This page has only limited features, please log in for full access.

Unclaimed
Xu Wang
Department of Electrical Engineering, Shanghai Jiao Tong University, 12474 Shanghai, China, 200240

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 17 May 2021 in IEEE Transactions on Sustainable Energy
Reads 0
Downloads 0

Hybrid AC/DC microgrids allow the integration of diverse renewable energy resources. However, the security and the reliability of hybrid AC/DC microgrid systems are challenged by various contingencies. In this paper, we propose a BESS sizing and siting approach which uses a combined stochastic programming and robust optimization method to cope with uncertainties of loads, wind power generation, and BESS charging/discharging efficiency. The proposed iterative solution enhances voltage and frequency regulation performances in an islanded hybrid AC/DC microgrid which is subject to post-contingency corrective rescheduling. A successive linear power flow approximation method is adopted to simulate post-fault power flows and represent static frequency characteristics of loads and generators. The proposed iterative solution for the mixed-integer linear programming (MILP) formulation can achieve computational tractability for determining pre-fault and post-fault initial operation points. Case studies corroborate the effectiveness of the proposed model and the solution method.

ACS Style

Kai Gong; Xu Wang; Chuanwen Jiang; Mohammad Shahidehpour; Xiaolin Liu; Zean Zhu. Security-Constrained Optimal Sizing and Siting of BESS in Hybrid AC/DC Microgrid Considering Post-Contingency Corrective Rescheduling. IEEE Transactions on Sustainable Energy 2021, PP, 1 -1.

AMA Style

Kai Gong, Xu Wang, Chuanwen Jiang, Mohammad Shahidehpour, Xiaolin Liu, Zean Zhu. Security-Constrained Optimal Sizing and Siting of BESS in Hybrid AC/DC Microgrid Considering Post-Contingency Corrective Rescheduling. IEEE Transactions on Sustainable Energy. 2021; PP (99):1-1.

Chicago/Turabian Style

Kai Gong; Xu Wang; Chuanwen Jiang; Mohammad Shahidehpour; Xiaolin Liu; Zean Zhu. 2021. "Security-Constrained Optimal Sizing and Siting of BESS in Hybrid AC/DC Microgrid Considering Post-Contingency Corrective Rescheduling." IEEE Transactions on Sustainable Energy PP, no. 99: 1-1.

Journal article
Published: 04 February 2021 in International Journal of Electrical Power & Energy Systems
Reads 0
Downloads 0

Hybrid renewable energy system (HRES) is an effective approach to aggregate multiple renewables efficiently. This paper focuses on the optimal operation of a hybrid system consisting of pumped hydro storage, cascade hydropower station, run-of-river hydropower station and photovoltaic plant. Various elements and contradictory aims like reliability and economy makes the operation of HRES complex. With this regard, a multi-objective optimization model is proposed to maximize both effective load carrying capability (ELCC) and economic revenue of the hybrid system. An improved normal boundary intersection (NBI) method is proposed to solve the multi-objective problem and obtain the Pareto surface. Moreover, to ensure the optimality of the results, big-M method, binary expansion method integrated with linearization transformation techniques are employed to linearize the model. Results are presented and discussed in the case study, which verify the feasibility of the model, demonstrate the effectiveness of the improved NBI method and illustrate the significance of involving ELCC into HRES operation.

ACS Style

Zean Zhu; Xu Wang; Chuanwen Jiang; Lingling Wang; Kai Gong. Multi-objective optimal operation of pumped-hydro-solar hybrid system considering effective load carrying capability using improved NBI method. International Journal of Electrical Power & Energy Systems 2021, 129, 106802 .

AMA Style

Zean Zhu, Xu Wang, Chuanwen Jiang, Lingling Wang, Kai Gong. Multi-objective optimal operation of pumped-hydro-solar hybrid system considering effective load carrying capability using improved NBI method. International Journal of Electrical Power & Energy Systems. 2021; 129 ():106802.

Chicago/Turabian Style

Zean Zhu; Xu Wang; Chuanwen Jiang; Lingling Wang; Kai Gong. 2021. "Multi-objective optimal operation of pumped-hydro-solar hybrid system considering effective load carrying capability using improved NBI method." International Journal of Electrical Power & Energy Systems 129, no. : 106802.

