Method for participating in clearing of electric power spot market in consideration of multi-agent aggregation

Abstract

The invention provides a method for participating in power spot market clearing in consideration of multi-subject aggregation, and relates to the technical field of power markets, and the method comprises the steps: designing an adjustable load side power utilization clustering algorithm, designing an algorithm considering energy storage charging and discharging market clearing, constructing a model for participating in market clearing in consideration of multi-subject aggregation, analyzing constraint conditions, and solving the model. The method has the advantages that the electricity utilization change rule of the load electricity utilization side based on the physical-market characteristics is analyzed, clustering fitting is conducted on the adjustable load side, and the influence of factors such as new energy and energy storage on output of a traditional generator set is fully considered; a strategy of participating in electric power spot market clearing by considering multi-body aggregation of a traditional unit, a new energy unit, energy storage power supply electric power, an adjustable load and the like is constructed, non-real-time balance of source loads is relieved, the on-site consumption capability of new energy is improved, technical support is provided for efficient integration and marketization operation of distributed user resources, and the system is suitable for large-scale popularization and application. And a fair, transparent and efficient spot transaction mechanism is provided for the electric power spot market.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and in particular to a method for clearing a power spot market taking into account the participation of multiple subjects in aggregation. Background Art

[0002] With the continuous deepening of power market reform and the rapid advancement of the construction of new power systems, the supply and demand sides of the power market bidding have undergone tremendous changes, and the power spot market has become a key reform direction. From the perspective of the main body of the power generation side, with the large-scale addition of new energy sources such as wind power and photovoltaics and energy storage to the power grid, the output of traditional power generating units has been affected, and the power structure has changed from controllable and continuous output characteristics to strong uncertainty and weak controllability characteristics. The addition of new energy and energy storage has brought dual challenges to the power balance and power security of the large power grid. From the perspective of the main body of the load power consumption side, most of the traditional spot market clearing adopts a unilateral bidding model, and users do not actually participate in the spot market competition. In order to promote the optimal allocation of resources and improve market efficiency, the load side has begun to participate in the market clearing mechanism from reporting quantity without quoting to reporting quantity and quoting. In the new environment, the demand side shows the characteristics of large regional differences and large changes in market supply and demand relations. This has brought huge challenges to the efficient participation of the power generation side and the demand side in the clearing of the power spot market. Therefore, it is urgent to efficiently integrate massive distributed resources to participate in spot transactions, fully mobilize the adjustable resources in all links of the power grid, especially the numerous load-side aggregation resources and energy storage resources and other new entities to participate in the electricity market, promote the efficient and safe consumption of renewable energy, and build a fair, safe and efficient electricity spot trading market.

[0003] The present invention integrates the influence of multiple subjects in the spot market, aggregates demand-side users to participate in market bidding through a clustering algorithm, and constructs an electricity spot market clearing method involving multiple subjects such as traditional generators, new energy generators, energy storage, and adjustable loads. By analyzing the complex constraints of multiple factors, it can efficiently respond to market mechanisms, conduct reasonable market clearing and settlement, improve the market's price clearing efficiency, provide technical support and experience reference for electricity market reform, and promote the improvement and innovation of electricity market rules and systems. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the shortcomings of the prior art, the present invention provides a method for clearing the electricity spot market taking into account the participation of multiple entities in the aggregate, which has the advantages of providing a fair, transparent and efficient spot trading mechanism for the electricity spot market, achieving the optimal configuration and utilization of decentralized user resources, and promoting market fairness and transparency, and solving the problems of large regional differences and large changes in market supply and demand relationships.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method for clearing the electricity spot market considering the aggregation of multiple subjects, including the design of an adjustable load-side electricity consumption clustering algorithm, the design of an algorithm considering the clearing of the energy storage charging and discharging market, the construction of a multi-subject aggregation participating in the market clearing model, constraint condition analysis and model solving, specifically including the following steps:

[0008] 1. By analyzing the electricity consumption characteristics of heterogeneous distributed users, coupled clustering is performed on the adjustable load side, and the energy storage clearing algorithm is designed by considering the participation of energy storage power sources in bilateral bidding;

[0009] 2. Construct a bilateral electricity spot market clearing strategy model involving multiple entities such as large-scale distributed power generation, energy storage, and adjustable loads;

[0010] 3. Conduct constraint analysis on strategic grid operation, traditional power supply and new energy output operation, energy storage charging and discharging operation, etc.;

[0011] 4. Propose a solution based on the established model and use the solution to obtain the result.

