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For the popular second-order conic program (SOCP) formulation of AC optimal power flow (OPF) in a radial network, this paper first shows that it does not have the strong duality property in general. Then, through a series of restrictive reformulations, we derive a set of closed-form sufficient conditions on network parameters that ensure its strong duality. Numerical studies on IEEE 33-bus, 69-bus test networks and two real-world distribution systems confirm that non-negligible duality gaps do exist in this SOCP formulation, and also demonstrate the validity of proposed sufficient conditions on closing the duality gap. Our results provide an analytical tool to ensure strong duality of the SOCP power flow formulation and to support algorithm developments for its complex extensions.
Xiaoyu Cao; Jianxue Wang; Bo Zeng. A Study on the Strong Duality of Second-Order Conic Relaxation of AC Optimal Power Flow in Radial Networks. IEEE Transactions on Power Systems 2021, PP, 1 -1.
AMA StyleXiaoyu Cao, Jianxue Wang, Bo Zeng. A Study on the Strong Duality of Second-Order Conic Relaxation of AC Optimal Power Flow in Radial Networks. IEEE Transactions on Power Systems. 2021; PP (99):1-1.
Chicago/Turabian StyleXiaoyu Cao; Jianxue Wang; Bo Zeng. 2021. "A Study on the Strong Duality of Second-Order Conic Relaxation of AC Optimal Power Flow in Radial Networks." IEEE Transactions on Power Systems PP, no. 99: 1-1.
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.
Multi-energy microgrid (MEMG) is a typical realization of multi-energy system. Its day-ahead dispatch is crucial for the economic operation of MEMG. However, previous research usually considered the efficiency of multi-energy coupling equipment in MEMG as a constant value neglecting the non-linear relationship of heterogeneous energy conversion. With this, day-ahead dispatch would result in inappropriate plans and further impair safe operation of MEMG. To deal with this challenge, this paper studies a novel day-ahead dispatch model for MEMG by considering variable operating conditions of multi-energy coupling equipment. Specifically, first, a novel day-ahead dispatch model of MEMG is proposed. The day-ahead dispatch model incorporates refined energy conversion constraints, and these constraints can capture variable operating conditions of multi-energy coupling equipment. Then, due to the non-linear terms with respect to variable operating conditions, a piecewise linearization method is utilized to transform the proposed model into a mixed-integer linear programming formulation. Finally, simulation results show that our approach can obtain a more reasonable day-ahead dispatch scheme with multiple advantages, such as improving the overall operating economy, avoiding multi-energy power curtailment, and enhancing the potential of demand response.
Weizhen Yong; Jianxue Wang; Zelong Lu; Fan Yang; Zilong Zhang; Jingdong Wei; Junfeng Wang. Day-ahead dispatch of multi-energy system considering operating conditions of multi-energy coupling equipment. Energy Reports 2021, 7, 100 -110.
AMA StyleWeizhen Yong, Jianxue Wang, Zelong Lu, Fan Yang, Zilong Zhang, Jingdong Wei, Junfeng Wang. Day-ahead dispatch of multi-energy system considering operating conditions of multi-energy coupling equipment. Energy Reports. 2021; 7 ():100-110.
Chicago/Turabian StyleWeizhen Yong; Jianxue Wang; Zelong Lu; Fan Yang; Zilong Zhang; Jingdong Wei; Junfeng Wang. 2021. "Day-ahead dispatch of multi-energy system considering operating conditions of multi-energy coupling equipment." Energy Reports 7, no. : 100-110.