Journal article
Published: 19 July 2019 in IET Renewable Power Generation
Reads 0
Downloads 0
ACS Style

Hao Cong; Xu Wang; Chuanwen Jiang. Robust coalitional game theoretic optimisation for cooperative energy hubs with correlated wind power. IET Renewable Power Generation 2019, 13, 2391 -2399.

AMA Style

Hao Cong, Xu Wang, Chuanwen Jiang. Robust coalitional game theoretic optimisation for cooperative energy hubs with correlated wind power. IET Renewable Power Generation. 2019; 13 (13):2391-2399.

Chicago/Turabian Style

Hao Cong; Xu Wang; Chuanwen Jiang. 2019. "Robust coalitional game theoretic optimisation for cooperative energy hubs with correlated wind power." IET Renewable Power Generation 13, no. 13: 2391-2399.

Research article
Published: 08 July 2019 in IET Generation, Transmission & Distribution
Reads 0
Downloads 0

The growing utilisation of natural gas and renewable energy resources brings more challenges to generation expansion planning problems. Short-term operational constraints are equally important for long-term capacity planning. This work studies the interdependence between electricity and natural gas systems and presents a combined market mechanism that allows two-stage energy trading and planning based on asynchronous electricity and gas markets. In the first stage, the gas market is cleared with the objective of maximum social welfare (lower level), at the same time obtaining the optimal strategies offered by gas producers and gas-fired units (upper level). In the second stage, generation companies and consumer companies aim to maximise their corresponding profits in the planning horizon (upper level), and electricity market is cleared on principle of maximum social welfare (lower level). Then, the authors develop a modified alternative direction method of multiplier algorithm to solve the two-stage nested bilevel model. To improve operational flexibility of expansion plans, uncertainties of renewable energy generation and integrated demand response are also included and analysed in different risk scenarios. Case studies validate the effectiveness of the proposed methodology.

ACS Style

Hao Cong; Xu Wang; Chuanwen Jiang. Two‐stage nested bilevel model for generation expansion planning in combined electricity and gas markets. IET Generation, Transmission & Distribution 2019, 13, 3443 -3454.

AMA Style

Hao Cong, Xu Wang, Chuanwen Jiang. Two‐stage nested bilevel model for generation expansion planning in combined electricity and gas markets. IET Generation, Transmission & Distribution. 2019; 13 (15):3443-3454.

Chicago/Turabian Style

Hao Cong; Xu Wang; Chuanwen Jiang. 2019. "Two‐stage nested bilevel model for generation expansion planning in combined electricity and gas markets." IET Generation, Transmission & Distribution 13, no. 15: 3443-3454.

Journal article
Published: 23 April 2019 in Energies
Reads 0
Downloads 0

Capable of aggregating multiple energy resources, the energy service company (ESCO) has been regarded as a promising alternative for improving power system flexibility and facilitate the consumption of renewable resources in the energy market. However, the issues have become significantly more serious related to the privacy and security of the data in consumption and trading. In this paper, we address the problem by proposing a privacy-preserving energy scheduling (PPES) model based on energy blockchain network. A Lagrangian relaxation method is applied to decompose the model into several individual optimal scheduling problems, and the individual scheduling problems are solved by consensus algorithm and smart contracts in energy blockchain network. The performance of the proposed model and method is evaluated with several case studies based on multiple energy nodes. Simulation results show the rationality and validity of the proposed method, and the model is conducive to the protection of environment and transparent scheduling of energy service companies (ESCOs). In addition, it can reflect the information of energy demand and supply to improve the privacy and security of data.

ACS Style

Shengmin Tan; Xu Wang; Chuanwen Jiang. Privacy-Preserving Energy Scheduling for ESCOs Based on Energy Blockchain Network. Energies 2019, 12, 1530 .

AMA Style

Shengmin Tan, Xu Wang, Chuanwen Jiang. Privacy-Preserving Energy Scheduling for ESCOs Based on Energy Blockchain Network. Energies. 2019; 12 (8):1530.

Chicago/Turabian Style

Shengmin Tan; Xu Wang; Chuanwen Jiang. 2019. "Privacy-Preserving Energy Scheduling for ESCOs Based on Energy Blockchain Network." Energies 12, no. 8: 1530.

