Day-ahead economic optimization scheduling method for virtual power plant

Abstract

The invention relates to a day-ahead economic optimization scheduling method for a virtual power plant. The method comprises the following steps: acquiring meteorological data, new energy power generation data, the number of electric vehicles, the mileage and the number of controllable loads participating in demand response in the virtual power plant; performing new energy power generation power prediction, electric load prediction and cold load prediction in the virtual power plant; grading basic electric loads participating in demand response, and constraining and compensating interruptible loads; establishing an electric vehicle battery loss cost model, and setting constraint conditions of electric vehicle charge and discharge power and charge capacity; constraining resources of a gas turbine, a cold storage tank, energy storage and a large power grid, and establishing a power balance condition of the virtual power plant; and solving the optimal scheduling model to obtain a day-ahead optimal scheduling scheme of the virtual power plant. Compared with the prior art, the method has the advantages that different levels of basic electric loads, electric vehicle battery losses and ice storage air conditioners are considered, and the economical efficiency of the virtual power plant is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant economic dispatch, and more particularly to a day-ahead economic optimization dispatch method for a virtual power plant. Background Art

[0002] In recent years, the large-scale grid connection and consumption of new energy have become important issues to be studied in scientific research. In order to alleviate the increasing pressure on the demand side of the power system, controllable loads have gradually attracted attention. With the continuous increase of new energy generating units and controllable loads, the problem of power uncertainty between the source and the load has gradually emerged, bringing new challenges to the new power system. As an advanced energy coordination management system, the "virtual power plant" can well solve the above problems.

[0003] A virtual power plant can aggregate devices such as new energy units, controllable loads, and energy storage through advanced communication technologies to form a whole to participate in the operation of the power system and the power market. Currently, there are already some virtual power plant projects at home and abroad. For example, the European FENIX virtual power plant project, the Dutch PMVPP project, and the Shenzhen virtual power plant project, etc.

[0004] There have been some studies on the day-ahead economic optimization dispatch of virtual power plants, mainly focusing on using energy storage to suppress the volatility of new energy and participate in the day-ahead market to obtain economic benefits. In addition, there are still other excellent characteristics of virtual power plants that can be brought into play in the optimization dispatch. The V2G electric vehicle technology in virtual power plants can effectively improve the stability and economy of the system and reduce carbon emissions; at the same time, the controllable loads in virtual power plants can be used as resources for demand response, bringing economy to users while optimizing the power system curve and enhancing the economy and stability of the power system. Currently, there is little effective scientific research on considering the battery life loss of electric vehicles and demand response in the economic optimization dispatch of virtual power plants.

[0005] After retrieval, the Chinese invention patent application publication number CN110188950B discloses a method for optimizing dispatching modeling of the power supply side and demand side of a virtual power plant based on multi-agent technology, including: establishing an optimized dispatching model of a virtual power plant using a multi-agent MAS control method: the optimized dispatching model of the virtual power plant includes a power supply side Agent, a demand side Agent, and a power grid Agent; the power supply side Agent includes wind power, photovoltaic power, and thermal power; the demand side Agent includes flexible loads and rechargeable electric vehicles; establishing an objective function of the virtual power plant; establishing constraint conditions for the operation of the virtual power plant; establishing a battery model and a rechargeable electric vehicle model. The dispatching model established by the modeling method of the present invention consists of two parts: the load side including electric vehicles and flexible loads and the power supply side including various power source types. Considering the demand response method, by optimizing the output of each unit on the power generation side, controlling the flexible loads on the load side, and coordinating the charging and discharging of electric vehicles, the ability of the virtual power plant to absorb new energy power generation is improved, and the revenue of the virtual power plant is increased. This existing patent application has problems of not considering the battery loss cost of electric vehicles, cold loads, and not grading the basic electric loads and compensating the interruptible loads in a graded manner.

[0006] How to improve the overall economy of the virtual power plant has become a technical problem to be solved. Summary of the Invention

[0007] The purpose of the present invention is to provide a day-ahead economic optimization dispatching method for a virtual power plant to overcome the defects existing in the above-mentioned prior art.

[0008] The purpose of the present invention can be achieved by the following technical solutions:

[0009] According to one aspect of the present invention, a day-ahead economic optimization dispatching method for a virtual power plant is provided, and the method includes the following steps:

[0010] Step S1, obtaining meteorological data, new energy power generation data, the number of electric vehicles, the driving mileage, and the controllable loads participating in demand response and their quantities inside the virtual power plant;

[0011] Step S2, using the data obtained in S1 to perform prediction of new energy power generation power, electric load prediction, and cold load prediction in the virtual power plant;

[0012] Step S3, grading the basic electric loads participating in demand response, and constraining and compensating the interruptible loads in the basic electric loads;

[0013] Step S4, establishing a battery loss cost model for electric vehicles, and setting constraint conditions for the charging and discharging power and the stored electricity of electric vehicles;

[0014] Step S5: Constrain the resources of the gas turbine, cold storage tank, energy storage, and large power grid, and establish the power balance condition of the virtual power plant;

[0015] Step S6: Solve the optimization scheduling model according to the objective function to obtain the day-ahead optimization scheduling plan of the virtual power plant.

