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Increasing penetration of electric vehicles (EVs) and gas vehicles (GVs) will endanger safe and stable operation of power-gas distribution network. Energy storage systems are considered effective tools to deal with the surge of charging demands brought by EV/GV and enhance energy supply reliability. Meanwhile, the coupling between power and gas distribution systems has been strengthened in recent years via gas turbines. This makes it possible and imperative to jointly optimize the configuration of electrical and gas storage systems and avoid the overinvestment commonly occurring in separate planning. To this end, this paper proposes a joint electrical and gas energy storage planning approach considering the interdependency between power-gas distribution network and transportation network. First, the semi-dynamic traffic assignment method is utilized to obtain EV/GV traffic flow and its transition between two adjacent periods. EV/GV traffic flow is directly related to EV/GV charging demands and can be further converted into their spatial–temporal distribution. Second, a novel second-order cone formulation is proposed to accurately describe the nonlinear operating characteristic of gas storage system. This can help to incorporate the constraints associated with the effective operation of gas storage systems into the planning model, while ensuring the computational tractability. Finally, the proposed planning problem is formulated as a mixed-integer second-order conic programming problem. A penalty convex-concave procedure algorithm is developed to ensure the exactness of all second-order cone relaxations in our proposed model. Numerical results indicate that joint planning strategy can fulfill the peak charging load while yielding to low investment cost.
Chenjia Gu; Yao Zhang; Jianxue Wang; Qingtao Li. Joint planning of electrical storage and gas storage in power-gas distribution network considering high-penetration electric vehicle and gas vehicle. Applied Energy 2021, 301, 117447 .
AMA StyleChenjia Gu, Yao Zhang, Jianxue Wang, Qingtao Li. Joint planning of electrical storage and gas storage in power-gas distribution network considering high-penetration electric vehicle and gas vehicle. Applied Energy. 2021; 301 ():117447.
Chicago/Turabian StyleChenjia Gu; Yao Zhang; Jianxue Wang; Qingtao Li. 2021. "Joint planning of electrical storage and gas storage in power-gas distribution network considering high-penetration electric vehicle and gas vehicle." Applied Energy 301, no. : 117447.
The objective of this research work is to analyze wind characteristics and to assess wind power potential by selecting the best fit probability distribution function of Jhimpir Sindh Pakistan. This type of detailed investigation helps wind power generation companies in selecting suitable wind turbine and provides information of wind characteristics of potential site. Eight probability distribution functions are tested on the wind speed data from January 2015 to July 2018. Frequency bins of Weibull and Rayleigh distribution with maximum probabilities of 0.1210 and 0.1143 are most closest representation of our data. In order to, quantitatively analysis which distribution function is best fitting the local wind regime, we have applied the coefficient-of-determination, Kolmogorov-Smirnov, Chi square, Cramer-von Mises, Anderson-Darling tests along with Akaike information and Bayesian information criterion. These statistical test are used to rank the empirical distribution functions in order to identify two distribution function better fitting the actual wind speed data. After selecting two best fitted distribution functions, we analyze wind power potential and compare the error of wind power density based on these distribution functions (Weibull and Rayleigh). The power densities reported varied from 73.67 to 648.73W/m 2 . Results indicate that power densities of Weibull and Rayleigh for the candidate site are 84.67–698.65W/m 2 and 83.67–1021.4W/m 2 , respectively. The highest error for Weibull and Rayleigh are 0.1850 and 0.5745, respectively. Whereas lowest error are 0.0178 and 0.0180, respectively. Complete analysis suggested that Weibull distribution function is the most suitable for Jhimpir Sindh Pakistan and the studied site is suitable for wind power production. In addition, comprehensive analysis of wind direction at the candidate site suggested that Eastern and Southeastern wind directions are predominant with 38.52% and 33.24% of the total time.
Muhammad Armoghan Khan; Yao Zhang; Jianxue Wang; Jingdong Wei; Muhammad Ali Raza; Aitizaz Ahmad; Yiping Yuan. Determination of Optimal Parametric Distribution and Technical Evaluation of Wind Resource Characteristics for Wind Power Potential at Jhimpir, Pakistan. IEEE Access 2021, 9, 70118 -70141.
AMA StyleMuhammad Armoghan Khan, Yao Zhang, Jianxue Wang, Jingdong Wei, Muhammad Ali Raza, Aitizaz Ahmad, Yiping Yuan. Determination of Optimal Parametric Distribution and Technical Evaluation of Wind Resource Characteristics for Wind Power Potential at Jhimpir, Pakistan. IEEE Access. 2021; 9 ():70118-70141.