Journal article
Published: 12 April 2019 in IEEE Transactions on Smart Grid
Reads 0
Downloads 0

When multiple distributed energy resource (DER) aggregators exist in a non-cooperative power market, the calculation of individual aggregator’s bidding strategies could encounter significant uncertainties for considering DERs and competing market participants’ bidding strategies. In this paper, a bi-level bidding strategy optimization model is proposed for a DER aggregator which utilizes wind power, ESS (energy storage system), and curtailable load. At the upper level, the designated aggregator’s bidding strategy is optimized considering the wind power uncertainty. The wind forecast error is modeled by an ambiguity set using the data-driven approach. The Information Gap Decision Theory (IGDT) method is employed in this paper to maximize the risk level the designated aggregator can bear for a certain level of expected payoff. By detecting the worst case in wind power generation, the upper-level model is linearized as a MILP. The designated aggregator submits its bids to the market using the linear utility function acquired from linear regression. At the lower level, the market clearing is carried out using competing market participants’ bidding strategy scenarios. The scenarios and the corresponding probability are modeled through a data-driven approach. The market clearing problem is linearized using Taylor series. The price signal is iterated between the two levels as the proposed bi-level model is solved. Numerical results prove the validity and effectiveness of the proposed IGDT-based method. It is shown that the aggregator can adjust either the bidding quantities or coefficients to reach an expected payoff level. The bidding strategies are affected by uncertainties of wind power and competing bidding strategies. For an expected payoff level, when the designated aggregator is posed to consider a higher risk of wind power uncertainty, the aggregator can only bear a lower risk level from competing bidding strategies and vice versa.

ACS Style

Bosong Li; Xu Wang; Mohammad Shahidehpour; Chuanwen Jiang; Zhiyi Li. DER Aggregator’s Data-Driven Bidding Strategy Using the Information Gap Decision Theory in a Non-Cooperative Electricity Market. IEEE Transactions on Smart Grid 2019, 10, 6756 -6767.

AMA Style

Bosong Li, Xu Wang, Mohammad Shahidehpour, Chuanwen Jiang, Zhiyi Li. DER Aggregator’s Data-Driven Bidding Strategy Using the Information Gap Decision Theory in a Non-Cooperative Electricity Market. IEEE Transactions on Smart Grid. 2019; 10 (6):6756-6767.

Chicago/Turabian Style

Bosong Li; Xu Wang; Mohammad Shahidehpour; Chuanwen Jiang; Zhiyi Li. 2019. "DER Aggregator’s Data-Driven Bidding Strategy Using the Information Gap Decision Theory in a Non-Cooperative Electricity Market." IEEE Transactions on Smart Grid 10, no. 6: 6756-6767.

Journal article
Published: 18 March 2019 in Applied Sciences
Reads 0
Downloads 0

This paper proposes a coordinated active–reactive power optimization model for an active distribution network with energy storage systems, where the active and reactive resources are handled simultaneously. The model aims to minimize the power losses, the operation cost, and the voltage deviation of the distribution network. In particular, the reactive power capabilities of distributed generators and energy storage systems are fully utilized to minimize power losses and improve voltage profiles. The uncertainties pertaining to the forecasted values of renewable energy sources are modelled by scenario-based stochastic programming. The second-order cone programming relaxation method is used to deal with the nonlinear power flow constraints and transform the original mixed integer nonlinear programming problem into a tractable mixed integer second-order cone programming model, thus the difficulty of problem solving is significantly reduced. The 33-bus and 69-bus distribution networks are used to demonstrate the effectiveness of the proposed approach. Simulation results show that the proposed coordinated optimization approach helps improve the economic operation for active distribution network while improving the system security significantly.

ACS Style

Lingling Wang; Xu Wang; Chuanwen Jiang; Shuo Yin; Meng Yang. Dynamic Coordinated Active–Reactive Power Optimization for Active Distribution Network with Energy Storage Systems. Applied Sciences 2019, 9, 1129 .

AMA Style

Lingling Wang, Xu Wang, Chuanwen Jiang, Shuo Yin, Meng Yang. Dynamic Coordinated Active–Reactive Power Optimization for Active Distribution Network with Energy Storage Systems. Applied Sciences. 2019; 9 (6):1129.

Chicago/Turabian Style

Lingling Wang; Xu Wang; Chuanwen Jiang; Shuo Yin; Meng Yang. 2019. "Dynamic Coordinated Active–Reactive Power Optimization for Active Distribution Network with Energy Storage Systems." Applied Sciences 9, no. 6: 1129.

