Bidding Optimization Strategy Of Combined Heat And Power Microgrids Considering The Dynamic Demand Response

The energy problems are getting more and more serious.The development of renewable energy such as wind and photovoltaics is one of the effective ways to solve energy problems.However,wind and photovoltaics are intermittent resources,and largescale resources access to grid poses great challenges to the stable operation of power systems.The emergence of microgrid provides an effective solution for the decentralized access of renewable energy.The coordinated operation of the power supply and heating system provides support for improving the utilization of renewable energy.At the same time,the demand response changes the user's energy habits,and makes the load curve more gradual,which is beneficial to the system to make better use of renewable energy,thereby reduces the consumption of fossil fuels.With the advancement of the electricity market reform,the bidding optimization strategy of microgrid can accurately reflect the clearing price of distributed energy,so that the microgrid can balance the output of distributed energy and realize the stable operation of microgrid.Therefore,it is important to study the bidding optimization strategy of microgrid.This paper combines The National Science Fund for Distinguished Young Scholars “Power System Reliability”(51725701),and conducts research and exploration on the bidding optimization strategy of combined heating and power micro-grid under the demand response,focusing on the relationship between demand elasticity coefficient and price.The impact of demand response on combined heat and power dispatching of microgrid,the impact of demand response on microgrid bidding optimization strategy,and the impact of unit's performance indexes on unit output of microgrid bidding.The main research contents of this paper are as follows:The demand response strategy usually uses the electricity price elasticity matrix to characterize the correlation between price changes and load changes.Therefore,accurately characterizing the price elasticity coefficient is a key factor in implementing demand response.In order to takes into account the influence of price on the elastic coefficient,this paper combines the equivalence matrix fuzzy clustering method to divide the peak-to-valley period,and then derives the functional relationship between the price elasticity coefficient and the price change through the supply-demand equilibrium relationship.Therefore,this paper establishes the electrothermal dynamic demand response mathematical model based on the time-of-use price.The results of different demand response method show that the model compared with the conventional model has improved the peak electric and heating load reduction ratios by 1.5% and 0.7%.the model can better realize the “peak clipping and valley filling”.In addition,the example also analyzes the impact of demand response ratios and user satisfaction on the load curve.Combined with the characteristics of combined heating and power micro-grid,in order to coordinate the output of distributed energy in the heating and power supply system,when implementing the time-of-use price in the microgrid,the demand response strategy is implemented to optimize the sales heating and electric price of the microgrid.This paper researches combined heating and power microgrid optimization dispatching model.The model is transformed into a two-layer optimization problem,the upper layer is the demand response model,and the lower layer is combined heating and power microgrid optimization dispatching model.This method has solved the nonlinear part of the demand response model.The results show that the model optimizes the distributed energy of diesel engines and combined heating and power units through demand response,which reduces the operating cost of the microgrid and improves the utilization ratios of renewable energy.In addition,the example also analyzes the impact of demand response ratios,wind and solar access ratios,and heat storage device on microgrid optimization.In order to determine the benefits of distributed energy,this paper proposes a twostage bidding optimization strategy for combined heating and power microgrid considering dynamic demand response.The model introduces the power performance index and differentiates the bidding unit.At the same time,it considers the load aggregator to participate in the market bidding,and combines the dynamic demand response to coordinate the output of each bidding unit.The model is transformed into a two-layer two-stage optimization problem.The upper layer is the demand response model,and the lower layer is the two-stage bidding optimization strategy of the microgrid.Through the analysis of the results of different model examples,the introduction of power performance index and load aggregators can increase the proportion of flexible and highquality resources and reduce the total system cost.In addition,through the analysis of the results of the demand response examples,the participation of demand response reduces the clearing price and output allocation during the peak period,and raises the clearing price and output allocation during the valley period,which help to stabilize market.