[0016] Preferably, the basic electrical loads participating in demand response are divided into first-class loads, second-class loads, and third-class loads according to the actual situation, and the interruptible loads and continuous interruptible loads of each level of basic electrical loads are constrained.

[0017] More preferably, the constraint on the interruptible load is specifically:

[0018] 0 ≤ P i cut (t) ≤ cil(i) * P i load (t)

[0019] P i cut (t - 1) ≤ 0.1 * P i load (t)

[0020] In the formula, P i cut (t) is the interruptible load of the i-th level load at time t; P i cut (t - 1) is the reducible load of the i-th level load at time t - 1; cil(i) is the proportion of interruptible load in the i-th level load; P i load (t) is the total load of the i-th level at time t;

[0021] The calculation of the compensation cost for the interruptible load is specifically:

[0022]

[0023] In the formula, is the compensation cost for the interruptible load at time t; is the compensation unit price of the i-th level interruptible load, and m is the total number of load classifications.

[0024] Preferably, the battery loss cost model of the electric vehicle is specifically:

[0025]

[0026] In the formula, is the total battery loss cost of the electric vehicle; n v is the number of electric vehicles; The purchase cost of the battery for the v-th electric vehicle; The number of charge and discharge cycles of the battery of the v-th electric vehicle over its entire life cycle; The battery capacity of the v-th electric vehicle; The available depth of discharge of the battery of the v-th electric vehicle; The discharge power of the v-th electric vehicle at time t; The discharge efficiency of the v-th electric vehicle; The driving distance of the v-th electric vehicle during the t time period; E v The energy demand of the v-th electric vehicle, representing the power required to be consumed per unit driving distance of the electric vehicle.

[0027] Preferably, the constraint conditions for the charging and discharging power and the stored electricity of the electric vehicle are specifically:

[0028]

[0029] In the formula, The stored electricity of the v-th electric vehicle at time t; and The upper and lower limits of the stored electricity of the v-th electric vehicle respectively; and The charging power and the discharging power of the v-th electric vehicle at time t respectively; and The upper limit of the charging power and the upper limit of the discharging power of the v-th electric vehicle respectively; Boolean variables and respectively represent whether the v-th electric vehicle is in the charging or discharging state during the t time period. If it is, it is 1; if not, it is 0; represents whether the v-th electric vehicle is in the grid-connected state at time t. If it is, it is 1; otherwise, it is 0; and The stored electricity at the start and end time periods of the v-th electric vehicle respectively; The charging efficiency of the v-th electric vehicle; The stored electricity of the v-th electric vehicle at time t - 1; The discharging efficiency of the v-th electric vehicle; The driving distance of the v-th electric vehicle during the t time period; E v The energy demand of the v-th electric vehicle, representing the power required to be consumed per unit driving distance of the electric vehicle; and respectively represent the stored electricity of the v-th electric vehicle at the 0th point and the 24th point.

[0030] Preferably, the constraint conditions for the gas turbine are specifically:

[0031]

[0032] -r d ≤g t -g t-1 ≤r u

[0033] In the formula, the Boolean variables and represent whether the gas turbine is working, starting, and stopping in the t period. If it is, it is 1; otherwise, it is 0. g t is the output of the gas turbine in the t period; g min and g max are the minimum and maximum output powers of the gas turbine respectively; r u and r d are the upward and downward ramp rates of the gas turbine respectively; represents whether the gas turbine was working in the (t - 1) period. If it was, it is 1; otherwise, it is 0. g t-1 is the output of the gas turbine in the (t - 1) period;

[0034] The constraint conditions of the cold storage tank are specifically as follows:

[0035]

[0036] A cold (t)=A coldch (t)-A colds (t)+A coldr (t)

[0037] A is (t)+A ir (t)≤1

[0038] S cold (t)=S cold (t - 1)+A colds (t)*ε ns -A coldr (t) / ε nr

[0039] P cold (t)=A coldch (t) / δ uch +A colds (t)*δ us +A coldr (t)*δ ur

[0040] In the formula, S cold (t) is the capacity of the cold storage tank at time t; is the upper limit of the capacity of the cold storage tank; A colds (t) is the cold storage amount of the cold storage tank at time t; A is(t) is the state of cold storage, being 1 if so, otherwise 0; is the maximum cold storage capacity; A coldr (t) is the cold release amount of the cold storage tank at time t; A ir (t) is the state of cold release, being 1 if so, otherwise 0; is the maximum cold release capacity; A cold (t) is the demand for actual cooling capacity at time t; A coldch (t) is the cooling capacity of the air-conditioning main unit at time t; S cold (t - 1) is the cold storage amount of the cold storage tank at time t - 1; ε ns is the cold storage efficiency of the cold storage tank; ε nr is the cold release efficiency of the cold storage tank; P cold (t) is the electric power of the refrigeration system; δ uch is the energy efficiency ratio of the refrigeration main unit; δ us is the cold storage energy efficiency ratio of the cold storage tank; δ ur is the energy efficiency ratio of cold release of the cold storage tank;