Journal article
Published: 02 March 2019 in Applied Sciences
Reads 0
Downloads 0

Coordination of a hydropower, combined heat and power (CHP), and battery energy storage system (BESS) with multiple renewable energy sources (RES) can effectively reduce the adverse effects of large-scale renewable energy integration in power systems. This paper proposes a concept of a renewable-based hybrid energy system and puts forward an optimal scheduling model of this system, taking into account the cost of operation and risk. An optimization method is proposed based on Latin hypercube sampling, scene reduction, and piecewise linearization. Firstly, a large number of samples were generated with the Latin hypercube sampling method according to the uncertainties, including the renewable resources availability, the load demand, and the risk aversion coefficients, and the generated samples were reduced with a scene reduction method. Secondly, the piecewise linearization method was applied to convert nonlinear constraints into linear to obtain the best results of each scene. Finally, the performance of the proposed model and method was evaluated based on case studies with real-life data. Results showed that the renewable-based hybrid system can not only reduce the intermittent and volatility of renewable resources but also ensure the smooth of tie-line power as much as possible. The proposed model and method are universal, feasible, and effective.

ACS Style

Shengmin Tan; Xu Wang; Chuanwen Jiang. Optimal Scheduling of Hydro–PV–Wind Hybrid System Considering CHP and BESS Coordination. Applied Sciences 2019, 9, 892 .

AMA Style

Shengmin Tan, Xu Wang, Chuanwen Jiang. Optimal Scheduling of Hydro–PV–Wind Hybrid System Considering CHP and BESS Coordination. Applied Sciences. 2019; 9 (5):892.

Chicago/Turabian Style

Shengmin Tan; Xu Wang; Chuanwen Jiang. 2019. "Optimal Scheduling of Hydro–PV–Wind Hybrid System Considering CHP and BESS Coordination." Applied Sciences 9, no. 5: 892.

Journal article
Published: 27 February 2019 in IEEE Transactions on Smart Grid
Reads 0
Downloads 0

DR (Demand Response) activation and dispatch introduce additional economic and security benefits to the power system operation and control. The strategies for DR activation and compensation are among critical market operation issues which continue to raise concerns among stakeholders. In this respect, DR cost allocation would be required to guarantee DR benefits to the ISO (Independent System Operator) and individual participating consumers. This paper proposes a DR activation strategy considering hourly system load and LMPs in which payoffs are optimized at effective LMPs (Locational Marginal Prices) where DR dispatch occurs. The paper presents a closed form solution for the DR activation strategy according to the FERC’s Net Benefits Test. A DR cost reallocation method is proposed using the contribution factor theory for managing financial risks which ensures market participants’ optimal payoff corresponding to the DR dispatch. The proposed framework is extended to consider practical DR activation strategies and cost reallocations. Case studies based on the IEEE 6-bus system and the IEEE 118-bus system illustrate the validity and effectiveness of the proposed DR activation strategy and cost reallocation method.

ACS Style

Bosong Li; Xu Wang; Mohammad Shahidehpour; Chuanwen Jiang; Zhiyi Li. Optimal DR Activation Strategy for Risk Aversion Considering Hourly Loads and Locational Prices. IEEE Transactions on Smart Grid 2019, 10, 6203 -6213.

AMA Style

Bosong Li, Xu Wang, Mohammad Shahidehpour, Chuanwen Jiang, Zhiyi Li. Optimal DR Activation Strategy for Risk Aversion Considering Hourly Loads and Locational Prices. IEEE Transactions on Smart Grid. 2019; 10 (6):6203-6213.

Chicago/Turabian Style

Bosong Li; Xu Wang; Mohammad Shahidehpour; Chuanwen Jiang; Zhiyi Li. 2019. "Optimal DR Activation Strategy for Risk Aversion Considering Hourly Loads and Locational Prices." IEEE Transactions on Smart Grid 10, no. 6: 6203-6213.

Review
Published: 18 December 2018 in IET Generation, Transmission & Distribution
Reads 0
Downloads 0
ACS Style

Gao Zhang; Chuanwen Jiang; Xu Wang. Comprehensive review on structure and operation of virtual power plant in electrical system. IET Generation, Transmission & Distribution 2018, 13, 145 -156.

