Photovoltaic power generation and cold storage load cooperative scheduling method and system

Abstract

The invention belongs to the technical field of network source coordinated control, and discloses a photovoltaic power generation and cold storage load coordinated scheduling method and system, and the method comprises the steps: obtaining energy data, and constructing a cold storage device model and a temperature model according to the energy data; taking the minimum net power consumption cost, the minimum photovoltaic light abandoning amount and the optimal comfort level as targets, and constructing a collaborative scheduling model of photovoltaic power generation and cold storage load based on a cold storage device model and a temperature model; and establishing constraint conditions of the collaborative scheduling model, and solving the collaborative scheduling model based on the constraint conditions to obtain a collaborative scheduling scheme of photovoltaic power generation and cold storage load. According to the invention, through comprehensive optimization of economic cost, light abandoning punishment and temperature comfort, an energy use strategy is adjusted in real time, and dynamic balance of each target is realized, so that the comfort of a user is ensured, the photovoltaic consumption capability and economical efficiency are remarkably improved, and the effects of energy conservation, emission reduction and cost reduction are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of network-source coordinated control, and in particular to a method and system for coordinated scheduling of photovoltaic power generation and cold storage loads. Background Art

[0002] Photovoltaic power generation, a key component of clean and renewable energy, has seen rapid growth in installed capacity. However, the intermittent and volatile nature of photovoltaic output, combined with the uncertainty of cooling load demand, has made it increasingly difficult to match energy supply and demand. Cooling loads, with their significant time-shifting characteristics, robust energy storage capacity, and inherent time synchronization with photovoltaic output, have become a key component in addressing the photovoltaic energy consumption dilemma and improving energy system efficiency.

[0003] The synergistic output of cooling loads and photovoltaics essentially resolves the core contradiction between renewable energy intermittency and load uncertainty through the spatiotemporal conversion of "electricity-cooling capacity." Against the backdrop of continuously increasing photovoltaic penetration and growing cooling load demand with global warming, synergy between the two has evolved from a "technical option" to a "system necessity," becoming a key component in building new power systems and efficient regional energy grids. However, existing methods for synergizing cooling loads and photovoltaics still have the following drawbacks:

[0004] The inability to accurately quantify the impact of cooling power on indoor temperature leads to decreased temperature control accuracy, which in turn affects user comfort. Furthermore, the lack of a priority strategy for energy allocation can easily lead to problems such as low photovoltaic utilization and high solar curtailment rates.

[0005] Therefore, how to provide a method and system for coordinated scheduling of photovoltaic power generation and cold storage loads is an urgent problem to be solved. Summary of the Invention

[0006] The embodiments of the present invention provide a method and system for coordinated scheduling of photovoltaic power generation and cooling load to solve the problem in the prior art that the intermittent and fluctuating photovoltaic output and the uncertainty of user cooling load demand make it more difficult to match energy supply and demand.

[0007] To provide a basic understanding of some aspects of the disclosed embodiments, the following is a brief summary. This summary is not intended to be a comprehensive review, identify key or essential elements, or delineate the scope of these embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the detailed description that follows.

[0008] According to a first aspect of an embodiment of the present invention, a method for coordinated scheduling of photovoltaic power generation and cold storage loads is provided.

[0009] In one embodiment, the method for coordinated scheduling of photovoltaic power generation and cold storage loads includes:

[0010] Obtain energy data and build a cold storage device model and a temperature model based on the energy data;

[0011] With the goals of minimizing net electricity costs, minimizing the amount of photovoltaic curtailment, and optimizing comfort, a coordinated scheduling model for photovoltaic power generation and cooling loads is constructed based on the cooling device model and temperature model.

[0012] The constraints of the collaborative scheduling model are established, and the collaborative scheduling model is solved based on the constraints to obtain a collaborative scheduling scheme for photovoltaic power generation and cooling storage loads.

[0013] In one embodiment, the energy data includes: a photovoltaic output curve generated based on historical photovoltaic power generation data and weather forecast data; an hourly predicted load of a cold storage device obtained using prediction technology; and indoor and outdoor temperature data.

[0014] In one embodiment, the expression of the cold storage device model is:

[0015]

[0016] Where, E(t+1) is the cooling capacity in period t+1; E(t) is the cooling capacity in period t; P ch (t) is the cooling power in period t; P dis (t) is the cooling power in period t; η ch is the cooling efficiency; η dis is the cooling efficiency; Δt is the time period length.