[0041] The constraint conditions of the energy storage are specifically as follows:

[0042]

[0043] S storage (1) = P sc (1) * λ sc -P sd (1) * λ sd

[0044] S storage (t) = S storage (t - 1) + P sc (t) * λ sc -P sd (t) * λ sd

[0045] In the formula, P sc (t) is the charging power of the energy storage at time t; is the maximum charging power of the energy storage; P sd (t) is the discharging power of the energy storage at time t; is the maximum discharging power of the energy storage; S storage (t) is the stored electricity amount of the energy storage at time t; is the maximum stored electricity amount of the energy storage; S storage (1) is the initial stored electricity amount of the energy storage system; P sc (1) and P sd (1) are respectively the initial charging power and discharging power of the energy storage; λ sc and λ sd are respectively the charging and discharging efficiencies of the energy storage; Sstorage (t - 1) is the stored electricity of the energy storage at time t - 1;

[0046] The electricity purchase and sale constraint conditions with the large power grid are as follows:

[0047] 0 ≤ S gb + S gs ≤ 1

[0048]

[0049] In the formula, S gb is the electricity purchase state, which is 1 if it is a purchase, otherwise 0; S gs is the electricity sale state, which is 1 if it is a sale, otherwise 0; P gb (t) and P gs (t) are the electricity purchase power and the electricity sale power at time t respectively; is the upper limit of the electricity purchase power; is the upper limit of the electricity sale power.

[0050] Preferably, the power balance condition of the virtual power plant is specifically:

[0051]

[0052] In the formula, P cold (t) is the electric power of the refrigeration system; P sc (t) is the charging power of the energy storage at time t; P load (t) is the load prediction result at time t; is the total interruptible load of all levels at time t; P gs (t) is the electricity sale power at time t; is for all n v the total charging power of the electric vehicles; P sd (t) is the discharging power of the energy storage at time t; P pv (t) is the predicted result of the photovoltaic power generation at time t; P gb (t) is the electricity purchase power at time t; g t is the output of the gas turbine in the t time period; is for all n v the total discharging power of the electric vehicles.

[0053] Preferably, the objective function is to minimize the total operating cost inside the virtual power plant. The total operating cost includes the electricity purchase and sale cost of the large power grid, the cost of the gas turbine, the battery loss cost of the electric vehicles, and the compensation cost of the interruptible load, specifically:

[0054]

[0055] In the formula, C is the total operating cost; is the power purchase and sale cost of the large power grid; is the cost of the gas turbine; is the battery loss cost of the electric vehicle; is the compensation cost of the interruptible load; P gb P(t) and P gs (t) are the power purchase and power sale at time t respectively; M gb M(t) is the unit price of power purchase at time t; M gs M(t) is the unit price of power sale at time t; M t1 is the fixed start-up cost of the gas turbine; M t2 is the piecewise linearization cost; M t3 is the start-stop cost; g(t) indicates whether the gas turbine works in the t period. If it does, it is 1; otherwise, it is 0; g t P(t) is the output of the gas turbine in the t period; n is the start or stop state of the gas turbine; v N is the number of electric vehicles.

[0056] Preferably, the meteorological data includes the total irradiance, temperature, humidity and wind speed data collected by the small meteorological station equipment installed inside the virtual power plant;

[0057] The new energy power generation data is the power generation data obtained by collecting the photovoltaic inverter;

[0058] The controllable loads of the demand response include the chilled water storage tank, electric vehicles and interruptible loads.

[0059] Preferably, the cooling load prediction is based on the meteorological data and historical cooling load data to predict the cooling capacity demand of the future chiller;

[0060] The new energy power generation power prediction is based on the meteorological data and historical total new energy power generation data to predict the new energy power generation power curve for a future period of time;

[0061] The electric load prediction is based on the meteorology, electricity price, number of personnel and planned output to predict the future electric load curve inside the virtual power plant.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1) The economic optimization dispatch method of the present invention takes into account the battery loss cost of electric vehicles and the demand response load, and classifies the basic electric load participating in the demand response, so that the basic electric loads at different levels, electric vehicles and ice storage air conditioners participate in the demand response simultaneously, effectively regulating the comprehensive energy inside the virtual power plant, reducing the operation cost, and improving the economy and stability of the virtual power plant.

[0064] 2) The present invention not only utilizes the demand response resources within the virtual power plant to increase economic benefits and optimize the power system curve, but also fully considers the battery loss cost of electric vehicles, avoids the problem of excessive losses in electric vehicles causing losses, reduces carbon emissions, and has a certain promoting effect on the economy and environmental protection of the power system.

[0065] 3) The present invention jointly regulates with the cold storage tank of the air conditioning unit. The cold storage tank stores cold when the cold load is low and the electricity price is cheap in the early stage of a day, and releases cold when the electricity price is relatively expensive; and combines with the cooling capacity of the refrigerator to meet the current cold load demand, which can optimize the power system load curve to a certain extent and improve the economy.

