A method and device for optimizing and dispatching a comprehensive energy system

Abstract

The present invention discloses a method and device for optimizing and scheduling an integrated energy system. The method includes obtaining the performance parameters, hourly operating parameters, and hourly demand values ​​of energy-consuming equipment in the integrated energy system within a period to be scheduled; constructing a dynamic programming model, wherein the decision optimization goal of the dynamic programming model is to minimize the total operating cost of each time period within the scheduling period, and its state transfer equation is determined based on the performance parameters, hourly operating parameters, and hourly demand values; optimizing the dynamic programming model to determine the scheduling plan for the integrated energy system. The present invention takes into account the changes in the performance parameters of the heat pump with the operating conditions and the coupled utilization of the heat pump, energy storage, etc., and solves the problem of underground soil thermal imbalance caused by the ground source heat pump in the coupled energy system. The present invention utilizes a genetic algorithm based on dynamic programming to solve the energy system scheduling problem, taking into account the global optimization and rapidity of solving the scheduling problem, and can make the system energy-efficient and operate stably for a long time.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system operation optimization, and specifically relates to an integrated energy system optimization scheduling method and device. Background Art

[0002] At present, achieving energy diversification and alleviating dependence and constraints on limited mineral energy are one of the important measures for my country's energy development strategy and energy structure adjustment. Heat pump is a device that can convert low-level heat energy that cannot be directly used into high-level heat energy that can be used. As one of the main devices for providing heat, it has been widely used in many fields for its environmental friendliness and energy conservation. Different types of heat pumps have their own advantages and disadvantages in application due to the different forms of energy used. For example, the heat of air source heat pump is easy to obtain, the system installation is less affected by the site, the installation is simple, and the system structure is simple, but the unit efficiency is low, and the efficiency is greatly affected by the outdoor temperature. The operation efficiency is low on cold days, and it may even not be used normally; the heat required by the ground source heat pump system is provided by the underground rock and soil layer. The system is not as susceptible to ambient temperature as the air source heat pump, but it is prone to underground soil thermal imbalance; the water source heat pump operates stably and reliably, has good heat exchange effect, high heat exchange efficiency, and small seasonal fluctuations, but it is easy to cause pollution and waste of water resources, has high requirements for water quality, and has a large initial investment.

[0003] Taking into account the complementarity of various types of heat pumps, the academic and engineering communities have conducted a large number of attempts to apply a combination of multiple heat pumps. The integrated energy system coupled with multiple heat pumps is currently widely used in scenarios such as parks and buildings. However, due to the lack of a complete optimization and scheduling strategy, the operation of the system is often based on manual experience, and it is difficult to maintain the optimal operating state. It is impossible to ensure efficient energy saving and long-term stable operation of the entire system, which leads to an increase in operating costs. Summary of the invention

[0004] The present invention provides a method and device for optimizing and dispatching an integrated energy system, thereby solving the technical problem that the existing integrated energy system cannot achieve high efficiency, energy saving and long-term stable operation.

[0005] The first aspect of the present invention discloses a method for optimizing and scheduling an integrated energy system, comprising:

[0006] Obtain the performance parameters, hourly operating parameters and hourly demand values ​​of energy-consuming equipment in the integrated energy system within the scheduling period;

[0007] Constructing a dynamic programming model, wherein the decision optimization goal of the dynamic programming model is to minimize the total operating cost of each time period within the scheduling cycle, and the state transfer equation of the dynamic programming model is determined according to the performance parameters, the hourly operating parameters and the hourly demand value;

[0008] The dynamic programming model is optimized to determine a scheduling plan for the integrated energy system.

[0009] Preferably, the energy supply equipment includes a heat storage device, a cold storage device and a heat pump unit;

[0010] The heat storage device and the cold storage device are both connected to the heat pump unit;

[0011] The heat pump unit includes a plurality of heat pumps, and the heat pump is at least one of an air source heat pump, a water source heat pump and a ground source heat pump.

