Electrolytic aluminum load group and wind power double-layer multi-time scale bidding method

Abstract

The invention discloses an electrolytic aluminum load group and wind power double-layer multi-time scale bidding method, which belongs to the technical field of new energy power generation, and comprises the following steps: constructing a wind power generator bidding model comprising a wind power generator benefit function and wind power plant group wind curtailment power constraint, and constructing an electrolytic aluminum load aggregator real-time bidding model; the method comprises the steps of bidding a wind power generator-electrolytic aluminum load aggregator real-time bilateral bidding solution, electrolytic aluminum load real-time electric quantity distribution and electrolytic aluminum load daily final electricity price optimization based on a KKT condition, changes an interaction mechanism of simultaneous bidding between each electrolytic aluminum load and the wind power generator under a traditional double-layer market structure, and improves the bidding efficiency of each electrolytic aluminum load. By introducing a real-time bidding strategy and a daily end pricing strategy, fair pricing among electrolytic aluminum loads is realized on the premise of ensuring accurate consumption of wind curtailment power.

Description

Technical Field

[0001] The present invention belongs to the technical field of renewable energy power generation, and specifically is a double-layer multi-time scale bidding method for an electrolytic aluminum load group and wind power. Background Art

[0002] With the increasing proportion of renewable energy in the power system, how to effectively absorb wind power has become one of the main challenges facing the power system. As a large-scale adjustable industrial load, electrolytic aluminum load has great potential to participate in the regulation process of the power market, which can effectively improve the utilization rate of wind power and promote the development of the power system towards clean and low-carbon. The electrolytic cell in the electrolytic aluminum production process has significant load regulation ability, but this regulation behavior is limited by the complex temperature-energy relationship. In this context, how to aggregate the electrolytic aluminum load group through a systematic method and enable it to participate in power market transactions efficiently has become an important way to solve the problem of wind power absorption. However, existing research on the bidding and trading mechanism for the interaction between electrolytic aluminum load and wind farm is still relatively limited, especially in the multi-level market structure, the interaction between transactions at different levels and different time scales is ignored, which hinders the balance between the stability of the long-term market and the flexibility of the short-term market. Therefore, it is particularly important to propose an innovative double-layer multi-time scale bidding method to cope with the dynamic interaction needs of the electrolytic aluminum load group and wind farm at multiple time scales. This method not only requires the coordination of participants at different market levels, but also improves the dynamic matching and market responsiveness of the electrolytic aluminum load group and wind power through a multi-time-scale price mechanism, thereby achieving more efficient power resource scheduling and optimizing wind power consumption effects. Summary of the invention

[0003] The technical solution of the present invention is as follows: a double-layer multi-time scale bidding method for electrolytic aluminum load group and wind power, comprising:

[0004] S1 builds a bidding model for wind power generators that includes the wind power generator benefit function and wind farm group wind power constraints;

[0005] S2 builds a real-time bidding model for electrolytic aluminum load aggregators;

[0006] S3 Real-time bilateral bidding solution between wind power generators and electrolytic aluminum load aggregators based on KKT conditions;

[0007] S4 real-time electricity distribution of electrolytic aluminum load;

[0008] Optimization of the end-of-day electricity price for S5 electrolytic aluminum load.

[0009] Furthermore, step S1 specifically comprises:

[0010] Benefits of wind power generators The maximum is the competitive optimization target of wind power generators, and the following objective function is constructed:

[0011]

[0012] Where, t is the real-time trading period of the electricity market; is the price of wind power; Penalize electricity prices for wind curtailment; The price of electricity for transmission and distribution; The amount of abandoned wind power to be absorbed; is the available abandoned wind power; the bidding optimization of wind power generators must meet the requirement that the abandoned wind power consumed is not greater than the available abandoned wind power, and the following constraints are obtained:

[0013]

[0014] Furthermore, step S2 is specifically the electrolytic aluminum load aggregator benefit function:

[0015]

[0016] In the formula, is the thermal power price during period t; is the interactive electricity price between the electrolytic aluminum load group and wind power at time t; is the abandoned wind power consumed by the electrolytic aluminum load group during period t; is the thermal power consumed by the electrolytic aluminum load group during period t; the equivalent aggregation temperature-energy dynamic characteristic model of the electrolytic aluminum load group:

