Coordinated Optimal Allocation of Distributed Generations in Smart Distribution Grids Considering Active Management and
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ORIGINAL ARTICLE
Coordinated Optimal Allocation of Distributed Generations in Smart Distribution Grids Considering Active Management and Contingencies Jia Liu1,2,3 · Pingliang Zeng1 · Yalou Li4 · Hao Xing1 Received: 2 April 2019 / Revised: 17 February 2020 / Accepted: 27 May 2020 © The Korean Institute of Electrical Engineers 2020
Abstract This study presents a multi-objective bi-level optimization model for distributed generation (DG) allocation in smart distribution grids integrating energy storage devices. As part of smart distribution grids, four active management schemes, coordinated on-load tap-changer voltage control, DG power factor control, DG curtailment and demand side management, are embedded in the proposed model. Uncertainties related to DGs, loads and contingencies and the capability of energy storage devices for peak shaving and renewable energy compensation are also inherent. The allocation model simulates the network transfer process to postpone the DG investment. The trade-off between the defined annual total cost and N-1 security margin index is achieved in the optimal allocation methodology considering operation thresholds and security improvements. The DG allocation solutions are solved by a hybrid algorithm. The correlated input parameters of the optimization problem, such as wind speed, illumination intensity and load, are generated using quasi Monte Carlo simulation and singular value decomposition and then simplified by fuzzy C-means clustering to improve the computation efficiency of optimal power flow. A modified 104-bus distribution case is used to demonstrate the effectiveness and flexibility of the proposed model. Keywords Smart distribution grid · Distributed generation allocation · Active management · N-1 contingency · Energy storage device Abbreviations AM Active management DN Distribution network DG Distributed generation DSM Demand side management DSSR Distribution system security region DNDEA Dynamic niche differential evolution algorithm ESD Energy storage device FCM Fuzzy C-means NBI Normal boundary intersection OLTC On-load tap-changer PVG Photovoltaic generation * Pingliang Zeng [email protected] 1
Department of Automation, Hangzhou Dianzi University, Hangzhou, China
2
Department of Electrical Engineering, Zhejiang University, Hangzhou, China
3
Hangzhou Kelin Electric Company, Hangzhou, China
4
China Electric Power Research Institute, Beijing, China
PDIPM Primal–dual interior point method QMCS Quasi Monte Carlo simulation SDG Smart distribution grid SSD Steady-state security distance SVD Singular value decomposition WTG Wind turbine generation ai , bi Number of WTGs and PVGs at bus i CI Annual DG investment cost COM,s DG operation and maintenance cost CAM,s DG AM cost CP,s Cost of energy injected from substations CCE,s Carbon emission cost CDSM,s DSM cost cIi,WTG , cIi,PVG Unit investment cost of WTGs and PVGs at bus i OM , Unit operation and maintenance cost of cOM c i,WTG i,PVG WTGs and PVGs at bus i AM , Unit AM
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