An intelligent park new energy power station scheduling optimization method and system

Abstract

An intelligent park new energy power station scheduling optimization method and system. In this solution, load types are set for loads according to the characteristics of each load in the intelligent park, and load models and comfort evaluation factors are constructed according to the characteristics of each type of load. Output power models are constructed for distributed power sources and distributed energy storage units with different characteristics in the intelligent park based on various influencing factors. Based on the comfort evaluation factors and output power models, the power flow in the intelligent park is scheduled using the day-ahead planning method. In this solution, by constructing a load model and according to the characteristics of the model, a user comfort coefficient is proposed to optimize the load scheduling in the new energy power station. In this solution, the output power of the distributed power source is constrained by the comfort evaluation factor of the load model, so that the output power of the distributed power source can meet the comfort of each load model and achieve the optimal power output of the distributed power source.

Description

Technical Field

[0001] The present invention relates to the field of energy Internet, and specifically to a method and system for optimizing the dispatching of new energy power stations in intelligent parks. Background Art

[0002] A park is composed of standard building complexes with complete supporting facilities, including energy supply, communication, transportation, warehousing, etc., which can meet the needs of industrial production and specific scientific experiments. Compared with traditional parks, intelligent parks integrate the most advanced information, communication, and renewable energy technologies. An advanced intelligent park can provide convenient and personalized services for people's work and life. Due to the obvious advantages of intelligent parks compared with traditional parks, the development of intelligent parks has attracted wide attention, thus accelerating the planning and construction pace of intelligent parks.

[0003] With the rapid development of renewable energy and Internet of Things technologies, the proportion of distributed power generation in intelligent parks has increased significantly. By increasing the power generation of renewable energy, intelligent parks can not only reduce the development of fossil energy but also promote the sustainable development of society. In addition, intelligent parks have the characteristic of local consumption of renewable energy and are considered an effective means to deal with energy efficiency. Therefore, it plays an important role in improving energy efficiency and promoting the utilization of low-carbon energy. Through research, it is found that most previous studies have taken the incentive electricity price as the main decision-making basis and only optimized the load with the economic cost as the goal. However, it should be noted that the simple cost optimization result lacks the value of practical application and seriously hinders the further development of intelligent parks.

[0004] At present, the operation and management of intelligent parks still lack a perfect user comfort evaluation method and research on the consumption of renewable energy. In the research on the optimization of controllable loads in intelligent parks, the same standard is mostly used to uniformly judge the user comfort level of all electrical appliances. However, for controllable loads, the impact of the transfer of different types of loads on user comfort varies greatly, resulting in a large error in the optimization result. Summary of the Invention

[0005] In order to solve the problem in the prior art that there are great differences in the impact of the transfer of different types of loads on user comfort, and the lack of classification of user comfort leads to a large error in the optimization result, the present invention provides a method and system for optimizing the dispatching of new energy power stations in intelligent parks.

[0006] The technical solution provided by the present invention is as follows:

[0007] A method for optimizing the dispatching of new energy power stations in intelligent parks, comprising:

[0008] Setting types for the loads according to the characteristics of each load in the intelligent park, constructing a load model and a comfort evaluation factor according to the characteristics of each type of load;

[0009] Build output power models for distributed power sources and distributed energy storage units with different characteristics in the smart park based on various influencing factors;

[0010] Based on the comfort evaluation factors and the output power models, use the day-ahead planning method to dispatch the power flow in the smart park.

[0011] Preferably, set types for the loads according to the characteristics of each load in the smart park, and build load models based on the characteristics of each type of load, including:

[0012] Set the non-sheddable loads and shiftable loads that must be powered during normal operation as important loads, and build important load models;

[0013] Set the loads whose usage periods are transferable but the total usage amount remains unchanged as transferable loads, and build transferable load models;

[0014] Set the loads that can be stopped for a period of time according to the dispatching requirements as interruptible loads, and build interruptible load models.

[0015] Preferably, the construction of the comfort evaluation factors for each load model includes:

[0016] Build a comfort evaluation factor for transferable loads according to the actual usage time and planned usage duration of the transferable load model;

[0017] Build a comfort evaluation factor for interruptible loads according to the interruption duration and the power during interruption of the interruptible load.

[0018] Preferably, the comfort evaluation factor for transferable loads is shown as the following formula:

[0019]

[0020] Where, is the comfort evaluation factor for transferable loads, t s is the actual start time of use, t α is the planned start time of use, t β is the planned end time of use.

[0021] Preferably, the comfort evaluation factor for interruptible loads is shown as the following formula:

[0022]

[0023] Where, φ IL is the comfort evaluation factor for interruptible loads, P ILi is the comfort evaluation factor for interruptible loads, Δt is the duration, UIL For the total amount of actual interrupted load during the optimization period, T IL For the total number of time periods when actual interrupted load is generated during the optimization period.

