An Optimization Scheduling Method for a CCHP Integrated Energy Utilization System

Abstract

The present invention belongs to the field of power technology, and particularly relates to an optimized scheduling method for a combined cooling, heating and power integrated energy utilization system, comprising the following steps: S1, obtaining the predicted power generation of photovoltaic and wind power, the predicted cooling, heating and power loads of users, and electricity price information during the optimization period; S2, taking the time-of-use electricity price, wind power, predicted wind power generation and load during each optimization period as the inputs of the system, using the gas turbine, conventional generator set, standby generator set and battery output as decision variables, and obtaining the optimal output of each generator set at each time period through the PSO-CS joint search algorithm. The present invention is used for the optimized scheduling of a combined cooling, heating and power integrated utilization system. Through the heuristic algorithm, the problems of multiple types of system equipment, complex coupling and difficult scheduling can be effectively solved, thereby improving the economic operation of the system. By combining the particle swarm optimization algorithm with the cuckoo algorithm, the problem that the particle swarm optimization algorithm is prone to falling into a local optimal solution in the later stage is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power, and particularly to an optimized scheduling method for a combined cooling, heating and power integrated energy utilization system. Background Technique

[0002] Combined cooling, heating and power, namely CCHP (Combined Cooling, Heating and Power), refers to using natural gas as the main fuel to drive gas power generation equipment such as gas turbines, micro gas turbines or internal combustion engine generators to operate. The generated electricity is supplied to meet the electricity demand of users. The waste heat discharged after the system generates electricity is used for heating and cooling users through waste heat recovery equipment (waste heat boilers or waste heat direct-fired machines, etc.). Through this method, the primary energy utilization rate of the entire system is greatly improved, and the cascade utilization of energy is realized. It can also provide grid-connected electricity for energy complementarity, and the economic benefits and efficiency of the entire system are correspondingly increased.

[0003] The CCHP system can make full use of the heat energy of natural gas, and the comprehensive energy utilization efficiency can reach more than 90%. At the same time, it can reduce the heating cost with natural gas as the fuel, spread part of the cost to the electricity bill, reduce the operation cost burden, and the extra initial investment cost compared with the conventional system can be recovered within 5 years by saving the operation cost.

[0004] Due to the outstanding advantages of CCHP in terms of energy conversion efficiency, it has a significant position in the energy field. However, in the actual application and scheduling process of the existing combined cooling, heating and power integrated energy utilization system, due to the large number of system equipment types and complex coupling, there are generally problems of difficult scheduling, and the operation cost is high, and it is impossible to achieve the optimized scheduling of the combined cooling, heating and power system considering the economic operation of the system. Summary of the Invention

[0005] (1) Technical Problems to be Solved

[0006] In view of the deficiencies of the prior art, the present invention provides an optimized scheduling method for a combined cooling, heating and power integrated energy utilization system, which solves the problems that in the actual application and scheduling process of the existing combined cooling, heating and power integrated energy utilization system, due to the large number of system equipment types and complex coupling, there are generally problems of difficult scheduling, and the operation cost is high, and it is impossible to achieve the optimized scheduling of the combined cooling, heating and power system considering the economic operation of the system.

[0007] (2) Technical Solutions

[0008] In order to achieve the above object, the present invention specifically adopts the following technical solutions:

[0009] An energy scheduling optimization method for a combined cooling, heating and power integrated energy utilization system, comprising: 1. Establishing a configuration, energy flow model and energy scheduling optimization model of the combined cooling, heating and power integrated energy system. 2. Proposing a joint search algorithm based on the particle swarm optimization algorithm and the cuckoo algorithm. This algorithm is divided into two layers. The first layer is the multi-population particle swarm optimization, and the optimized result is sent to the second layer for optimization by the cuckoo algorithm. Finally, the optimized value is returned to the first layer. The algorithm proposed by the present invention can effectively solve the problem that the particle swarm optimization algorithm is prone to falling into local optimal solutions, improve the quality of the final solution, and thus improve the economic efficiency of the actual operation of the system.

[0010] The combined cooling, heating and power integrated energy utilization system proposed by the present invention includes wind power and photovoltaic power generation equipment, a conventional generator set and a standby generator set fueled by diesel, a gas turbine and a boiler fueled by natural gas, as well as an energy storage system, a waste heat recovery system, an absorption chiller and an air conditioner. Among them, the gas turbine not only generates electricity, but also can generate heat through the waste heat recovery system, and the excess heat can be used for refrigeration by the absorption chiller. The user load is the cooling, heating and power load. There can also be power exchange between the system and the power grid, and the electricity price adopts the stepped electricity price.

[0011] A management optimization scheduling method for a combined cooling, heating and power integrated energy utilization system proposed by the present invention aims to optimize the scheduling of the combined cooling, heating and power integrated energy utilization system on the premise of considering the operating cost and environmental governance cost, and obtain the equipment scheduling strategies corresponding to each optimization period, so as to achieve the purpose of energy conservation and emission reduction. Before optimizing the scheduling of the combined cooling, heating and power integrated energy utilization system, the research object is first defined, and models are established for different types of loads in the combined cooling, heating and power integrated energy utilization system.