[0012] Preferably, the design of the adjustable load side electricity consumption clustering algorithm includes obtaining the adjustable load side electricity consumption market demand information, analyzing the historical electricity consumption data of heterogeneous distributed users, clustering power users based on the changing rules and correlations of the user's physical-market characteristics, and building the coupling relationship between users. The specific steps are as follows:

[0013] 1) Obtain the basic industry classification of users, and then sort the users' electricity loads in different time periods according to the preset sorting rules, and obtain the starting time point sets respectively;

[0014] 2) Obtain the preset ranking according to the preset selection rules, and then h =round(m / (k+1))*h-1 to obtain the ranking of the preset number of clusters, where P is the hth ranking, 1≤h≤k, k is the number of clusters, and m is the number of power load users. Based on this, the load-side power demand function is predicted. is the load forecast value of load node b at the power consumption side during period t, through Participate in bidding transactions in the spot electricity market.

[0015] Preferably, the algorithm design considering the energy storage charging and discharging market clearing needs to consider the participation of energy storage in bilateral bidding, and construct a clearing strategy considering energy storage based on the large-scale decentralized user power load forecast data, traditional power sources and new energy power generation output forecast data, with the goal of maximizing the charging and discharging benefits.

[0016]

[0017] Among them, Tmax and Tmin are the time periods with the highest and lowest electricity prices in the time-of-use electricity price. is the time-of-use electricity price at the node where the energy storage S is located during period t, are the charging power and discharging power of the energy storage S period t, ΔT is the period interval, and the constraints are:

[0018]

[0019] in, They are the maximum discharge power and maximum charging power of energy storage S, respectively. is the maximum charge limit of energy storage S, γ s It is the loss coefficient of energy storage S converted to the charging side.

[0020] Preferably, the model for constructing a market clearing strategy in which multiple subjects participate in the aggregation includes obtaining the market declaration information on the power generation side, taking the optimal power generation cost as the goal, and constructing a power spot market clearing method with the bilateral participation of multiple subjects including large-scale distributed power sources, energy storage, and adjustable loads, wherein the traditional power source declares power in a time-divided manner, and the new energy source declares flexible power in a way of supplementing the traditional power source. Energy storage can participate in the bilateral market. Under the traditional time-divided power declaration mode, the maximum and minimum power generation output, the maximum and minimum climbing capacity of the generator sets in operation of the power generation subject, the prices of each segment and the power generation output limits of each segment, the new energy power generation subject declares the maximum power generation output of the corresponding stage power generation and price, and the objective function obtains the minimum power generation cost. The model is as follows:

[0021]

[0022] Among them, Min C UC is the optimization target of the combined model, NG is the number of traditional power generation units using the time-based output declaration mode; NH is the number of new energy sources using the flexible trading mode to supplement traditional power sources; NT is the number of optimized time periods under the segmented output declaration mode, ΔT is the corresponding time interval, and NHT is the number of hours in the optimized time period, and both should satisfy NT*ΔT=NHT; is the clearing price of the traditional power generator set g in period t, is the clearing power output of the traditional power generator set in period g; is the start-stop cost of traditional power supply, They are respectively the startup state variable and the shutdown state variable of the traditional power supply g; They are the declared price and electricity quantity for flexible trading of new energy in the hth period and htth period, is the state variable of whether the new energy h-th period flexible transaction is successful, is the charging power of the energy storage during period S, The declared price of the electric energy storage S converted to the charging side, The curtailed wind and solar power of renewable energy in period n and t.

[0023] Preferably, the constraint analysis includes power grid operation constraints, traditional power supply operation constraints, energy storage power supply operation constraints and new energy power supply operation constraints;

[0024] 1) Power grid operation constraints

[0025] The power generation side includes the clearing power generation output of traditional power generation units, the clearing power generation output of new energy sources, and the clearing output of energy storage charging and discharging. The power generation side output is balanced with the load on the demand side, and the power generation side output transmission capacity must be between the maximum and minimum transmission capacities. The formula is as follows:

[0026]

[0027] in, are the net charging and discharging power and charging power of energy storage s in time period t, is the power load of node b in period t, NB is the number of load nodes, are the maximum and minimum transmission capacities of the operating section ns, G ns,g , G ns,n , G ns,s , G b,n They are the flow transfer distribution factors of the operating section ns and the traditional power source g, the new energy source n, the electric energy storage s participating in the bilateral transaction, and the load node b respectively; and they are the predicted maximum output of the new energy source n in the time period t.