[0024] Preferably, the output power model for distributed power sources and distributed energy storage units with different characteristics in the smart park based on various influencing factors includes:

[0025] Construct an objective function according to the output power of the distributed power source and the total power consumption duration of the load model;

[0026] Construct an output power model based on the comfort constraint conditions of the load model and the consumption rate constraint conditions of renewable energy.

[0027] Preferably, the objective function is as shown in the following formula:

[0028]

[0029] Among them, C is the daily energy cost of the smart park, C i Is the energy cost of the i-th distributed power source, including the energy cost of new energy power generation, the energy cost of distributed fuel cells, and the energy cost of battery energy storage units, P i (t) is the output power of the i-th distributed power source at time t, T is the total number of time periods, M is the number of distributed generators, and ΔT is the planned time period.

[0030] Preferably, the comfort constraint conditions are as shown in the following formula:

[0031]

[0032]

[0033] Among them, Is the actual comfort of the shiftable load under the scheduling of the output power model; Is the maximum value of the comfort of the shiftable load, Is the actual comfort of the interruptible load under the scheduling of the output power model, Is the maximum value of the comfort of the interruptible load.

[0034] Preferably, based on the comfort evaluation factor and the output power model, the power flow in the smart park is scheduled by using the day-ahead planning method, including:

[0035] Based on the comfort evaluation factor of the load model, obtain the maximum value of the comfort of the shiftable load and the maximum value of the comfort of the interruptible load;

[0036] Taking the maximum value of the comfort level of the transferable load and the maximum value of the comfort level of the interruptible load as constraints, the output powers of the distributed power sources and the distributed energy storage units in the output power model are solved through the particle swarm optimization algorithm;

[0037] The output power is scheduled through the day-ahead planning method to supply power to the load model.

[0038] An intelligent park new energy power station dispatching optimization system, the system includes:

[0039] Load model modeling module: Set types for the loads according to the characteristics of each load in the intelligent park, and construct a load model and a comfort evaluation factor according to the characteristics of each type of load;

[0040] Output power modeling module: Construct an output power model for the distributed power sources and distributed energy storage units with different characteristics in the intelligent park based on various influencing factors;

[0041] Dispatching module: Based on the comfort evaluation factor and the output power model, the power flow in the intelligent park is dispatched by using the day-ahead planning method.

[0042] Preferably, the load model modeling module includes:

[0043] Important load modeling sub-module: Set the non-removable load and the shifted load that must be supplied with power under normal operating conditions as important loads, and construct an important load model;

[0044] Transferable load modeling sub-module: Set the load whose usage period can be transferred but the total usage amount remains unchanged as a transferable load, and construct a transferable load model;

[0045] Interruptible load modeling sub-module: Set the load that can be stopped from being used for a period of time according to the dispatching requirements as an interruptible load, and construct an interruptible load model.

[0046] Preferably, the load model modeling module further includes:

[0047] First comfort evaluation factor modeling sub-module: Construct a transferable load comfort evaluation factor according to the actual usage time and planned usage duration of the transferable load model;

[0048] Second comfort evaluation factor modeling sub-module: Construct an interruptible load comfort evaluation factor according to the interruption duration and the power of the interrupted load.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] The technical solution provided by the present invention includes: setting types for loads according to the characteristics of each load in the smart park, constructing load models and comfort evaluation factors based on the characteristics of each type of load; constructing output power models for distributed power sources and distributed energy storage units with different characteristics in the smart park based on various influencing factors; and scheduling the power flow in the smart park by using the day-ahead planning method. In this solution, by constructing a load model and according to the characteristics of the model, a user comfort coefficient related to the model is proposed to optimize the load scheduling in the new energy power station. In this solution, the output power of the distributed power source is constrained by the comfort evaluation factor of the load model, so that the output power of the distributed power source can meet the comfort of each load model and achieve the optimal number of distributed power sources. Description of the Drawings

[0051] Figure 1 It is a flowchart of an optimization method for a new energy power station according to the present invention;

[0052] Figure 2 It is a calculation flowchart of the particle swarm model in an embodiment of the present invention. Detailed Embodiments

[0053] To better understand the present invention, the content of the present invention will be further described below in conjunction with the accompanying drawings of the specification and examples.

[0054] Embodiment 1:

[0055] This embodiment provides an optimization method for scheduling a new energy power station in a smart park, and the method flowchart is as Figure 1 shown.

[0056] S1: Set types for loads according to the characteristics of each load in the smart park, and construct load models and comfort evaluation factors based on the characteristics of each type of load.

[0057] Compared with traditional parks, smart parks integrate the most advanced information, communication, and renewable energy technologies, which enable smart parks to interact information with power generation systems, storage systems, and loads, and clearly classify power loads and performance. The configuration of the energy storage system capacity can help smart parks optimize the power flow on multiple time scales.

[0058] This patent takes a distributed power generation system installed with photovoltaic power generation, wind power generation, and fuel cells as an example to illustrate the typical structure of the distributed power generation system. In addition, lithium batteries are also used as energy storage systems.

