A comprehensive energy optimization scheduling control method based on dynamic programming

Abstract

The present invention discloses a comprehensive energy optimization scheduling control method based on dynamic programming, comprising: step 1: constructing a comprehensive energy model covering photovoltaic power generation, energy storage batteries, ice storage tanks, air source heat pumps, chillers and load ends; step 2: decoupling the comprehensive energy model using a sequential decoupling method to obtain a thermal system, a cold system and an electrical system; step 3: optimizing the thermal system, the cold system and the electrical system respectively using a dynamic programming algorithm to obtain the optimal operating state solution set of the thermal system, the cold system and the electrical system; the present invention can effectively coordinate the energy flow and load distribution between the cold system, the thermal system and the electrical system, thereby maximizing energy efficiency and minimizing operating costs, improving comprehensive energy utilization efficiency, reducing operating costs and ensuring energy supply reliability.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy system optimization and scheduling, and in particular relates to an integrated energy optimization and scheduling control method based on dynamic programming. Background Art

[0002] With the rapid development of renewable energy, integrated energy systems have attracted much attention for their ability to integrate multiple energy forms such as cooling, heating and electricity and improve energy efficiency. However, the complex coupling relationship between different energy subsystems, time-varying energy prices, and fluctuating energy loads have made the dispatching and control of integrated energy systems face many challenges. Traditional single energy control or simple empirical dispatching strategies are difficult to achieve overall optimization, resulting in energy waste and rising operating costs. The current energy dispatching strategy is difficult to fully coordinate the coupling relationship between the cooling system, the heating system, and the electricity system, and it is difficult to achieve global optimal dispatching under dynamically changing demand and external environment. Summary of the invention

[0003] The purpose of the present invention is to provide a comprehensive energy optimization scheduling control method based on dynamic programming to solve the problem that the current energy scheduling strategy is difficult to fully coordinate the coupling relationship between the cold system, the hot system, and the electric system, and it is difficult to achieve global optimal scheduling under dynamically changing demand and external environment.

[0004] The present invention adopts the following technical solution: a comprehensive energy optimization scheduling control method based on dynamic programming, comprising:

[0005] Step 1: Build a comprehensive energy model covering photovoltaic power generation, energy storage batteries, ice storage tanks, air source heat pumps, chillers and load ends;

[0006] Step 2: Decouple the comprehensive energy model using a sequential decoupling method to obtain the heat system, cooling system, and electrical system;

[0007] Step 3: Use the dynamic programming algorithm to optimize the thermal system, cooling system and electrical system respectively to obtain the optimal operating state solution set of the thermal system, cooling system and electrical system.

[0008] Furthermore, the decoupling method in step 2 is:

[0009] Step 201: Optimizing the heat system and the cooling system using a dynamic programming algorithm to obtain the optimal operating states of the heat system and the cooling system when the heat load and the cooling load are met;

[0010] Step 202: Calculate the power consumption of the air source heat pump and the power consumption of the chiller according to the optimal operating states of the air source heat pump in the current hot system and the chiller in the cold system;

[0011] Step 203: Add the power consumption of the air source heat pump and the power consumption of the chiller to the power load of the power system to obtain a new power load;

[0012] Step 204: Solve the optimal operating state of the current power system based on the new power load, and gradually approach the optimal solution of the comprehensive energy model through multiple iterations.

[0013] Furthermore, the decision variable for thermal system optimization in step 3 is the on / off state of the air source heat pump.

[0014] Furthermore, the decision variable for cooling system optimization in step 3 is the amount of ice melted or stored in the ice storage tank.

[0015] Furthermore, the decision variable for power system optimization in step 3 is the charging power or discharging power of the energy storage battery.

[0016] The beneficial effects of the present invention are:

[0017] The present invention uses a dynamic programming algorithm to achieve global optimization of energy scheduling through step-by-step calculation and system coupling optimization, solves the scheduling problems of the three parts of cooling, heating and electricity in the integrated energy system, and improves the energy efficiency of the integrated energy system, and the adaptability and real-time performance of the control strategy;

[0018] The present invention simplifies the complexity of the comprehensive energy scheduling problem by optimizing the cooling system and the heating system in steps, while retaining the coupling relationship between the systems, comprehensively considering the electric load demand of the cooling and heating systems and the fluctuation of external electricity prices, optimizing the charging and discharging strategy of the electric system, reducing the operating cost, fully considering the uncertainty of renewable energy such as photovoltaic and solar thermal energy, and improving the flexibility and sustainability of system operation;

[0019] The present invention uses a dynamic programming algorithm to optimize the decoupled cooling, heating and electricity systems in steps, finds the relatively optimal operating state solution set, and makes adaptive adjustments according to environmental changes, thereby improving the flexibility and adaptability of the operation of the integrated energy system.