AMA Style

Gao Zhang, Chuanwen Jiang, Xu Wang. Comprehensive review on structure and operation of virtual power plant in electrical system. IET Generation, Transmission & Distribution. 2018; 13 (2):145-156.

Chicago/Turabian Style

Gao Zhang; Chuanwen Jiang; Xu Wang. 2018. "Comprehensive review on structure and operation of virtual power plant in electrical system." IET Generation, Transmission & Distribution 13, no. 2: 145-156.

Journal article
Published: 19 October 2018 in IEEE Transactions on Sustainable Energy
Reads 0
Downloads 0

Aggregation is an effective way of collective management of demand-side resources (DSRs). As an independent entity in electricity market, DSR aggregator can participate in both the energy and ancillary services markets. The DSR aggregator's optimal bidding strategy is subject to market price uncertainties and resource variability. In this paper, spatial correlation of wind power generation is considered in developing a robust DSR bidding strategy which takes into account individual and cooperative aggregators. The proposed model is transformed into and solved as a MISOCP. The Aumann-Shapley procedure is applied in this paper to allocate the payoff among cooperative DSR aggregators considering potential uncertainties. By replacing binary variables with their optimal solution value, the original MISOCP is transformed into an SOCP and Lagrange multipliers are employed for the implantation of a discrete Aumann-Shapley procedure. Case studies verify the feasibility and effectiveness of the proposed bidding model and payoff allocation procedure.

ACS Style

Bosong Li; Xu Wang; Mohammad Shahidehpour; Chuanwen Jiang; Zhiyi Li. Robust Bidding Strategy and Profit Allocation for Cooperative DSR Aggregators With Correlated Wind Power Generation. IEEE Transactions on Sustainable Energy 2018, 10, 1904 -1915.

AMA Style

Bosong Li, Xu Wang, Mohammad Shahidehpour, Chuanwen Jiang, Zhiyi Li. Robust Bidding Strategy and Profit Allocation for Cooperative DSR Aggregators With Correlated Wind Power Generation. IEEE Transactions on Sustainable Energy. 2018; 10 (4):1904-1915.

Chicago/Turabian Style

Bosong Li; Xu Wang; Mohammad Shahidehpour; Chuanwen Jiang; Zhiyi Li. 2018. "Robust Bidding Strategy and Profit Allocation for Cooperative DSR Aggregators With Correlated Wind Power Generation." IEEE Transactions on Sustainable Energy 10, no. 4: 1904-1915.

Journal article
Published: 24 August 2018 in IEEE Transactions on Power Systems
Reads 0
Downloads 0

Increasingly the prevalence of electric vehicles (EVs) calls for an effective planning of charging stations by coupling transportation network (TN) with power distribution network (PDN). This paper conducts an interdisciplinary study on the coordinated planning strategy of EV charging stations and coupled traffic-electric network. A comprehensive model is proposed, which determines the optimal expansion strategies for both TN and PDN, including sites and sizes of new charging stations, charging spots, TN lanes and PDN lines. In TN, we use an unconstrained traffic assignment model (UTAM) to explicitly capture the steady-state distribution of traffic flows. In PDN, operating conditions are described by a linearized DistFlow. Moreover, EV charging demands and interdependency of TN and PDN are characterized by EV charging station location model (ECSLM). A mixed-integer linear programming (MILP) with UTAM constraints is formulated for the coordinated planning model, where a global optimal solution is obtained through a deterministic branch-and-bound method. Numerical experiments on a coupled traffic-electric network are conducted to validate the effectiveness of the proposed model and the solution method.

ACS Style

Xu Wang; Mohammad Shahidehpour; Chuanwen Jiang; Zhiyi Li. Coordinated Planning Strategy for Electric Vehicle Charging Stations and Coupled Traffic-Electric Networks. IEEE Transactions on Power Systems 2018, 34, 268 -279.

AMA Style

Xu Wang, Mohammad Shahidehpour, Chuanwen Jiang, Zhiyi Li. Coordinated Planning Strategy for Electric Vehicle Charging Stations and Coupled Traffic-Electric Networks. IEEE Transactions on Power Systems. 2018; 34 (1):268-279.

Chicago/Turabian Style

Xu Wang; Mohammad Shahidehpour; Chuanwen Jiang; Zhiyi Li. 2018. "Coordinated Planning Strategy for Electric Vehicle Charging Stations and Coupled Traffic-Electric Networks." IEEE Transactions on Power Systems 34, no. 1: 268-279.