[0059] Equipment Power Modeling:

[0060] 1) New energy power generation.

[0061] New energy power generation mainly includes power generation methods such as solar power generation, wind power generation, geothermal power generation, and tidal power generation. Compared with traditional primary energy power generation, new energy power generation generally has the characteristics of less pollution and large reserves, and is widely used in intelligent parks to meet power demands. Considering that the experimental pilot area in this patent is rich in solar and wind energy resources, and solar and wind energy are complementary in time, this patent selects photovoltaic power generation units and wind power generation units as the power generation devices in the intelligent park.

[0062] ① Photovoltaic power generation unit.

[0063] Photovoltaic power generation has the characteristics of no noise and no pollution, and the photovoltaic power generation unit can be combined with buildings to form building-integrated photovoltaics to save a large amount of space. In order to reduce environmental pollution and floor area, in this patent, the photovoltaic power generation unit is used to provide the electrical load of the park.

[0064] Since the output of the photovoltaic power generation unit is affected by factors such as solar radiation intensity and environmental temperature, the power output of the photovoltaic power generation unit is generally corrected based on the system output under standard test conditions (STC), and its actual power output can be expressed as:

[0065]

[0066] In the formula: P pv is the actual output power of the photovoltaic power generation unit, P STC is the maximum output power of the photovoltaic power generation unit under STC, G s is the actual solar radiation intensity, G STC is the solar radiation intensity under STC, k is the power temperature coefficient, T c is the actual working temperature of the battery panel, and T0 is the reference ambient temperature (25 °C).

[0067] ② Wind power generation unit.

[0068] Compared with photovoltaic power generation, wind power generation also has the characteristics of clean and environmental protection, and the cost of wind power generation is relatively low. In addition, photovoltaic power generation and wind power generation are complementary. Through wind-solar complementarity, the impact of the uncertainty and intermittency of clean energy power generation on the park system can be reduced. In order to reduce the power generation cost and improve the power supply reliability of the park, this patent adds the wind power generation unit to the intelligent park system to jointly supply the electrical load of the park with the photovoltaic power generation unit.

[0069] According to the operating characteristics of wind turbines, wind turbines can be divided into constant-speed wind turbines and variable-speed wind turbines, and the power output characteristics of different wind turbines will be different. Compared with constant-speed wind turbines, variable-speed constant-frequency wind turbines have the characteristics of small power fluctuations, high cost efficiency, and light support structures, and are widely used in wind power projects at the park level. Its power output model can be expressed by the following formula:

[0070]

[0071] Where:

[0072]

[0073]

[0074] In the formula: P w is the output power of the wind turbine, P0 is the rated power, v ci , v r and v co are the cut-in wind speed, rated wind speed and cut-out wind speed respectively, v represents the actual wind speed, and a, b represent the wind power coefficient.

[0075] 2) Controllable distributed generation unit.

[0076] Controllable distributed power sources have the characteristics of high energy efficiency, flexible use, and low environmental dependence. When new energy generation cannot meet the load and the grid electricity price is high, the controllable distributed power source can be started for power generation. In this patent, the controllable distributed power source is a fuel cell, and its output power is as follows:

[0077] P FC = η FC ×P FCin (5)

[0078] In the formula: P FC is the output power of the fuel cell power generation unit, P FCin is the input power of the fuel cost power generation unit, and η FC is the power conversion efficiency of the fuel cell power generation unit.

[0079] 3) Battery energy storage unit.

[0080] In smart parks, the proportion of renewable energy generation is relatively large. To balance its random fluctuations, improve power quality, and maintain system stability, a certain amount of battery energy storage units are generally equipped.

[0081] The state of charge (SOC) of a battery is an important parameter that needs to be monitored. The SOC of energy storage at each time period reflects the proportion of the remaining capacity of the battery in the total capacity at the current time period, and is closely related to indicators such as the flexibility and load shedding rate of the entire system. Therefore, the SOC of the energy storage system is an important decision variable in the charging and discharging process of energy storage, and its calculation formula is:

[0082]

[0083] In the formula: SOC es (t) is the state of charge of energy storage at time t, δ is the self-discharge efficiency of energy storage, Δt is the scheduling time interval, P es (t) is the charging and discharging power of energy storage at time t. It is stipulated that it is negative during charging and positive during discharging. E es is the maximum capacity of energy storage, η es is the charging and discharging efficiency of energy storage at time t.

[0084] Equipment energy cost modeling:

[0085] 1) The energy cost of new energy power generation.

[0086] Since wind energy and solar energy are widely distributed, this part mainly considers the operating costs of wind power and photovoltaic power generation. The operating costs of new energy power generation include the operation and maintenance costs of wind power and photovoltaic power generation and the system reserve capacity cost.

