption of five types of crop planting. The results illustrate that the power consumption is primarily driven by artificial lighting compensation system.Item Open Access Aviation-to-grid flexibility through electric aircraft charging(IEEE, 2021-10-16) Guo, Zekun; Zhang, Jinning; Zhang, Rui; Zhang, XinThis paper proposes a new concept of Aviation-to-Grid (A2G) that utilizes electric aircraft (EA) charging to provide flexibility to the power grid. Smart EA charging system with battery swap method is developed using PV, gas turbine, and grid electricity. Hourly energy dispatch strategy is produced based on the mixed integer linear programming method to meet electrified aviation and provide A2G frequency response. Case studies are conducted in 8 major UK airports considering seasonal flight schedules and power system scenarios. Results show that the EA charging system can provide effective primary and secondary frequency response to improve the frequency nadir by 0.2 - 0.3 Hz under grid disturbance. The total A2G frequency response capacity across the 8 UK airport can reach up to 1,300 MW overnight and up to 900 MW daytime. Annual A2G frequency response revenue is 46.58 million pounds, which can cover 19.8% to 30% of EA charging costs.Item Open Access Chance-constrained optimal dispatch of integrated electricity and natural gas systems considering medium and long-term electricity transactions(IEEE, 2019-08-01) Wu, Gang; Xiang, Yue; Liu, Junyong; Zhang, Xin; Tang, ShuoyaA novel stochastic optimal dispatch model considering medium and long-term electricity transaction for a wind power integrated energy system by using chance constrained programming is proposed. The electricity contract decomposition problem is introduced into the day-ahead optimal dispatch plan formulation progress. Considering the case that decomposition results may be not executable in the dispatch plan, a coordinated optimization strategy based on Lagrange multiplier is proposed to locate the infeasible factors and eliminate the non executable electric quantity. At the same time, the uncertainties and correlation of wind power are considered in the dispatch model, and the original stochastic dispatch problem is transformed into a mixed integer second-order cone programming problem based on second-order cone relaxation and deterministic transformation of chance constraints. Case study results demonstrate the validity of the proposed methodItem Open Access Economic-effective multi-energy management with voltage regulation networked with energy hubs(IEEE, 2020-10-15) Zhao, Pengfei; Gu, Chenghong; Hu, Zechun; Zhang, Xin; Chen, Xinlei; Hernando-Gil, Ignacio; Ding, YuchengThis paper develops a novel two-stage coordinated volt-pressure optimization (VPO) for integrated energy systems (IES) networked with energy hubs considering renewable energy sources. The promising power-to-gas (P2G) facilities are used for improving the interdependency of the IES. The proposed VPO contains the traditional volt-VAR optimization functionality to mitigate the voltage deviation while ensuring a satisfying gas quality due to the hydrogen mixture. In addition to the conventional voltage regulating devices, i.e., on-load tap changers and capacitor banks, P2G converter and gas storage are used to address the voltage fluctuation problem caused by renewable penetration. Moreover, an effective two-stage distributionally robust optimization (DRO) based on Wassersteain metric is utilized to capture the renewable uncertainty with tractable robust counterpart reformulations. The Wasserstein-metric based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Extensive case studies are conducted in a modified IEEE 33-bus system connected with a 20-node gas system. The proposed VPO problem enables to provide a voltage-regulated economic operation scheme with gas quality ensured that contributes high-quality but low-cost multi-energy supply to customers.Item Open Access Energy performance and life cycle cost assessments of a photovoltaic/thermal assisted heat pump system(Elsevier, 2020-06-26) Cui, Yuanlong; Zhu, Jie; Zoras, Stamatis; Qiao, Yaning; Zhang, XinA photovoltaic/thermal module assisted heat pump system is investigated in this paper, which provides electrical and thermal energy for a domestic building. In-depth evaluation on the system energy production is conducted based on the finite difference method for a long-term operating period. The 25 years’ system life cycle cost is assessed via the Monte Carlo simulation under the Feed-in Tariff (FiT) and Renewable Heat Incentive schemes, the annual energy savings, income and payback period (PBP) are compared for the FiT and Smart Export Guarantee (SEG) schemes. The technical analysis results illustrate that the system is able to fulfil the building thermal and electrical energy demands from April to October and from May to August, respectively, and the extra electricity of 229.47 kWh is fed into the grid. The economic assessment results clarify that the system achieves a net present value (NPV) of £38,990 and has a PBP of 4.15 years. Meanwhile, the economic sensitive analyses reveal that the high discount rate reduces the system NPV whereas the high investment cost