[0017]

[0018] In the formula, and are the equivalent temperatures of the electrolytic aluminum load group during period t-1 and period t respectively; is the power consumption of the electrolytic aluminum load group during period t; is the feed rate of the electrolytic aluminum load group during period t; The amount of heat required to heat each ton of alumina to the reaction temperature; E is the raw material reaction rate of the electrolytic aluminum load group during period t; rea The heat required for electrolysis of each ton of alumina raw material; T room is the indoor temperature; G Als Equivalent thermal conductivity of the electrolytic cell of the electrolytic aluminum load group; C Als is the equivalent heat capacity of the electrolyte of the electrolytic aluminum load group; Δt is the real-time trading cycle; the price-temperature demand function of the electrolytic aluminum load group:

[0019]

[0020] In the formula, is the equivalent electricity price of the electrolytic aluminum load group; Als-max is the elastic price ceiling of the electrolytic aluminum load group; Als-min is the lower limit of the elastic price of the electrolytic aluminum load group; T Als-max T is the upper limit of equivalent temperature of electrolytic aluminum load group; Als-min The lower limit of equivalent temperature of electrolytic aluminum load group and price balance constraint:

[0021]

[0022] Furthermore, step S3 specifically converts the optimization problems of (2-1) to (2-5) into Karush-Kuhn-Tucker (KKT) conditions, considers the constraints (1-2), takes (1-1) as the objective function, performs optimization and solves, and obtains the transaction price and transaction volume in period t:

[0023] Further, step S4 is specifically, in the trading period t, taking the minimum maximum temperature dissatisfaction index in the electrolytic aluminum load numbered 1, 2, ..., GN as the optimization target, as follows:

[0024]

[0025] In the formula, It is the temperature dissatisfaction index of electrolytic aluminum load g during period t; is the temperature of the electrolytic cell with electrolytic aluminum load g during period t; The optimal reaction temperature of the electrolytic cell with electrolytic aluminum load g and the power distribution constraint are:

[0026]

[0027] Wherein, g is the electrolytic aluminum load serial number, g = 1, 2, ..., GN; GN is the total number of electrolytic aluminum loads; is the power consumption of the g-th electrolytic aluminum load, and the temperature-energy dynamic characteristic constraint of the electrolytic aluminum load:

[0028]

[0029] In the formula, and are the electrolytic cell temperatures of the electrolytic aluminum load g during the period t-1 and period t respectively; is the feed rate of electrolytic aluminum load g during period t; The amount of heat required to heat each ton of alumina to the reaction temperature; E is the raw material reaction rate of electrolytic aluminum load g in time period t; rea The heat required for electrolysis of one ton of alumina raw material; h elt is the heat dissipation coefficient; Aelt is the heat dissipation area of ​​the tank; c elt is the specific heat capacity of the electrolyte; m elt is the electrolyte mass; Δt is the real-time trading period. Based on the above optimization model, the power consumption of electrolytic aluminum load g during trading period t is finally obtained.

[0030] Further, step S5 is specifically, in all trading cycles of a day with t=1, 2, ..., TN, execute 1, 2, 3, 4, and obtain and The optimization goal is to minimize the maximum electricity price dissatisfaction index in the electrolytic aluminum load numbered 1, 2, ..., GN, as follows:

[0031]

[0032] In the formula, is the electricity price dissatisfaction index of electrolytic aluminum load g; is the expected end-of-day electricity price for electrolytic aluminum load g; is the end-of-day electricity price of electrolytic aluminum load g, and the expected end-of-day electricity price constraint is:

[0033]

[0034] In the formula, is the expected real-time price of electrolytic aluminum load g; is the elastic price ceiling of electrolytic aluminum load g; is the lower limit of the elastic price of electrolytic aluminum load g; is the upper temperature limit of electrolytic aluminum load g; Aluminum electrolytic load The lower limit of temperature of aluminum electrolytic load g, electricity price balance constraint:

[0035]

[0036] In the formula, is the virtual real-time electricity price of electrolytic aluminum load g; is the aggregation cost coefficient of electrolytic aluminum load g.

