Self-scheduling method of industrial park participating in demand response market for virtual power plant

Abstract

The invention provides a self-scheduling method for an industrial park participating in a demand response market for a virtual power plant, and belongs to the technical field of power system automation, and the method comprises the steps: constructing an uncertainty factor model of a system, including electricity price, photovoltaic output, load and the like; constructing a deterministic optimization scheduling problem model based on complete information; constructing a two-stage minimum and maximum regret value optimization scheduling problem based on uncertainty factors, wherein the two-stage minimum and maximum regret value optimization scheduling problem comprises a system scheduling objective function and constraint conditions; according to the method, a two-stage minimum and maximum regret value optimization scheduling problem is reconstructed and decomposed to obtain a main problem and a sub-problem, the constructed optimization problem can be solved by adopting a column and constraint generation algorithm, and the method not only can improve the robustness and adaptability of a scheduling strategy, but also can improve the scheduling efficiency. And the regret value caused by uncertainty can be reduced to the greatest extent, so that scientific and reasonable scheduling decision support is provided for the industrial park to participate in the demand response market.

Description

Technical Field

[0001] The present invention relates to the field of power system automation technology, and in particular to a self-dispatching method for an industrial park participating in a demand response market for a virtual power plant. Background Art

[0002] With the development of energy internet and smart grid, virtual power plants, as a new type of energy management and scheduling method, have gradually attracted widespread attention. As a concentrated area of energy consumption and production, industrial parks have great load regulation potential and are an important part of virtual power plants. Incorporating industrial parks into the framework of virtual power plants and participating in the demand response market can effectively improve energy utilization efficiency, reduce operating costs, improve grid stability, and promote the absorption of renewable energy. However, due to the uncertainty of factors such as electricity prices, photovoltaic output and load, the scheduling optimization of industrial parks in demand response faces many challenges. Traditional scheduling strategies are often difficult to cope with these uncertainties, resulting in insufficient robustness and adaptability of scheduling results. Therefore, there is an urgent need for an optimization scheduling method that can fully consider uncertainty factors to achieve more efficient and stable energy management. The present invention proposes a self-scheduling strategy for industrial parks participating in the demand response market for virtual power plants. By constructing an uncertainty factor model, a deterministic optimization scheduling problem model and a two-stage minimum and maximum regret optimization scheduling problem based on uncertainty factors, the scheduling problem is reconstructed and decomposed. This method can not only improve the robustness and adaptability of the scheduling strategy, but also minimize the regret value caused by uncertainty, thereby providing scientific and reasonable scheduling decision support for industrial parks participating in the demand response market. Summary of the Invention

[0003] The main purpose of this paper is to propose a self-scheduling method for industrial parks participating in the demand response market, targeting virtual power plants. This method reconstructs and decomposes the scheduling problem by constructing an uncertainty factor model, a deterministic optimization scheduling problem model, and a two-stage minimum-maximum regret optimization scheduling problem based on uncertainty factors. This method not only improves the robustness and adaptability of the scheduling strategy but also minimizes the regret caused by uncertainty, thereby providing scientific and reasonable scheduling decision support for industrial parks participating in the demand response market.

[0004] To achieve the above objectives, embodiments of the present disclosure provide a self-scheduling method for an industrial park participating in a demand response market for a virtual power plant. The method is applied to a virtual power plant system comprising a photovoltaic unit, a power load, and an energy storage system. The method comprises:

[0005] Construct uncertainty factor model of the system;

[0006] Construct a deterministic optimization scheduling problem model based on complete information;

[0007] Construct a two-stage minimum-maximum regret optimization scheduling problem based on uncertainty factors;

[0008] Reconstruct the two-stage minimum-maximum regret optimization scheduling problem;

[0009] Decompose the two-stage minimum-maximum regret optimization scheduling problem;

[0010] According to the uncertainty factor model of the constructed system, the deterministic optimization scheduling problem model based on complete information, the two-stage minimum-maximum regret optimization scheduling problem based on uncertainty factors, the reconstruction of the two-stage minimum-maximum regret optimization scheduling problem, and the decomposition of the two-stage minimum-maximum regret optimization scheduling problem, a self-scheduling method for industrial parks participating in the demand response market for virtual power plants is constructed.