causes a long PBP to realize the positive NPV. Compared with the SEG scheme, the FiT is the most cost-effective method for renewable electricity generation and has the shortest PBPItem Open Access An explicit formula based estimation method for distribution network reliability(IEEE, 2019-10-31) Xiang, Yue; Su, Yunche; Wang, Yang; Liu, Junyong; Zhang, XinAn improved explicit estimation algorithm is proposed for reliability estimation of distribution network. Firstly, hierarchical clustering is used to identify and cluster typical feeders based on topology structure. Secondly, the explicit formula of reliability indices under each typical feeder topology is derived by regression analysis, to establish the model for network reliability estimation. Numerical simulations show the suitability of the proposed method in obtaining accurate reliability index for diversified network topology.Item Open Access A fixed-point based distributed method for energy flow calculation in multi-energy systems(IEEE, 2020-01-15) Zhang, Gang; Zhang, Feng; Meng, Ke; Zhang, Xin; Dong, ZhaoyangMulti-energy flow calculation (M-EFC) is an essential tool for the coordinated analysis of strongly coupled electricity-gas-heating systems. However, the separate management of these subsystems poses a considerable challenge for designing a fast and reliable M-EFC method. In this paper, a fixed-point based distributed method is proposed for the M-EFC problem. The proposed method can preserve the autonomy of subsystems due to limited information exchange during the solution process. Moreo-ver, the fast and reliable convergence is achieved according to the proposed sufficient conditions based on the fixed-point theory. Besides, the proposed method is availa-ble for multi-energy systems (MES) with various coupling relationships and different structures of information ex-change. Simulations on a MES demonstrate that the pro-posed method has remarkable superiority compared to the unified Newton-Raphson method in computation time, accuracy and robustness against data loss.Item Open Access Mobile emergency generator planning in resilient distribution systems: a three-stage stochastic model with nonanticipativity constraints(IEEE, 2020-06-19) Zhang, Gang; Zhang, Feng; Zhang, Xin; Wang, Zhaoyu; Meng, Ke; Dong, Zhao YangMobile emergency generators (MEGs) can effec-tively restore critical loads as flexible backup resources after power network disturbance from extreme events, thereby boosting the distribution system resilience. Therefore, MEGs are re-quired to be optimally allocated and utilized. For this purpose, a novel three-stage stochastic planning model is proposed for MEG allocation of resilient distribution systems in consideration of planning stage (PLS), preventive response stage (PRS) and emergency response stage (ERS). Moreover, the nonanticipativity constraints are proposed to guarantee that the MEG allocation decisions are dependent on the stage-based uncertainties. Specifically, in the PLS, the intensity uncertainty (IU) of disasters and the outage uncertainty (OU) incurred by a given disaster are considered with probability-weighted scenarios for the effective MEG allocation. Then, with the IU that can be observed in the PRS, the MEGs are pre-positioned in the consideration of OU. It is noted that the pre-position decisions should only correspond to the IU realizations, according to nonanticipativity constraints. Last, with the further realization of OU in the ERS, the MEGs are re-routed from the pre-position to the target location, so that the provisional microgrids can be formed to restore critical loads. The proposed planning model can be large-scale due to multiple sce-narios. Therefore, the progressive hedging algorithm (PHA) is customized to reduce the computational burden. The simulation results in 13 and 123 node distribution systems show the effec-tiveness and superiority of the proposed three-stage MEG plan-ning model over the traditional two-stage model.Item Open Access A multi-disaster-scenario distributionally robust planning model for enhancing the resilience of distribution systems(Elsevier, 2020-05-26) Zhang, Gang; Zhang, Feng; Zhang, Xin; Wu, Qiuwei; Meng, KeResilience oriented network planning provides an effective solution to protect the distribution system from natural disasters by the pre-planned line hardening and backup generator allocation. In this paper, a multi-disaster-scenario based distributionally robust planning model (MDS-DRM) is proposed to hedge against two types of natural disaster-related uncertainties: random offensive resources (ORs) of various natural disasters, and random probability distribution of line outages (PDLO) that are incurred by a certain natural disaster. The OR uncertainty is represented by the defined probability-weighted scenarios with stochastic programming, and the PDLO uncertainty is modeled as the moment based ambiguity sets. Moreover, the disaster recovery strategies of network reconfiguration and microgrid formation are integrated into the pre-disaster network planning for resilience enhancement in both planning and operation stages. Then, a novel primal cut based decomposition solution method is proposed to improve