[0020] The present invention can also well control the solution rate and time by adjusting parameters, provide a reliable and real-time optimization control strategy, improve the energy efficiency and control accuracy of the integrated energy system, fully tap the energy-saving potential of the integrated energy system, and reduce energy consumption;

[0021] The present invention can effectively coordinate the energy flow and load distribution between the cooling system, the heating system and the electrical system, thereby maximizing energy efficiency and minimizing operating costs, improving comprehensive energy utilization efficiency, reducing operating costs and ensuring energy supply reliability. DETAILED DESCRIPTION

[0022] The present invention is described in detail below in conjunction with specific implementation modes.

[0023] The present invention discloses a comprehensive energy optimization dispatching control method based on dynamic programming, comprising:

[0024] Step 1: Construct a comprehensive energy model covering photovoltaic power generation, energy storage batteries, ice storage tanks, air source heat pumps, chillers and load ends; the comprehensive energy model covers power generation equipment (photovoltaic power generation equipment), energy storage equipment (energy storage batteries, ice storage tanks), energy conversion equipment (air source heat pumps, chillers), energy supply networks (considering energy losses in the networks), and load ends (heat load, cooling load, and electrical load).

[0025] Step 2: Decouple the comprehensive energy model using a sequential decoupling method to obtain a heat system, a cooling system, and an electrical system; the decoupling method in step 2 is:

[0026] Step 201: Optimizing the heat system and the cooling system using a dynamic programming algorithm to obtain the optimal operating states of the heat system and the cooling system when the heat load and the cooling load are met;

[0027] Step 202: Calculate the power consumption of the air source heat pump and the power consumption of the chiller according to the optimal operating states of the air source heat pump in the current hot system and the chiller in the cold system;

[0028] Step 203: Add the power consumption of the air source heat pump and the power consumption of the chiller to the power load of the power system to obtain a new power load;

[0029] Step 204: Solve the optimal operating state of the current power system based on the new power load, and gradually approach the optimal solution of the comprehensive energy model through multiple iterations.

[0030] Step 3: Use the dynamic programming algorithm to optimize the thermal system, cooling system and electrical system respectively to obtain the optimal operating state solution set of the thermal system, cooling system and electrical system.

[0031] Among them, the decision variable for thermal system optimization in step 3 is the on / off state of the air source heat pump, the decision variable for cold system optimization is the amount of ice melted or stored in the ice storage tank, and the decision variable for electrical system optimization is the charging power or discharging power of the energy storage battery.

[0032] Example 1

[0033] Step 1: Build a comprehensive energy model covering photovoltaic power generation, energy storage batteries, ice storage tanks, air source heat pumps, chillers and loads

[0034] Step 2: Decouple the comprehensive energy model using the sequential decoupling method to obtain the heat system, cooling system and electrical system

[0035] Step 3: Use the dynamic programming algorithm to optimize the thermal system, cooling system and electrical system respectively to obtain the optimal operating state solution set of the thermal system, cooling system and electrical system.

[0036] The specific steps of optimizing the thermal system to obtain the optimal operating state solution set of the thermal system include:

[0037] Define the parameters:

[0038] t: time step (the tth moment in the time series).

[0039] s: Storage state of the thermal system (heat storage state of the water storage tank).

[0040] m: Working status of thermal system equipment (on / off status of air source heat pump).

[0041] dp[t][s][m]: minimum total cost of the thermal system at time t, when the storage state is s and the device state is m.

[0042] s_t+1: storage status at time t+1.

[0043] m_t+1: The device switch status at time t+1.

[0044] total_cost: the total cost required to run at time t (including operating cost and electricity cost).

[0045] initialization:

[0046] m=2 2 , there are currently two air source heat pumps, each with a switch state of 0 / 1, that is, there are a total of 4 switch state combinations.

[0047] s_max: Maximum storage heat of the hot water tank.

[0048] work_hour=24, the working hours are 24, and the results are optimized once every hour.

[0049] dp = np.full((work_hour+1,s_max+1,machine_states),np.inf), initialize the state transfer table, where the machine_states thermal system has 4 values, 0, 1, 2, and 3, where 0 represents 00, i.e., heat pump 1 is off and heat pump 2 is off, 1 represents 01, i.e., heat pump 1 is off and heat pump 2 is on, 2 represents 10, i.e., heat pump 1 is on and heat pump 2 is off, and 3 represents 11, i.e., heat pump 1 is on and heat pump 2 is on.

[0050] At the last time step t=24, let dp[t][s][m]=0, that is, at this moment, the full-day optimization has ended and the cost is 0.