[0017] In one embodiment, the temperature model is expressed as:

[0018] T in (t+1)=T in (t)+α(T out (t)-T in (t))Δt-β(P cool (t)+P dis (t))Δt;

[0019] Where, T in (t+1) is the indoor temperature at time period t+1; T in (t) is the indoor temperature in time period t; T out (t) is the outdoor temperature during period t; T set is the user-set temperature; α is the heat transfer coefficient; β is the cooling efficiency coefficient; Δt is the time period.

[0020] In one embodiment, the expression of the coordinated scheduling model of photovoltaic power generation and cold storage load is:

[0021] min(λ1C cost+λ2C pv,curt +λ3C comfort );

[0022] In the formula, λ1, λ2 and λ3 are weight coefficients; C cost represents the net electricity cost; C pv,curt Indicates the amount of photovoltaic curtailment; C comfort Indicates comfort;

[0023] The net electricity cost is expressed as:

[0024]

[0025] The expression of the amount of photovoltaic curtailment is:

[0026]

[0027] The expression of the comfort level is:

[0028]

[0029] Where C cost is the net electricity cost; C pv,curt is the amount of photovoltaic curtailment; C comfort For comfort; P grid (t) is the purchased power in period t; p(t) is the electricity price; P sell (t) is the photovoltaic surplus power sold in period t; p sell (t) is the electricity price; P curt (t) is the photovoltaic power curtailment power in period t; c curt is the penalty cost for power curtailment; Δt is the time period length; T in (t) is the indoor temperature in time period t; T set Set the temperature for the user.

[0030] In one embodiment, the constraints of the collaborative scheduling model include power balance constraints, surplus power allocation constraints, cold storage device limitation constraints, temperature comfort constraints, and grid interaction limitation constraints.

[0031] In one embodiment, solving the coordinated scheduling model based on the constraint conditions to obtain a coordinated scheduling scheme for photovoltaic power generation and cooling storage loads includes:

[0032] The quadratic term of temperature comfort C comfort Convert it into a piecewise linear constraint and set the charging and discharging states of the cold storage device as preset variables to solve the coordinated scheduling model and obtain the optimal coordinated scheduling plan for photovoltaic power generation and cold storage load in the future time period;

[0033] The scheduling of photovoltaic power generation and cooling storage load is performed through the optimal coordinated scheduling scheme.

[0034] According to a second aspect of an embodiment of the present invention, a photovoltaic power generation and cold storage load coordinated scheduling system is provided.

[0035] In one embodiment, the photovoltaic power generation and cold storage load coordinated scheduling system includes:

[0036] Energy data acquisition module, used to obtain energy data and build a cold storage device model and a temperature model based on the energy data;

[0037] The scheduling model construction module is used to build a coordinated scheduling model for photovoltaic power generation and cooling load based on the cooling device model and temperature model, with the goals of minimizing net electricity cost, minimizing photovoltaic curtailment, and optimizing comfort.

[0038] The scheduling model solving module is used to establish the constraints of the collaborative scheduling model, solve the collaborative scheduling model based on the constraints, and obtain the collaborative scheduling plan for photovoltaic power generation and cooling storage load.

[0039] In one embodiment, the energy data includes: a photovoltaic output curve generated based on historical photovoltaic power generation data and weather forecast data; an hourly predicted load of a cold storage device obtained using prediction technology; and indoor and outdoor temperature data.

[0040] In one embodiment, the expression of the cold storage device model is:

[0041]

[0042] Where, E(t+1) is the cooling capacity in period t+1; E(t) is the cooling capacity in period t; P ch (t) is the cooling power in period t; P dis (t) is the cooling power in period t; η ch is the cooling efficiency; η dis is the cooling efficiency; Δt is the time period length.

[0043] In one embodiment, the temperature model is expressed as:

[0044] T in (t+1)=T in (t)+α(T out (t)-T in (t))Δt-β(P cool (t)+P dis (t))Δt;

[0045] Where, T in (t+1) is the indoor temperature at time period t+1; T in (t) is the indoor temperature in time period t; Tout (t) is the outdoor temperature during period t; T set is the user-set temperature; α is the heat transfer coefficient; β is the cooling efficiency coefficient; Δt is the time period.