Chicago/Turabian StyleMuhammad Armoghan Khan; Yao Zhang; Jianxue Wang; Jingdong Wei; Muhammad Ali Raza; Aitizaz Ahmad; Yiping Yuan. 2021. "Determination of Optimal Parametric Distribution and Technical Evaluation of Wind Resource Characteristics for Wind Power Potential at Jhimpir, Pakistan." IEEE Access 9, no. : 70118-70141.
To enhance industrial park's economic gains and effectively allocate its electricity bill among industrial users with combined heat and power (CHP) units and photovoltaic (PV) panels, this paper proposes a distribution locational marginal price (DLMP)-based bi-level demand management approach. The upper level optimizes dispatching decisions of industrial users with the objective of minimizing their energy bills, and the lower level is a DLMP-based market clearing problem to minimize the two-part tariff cost of the industrial park operator. In order to solve the proposed bi-level model efficiently, it is first equivalently converted into a single-level mathematical programming with equilibrium constraints (MPEC), and then reformulated as a mixed-integer second-order conic programming (MISOCP) model by linearizing bilinear terms. Numerical results demonstrate the effectiveness of our proposed bi-level method in lowering industrial park's electricity bill and achieving effective allocation among users.
Jingdong Wei; Yao Zhang; Jianxue Wang; Lei Wu. Distribution LMP-Based Demand Management in Industrial Park via a Bi-Level Programming Approach. IEEE Transactions on Sustainable Energy 2021, 12, 1695 -1706.
AMA StyleJingdong Wei, Yao Zhang, Jianxue Wang, Lei Wu. Distribution LMP-Based Demand Management in Industrial Park via a Bi-Level Programming Approach. IEEE Transactions on Sustainable Energy. 2021; 12 (3):1695-1706.
Chicago/Turabian StyleJingdong Wei; Yao Zhang; Jianxue Wang; Lei Wu. 2021. "Distribution LMP-Based Demand Management in Industrial Park via a Bi-Level Programming Approach." IEEE Transactions on Sustainable Energy 12, no. 3: 1695-1706.
As the need for clean energy increases, massive distributed energy resources are deployed, strengthening the interdependence of multi-carrier energy systems. This has raised concerns on the electricity-heat system’s co-operation for lower operation costs, higher energy efficiency, and higher flexibility. This paper discusses the co-operation of integrated electricity–heat system. In the proposed model, network constraints in both systems are considered to guarantee system operations’ security: the branch flow model is utilized to describe the electricity network, while a convexified model considering variable mass flow and temperature dynamics is adopted to describe the heat network. Additionally, novel models for heat pumps and the stratified water tank are proposed to represent the physical system more accurately. Finally, to preserve the information privacy of separate systems, a distributed algorithm is proposed based on the alternating direction method of multipliers (ADMM). Numerical studies show that the co-operation could provide a more economical and reliable solution than the decoupled operation of the heat network and electricity network. Moreover, the ADMM-based algorithm could derive solutions very close to the optimum provided by centralized optimization.
Yang Chen; Yao Zhang; Jianxue Wang; Zelong Lu. Optimal Operation for Integrated Electricity–Heat System with Improved Heat Pump and Storage Model to Enhance Local Energy Utilization. Energies 2020, 13, 6729 .
AMA StyleYang Chen, Yao Zhang, Jianxue Wang, Zelong Lu. Optimal Operation for Integrated Electricity–Heat System with Improved Heat Pump and Storage Model to Enhance Local Energy Utilization. Energies. 2020; 13 (24):6729.
Chicago/Turabian StyleYang Chen; Yao Zhang; Jianxue Wang; Zelong Lu. 2020. "Optimal Operation for Integrated Electricity–Heat System with Improved Heat Pump and Storage Model to Enhance Local Energy Utilization." Energies 13, no. 24: 6729.
The accuracy of wind power forecasting depends a great deal on the data quality, which is so susceptible to cybersecurity attacks. In this paper, we study the cybersecurity issue of short-term wind power forecasting. We present one class of data attacks, called false data injection attacks, against wind power deterministic and probabilistic forecasting. We show that any malicious data can be injected to historical data without being discovered by one of the commonly-used anomaly detection techniques. Moreover, we testify that attackers can launch such data attacks even with limited resources. To study the impact of data attacks on the forecasting accuracy, we establish the framework of simulating false data injection attacks using the Monte Carlo method. Then, the robustness of six representative wind power forecasting models is tested. Numerical results on real-world data demonstrate that the support vector machine and k-nearest neighbors combined with kernel density estimator are the most robust deterministic and probabilistic forecasting ones among six representative models, respectively. Nevertheless, none of them can issue accurate forecasts under very strong false data attacks. This presents a serious challenge to the community of wind power forecasting. The challenge is to study robust wind power forecasting models dealing with false data attacks.