Journal article
Published: 20 June 2018 in IEEE Transactions on Smart Grid
Reads 0
Downloads 0

Traffic lights play a critical role in mitigating traffic congestions, which can be energized by distributed generators (DGs) when power outages occur in urban areas. This paper studies the resilience enhancement strategy by line hardening and DG placement when outages occur in distribution lines and traffic lights in the coupled power distribution system and urban transportation system (PDS-UTS). A tri-level optimization model is formulated with a limited budget for line hardening and DG placement to minimize the cost of load shedding and aggregated vehicle travel time. The first level is to determine line hardening and DG placement strategies, the second level is to search for the worst case of faulted lines that would maximize load shedding and aggregated vehicle travel time, and the third level is to minimize the corresponding costs of load shedding and travel. In UTS, a dynamic user equilibrium (DUE) model is established in a cell transmission model (CTM) and solved by a linear complementarity approach. As the number of unavailable lines and traffic lights are definite in the inner-most level, the coupled PDS-UTS is considered as two decoupled systems. Accordingly, the tri-level model is converted into an equivalent bi-level model through Karush-Kuhn-Tucker (KKT) conditions, which is then solved by a greedy search method. Case studies corroborate the effectiveness of the proposed model and relevant solution method for the coupled PDS-UTS.

ACS Style

Xu Wang; Mohammad Shahidehpour; Chuanwen Jiang; Zhiyi Li. Resilience Enhancement Strategies for Power Distribution Network Coupled With Urban Transportation System. IEEE Transactions on Smart Grid 2018, 10, 4068 -4079.

AMA Style

Xu Wang, Mohammad Shahidehpour, Chuanwen Jiang, Zhiyi Li. Resilience Enhancement Strategies for Power Distribution Network Coupled With Urban Transportation System. IEEE Transactions on Smart Grid. 2018; 10 (4):4068-4079.

Chicago/Turabian Style

Xu Wang; Mohammad Shahidehpour; Chuanwen Jiang; Zhiyi Li. 2018. "Resilience Enhancement Strategies for Power Distribution Network Coupled With Urban Transportation System." IEEE Transactions on Smart Grid 10, no. 4: 4068-4079.

Journal article
Published: 29 December 2017 in IEEE Transactions on Sustainable Energy
Reads 0
Downloads 0

Line hardening refers to strengthening certain distribution lines that will not be subject to outages during extreme conditions. Line hardening provides a resilient solution in the case of major faults in a radial or meshed distribution system. We propose a robust optimal line hardening method coupled with multiple provisional microgrids to improve the distribution system resilience against worst N-k contingencies. A tri-level optimal model is considered with the objectives of minimizing the costs of line hardening and the operation of multiple islanded provisional microgrids, which could include the cost of load shedding in each provisional microgrid considering the worst N-k contingencies. Specifically, a two-stage robust optimization model is formulated and Benders decomposition (BD) algorithm coupled with iterative relaxation procedure (IRP) is developed to enable the tractable computation. In our work, the master problem determines line hardening strategies and the subproblem discovers the impact of the worst N-k contingencies corresponding to the optimal operation of reformed provisional microgrids. The second-stage max-min subproblem is solved efficiently using linearization techniques, duality theory and IRP. Our computational results for the IEEE distribution test systems validate the effectiveness of the proposed model and reveal that distributed generation is critical in increasing the resilience of distribution systems corresponding to the worst N-k contingencies in provisional microgrids.

ACS Style

Xu Wang; Zhiyi Li; Mohammad Shahidehpour; Chuanwen Jiang. Robust Line Hardening Strategies for Improving the Resilience of Distribution Systems With Variable Renewable Resources. IEEE Transactions on Sustainable Energy 2017, 10, 386 -395.

AMA Style

Xu Wang, Zhiyi Li, Mohammad Shahidehpour, Chuanwen Jiang. Robust Line Hardening Strategies for Improving the Resilience of Distribution Systems With Variable Renewable Resources. IEEE Transactions on Sustainable Energy. 2017; 10 (1):386-395.

Chicago/Turabian Style

Xu Wang; Zhiyi Li; Mohammad Shahidehpour; Chuanwen Jiang. 2017. "Robust Line Hardening Strategies for Improving the Resilience of Distribution Systems With Variable Renewable Resources." IEEE Transactions on Sustainable Energy 10, no. 1: 386-395.