[0046] In one embodiment, the expression of the coordinated scheduling model of photovoltaic power generation and cold storage load is:

[0047] min(λ1C cost +λ2C pv,curt +λ3C comfort );

[0048] In the formula, λ1, λ2 and λ3 are weight coefficients; C cost represents the net electricity cost; C pv,curt Indicates the amount of photovoltaic curtailment; C comfort Indicates comfort;

[0049] The net electricity cost is expressed as:

[0050]

[0051] The expression of the amount of photovoltaic curtailment is:

[0052]

[0053] The expression of the comfort level is:

[0054]

[0055] Where C cost is the net electricity cost; C pv,curt is the amount of photovoltaic curtailment; C comfort For comfort; P grid (t) is the purchased power in period t; p(t) is the electricity price; P sell (t) is the photovoltaic surplus power sold in period t; p sell (t) is the electricity price; P curt (t) is the photovoltaic power curtailment power in period t; c curt is the penalty cost for power curtailment; Δt is the time period length; T in (t) is the indoor temperature in time period t; T set Set the temperature for the user.

[0056] In one embodiment, the constraints of the collaborative scheduling model include power balance constraints, surplus power allocation constraints, cold storage device limitation constraints, temperature comfort constraints, and grid interaction limitation constraints.

[0057] In one embodiment, solving the coordinated scheduling model based on the constraint conditions to obtain a coordinated scheduling scheme for photovoltaic power generation and cooling storage loads includes:

[0058] The quadratic term of temperature comfort C comfort Convert it into a piecewise linear constraint and set the charging and discharging states of the cold storage device as preset variables to solve the coordinated scheduling model and obtain the optimal coordinated scheduling plan for photovoltaic power generation and cold storage load in the future time period;

[0059] The scheduling between photovoltaic power generation and cooling storage load is performed through the optimal coordinated scheduling scheme.

[0060] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0061] In one embodiment, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0062] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0063] In one embodiment, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0064] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0065] 1. The present invention establishes a state equation and a temperature dynamic equation for the cold storage device to quantify the impact of cooling power on indoor temperature, thereby providing a basis for energy scheduling. It also dynamically allocates energy based on photovoltaic output in the priority order of "cooling → cold storage → electricity sales → power abandonment", ensuring that cooling needs are met first, maximizing photovoltaic utilization efficiency, and thus reducing energy waste.

[0066] 2. The present invention adjusts the energy use strategy in real time through comprehensive optimization of economic costs, penalties for abandoned solar power, and temperature comfort, achieving a dynamic balance among various objectives. This significantly improves the photovoltaic absorption capacity and economy while ensuring user comfort, achieving the effects of energy conservation, emission reduction, and cost reduction.

[0067] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0069] Figure 1This is a flow chart showing a method for coordinated scheduling of photovoltaic power generation and cooling storage loads according to an exemplary embodiment;

[0070] Figure 2 This is a principle block diagram of a photovoltaic power generation and cold storage load coordinated scheduling system according to an exemplary embodiment;

[0071] Figure 3 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0072] The following description and accompanying drawings sufficiently illustrate the specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in or substituted for portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims, including all available equivalents thereof. Herein, the terms "first," "second," and the like are used solely to distinguish one element from another and do not require or imply any actual relationship or order between these elements. In practice, the first element can also be referred to as the second element, and vice versa. Furthermore, the terms "comprise," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a structure, device, or apparatus comprising a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such structure, device, or apparatus. Without further limitation, an element defined by the phrase "comprising a..." does not preclude the presence of other identical elements in the structure, device, or apparatus comprising the element. The various embodiments herein are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Similar or identical parts between the various embodiments can be referenced to each other.

[0073] The terms "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like used herein to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are intended only to facilitate the description of this document and simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the present invention. In the description herein, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, they can be mechanical or electrical connections, or they can be internal connections between two elements, they can be directly connected, or they can be indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0074] As used herein, unless otherwise specified, the term "plurality" means two or more.

[0075] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0076] In this article, the term "and / or" is used to describe the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0077] It should be understood that, although the various steps in the flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps may be performed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but may be performed at different times. The execution order of these sub-steps or stages is not necessarily to be performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0078] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software so that the processor can call and execute the operations corresponding to the above modules.