[0087] The operation and maintenance costs of wind power and photovoltaic power generation can be expressed as:

[0088]

[0089] In the formula: k w 、k pv are the operation and maintenance cost coefficients of wind power and photovoltaic power generation respectively, P w 、P pv are the output powers of the wind turbine and the photovoltaic power generation unit at time t respectively.

[0090] The large-scale grid connection of wind power and photovoltaic power has increased the uncertainty of power grid operation. Therefore, more capacity needs to be reserved to cope with prediction errors and emergencies. The additional cost of the spinning reserve capacity can be expressed as:

[0091]

[0092] In the formula: C Er is the spinning reserve capacity cost, k r is the system reserve cost coefficient, P Lt is the load value at time t, and L, F, and G are the prediction error rates of the load, wind power, and photovoltaic power respectively.

[0093] Therefore, the total energy cost of new energy power generation including wind power and photovoltaic power is expressed as:

[0094] C E = C Eop + C Er (9)

[0095] 2) Energy cost of distributed fuel cells.

[0096] The energy cost of distributed fuel cells generally includes fuel cost, operation and maintenance cost, and emission fine cost, which are specifically as follows:

[0097]

[0098] In the formula: C FC,f is the fossil fuel cost of the fuel cell, C op is the operation and maintenance cost, C yen is the fine cost of the yth pollutant.

[0099] The mathematical expressions of the fuel cost and the output of the fuel cell system can be expressed as:

[0100]

[0101] In the formula: P FC is the output power of the fuel cell power generation unit, η FC is the power generation efficiency of the fuel cell system, C FC,f is the fossil fuel cost of the fuel cell, c is the unit natural gas price, L is the calorific value of natural gas, θ FC is the time interval of the fuel cell system.

[0102] 3) Energy cost of the battery energy storage unit.

[0103] With the continuous progress of technology, the performance of lithium batteries has been significantly improved compared with before. It has excellent characteristics such as high energy density, large output power, and long service life, making it widely used in various fields. Therefore, this part mainly considers using lithium batteries as the battery energy storage unit of the system.

[0104] After charging and discharging, lithium batteries can, on the one hand, compensate for the intermittency of new energy power generation to reduce wind or light curtailment, on the other hand, shave peaks and fill valleys to smooth the system power fluctuation, and on the other hand, cause life loss of itself, increasing the operation cost. The economic optimization of the microgrid depends on the accurate modeling of the operation cost of lithium batteries.

[0105] ① Operation cost of the battery energy storage unit.

[0106] The depth of discharge refers to the percentage of the energy discharged by a lithium battery during operation to its rated capacity. The depth of discharge is highly related to the lifespan of the lithium battery. An increase in the depth of discharge of the lithium battery will lead to a shortening of the operating lifespan. Therefore, during the use of lithium batteries, deep discharge should be avoided as much as possible.

[0107] Here, the rainflow counting method is used to statistically analyze the relationship between the operating lifespan and the depth of discharge of the lithium battery, and it is fitted as:

[0108] N life (t) = -3278D od (t) 4 -5D od (t) 3 +12823D od (t) 2 -14122D od (t) + 5112 (12)

[0109] In the formula: D od (t) represents the depth of discharge of the lithium battery in the t time period, and N life (t) represents the cycle life of the lithium battery in the t time period at the depth of discharge D od (t).

[0110] Combined with the theoretical basis such as the relationship between the operating lifespan and the depth of discharge of the lithium battery and the throughput method for estimating the lifespan loss of the storage battery, this patent derives the operating cost function considering the cycle life of the lithium battery as:

[0111]

[0112] In the formula: C Br (t) represents the operating cost of the lithium battery in the t time period, and C inv represents the initial investment cost of the lithium battery; I ch (t) is a 0-1 integer variable, taking 1 when the lithium battery is in the charging state in the t time period; I dis (t) is a 0-1 integer variable, taking 1 when the lithium battery is in the discharging state in the t time period; P ch (t) represents the charging power of the lithium battery in the t time period, and P dis (t) represents the discharging power of the lithium battery in the t time period, and E LB represents the rated capacity of the lithium battery, and Δt is the duration of each time period.

[0113] Through the establishment of this formula for the operating cost model of the lithium battery, the operating cost of the lithium battery can be accurately estimated quantitatively, and at the same time, the loss of the operating lifespan of the lithium battery is naturally incorporated into the objective function, transforming the multi-objective optimization problem into a single-objective optimization problem and reducing the complexity of the nonlinear programming calculation.

[0114] ② Maintenance cost of the battery energy storage unit.

[0115] This part presents a calculation method for the maintenance cost of lithium batteries, which is directly proportional to the absolute value of the charge and discharge power of lithium batteries:

[0116] C Bo (t) = K Bo × |I ch (t)·P ch (t) + I dis (t)·P dis (t)| × Δt (14)

[0117] In the formula: C Bo (t) represents the maintenance cost of the lithium battery within the time period t, and K Bo represents the maintenance cost coefficient of the lithium battery.

