Research on Hierarchical and Distributed Control for Smart Generation Based on Virtual Wolf Pack Strategy

Nowadays, haze has become a big trouble in our society. One of the significant solutions is to introduce renewable energy on a large scale. How to ensure that power system can adapt to the integration and consumption of new energy very well has become a scientific issue. A smart generation control which is called hierarchical and distributed control based on virtual wolf pack strategy is explored in this study. The proposed method is based on multiagent system stochastic consensus game principle. Meanwhile, it is also integrated into the new win-lose judgment criterion and eligibility trace. The simulations, conducted on the modified power system model based on the IEEE two-area load frequency control and Hubei power grid model in China, demonstrate that the proposed method can obtain the optimal collaborative control of AGC units in a given regional power grid. Compared with some smart methods, the proposed one can improve the closed-loop system performances and reduce the carbon emission. Meanwhile, a faster convergence speed and stronger robustness are also achieved.

[1]  Goshaidas Ray,et al.  A new approach to the design of robust load-frequency controller for large scale power systems , 1999 .

[2]  Bikramjit Banerjee,et al.  Adaptive policy gradient in multiagent learning , 2003, AAMAS '03.

[3]  Noradin Ghadimi,et al.  An analytical methodology for assessment of smart monitoring impact on future electric power distribution system reliability , 2015, Complex..

[4]  Gonzalo Abad,et al.  Online Reference Limitation Method of Shunt-Connected Converters to the Grid to Avoid Exceeding Voltage and Current Limits Under Unbalanced Operation—Part I: Theory , 2015, IEEE Transactions on Energy Conversion.

[5]  Mohsen Mohammadi,et al.  Wavelet neural network based on islanding detection via inverter-based DG , 2015, Complex..

[6]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[7]  E. Karaman,et al.  Multilayer-Winding Versus Switched-Flux Permanent-Magnet AC Machines for Gearless Applications in Clean-Energy Systems , 2012, IEEE Transactions on Industry Applications.

[8]  Tao Yu,et al.  Stochastic Optimal Relaxed Automatic Generation Control in Non-Markov Environment Based on Multi-Step $Q(\lambda)$ Learning , 2011, IEEE Transactions on Power Systems.

[9]  Sridhar Mahadevan,et al.  Hierarchical multi-agent reinforcement learning , 2001, AGENTS '01.

[10]  Lei Xi,et al.  A novel multi-agent decentralized win or learn fast policy hill-climbing with eligibility trace algorithm for smart generation control of interconnected complex power grids☆ , 2015 .

[11]  D. Ernst,et al.  Power systems stability control: reinforcement learning framework , 2004, IEEE Transactions on Power Systems.

[12]  Yasser Abdel-Rady I. Mohamed,et al.  Suppression of Low- and High-Frequency Instabilities and Grid-Induced Disturbances in Distributed Generation Inverters , 2011, IEEE Transactions on Power Electronics.

[13]  Noradin Ghadimi,et al.  An adaptive neuro-fuzzy inference system for islanding detection in wind turbine as distributed generation , 2015, Complex..

[14]  Manuela M. Veloso,et al.  Multiagent learning using a variable learning rate , 2002, Artif. Intell..

[15]  Charles E. Fosha,et al.  Optimum Megawatt-Frequency Control of Multiarea Electric Energy Systems , 1970 .

[16]  Peter Dayan,et al.  Q-learning , 1992, Machine Learning.

[17]  Tao Yu,et al.  Distributed multi-step Q(λ) learning for Optimal Power Flow of large-scale power grids , 2012 .

[18]  Olle I. Elgerd,et al.  Electric Energy Systems Theory: An Introduction , 1972 .

[19]  Ali Feliachi,et al.  NERC compliant decentralized load frequency control design using model predictive control , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[20]  Alireza Noruzi,et al.  A new method for probabilistic assessments in power systems, combining monte carlo and stochastic-algebraic methods , 2015, Complex..

[21]  Lei Xi,et al.  Multiagent Stochastic Dynamic Game for Smart Generation Control , 2016 .

[22]  Koushik Kar,et al.  Collaborative Energy and Thermal Comfort Management Through Distributed Consensus Algorithms , 2015, IEEE Transactions on Automation Science and Engineering.

[23]  Tao Yu,et al.  Robust collaborative consensus algorithm for decentralized economic dispatch with a practical communication network , 2016 .

[24]  OVEIS ABEDINIA,et al.  A new metaheuristic algorithm based on shark smell optimization , 2016, Complex..