The increasing of distributed energy and flexible load incentivize market participants to participate in a more active market. A coordinated peer to peer (P2P) trading model with an aggregated alliance and reserve purchasing is proposed in this paper. Under such a coordinated trading model, the market participants form an alliance, where the agents perform aggregated alliance and purchase reserve from a mobile energy storage supplier. To reduce the risk of deviation penalty in P2P trading, the agents seek to maximize the welfare of the entire alliance in the P2P process, while considering product differentiation and deviation risks. The proposed trading model design comprises: (i) a coordinated P2P market design with the aggregated alliance and reserve purchasing, (ii) a two-stage P2P market-clearing model to maximize trading utility while reducing the risk of deviation. To solve this two-stage multivariable coupling problem in a distributed way, we propose a primal–dual based ADMM method. Through the case study, compared with the traditional P2P trading model, the proposed market model can reduce the deviation penalty and improve the comprehensive welfare, while the convergence can be effectively guaranteed.
Zelong Lu; Jianxue Wang; Weizhen Yong; Zhiwei Tang; Meng Yang; Bin Zhang. Coordinated P2P electricity trading model with aggregated alliance and reserve purchasing for hedging the risk of deviation penalty. Energy Reports 2021, 7, 426 -435.
AMA StyleZelong Lu, Jianxue Wang, Weizhen Yong, Zhiwei Tang, Meng Yang, Bin Zhang. Coordinated P2P electricity trading model with aggregated alliance and reserve purchasing for hedging the risk of deviation penalty. Energy Reports. 2021; 7 ():426-435.
Chicago/Turabian StyleZelong Lu; Jianxue Wang; Weizhen Yong; Zhiwei Tang; Meng Yang; Bin Zhang. 2021. "Coordinated P2P electricity trading model with aggregated alliance and reserve purchasing for hedging the risk of deviation penalty." Energy Reports 7, no. : 426-435.
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.
With the large‐scale integration of centralized renewable energy (RE), the problem of RE curtailment and system operation security is becoming increasingly prominent. As a promising solution technology, energy storage system (ESS) has gradually gained attention in many fields. However, without meticulous planning and benefit assessment, installing ESSs may lead to a relatively long payback period, and it could be a barrier to properly guiding industry planning and development. In this article, we present a comprehensive framework to incorporate both the investment and operational benefits of ESS, and quantitatively assess operational benefits (ie, energy transfer and ancillary services benefits). The time‐sequential operation simulation method is introduced to quantify the different operational benefits more accurately. Finally, we analyze the coupling relationships among these benefits and design a decoupling method to separate them. A case study on a modified practical power system is investigated. Numerical results show that the operational benefits of ESS are fully investigated and properly measured. In addition, ESSs' operational benefits will increase with the RE penetration and proper selection of the installed capacity of ESSs.
Chenjia Gu; Jianxue Wang; Qian Yang; Xiuli Wang. Assessing operational benefits of large‐scale energy storage in power system: Comprehensive framework, quantitative analysis, and decoupling method. International Journal of Energy Research 2021, 45, 10191 -10207.
AMA StyleChenjia Gu, Jianxue Wang, Qian Yang, Xiuli Wang. Assessing operational benefits of large‐scale energy storage in power system: Comprehensive framework, quantitative analysis, and decoupling method. International Journal of Energy Research. 2021; 45 (7):10191-10207.
Chicago/Turabian StyleChenjia Gu; Jianxue Wang; Qian Yang; Xiuli Wang. 2021. "Assessing operational benefits of large‐scale energy storage in power system: Comprehensive framework, quantitative analysis, and decoupling method." International Journal of Energy Research 45, no. 7: 10191-10207.
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 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.
The advanced switching techniques enable the topology reconfiguration of microgrids (MGs) in active distribution network. In this paper, we enhance and generalize the traditional reconfiguration strategy resorting to the concept of “dynamic MGs” (i.e., the reorganization of MGs boundaries), to achieve a higher operational feasibility against the emergency islandings. Also, a risk-averse two-stage mixed integer conic program model is presented to support the networked MGs planning with generalized reconfiguration decisions. The MGs capacity expansion and seasonal reconfiguration decisions are made in the first stage, and validated under stochastic islanding scenarios in the second stage, where the network operations are captured by a second-order conic program (SOCP). Furthermore, a conditional value-at-risk (CVaR) measure is involved to quantitatively control the islanding risks. By theoretically proving the strong duality of the SOCP subproblem, we develop and customize Benders decomposition method with the guaranteed finite convergence to the optimal value. Finally, numerical results on 33- and 56-bus networked MGs validate the effectiveness of proposed reconfiguration strategy as well as planning approach. Our method demonstrates a cost-saving up to 22.56% when comparing to the traditional scheme with fixed MGs boundaries.