[0118] Therefore, when the lithium battery is used as the system battery energy storage unit, its energy cost is expressed as follows:

[0119] C B = C Br (t) + C Bo (t) (15)

[0120] In the formula: C B is the operating cost of the battery energy storage unit.

[0121] Step 2 analyzes the characteristics of different types of loads, divides the controllable loads in the smart park into transferable loads and interruptible loads, and proposes user comfort coefficients related to transferable loads and interruptible loads according to their respective characteristics:

[0122] (1) Load classification and modeling.

[0123] This patent divides the loads into three categories according to the importance of the actual electricity consumption needs of users: important loads, transferable loads, and interruptible loads. Among them, important loads are the loads that must be satisfied under normal operating conditions and cannot be cut off or shifted. They mainly include basic lighting equipment, production equipment, etc.; transferable loads are the loads that can be transferred on the time scale according to the dispatching requirements, that is, the loads in a certain time period are transferred to another time period without changing the total electricity consumption. They mainly include household electric cookers, electric vehicles, etc.; interruptible loads are the loads that can be stopped for a period of time according to the dispatching requirements. They mainly include air conditioners, electric heating equipment, etc.

[0124] The power supply durations of different types of transferable loads are different, and the load powers of the same type of load in each time period during the power supply duration are also different. Therefore, when shifting the load, not only the transferred load quantity and the transferred-out load quantity in the dispatching time period t need to be considered, but also the influence of the transferred-in and transferred-out loads in the previous time period on the load in the dispatching time period t needs to be considered. The transferable load H in the time period tTL (t) expression is:

[0125] H TL H(t) = H TLC (t - 1) + H TLI ( t ) - H TLO ( t ) (16)

[0126] In the formula, H TLC (t - 1) is the load from the (t - 1) period to the t period, H TLI (t) is the transferred-in load in the t period, H TLO (t) is the transferred-out load in the t period.

[0127] Where:

[0128]

[0129]

[0130]

[0131] In the formula: N is the total number of continuous loads in the (t - 1) period, J is the total number of transferred-in loads in the t period, K is the total number of transferred-out loads in the t period, P TLCi_t-1 is the power of the i-th continuous load in the (t - 1) period, P TLIi_t is the power of the i-th transferred-in load in the t period, P TLOi_t is the power of the i-th transferred-out load in the t period.

[0132] When the wind and light fluctuate downward based on the day-ahead prediction value and the upward regulation margin of the controllable distributed power source is insufficient, some interruptible loads need to be cut off at this time to ensure the normal operation of the microgrid. The load H IL (t) to be cut off in each dispatching period is:

[0133] H IL (t) = max{ΔP r_d (t) - f fc_u (t), 0} (20)

[0134] In the formula: ΔP r_d (t) is the power of the wind and light fluctuating downward based on the day-ahead prediction in the t period; f fc_u (t) is the upward regulation margin of the controllable distributed power source (fuel cell) in the t period.

[0135] (2) Definition of comfort coefficient.

[0136] Through research, it is found that most previous studies have taken the incentive electricity price as the main decision-making basis and only optimized the load with the economic cost as the goal. However, it should be noted that the result of pure cost optimization lacks the value of practical application because cutting or transferring the load will surely affect the user comfort. As the main electricity consumers, the comfort and satisfaction of users' electricity consumption are important indicators for evaluating the applicability of the park operation strategy, which will have an important impact on the implementation and promotion of the operation strategy. The optimization result without considering user comfort will have a large deviation. Based on the cost optimization, this patent considers the user's electricity consumption comfort and establishes an influencing factor of electricity consumption comfort.

[0137] For different types of loads, the user comfort requirements are also different. For equipment such as cookers and cars, the user comfort will decrease as the time interval between the load transfer-in and transfer-out extends. When the load transfer-in time is closer to the transfer-out time, the impact on the user's energy consumption is smaller and the user comfort is higher. The user comfort evaluation factor for transferable loads established in this patent The expression is as follows:

[0138]

[0139] In the formula: t s ,t α ,t β are respectively the actual start time, planned start time and planned end time of the transferable load equipment, that is, the start and end times of the normal use of the equipment without considering load transfer. The smaller the value of

[0140] is, the smaller the difference between the load transfer-in time and the transfer-out time, and the better the user energy consumption comfort. Different from transferable loads, interruptible loads only have a cutting process and no transfer-in process. The main factor affecting user comfort is the magnitude of the actual interrupted load power per unit time period. For the same amount of interrupted load, when all the interrupted loads are cut in one time period, the impact on user comfort is the greatest. When all the loads are dispersed and cut in multiple time periods, the interrupted load power in a single time period will decrease and the user comfort will increase. The more evenly distributed the interruptible load is in multiple time periods, the smaller the impact on user comfort. On the contrary, the more concentrated the interruptible load distribution time period is, the lower the user energy consumption comfort. The user comfort evaluation factor φ of interruptible loads IL The expression is as follows:

[0141]

[0142] In the formula: T IL is the total number of time periods with actual interrupted loads during the optimization period, P ILi is the interrupted load power in the i-th time period with interrupted loads, U ILTo optimize the total amount of actual interrupted load within the cycle; The smaller the value, the more evenly the total interrupted load is distributed, and the better the user's energy consumption comfort.