[0012] Preferably, the state transfer equation of the dynamic programming model includes a state transfer equation corresponding to the heat storage device and a state transfer equation corresponding to the cold storage device;

[0013] The state transfer equation corresponding to the heat storage device is as shown in the first formula, and the first formula is:

[0014]

[0015] In the formula, is the heat storage capacity of the heat storage device at the end of time period t within the scheduling period, is the heat storage capacity of the heat storage device at the end of time period t-1 in the scheduling cycle, δ h is the dissipation coefficient of the heat storage device, is the heat storage efficiency of the heat storage device, is the heat release efficiency of the heat storage device, Δt is the duration of the t period in the scheduling cycle, V t h Determined according to the second formula, the second formula is:

[0016]

[0017] In the formula, is the output value of the heating capacity of the ith heat pump in the heat pump unit during period t, M HP is the total number of heat pumps in the heat pump unit, is the heat load demand value of the energy-consuming equipment during period t;

[0018] The state transfer equation corresponding to the cold storage device is as shown in the third formula, and the third formula is:

[0019]

[0020] In the formula, is the cold storage capacity of the cold storage device at the end of time period t within the scheduling cycle, is the cold storage capacity of the cold storage device at the end of period t-1 in the scheduling cycle, δ c is the dissipation coefficient of the cold storage device, is the cold storage efficiency of the cold storage device, is the cooling efficiency of the cold storage device, Δt is the duration of the t period in the scheduling cycle, V t c Determined according to the fourth formula, the fourth formula is:

[0021]

[0022] In the formula, is the output value of the cooling capacity of the i-th heat pump in the heat pump unit during period t, M HP is the total number of heat pumps in the heat pump unit, is the cooling load demand value of the energy-consuming equipment during period t.

[0023] Preferably, the is determined according to the fifth formula, which is:

[0024]

[0025] In the formula, is the input power of the i-th heat pump in the heat pump unit during period t, is the heating coefficient of the i-th heat pump in the heat pump unit in period t, and the heating coefficient is determined according to the operating parameters of the i-th heat pump;

[0026] Said is determined according to the sixth formula, which is:

[0027]

[0028] In the formula, is the cooling input power of the i-th heat pump in the heat pump unit during period t, is the refrigeration coefficient of the i-th heat pump in the heat pump unit in period t, and the refrigeration coefficient is determined according to the operating parameters of the i-th heat pump.

[0029] Preferably, when there is a ground source heat pump in the heat pump, the state transfer equation of the dynamic programming model also includes a state transfer equation corresponding to the ground source heat pump;

[0030] The state transfer equation corresponding to the ground source heat pump is determined according to the seventh formula, which is:

[0031]

[0032] In the formula, is the total amount of heat extracted from the soil or released to the soil by the ground-source heat pump at the end of time period t within the scheduling cycle, is the total amount of heat extracted from the soil or released to the soil by the ground-source heat pump at the end of period t-1 within the scheduling cycle, is the output value of the ground source heat pump heating capacity during period t, is the output value of the ground source heat pump cooling capacity during period t, COP t GHP,h is the heating coefficient of the ground source heat pump during period t, COP t GHP,c is the cooling coefficient of the ground source heat pump during period t.

[0033] Preferably, the The constraint conditions that need to be satisfied are as shown in the eighth formula, which is:

[0034]

[0035]

[0036] In the formula, and are the optimal output reference values ​​of the ground source heat pump on the dispatching day d, and The lower and upper limit adjustment coefficients of the output of the ground source heat pump on the dispatch day d when heating. and They are the lower limit adjustment coefficient and upper limit adjustment coefficient of the output of the ground source heat pump on the dispatch day d, and are the total amount of heat extracted from the soil (heating) or released to the soil (cooling) by the ground source heat pump on the scheduling day d, and are the total amount of heat extracted from the soil (heating) or released to the soil (cooling) by the ground source heat pump on the scheduling day d when the t period is the last period T in the heating or cooling mode. The corresponding form.

[0037] Preferably, the energy supply device further includes a power storage device, and accordingly, the state transfer equation of the dynamic programming model further includes a state transfer equation corresponding to the power storage device;

[0038] The state transfer equation corresponding to the power storage device is as shown in the ninth formula, and the ninth formula is:

[0039]

[0040] In the formula, SOC t The state of charge of the storage device at the end of time period t in the scheduling cycle, SOC t-1 The state of charge of the storage device at the end of time period t-1 within the scheduling cycle, is the charging efficiency of the power storage device, is the discharge efficiency of the power storage device, E r is the rated capacity of the storage device, V t e Determined according to the tenth formula, the tenth formula is:

[0041]

[0042] In the formula, and are the interactive power of the external power grid connected to the power storage device during period t, the photovoltaic power generation power and the wind turbine power generation power, respectively. is the electric load demand value of the energy-consuming equipment in period t, is the input power of the ith heat pump in the heat pump unit during period t, M HP is the total number of heat pumps in the heat pump unit.