[0066] 4) The present invention classifies the basic electric load. When the electricity price is high, considering the high current demand response compensation cost, the interruptible loads of the first-level load, second-level load, and third-level load are interrupted to make the overall economic efficiency reach the best. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a schematic diagram of the overall process in the present invention;

[0068] Figure 2 is a diagram of the driving distance of the vehicle;

[0069] Figure 3 is a diagram of the electricity purchase and sale prices;

[0070] Figure 4 is a schematic diagram of the basic scheduling result of the aggregation unit;

[0071] Figure 5 is a schematic diagram of the regulation result of the air conditioning unit;

[0072] Figure 6 is a schematic diagram of the scheduling result of BYD electric vehicles;

[0073] Figure 7 is a schematic diagram of the scheduling result of Nissan electric vehicles;

[0074] Figure 8 is a schematic diagram of the scheduling result of the interruptible load;

[0075] Figure 9 Schematic diagram of the optimization result of energy storage under time-of-use electricity price. DETAILED DESCRIPTION OF THE INVENTION

[0076] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0077] This embodiment relates to a virtual power plant day-ahead economic optimization scheduling method, as Figure 1 follows. The method includes the following steps:

[0078] Step S1: Obtain meteorological data, new energy power generation data, the number of electric vehicles, driving mileage, and controllable loads participating in demand response and their quantities within the virtual power plant;

[0079] Step S2: Combine data such as meteorological information, electricity price information, the number of personnel, planned output, etc. to perform new energy power generation power prediction, basic electricity load prediction, and cooling load prediction;

[0080] Step S3: Classify the basic electricity load of demand response resources, and constrain interruptible loads and continuous interruptible loads;

[0081] Step S4: Establish an electric vehicle battery loss cost model, and set constraints such as the charging and discharging power and storage capacity of electric vehicles;

[0082] Step S5: Constrain resources such as gas turbines, chilled water storage tanks, energy storage, and the large power grid, and establish the power balance condition of the entire system;

[0083] Step S6: Solve the optimization scheduling model according to the objective function to obtain the day-ahead optimization scheduling plan of the virtual power plant.

[0084] In step S1, the meteorological data are data such as total irradiance, temperature, humidity, wind speed, etc. collected by small meteorological station equipment installed inside the virtual power plant; the new energy power generation data are the power generation data collected by collecting the photovoltaic inverters; the quantities of controllable loads participating in demand response include data such as chilled water storage tanks, electric vehicles, and interruptible loads.

[0085] In step S2, the new energy power generation power prediction mainly predicts the power generation power curve for a future period of time. It is necessary to combine historical meteorological data and historical total power generation data as a data set, input it into a machine learning algorithm for model training, and then use high-precision weather forecast data as the input for future prediction to obtain the future photovoltaic power generation power curve.

[0086] Electric load forecasting mainly forecasts the entire electricity load curve within the virtual power plant. It is necessary to combine multiple factors affecting load operation, such as meteorology, electricity price, number of personnel, planned output, etc. Input multiple influencing factors and historical load data into the machine learning model to obtain the future electricity load curve.

[0087] Cooling load forecasting is to forecast the cooling capacity demand of the future chiller. It is necessary to select appropriate input and output pairs for the machine learning model according to data such as outdoor temperature, outdoor humidity, outdoor irradiance, and cooling load collected by the meteorological station for training, and perform cooling load forecasting based on the trained model. The cooling load is usually the cooling load data measured by the energy meter installed on the main pipeline of the chiller. The calculation method adopted by the energy meter is as follows:

[0088] c·G·Δt=Q

[0089] In the formula, c is the specific heat capacity of water; G is the flow rate of chilled water; Δt is the temperature difference between the supply and return water of chilled water; Q is the cooling load.

[0090] In step S3, the basic electricity load participating in demand response is classified, mainly divided into first-level load, second-level load, and third-level load according to the actual situation, and the interruptible load and continuous interruptible load of each level are restricted. Usually, the higher the interruption level, the higher the compensation cost. The constraint formulas for the interruptible load and continuous interruptible load are as follows:

[0091] 0≤P i cut (t)≤cil(i)*P i load (t)

[0092] P i cut (t-1)≤0.1*P i load (t)

[0093] In the formula, P i cut (t) is the interruptible load of the i-th level load at time t; cil(i) is the proportion of the interruptible load in the i-th level load; P i load (t) is the total load of the i-th level at time t; P i cut (t-1) is the load that can be reduced by the i-th level load at time t-1.

[0094] The compensation cost for the interruptible load is as follows:

[0095]

[0096] In the formula, is the compensation cost of interruptible load at time t; is the compensation unit price of the i-th level of interruptible load, and m is the total number of load levels.

[0097] In step S4, the battery loss cost model of the electric vehicle is expressed as:

[0098]

[0099] In the formula, is the total battery loss cost of the electric vehicle; n v is the number of electric vehicles; is the battery purchase cost of the v-th electric vehicle; is the number of charge and discharge cycles of the v-th electric vehicle battery in its entire life cycle; is the battery capacity of the v-th electric vehicle; is the available battery discharge depth of the v-th electric vehicle; is the discharge power of the v-th electric vehicle at time t; is the discharge efficiency of the v-th electric vehicle; is the driving distance of the v-th electric vehicle in the t period; E v is the energy demand of the v-th electric vehicle, indicating the power consumed by the electric vehicle per unit driving distance.