[0143] Step 3 proposes a system optimization strategy, which uses the day-ahead planning method to optimize the power flow in the smart campus. This patent aims to minimize the daily energy cost and optimizes the power flow in the smart campus by setting certain constraints to keep the comfort level and the renewable energy consumption rate within the set range:

[0144] To balance the computational speed and accuracy of the simulation results, this patent uses the day-ahead planning method to optimize the power flow in the smart campus. The focus of the day-ahead plan is to optimize the daily energy cost over 24 hours.

[0145] S2: Based on various influencing factors, construct output power models for distributed power sources and distributed energy storage units with different characteristics in the smart campus.

[0146] (1) Objective function.

[0147] This study aims to optimize the power flow in the smart campus with the daily energy cost as the objective. For the smart campus, the daily energy cost mainly consists of two parts, namely the cost of renewable energy and fuel cells. Therefore, the objective function can be expressed as:

[0148]

[0149] In the formula: C is the daily energy cost of the smart campus, C i is the energy cost of the i-th distributed power source, including the energy cost of new energy generation, the energy cost of distributed fuel cells, and the energy cost of battery energy storage units, P i (t) is the output power of the i-th distributed power source at time t, T is the total number of time periods, M is the number of distributed generators, and ΔT is the planned time period.

[0150] (2) Constraints.

[0151] 1) Power limit condition.

[0152] To improve the reliability of energy supply and avoid system load loss, the power generation must always be greater than the campus electricity demand. In addition, due to the short transmission distance within the campus, transmission losses are ignored. The power limit condition is as follows:

[0153] P pv (t)+P w (t)+P FC (t)+P es (t)=P loads (t) (24)

[0154] Where: P loads (t) is the total load of the smart park at time t.

[0155] 2) Fuel cell operation limit.

[0156] As a typical fossil generator, the fuel cell is restricted by its rated maximum output power and minimum output power, and its output power can be written as:

[0157] P FCmin <P FC (t) < P FCmax (25)

[0158] In addition, the operation of the fuel cell is also restricted by its ramp rate, which can be described as:

[0159] -ΔP FCdmax <P FC (t + 1) - P FC (t) < ΔP FCumax (26)

[0160] Where: ΔP FCdmax is the maximum descent rate of the fuel cell operation, and ΔP FCumax is the maximum ascent rate of the fuel cell operation.

[0161] 3) Battery operation limit.

[0162] To ensure the safe operation of the battery, its charge and discharge power needs to be restricted within a certain range, which can be written as:

[0163] -P escmax <P es (t) < P esdmax (27)

[0164] Where: P escmax is the maximum charging power of the energy storage system, and P esdmax is the maximum discharging power of the energy storage system.

[0165] In addition, to extend the life of the battery, the state of charge of the energy storage system needs to be restricted within a certain range. Therefore, the inequality constraint of the state of charge can be expressed as:

[0166] SOC esmin <SOC es (t) < SOC esmax (28)

[0167] Where: SOC esmin is the lower limit of the state of charge of the energy storage system, and SOC esmax is the upper limit of the state of charge of the energy storage system.

[0168] 4) User comfort limit.

[0169] Considering that the reduction of interruptible load and the movement of shiftable load have a direct impact on the user comfort level, it is necessary to set the threshold of the user comfort level coefficient related to interruptible and shiftable loads:

[0170]

[0171]

[0172] In the formula: is the maximum coefficient of user comfort related to interruptible load, is the maximum coefficient of user comfort related to shiftable load.

[0173] 5) Renewable energy consumption rate limit.

[0174] In order to make full use of the installed capacity of renewable energy generating units and reduce carbon emissions, it is necessary to design the renewable energy consumption rate.

[0175] γ min <γ≤γ max (31)

[0176] In the formula: γ is the renewable energy consumption rate, γ min is the minimum renewable energy consumption rate, γ max is the maximum renewable energy consumption rate.

[0177] Step 4 Based on the above research content, under the proposed equality constraints and inequality constraints, select the particle swarm optimization algorithm to optimize the daily energy cost of the above intelligent park:

[0178] S3: Based on the comfort evaluation factor and the output power model, use the day-ahead planning method to dispatch the power flow in the intelligent park.

[0179] Under the equality constraints and inequality constraints, select the particle swarm optimization algorithm to optimize the daily energy cost of the above intelligent park, and the calculation flow chart is as Figure 2 shown:

[0180] Step (1): Input system coefficients (including the operating parameters of each generator, load, gas price, and renewable energy output);

[0181] Step (2): Set the minimum / maximum renewable energy consumption rate γ min and γ max ;

[0182] Step (3): Initialize the operation parameters of the particle swarm algorithm (particle swarm size, particle dimension);

[0183] Step (4): Based on the constraint conditions of Equations (24-28), and set the objective function of Equation (23) as the fitness value in the particle swarm algorithm. If the termination condition is reached, proceed to the next step; otherwise, restart the steps after initialization in the particle swarm algorithm according to the process.