[0037] The beneficial effects of the present invention are:

[0038] The present invention changes the interactive mechanism of simultaneous bidding between various electrolytic aluminum loads and wind power generators under the traditional two-tier market structure, and achieves fair pricing among various electrolytic aluminum loads under the premise of ensuring accurate absorption of abandoned wind power by introducing real-time bidding strategy and end-of-day pricing strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 For the method flow.

[0040] Figure 2 Schematic diagram of electrolytic aluminum load and wind power trading electricity price. DETAILED DESCRIPTION

[0041] Building a real-time bidding model for wind power generators

[0042] A bidding model for wind power generators including the wind power generator benefit function and wind farm group wind power constraints is constructed as follows:

[0043] Wind power generator benefit function

[0044] Benefits of wind power generators The maximum is the competitive optimization target of wind power generators, and the following objective function is constructed:

[0045]

[0046] Where, t is the real-time trading period of the electricity market; is the price of wind power; Penalize electricity prices for wind curtailment; The price of electricity for transmission and distribution; The amount of abandoned wind power to be absorbed; To utilize the abandoned wind power.

[0047] Wind farm group wind power curtailment constraints

[0048] The competitive optimization of wind power generators must meet the requirement that the amount of abandoned wind power consumed is not greater than the amount of abandoned wind power available, and the following constraints are obtained:

[0049]

[0050] Constructing a real-time bidding model for electrolytic aluminum load aggregators

[0051] Electrolytic aluminum load aggregator benefit function

[0052]

[0053] In the formula, is the thermal power price during period t; is the interactive electricity price between the electrolytic aluminum load group and wind power at time t; is the abandoned wind power consumed by the electrolytic aluminum load group during period t; is the thermal power consumed by the electrolytic aluminum load group during period t;

[0054] Equivalent Aggregation Temperature-Energy Dynamic Characteristic Model of Electrolytic Aluminum Load Group

[0055]

[0056] In the formula, and are the equivalent temperatures of the electrolytic aluminum load group during period t-1 and period t respectively; is the power consumption of the electrolytic aluminum load group during period t; is the feed rate of the electrolytic aluminum load group during period t; The amount of heat required to heat each ton of alumina to the reaction temperature; E is the raw material reaction rate of the electrolytic aluminum load group during period t; rea The heat required for electrolysis of each ton of alumina raw material; T room is the indoor temperature; G Als Equivalent thermal conductivity of the electrolytic cell of the electrolytic aluminum load group; C Als is the electrolyte equivalent heat capacity of the electrolytic aluminum load group; Δt is the real-time trading cycle.

[0057] Price-temperature demand function of electrolytic aluminum load group

[0058]

[0059] In the formula, is the equivalent electricity price of the electrolytic aluminum load group; Als-max is the elastic price ceiling of the electrolytic aluminum load group; Als-min is the lower limit of the elastic price of the electrolytic aluminum load group; T Als-max T is the upper limit of equivalent temperature of electrolytic aluminum load group; Als-min Lower limit of equivalent temperature of electrolytic aluminum load group.

[0060] Price Equilibrium Constraint

[0061]

[0062] Solution of real-time bilateral bidding between wind power generators and aluminum electrolytic load aggregators based on KKT conditions

[0063] Convert the optimization problems in 2 to KKT conditions, convert the optimization problems (2-1) to (2-5) to Karush-Kuhn-Tucker (KKT) conditions, consider the constraints (1-2), and use (1-1) as the objective function to optimize and solve, and obtain the transaction price and transaction volume in period t:

[0064] Real-time power distribution of electrolytic aluminum load

[0065] Taking (4-1) as the optimization objective and (4-2) and (4-3) as constraints, the total transaction power obtained by optimizing the bidding in 3 is Decomposed into each electrolytic aluminum load. The specific optimization allocation model is as follows:

[0066] Optimization goals

[0067] In the t trading period, the optimization goal is to minimize the maximum temperature dissatisfaction index in the electrolytic aluminum load numbered 1, 2, ..., GN, as follows:

[0068]

[0069] In the formula, It is the temperature dissatisfaction index of electrolytic aluminum load g during period t; is the temperature of the electrolytic cell with electrolytic aluminum load g during period t; It is the optimal reaction temperature of the electrolytic cell with electrolytic aluminum load g.