[0028] 2) Traditional power supply operation constraints

[0029] Considering the upper and lower limits of traditional power output, clearing price constraints, and ramp capability constraints, the formula is as follows:

[0030]

[0031]

[0032] in, They are the maximum and minimum power output of the traditional power source g, They are the maximum and minimum climbing capabilities of the traditional power supply g, They are respectively the power generation output of renewable energy in period n and t, and the abandoned wind and solar power in period n and t, is the predicted maximum output of the new energy source in period n and t, NP is the maximum number of declared sections in the segmented power declaration mode of the traditional power source, They are the upper and lower limits of the power output of each section respectively. Since the power output of each section is continuous, the maximum power output of the previous section is equal to the minimum power output of the next section.

[0033] 3) Energy storage power supply operation constraints

[0034] Considering the maximum discharge and maximum charging power constraints of the energy storage power supply, losses also need to be considered during the charging and discharging process. The formula is as follows:

[0035]

[0036] in, They represent the charging and discharging power of the energy storage S at time t respectively.

[0037] 4) Constraints on operation of new energy power sources

[0038] According to the operation demand of new energy, considering the constraints of power generation during the optimization period, the power generation limit constraints during the specific period, and the operating conditions that must be met such as the declared power, the formula is as follows:

[0039]

[0040] Among them, E H,Min 、E H,Max They are the upper and lower limits of the total renewable energy power generation during the entire optimization period. They are the upper and lower limits of the power generation of the new energy h during the entire optimization period according to the operation needs, They are the upper and lower limits of the total power generation of all renewable energy sources in a specific period of time t.

[0041] Preferably, the model is solved by using commercial software such as Cplex or adopting a branch and bound optimization method to solve a power spot market clearing model that takes into account the participation of multiple entities including distributed power sources, energy storage, and adjustable loads.

[0042] (III) Beneficial effects

[0043] Compared with the prior art, the present invention provides a method for clearing the electricity spot market considering the participation of multiple entities in the aggregate, which has the following beneficial effects:

[0044] This method considers the participation of multiple subjects in the electricity spot market clearing. By analyzing the electricity consumption change law based on the physical-market characteristics of the load power consumption side, clustering fitting is performed on the adjustable load side, and the impact of new energy, energy storage and other factors on the output of traditional power generating units is fully considered. A multi-subject aggregation participation strategy for the electricity spot market is constructed considering traditional units, new energy units, energy storage power, adjustable loads, etc., to alleviate the non-real-time balance of source and load, improve the local consumption capacity of new energy, and provide technical support for the efficient integration and market-oriented operation of distributed user resources. It provides a fair, transparent and efficient spot trading mechanism for the electricity spot market, realizes the optimal allocation and utilization of distributed user resources, promotes market fairness and transparency, provides technical support and experience reference for electricity market reform, promotes the improvement and innovation of electricity market rules and systems, and helps achieve national goals and the implementation of new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic diagram of the method for clearing the electricity spot market that considers the participation of multiple entities in the electricity spot market proposed by the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Embodiment A method for clearing the electricity spot market by considering the aggregation of multiple subjects, firstly analyzes the user's physical-market characteristics, clusters the adjustable load side users using the K-means clustering algorithm, then considers the optimal benefits of energy storage in the spot market, constructs a market clearing strategy involving traditional generators, new energy generators, energy storage, adjustable load side, etc., and analyzes a variety of realistic constraints to model and solve them. The method comprises the following steps:

[0048] Step 1: First, analyze the user-side user physical-market characteristics, obtain the preset ranking according to the preset selection rules, and then use the formula P h =round(m / (k+1))*h-1Get the ranking of the preset number of clusters and predict the load side power demand function

[0049] Step 2: Consider energy storage to participate in bilateral bidding, and build a clearing strategy that considers energy storage based on the forecast data of large-scale decentralized user power load, traditional power sources and new energy power generation output forecast data to achieve the goal of maximizing charging and discharging benefits. The formula is:

[0050] Step 3: Obtain the market declaration information on the power generation side, and with the goal of optimizing the power generation cost, construct a power spot market clearing method with bilateral participation of heterogeneous distributed users such as large-scale distributed power sources, energy storage, and adjustable loads. The formula is:

[0051]

[0052] Step 4: Analyze various realistic constraints, including grid operation, traditional power supply operation, energy storage power supply operation, and new energy power supply operation.

[0053] Step 5: Use commercial software such as Cplex or adopt branch and bound optimization method to solve the model.