[0100] The constraints for the charge and discharge power and the stored power of the electric vehicle are as follows:

[0101]

[0102] In the formula, is the stored power of the v-th electric vehicle at time t; and are the upper and lower limits of the stored power of the v-th electric vehicle, respectively; and are the charge power and discharge power of the v-th electric vehicle at time t, respectively; and are the upper limits of the charge power and discharge power of the v-th electric vehicle, respectively; The Boolean variables and indicate whether the v-th electric vehicle is in the charging or discharging state during the t period. If it is, it is 1; if not, it is 0; indicates whether the v-th electric vehicle is connected to the power grid at time t. If it is, it is 1; otherwise, it is 0; and are the stored powers of the v-th electric vehicle at the start and end of the period, respectively; is the charge efficiency of the v-th electric vehicle; is the stored power of the v-th electric vehicle at time t - 1.

[0103] In step S5, the constraint conditions of the gas turbine are as follows:

[0104]

[0105] -r d ≤g t -g t-1 ≤r u

[0106] In the formula, the Boolean variables μ t o 、 and represent whether the gas turbine is operating, starting, and stopping in period t. If it is, it is 1; otherwise, it is 0. g t is the output of the gas turbine in period t. g min and g max are the minimum and maximum output powers of the gas turbine. r u and r d are the upward and downward ramp rates of the gas turbine respectively; represents whether the gas turbine was operating in period t - 1. If it was, it is 1; otherwise, it is 0. g t-1 is the output of the gas turbine in period t - 1.

[0107] The constraint conditions of the cold storage tank are as follows:

[0108]

[0109] A cold (t) = A coldch (t) - A colds (t) + A coldr (t)

[0110] A is (t) + A ir (t) ≤ 1

[0111] S cold (t) = S cold (t - 1) + A colds (t) * ε ns -A coldr (t) / ε nr

[0112] P cold (t) = A coldch (t) / δ uch +A colds (t) * δ us +A coldr (t) * δ ur

[0113] In the formula, Scold (t) is the capacity of the cold storage tank at time t; is the upper limit of the capacity of the cold storage tank; A colds (t) is the cold storage amount of the cold storage tank at time t; A is (t) is the cold storage state, which is 1 if it is in the cold storage state, otherwise 0; is the maximum cold storage amount; A coldr (t) is the cold release amount of the cold storage tank at time t; A ir (t) is the cold release state, which is 1 if it is in the cold release state, otherwise 0; is the maximum cold release amount; A cold (t) is the demand for the actual cooling capacity at time t; A coldch (t) is the cooling capacity of the air-conditioning main unit at time t; S cold (t - 1) is the cold storage amount of the cold storage tank at time t - 1; ε ns is the cold storage efficiency of the cold storage tank; ε nr is the cold release efficiency of the cold storage tank; P cold (t) is the electric power of the refrigeration system; δ uch is the energy efficiency ratio of the refrigeration main unit; δ us is the cold storage energy efficiency ratio of the cold storage tank; δ ur is the cold release energy efficiency ratio of the cold storage tank.

[0114] The constraints for energy storage are as follows:

[0115]

[0116] S storage (1) = P sc (1) * λ sc - P sd (1) * λ sd

[0117] S storage (t) = S storage (t - 1) + P sc (t) * λ sc - P sd (t) * λ sd

[0118] In the formula, P sc (t) is the charging power of the energy storage at time t; is the maximum charging power of the energy storage; P sd (t) is the discharging power of the energy storage at time t; is the maximum discharging power of the energy storage; S storage (t) is the stored electricity amount of the energy storage at time t; is the maximum stored electricity amount of the energy storage; S storage (1) is the initial stored electricity amount of the energy storage system; P sc(1) and P sd (1) is the initial charge-discharge power of the energy storage; λ sc and λ sd are the charging and discharging efficiencies of the energy storage respectively; S storage (t - 1) is the stored electricity of the energy storage at time t - 1.

[0119] The constraints on power purchase and sale with the large power grid are as follows:

[0120] 0 ≤ S gb + S gs ≤ 1

[0121]

[0122] In the formula, S gb is the power purchase state, which is 1 if power is purchased, otherwise 0; S gs is the power sale state, which is 1 if power is sold, otherwise 0; P gb (t) and P gs (t) are the power purchase power and power sale power at time t respectively; is the upper limit of the power purchase power; is the upper limit of the power sale power.

[0123] The power balance condition is as follows:

[0124]

[0125] In the formula, P cold (t) is the electric power of the refrigeration system; P sc (t) is the charging power of the energy storage at time t; P load (t) is the load prediction result at time t; is the total interruptible load of the first, second, and third levels at time t; P gs (t) is the power sale power at time t; is n v the total charging power of electric vehicles; P sd (t) is the discharging power of the energy storage at time t; P pv (t) is the predicted result of the photovoltaic power generation at time t; P gb (t) is the power purchase power at time t; g t is the output of the gas turbine in time period t; is n v the total discharging power of electric vehicles.

