Multi-Objective Bilevel Coordinated Planning of Distributed Generation and Distribution Network Frame Based on Multiscenario Technique Considering Timing Characteristics

This paper presents a novel approach to planning distributed generation (DG) and distribution network frames based on a multiscenario technique. In view of the uncertainties of the load and intermittence of the DG output, the annual timing characteristics are analyzed, and the daily load and output of DG are divided into several typical situations according to respective influence factors. In the target planning year, the uncertain units are simulated using the typical daily forecast values, then the probability models are established considering the forecast errors; finally, multiple scenarios are achieved through Latin hypercube sampling and sample reduction. In view of the interaction of DG and distribution network frames, a bilevel coordinated planning model is proposed, in which the upper level planning is aimed to achieve the integrated optimal decision and the goal of the lower level planning is to comprehensively consider the benefits of DG. Finally, hybrid chaos binary particle swarm optimization based on Pareto set theory and niche sharing is applied to make a nested solving of the model, and the superiority and effectiveness of the proposed model are verified in the IEEE 33-node and 69-node distribution systems as the test cases.

[1]  Javier Contreras,et al.  Joint expansion planning of distributed generation and distribution networks , 2015 .

[2]  Chandan Kumar Chanda,et al.  Placement of wind and solar based DGs in distribution system for power loss minimization and voltage stability improvement , 2013 .

[3]  Rahmat-Allah Hooshmand,et al.  Distribution network expansion planning and DG placement in the presence of uncertainties , 2015 .

[4]  Ehab F. El-Saadany,et al.  Long-term multi-objective distribution network planning by DG allocation and feeders’ reconfiguration , 2013 .

[5]  Mauricio E. Samper,et al.  Investment Decisions in Distribution Networks Under Uncertainty With Distributed Generation—Part I: Model Formulation , 2013, IEEE Transactions on Power Systems.

[6]  S. M. El-Debeiky,et al.  Long-Term Load Forecasting for Fast-Developing Utility Using a Knowledge-Based Expert System , 2002, IEEE Power Engineering Review.

[7]  W. El-khattam,et al.  Optimal investment planning for distributed generation in a competitive electricity market , 2004, IEEE Transactions on Power Systems.

[8]  A. Soroudi,et al.  Possibilistic-Scenario Model for DG Impact Assessment on Distribution Networks in an Uncertain Environment , 2012, IEEE Transactions on Power Systems.

[9]  Tomonobu Senjyu,et al.  Optimal Voltage Control Using Inverters Interfaced With PV Systems Considering Forecast Error in a Distribution System , 2014, IEEE Transactions on Sustainable Energy.

[10]  Hamid Lesani,et al.  Integrated distribution network expansion planning incorporating distributed generation considering uncertainties, reliability, and operational conditions , 2015 .

[11]  G. Harrison,et al.  DG Impact on Investment Deferral: Network Planning and Security of Supply , 2010, IEEE Transactions on Power Systems.

[12]  Ryuichi Yokoyama,et al.  Influence of large-scale grid-connected photovoltaic system on distribution networks , 2012 .

[13]  Wang Xiaomin.  Seasonal variation of ground wind velocity in recent 47 years in Hebei and its influence factors , 2010 .

[14]  Nima Amjady,et al.  A new evolutionary solution method for dynamic expansion planning of DG-integrated primary distribution networks , 2014 .

[15]  S. C. Srivastava,et al.  A Generalized Approach for DG Planning and Viability Analysis Under Market Scenario , 2013, IEEE Transactions on Industrial Electronics.

[16]  J.H. Zhang,et al.  Probabilistic Load Flow Evaluation With Hybrid Latin Hypercube Sampling and Cholesky Decomposition , 2009, IEEE Transactions on Power Systems.

[17]  Wei Yuan,et al.  Robust Optimization-Based Resilient Distribution Network Planning Against Natural Disasters , 2016, IEEE Transactions on Smart Grid.

[18]  Witold Pedrycz,et al.  Time series long-term forecasting model based on information granules and fuzzy clustering , 2015, Eng. Appl. Artif. Intell..

[19]  Alireza Soroudi,et al.  Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty , 2012 .

[20]  Min-xiang Huang,et al.  Multi-Objective Optimal Power Flow Considering Transient Stability Based on Parallel NSGA-II , 2015, IEEE Transactions on Power Systems.

[21]  Tang Wei,et al.  Distributed Generators Planning Considering Benefits for Distribution Power Company and Independent Power Suppliers , 2011 .

[22]  Wang Weizhou.  Optimization of Transmission Network Maintenance Scheduling Based on Niche Multi-objective Particle Swarm Algorithm , 2013 .