[0011] In some embodiments, the step of constructing the uncertainty factor model of the system includes:

[0012] Constructing a price uncertainty model for the electricity market;

[0013] Constructing a photovoltaic output uncertainty model;

[0014] Constructing power load uncertainty model;

[0015] According to the construction of the electricity market price uncertainty model, the construction of the photovoltaic output uncertainty model, and the construction of the power load uncertainty model, an uncertainty factor model of the system is constructed.

[0016] In some embodiments, constructing a deterministic optimization scheduling problem model based on complete information includes:

[0017] Construct the objective function of the deterministic optimization scheduling problem based on complete information;

[0018] Construct the constraints of deterministic optimization scheduling problem under complete information;

[0019] According to the said construction of the deterministic optimization scheduling problem objective function based on complete information and the said construction of the deterministic optimization scheduling problem constraint conditions based on complete information, a deterministic optimization scheduling problem model based on complete information is constructed.

[0020] In some embodiments, constructing a deterministic optimization scheduling problem objective function based on complete information includes:

[0021] Constructing a generator set operating cost model;

[0022] Construct an energy storage system operation aging model;

[0023] Construct transformer operation aging model;

[0024] Construct a transaction cost model for participating in the electricity market;

[0025] According to the construction of the generator set operation cost model, the construction of the energy storage system operation aging model, the construction of the transformer operation aging model, and the construction of the transformer operation aging model, a deterministic optimization scheduling problem objective function based on complete information is constructed.

[0026] In some embodiments, constructing the deterministic optimization scheduling problem constraint conditions under complete information includes:

[0027] Construct the generator set operation constraint model;

[0028] Construct an energy storage system operation constraint model;

[0029] Construct transformer operation constraint model;

[0030] Construct a model of constraints for participating in electricity market transactions;

[0031] Construct a system power balance constraint model;

[0032] According to the construction of the generator set operation constraint model, the construction of the energy storage system operation constraint model, the construction of the transformer operation constraint model, the construction of the transformer operation constraint model, and the construction of the system power balance constraint model, a deterministic optimization scheduling problem objective function based on complete information is constructed.

[0033] In some embodiments, constructing a two-stage minimum-maximum-regret optimization scheduling problem based on uncertainty factors includes:

[0034] Construct a two-stage minimum-maximum regret optimization scheduling problem objective function;

[0035] Construct constraints for a two-stage minimum-maximum regret optimization scheduling problem;

[0036] According to the objective function of constructing the two-stage minimum-maximum regret optimization scheduling problem and the constraint conditions of constructing the two-stage minimum-maximum regret optimization scheduling problem, a two-stage minimum-maximum regret optimization scheduling problem based on uncertainty factors is constructed.

[0037] In some embodiments, the reconstructing and decomposing the two-stage minimum-maximum-regret optimization scheduling problem includes:

[0038] Define the first-stage decision variables and the second-stage decision variables;

[0039] Reconstruct the objective function of the two-stage minimum-maximum regret optimization scheduling problem;

[0040] According to the definition of the first-stage decision variables and the second-stage decision variables, the reconstruction of the objective function of the two-stage minimum-maximum-regret optimization scheduling problem, and the decomposition of the optimization problem into a main problem and sub-problems, the two-stage minimum-maximum-regret optimization scheduling problem is reconstructed and decomposed.