Journal article
Published: 29 December 2017 in IEEE Transactions on Power Systems
Reads 0
Downloads 0

This paper introduces an efficient method for calculating the three-phase power flow in a loop-based microgrid. The proposed method incorporates the conventional Newton-Raphson (NR) iterative approach in a backward/forward sweep (BFS) algorithm for power distribution network analyses. Conventional compensation-based approaches are commonly used to account for loop breakpoints (LBPs) and PV nodes. However, the efficiency and the convergence of traditional solutions deteriorate as the number of loops and PV nodes increases. In this paper, we convert microgrid loops into radial structures by breaking up LBPs, when PV nodes connected to distributed generators (DGs) are regulated with scheduled constant voltage magnitudes. Then, we apply a three-phase BFS-based power flow method with an acceptable convergence for radial distribution networks. Next, we use the NR method for power mismatch corrections at LBPs and PV nodes. Finally, the proposed method is extended to islanded microgrids by introducing the system frequency as a variable. We label the proposed loop-based method an NR-BFS power flow calculation scheme, which combines NR and BFS methods for microgrid solutions. The solution of the proposed algorithm, which signifies the application of the improved BFS method, is applicable to active distribution systems with several loops and DGs. The simulation results demonstrate the efficiency of the proposed method in the loop-based microgrid applications.

ACS Style

Xu Wang; Mohammad Shahidehpour; Chuanwen Jiang; Wei Tian; Zhiyi Li; Yiyun Yao. Three-Phase Distribution Power Flow Calculation for Loop-Based Microgrids. IEEE Transactions on Power Systems 2017, 33, 3955 -3967.

AMA Style

Xu Wang, Mohammad Shahidehpour, Chuanwen Jiang, Wei Tian, Zhiyi Li, Yiyun Yao. Three-Phase Distribution Power Flow Calculation for Loop-Based Microgrids. IEEE Transactions on Power Systems. 2017; 33 (4):3955-3967.

Chicago/Turabian Style

Xu Wang; Mohammad Shahidehpour; Chuanwen Jiang; Wei Tian; Zhiyi Li; Yiyun Yao. 2017. "Three-Phase Distribution Power Flow Calculation for Loop-Based Microgrids." IEEE Transactions on Power Systems 33, no. 4: 3955-3967.

Journal article
Published: 18 August 2014 in IEEE Transactions on Power Systems
Reads 0
Downloads 0

Most existing carbon emission management strategies only control the total carbon emission without focusing on both the regional carbon emission and the stochastic properties of the system. Correlated regional loads and unpredictable renewable energies in the power system make regional carbon emission management (RCEM) increasingly challenging and necessary. A complex multi-objective RCEM model based on probabilistic power flow (PPF) considering correlated variables is contributed in this paper. The three objective functions to be minimized are 1) the cost of electricity generated, 2) the total carbon emission, and 3) the carbon emission difference among regions which reflects the regional carbon emission imbalance from the supply side. A new clonal selection algorithm (CSA) coupled with a fuzzy satisfying decision method and an extended 2 m +1 point estimate method (PEM) is proposed to solve this multi-objective RCEM model. The proposed method is illustrated through IEEE 30-bus, IEEE 118-bus and simplified Shanghai case studies. The proposed model can help reduce the total carbon emission, control regional carbon emission, prevent probabilistic congested lines from overloading, and choose the most suitable region for wind farms (WFs).

ACS Style

Xu Wang; Yu Gong; Chuanwen Jiang. Regional Carbon Emission Management Based on Probabilistic Power Flow With Correlated Stochastic Variables. IEEE Transactions on Power Systems 2014, 30, 1094 -1103.

AMA Style

Xu Wang, Yu Gong, Chuanwen Jiang. Regional Carbon Emission Management Based on Probabilistic Power Flow With Correlated Stochastic Variables. IEEE Transactions on Power Systems. 2014; 30 (2):1094-1103.

Chicago/Turabian Style

Xu Wang; Yu Gong; Chuanwen Jiang. 2014. "Regional Carbon Emission Management Based on Probabilistic Power Flow With Correlated Stochastic Variables." IEEE Transactions on Power Systems 30, no. 2: 1094-1103.