[0054] To sum up, the purpose of the present invention is to overcome the shortcomings of the prior art, comprehensively consider the impact of distributed renewable energy on grid operation, the participation of energy storage in bilateral bidding, and the participation of adjustable load side in quotation market bidding, and provide a method for clearing the electricity spot market that considers the aggregation of multiple subjects, and analyzes the constraints of multiple influencing factors, and then formulates an efficient and fair market clearing strategy. Through the present invention, efficient bidding operation between the power generation side such as new energy, energy storage, and traditional units and the power consumption side of adjustable loads is achieved, thereby improving the fairness, flexibility and economy of the electricity market.

[0055] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise one" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0056] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for clearing the electricity spot market considering the participation of multiple entities, characterized by: It includes the design of an adjustable load-side electricity clustering algorithm, the design of an algorithm considering the clearing of the energy storage charging and discharging market, the construction of a multi-agent aggregation participating in the market clearing model, constraint analysis and model solving, specifically including the following steps: 1) By analyzing the electricity consumption characteristics of heterogeneous distributed users, coupled clustering is performed on the adjustable load side electricity consumption, and the energy storage clearing algorithm is designed considering the participation of energy storage power sources in bilateral bidding; 2) Construct a bilateral trading electricity spot market clearing strategy model involving multiple entities such as large-scale distributed power generation, energy storage, and adjustable loads; 3) Conduct constraint analysis on strategic grid operation, traditional power supply and new energy output operation, energy storage charging and discharging operation, etc.; 4) Propose a solution method based on the established model and use the solution method to obtain the result.

2. A method for clearing the electricity spot market considering the participation of multiple entities in the aggregate as claimed in claim 1, characterized in that: The design of the adjustable load side electricity clustering algorithm includes obtaining the adjustable load side electricity market demand information, analyzing the historical electricity consumption data of heterogeneous distributed users, clustering power users based on the changing rules and correlation of the user's physical-market characteristics, and building the coupling relationship between users. The specific steps are as follows: 1) Obtain the basic industry classification of users, and then sort the users' electricity loads in different time periods according to the preset sorting rules, and obtain the starting time point sets respectively; 2) Obtain the preset ranking according to the preset selection rules, and then select the ranking according to the formula P h =round(m / (k+1))*h-1 to obtain the ranking of the preset number of clusters, where P is the hth ranking, 1≤h≤k, k is the number of clusters, and m is the number of power load users. Based on this, the load-side power demand function is predicted. is the load forecast value of load node b at the power consumption side during period t, through Participate in bidding transactions in the spot electricity market.

3. A method for clearing the electricity spot market considering the participation of multiple entities in the aggregate as claimed in claim 2, characterized in that: The algorithm design considering the energy storage charging and discharging market clearing needs to consider the participation of energy storage in bilateral bidding, and build a clearing strategy considering energy storage based on the large-scale decentralized user power load forecast data, traditional power sources and new energy power generation output forecast data, with the goal of maximizing the charging and discharging benefits.

4. A method for clearing the electricity spot market considering the participation of multiple entities in the aggregate as claimed in claim 3, characterized in that: The model for constructing a market clearing strategy with the participation of multiple subjects includes obtaining the market declaration information on the power generation side, taking the optimal power generation cost as the goal, and constructing a power spot market clearing method with the bilateral participation of multiple subjects including large-scale distributed power sources, energy storage, and adjustable loads. Traditional power sources declare power in a time-sharing manner, and new energy uses a flexible power declaration method to supplement traditional power sources. Energy storage can participate in the bilateral market. Under the traditional time-sharing power declaration mode, the power generation subject has the maximum and minimum power generation output of the generator sets in operation, the maximum and minimum climbing capabilities, the prices of each segment and the power generation output limits of each segment. The new energy power generation subject declares the power generation and price of the corresponding stage with the maximum power generation output.

5. A method for clearing the electricity spot market considering the participation of multiple entities in the aggregate as claimed in claim 4, characterized in that: The constraint condition analysis includes power grid operation constraints, traditional power supply operation constraints, energy storage power supply operation constraints and new energy power supply operation constraints.

6. A method for clearing the electricity spot market considering the participation of multiple entities in the aggregate as claimed in claim 5, characterized in that: The model is solved by using commercial software such as Cplex or adopting a branch and bound optimization method to solve a multi-agent aggregation participation electricity spot market model considering distributed power sources, energy storage, and adjustable loads.

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