Xiaoyu Cao; Jianxue Wang; Jianhui Wang; Bo Zeng. A Risk-Averse Conic Model for Networked Microgrids Planning With Reconfiguration and Reorganizations. IEEE Transactions on Smart Grid 2019, 11, 696 -709.
AMA StyleXiaoyu Cao, Jianxue Wang, Jianhui Wang, Bo Zeng. A Risk-Averse Conic Model for Networked Microgrids Planning With Reconfiguration and Reorganizations. IEEE Transactions on Smart Grid. 2019; 11 (1):696-709.
Chicago/Turabian StyleXiaoyu Cao; Jianxue Wang; Jianhui Wang; Bo Zeng. 2019. "A Risk-Averse Conic Model for Networked Microgrids Planning With Reconfiguration and Reorganizations." IEEE Transactions on Smart Grid 11, no. 1: 696-709.
In the smart grid era, high granular data play an important role in providing an enormous amount of information for industry and commerce, both temporally and spatially. With massive data, a hierarchical structure can be constructed, containing load series at diverse levels. With the fluctuation and uncertainty of power supply and demand increasing rapidly, hierarchical probabilistic load forecasting is necessary for a hierarchy formed by power system network, which can provide comprehensive information on electricity consumption at different levels. System operators or power market participants can make coherent decisions based on coherent forecasting. The challenge for hierarchical probabilistic load forecasting is how to produce probabilistically coherent forecasts. In order to simplify the prediction procedure and improve the prediction accuracy, an effective approach that could generate probabilistically coherent forecasts for a hierarchy is introduced in this paper. The proposed methodology has three major achievements: 1) A naive multiple linear regression model is proposed for bottom-level series; 2) A novel approach of combining quantile regression and empirical copulas is proposed to estimate the joint distribution of random variables; 3) To improve the prediction accuracy, a weighted correction method based on constrained quantile regression is introduced to adjust predictive distributions at the bottom level. In case studies, the effectiveness of our proposed method is verified by using two public datasets. Compared with four benchmarks, evaluation results show that the proposed approach makes a better performance.
Tianhui Zhao; Jianxue Wang; Yao Zhang. Day-Ahead Hierarchical Probabilistic Load Forecasting With Linear Quantile Regression and Empirical Copulas. IEEE Access 2019, 7, 80969 -80979.
AMA StyleTianhui Zhao, Jianxue Wang, Yao Zhang. Day-Ahead Hierarchical Probabilistic Load Forecasting With Linear Quantile Regression and Empirical Copulas. IEEE Access. 2019; 7 (99):80969-80979.
Chicago/Turabian StyleTianhui Zhao; Jianxue Wang; Yao Zhang. 2019. "Day-Ahead Hierarchical Probabilistic Load Forecasting With Linear Quantile Regression and Empirical Copulas." IEEE Access 7, no. 99: 80969-80979.
With the large-scale integration of renewable generation, energy storage system (ESS) is increasingly regarded as a promising technology to provide sufficient flexibility for the safe and stable operation of power systems under uncertainty. This paper focuses on grid-scale ESS planning problems in transmission-constrained power systems considering uncertainties of wind power and load. A scenario-based chance-constrained ESS planning approach is proposed to address the joint planning of multiple technologies of ESS. Specifically, the chance constraints on wind curtailment are designed to ensure a certain level of wind power utilization for each wind farm in planning decision-making. Then, an easy-to-implement variant of Benders decomposition (BD) algorithm is developed to solve the resulting mixed integer nonlinear programming problem. Our case studies on an IEEE test system indicate that the proposed approach can co-optimize multiple types of ESSs and provide flexible planning schemes to achieve the economic utilization of wind power. In addition, the proposed BD algorithm can improve the computational efficiency in solving this kind of chance-constrained problems.