[0043] Preferably, the decision optimization target of the dynamic programming model is as in the eleventh formula, and the eleventh formula is:

[0044]

[0045] In the formula, is the maintenance cost of the energy output per unit of the i-th energy supply equipment, is the energy output of the ith energy supply device in period t, Ep t is the electricity price in period t, is the amount of electricity purchased during period t, X t is a decision variable and It is the total amount of heat extracted from the soil or released to the soil by the ground source heat pump during the scheduling period. is the heat storage capacity of the heat storage device at the end of time period t within the scheduling period, is the cold storage capacity of the cold storage device at the end of time period t in the scheduling cycle, SOC t The state of charge of the storage device at the end of time period t within the scheduling cycle.

[0046] Preferably, optimizing the dynamic programming model specifically includes:

[0047] The dynamic programming model is optimized using a genetic algorithm.

[0048] The second aspect of the present invention discloses a comprehensive energy system optimization scheduling device, comprising:

[0049] An information acquisition module, which is used to obtain performance parameters, hourly operating parameters and hourly demand values ​​of energy-consuming equipment in the integrated energy system within the scheduling period;

[0050] A model building module, the model building module is used to build a dynamic programming model, the decision optimization goal of the dynamic programming model is to minimize the total operating cost of each time period in the scheduling cycle, and the state transfer equation of the dynamic programming model is determined according to the performance parameters, the hourly operating parameters and the hourly demand value;

[0051] A scheduling scheme determination module is used to optimize the dynamic programming model and determine the scheduling scheme of the integrated energy system.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The present invention proposes an integrated energy system optimization scheduling method and device, which are particularly suitable for an integrated energy system coupled with multiple heat pumps.

[0054] The method of the present invention fully considers the changes in heat pump performance parameters with operating conditions, as well as the coupled utilization of photovoltaics, heat pumps, energy storage, etc., and is closer to the actual situation. When there is a ground-source heat pump, the cold and heat balance operating constraints of the ground-source heat pump are fully considered, and the optimal output plan is solved by the ground-source heat pump daily output optimization model. The optimal output plan constraints of the ground-source heat pump are established, and the problem of underground soil thermal imbalance is avoided. Furthermore, the present invention proposes a genetic algorithm based on dynamic programming to solve the comprehensive energy scheduling problem of multiple heat pump coupling, which takes into account the global optimization and rapidity of solving the scheduling problem, can reduce the system operating costs, and better guide the economic, efficient, and environmentally friendly operation of the multi-heat pump coupled comprehensive energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Attached Figure 1 A flow chart of the integrated energy system optimization scheduling method of the present invention;

[0056] Attached Figure 2 It is a structural schematic diagram of the comprehensive energy system optimization scheduling device of the present invention.

[0057] In the figure, 101 is an information acquisition module, 102 is a model building module, and 103 is a scheduling solution determination module. DETAILED DESCRIPTION

[0058] The technical scheme of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation cases. It should be understood that the following embodiments are only exemplary descriptions and explanations of the present invention and should not be construed as limiting the scope of protection of the present invention. All technologies implemented based on the above content of the present invention are included in the scope that the present invention is intended to protect.

[0059] The first aspect of the present invention discloses a method for optimizing and dispatching an integrated energy system. Figure 1 As shown, including:

[0060] Step 1: Obtain the performance parameters, hourly operating parameters and hourly demand values ​​of energy-consuming equipment in the integrated energy system within the scheduling period.

[0061] The energy supply equipment in the comprehensive energy system of the present invention may only include a heat storage device and a cold storage device connected to a heat pump unit; wherein the heat pump unit includes multiple heat pumps, and the heat pump is at least one of an air source heat pump, a water source heat pump and a ground source heat pump.

[0062] The hourly output model of the heat pump in the above heat pump unit is as follows:

[0063] Heating mode:

[0064]

[0065] Cooling mode:

[0066]

[0067] In the formula, is the input power of the heat pump unit for heating and cooling during period t, in kW; is the heating and cooling output of the heat pump unit during period t, in kW; is the heating and cooling performance coefficient of the heat pump unit during period t. Among them, the COP of the heat pump unit is affected by various operating parameters, such as the load rate p t , water supply temperature T t 0 , ambient temperature T t e etc., therefore,

[0068] COP t =f(p t ,T t 0 ,T t e ) (3)

[0069] As an example of implementation, the coefficient of performance of an air source heat pump can be fitted to a second-order polynomial by data fitting:

[0070]

[0071] For ground source heat pumps, their performance coefficient is less affected by ambient temperature and can be simplified as

[0072]

[0073] The hourly output model of the above heat storage device and cold storage device is:

[0074] Heat storage device:

[0075]

[0076] Cold storage device:

[0077]

[0078] In the formula, They are the heat storage capacity of the heat storage device and the cold storage capacity of the cold storage device at the end of period t, in kWh; and are the heat storage power and heat release power of the heat storage device during period t, in kW; and are the storage power and cooling power of the cold storage device during period t, in kW; δ h and δ c are the dissipation coefficients of the heat storage device and the cold storage device respectively; and are the cold storage efficiency and cooling efficiency of the cold storage device, and are the heat storage efficiency and heat release efficiency of the heat storage device respectively.