[0126] In step S6, an optimal scheduling model is established, and its objective function is to minimize the total operating cost within the virtual power plant.

[0127]

[0128]

[0129] Wherein, C is the total operating cost; is the cost of purchasing and selling electricity from the large power grid; is the cost of the gas turbine; is the battery loss cost of the electric vehicle; is the compensation cost of the demand response interruptible load; P gb (t) and P gs (t) are the electricity purchase power and electricity selling power at time t respectively; M gb (t) is the electricity purchase unit price at time t; M gs (t) is the electricity selling unit price at time t; M t1 is the fixed start-up cost of the gas turbine; M t2 is the piecewise linearization cost; M t3 is the start-stop cost; indicates whether the gas turbine is working in the t period. If it is, it is 1; otherwise, it is 0; g t is the output of the gas turbine in the t period; is the start-up or stop state of the gas turbine.

[0130] This embodiment also relates to a day-ahead economic optimal dispatch method for a virtual power plant. Taking a day-ahead economic optimal dispatch case of a virtual power plant as an example, the actual application of the invention is explained.

[0131] It is assumed that there are two types of electric vehicles in the virtual power plant, 1000 BYD vehicles and 1000 Nissan electric vehicles. The battery capacity of each BYD vehicle is 57 kWh, and the battery capacity of each Nissan vehicle is 24 kWh. Both are 0.9; Both are 0.9; Both are 5000 times; Both are 200,000; The daily driving distances of the two types of cars are as Figure 2 shown.

[0132] The interruptible load ratio cil(i) and compensation unit price are respectively: The interruptible ratio of the third-level load cil(3) is 0.15, is 500 yuan / MWh; The interruptible ratio of the second-level load cil(2) is 0.1, is 700 yuan / MWh; The interruptible ratio of the first-level load cil(1) is 0.08, is 800 yuan / MWh.

[0133] The g of the gas turbine max and g min are 3.31 MW and 1.3 MW respectively; r u and r dBoth are 1.5 MW; M t1 is 600; M t2 is 300; M t3 is 600.

[0134] For the cold storage tank is 24 MW; and both are 5 MW; ε ns is 0.95; ε nr is 0.92; δ uch is 5.6; δ us is the cold storage energy efficiency ratio of the cold storage tank; δ us The cold release energy efficiency ratio of the cold storage tank.

[0135] The power purchase and sale parameters of the large power grid and both are 20 MW, and the power purchase price and the power sale price are shown in Figure 3 as shown.

[0136] The parameters of energy storage and both are 1 MW; is 4 MW; λ sc and λ sd both are 0.95.

[0137] Based on the above case data, an economic optimization dispatch model with the lowest total operating cost is established. First, the basic dispatch results of the aggregation unit are obtained, as shown in Figure 4 as shown, where the load and the PV curve are both prediction results. The energy storage is charged when the electricity price is relatively cheap and discharged during the peak electricity price, meeting the economic requirements.

[0138] The regulation results of the air-conditioning unit are as shown in Figure 5 as shown. The cold storage tank stores cold when the cold load is low and the electricity price is cheap in the early stage of a day, and releases cold when the electricity price is relatively expensive; and combines with the cooling capacity of the chiller to meet the current cold load demand, which can optimize the profile of the load curve to a certain extent. The current cold load data is the cold load prediction result.

[0139] Figure 6 and Figure 7 are the charge and discharge dispatch curves of two different vehicle models. Under the guidance of the demand response mechanism, electric vehicles can play a role in peak shaving and valley filling.

[0140] Figure 8For the scheduling result of interruptible load, the disconnection ratio of interruptible load fluctuates with the electricity price. When the electricity price is at a low point, the disconnection of primary load and secondary load is basically not considered. When the electricity price is at a high point, considering the relatively high current demand response compensation cost, the interruptible load of primary load and secondary load can be considered to be interrupted to achieve the best overall economic efficiency.

[0141] Figure 9 It is the optimization result diagram of energy storage under time-of-use electricity price. The charging and discharging rules of energy storage also fluctuate with the electricity price. It charges when the electricity price is relatively cheap and discharges when the electricity price is at a peak, meeting the requirements of overall economy.

[0142] Through the above analysis, it can be seen that a virtual power plant day-ahead economic optimization scheduling method proposed by the present invention can not only increase economic benefits and optimize the power system curve by using the demand response resources within the virtual power plant, but also fully consider the battery loss cost of V2G electric vehicles, avoiding the problem of excessive loss of electric vehicles causing losses. This optimization scheduling method has a certain promoting effect on the economy and environmental protection of the power system.

[0143] The electronic device of the present invention includes a central processing unit (CPU), which can execute various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or computer program instructions loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM, and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0144] Multiple components in the device are connected to the I / O interface, including: an input unit, such as a keyboard, a mouse, etc.; an output unit, such as various types of displays, speakers, etc.; a storage unit, such as a disk, an optical disc, etc.; and a communication unit, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit allows the device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0145] The processing unit executes the various methods and processes described above. For example, in some embodiments, the method can be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device via the ROM and / or the communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of the method described above can be executed. Alternatively, in other embodiments, the CPU can be configured to execute the method by any other appropriate means (e.g., by means of firmware).