[0184] Step (5): Determine whether the renewable energy consumption rate meets the set requirements. If it does, proceed to the next step; otherwise, return to Step (3).

[0185] Step (6): Calculate the user comfort level coefficient related to interruptible and shiftable loads, and determine whether the value is within the set interval. If it is satisfied, end; otherwise, return to Step (2).

[0186] Through the flow chart of this particle swarm optimization-based model, on the basis of considering user comfort and meeting a certain renewable energy consumption rate, the daily energy cost of the smart campus can be minimized, achieving economic benefits.

[0187] Embodiment 2:

[0188] This embodiment provides a dispatching and optimization system for a new energy power station in a smart campus, including:

[0189] Load model modeling module: Set the types of loads according to the characteristics of each load in the smart campus, and construct a load model and a comfort evaluation factor according to the characteristics of each type of load.

[0190] Output power modeling module: Based on various influencing factors, construct output power models for distributed power sources and distributed energy storage units with different characteristics in the smart campus.

[0191] Dispatching module: Based on the comfort evaluation factor and the output power model, use the day-ahead planning method to dispatch the power flow in the smart campus.

[0192] The load model modeling module includes:

[0193] Important load modeling sub-module: Set the non-switchable loads and shifted loads that must be supplied during normal operation as important loads, and construct an important load model.

[0194] Shiftable load modeling sub-module: Set the loads whose usage time periods can be shifted but the total usage amount remains unchanged as shiftable loads, and construct a shiftable load model.

[0195] Interruptible load modeling sub-module: Set the loads that can be stopped from being used for a period of time according to dispatching requirements as interruptible loads, and construct an interruptible load model.

[0196] The load model modeling module further includes:

[0197] The first comfort evaluation factor modeling sub-module: construct a transferable load comfort evaluation factor according to the actual usage time and planned usage duration of the transferable load model;

[0198] The second comfort evaluation factor modeling sub-module: construct an interruptible load comfort evaluation factor according to the interruption duration of the interruptible load and the interruptible load power.

[0199] The transferable load comfort evaluation factor constructed in the first comfort evaluation factor modeling sub-module is shown in the following formula:

[0200]

[0201] where is the transferable load comfort evaluation factor, t s is the actual start time of use, t α is the planned start time of use, t β is the planned end time of use.

[0202] The interruptible load comfort evaluation factor constructed in the second comfort evaluation factor modeling sub-module is shown in the following formula:

[0203]

[0204] where φ IL is the interruptible load comfort evaluation factor, P ILi is the interruptible load comfort evaluation factor, Δt is the duration, U IL is the total amount of actual interruptible load within the optimization period, T IL is the total number of time periods when actual interruptible load is generated within the optimization period.

[0205] The output power modeling module includes:

[0206] The objective function construction sub-module: construct an objective function according to the output power of the distributed power source and the total electricity consumption duration of the load model;

[0207] The output power modeling sub-module: construct an output power model based on the comfort constraint conditions of the load model and the consumption rate constraint conditions of renewable energy.

[0208] The objective function constructed in the objective function construction sub-module is shown in the following formula:

[0209]

[0210] where C is the daily energy cost of the smart park, C i$C_{i}$ is the energy cost of the $i$-th distributed power source, including the energy cost of new energy power generation, the energy cost of distributed fuel cells, and the energy cost of battery energy storage units, $P$ i (t) is the output power of the $i$-th distributed power source at time $t$, $T$ is the total number of time periods, $M$ is the number of distributed generators, and $\Delta T$ is the planned time period.

[0211] The comfort constraint conditions in the output power modeling sub-module are as shown in the following formula:

[0212]

[0213]

[0214] Among them, is the actual comfort of the shiftable load under the output power model scheduling; is the maximum comfort of the shiftable load, is the actual comfort of the interruptible load under the output power model scheduling, is the maximum comfort of the interruptible load.

[0215] The scheduling module includes:

[0216] Maximum comfort acquisition sub-module: Based on the comfort evaluation factor of the load model, obtain the maximum comfort of the shiftable load and the maximum comfort of the interruptible load;

[0217] Output power solution sub-module: Take the maximum comfort of the shiftable load and the maximum comfort of the interruptible load as constraint conditions, and solve the output power of the distributed power source and the distributed energy storage unit in the output power model through the particle swarm algorithm;

[0218] Scheduling sub-module: Schedule the output power through the day-ahead planning method and supply power to the load model.

[0219] Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0220] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0221] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce a means for realizing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0222] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means that realizes the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0223] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0224] The above are only embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention pending approval.