the computational efficiency of the proposed model. In particular, the equivalent reformulation of the original MDS-DRM is first derived to eliminate the PDLO-related variables. Then, the reformulation problem is solved by the proposed primal cut based decomposition method and linearization techniques. Finally, Simulation results are demonstrated for IEEE 13-node, 33-node and 135-node distribution systems to validate the effectiveness of the proposed method in enhancing the disaster-induced network resilience.Item Open Access Optimal CHP planning in integrated energy systems considering network charges(Elsevier, 2019-07-26) Wang, Hantao; Gu, Chenghong; Zhang, Xin; Li, FurongThis paper proposes a novel optimal planning model for combined heat and power (CHP) in multiple energy systems of natural gas and electricity to benefit both networks by deferring investment for network owners and reducing use-of-system (UoS) charge for network users. The new planning model considers the technical constraints of both electricity and natural gas systems. A two-stage planning approach is proposed to determine the optimal site and size of CHPs. In the first stage, a long-run incremental cost matrix is designed to reflect CHP locational impact on both natural gas and electricity network investment, used as a criterion to choose the optimal location. In the second stage, CHP size is determined by solving an integrated optimal model with the objective to minimize total incremental network investment costs. The proposed method is resolved by the interior-point method and implemented on a practically integrated electricity and natural gas systems. Two case studies are conducted to test the performance for single and multiple CHPs cases. This paper enables cost-efficient CHP planning to benefit integrated natural gas and electricity networks and network users in terms of reduced network investment cost and consequently reduced UoS charges.Item Open Access Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks(Elsevier, 2022-11-15) Lai, Chun Sing; Chen, Dashen; Zhang, Jinning; Zhang, Xin; Xu, Xu; Taylor, Gareth A.; Lai, Loi LeiLarge-scale integration of battery energy storage systems (BESS) in distribution networks has the potential to enhance the utilization of photovoltaic (PV) power generation and mitigate the negative effects caused by electric vehicles (EV) fast charging behavior. This paper presents a novel deep reinforcement learning-based power scheduling strategy for BESS which is installed in an active distribution network. The network includes fast EV charging demand, PV power generation, and electricity arbitrage from main grid. The aim is to maximize the profit of BESS operator whilst maintaining voltage limits. The novel strategy adopts a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and requires forecasted PV power generation and EV smart charging demand. The proposed strategy is compared with Deep Deterministic Policy Gradient (DDPG), Particle Swarm Optimization and Simulated Annealing algorithms to verify its effectiveness. Case studies are conducted with smart EV charging dataset from Project Shift (UK Power Networks Innovation) and the UK photovoltaic dataset. The Internal Rate of Return results with TD3 and DDPG algorithms are 9.46% and 8.69%, respectively, which show that the proposed strategy can enhance power scheduling and outperforms the mainstream methods in terms of reduced levelized cost of storage and increased net present value.Item Open Access Reactive coordinated optimal operation of distributed wind generation(Elsevier, 2020-11-26) Xiang, Yue; Zhou, Lili; Huang, Yuan; Zhang, Xin; Liu, Youbo; Liu, JunyongLarge-scale distributed wind generation (DWG) integration brings new challenges to the optimal operation of the distribution network. The reactive supports from wind turbines (WTs) and reactive power resources can improve both the operation economy and renewable energy consumption. In this paper, a multi-period reactive coordinated optimal operation model for DWG in the distribution network is established. The active-reactive power coordination characteristics of two typical types of WTs are considered and the operating strategy of reactive power resources is integrated in the model. The second-order cone programming (SOCP) is developed to transform the original nonlinear power flow model into a linear and convex model, which would significantly improve the power flow calculation efficiency for DWG penetrated distribution network. The simulation results show that the integration of reactive power resources can further promote the consumption of DWG and improve the operating profits of the distribution network.Item Open Access Reactive power optimization in integrated electricity and gas systems(IEEE, 2020-05-28) Zhao, Pengfei; Gu, Chenghong; Xiang, Yue; Zhang, Xin; Shen, Yichen; Li, ShuangqiVolt/VAR optimization (VVO) is one important operation in distribution systems to maintain acceptable voltage profiles. However, the high penetration of renewable generation poses severe challenges to VVO, leading to voltage deviation and fluctuation. This is further complicated by the growing coupling between the electricity and natural