Yao Zhang; Fan Lin; Ke Wang. Robustness of Short-Term Wind Power Forecasting Against False Data Injection Attacks. Energies 2020, 13, 3780 .
AMA StyleYao Zhang, Fan Lin, Ke Wang. Robustness of Short-Term Wind Power Forecasting Against False Data Injection Attacks. Energies. 2020; 13 (15):3780.
Chicago/Turabian StyleYao Zhang; Fan Lin; Ke Wang. 2020. "Robustness of Short-Term Wind Power Forecasting Against False Data Injection Attacks." Energies 13, no. 15: 3780.
The increasing penetration of renewable energy brings great challenges to the planning and operation of power systems. To deal with the fluctuation of renewable energy, the main focus of current research is on incorporating the detailed operation constraints into generation expansion planning (GEP) models. In most studies, the traditional objective function of GEP is to minimize the total cost (including the investment and operation cost). However, in power systems with high penetration of renewable energy, more attention has been paid to increasing the utilization of renewable energy and reducing the renewable energy curtailment. Different from the traditional objective function, this paper proposes a new objective function to maximize the accommodation of renewable energy during the planning horizon, taking into account short-term operation constraints and uncertainties from load and renewable energy sources. A power grid of one province in China is modified as a case study to verify the rationality and effectiveness of the proposed model. Numerical results show that the proposed GEP model could install more renewable power plants and improve the accommodation of renewable energy compared to the traditional GEP model.
Qingtao Li; Jianxue Wang; Yao Zhang; Yue Fan; Guojun Bao; Xuebin Wang. Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources. Sustainability 2020, 12, 1083 .
AMA StyleQingtao Li, Jianxue Wang, Yao Zhang, Yue Fan, Guojun Bao, Xuebin Wang. Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources. Sustainability. 2020; 12 (3):1083.
Chicago/Turabian StyleQingtao Li; Jianxue Wang; Yao Zhang; Yue Fan; Guojun Bao; Xuebin Wang. 2020. "Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources." Sustainability 12, no. 3: 1083.
In this paper, we study the multi-period planning problem of multi-energy microgrids considering the long-term uncertainty (i.e., the declining trend of battery storage investment cost) and the short-term uncertainty (i.e., renewable energy generation and electrical/heat load). We first present the joint deterministic multi-period planning approach for multi-energy microgrid coupling electricity and heat carriers. Then, an information gap decision (IGD)-based multi-energy microgrid multi-period planning model dealing with the long-term uncertainty is proposed, and the proposed model is further converted into a mixed integer linear planning (MILP) IGD-based planning model. Next, to coordinate the long-term uncertainty and the short-term uncertainty in multi-energy microgrid planning problems, we develop a chance constrained (CC) IGD-based multi-period planning model and then convert such model into a MILP CC-IGD equivalence. Finally, the strengthened bilinear Benders decomposition (SBBD) algorithm is adopted to efficiently solve our proposed MILP CC-IGD model for large-scale multi-energy microgrid planning problems. Our numerical results demonstrate the advantage of the joint planning of electricity and heat supply systems in multi-energy microgrids. Case studies verify the effectiveness of considering multi-type uncertainties in multi-energy microgrid planning, especially the declining trend uncertainty of battery storage investment cost. Experimental results also show that the SBBD algorithm is more efficient on computing our proposed MILP CC-IGD model compared to commercial solvers, such as CPLEX.
Jingdong Wei; Yao Zhang; Jianxue Wang; Xiaoyu Cao; Muhammad Armoghan Khan. Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method. Applied Energy 2019, 260, 114188 .
AMA StyleJingdong Wei, Yao Zhang, Jianxue Wang, Xiaoyu Cao, Muhammad Armoghan Khan. Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method. Applied Energy. 2019; 260 ():114188.
Chicago/Turabian StyleJingdong Wei; Yao Zhang; Jianxue Wang; Xiaoyu Cao; Muhammad Armoghan Khan. 2019. "Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method." Applied Energy 260, no. : 114188.
As the rapid development of natural-gas fired units (NGUs), power systems begin to rely more on a natural gas system to supply the primary fuel. On the other hand, natural gas system contingency might cause the nonavailability of NGUs and inevitably jeopardize power system security. To address this issue, this paper studies security-constrained joint expansion planning problems for this combined energy system. We develop a computationally efficient mixed-integer linear programming (MILP) approach that simultaneously considers N-1 contingency in both natural gas system and electricity power system. To reduce the combinatorial search space of MILP models, an extension of a reduced disjunctive model is proposed to decrease the numbers of binary and continuous variables as well as constraints. The involving nonlinear terms in N-1 constraints are exactly linearized without sacrificing any optimality. Numerical results on two typical integrated energy systems demonstrate the necessity of extending N-1 criterion to the whole network of a combined energy system. Experimental results also show that compared with the conventional approach, our proposed MILP approach achieves a great computational performance improvement in solving security-constrained co-optimization expansion planning problems.