[0070] 4.2 Power Distribution Constraints

[0071]

[0072] Wherein, g is the electrolytic aluminum load serial number, g = 1, 2, ..., GN; GN is the total number of electrolytic aluminum loads; is the power consumption of the g-th electrolytic aluminum load.

[0073] Temperature-energy dynamic characteristics constraints of electrolytic aluminum load

[0074]

[0075] In the formula, and are the electrolytic cell temperatures of the electrolytic aluminum load g during the period t-1 and period t respectively; is the feed rate of electrolytic aluminum load g during period t; The amount of heat required to heat each ton of alumina to the reaction temperature; E is the raw material reaction rate of electrolytic aluminum load g in time period t; rea The heat required for electrolysis of one ton of alumina raw material; h elt is the heat dissipation coefficient; A elt is the heat dissipation area of ​​the tank; c elt is the specific heat capacity of the electrolyte; m elt is the electrolyte mass; Δt is the real-time trading cycle.

[0076] Based on the above optimization model, the power consumption of electrolytic aluminum load g during trading period t is finally obtained:

[0077] Optimization of the end-of-day electricity price for electrolytic aluminum load

[0078] In all trading cycles of a day with t=1,2,…,TN, execute 1, 2, 3, 4 and get and Finally, taking (5-1) as the optimization objective and (5-2) to (5-5) as the constraints, the end-of-day electricity price of each electrolytic aluminum load is optimized and determined. as follows:

[0079] Optimization goals

[0080] The optimization goal is to minimize the maximum electricity price dissatisfaction index in the electrolytic aluminum load numbered 1, 2, ..., GN, as follows:

[0081]

[0082] In the formula, is the electricity price dissatisfaction index of electrolytic aluminum load g; is the expected end-of-day electricity price for electrolytic aluminum load g; is the end-of-day electricity price for electrolytic aluminum load g.

[0083] Expected end-of-day electricity price constraint

[0084]

[0085] In the formula, is the expected real-time price of electrolytic aluminum load g; is the elastic price ceiling of electrolytic aluminum load g; is the lower limit of the elastic price of electrolytic aluminum load g; is the upper temperature limit of electrolytic aluminum load g; Electrolytic aluminum load The lower limit of temperature of electrolytic aluminum load g.

[0086] Electricity price balance constraint

[0087]

[0088] In the formula, is the virtual real-time electricity price of electrolytic aluminum load g; is the aggregation cost coefficient of electrolytic aluminum load g.

[0089] The above is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent replacement and improvement made by any person skilled in the art within the technical scope disclosed by the present invention and within the spirit and principle of the present invention shall be covered within the protection scope of the present invention. At the same time, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0090] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

Claims

1. A dual-layer multi-time scale bidding method for electrolytic aluminum load group and wind power, characterized in that: The following steps are involved: S1 builds a bidding model for wind power generators that includes the wind power generator benefit function and wind farm group wind power constraints; S2 builds a real-time bidding model for electrolytic aluminum load aggregators; S3 Real-time bilateral bidding solution between wind power generators and electrolytic aluminum load aggregators based on KKT conditions; S4 real-time electricity distribution of electrolytic aluminum load; Optimization of the end-of-day electricity price for S5 electrolytic aluminum load.

2. The method for adjusting the electrolytic aluminum load group and wind power in two layers and multiple time scales according to claim 1 is characterized in that: Step S1 specifically comprises: Benefits of wind power generators The maximum is the competitive optimization target of wind power generators, and the following objective function is constructed: Where, t is the real-time trading period of the electricity market; is the price of wind power; Penalize electricity prices for wind curtailment; The price of electricity for transmission and distribution; The amount of abandoned wind power to be absorbed; is the available abandoned wind power; the bidding optimization of wind power generators must meet the requirement that the abandoned wind power consumed is not greater than the available abandoned wind power, and the following constraints are obtained:

