Optical storage collaborative optimization operation system participating in demand response

Abstract

The invention relates to optical storage system optimization scheduling, in particular to an optical storage collaborative optimization operation system participating in demand response, and the system comprises a demand response information receiving module which records a clearing result disclosed to a market subject by a scheduling center through a transaction system; the prediction module is used for predicting the user electrical load and the photovoltaic power generation power by constructing a user electrical load prediction model and a photovoltaic power generation power prediction model; and the energy storage optimization scheduling strategy generation module is used for receiving the related parameters of the unit equipment and the user electrical load predicted value and the photovoltaic generation power predicted value sent by the prediction module, establishing an optical storage system optimization operation model with a demand response mechanism for comprehensively considering the time-of-use electricity price and excitation, and solving by adopting an operation planning optimization algorithm. An energy storage optimization scheduling strategy is obtained; according to the technical scheme provided by the invention, the defect of unstable power grid operation caused by difficulty in effective optimization scheduling of the optical storage system in the prior art can be effectively overcome.

Description

Technical Field

[0001] The present invention relates to optimized scheduling of a photovoltaic storage system, and in particular to a photovoltaic storage coordinated optimized operation system participating in demand response. Background Art

[0002] As the penetration rate of photovoltaic power continues to increase, the uncertainty and intermittency of photovoltaic output have become key factors affecting the stable operation of distribution networks. As a flexible resource, energy storage systems play an important role in solving the uncertainty problem of photovoltaic output. On the one hand, distributed photovoltaic storage systems can be dispatched uniformly by the power grid as a flexible resource. On the other hand, they are greatly affected by market mechanisms. Unreasonable market mechanisms may cause the economy and stability of the power grid to decline.

[0003] In the electricity market, demand response, as an emerging flexible resource management strategy, is gradually being widely used. It can guide users to adjust their electricity consumption behavior within a specific time, so as to achieve the purpose of peak shaving and valley filling. Demand response includes two main types: price-based demand response and incentive-based demand response. The former implements policies such as time-of-use electricity prices, while the latter encourages users to respond to grid dispatch instructions through direct economic compensation and other means. Time-of-use electricity prices are a commonly used electricity price strategy in China, which can effectively reflect the difference in power supply costs at different times of the grid.

[0004] However, existing solar-storage systems often lack a complete set of optimized scheduling strategies when participating in demand response, and thus cannot fully utilize the advantages of demand response based on time-of-use electricity prices and incentives. In this case, the power grid may experience problems such as mismatch between supply and demand and poor peak-shaving and valley-filling effects. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a photovoltaic-storage collaborative optimization operation system participating in demand response, which can effectively overcome the defects of the prior art that the grid operation is unstable due to the difficulty in effectively optimizing the scheduling of the photovoltaic-storage system.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A photovoltaic energy storage coordinated optimization operation system participating in demand response, comprising a demand response information receiving module, a data processing and storage module, a prediction module, an energy storage optimization scheduling strategy generation module and an energy storage optimization scheduling strategy delivery module;

[0010] The demand response information receiving module records the clearing results disclosed by the dispatch center to market entities through the trading system;

[0011] Data processing and storage module, which receives photovoltaic operation data, energy storage operation data, power load data and electricity price data, and performs data cleaning and data storage;

[0012] The prediction module predicts the user's power load and photovoltaic power generation by constructing a user's power load prediction model and a photovoltaic power generation power prediction model;

[0013] The energy storage optimization scheduling strategy generation module receives the relevant parameters of the unit equipment and the user power load forecast value and photovoltaic power generation power forecast value sent by the prediction module, establishes an optimal operation model of the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives, and uses the operations optimization algorithm to solve it to obtain the energy storage optimization scheduling strategy;

[0014] The energy storage optimization scheduling strategy sending module receives the energy storage optimization scheduling strategy sent by the energy storage optimization scheduling strategy generation module, and sends it to the energy storage control end to realize the optimized operation control of energy storage.

[0015] Preferably, the demand response information receiving module records the clearing results disclosed by the dispatch center to the market entities through the trading system, including:

[0016] Record the clearing results including response area, response time, response capacity and clearing price disclosed by the dispatching center to market entities through the trading system.

[0017] Preferably, the energy storage optimization scheduling strategy generation module receives the relevant parameters of the unit equipment and the user power load prediction value and the photovoltaic power generation power prediction value sent by the prediction module, establishes an optimization operation model of the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives, and uses an operations research optimization algorithm to solve it to obtain an energy storage optimization scheduling strategy, including:

[0018] S1. Setting the guiding electricity price;

[0019] S2, determine the solution variables;

[0020] S3. Set constraints;

[0021] S4, set the objective function;

[0022] S5. Combine the constraints and objective functions to establish an optimal operation model for the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives. Use the operations research optimization solver to solve the model and obtain the energy storage optimization scheduling strategy.