[0079] The energy supply equipment in the comprehensive energy system of the present invention may also include a heat storage device, a cold storage device and an electric storage device. The electric storage device is connected to an external power grid, a photovoltaic power generation device and a wind power generation device. The heat storage device and the cold storage device are both connected to a heat pump unit, and the heat pump unit includes a plurality of heat pumps, and the heat pump is at least one of an air source heat pump, a water source heat pump and a ground source heat pump.

[0080] When the storage device is included, the hourly output model of the battery is expressed as:

[0081]

[0082] In the formula, SOC t is the state of charge of the storage device at the end of period t; are the charging power and discharging power of the storage device during period t, in kW; E is the charging efficiency and discharging efficiency of the power storage device respectively; r is the rated capacity of the storage device.

[0083] The operation of the integrated energy system not only meets the constraints of the above equipment output model, but also needs to meet the constraints of cooling, heating and electricity power balance, equipment output, energy storage device storage and release, and transmission capacity constraints with the external power grid. The constraints are expressed as follows:

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] SOC min ≤SOC t ≤SOC max (twenty one)

[0097]

[0098] In the formula, are the electricity, heating and cooling load demand values ​​in period t respectively; They are the interaction power between the integrated energy system and the external power grid, photovoltaic power generation and wind turbine power generation during period t respectively; are the upper and lower limits of the output of heat pump i respectively; They are the upper and lower limits of heat storage power, heat release power and heat storage capacity of the heat storage device respectively; They are the upper and lower limits of the cold storage power, the upper and lower limits of the cold release power, and the upper and lower limits of the cold storage capacity of the cold storage device; SOC max , SOC min They are the upper and lower limits of battery charging power, upper and lower limits of discharging power, and upper and lower limits of state of charge; It is the maximum upper limit of electricity selling capacity and electricity buying capacity for the interaction between the integrated energy system and the external power grid.

[0099] In order to ensure that the dispatch result of this dispatch cycle does not affect the next dispatch cycle, the energy storage state of the energy storage equipment at the end of the dispatch cycle is required to be restored to the vicinity of the initial value of the dispatch cycle, that is:

[0100]

[0101]

[0102] -ΔSOC≤SOC T -SOC0≤ΔSOC (25)

[0103] In the formula, ΔS h , ΔS c , ΔSOC are the absolute values ​​of deviations allowed by the heat storage, cold storage and electrical storage devices at the beginning and end of the scheduling period; T is the last period in the scheduling cycle.

[0104] The hourly demand value of the energy-consuming equipment in the comprehensive energy system of the present invention is determined based on factors such as weather and day type within the scheduling period.

[0105] The waiting-for-scheduling period in the present invention may be several hours, one day, several days, several months, etc.

[0106] Step 2: Construct a dynamic programming model. The decision optimization goal of the dynamic programming model is to minimize the total operating cost in each period within the scheduling cycle. The state transfer equation of the dynamic programming model is determined based on performance parameters, hourly operating parameters and hourly demand values.

[0107] The present invention adopts a forward dynamic programming method to transform the integrated energy system scheduling problem into a multi-stage decision-making problem and establishes a dynamic programming model for the integrated energy system.

[0108] Aiming at the optimization scheduling problem of integrated energy system, the decision-making stages of the scheduling problem are divided according to the time domain.

[0109] In the integrated energy system optimization scheduling method, the scheduling target is to minimize the system operation cost. The specific function is expressed as:

[0110]

[0111] Where F represents the total cost of system operation, including equipment operation and maintenance costs and external power costs. T is the last period of the scheduling cycle, N is the total number of devices in the integrated energy system, is the operating cost corresponding to the energy output per unit of equipment i, is the energy output of device i in period t, Ep t , are the electricity price and electricity purchase quantity in period t respectively.