[0079] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0080] Figure 1 An embodiment of the photovoltaic power generation and cold storage load coordinated scheduling method of the present invention is shown.

[0081] In this optional embodiment, the method for coordinated scheduling of photovoltaic power generation and cold storage load includes:

[0082] Step S101: Acquire energy data, and construct a cold storage device model and a temperature model based on the energy data;

[0083] Step S102: With the goals of minimizing net electricity costs, minimizing the amount of photovoltaic curtailment, and optimizing comfort, a coordinated scheduling model for photovoltaic power generation and cooling load is constructed based on the cooling device model and the temperature model;

[0084] Step S103: Establish constraints for the collaborative scheduling model, solve the collaborative scheduling model based on the constraints, and obtain a collaborative scheduling solution for photovoltaic power generation and cooling storage load.

[0085] In this optional embodiment, the energy data includes: a photovoltaic output curve generated based on historical photovoltaic power generation data and weather forecast data; an hourly predicted load of the cold storage device obtained using prediction technology; and indoor and outdoor temperature data.

[0086] In this optional embodiment, the expression of the cold storage device model is:

[0087]

[0088] Where, E(t+1) is the cooling capacity in period t+1; E(t) is the cooling capacity in period t; P ch (t) is the cooling power in period t; P dis (t) is the cooling power in period t; η ch is the cooling efficiency; η dis is the cooling efficiency; Δt is the time period length.

[0089] In this optional embodiment, the expression of the temperature model is:

[0090] T in (t+1)=T in (t)+α(T out (t)-T in (t))Δt-β(P cool (t)+P dis (t))Δt;

[0091] Where, T in (t+1) is the indoor temperature at time period t+1; T in (t) is the indoor temperature in time period t; T out (t) is the outdoor temperature during period t; T set is the user-set temperature; α is the heat transfer coefficient; β is the cooling efficiency coefficient; Δt is the time period.

[0092] In this optional embodiment, the expression of the coordinated scheduling model of photovoltaic power generation and cold storage load is:

[0093] min(λ1C cost +λ2C pv,curt +λ3C comfort );

[0094] In the formula, λ1, λ2 and λ3 are weight coefficients; C cost represents the net electricity cost; C pv,curt Indicates the amount of photovoltaic curtailment; C comfort Indicates comfort;

[0095] The net electricity cost is expressed as:

[0096]

[0097] The expression of the amount of photovoltaic curtailment is:

[0098]

[0099] The expression of the comfort level is:

[0100]

[0101] Where C cost is the net electricity cost; C pv,curt is the amount of photovoltaic curtailment; C comfort For comfort; P grid (t) is the purchased power in period t; p(t) is the electricity price; P sell (t) is the photovoltaic surplus power sold in period t; p sell (t) is the electricity price; P curt (t) is the photovoltaic power curtailment power in period t; c curt is the penalty cost for power curtailment; Δt is the time period length; T in (t) is the indoor temperature in time period t; T set Set the temperature for the user.

[0102] In this optional embodiment, the constraints of the collaborative scheduling model include power balance constraints, surplus power distribution constraints, cold storage device limitation constraints, temperature comfort constraints and grid interaction limitation constraints.

[0103] In this optional embodiment, solving the coordinated scheduling model based on the constraint conditions to obtain a coordinated scheduling scheme for photovoltaic power generation and cooling storage loads includes:

[0104] The quadratic term of temperature comfort C comfort Convert it into a piecewise linear constraint and set the charging and discharging states of the cold storage device as preset variables to solve the coordinated scheduling model and obtain the optimal coordinated scheduling plan for photovoltaic power generation and cold storage load in the future time period;

[0105] The scheduling of photovoltaic power generation and cooling storage load is performed through the optimal coordinated scheduling scheme.

[0106] Figure 2 An embodiment of the photovoltaic power generation and cold storage load coordinated scheduling system of the present invention is shown.

[0107] In this optional embodiment, the photovoltaic power generation and cold storage load coordinated scheduling system includes:

[0108] Energy data acquisition module 201, used to obtain energy data and build a cold storage device model and a temperature model based on the energy data;

[0109] The scheduling model building module 202 is used to build a coordinated scheduling model for photovoltaic power generation and cooling load based on the cooling device model and temperature model, with the goals of minimizing net electricity cost, minimizing photovoltaic curtailment, and optimizing comfort.

