Research On Pricing Decisions For Diversified Energy Utilization In Integrated Energy Systems Under Uncertainty

The large-scale development of renewable energy is important for achieving carbon peaking and neutrality.However,the randomness and intermittency of renewable energy bring many challenges to the operational planning of power systems.The comprehensive energy system can be an excellent means to support the on-site consumption of renewable energy due to its multi-energy coupling transformation characteristics.Energy price is an important factor and key parameter for the operation and decision-making of multiple energy entities under the new power system.A good energy Price system and pricing mechanism can actively promote and guarantee the development of multiple energy businesses through the guidance of Price signals,reasonably allocate energy resources,improve energy efficiency,reduce energy costs,reduce carbon emissions,and provide users with reliable,economic,high-quality and clean energy supply,Promote the healthy and sustainable development of the energy market.In the development of the integrated energy system,different from the separate and decentralized pricing mechanism of energy under the Wet market model,the multiple market players are interrelated and deeply coupled,which makes all kinds of energy market players need to consider the behaviors and decisions of other market players in addition to their own technical and economic characteristics when pricing,thus making the issue of energy pricing of multiple market players more complex.In addition,the current energy Price system could be better.The form of energy pricing is single,and the mechanism is imperfect.The coupling relationship between various energy entities needs to be considered.The economic leverage is limited,and the energy resource allocation capacity needs to be increased,and the energy utilization efficiency and carbon emission potential need to be improved.In addition,the optimization operation of the comprehensive energy system is also affected by the random fluctuations of renewable energy sources such as wind power and photovoltaic.In order to address the limitations of the traditional statistical model,which struggles to account for the complex correlations and relationships between renewable energy outputs like wind power and photovoltaic,and has limited practicality,we need a better solution.This paper proposes a Generative model of renewable energy output scenarios such as wind power and photovoltaic based on the reasoning Generative adversarial network(AE-GAN).The AEGAN model can learn the mapping relationship between the noise distribution that meets the prediction conditions and the output scene set and use distance functions to distinguish the distribution differences between sample sets.Design a network structure suitable for generating renewable energy output scenarios through learning the characteristics of renewable energy output;Through the game learning between the generator and discriminator in AE-GAN,the generator learns the Conditional probability mapping of the output scenario so that the day ahead scenario can be generated.The calculation results show that the AEGAN model used in this article can effectively learn the characteristics of renewable energy output and accurately generate renewable energy output scenarios.In response to the current issue of only considering energy quantity while neglecting energy efficiency in the capacity and investment planning of the comprehensive energy system,and considering that the energy efficiency value comprehensively considers the quantity and quality of energy,it is a more reasonable standard to measure the effective utilization level of the comprehensive energy system.This article proposes a multi-objective capacity and investment planning model for the comprehensive energy system based on improving energy efficiency and economy,And consider non deterministic factors such as renewable energy output in the system to determine the capacity and investment cost of various equipment in the comprehensive energy system planning process.In addition,to address the impact of uncertain factors,a distributed robust planning model for the comprehensive energy system based on AE-GAN was constructed based on the output scenarios generated in Chapter 3,and corresponding transformation and solution methods were proposed.Finally,the comprehensive energy system of a vacation town in Shandong Province.China was selected as the research object for numerical analysis.The results showed that after considering energy efficiency and using the method proposed in this paper,the total annual operating cost of the system can be effectively reduced by about 6%.In response to the problems of existing energy pricing methods that are difficult to apply to multi energy coupling in integrated energy systems,traditional node marginal electricity prices that are difficult to satisfy incentive compatibility in nonlinear(non convex)economic scheduling problems,and investment costs that are difficult to effectively recover,this paper constructs a diversified energy pricing system for a single integrated energy system,including the general form of a clearing model,The convex hull pricing method is introduced to address the non convex problem of unit combinations in the model,and it is extended to the convex hull node energy price to achieve energy price decisions for various energy sources such as electricity,gas,and heat in the comprehensive energy system.At the same time,the pricing system also includes diversified energy capacity and price decisions based on the comprehensive energy system capacity and investment planning to ensure effective and effective recovery of investment costs.Finally,a case study was conducted on a single integrated energy system in a resort town in Shandong Province,analyzing the changes in node electricity prices,gas prices,cold prices,and heat prices when transmission congestion occurs in the power,natural gas,or thermal subsystems.The above example results indicate that the energy price decision-making strategy of the comprehensive energy system takes into account the mutual influence between multiple energy systems,and can provide an effective and reasonable pricing mechanism,providing a reference for quantifying the mutual influence between multiple energy sources.With the development of integrated energy systems,entities with different business entities can form a system cluster for internal energy interaction to achieve optimal resource allocation on a larger scale.However,various comprehensive energy entities are motivated by privacy protection and self-interest,making it difficult to determine the price of energy interaction between each entity.This paper proposes an interactive energy price Decision model based on the Nash Hasanyi bargaining game for decentralized collaboration of integrated energy system clusters.This model can reflect the Bargaining power and participants’ contribution in cooperation to ensure good interactive price decision-making.To protect the privacy of each participant and improve convergence efficiency,an improved alternating direction multiplier algorithm is used to solve the above problem in a distributed manner.The simulation results show that the price determined through this method can effectively stimulate the interaction enthusiasm of various entities and reduce operating costs.Compared with the standalone operation mode,through joint operation,the operating costs of each comprehensive energy system have been reduced by 19.59%,18.18%,and 16.61%,respectively.In addition,the improved ADMM algorithm also improves the solving efficiency of the system.