Yao Zhang; Yuan Hu; Jin Ma; Zhaohong Bie. A Mixed-Integer Linear Programming Approach to Security-Constrained Co-Optimization Expansion Planning of Natural Gas and Electricity Transmission Systems. IEEE Transactions on Power Systems 2018, 33, 6368 -6378.
AMA StyleYao Zhang, Yuan Hu, Jin Ma, Zhaohong Bie. A Mixed-Integer Linear Programming Approach to Security-Constrained Co-Optimization Expansion Planning of Natural Gas and Electricity Transmission Systems. IEEE Transactions on Power Systems. 2018; 33 (6):6368-6378.
Chicago/Turabian StyleYao Zhang; Yuan Hu; Jin Ma; Zhaohong Bie. 2018. "A Mixed-Integer Linear Programming Approach to Security-Constrained Co-Optimization Expansion Planning of Natural Gas and Electricity Transmission Systems." IEEE Transactions on Power Systems 33, no. 6: 6368-6378.
Using off-site predictors to capture spatio-temporal correlations among geographically distributed wind farms is seen as one solution to improve the forecast accuracy of wind power generation. However, in practice, wind farm operators are usually unwilling to share their private data with each other because of competitive reasons and security considerations. To address this issue, this paper presents how wind power probabilistic forecasting using off-site information could be achieved in a privacy-preserving and distributed fashion. Wind power probabilistic forecasts are created by means of multiple quantile regression. The original large-scale forecasting problem is first decomposed into a large number of small-scale subproblems. The subproblem can be computed locally on each farm. Then, the closed-form solution to the subproblem is derived exactly for achieving high computational efficiency. The proposed approach offers a flexible framework for using off-site information, but without having to exchange commercially sensitive data among all participants. It relies on the alternating direction method of multipliers algorithm to achieve the cooperation among all participants and finally converges to the optimal solution. Case studies with real-world data validate improvements in the forecast accuracy when considering spatio-temporal correlations. Distributed approaches also show higher computational efficiency than traditional centralized approaches.
Yao Zhang; Jianxue Wang. A Distributed Approach for Wind Power Probabilistic Forecasting Considering Spatio-Temporal Correlation Without Direct Access to Off-Site Information. IEEE Transactions on Power Systems 2018, 33, 5714 -5726.
AMA StyleYao Zhang, Jianxue Wang. A Distributed Approach for Wind Power Probabilistic Forecasting Considering Spatio-Temporal Correlation Without Direct Access to Off-Site Information. IEEE Transactions on Power Systems. 2018; 33 (5):5714-5726.
Chicago/Turabian StyleYao Zhang; Jianxue Wang. 2018. "A Distributed Approach for Wind Power Probabilistic Forecasting Considering Spatio-Temporal Correlation Without Direct Access to Off-Site Information." IEEE Transactions on Power Systems 33, no. 5: 5714-5726.
This letter presents an extension of reduced disjunctive model (RDM) to consider N-1 criterion in multi-stage transmission expansion planning (TEP). This extension is realized by exactly linearizing nonlinear terms induced by N-1 contingency constraints. Compared with the traditional approach, the extended RDM reduces the number of binary variables and constraints. Numerical results of three test systems indicate that the proposed approach significantly improves the computational performance without sacrificing the optimality of TEP problem.
Yao Zhang; Jianxue Wang; Yunhao Li; Xiuli Wang. An Extension of Reduced Disjunctive Model for Multi-Stage Security-Constrained Transmission Expansion Planning. IEEE Transactions on Power Systems 2017, 33, 1092 -1094.
AMA StyleYao Zhang, Jianxue Wang, Yunhao Li, Xiuli Wang. An Extension of Reduced Disjunctive Model for Multi-Stage Security-Constrained Transmission Expansion Planning. IEEE Transactions on Power Systems. 2017; 33 (1):1092-1094.
Chicago/Turabian StyleYao Zhang; Jianxue Wang; Yunhao Li; Xiuli Wang. 2017. "An Extension of Reduced Disjunctive Model for Multi-Stage Security-Constrained Transmission Expansion Planning." IEEE Transactions on Power Systems 33, no. 1: 1092-1094.