[0146] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), Systems on Chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

[0147] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general purpose computer, a special purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, executed partially on the machine as an independent software package and partially on a remote machine, or executed entirely on a remote machine or server.

[0148] In the context of the present invention, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include electrical connections based on one or more wires, portable computer disks, hard disks, Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM or Flash Memory), optical fibers, portable Compact Disc Read Only Memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0149] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for day-ahead economic optimization scheduling of a virtual power plant, characterized in that: The method comprises the following steps: Step S1, obtaining meteorological data, new energy power generation data, number of electric vehicles, mileage, and controllable loads participating in demand response and their number within the virtual power plant; Step S2, using the data obtained in S1 to predict the power generation, electric load and cooling load of new energy in the virtual power plant; Step S3: classify the basic electric loads participating in the demand response, and constrain and compensate the interruptible loads in the basic electric loads; Step S4, establishing an electric vehicle battery loss cost model, and setting constraints on the electric vehicle charging and discharging power and storage capacity; Step S5: constraining the resources of the gas turbine, cold storage tank, energy storage and large power grid, and establishing the power balance condition of the virtual power plant; Step S6: Solve the optimization scheduling model according to the objective function to obtain the day-ahead optimization scheduling plan of the virtual power plant.

2. The method for day-ahead economic optimization scheduling of a virtual power plant according to claim 1, characterized in that: The basic electric load participating in demand response is divided into primary load, secondary load and tertiary load according to actual conditions, and the interruptible load of each level of basic electric load is constrained and compensated.

3. The method for day-ahead economic optimization scheduling of a virtual power plant according to claim 2, characterized in that: The specific constraints on interruptible loads are: 0≤P i cut (t)≤cil(i)*P i load (t) P i cut (t-1)≤0.1*P i load (t) Where P i cut (t) is the interruptible load of the i-th level load at time t; P i cut (t-1) is the load that can be reduced at the i-th level load at time t-1; cil(i) is the proportion of interruptible load in the i-th level load; P i load (t) is the total load of level i at time t; The compensation cost for interruptible load is calculated as follows: In the formula, is the compensation cost for the interruptible load at time t; is the compensation unit price of the i-th level interruptible load, and m is the total number of load levels.

4. The method for day-ahead economic optimization scheduling of a virtual power plant according to claim 1, characterized in that: The electric vehicle battery loss cost model is specifically as follows: In the formula, is the total battery loss cost of electric vehicles; n v is the number of electric vehicles; The cost of purchasing the battery for the vth electric car; is the number of charge and discharge cycles of the vth electric vehicle battery in its entire life cycle; is the battery capacity of the vth electric car; is the available battery discharge depth of the vth electric vehicle; is the discharge power of the vth electric vehicle at time t; is the discharge efficiency of the vth electric vehicle; is the travel distance of the vth electric vehicle in period t; E v is the energy demand of the vth electric car, which represents the power consumed by the electric car per unit driving distance.

5. The method for day-ahead economic optimization scheduling of a virtual power plant according to claim 1, characterized in that: The constraints on the charging and discharging power and storage capacity of the electric vehicle are specifically: In the formula, is the power storage capacity of the vth electric vehicle at time t; and are the upper and lower limits of the power storage capacity of the vth electric vehicle respectively; and are the charging power and discharging power of the vth electric vehicle at time t respectively; and are the upper limit of charging power and the upper limit of discharging power of the vth electric vehicle respectively; Boolean variables and Respectively indicate whether the vth electric vehicle is in the charging or discharging state during period t, if yes, it is 1, otherwise, it is 0; Indicates whether the vth electric vehicle is connected to the power grid at time t, if yes, it is 1, otherwise it is 0; and are the storage capacity of the beginning and end period of the vth electric vehicle respectively; is the charging efficiency of the vth electric vehicle; is the power storage capacity of the vth electric vehicle at time t-1; is the discharge efficiency of the vth electric vehicle; is the travel distance of the vth electric vehicle in period t; E v is the energy demand of the vth electric vehicle, which represents the power consumed by the electric vehicle per unit driving distance; and They represent the power storage of the vth electric car at the 0th point and the 24th point respectively.