[0023] Preferably, setting a guiding electricity price in S1 includes:

[0024] The following formula is used to set the guiding electricity price:

[0025]

[0026] Among them, c buy,t is the guidance electricity price at time t, is the power purchased by the city electricity at time t, P linit,t is the target power purchase limit at time t. When the demand response is peak shaving response, When the demand response is valley filling response, is the time-of-use electricity price at time t, α is the penalty factor, α<0;

[0027] When the power purchased by the city electricity at time t Not equal to the target power purchase limit P at time t linit,t When the power is purchased by the city power Biased towards target power purchase limit P linit,t Adjust to further obtain demand response benefits.

[0028] Preferably, determining the solution variables in S2 includes:

[0029] The energy storage charging power at time t Energy storage discharge power Power purchased from the mains And the power sold by the city electricity as the solution variable.

[0030] Preferably, the constraints set in S3 include:

[0031] S31. Use the following formula to set the energy storage device constraint conditions:

[0032]

[0033] in, They are the maximum charging power and the maximum discharging power of energy storage, respectively. They are the energy storage charging mark and energy storage discharging mark at time t, and

[0034] They are the remaining energy storage capacity at time t and the remaining energy storage capacity at time t-1, Δt is the time interval, They are energy storage charging efficiency and energy storage discharging efficiency respectively;

[0035] are the minimum state of charge and maximum state of charge of the energy storage device, Q ES is the rated capacity of energy storage;

[0036] S32. Use the following formula to set the power balance constraint of the photovoltaic storage system:

[0037]

[0038] Among them, P load,t is the user's electricity load at time t, P pv,t is the photovoltaic power generation at time t.

[0039] Preferably, the objective function is set in S4, including:

[0040] The objective function is to minimize the operating cost of the photovoltaic storage system:

[0041]

[0042] Among them, C cost is the operating cost of the photovoltaic storage system, c sell,t is the electricity price at time t, and T is the operating time of the photovoltaic storage system.

[0043] Preferably, in S5, a photovoltaic storage system optimization operation model with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives is established by combining constraints and objective functions, and an operations research optimization solver is used to solve the model to obtain an energy storage optimization scheduling strategy, including:

[0044] S51. Combine constraints and objective functions to establish an optimal operation model for the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives;

[0045] S52, receiving the relevant parameters of the unit equipment and the user power load prediction value and the photovoltaic power generation power prediction value sent by the prediction module, and inputting them into the photovoltaic storage system optimization operation model;

[0046] S53. Use the operations research optimization solver to solve the optimal operation model of the photovoltaic energy storage system to obtain the energy storage optimization scheduling strategy.

[0047] (III) Beneficial effects

[0048] Compared with the prior art, the photovoltaic energy storage coordinated optimization operation system participating in demand response provided by the present invention has the following beneficial effects:

[0049] 1) The introduction of guided electricity prices and the demand response mechanism that can comprehensively consider time-of-use electricity prices and incentives have increased the economic benefits of the system while also improving energy efficiency;

[0050] 2) This solution helps to achieve a more stable balance between electricity supply and demand. By intelligently optimizing the scheduling of the photovoltaic storage system, it can effectively alleviate the peak load pressure of the power grid and improve the overall stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 It is a schematic diagram of the system of the present invention;

[0053] Figure 2 A schematic diagram of a flow chart of the energy storage optimization scheduling strategy generation module in the present invention generating an energy storage optimization scheduling strategy. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0055] A photovoltaic storage collaborative optimization operation system that participates in demand response, such as Figure 1 As shown, it includes a demand response information receiving module, a data processing and storage module, a prediction module, an energy storage optimization scheduling strategy generation module and an energy storage optimization scheduling strategy delivery module;

[0056] The demand response information receiving module records the clearing results disclosed by the dispatch center to market entities through the trading system;

[0057] Data processing and storage module, which receives photovoltaic operation data, energy storage operation data, power load data and electricity price data, and performs data cleaning and data storage;

[0058] The prediction module predicts the user's power load and photovoltaic power generation by constructing a user's power load prediction model and a photovoltaic power generation power prediction model;

[0059] The energy storage optimization scheduling strategy generation module receives the relevant parameters of the unit equipment and the user power load forecast value and photovoltaic power generation power forecast value sent by the prediction module, establishes an optimal operation model of the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives, and uses the operations optimization algorithm to solve it to obtain the energy storage optimization scheduling strategy;

[0060] The energy storage optimization scheduling strategy sending module receives the energy storage optimization scheduling strategy sent by the energy storage optimization scheduling strategy generation module, and sends it to the energy storage control end to realize the optimized operation control of energy storage.