[23]  Yajing Gao,et al.  Multi-Objective Planning of Multi-Type Distributed Generation Considering Timing Characteristics and Environmental Benefits , 2014 .

[24]  Mauricio E. Samper,et al.  Investment Decisions in Distribution Networks Under Uncertainty With Distributed Generation—Part II: Implementation and Results , 2013, IEEE Transactions on Power Systems.

[25]  Xu Lian.  Mid- and long-term daily load curve forecasting based on functional nonparametric regression model , 2015 .

[26]  Xu Yang,et al.  Integrated Planning for Transition to Low-Carbon Distribution System With Renewable Energy Generation and Demand Response , 2014, IEEE Transactions on Power Systems.

[27]  Reza Noroozian,et al.  Optimal siting and sizing of distributed generation accompanied by reconfiguration of distribution networks for maximum loss reduction by using a new UVDA-based heuristic method , 2016 .

[28]  Hamid Lesani,et al.  Renewable power generation employed in an integrated dynamic distribution network expansion planning , 2015 .

[29]  Mehdi Ehsan,et al.  A possibilistic-probabilistic tool for evaluating the impact of stochastic renewable and controllable power generation on energy losses in distribution networks - A case study , 2011 .

[30]  G. Ledwich,et al.  Integrated Distribution Systems Planning to Improve Reliability Under Load Growth , 2012, IEEE Transactions on Power Delivery.

[31]  Zhongfu Tan,et al.  A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response , 2016 .

[32]  Abbas Rabiee,et al.  Corrective Voltage Control Scheme Considering Demand Response and Stochastic Wind Power , 2014, IEEE Transactions on Power Systems.

[33]  Mehdi Ehsan,et al.  Probabilistic dynamic multi-objective model for renewable and non-renewable distributed generation planning , 2011 .

[34]  Werner Römisch,et al.  Scenario Reduction Algorithms in Stochastic Programming , 2003, Comput. Optim. Appl..

[35]  Jianhui Wang,et al.  Robust Optimization Based Optimal DG Placement in Microgrids , 2014, IEEE Transactions on Smart Grid.

[36]  Kai Zou,et al.  Distribution System Planning With Incorporating DG Reactive Capability and System Uncertainties , 2012, IEEE Transactions on Sustainable Energy.

[37]  Liu Ye,et al.  A scenario-based robust transmission network expansion planning method for consideration of wind power uncertainties , 2016 .

[38]  Yuan Liu,et al.  Optimal sitting and sizing of DGs in distribution system considering time sequence characteristics of loads and DGs , 2015 .

[39]  C. Borges,et al.  Active distribution network integrated planning incorporating distributed generation and load response uncertainties , 2011, 2012 IEEE Power and Energy Society General Meeting.

[40]  Paul Giorsetto,et al.  Development of a New Procedure for Reliability Modeling of Wind Turbine Generators , 1983, IEEE Transactions on Power Apparatus and Systems.

[41]  Hantao Cui,et al.  Day-ahead coordinated operation of utility-scale electricity and natural gas networks considering demand response based virtual power plants , 2016 .

[42]  Jianhua Liu,et al.  The Analysis and Improvement of Binary Particle Swarm Optimization , 2009, 2009 International Conference on Computational Intelligence and Security.

[43]  Faruk Ugranli,et al.  Multiple-distributed generation planning under load uncertainty and different penetration levels , 2013 .

[44]  E.F. El-Saadany,et al.  Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization , 2010, IEEE Transactions on Power Systems.

[45]  Faruk Ugranli,et al.  Comparison of single- and multiple-distributed generation concepts in terms of power loss, voltage profile, and line flows under uncertain scenarios , 2015 .

[46]  K. Mumtaz.  An Analysis on Density Based Clustering of Multi Dimensional Spatial Data , 2010 .

[47]  Hans-Peter Waldl,et al.  ASSESSING THE UNCERTAINTY OF WIND POWER PREDICTIONS WITH REGARD TO SPECIFIC WEATHER SITUATIONS , 2022 .

[48]  L.A. Kojovic,et al.  Summary of Distributed Resources Impact on Power Delivery Systems , 2008, IEEE Transactions on Power Delivery.

[49]  Sanjib Ganguly,et al.  Distributed Generation Allocation on Radial Distribution Networks Under Uncertainties of Load and Generation Using Genetic Algorithm , 2015, IEEE Transactions on Sustainable Energy.

[50]  Zhipeng Liu,et al.  Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Uncertainties , 2011, IEEE Transactions on Power Delivery.