[0041] In some embodiments, decomposing the two-stage minimum-maximum-regret optimization scheduling problem includes:

[0042] Define subproblems of max-min form;

[0043] Define auxiliary variables;

[0044] Construct an optimization model for the main problem;

[0045] According to the definition of sub-problems in the form of max-min, the definition of auxiliary variables, and the construction of the optimization model of the main problem, the two-stage minimum-maximum regret optimization scheduling problem is decomposed. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of a self-scheduling method for an industrial park to participate in a demand response market for a virtual power plant according to the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0048] With the development of the Energy Internet and smart grids, virtual power plants (VPPs) have gradually attracted widespread attention as a new energy management and scheduling method. Industrial parks, as concentrated areas of energy consumption and production, have significant load regulation potential and are a key component of VPPs. Incorporating industrial parks into the framework of VPPs and participating in the demand response market can effectively improve energy efficiency, reduce operating costs, enhance grid stability, and promote the integration of renewable energy. However, due to the uncertainty of factors such as electricity prices, photovoltaic output, and load, the scheduling optimization of industrial parks in demand response faces many challenges. Traditional scheduling strategies often have difficulty in coping with these uncertainties, resulting in insufficient robustness and adaptability of scheduling results. Therefore, an optimized scheduling method that can fully account for these uncertainties is urgently needed to achieve more efficient and stable energy management.

[0049] Based on this, the main purpose of the embodiments of the present disclosure is to propose a self-scheduling method for industrial parks to participate in the demand response market for virtual power plants, which is specifically explained through the following embodiments.

[0050] Reference Figure 1 According to an embodiment of the present disclosure, a self-scheduling method for an industrial park for a virtual power plant to participate in a demand response market is applied to a virtual power plant system, wherein the virtual power plant system includes a photovoltaic unit, a power load, and an energy storage system. A self-scheduling method for an industrial park for a virtual power plant to participate in a demand response market includes but is not limited to steps S110 to S150.

[0051] S110, build the uncertainty factor model of the system;

[0052] S120, construct a deterministic optimization scheduling problem model based on complete information;

[0053] S130, construct a two-stage minimum-maximum regret optimization scheduling problem based on uncertainty factors;

[0054] S140, reconstructing the two-stage minimum-maximum regret optimization scheduling problem;

[0055] S150, decomposing the two-stage minimum-maximum regret optimization scheduling problem.

[0056] In step S110, the uncertainty factor model of the system is constructed:

[0057]

[0058] Among them, u represents the uncertainty variable, U represents the uncertainty set; u Re (t),u L (t), They represent the photovoltaic output set, power load set and power market transaction price set respectively; Represents the predicted values of photovoltaic output, power load and power market transaction price; Indicates the minimum fluctuation of photovoltaic output, power load and power market transaction price; represents the maximum value of the fluctuation of photovoltaic output, power load and power market transaction price; γ Re , γ L 、 Represents the uncertainty factor adjustment coefficient.

[0059] In step S120, a deterministic optimization scheduling problem model based on complete information is constructed. The optimal solution profit Q(u) for a given scenario s can be obtained by solving the following deterministic problem. The system has the following components:

[0060] Generator set:

[0061] The operating cost of the generator set C(P G,i (t)) is usually expressed as the generated power P G,i The quadratic function form of (t) is as follows:

[0062]

[0063] Among them, a 1,i 、b 1,i 、c 1,i Represents the cost coefficient of the generator set.

[0064] The operating constraints of the generator set are as follows:

[0065]

[0066] in, and Indicates the minimum and maximum output of the generator set. and Indicates the ramp power limit of the generator set output.

[0067] Energy storage system:

[0068] The degradation cost of the electric energy storage unit is linearized and expressed as follows:

[0069]

[0070] Among them, a2, b2, c2, and d2 are the coefficients of battery degradation cost.