[0061] ① The demand response information receiving module records the clearing results disclosed by the dispatch center to market entities through the trading system, including:

[0062] Record the clearing results including response area, response time, response capacity and clearing price disclosed by the dispatching center to market entities through the trading system.

[0063] ② The energy storage optimization scheduling strategy generation module receives the relevant parameters of the unit equipment and the user power load forecast value and photovoltaic power generation power forecast value sent by the prediction module, establishes an optimal operation model of the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives, and uses the operations optimization algorithm to solve it to obtain the energy storage optimization scheduling strategy, such as Figure 2 As shown, including:

[0064] S1. Setting the guiding electricity price;

[0065] S2, determine the solution variables;

[0066] S3. Set constraints;

[0067] S4, set the objective function;

[0068] S5. Combine the constraints and objective functions to establish an optimal operation model for the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives. Use the operations research optimization solver to solve the model and obtain the energy storage optimization scheduling strategy.

[0069] 1) Set the guiding electricity price in S1, including:

[0070] The following formula is used to set the guiding electricity price:

[0071]

[0072] Among them, c buy,t is the guidance electricity price at time t, is the power purchased by the city electricity at time t, P linit,t is the target power purchase limit at time t. When the demand response is peak shaving response, When the demand response is valley filling response, is the time-of-use electricity price at time t, α is the penalty factor, α<0;

[0073] When the power purchased by the city electricity at time t Not equal to the target power purchase limit P at time t linit,t When the power is purchased by the city power Biased towards target power purchase limit P linit,t Adjust to further obtain demand response benefits.

[0074] 2) Determine the solution variables in S2, including:

[0075] The energy storage charging power at time t Energy storage discharge power Power purchased from the mains And the power sold by the city electricity as the solution variable.

[0076] 3) Set constraints in S3, including:

[0077] S31. Use the following formula to set the energy storage device constraint conditions:

[0078]

[0079] in, They are the maximum charging power and the maximum discharging power of energy storage, respectively. They are the energy storage charging mark and energy storage discharging mark at time t, and

[0080] They are the remaining energy storage capacity at time t and the remaining energy storage capacity at time t-1, Δt is the time interval, They are energy storage charging efficiency and energy storage discharging efficiency respectively;

[0081] are the minimum state of charge and maximum state of charge of the energy storage device, Q ES is the rated capacity of energy storage;

[0082] S32. Use the following formula to set the power balance constraint of the photovoltaic storage system:

[0083]

[0084] Among them, P load,t is the user's electricity load at time t, P pv,t is the photovoltaic power generation at time t.

[0085] 4) Set the objective function in S4, including:

[0086] The objective function is to minimize the operating cost of the photovoltaic storage system:

[0087]

[0088] Among them, C cost is the operating cost of the photovoltaic storage system, c sell,t is the electricity price at time t, and T is the operating time of the photovoltaic storage system.

[0089] 5) In S5, the constraints and objective functions are combined to establish an optimal operation model of the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives. The operation research optimization solver is used to solve the problem and obtain the energy storage optimization scheduling strategy, including:

[0090] S51. Combine constraints and objective functions to establish an optimal operation model for the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives;

[0091] S52, receiving the relevant parameters of the unit equipment and the user power load prediction value and the photovoltaic power generation power prediction value sent by the prediction module, and inputting them into the photovoltaic storage system optimization operation model;

[0092] S53. Use the operations research optimization solver to solve the optimal operation model of the photovoltaic energy storage system to obtain the energy storage optimization scheduling strategy.

[0093] The technical solution of this application introduces a guiding electricity price, and can comprehensively consider the time-of-use electricity price and the demand response mechanism of incentives, which increases the economic benefits of the system and improves the efficiency of energy use. This solution helps to achieve a more stable balance between electricity supply and demand. Through intelligent optimization and scheduling of the photovoltaic storage system, it can effectively alleviate the peak load pressure of the power grid and improve the overall stability of the power system.