[0110] The scheduling model solving module 203 is used to establish the constraints of the collaborative scheduling model, solve the collaborative scheduling model based on the constraints, and obtain a collaborative scheduling solution for photovoltaic power generation and cooling storage load.

[0111] The photovoltaic power generation and cold storage load coordinated scheduling method provided by the present invention is further described below.

[0112] The method for coordinated scheduling of photovoltaic power generation and cold storage loads provided by the present invention includes system modeling, objective function, constraint conditions, solution and other steps. Specifically:

[0113] The scheduling period is divided into T time periods, t∈{1,2,...,T}, with a time period length of Δt. The model includes the following parts:

[0114] (1) Cold storage device model:

[0115]

[0116] Where, E(t) is the cooling capacity (kWh) in period t; E(t+1) is the cooling capacity in period t+1;

[0117] P ch (t),P dis (t) is the charging / discharging cooling power (kW) in period t, satisfying 0≤P ch (t)≤P ch,max ,0≤P dis (t)≤P dis,max , η ch ,η dis is the charging / discharging cooling efficiency (0~1); Δt is the time period length.

[0118] (2) Temperature model:

[0119] T in (t+1)=T in (t)+α(T out (t)-T in (t))Δt-β(P cool (t)+P dis (t))Δt;

[0120] Where, T in (t+1) is the indoor temperature at time period t+1; T in (t),Tout (t) is the indoor / outdoor temperature in period t (℃); T set is the user-set temperature; α is the heat transfer coefficient (kW / °C); β is the cooling efficiency coefficient (°C / kWh); P cool (t) is the instantaneous cooling power in time period t (kW); P dis (t) is the cooling power (kW) in period t.

[0121] The constraints include:

[0122] (1) Power balance

[0123] P pv (t)+P grid (t) = P cool (t)+P ch (t)+P other (t)+P sell (t)+P curt (t);

[0124] Where, P pv (t) is the predicted photovoltaic output in time period t (kW); P grid (t) is the purchased power (kW) in period t, and the electricity price is p(t) (yuan / kWh); P sell (t) is the photovoltaic surplus power sold in period t (kW), and the selling price is p sell (t); P curt (t) is the photovoltaic power curtailment power in period t (kW), and the unit curtailment penalty cost is c curt (yuan / kWh); P other (t) is other fixed loads (such as lighting, equipment) during period t; P ch (t) is the cooling power (kW) during time period t.

[0125] (2) Surplus power allocation constraints

[0126] P pv (t)-(P cool (t)+P ch (t))=P sell (t)+P curt (t);

[0127] Photovoltaic output is prioritized for cooling and cold storage, with surplus power used for electricity sales or abandonment.

[0128] (3) Limitations of cold storage devices

[0129] Capacity Limitation:

[0130] E min ≤E(t)≤E max .

[0131] (4) Temperature comfort

[0132] T min ≤T in (t)≤T max ;

[0133] (5) Grid interaction restrictions

[0134] Power sales:

[0135] 0≤P sell (t)≤P sell,max (t);

[0136] Abandoned power:

[0137] P curt (t)≥0;

[0138] Among them, the objective function is:

[0139] min(λ1C cost +λ2C pv,curt +λ3C comfort );

[0140] In the formula, the weight coefficient satisfies λ1+λ2+λ3=1, and the specific items are as follows:

[0141] (1) The economic objective is the net electricity cost (electricity purchase cost - electricity sales revenue + power abandonment penalty):

[0142]

[0143] (2) The energy efficiency goal is to minimize the amount of photovoltaic power wasted:

[0144]

[0145] (3) User comfort target is the penalty for indoor temperature deviation from the set value:

[0146]

[0147] Where C cost is the net electricity cost; C pv,curt is the amount of photovoltaic curtailment; C comfort For comfort.