gas systems. To resolve the unacceptable voltage deviation under energy system interdependency, this article proposes a cooptimization of VVO for an integrated electricity and gas system (IEGS) with uncertain renewable generation. A two-stage data-driven distributionally robust optimization is developed to model the coordinated optimization problem, which determines the two-stage VVO and operation schemes with dispatch and corrective adjustment through active power regulation and reactive power support in both day-ahead and real-time stage. A semidefinite programming is reformulated to ensure the tractability and the proposed problem is solved by a constraint generation framework. Simulation studies are conducted on a 33-bus-6-node IEGS. Case studies demonstrate that the interdependency between electricity and gas systems reduces the significant operation cost and voltage rise. It, thus, can benefit integrated system operators with a powerful operation tool to manage the systems with fewer costs but integrate more renewable energy while maintaining the high supply quality.Item Open Access Robust optimization-based energy storage operation for system congestion management(IEEE, 2019-08-19) Yan, Xiaohe; Gu, Chenghong; Zhang, Xin; Li, FurongPower system operation faces an increasing level of uncertainties from renewable generation and demand, which may cause large-scale congestion under an ineffective operation. This article applies energy storage (ES) to reduce system peak and the congestion by the robust optimization, considering the uncertainties from the ES state-of-charge (SoC), flexible load, and renewable energy. First, a deterministic operation model for the ES, as a benchmark, is designed to reduce the variance of the branch power flow based on the least-squares concept. Then, a robust model is built to optimize the ES operation with the uncertainties in the severest case from the load, renewable energy, and ES SoC that are converted into branch flow budgeted uncertainty sets by the cumulant and Gram–Charlier expansion methods. The ES SoC uncertainty is modeled as an interval uncertainty set in the robust model, solved by the duality theory. These models are demonstrated on a grid supply point to illustrate the effectiveness of a congestion management technique. Results illustrate that the proposed ES operation significantly improves system performance in reducing the system congestion. This robust optimization-based ES operation can further increase system flexibility to facilitate more renewable energy and flexible demand without triggering the large-scale network investment.Item Open Access Sequential disaster recovery model for distribution systems with co-optimization of maintenance and restoration crew dispatch(IEEE, 2020-05-12) Zhang, Gang; Zhang, Feng; Zhang, Xin; Meng, Ke; Yang Dong, ZhaoTo efficiently restore electricity customers from a large-scale blackout, this paper proposes a novel mixed-integer linear programing (MILP) model for the optimal disaster recovery of power distribution systems. In the proposed recovery scheme, the maintenance crews (MCs) are scheduled to repair damaged components, and the restoration crews (RCs) are dispatched to switch on the manual switches. Then, the MC and RC dispatch models are integrated into the disaster recovery scheme, which will generate an optimal sequence of control actions for distributed generation (DG), controllable load, and remote/ manual switches. Besides, to address the time scale related challenges in the model formulation, the technical constraints for system operation are investigated in each energization step rather than time step, hence the co-optimization problem is formulated as an “event-based” model with variable time steps. Consequently, the disaster recovery, MC dispatch and RC dispatch are properly cooperated, and the whole distribution systems can be restored step by step. Last, the effectiveness of the co-optimization model is validated in the modified IEEE 123 bus test distribution system.Item Open Access Slope-based shape cluster method for smart metering load profiles(IEEE, 2020-01-10) Xiang, Yue; Hong, Juhua; Yang, Zhiyu; Wang, Yang; Huang, Yuan; Zhang, Xin; Chai, Yanxin; Yao, HaotianCluster analysis is used to study the group of load profiles from smart meters to improve the operability in distribution network. The traditional K-means clustering analysis method employs Euclidean distance as similarity measurement, which is insufficient in reflecting the shape similarities of load profiles. In this work, we propose a novel shape cluster method based on the segmented slope of load profiles. Compared with traditional K-means and two improved algorithms, the proposed method can improve the clustering accuracy and efficiency by capturing the shape features of smart metering load profiles.Item Open Access Techno-economic assessment of wireless charging systems for airport electric shuttle buses(Elsevier, 2023-03-17) Guo, Zekun; Lai, Chun Sing; Luk, Patrick Chi-Kwong; Zhang, XinFlightpath 2050, the European Commission's vision for aviation, requires that the aviation industry achieves a 75 % reduction in CO2 emissions per passenger mile and airports become