Research On Power Generation And Transmission Scheduling Based On Non-cooperative Game

Over the past few years,the scope of electricity market reform in China is gradually expanding.In a deregulated environment of the electricity market,increase profit(or reduce cost)through competition in power generation and transmission scheduling are problems must be taken into consideration for power plant.Besides,with the increasing penetration in power system,renewable energy power plants such as wind power plants and solar power plants have great influence on the stability and safety of the grid.This dissertation proposed optimization method for power generation scheduling and transmission scheduling of power plants in the deregulated electricity market.Firstly,two-part pricing mechanism based generation cost model of thermal power plants,wind power plants,solar power plants,hydro power plants and nuclear power plants were provided,which considering with the characteristics of various types of power plants in the process of investment construction,operation and maintenance.Besides,the carbon emission factor and reserve cost were included in the generation cost model by taking into account the environmental issues and space-time characteristics.Secondly,renewable energy power plants are regarded as market participants in the same way as thermal power plants to bid in electricity market.Power plants can not only bid in the day-ahead market,but also rebid in the intraday market,which provides an effective way to solve the problem casued by difficult prediction for output of renewable energy power plants.A game-theoretic bidding optimization method based on bilevel programming was presented to maximize profit of power plants,where power plants are at the upper level and utility companies are at the lower level.The competition among the multiple power plants is formulated as a non-cooperative game in which bidding curves are their strategies.Simulation results were performed and discussed to verify the feasibility and effectiveness of the proposed non-cooperative game based bilevel optimization approach.Then,power plants can sell electricity to users directly in the deregulated electricity market,but they need to pay wheeling cost to utility companies for providing services of electricity transmission.Considering the transmission capacity constraints,wheeling cost model including congestion cost and network loss cost is eatablished based on embedded cost methods.Each power plant selects the proportion of energy transported by each transmission line in total energy generated by the power plant.The degree of congestion on transmission line is related to transmission scheduling of each power plant.By assuming power plants are selfish and rational,a non-cooperative game approach is proposed for multiple power plants to lower their wheeling cost,where strategies of the non-cooperative game are the power transmission scheduling of power plant.In addition,it is proved that there exists at least one pure-strategy Nash equilibrium of the formulated power transmission game,and a distributed algorithm was provided to realize the optimization in terms of minimizing the wheeling cost.Simulations results show that wheeling cost of power plants are decreased,which verify the effectiveness of the proposed non-cooperative game approach for the power plants to reduce wheeling cost.Finally,in order to promote renewable energy consumption,a wind-solar-thermal hybrid system is consumed with long distance by ultra-high voltage transmission lines.Cost model considering with generation cost,wheeling cost,carbon emissions cost and government subsidy was provided.Apart from the balance of power supply and demand constraints,unit output constraints and transmission line capacity constraints,the ramp rate constraints are considered.The clearing power generation and the market clearing price are determined after bidding process is finished.Power plants maximize their profits by using bilevel optimization method based on non-cooperative game,where generalized Nash equilibrium as the optimal solution.The effectiveness of the proposed optimization method for long distance consumption was verified through simulation under different operation conditions.