[0148] For example, a commercial building on a certain day in July in summer: 24 hours (T = 24, Δt = 1h);

[0149] Photovoltaic output: Noon peak 500kW, total daily output 2800kWh (typical sunny day data); Cooling demand: 200kW during the daytime peak period (12:00-18:00), 50kW during the nighttime off-peak period; Time-of-use electricity price (yuan / kWh): 0.3 yuan / kWh during the off-peak period (0:00-8:00);

[0150] During normal hours (8:00-12:00, 20:00-24:00), the price is 0.6 yuan / kWh;

[0151] During peak hours (12:00-20:00), the price is 1.5 yuan / kWh;

[0152] Electricity sales price: uniform 0.8 yuan / kWh, penalty cost for power abandonment 0.5 yuan / kWh;

[0153] Cold storage device parameters: capacity is E max =1000kWh, the initial cooling capacity is E initial =200kWh;

[0154] Charge / discharge efficiency: η ch =0.9,η dis =0.85;

[0155] Maximum charge / discharge power: P chmax =150kW, P dismax =120kW;

[0156] Temperature model parameters: set temperature T set =24℃, the allowable fluctuation range is [22℃, 26℃];

[0157] Heat transfer coefficient α = 0.02kW / ℃, refrigeration efficiency coefficient β = 0.05℃ / kWh.

[0158] The photovoltaic power generation and cold storage load coordinated scheduling method provided by the present invention will be further described below in conjunction with specific implementation methods.

[0159] Step 1: Input data preprocessing

[0160] Photovoltaic output curve: generated based on historical data and weather forecast (e.g. P_pv = 500kW at 12:00 noon).

[0161] Cooling demand forecast: Use the forecast system's hourly load forecast (e.g. 14:00 D(t) = 200kW).

[0162] Temperature data: outdoor temperature T out (t) Taken from weather forecast (e.g. 32°C at noon, 25°C at night).

[0163] Step 2: Build an optimization model

[0164] Objective function (weighted weights λ1 = 0.6, λ2 = 0.2, λ3 = 0.2):

[0165] min(0.6C cost +0.2C pv,curt +0.2C comfort );

[0166] Among them, (high price electricity purchase during peak period):

[0167] C cost =∑[1.5P grid (t)-0.8P sell (t)+0.5P curt (t)];

[0168] C pv,curt =∑P curt (t);

[0169] C comfort =∑(T in (t)-24) 2 ;

[0170] Cold storage dynamics:

[0171]

[0172] Temperature dynamics:

[0173] T in (t+1)=T in (t)+0.02(T out (t)-T in (t))-0.05(P cool (t)+P dis (t));

[0174] Step 3: Constraint modeling

[0175] Power Balance:

[0176] P pv (t)+P grid (t) = P cool (t)+P ch (t)+P sell (t)+P curt (t);

[0177] Residual power allocation constraints:

[0178] P pv (t)-(P cool (t)+P ch (t))=P sell(t)+P curt (t);

[0179] Restrictions on cold storage devices:

[0180] 22≤T in (t)≤26;

[0181] The electricity sales power (grid interaction limit) meets the following requirements:

[0182] 0≤P sell (t)≤120kW;

[0183] The curtailed power (grid interaction limit) meets the following requirements:

[0184] P curt (t)≥0;

[0185] Step 4: Solving algorithm

[0186] The quadratic term of temperature comfort C comfort Convert to piecewise linear constraints (such as T in (t) -24 ≤ 2℃). Define the charging / discharging state of the cold storage device as a 0-1 variable (such as δ ch (t)∈{0,1} indicates whether charging is on), the forecast data is updated every 4 hours, and the scheduling plan for the next 24 hours is solved in a rolling manner.

[0187] Step 5: Optimize result output

[0188] During off-peak hours (2:00-6:00), the power consumption is P ch =150kW charging, peak power period (14:00-18:00) dis =120kW cooling, electricity sales power P during the noon photovoltaic surplus period (12:00-13:00) sell =180kW, the indoor temperature is maintained at 23.5-25.8℃ throughout the day (in compliance with comfort constraints).

[0189] Table 1: Result analysis table

[0190]

[0191]

[0192] As shown in Table 1, the beneficial effects of the present invention include:

[0193] Economic improvement mechanism

[0194] Revenue from electricity sales: During the noon period of excess photovoltaic power, 180kW of electricity is sold to the grid, and the revenue = 180×0.8×1h = 144 yuan.

[0195] Time-shifting cold storage: cold storage is used during off-peak hours (electricity price 0.3 yuan / kWh), and cold storage is used during peak hours (electricity price 1.5 yuan / kWh) to replace expensive electricity purchases. Cost savings = (1.5-0.3) × 120kW × 4h = 576 yuan.