Yunhao Li; Jianxue Wang; Chenjia Gu; Jinshan Liu; Zhengxi Li. Investment optimization of grid-scale energy storage for supporting different wind power utilization levels. Journal of Modern Power Systems and Clean Energy 2019, 7, 1721 -1734.
AMA StyleYunhao Li, Jianxue Wang, Chenjia Gu, Jinshan Liu, Zhengxi Li. Investment optimization of grid-scale energy storage for supporting different wind power utilization levels. Journal of Modern Power Systems and Clean Energy. 2019; 7 (6):1721-1734.
Chicago/Turabian StyleYunhao Li; Jianxue Wang; Chenjia Gu; Jinshan Liu; Zhengxi Li. 2019. "Investment optimization of grid-scale energy storage for supporting different wind power utilization levels." Journal of Modern Power Systems and Clean Energy 7, no. 6: 1721-1734.
This paper presents a chance constrained stochastic conic program model for networked microgrids planning. Under a two-stage optimization framework, we integrate a multi-site microgrids investment problem and two sets of operational problems that correspond to the grid-connected and islanding modes, respectively. To handle the uncertain nature of renewable energy generation and load variation, as well as the contingent islanding caused by external disruptions, stochastic scenarios are employed to capture randomness and a joint chance constraint is introduced to control the operational risks. A second-order conic program (SOCP) formulation is also utilized to accurately describe the AC optimal power flow (OPF) in operational problems. As the resulting mixed integer SOCP model is computationally difficult, we customize the bilinear Benders decomposition with non-trivial enhancement techniques to deal with practical instances. Numerical results on 5-and 69-bus networked microgrids demonstrate the effectiveness of the proposed planning model and the superior performance of our solution algorithm.
Xiaoyu Cao; Jianxue Wang; Bo Zeng. Networked Microgrids Planning Through Chance Constrained Stochastic Conic Programming. IEEE Transactions on Smart Grid 2019, 10, 6619 -6628.
AMA StyleXiaoyu Cao, Jianxue Wang, Bo Zeng. Networked Microgrids Planning Through Chance Constrained Stochastic Conic Programming. IEEE Transactions on Smart Grid. 2019; 10 (6):6619-6628.
Chicago/Turabian StyleXiaoyu Cao; Jianxue Wang; Bo Zeng. 2019. "Networked Microgrids Planning Through Chance Constrained Stochastic Conic Programming." IEEE Transactions on Smart Grid 10, no. 6: 6619-6628.
Electric load is hierarchically organized based on geography, which requires hierarchical forecasts covering all levels to support decision makings of power system operations. A trivial way to implement hierarchical forecasts is to independently generate load forecasts at each level using state-of-the-art techniques. However, these independently-generated forecasts may not satisfy hierarchical structures, i.e., the sum of lower-level forecasts cannot add up exactly to upper-level forecasts. To deal with this problem, this letter presents a quadratic programming (QP) model to optimally adjust load forecasts independently generated at each level of a hierarchy. The proposed model obtains the “best” forecasts as close as possible to base forecasts but also satisfy the aggregate consistency defined by hierarchical structures. Numerical results using real-world data validate the effectiveness of the proposed approach in two application scenarios (i.e., bulk power systems and power distribution networks). The accuracy improvement of the proposed approach over either base forecasts or bottom-up forecasts can be observed at all levels of the hierarchy.
Yao Zhang; Jianxue Wang; Tianhui Zhao. Using Quadratic Programming to Optimally Adjust Hierarchical Load Forecasting. IEEE Transactions on Power Systems 2018, 1 -1.
AMA StyleYao Zhang, Jianxue Wang, Tianhui Zhao. Using Quadratic Programming to Optimally Adjust Hierarchical Load Forecasting. IEEE Transactions on Power Systems. 2018; (99):1-1.