[0071] The constraints that the energy storage system must meet during operation include:

[0072]

[0073] in, and Represents the charging power P of the energy storage unit ch (t) and discharge power P dis The upper limit of (t); U bat (t) is a 0-1 variable indicating the charge and discharge state of the energy storage unit. When the value is 1, it indicates the charge state, and when the value is 0, it indicates the discharge state. SOC(t) indicates the charge state of the energy storage unit. min With SOC max are the upper and lower limits of the state of charge respectively; E bat (t) is the stored energy at time t, and are the maximum / minimum remaining capacity allowed for the energy storage unit during the scheduling process. The main purpose of this constraint is to prevent overcharge / overdischarge of energy storage and extend its service life; η ch and η dis are the charging and discharging energy efficiencies of the electric energy storage unit, respectively.

[0074] Therefore, the deterministic optimization scheduling problem model based on complete information is as follows:

[0075]

[0076] Among them, P Bid (t) represents the amount of regulation in the participating market; D T Determined by the specific performance indicators of the transformer model used, K T (t) is the ratio of transformer load to rated load; C T is the transformer purchase cost; T L The planned usage time of the transformer; and represents the real-time power purchase and sales caused by power imbalance during the day; ρ1 and ρ2 are penalty cost coefficients;

[0077] In step S130, a two-stage minimum-maximum regret optimization scheduling problem based on uncertainty factors is constructed. In the two-stage minimum-maximum regret problem, the decision of the first stage includes P G,i (t) and P Bid (t), the second stage is SOC(t). Define regret value Reg(P Bid (t),P G,i (t)):

[0078]

[0079] The goal of the min-max regret problem is to determine the best first-stage decision whose regret is minimized among all feasible first-stage decisions:

[0080]

[0081] st(3),(5)

[0082]

[0083] In step S140, the two-stage minimum-maximum regret optimization scheduling problem is reconstructed, and Y and X are defined as the first-stage decision variables and the second-stage decision variables of the minimum-maximum regret problem. Reg(Y) is expressed as follows:

[0084]

[0085] Among them, f(Y,s), g(Y), h(X), l(X), and m(X) correspond to each term of the objective function in (6).

[0086]

[0087] Since the generator operating cost will not have an impact on the second stage problem, g(Y) is removed from the optimization problem.

[0088] Therefore, the minimum maximum regret optimization scheduling problem can be expressed as follows:

[0089]

[0090] In step S150, the two-stage minimum-maximum regret optimization scheduling problem is decomposed.

[0091] From the problem definition:

[0092]

[0093] Using the auxiliary variable ε to represent the target value of the slave problem (12), the main problem is defined as follows:

[0094]

[0095] Among them, X l is a new variable added to the main problem at the lth iteration; and It is the result obtained from solving the problem in the lth iteration.

[0096] Compared with the existing technology, the method of the present invention can not only improve the robustness and adaptability of the scheduling strategy, but also minimize the regret value caused by uncertainty, thereby providing scientific and reasonable scheduling decision support for industrial parks to participate in the demand response market.

[0097] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the content disclosed in the present invention should be included in the protection scope recorded in the claims.

Claims

1. A self-scheduling method for industrial parks participating in a demand response market for virtual power plants, characterized in that: Applied to a virtual power plant system, the virtual power plant system includes a photovoltaic unit, a power load, and an energy storage system, and the method includes: Construct uncertainty factor model of the system; Construct a deterministic optimization scheduling problem model based on complete information; Construct a two-stage minimum-maximum regret optimization scheduling problem based on uncertainty factors; Reconstruct the two-stage minimum-maximum regret optimization scheduling problem; Decompose the two-stage minimum-maximum regret optimization scheduling problem; According to the uncertainty factor model of the constructed system, the deterministic optimization scheduling problem model based on complete information, the two-stage minimum-maximum regret optimization scheduling problem based on uncertainty factors, the reconstruction of the two-stage minimum-maximum regret optimization scheduling problem, and the decomposition of the two-stage minimum-maximum regret optimization scheduling problem, a self-scheduling method for industrial parks participating in the demand response market for virtual power plants is constructed.