[0196] Energy efficiency optimization

[0197] Control of curtailed solar power rate: Through priority allocation (cooling → cold storage → electricity sales → curtailed electricity), the curtailed solar power rate was reduced from 15% to 2%.

[0198] Cold storage utilization rate: The average daily charging / discharging cycle efficiency of the cold storage device is 76% (η ch ×η dis =0.765).

[0199] Comfort guarantee

[0200] Dynamic temperature adjustment: When the outdoor temperature is 32℃ at 14:00, the P dis =120kW cooling + instant cooling P cool =80kW, maintain T in =25.2℃.

[0201] Through dynamic priority allocation, time-of-use electricity price response and temperature-cold storage coupled control, the present invention significantly improves the photovoltaic absorption rate and economy (cost reduction by 43%), while ensuring user comfort (temperature deviation improvement by 68%).

[0202] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment are implemented.

[0203] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0204] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0205] In addition, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0206] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0207] The present invention is not limited to the structures described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for coordinated scheduling of photovoltaic power generation and cold storage load, characterized in that: The method includes: Obtain energy data and build a cold storage device model and a temperature model based on the energy data; With the goals of minimizing net electricity costs, minimizing the amount of photovoltaic curtailment, and optimizing comfort, a coordinated scheduling model for photovoltaic power generation and cooling loads is constructed based on the cooling device model and temperature model. The constraints of the collaborative scheduling model are established, and the collaborative scheduling model is solved based on the constraints to obtain a collaborative scheduling scheme for photovoltaic power generation and cooling storage loads.

2. The method for coordinated scheduling of photovoltaic power generation and cold storage load according to claim 1, characterized in that: The energy data includes: Photovoltaic output curve generated based on historical photovoltaic power generation data and weather forecast data; Hourly forecast load of the cold storage device obtained using forecasting technology; Indoor and outdoor temperature data.

3. The method for coordinated scheduling of photovoltaic power generation and cold storage load according to claim 2, characterized in that: The expression of the cold storage device model is: Where, E(t+1) is the cooling capacity in period t+1; E(t) is the cooling capacity in period t; P ch (t) is the cooling power in period t; P dis (t) is the cooling power in period t; η ch is the cooling efficiency; η dis is the cooling efficiency; Δt is the time period length.

4. The method for coordinated scheduling of photovoltaic power generation and cold storage load according to claim 3, characterized in that: The expression of the temperature model is: T in (t+1)=T in (t)+α(T out (t)-T in (t))Δt-β(P cool (t)+P dis (t))Δt; Where, T in (t+1) is the indoor temperature at time period t+1; T in (t) is the indoor temperature in time period t; T out (t) is the outdoor temperature during period t; T set is the user-set temperature; α is the heat transfer coefficient; β is the cooling efficiency coefficient; Δt is the time period.

5. The method for coordinated scheduling of photovoltaic power generation and cold storage load according to claim 4, characterized in that: The expression of the coordinated scheduling model of photovoltaic power generation and cold storage load is: min(λ1C cost +λ2C pv,curt +λ3C comfort ); In the formula, λ1, λ2 and λ3 are weight coefficients; C cost represents the net electricity cost; C pv,curt Indicates the amount of photovoltaic curtailment; C comfort Indicates comfort; The net electricity cost is expressed as: The expression of the amount of photovoltaic abandoned light is: The expression of the comfort level is: Where, v cost is the net electricity cost; C pv,curt is the amount of photovoltaic curtailment; C comfort For comfort; P grid (t) is the purchased power in period t; p(t) is the electricity price; P sell (t) is the photovoltaic surplus power sold in period t; p sell (t) is the electricity price; P curt (t) is the photovoltaic power curtailment power in period t; c curt is the penalty cost for power curtailment; Δt is the time period length; T in (t) is the indoor temperature in time period t; T set Set the temperature for the user.

6. The method for coordinated scheduling of photovoltaic power generation and cold storage load according to claim 5, characterized in that: The constraints of the collaborative scheduling model include power balance constraints, surplus power distribution constraints, cold storage device limitation constraints, temperature comfort constraints and grid interaction limitation constraints.