[0112] Based on formula (26), the decision optimization goal of the dynamic programming model of the present invention is to minimize the total operating cost of each period in the scheduling cycle, specifically:

[0113]

[0114] In the formula, is the maintenance cost of the energy output per unit of the i-th energy supply equipment, is the energy output of the ith energy supply device in period t, Ep t is the electricity price in period t, is the amount of electricity purchased during period t, X t is a decision variable and in, is the total amount of heat extracted from the soil or released to the soil by the ground source heat pump during the scheduling period T. is the heat storage capacity of the heat storage device at the end of time period t within the scheduling period, is the cold storage capacity of the cold storage device at the end of time period t in the scheduling cycle, SOC t The state of charge of the storage device at the end of time period t within the scheduling cycle.

[0115] When the energy supply equipment only includes a heat storage device and a cold storage device connected to the heat pump unit, the state transfer equation of the dynamic programming model includes a state transfer equation corresponding to the heat storage device and a state transfer equation corresponding to the cold storage device;

[0116] The state transfer equation corresponding to the heat storage device is as follows:

[0117]

[0118] In the formula, is the heat storage capacity of the heat storage device at the end of time period t within the scheduling period, is the heat storage capacity of the heat storage device at the end of time period t-1 in the scheduling cycle, δ h is the dissipation coefficient of the heat storage device, is the heat storage efficiency of the heat storage device, is the heat release efficiency of the heat storage device, Δt is the duration of the t period in the scheduling cycle, V t h As shown in formula (29):

[0119]

[0120] In the formula, is the output value of the heating capacity of the ith heat pump in the heat pump unit during period t, M HP is the total number of heat pumps in the heat pump unit, is the heat load demand value of the energy-consuming equipment during period t;

[0121] In formula (29) As shown in formula (30):

[0122]

[0123] In the formula, is the input power of the i-th heat pump in the heat pump unit during period t, is the heating coefficient of the i-th heat pump in the heat pump unit during period t. The heating coefficient is determined based on the operating parameters of the i-th heat pump.

[0124] The state transfer equation corresponding to the cold storage device of the present invention is as follows:

[0125]

[0126] In the formula, is the cold storage capacity of the cold storage device at the end of time period t within the scheduling cycle, is the cold storage capacity of the cold storage device at the end of period t-1 in the scheduling cycle, δ c is the dissipation coefficient of the cold storage device, is the cold storage efficiency of the cold storage device, is the cooling efficiency of the cold storage device, Δt is the duration of the t period in the scheduling cycle, V t c As shown in formula (32):

[0127]

[0128] In the formula, is the output value of the cooling capacity of the i-th heat pump in the heat pump unit during period t, M HP is the total number of heat pumps in the heat pump unit, is the cooling load demand value of the energy-consuming equipment during period t.

[0129] In formula (32), It is determined according to formula (33):

[0130]

[0131] In the formula, is the cooling input power of the i-th heat pump in the heat pump unit during period t, is the refrigeration coefficient of the i-th heat pump in the heat pump unit during period t. The refrigeration coefficient is determined based on the operating parameters of the i-th heat pump.

[0132] When there is a ground source heat pump in the heat pump, the state transfer equation of the dynamic programming model also includes the state transfer equation corresponding to the ground source heat pump;

[0133] The state transfer equation corresponding to the ground source heat pump is as follows:

[0134]

[0135] In the formula, is the total amount of heat extracted from the soil or released to the soil by the ground-source heat pump at the end of time period t within the scheduling cycle, is the total amount of heat extracted from the soil or released to the soil by the ground-source heat pump at the end of period t-1 within the scheduling cycle, is the output value of the ground source heat pump heating capacity during period t, is the output value of the ground source heat pump cooling capacity during period t, COP t GHP,h is the heating coefficient of the ground source heat pump during period t, COP t GHP,c is the cooling coefficient of the ground source heat pump during period t.

[0136] For the ground source heat pump system, in addition to the constraints of the above formulas 1 to 25, the dynamic balance of heat release and heat absorption of the soil in the cooling season and the heating season should be ensured to avoid the problem of thermal imbalance of the underground soil. Therefore, the total output of the ground source heat pump in the scheduling period should meet the optimal output plan constraint, that is, And the constraints that need to be satisfied are as follows:

[0137]

[0138]

[0139] In the formula, and are the total amount of heat extracted from the soil (heating) or released to the soil (cooling) by the ground source heat pump on the scheduling day d, and are the total amount of heat extracted from the soil (heating) or released to the soil (cooling) by the ground source heat pump on the scheduling day d when the t period is the last period T in the heating or cooling mode. The corresponding form, and The lower and upper limit adjustment coefficients of the output of the ground source heat pump on the dispatch day d when heating. and They are the lower limit adjustment coefficient and upper limit adjustment coefficient of the output of the ground source heat pump on the dispatch day d. The above coefficients can be set according to the weather on the dispatch day and the predicted total load demand. and are the optimal output reference values ​​of the ground source heat pump on the dispatch day d, which can be obtained based on the following daily output optimization model based on all constraints of formulas 1 to 25 and formula 35:

[0140]

[0141]

[0142]

[0143]

[0144] Where N h , N c They are the number of remaining heating and cooling typical cycle types from the waiting scheduling cycle to the end of the entire heating and cooling cycle (typical year); They are the remaining days corresponding to the dth typical heating or cooling cycle in the interval from the waiting scheduling period to the end of the entire heating and cooling cycle (typical year); They are the sum of heat and cold actually extracted from the soil by the ground source heat pump from the beginning of the corresponding cooling and heating cycle to the day before the scheduled cycle; D h , D c The above model optimizes the daily output with the goal of minimizing the operating cost of the entire cooling and heating cycle (typical year). To solve the above model, in order to improve the calculation efficiency, the performance coefficient of the heat pump can be approximated as a linear function of the ambient temperature.

[0145] Preferably, when the energy supply device further includes a power storage device, accordingly, the state transfer equation of the dynamic programming model further includes a state transfer equation corresponding to the power storage device;

[0146] The state transfer equation corresponding to the power storage device is as follows:

[0147]

[0148] In the formula, SOC t The state of charge of the storage device at the end of time period t in the scheduling cycle, SOC t-1 The state of charge of the storage device at the end of time period t-1 within the scheduling cycle, is the charging efficiency of the power storage device, is the discharge efficiency of the power storage device, E r is the rated capacity of the storage device, V t e As shown in formula (41):

[0149]

[0150] In the formula, and are the interactive power of the external power grid connected to the power storage device during period t, the photovoltaic power generation power, and the wind turbine power generation power, respectively. is the electric load demand value of the energy-consuming equipment in period t, is the input power of the ith heat pump in the heat pump unit during period t, M HP is the total number of heat pumps in the heat pump unit.

[0151] When the power storage device and the heat pump include a ground source heat pump, the state variables in the dynamic programming model of the present invention select the energy storage state of the energy storage device at the end of each time period and the cumulative output state of the ground source heat pump on the same day, which is expressed as: The decision variables are the controllable power supply, the output of cold and hot sources, and the interactive power between the microgrid and the external network in each period, which can be expressed as:

[0152] Since the state variable can take values ​​in a continuous interval, it is necessary to discretize the state variable space when applying dynamic programming. In order to improve the optimization effect near the near-optimal solution, the present invention discretizes the state variable space based on the normal distribution random sequence. For example, given a mean μ∈(0,1), use the central limit theorem to generate K random numbers x in the interval [0,1] k ~N(μ,σ 2 ), σ is recommended to be 0.4. During the generation process, if the generated number is outside the interval [0,1], it will be discarded until the number in the interval reaches K. Then K states are generated by formula (42) That is, it is the available set of its state variable space, and the same is true for other state variables.

[0153]

[0154] Step 3: Use genetic algorithm to optimize the dynamic programming model and determine the scheduling plan for the integrated energy system.

[0155] The steps of the genetic algorithm based on dynamic programming include:

[0156] Step 1: Initialize the discrete number of states K, the initial state X0, the initial population number, the maximum number of iterations, the selection probability and the mutation probability of the integrated energy dynamic programming model; the optimization variables of the genetic algorithm include the discretized mean of the state variables in each period T (corresponding to ) and the state variable values ​​during the period t = T

[0157] Step 2: Encode all optimization variables in decimal form and randomly generate the initial population under the constraints of the upper and lower limits of each variable;

[0158] Step 3: For the discretized mean corresponding to each individual in the population, discretize the state variable space of each stage based on the normal distribution random sequence, and solve the scheduling problem based on dynamic programming in the corresponding available set. If there is a solution, record the obtained optimization solution U t , X t and the corresponding J T (X T );

[0159] Step 4: Compare the J corresponding to each individual T (X T ), save the optimal solution and the corresponding optimization scheduling plan, and update the discretization mean for the individual that obtains the optimization plan Where α is a random number, α∈[0,1], is the state variable X of the optimization solution tThe corresponding original random number value.

[0160] Step 5: Perform selection, crossover, or mutation operations;

[0161] Step 6: Determine whether the termination condition is reached. If so, output the optimal solution and the corresponding optimized scheduling scheme; if not, return to Step 3.