6. The method for day-ahead economic optimization scheduling of a virtual power plant according to claim 1, characterized in that: The constraints of the gas turbine are specifically: -r d ≤g t -g t-1 ≤r u In the formula, the Boolean variable and Indicates whether the gas turbine is working, started or stopped during period t, if yes, it is 1, otherwise, it is 0; g t is the output of the gas turbine during period t; g min and g max are the minimum and maximum output power of the gas turbine respectively; r u and r d are the upward and downward ramp rates of the gas turbine, respectively; Indicates whether the gas turbine is working during the period t-1, if yes, it is 1, otherwise it is 0; g t-1 is the output of the gas turbine during period t-1; The constraints of the cold storage tank are specifically: A cold (t)=A coldch (t)-A colds (t)+A coldr (t) A is (t)+A ir (t)≤1 S cold (t)=S cold (t-1)+A colds (t)*ε ns -A coldr (t) / ε nr P cold (t)=A coldch (t) / δ uch +A colds (t)*δ us +A coldr (t)*δ ur In the formula, S cold (t) is the capacity of the cold storage tank at time t; A is the upper limit of the cold storage tank capacity; colds (t) is the cold storage capacity of the cold storage tank at time t; A is (t) is the state of cold storage, which is 1 if yes, and 0 otherwise; is the maximum cooling capacity; A coldr (t) is the cooling capacity of the cold storage tank at time t; A ir (t) is the state of cooling release, which is 1 if yes, and 0 otherwise; A is the maximum cooling capacity; cold (t) is the actual cooling capacity demand at time t; A coldch (t) is the cooling capacity of the air conditioner at time t; S cold (t-1) is the cold storage capacity of the cold storage tank at time t-1; ε ns is the cold storage efficiency of the cold storage tank; nr is the cooling efficiency of the cold storage tank; P cold (t) is the electrical power of the refrigeration system; δ uch is the energy efficiency ratio of the refrigeration host; us is the cold storage energy efficiency ratio of the cold storage tank; δ ur The energy efficiency ratio of releasing cold from the cold storage tank; The energy storage constraints are specifically: S storage (1)=P sc (1)*l sc -P sd (1)*l sd S storage (t)=S storage (t-1)+P sc (t)*λ sc -P sd (t)*λ sd Where P sc (t) is the charging power of the energy storage at time t; is the maximum charging power of energy storage; P sd (t) is the discharge power of the energy storage at time t; is the maximum discharge power of energy storage; S storage (t) is the storage capacity of the energy storage at time t; is the maximum storage capacity of energy storage; S storage (1) is the initial storage capacity of the energy storage system; P sc (1) and P sd (1) are the initial charging power and discharging power of the energy storage; λ sc and λ sd are the charging and discharging efficiencies of energy storage, respectively; S storage (t-1) is the storage capacity of the energy storage at time t-1; The constraints on electricity purchase and sale with the large power grid are as follows: 0≤S gb +S gs ≤1 In the formula, S gb If it is the electricity purchasing state, it is 1, otherwise it is 0; S gs If it is the electricity selling state, it is 1, otherwise it is 0; P gb (t) and P gs (t) are the purchased power and sold power at time t respectively; The upper limit of the power purchased; The upper limit of electricity sales power.

7. The method for day-ahead economic optimization scheduling of a virtual power plant according to claim 1, characterized in that: The power balance condition of the virtual power plant is specifically: Where P cold (t) is the electrical power of the refrigeration system; P sc (t) is the charging power of the energy storage at time t; P load (t) is the load forecast result at time t; is the total interruptible load of all levels at time t; P gs (t) is the electricity sales power at time t; For all n v The total charging power of electric vehicles; P sd (t) is the discharge power of the energy storage at time t; P pv (t) is the photovoltaic power generation power prediction result at time t; P gb (t) is the purchased power at time t; g t is the output of the gas turbine during period t; For all n v The total discharge power of an electric vehicle.

8. The method for day-ahead economic optimization scheduling of a virtual power plant according to claim 1, characterized in that: The objective function is to minimize the total operating cost within the virtual power plant, which includes the purchase and sale costs of electricity from the large power grid, the cost of gas turbines, the battery loss costs of electric vehicles, and the compensation costs of interruptible loads. Specifically, Where C is the total operating cost; The cost of purchasing and selling electricity for the large power grid; For the cost of gas turbines; Cost of battery depletion for electric vehicles; P is the compensation cost for interruptible load; gb (t) and P gs (t) are the purchased power and sold power at time t; M gb (t) is the unit price of electricity at time t; M gs (t) is the unit price of electricity sold at time t; M t1 is the fixed startup cost of the gas turbine; M t2 is the piecewise linearization cost; M t3 Start-stop fees; Indicates whether the gas turbine is working during period t, if yes, it is 1, otherwise it is 0; g t is the output of the gas turbine during period t; The gas turbine is started or stopped; n v is the number of electric vehicles.

9. The method for day-ahead economic optimization scheduling of a virtual power plant according to claim 1, characterized in that: The meteorological data include total irradiance, temperature, humidity and wind speed data collected by a small meteorological station device installed inside the virtual power plant; The new energy power generation data is the power generation data obtained by collecting photovoltaic inverters; The controllable loads for demand response include cold storage tanks, electric vehicles, and interruptible loads.

10. The method for day-ahead economic optimization scheduling of a virtual power plant according to claim 1, characterized in that: The cooling load forecast is based on meteorological data and historical cooling load data to forecast the cooling capacity demand of the future refrigeration host; The new energy power generation prediction is based on meteorological data and historical new energy total power generation data to predict the new energy power generation curve for a period of time in the future; The electricity load forecast is based on weather, electricity prices, number of personnel and planned output, and predicts the future electricity load curve within the virtual power plant.

Images