Claims

1. A scheduling optimization method for a new energy power station in an intelligent park, characterized in that, Including: Setting types for loads according to the characteristics of each load in the smart park, constructing load models and comfort evaluation factors based on the characteristics of each type of load; Constructing output power models for distributed power sources and distributed energy storage units with different characteristics in the smart park based on various influencing factors; Based on the comfort evaluation factors and output power models, using the day-ahead planning method to dispatch the power flow in the smart park; The setting of load types according to the characteristics of each load in the smart park and the construction of load models based on the characteristics of each type of load include: Setting the non-disconnectable loads and shiftable loads that must be supplied with power under normal operating conditions as important loads and constructing important load models; Setting the loads whose usage time periods can be transferred but the total usage amount remains unchanged as transferable loads and constructing transferable load models; Setting the loads that can be stopped for a period of time according to the dispatching requirements as interruptible loads and constructing interruptible load models; The construction of comfort evaluation factors for each load model includes: Constructing a transferable load comfort evaluation factor according to the actual usage time and planned usage duration of the transferable load model; Constructing an interruptible load comfort evaluation factor according to the interruption duration and interruption load power of the interruptible load; The construction of output power models for distributed power sources and distributed energy storage units with different characteristics in the smart park based on various influencing factors includes: Constructing an objective function according to the output power of the distributed power source and the total power consumption duration of the load model; Constructing an output power model based on the comfort constraint conditions of the load model and the consumption rate constraint conditions of renewable energy.

2. The method according to claim 1, characterized in that The transferable load comfort evaluation factor is shown as follows: Among them, is the evaluation factor of transferable load comfort, and t s is the actual start time of use, tα is the planned start time of use, and t β is the planned end time of use.

3. The method according to claim 1, characterized in that The interruptible load comfort evaluation factor is shown as follows: Among them, φ IL is the comfort evaluation factor of interruptible load, P ILi is the comfort evaluation factor of interruptible load, Δt is the duration, U IL is the total amount of actual interrupted load during the optimization period, T IL is the total number of time periods when actual interrupted load occurs during the optimization period.

4. The method according to claim 1, wherein The objective function is shown as follows: Among them, C is the daily energy cost of the intelligent park, C i is the energy cost of the i-th distributed power source, including the energy cost of new energy power generation, the energy cost of distributed fuel cells, and the energy cost of battery energy storage units, P i (t) is the output power of the i-th distributed power source at time t, T is the total number of time periods, M is the number of distributed generators, and ΔT is the planned time period.

5. The method according to claim 1, wherein The comfort constraint conditions are shown as follows: Among them, is the actual comfort level of the shiftable load under the output power model scheduling; is the maximum value of the comfort level of the shiftable load, is the actual comfort level of the interruptible load under the output power model scheduling, is the maximum value of the comfort level of the interruptible load.

6. The method according to claim 5, characterized in that, The method of using day-ahead planning to dispatch the power flow in the smart park based on the comfort evaluation factors and output power models includes: Based on the comfort evaluation factors of the load model, obtaining the maximum transferable load comfort value and the maximum interruptible load comfort value; Taking the maximum transferable load comfort value and the maximum interruptible load comfort value as constraint conditions, and solving the output powers of the distributed power source and the distributed energy storage unit in the output power model through the particle swarm optimization algorithm; Dispatching the output power through the day-ahead planning method and supplying power to the load model.

7. An intelligent park new energy power station dispatching optimization system, characterized in that The system includes: Load model modeling module: Setting types for loads according to the characteristics of each load in the smart park, constructing load models and comfort evaluation factors based on the characteristics of each type of load; Output power modeling module: Constructing output power models for distributed power sources and distributed energy storage units with different characteristics in the smart park based on various influencing factors; Dispatching module: Based on the comfort evaluation factors and output power models, using the day-ahead planning method to dispatch the power flow in the smart park; The load model modeling module includes: Important load modeling sub-module: Set the non-disconnectable load and shiftable load that must be powered during normal operation as important loads, and construct an important load model; Transferable load modeling sub-module: Set the load whose usage period is transferable but the total load usage remains unchanged as a transferable load, and construct a transferable load model; Interruptible load modeling sub-module: Set the load that can be stopped from being used for a period according to the scheduling requirements as an interruptible load, and construct an interruptible load model; The load model modeling module further includes: First comfort evaluation factor modeling sub-module: Construct a comfort evaluation factor for the transferable load according to the actual usage time and planned usage duration of the transferable load model; Second comfort evaluation factor modeling sub-module: Construct a comfort evaluation factor for the interruptible load according to the interruption duration and interruption load power of the interruptible load; The output power modeling module includes: Objective function construction sub-module: Construct an objective function according to the output power of the distributed power source and the total power consumption duration of the load model; Output power modeling sub-module: Construct an output power model based on the comfort constraint conditions of the load model and the consumption rate constraint conditions of renewable energy.

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