[0162] In Step 3, the steps for solving the scheduling problem based on dynamic programming are as follows:

[0163] Step 3-1: t = 1, J0(X0) = 0;

[0164] Step 3-2: For each X in the available set of the state variable space at stage t t , under the conditions of satisfying the state transition equation and the operating constraints of the integrated energy system, use the method of preferentially allocating from high to low energy efficiency to solve for X t-1 →X t corresponding If no feasible and can be found, then set this value to a very large number M;

[0165] Step 3-3: Calculate J t (X t );

[0166] Step 3-4: If t < T, then t = t + 1, return to Step 3-2; otherwise, output J T (X T ) and the corresponding optimized scheme U t and X t .

[0167] The second aspect of the present invention discloses an integrated energy system optimized scheduling device, the structure of which is shown in Figure 2 , including an information acquisition module 101, a model construction module 102, and a scheduling scheme determination module 103.

[0168] Among them, the information acquisition module 101 is used to obtain the performance parameters, hourly operation parameters of the energy supply equipment in the integrated energy system during the to-be-scheduled period, and the hourly demand values of the energy-consuming equipment;

[0169] The model construction module 102 is used to construct a dynamic programming model. The decision optimization objective of the dynamic programming model is to minimize the total operating cost in each period during the scheduling period. The state transition equation of the dynamic programming model is determined according to the performance parameters, hourly operation parameters, and hourly demand values;

[0170] The scheduling scheme determination module 103 is used to optimize the dynamic programming model to determine the scheduling scheme of the integrated energy system.

[0171] The method and device of the present invention fully consider the changes in heat pump performance parameters with operating conditions, as well as the coupled utilization of photovoltaics, heat pumps, energy storage, etc., and are closer to the actual situation. When there is a ground-source heat pump, the cold and heat balance operating constraints of the ground-source heat pump are fully considered, and the optimal output plan is solved by the ground-source heat pump daily output optimization model. The optimal output plan constraints of the ground-source heat pump are established, and the problem of underground soil thermal imbalance is avoided. Furthermore, the present invention proposes a genetic algorithm based on dynamic programming to solve the comprehensive energy scheduling problem of multiple heat pump coupling, which takes into account the global optimization and rapidity of solving the scheduling problem, can reduce the system operating costs, and better guide the economic, efficient, and environmentally friendly operation of the multi-heat pump coupled comprehensive energy system.

Claims

1. A method for optimizing and dispatching a comprehensive energy system, characterized in that: include: Obtain the performance parameters, hourly operating parameters and hourly demand values ​​of energy-consuming equipment in the integrated energy system within the scheduling period; Constructing a dynamic programming model, wherein the decision optimization goal of the dynamic programming model is to minimize the total operating cost of each time period within the scheduling cycle, and the state transfer equation of the dynamic programming model is determined according to the performance parameters, the hourly operating parameters and the hourly demand value; Optimizing the dynamic programming model to determine a scheduling plan for the integrated energy system; The energy supply equipment includes a heat storage device, a cold storage device and a heat pump unit; The heat storage device and the cold storage device are both connected to the heat pump unit; The heat pump unit includes a plurality of heat pumps, and the heat pump is at least one of an air source heat pump, a water source heat pump and a ground source heat pump; The state transfer equation of the dynamic programming model includes a state transfer equation corresponding to the heat storage device and a state transfer equation corresponding to the cold storage device; The state transfer equation corresponding to the heat storage device is as shown in the first formula, and the first formula is: In the formula, is the heat storage capacity of the heat storage device at the end of time period t within the scheduling period, is the heat storage capacity of the heat storage device at the end of time period t-1 in the scheduling cycle, δ h is the dissipation coefficient of the heat storage device, is the heat storage efficiency of the heat storage device, is the heat release efficiency of the heat storage device, Δt is the duration of the t period in the scheduling cycle, V t h Determined according to the second formula, the second formula is: In the formula, is the output value of the heating capacity of the ith heat pump in the heat pump unit during period t, M HP is the total number of heat pumps in the heat pump unit, is the heat load demand value of the energy-consuming equipment during period t; The state transfer equation corresponding to the cold storage device is as shown in the third formula, and the third formula is: In the formula, is the cold storage capacity of the cold storage device at the end of time period t within the scheduling cycle, is the cold storage capacity of the cold storage device at the end of period t-1 in the scheduling cycle, δ c is the dissipation coefficient of the cold storage device, is the cold storage efficiency of the cold storage device, is the cooling efficiency of the cold storage device, Δt is the duration of the t period in the scheduling cycle, V t c Determined according to the fourth formula, the fourth formula is: In the formula, is the output value of the cooling capacity of the i-th heat pump in the heat pump unit during period t, M HP is the total number of heat pumps in the heat pump unit, is the cooling load demand value of the energy-consuming equipment during period t; When the heat pump includes a ground source heat pump, the state transfer equation of the dynamic programming model also includes a state transfer equation corresponding to the ground source heat pump; The state transfer equation corresponding to the ground source heat pump is determined according to the seventh formula, which is: In the formula, is the total amount of heat extracted from the soil or released to the soil by the ground-source heat pump at the end of time period t within the scheduling cycle, is the total amount of heat extracted from the soil or released to the soil by the ground-source heat pump at the end of period t-1 within the scheduling cycle, is the output value of the ground source heat pump heating capacity during period t, is the output value of the ground source heat pump cooling capacity during period t, COP t GHP,h is the heating coefficient of the ground source heat pump during period t, COP t GHP,c is the cooling coefficient of the ground source heat pump during period t; The decision optimization target of the dynamic programming model is as shown in the eleventh formula, and the eleventh formula is: In the formula, is the maintenance cost of the energy output per unit of the i-th energy supply equipment, is the energy output of the ith energy supply device in period t, Ep t is the electricity price in period t, is the amount of electricity purchased during period t, X t is a decision variable and It is the total amount of heat that the ground source heat pump extracts from the soil or releases to the soil during the scheduling period. is the heat storage capacity of the heat storage device at the end of time period t within the scheduling period, is the cold storage capacity of the cold storage device at the end of time period t in the scheduling cycle, SOC t The state of charge of the storage device at the end of time period t within the scheduling cycle.