7. The method for coordinated scheduling of photovoltaic power generation and cold storage load according to claim 6, characterized in that: The collaborative scheduling model is solved based on the constraint conditions to obtain the collaborative scheduling scheme of photovoltaic power generation and cooling storage load, including: The quadratic term of temperature comfort C comfort Convert it into a piecewise linear constraint and set the charging and discharging states of the cold storage device as preset variables to solve the coordinated scheduling model and obtain the optimal coordinated scheduling plan for photovoltaic power generation and cold storage load in the future time period; The scheduling of photovoltaic power generation and cooling storage load is performed through the optimal coordinated scheduling scheme.

8. A photovoltaic power generation and cold storage load coordinated scheduling system, characterized in that: The system includes: Energy data acquisition module, used to obtain energy data and build a cold storage device model and a temperature model based on the energy data; The scheduling model construction module is used to build a coordinated scheduling model for photovoltaic power generation and cooling load based on the cooling device model and temperature model, with the goals of minimizing net electricity cost, minimizing photovoltaic curtailment, and optimizing comfort. The scheduling model solving module is used to establish the constraints of the collaborative scheduling model, solve the collaborative scheduling model based on the constraints, and obtain the collaborative scheduling plan for photovoltaic power generation and cooling storage load.

9. The photovoltaic power generation and cold storage load coordinated scheduling system according to claim 8, characterized in that: The energy data includes: Photovoltaic output curve generated based on historical photovoltaic power generation data and weather forecast data; Hourly forecast load of the cold storage device obtained using forecasting technology; Indoor and outdoor temperature data.

10. The photovoltaic power generation and cold storage load coordinated scheduling system according to claim 9, characterized in that: The expression of the cold storage device model is: Where, E(t+1) is the cooling capacity in period t+1; E(t) is the cooling capacity in period t; P ch (t) is the cooling power in period t; P dis (t) is the cooling power in period t; η ch is the cooling efficiency; η dis is the cooling efficiency; Δt is the time period length.

11. The photovoltaic power generation and cold storage load coordinated scheduling system according to claim 10, characterized in that: The expression of the temperature model is: T in (t+1)=T in (t)+α(T out (t)-T in (t))Δt-β(P cool (t)+P dis (t))Δt; Where, T in (t+1) is the indoor temperature at time period t+1; T in (t) is the indoor temperature in time period t; T out (t) is the outdoor temperature during period t; T set is the user-set temperature; α is the heat transfer coefficient; β is the cooling efficiency coefficient; Δt is the time period.

12. The photovoltaic power generation and cold storage load coordinated scheduling system according to claim 11, characterized in that: The expression of the coordinated scheduling model of photovoltaic power generation and cold storage load is: min(λ1C cost +λ2C pv,curt +λ3C comfort ); In the formula, λ1, λ2 and λ3 are weight coefficients; C cost represents the net electricity cost; C pv,curt Indicates the amount of photovoltaic curtailment; C comfort Indicates comfort; The net electricity cost is expressed as: The expression of the amount of photovoltaic abandoned light is: The expression of the comfort level is: Where C cost is the net electricity cost; C pv,curt is the amount of photovoltaic curtailment; C comfort For comfort; P grid (t) is the purchased power in period t; p(t) is the electricity price; P sell (t) is the photovoltaic surplus power sold in period t; p sell (t) is the electricity price; P curt (t) is the photovoltaic power curtailment power in period t; c curt is the penalty cost for power curtailment; Δt is the time period length; T in (t) is the indoor temperature in time period t; T set Set the temperature for the user.

13. The photovoltaic power generation and cold storage load coordinated scheduling system according to claim 12, characterized in that: The constraints of the collaborative scheduling model include power balance constraints, surplus power distribution constraints, cold storage device limitation constraints, temperature comfort constraints and grid interaction limitation constraints.

14. The photovoltaic power generation and cold storage load coordinated scheduling system according to claim 13, characterized in that: The collaborative scheduling model is solved based on the constraint conditions to obtain the collaborative scheduling scheme of photovoltaic power generation and cooling storage load, including: The quadratic term of temperature comfort C comfort Convert it into a piecewise linear constraint and set the charging and discharging states of the cold storage device as preset variables to solve the coordinated scheduling model and obtain the optimal coordinated scheduling plan for photovoltaic power generation and cold storage load in the future time period; The scheduling between photovoltaic power generation and cooling storage load is performed through the optimal coordinated scheduling scheme.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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