Chicago/Turabian StyleYao Zhang; Jianxue Wang; Tianhui Zhao. 2018. "Using Quadratic Programming to Optimally Adjust Hierarchical Load Forecasting." IEEE Transactions on Power Systems , no. 99: 1-1.
This letter presents a feasibility and profit based planning guidance for the regulatory of Distributed Generation (DG) integration. Different from the conventional planning frameworks, our approach yields a capacity interval enveloped by feasibility and profit bounds for DG installation, instead of a particular capacity scheme. On one hand, the feasibility bound is determined through multi-period stochastic optimal power flow to enforce the feasibility constraints of the distribution network operation subject to the uncertainties of DG output. On the other hand, the profit bound is derived using chance constrained program to guide profitable DG investment under controllable risk level. Numerical tests on IEEE 33-bus distribution system demonstrate the effectiveness of the proposed approach.
Xiaoyu Cao; Jianxue Wang; Bo Zeng. Distributed Generation Planning Guidance Through Feasibility and Profit Analysis. IEEE Transactions on Smart Grid 2018, 9, 5473 -5475.
AMA StyleXiaoyu Cao, Jianxue Wang, Bo Zeng. Distributed Generation Planning Guidance Through Feasibility and Profit Analysis. IEEE Transactions on Smart Grid. 2018; 9 (5):5473-5475.
Chicago/Turabian StyleXiaoyu Cao; Jianxue Wang; Bo Zeng. 2018. "Distributed Generation Planning Guidance Through Feasibility and Profit Analysis." IEEE Transactions on Smart Grid 9, no. 5: 5473-5475.
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 study presents a risk-averse stochastic unit commitment (SUC) model which considers the loss-of-load risk caused by wind power uncertainty. The expected cost of loss-of-load is usually considered in the conventional scenario-based SUC model. However, even if the expected risk of loss-of-load induced by all wind scenarios is low, the risk induced by some extreme scenarios can be very high. Thus, there is a strong will to better control the risk in these cases with high costs but low probabilities. In this study, the management of loss-of-load risk in worst scenarios is addressed by the conditional value-at-risk (CVaR). The proposed SUC model is built in a mixed-integer linear programming formulation and finally solved by a modified Benders decomposition algorithm with two enhancement strategies (Jensen's inequality and multiple cuts generated from all subproblems). Case studies demonstrate that the loss-of-load cost in extreme scenarios decreases after the inclusion of CVaR in the proposed SUC model. The proposed model can also provide multiple unit commitment schedules with different levels of loss-of-load risk. Using enhancement strategies in Benders decomposition drastically reduces the total number of iterations, verifying the effectiveness of the modified Benders decomposition algorithm.
Yao Zhang; Jianxue Wang; Tao Ding; Xifan Wang. Conditional value at risk‐based stochastic unit commitment considering the uncertainty of wind power generation. IET Generation, Transmission & Distribution 2017, 12, 482 -489.
AMA StyleYao Zhang, Jianxue Wang, Tao Ding, Xifan Wang. Conditional value at risk‐based stochastic unit commitment considering the uncertainty of wind power generation. IET Generation, Transmission & Distribution. 2017; 12 (2):482-489.
Chicago/Turabian StyleYao Zhang; Jianxue Wang; Tao Ding; Xifan Wang. 2017. "Conditional value at risk‐based stochastic unit commitment considering the uncertainty of wind power generation." IET Generation, Transmission & Distribution 12, no. 2: 482-489.