2. The method according to claim 1, characterized in that: Said is determined according to the fifth formula, which is: In the formula, is the input power of the i-th heat pump in the heat pump unit during period t, is the heating coefficient of the i-th heat pump in the heat pump unit in period t, and the heating coefficient is determined according to the operating parameters of the i-th heat pump; Said is determined according to the sixth formula, which is: In the formula, is the cooling input power of the i-th heat pump in the heat pump unit during period t, is the refrigeration coefficient of the i-th heat pump in the heat pump unit in period t, and the refrigeration coefficient is determined according to the operating parameters of the i-th heat pump.

3. The method according to claim 1, characterized in that: Said The constraint conditions that need to be satisfied are as shown in the eighth formula, which is: In the formula, and are the optimal output reference values ​​of the ground source heat pump on the dispatching day d, and The lower and upper limit adjustment coefficients of the output of the ground source heat pump on the dispatch day d when heating. and They are the lower limit adjustment coefficient and upper limit adjustment coefficient of the output of the ground source heat pump on the dispatch day d, and are the total amount of heat extracted from or released to the soil by the ground source heat pump on the scheduling day d, and are the total amount of heat removed from or released to the soil by the ground source heat pump on the scheduling day d when the t period is the last period T in the heating or cooling mode on the scheduling day d. The corresponding form.

4. The method according to claim 1, characterized in that: The energy supply device also includes a power storage device, and accordingly, the state transfer equation of the dynamic programming model also includes a state transfer equation corresponding to the power storage device; The state transfer equation corresponding to the power storage device is as shown in the ninth formula, and the ninth formula is: In the formula, SOC t The state of charge of the storage device at the end of time period t in the scheduling cycle, SOC t-1 The state of charge of the storage device at the end of time period t-1 within the scheduling cycle, is the charging efficiency of the power storage device, is the discharge efficiency of the power storage device, E r is the rated capacity of the storage device, V t e Determined according to the tenth formula, the tenth formula is: In the formula, and are the interactive power of the external power grid connected to the power storage device during period t, the photovoltaic power generation power and the wind turbine power generation power, respectively. is the electric load demand value of the energy-consuming equipment in period t, is the input power of the ith heat pump in the heat pump unit during period t, M HP is the total number of heat pumps in the heat pump unit.

5. The method according to claim 1, characterized in that: Optimizing the dynamic programming model specifically includes: The dynamic programming model is optimized using a genetic algorithm.

6. A comprehensive energy system optimization and scheduling device based on the method according to any one of claims 1 to 5, characterized in that: include: An information acquisition module, which is used to obtain performance parameters, hourly operating parameters and hourly demand values ​​of energy-consuming equipment in the integrated energy system within the scheduling period; A model building module, the model building module is used to build a dynamic programming model, the decision optimization goal of the dynamic programming model is to minimize the total operating cost of each time period in the scheduling cycle, and the state transfer equation of the dynamic programming model is determined according to the performance parameters, the hourly operating parameters and the hourly demand value; A scheduling scheme determination module is used to optimize the dynamic programming model and determine the scheduling scheme of the integrated energy system.

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