[0051] State transfer equation:

[0052] For each time t, heat storage s and heat pump state m, enumerate all possible startup combinations of the heat pump (p1, p2) and calculate the heat storage and total cost at the next moment:

[0053] Status Update:

[0054] s_t+1=s+p1·airhot_hotpower1+p2·airhot_hotpower2-loads[t]

[0055] Among them, p1 is the switching state of air source heat pump 1, p2 is the switching state of air source heat pump 2; airhot_hotpower1 is the thermal power of air source heat pump 1, and airhot_hotpower2 is the thermal power of air source heat pump 2.

[0056] Operation cost calculation:

[0057] total_cost=(p1·airhot_power1+p2·airhot_power2)·electric_price[t]

[0058] Among them, airhot_power1 is the electric power of air source heat pump 1, airhot_power2 is the electric power of air source heat pump 2, and electric_price[t] is the electricity price at time t.

[0059] If the heat pump switches from off to on, there are also additional startup costs:

[0060] M1=0∧p1≠0: increase airhot1_start_cost

[0061] M2=0∧p2≠0: increase airhot2_start_cost

[0062] Among them, airhot1_start_cost and airhot2_start_cost are the start and stop costs of heat pump 1 and heat pump 2; M1 is the switch state of heat pump 1, 0 represents off; M2 is the switch state of heat pump 2, 0 represents off.

[0063] Dynamic programming solution process:

[0064] Time reverse calculation: calculate in reverse order from t=23 to t=0.

[0065] Status Update:

[0066] Storage status update: Update the status of the water storage tank according to the device output heat and the user's heat load demand: s_t+1=s_t+Qin-Qout, where Qin is the heat output of the device and Qout is the heat required by the user.

[0067] Device status switching: The device can switch between different working modes M, for example: starting and stopping an air source heat pump.

[0068] Target optimization:

[0069] At each time step and state, calculate dp[t][s][m] = dp[t+1][s_t+1][m_t+1] + total_cost

[0070] If the new cost is less than the current optimal value, update dp[t][s][m] and decision[t][s][m], and decision[t][s][m] records the action selected in the current state.

[0071] Result output: Determine the optimal strategy through backtracking. At each time step, read the action recorded in decision[t][s][m], and then enter the next state to read the best action in sequence.

[0072] The specific steps of optimizing the cooling system to obtain the optimal operating state solution set of the cooling system include:

[0073] Define the parameters:

[0074] q1, q2, q3: represent the cooling capacity of the three refrigeration units respectively.

[0075] t: time step (the tth moment in the time series).

[0076] s: Storage status of the cold system (cold storage status of the ice storage tank).

[0077] m: Working status of cooling system equipment (on / off status of chiller).

[0078] dp[t][s][m]: minimum total cost of the cold system at time t, when the storage state is s and the device state is m.

[0079] s_t+1: storage system status at time t+1.

[0080] m_+1: The device switch status at time t+1.

[0081] total_cost: the total cost required to run at time t (including operating cost and electricity cost).

[0082] initialization:

[0083] m=2 3 , there are currently three refrigeration hosts, each with a switch status of 0 / 1, that is, there are a total of 8 switch status combinations.

[0084] s_max: the maximum capacity of the ice storage tank.

[0085] work_hour=24, the working hours are 24, and the results are optimized once every hour.

[0086] dp = np.full((work_hour+1,s_max+1,machine_states),np.inf), initialize the state transition table. Among them, the machine_states cold system has 8 values, which are 0, 1, 2, 3----7, and represent the switch state combination of the three refrigeration units. 0, 1, 2, 3----7 represent 000, 001, 010, 011-----111, among which 0 represents off and 1 represents on.

[0087] At the last time step t=24, let dp[t][s][m]=0, that is, at this moment, the full-day optimization has ended and the cost is 0.

[0088] State transfer equation:

[0089] For each time t, ice storage capacity s and host state m, enumerate the startup combinations (q1, q2, q3) corresponding to all possible ice storage tank actions ice_action of the ice storage tank, and calculate the ice storage capacity and total cost at the next time.

[0090] Status update: s_t+1 = s-ice_action

[0091] Operation cost calculation: total_cost = E(q1,q2,q3)·electric_price[t], where E(q1,q2,q3) is the electric power of the refrigeration unit and electric_price[t] is the electricity price at time t.

[0092] If the cooling host switches from off to on, the startup cost will be increased:

[0093] N1=0∧p1≠0: increase p1_start_cost

[0094] N2=0∧p2≠0: increase p2_start_cost

[0095] N3=0∧p3≠0: increase p3_start_cost

[0096] p1_start_cost, p2_start_cost, and p3_start_cost are the start and stop costs of the equipment; N1 is the switch status of refrigeration unit 1, 0 represents off; N2 is the switch status of refrigeration unit 2, 0 represents off; N3 is the switch status of refrigeration unit 3, 0 represents off.

[0097] Dynamic programming solution process:

[0098] Time reverse calculation: calculate in reverse order from t=23 to t=0.