2. The self-scheduling method for industrial parks participating in the demand response market for virtual power plants according to claim 1 is characterized in that: The uncertainty factor model of the construction system includes: Constructing a price uncertainty model for the electricity market; Constructing a photovoltaic output uncertainty model; Constructing power load uncertainty model; According to the construction of the electricity market price uncertainty model, the construction of the photovoltaic output uncertainty model, and the construction of the power load uncertainty model, an uncertainty factor model of the system is constructed.

3. The self-scheduling method for an industrial park participating in a demand response market for a virtual power plant according to claim 1, characterized in that: The construction of a deterministic optimization scheduling problem model based on complete information includes: Construct the objective function of the deterministic optimization scheduling problem based on complete information; Construct the constraints of deterministic optimization scheduling problem under complete information; According to the said construction of the deterministic optimization scheduling problem objective function based on complete information and the said construction of the deterministic optimization scheduling problem constraint conditions based on complete information, a deterministic optimization scheduling problem model based on complete information is constructed.

4. The self-scheduling method for an industrial park participating in a demand response market for a virtual power plant according to claim 3 is characterized in that: The objective function of the deterministic optimization scheduling problem under complete information is constructed, including: Constructing a generator set operating cost model; Construct an energy storage system operation aging model; Construct transformer operation aging model; Construct a transaction cost model for participating in the electricity market; According to the construction of the generator set operation cost model, the construction of the energy storage system operation aging model, the construction of the transformer operation aging model, and the construction of the power market transaction cost model, a deterministic optimization scheduling problem objective function based on complete information is constructed.

5. The self-scheduling method for an industrial park participating in a demand response market for a virtual power plant according to claim 3 is characterized in that: The construction is based on the constraints of the deterministic optimization scheduling problem under complete information, including: Construct the generator set operation constraint model; Construct an energy storage system operation constraint model; Construct transformer operation constraint model; Construct a model of constraints for participating in electricity market transactions; Construct a system power balance constraint model; According to the said construction of generator set operation constraint model, the said construction of energy storage system operation constraint model, the said construction of transformer operation constraint model, the said construction of power market transaction participation constraint model, and the said construction of system power balance constraint model, a deterministic optimization scheduling problem objective function based on complete information is constructed.

6. The self-scheduling method for an industrial park participating in a demand response market for a virtual power plant according to claim 1, characterized in that: The two-stage minimum-maximum regret optimization scheduling problem based on uncertainty factors is constructed, including: Construct a two-stage minimum-maximum regret optimization scheduling problem objective function; Construct constraints for a two-stage minimum-maximum regret optimization scheduling problem; According to the objective function of constructing the two-stage minimum-maximum regret optimization scheduling problem and the constraint conditions of constructing the two-stage minimum-maximum regret optimization scheduling problem, a two-stage minimum-maximum regret optimization scheduling problem based on uncertainty factors is constructed.

7. The self-scheduling method for an industrial park participating in a demand response market for a virtual power plant according to claim 1, characterized in that: The two-stage minimum-maximum regret optimization scheduling problem is reconstructed and decomposed, including: Define the first-stage decision variables and the second-stage decision variables; Reconstruct the objective function of the two-stage minimum-maximum regret optimization scheduling problem; According to the definition of the first-stage decision variables and the second-stage decision variables, the reconstruction of the objective function of the two-stage minimum-maximum-regret optimization scheduling problem, and the decomposition of the optimization problem into a main problem and sub-problems, the two-stage minimum-maximum-regret optimization scheduling problem is reconstructed and decomposed.

8. The self-scheduling method for an industrial park participating in a demand response market for a virtual power plant according to claim 1, characterized in that: The two-stage minimum-maximum regret optimization scheduling problem is decomposed into: Define subproblems of max-min form; Define auxiliary variables; Construct an optimization model for the main problem; According to the definition of sub-problems in the form of max-min, the definition of auxiliary variables, and the construction of the optimization model of the main problem, the two-stage minimum-maximum regret optimization scheduling problem is decomposed.

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