[0094] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A photovoltaic energy storage coordinated optimization operation system participating in demand response, characterized by: It includes a demand response information receiving module, a data processing and storage module, a prediction module, an energy storage optimization scheduling strategy generation module, and an energy storage optimization scheduling strategy delivery module; The demand response information receiving module records the clearing results disclosed by the dispatch center to market entities through the trading system; Data processing and storage module, which receives photovoltaic operation data, energy storage operation data, power load data and electricity price data, and performs data cleaning and data storage; The prediction module predicts the user's power load and photovoltaic power generation by constructing a user's power load prediction model and a photovoltaic power generation power prediction model; The energy storage optimization scheduling strategy generation module receives the relevant parameters of the unit equipment and the user power load forecast value and photovoltaic power generation power forecast value sent by the prediction module, establishes an optimal operation model of the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives, and uses the operations optimization algorithm to solve it to obtain the energy storage optimization scheduling strategy; The energy storage optimization scheduling strategy sending module receives the energy storage optimization scheduling strategy sent by the energy storage optimization scheduling strategy generation module, and sends it to the energy storage control end to realize the optimized operation control of energy storage.

2. The photovoltaic energy storage coordinated optimization operation system participating in demand response according to claim 1 is characterized in that: The demand response information receiving module records the clearing results disclosed by the dispatch center to the market entities through the trading system, including: Record the clearing results including response area, response time, response capacity and clearing price disclosed by the dispatching center to market entities through the trading system.

3. The photovoltaic energy storage coordinated optimization operation system for demand response according to claim 1 is characterized in that: The energy storage optimization scheduling strategy generation module receives the relevant parameters of the unit equipment and the user power load prediction value and photovoltaic power generation power prediction value sent by the prediction module, establishes an optimal operation model of the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives, and uses an operations optimization algorithm to solve it to obtain an energy storage optimization scheduling strategy, including: S1. Setting the guiding electricity price; S2, determine the solution variables; S3. Set constraints; S4, set the objective function; S5. Combine the constraints and objective functions to establish an optimal operation model for the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives. Use the operations research optimization solver to solve the model and obtain the energy storage optimization scheduling strategy.

4. The photovoltaic energy storage coordinated optimization operation system participating in demand response according to claim 3 is characterized in that: The guiding electricity price is set in S1, including: The following formula is used to set the guiding electricity price: Among them, c buy,t is the guidance electricity price at time t, is the power purchased by the city electricity at time t, P linit,t is the target power purchase limit at time t. When the demand response is peak shaving response, When the demand response is valley filling response, is the time-of-use electricity price at time t, α is the penalty factor, α<0; When the power purchased by the city electricity at time t Not equal to the target power purchase limit P at time t linit,t When the power is purchased by the city power Biased towards target power purchase limit P linit,t Adjust to further obtain demand response benefits.

5. The photovoltaic energy storage coordinated optimization operation system participating in demand response according to claim 4 is characterized in that: The variables to be solved are determined in S2, including: The energy storage charging power at time t Energy storage discharge power Power purchased from the mains And the power sold by the city electricity as the solution variable.

6. The photovoltaic-storage collaborative optimization operation system participating in demand response according to claim 5 is characterized in that: Set constraints in S3, including: S31. Use the following formula to set the energy storage device constraint conditions: in, They are the maximum charging power and the maximum discharging power of energy storage, respectively. They are the energy storage charging mark and energy storage discharging mark at time t, and They are the remaining energy storage capacity at time t and the remaining energy storage capacity at time t-1, Δt is the time interval, They are energy storage charging efficiency and energy storage discharging efficiency respectively; are the minimum state of charge and maximum state of charge of the energy storage device, Q ES is the rated capacity of energy storage; S32. Use the following formula to set the power balance constraint of the photovoltaic storage system: Among them, P load,t is the user's electricity load at time t, P pv,t is the photovoltaic power generation at time t.

7. The photovoltaic energy storage coordinated optimization operation system participating in demand response according to claim 6 is characterized in that: The objective function is set in S4, including: The objective function is to minimize the operating cost of the photovoltaic storage system: Among them, C cost is the operating cost of the photovoltaic storage system, c sell,t is the electricity price at time t, and T is the operating time of the photovoltaic storage system.

8. The photovoltaic energy storage coordinated optimization operation system participating in demand response according to claim 7 is characterized in that: In S5, the constraints and objective functions are combined to establish an optimal operation model of the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives. The operation research optimization solver is used to solve the problem and obtain the energy storage optimization scheduling strategy, including: S51. Combine constraints and objective functions to establish an optimal operation model for the photovoltaic storage system with a demand response mechanism that comprehensively considers time-of-use electricity prices and incentives; S52, receiving the relevant parameters of the unit equipment and the user power load prediction value and the photovoltaic power generation power prediction value sent by the prediction module, and inputting them into the photovoltaic storage system optimization operation model; S53. Use the operations research optimization solver to solve the optimal operation model of the photovoltaic energy storage system to obtain the energy storage optimization scheduling strategy.

Images