emission-free by 2050. Airport shuttle buses in the airfields are going to be electrified to reduce ground emissions. Simultaneously, the airfield movement space and time schedules are becoming more limited for adopting stationary charging facilities for electrified ground vehicles. Therefore, the dynamic wireless charging technology becomes a promising technology to help improve the stability of electrification of the airfield transport network. This paper proposes a techno-economic assessment of wireless charging, wired charging, and conventional technologies for electrifying airport shuttle buses. A bi-level planning optimisation approach combines the multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-III) and mixed integer linear programming (MILP) algorithm to handle a large number of decision variables and constraints generated from the investigated problem. The airport shuttle bus transport is simulated through a multi-agent-based model (MABM) approach. Four case studies are analysed for illustrating the techno-economic feasibility of wireless charging technology for airport electric shuttle buses. The results show that the wireless charging technology enables the electric shuttle buses to carry smaller batteries while conducting the same as tasks conventional diesel/petrol vehicles and the bi-directional wireless charging technology could help mitigate the impact of electrification of shuttle buses on the distribution network.Item Unknown Techno-economic design of energy systems for airport electrification: a hydrogen-solar-storage integrated microgrid solution(Elsevier, 2021-01-02) Xiang, Yue; Cai, Hanhu; Liu, Junyong; Zhang, XinCan aviation really become less polluting? The electrification of airport energy system as a micro-grid is a promising solution to achieve zero emission airport operation, however such electrification approach presents the engineering challenge of integrating new energy resources, such as hydrogen supply and solar energy as attractive options to decarbonize the present system. This paper explores the techno-economic benefits of integrating hydrogen supply, electric auxiliary power unit (APU) of aircraft, electric vehicles, photovoltaic energy (PV), and battery storage system into electrified airport energy system. The hydrogen fuel cell generation provides great flexibility to supply aircraft at remote stands, and reduces the carbon emissions caused by traditional fuel-powered APU. A mixed integer linear programming optimization method based on life cycle theory is developed for capacity sizing of hydrogen energy system, PV and battery storage, with optimization objective of minimizing the total economic costs as well as considering environmental benefits of the proposed airport microgrid system. Case studies are conducted by five different energy integration scenarios with techno-economic and environmental assessments to quantify the benefits of integrating hydrogen and renewable energy into airport. Compared with the benchmark scenarios, the integration of hydrogen energy system reduced the total annual costs and carbon emissions of airport energy system by 41.6% and 67.29%, respectively. Finally, sensitivity analysis of key system parameters such as solar irradiance, grid emission factor, elctricity price, carbon tax, unit investment cost of hydrogen energy system have been investigated to inform the design of hydrogen-solar-storage integrated energy system for future airport electrification.Item Unknown Techno-economic-environmental evaluation of aircraft propulsion electrification: Surrogate-based multi-mission optimal design approach(Elsevier, 2023-01-10) Zhang, Jinning; Roumeliotis, Ioannis; Zhang, Xin; Zolotas, ArgyriosDriven by the sustainability initiatives in the aviation sector, the emerging technologies of aircraft propulsion electrification have been identified as the promising approach to realize sustainable and decarbonized aviation. This study proposes a surrogate-based multi-mission optimal design approach for aircraft propulsion electrification, which innovatively incorporates realistic aviation operations into the electric aircraft design, with the aim of improving the overall aircraft fuel economy over multiple flight missions and conditions in practical scenarios. The proposed optimal design approach starts with the flight route data analysis to cluster the flight operational data using gaussian mixture model, so that a concise representation of flight mission profiles can be achieved. Then, an optimal orthogonal array-based Latin hypercubes are employed to generate sampling points of design variables for electrified aircraft propulsion. The mission analysis is performed with coupled propulsion-airframe integration in order to propose energy management strategy for mission-dependent aircraft performance. Consequently, fuel economy surrogate model is established via support vector machines to obtain the optimal design points of electrified aircraft propulsion. For assessing the viability of novel propulsion technologies, techno-economic evaluation is conducted using sensitivity analysis and breakeven electricity prices under a series of environmental regulatory policy scenarios.