3. The method for adjusting the electrolytic aluminum load group and wind power in two layers and multiple time scales according to claim 1 is characterized in that: Step S2 is specifically the electrolytic aluminum load aggregator benefit function: In the formula, is the thermal power price during period t; is the interactive electricity price between the electrolytic aluminum load group and wind power at time t; is the abandoned wind power consumed by the electrolytic aluminum load group during period t; is the thermal power consumed by the electrolytic aluminum load group during period t; the equivalent aggregation temperature-energy dynamic characteristic model of the electrolytic aluminum load group: In the formula, and are the equivalent temperatures of the electrolytic aluminum load group during period t-1 and period t respectively; is the power consumption of the electrolytic aluminum load group during period t; is the feed rate of the electrolytic aluminum load group during period t; The amount of heat required to heat each ton of alumina to the reaction temperature; E is the raw material reaction rate of the electrolytic aluminum load group during period t; rea The amount of heat required to electrolyze each ton of alumina raw material; T room is the indoor temperature; G Als Equivalent thermal conductivity of the electrolytic cell of the electrolytic aluminum load group; C Als is the equivalent heat capacity of the electrolyte of the electrolytic aluminum load group; Δt is the real-time trading cycle; the price-temperature demand function of the electrolytic aluminum load group: In the formula, is the equivalent electricity price of the electrolytic aluminum load group; Als-max It is an elastic price cap for the electrolytic aluminum load group; λ Als-min is the lower limit of the elastic price of the electrolytic aluminum load group; T Als-max It is the upper limit of equivalent temperature of electrolytic aluminum load group; T Als-min The lower limit of equivalent temperature of electrolytic aluminum load group and price balance constraint:

4. The method for bidding for electrolytic aluminum load group and wind power at two layers and multiple time scales according to claim 1 is characterized in that: Step S3 specifically converts the optimization problems of (2-1) to (2-5) into Karush-Kuhn-Tucker (KKT) conditions, considers the constraints (1-2), takes (1-1) as the objective function, performs optimization and solves, and obtains the transaction price and transaction volume in period t:

5. The method for bidding for electrolytic aluminum load group and wind power at two layers and multiple time scales according to claim 1 is characterized in that: Step S4 is specifically, in the trading period t, taking the minimum maximum temperature dissatisfaction index in the electrolytic aluminum load numbered 1, 2, ..., GN as the optimization target, as follows: In the formula, It is the temperature dissatisfaction index of electrolytic aluminum load g during period t; is the temperature of the electrolytic cell with electrolytic aluminum load g during period t; The optimal reaction temperature of the electrolytic cell with electrolytic aluminum load g and the power distribution constraint are: Wherein, g is the electrolytic aluminum load serial number, g = 1, 2, ..., GN; GN is the total number of electrolytic aluminum loads; is the power consumption of the g-th electrolytic aluminum load, and the temperature-energy dynamic characteristic constraint of the electrolytic aluminum load: In the formula, and are the electrolytic cell temperatures of the electrolytic aluminum load g during the period t-1 and period t respectively; is the feed rate of electrolytic aluminum load g during period t; The amount of heat required to heat each ton of alumina to the reaction temperature; E is the raw material reaction rate of electrolytic aluminum load g in time period t; rea The heat required for electrolysis of one ton of alumina raw material; h elt is the heat dissipation coefficient; A elt is the heat dissipation area of ​​the tank; c elt is the specific heat capacity of the electrolyte; m elt is the electrolyte mass; Δt is the real-time trading period. Based on the above optimization model, the power consumption of electrolytic aluminum load g during trading period t is finally obtained.

6. The method for bidding for electrolytic aluminum load group and wind power at two layers and multiple time scales according to claim 1 is characterized in that: Step S5 specifically includes executing 1, 2, 3, 4 in all trading cycles of a day with t=1, 2, ..., TN, and obtaining and The optimization goal is to minimize the maximum electricity price dissatisfaction index in the electrolytic aluminum load numbered 1, 2, ..., GN, as follows: In the formula, is the electricity price dissatisfaction index of electrolytic aluminum load g; is the expected end-of-day electricity price for electrolytic aluminum load g; is the end-of-day electricity price of electrolytic aluminum load g, and the expected end-of-day electricity price constraint is: In the formula, is the expected real-time price of electrolytic aluminum load g; is the elastic price ceiling of electrolytic aluminum load g; is the lower limit of the elastic price of electrolytic aluminum load g; is the upper temperature limit of electrolytic aluminum load g; Aluminum electrolytic load The lower limit of temperature of aluminum electrolytic load g, electricity price balance constraint: In the formula, is the virtual real-time electricity price of electrolytic aluminum load g; is the aggregation cost coefficient of electrolytic aluminum load g.

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