This paper presents a chance constrained information gap decision model for multi-period microgrid expansion planning (MMEP) considering two categories of uncertainties, namely random and non-random uncertainties. The main task of MMEP is to determine the optimal sizing, type selection, and installation time of distributed energy resources (DER) in microgrid. In the proposed formulation, information gap decision theory (IGDT) is applied to hedge against non-random uncertainties of long-term demand growth. Then, chance constraints are imposed in the operational stage to address the random uncertainties of hourly renewable energy generation and load variation. The objective of chance constrained IGD model is to maximize the robustness level of DER investment meanwhile satisfying a set of operational constraints with high probability. The integration of IGDT and chance constrained program, however, makes it very challenging to compute. To address this challenge, we propose and implement a strengthened bilinear Benders decomposition method. Finally, the effectiveness of proposed planning model is verified through the numerical studies on both the simple and practical complex microgrid. Also, our new computational method demonstrates a superior solution capacity and scalability. Compared to directly using a professional mixed integer programming solver, it could reduce the computational time by orders of magnitude.
Xiaoyu Cao; Jianxue Wang; Bo Zeng. A Chance Constrained Information-Gap Decision Model for Multi-Period Microgrid Planning. IEEE Transactions on Power Systems 2017, 33, 2684 -2695.
AMA StyleXiaoyu Cao, Jianxue Wang, Bo Zeng. A Chance Constrained Information-Gap Decision Model for Multi-Period Microgrid Planning. IEEE Transactions on Power Systems. 2017; 33 (3):2684-2695.
Chicago/Turabian StyleXiaoyu Cao; Jianxue Wang; Bo Zeng. 2017. "A Chance Constrained Information-Gap Decision Model for Multi-Period Microgrid Planning." IEEE Transactions on Power Systems 33, no. 3: 2684-2695.
In this paper, we study N-1 contingency-constrained transmission expansion planning (TEP) problems considering the uncertainty of wind power output. TEP problem is formulated as a chance-constrained stochastic programming. Our model ensures that a large portion of wind power generation will be utilized with a high probability. Furthermore, we present a bilinear mixed integer formulation of chance constraint, and then derive its linear counterpart. Finally, the computational result indicates that increasing the utilization portion of wind power generation may increase the total investment cost of transmission lines. Our experiments also verify that the bilinear mixed integer formulation is stronger than the widely adopted Big-M linear formulation.
Yao Zhang; Jianxue Wang; Yunhao Li; Xiaoyu Cao. Chance-constrained transmission expansion planning with guaranteed wind power utilization. 2017 IEEE Power & Energy Society General Meeting 2017, 1 -5.
AMA StyleYao Zhang, Jianxue Wang, Yunhao Li, Xiaoyu Cao. Chance-constrained transmission expansion planning with guaranteed wind power utilization. 2017 IEEE Power & Energy Society General Meeting. 2017; ():1-5.
Chicago/Turabian StyleYao Zhang; Jianxue Wang; Yunhao Li; Xiaoyu Cao. 2017. "Chance-constrained transmission expansion planning with guaranteed wind power utilization." 2017 IEEE Power & Energy Society General Meeting , no. : 1-5.
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.
Probabilistic forecasts provide quantitative information in relation to energy uncertainty, which is essential for making better decisions on the operation of power systems with an increasing penetration of wind power. On the basis of the kk-nearest neighbors algorithm and a kernel density estimator method, this paper presents a general framework for the probabilistic forecasting of renewable energy generation, especially for wind power generation. It is a direct and non-parametric approach. Firstly, the kk-nearest neighbors algorithm is used to find the kk closest historical examples with characteristics similar to the future weather condition of wind power generation. Secondly, a novel kernel density estimator based on a logarithmic transformation and a boundary kernel is used to construct wind power predictive density based on the kk closest historical examples. The effectiveness of this approach has been confirmed on the real data provided for GEFCom2014. The evaluation results show that the proposed approach can provide good quality, reliable probabilistic wind power forecasts.
Yao Zhang; Jianxue Wang. K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting. International Journal of Forecasting 2016, 32, 1074 -1080.
AMA StyleYao Zhang, Jianxue Wang. K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting. International Journal of Forecasting. 2016; 32 (3):1074-1080.
Chicago/Turabian StyleYao Zhang; Jianxue Wang. 2016. "K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting." International Journal of Forecasting 32, no. 3: 1074-1080.