Optimal scheduling of active distribution network based on demand respond theory

With the increasing penetration of renewable power source into the distribution network, maintaining system reliability has been a challenging issue due to the uncertainty and volatility characteristics of renewable power source. Based on the robust optimization theory, this paper proposed a multi-stage robust optimal scheduling of active distribution to maintain the voltage within the allowed range as well as develop the operation of distribution network. This methodology applies the demand responds method into the model in order to make full advantage of elastic load adjustment. The proposed method is accomplished in three phases. In the first stage, in order to determine the uncertain parameters, renewable energy output uncertainty is described by uncertain set. Meanwhile, the extreme scenario method is adopted to cut down the field of sets. In the second stage, the elastic load is used to moderate the load fluctuation by applying demand respond theory. In the third stage, based on the bi-level planning, distributed generation's (DG's) reactive output adjustment cooperates with traditional voltage regulation method, which reduces the regulating times of switching device operations and system network loss. Furthermore, the economy and stability of system operation improve significantly. Finally, the efficiency of the built model is analyzed using the PG&E 69-bus system.

[1]  Audun Botterud,et al.  Modeling and simulation of price elasticity of demand using an agent-based model , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[2]  M. M. A. Salama,et al.  Management Scheme for Increasing the Connectivity of Small-Scale Renewable DG , 2014, IEEE Transactions on Sustainable Energy.

[3]  Hossein Haghighat,et al.  Stochastic and Chance-Constrained Conic Distribution System Expansion Planning Using Bilinear Benders Decomposition , 2017, IEEE Transactions on Power Systems.

[4]  Yongpei Guan,et al.  A Chance-Constrained Two-Stage Stochastic Program for Unit Commitment With Uncertain Wind Power Output , 2012 .

[5]  Young-Jin Kim,et al.  Coordinated Control of a DG and Voltage Control Devices Using a Dynamic Programming Algorithm , 2013, IEEE Transactions on Power Systems.

[6]  Aouss Gabash,et al.  Active-Reactive Optimal Power Flow in Distribution Networks With Embedded Generation and Battery Storage , 2012, IEEE Transactions on Power Systems.

[7]  Ruiwei Jiang,et al.  Robust Unit Commitment With Wind Power and Pumped Storage Hydro , 2012, IEEE Transactions on Power Systems.

[8]  D. Kirschen Demand-side view of electricity markets , 2003 .

[9]  D. Kirschen,et al.  Quantifying the Effect of Demand Response on Electricity Markets , 2007, IEEE Transactions on Power Systems.

[10]  Janusz Bialek,et al.  Generation curtailment to manage voltage constraints in distribution networks , 2007 .

[11]  Li Yan-ping,et al.  An electricity charge strategy based on dynamic rewards and punishment , 2014, The 26th Chinese Control and Decision Conference (2014 CCDC).

[12]  Scott Backhaus,et al.  A robust approach to chance constrained optimal power flow with renewable generation , 2017 .

[13]  Alireza Karimi,et al.  Plug-and-Play Robust Voltage Control of DC Microgrids , 2018, IEEE Transactions on Smart Grid.

[14]  Yongpei Guan,et al.  A Chance-Constrained Two-Stage Stochastic Program for Unit Commitment With Uncertain Wind Power Output , 2012, IEEE Transactions on Power Systems.

[15]  Vinod Khadkikar,et al.  Incorporating PV Inverter Control Schemes for Planning Active Distribution Networks , 2015, IEEE Transactions on Sustainable Energy.

[16]  Z. Dong,et al.  A Modified Differential Evolution Algorithm With Fitness Sharing for Power System Planning , 2008, IEEE Transactions on Power Systems.