[0099] Status Update:

[0100] Storage status update: Update the status of the ice storage tank according to ice_action: s_t+1=s_t-ice_action, where ice_action is the action of the ice storage tank.

[0101] Device status switching: Host status m changes to the start / stop status of the three hosts

[0102] N1=m&1

[0103] N2=(m>>1)&1

[0104] N3=(m>>2)&1

[0105] Target optimization:

[0106] At each time step and state, calculate dp[t][s][m] = dp[t+1][s_t+1][m_t+1] + total_cost

[0107] If the new cost is less than the current optimal value, update dp[t][s][m] and decision[t][s][m], and decision[t][s][m] records the action selected in the current state.

[0108] Result output: Determine the optimal strategy through backtracking. At each time step, read the action recorded in decision[t][s][m], and then enter the next state to read the best action in sequence.

[0109] The specific steps of optimizing the electric system to obtain the optimal operating state solution set of the electric system include:

[0110] Define the parameters:

[0111] t: time step (the tth moment in the time series).

[0112] s: Storage state of the electrical system (power storage state of the energy storage battery).

[0113] dp[t][s]: The minimum total cost of the power system at time t when the storage state is s.

[0114] s_t+1: storage system status at time t+1.

[0115] total_cost: the total cost required to run at time t (including operating cost and electricity cost).

[0116] initialization:

[0117] s_max: Maximum capacity of the energy storage battery.

[0118] work_hour=24, the working hours are 24, and the results are optimized once every hour.

[0119] dp = np.full((work_hour+1,s_max+1),np.inf), initialize the state transition table

[0120] At the last time step t=24, let dp[t][s]=0, that is, at this moment, the full-day optimization has ended and the cost is 0.

[0121] State transfer equation:

[0122] For each time t and power s, enumerate all possible actions p of the battery and calculate the power and total cost at the next moment:

[0123] State update: s_t+1=sp, where p is the battery action, such as charging 20%, discharging 20%, etc.

[0124] Operation cost calculation: total_cost = (loads[t]–p–pv_power[t])*electric_price[t]

[0125] Where p is the battery re-discharge amount, loads[t] is all the electrical loads at the current moment, pv_power[t] is the photovoltaic power generation at the current moment, and electric_price[t] is the electricity price at time t.

[0126] Dynamic programming solution process:

[0127] Time reverse calculation: calculate in reverse order from t=23 to t=0.

[0128] Status Update:

[0129] Storage status update: According to the battery action, update the battery status: s_t+1=s_t-p.

[0130] Target optimization:

[0131] At each time step and state, calculate dp[t][s] = dp[t+1][s_t+1] + total_cost

[0132] If the new cost is less than the current optimal value, update dp[t][s] and decision[t][s], where decision[t][s] records the action selected in the current state.

[0133] Result output: Determine the optimal strategy through backtracking. At each time step, read the action recorded in decision[t][s], and then enter the next state to read the best action in sequence.

[0134] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A comprehensive energy optimization scheduling control method based on dynamic programming, characterized in that: include: Step 1: Build a comprehensive energy model covering photovoltaic power generation, energy storage batteries, ice storage tanks, air source heat pumps, chillers and load ends; Step 2: Decouple the comprehensive energy model using a sequential decoupling method to obtain the heat system, cooling system, and electrical system; Step 3: Use the dynamic programming algorithm to optimize the thermal system, cooling system and electrical system respectively to obtain the optimal operating state solution set of the thermal system, cooling system and electrical system.

2. According to claim 1, a comprehensive energy optimization scheduling control method based on dynamic programming is characterized in that: The decoupling method in step 2 is: Step 201: Optimizing the heat system and the cooling system using a dynamic programming algorithm to obtain the optimal operating states of the heat system and the cooling system when the heat load and the cooling load are met; Step 202: Calculate the power consumption of the air source heat pump and the power consumption of the chiller according to the optimal operating states of the air source heat pump in the current hot system and the chiller in the cold system; Step 203: Add the power consumption of the air source heat pump and the power consumption of the chiller to the power load of the power system to obtain a new power load; Step 204: Solve the optimal operating state of the current power system based on the new power load, and gradually approach the optimal solution of the comprehensive energy model through multiple iterations.

3. The comprehensive energy optimization scheduling control method based on dynamic programming according to claim 1 is characterized in that: The decision variable for thermal system optimization in step 3 is the on / off state of the air source heat pump.

4. The comprehensive energy optimization scheduling control method based on dynamic programming according to claim 1 is characterized in that: The decision variable for cooling system optimization in step 3 is the amount of ice melted or stored in the ice storage tank.

5. The comprehensive energy optimization scheduling control method based on dynamic programming according to claim 1 is characterized in that: The decision variable for power system optimization in step 3 is the charging power or discharging power of the energy storage battery.