[0012] Based on the above model, taking Figure 1 the combined cooling, heating and power integrated energy utilization system shown as an example, the specific explanation of the system energy management optimization scheduling method is as Figure 2 shown. The method includes:

[0013] S1. Obtaining the predicted power generation of photovoltaic and wind power, the predicted cooling, heating and power loads of users, and the electricity price information during the optimization period;

[0014] S2. Taking the time-of-use electricity price, wind power, predicted wind power generation and load during each optimization period as the input of the system, using the gas turbine, conventional generator set, standby generator set and battery output as decision variables, and obtaining the optimal output of each generator set at each time period through the PSO-CS joint search algorithm.

[0015] Furthermore, in step S2, for the optimization problem of the combined cooling, heat and power integrated energy utilization system, taking the output powers of the gas turbine, conventional generator set, standby generator set and battery as decision variables, considering the time-of-use electricity price information and demand response information of the dispatching system, an optimization dispatching model for the combined cooling, heat and power integrated energy utilization system is constructed. The optimization dispatching model for the combined cooling, heat and power integrated energy utilization system includes:

[0016] Taking the operating cost and environmental governance cost as the objectives and the power balance and the power limits of each output unit as the constraint conditions, the equipment dispatching strategy is optimized to construct an optimization dispatching model for the combined cooling, heat and power integrated energy utilization system. The objective function is as follows:

[0017]

[0018] In the formula, cos t tot is the total daily operating cost, is the operating cost of equipment t at time i, is the environmental governance cost of equipment t at time i, is the fuel cost of equipment t at time i.

[0019] Furthermore, in step S2, the PSO-CS joint search algorithm is as follows: The lower layer of the algorithm is divided into multiple communities. In each community, the PSO algorithm update formula is used, and then the optimization result is sent to the upper layer for CS algorithm optimization, and the optimized result is returned to the upper layer. The speed and position update formulas of the PSO algorithm are as follows:

[0020] v i,k+1 =ω*v i,k +c 1 *rand.*(position i,best -position i,k )+c 2 *rand.*(position best -position i,k )

[0021] +c 3 *rand.*(position sc -position i,k )

[0022] position i,k+1 =position i,k +v i,k+1

[0023] In the formula, v i,k is the velocity of the i-th particle at the k-th iteration, rand is a random number vector between 0 and 1, and positioni,best is the position of the historical optimal solution for the i-th particle, position best is the position of the current optimal particle, position sc is the position of the optimal particle in the population where the particle is located; ω, c 1 、c 2 、c 3 In the present invention, they are taken as 0.7, 1.5, 1.5, 1.5; if the updated particle velocity exceeds the allowable range, the upper or lower limit of the velocity is taken as the particle's velocity; similar to the velocity update formula, if the particle position exceeds the allowable range, the upper or lower limit of the position is taken as the particle's position;

[0024] The update formula of the CS algorithm is as follows:

[0025] The CS algorithm mainly includes two steps: updating the nest position and reconstructing the nest; the nest position is updated using Levy flight. If the nest position after flight is better than the original position, the nest position is updated to the position after flight, and then it is determined whether to reconstruct the nest according to the probability; The Levy flight formula and the nest reconstruction formula are as follows:

[0026] X t+1 =X t +α.*Levy(β)

[0027] where X t is the nest position, α is the step size scaling factor, which is taken as 1 in the present invention, Levy(β) is the Levy flight random path, and β is taken as 1.5 in the present invention;

[0028] The Levy flight random path is:

[0029]

[0030] In the formula where u~N(0,σ 2 ), v~N(0,1),

[0031] The nest reconstruction formula is

[0032] X t+1 =X t +rand.*Heaviside(Pa - ε).*(X i -X j )

[0033] In the formula, Heaviside(x) is the step function, which is equal to 1 when x is greater than 0 and equal to 0 otherwise, X i 、X j are any two nest positions; Pa is the nest reconstruction probability, and ε is a random number between 0 and 1.

[0034] (III) Beneficial Effects

[0035] Compared with the prior art, the present invention provides an optimized scheduling method for a combined cooling, heating and power integrated energy utilization system, having the following beneficial effects:

[0036] 1. The present invention is used for the optimized scheduling of a combined cooling, heating and power integrated utilization system. Through a heuristic algorithm, it can effectively solve the problems of a large number of system equipment types, complex coupling, and difficult scheduling, thereby improving the economic efficiency of system operation.

[0037] 2. The present invention combines the particle swarm optimization algorithm and the cuckoo algorithm to solve the problem that the particle swarm optimization algorithm is prone to falling into a local optimal solution in the later stage, greatly improving the ability to find the optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the structure and energy flow of the combined cooling, heating and power integrated energy utilization system proposed by the present invention;

[0039] Figure 2 It is a schematic diagram of the steps of the energy optimization scheduling method proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the 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.

[0041] Embodiment

[0042] As Figure 1-2 shown, an optimized scheduling method for the management of a combined cooling, heating and power integrated energy utilization system proposed in an embodiment of the present invention aims to optimize the scheduling of the combined cooling, heating and power integrated energy utilization system on the premise of considering the operating cost and environmental governance cost, and obtain the equipment scheduling strategy corresponding to each optimized period, so as to achieve the purpose of energy conservation and emission reduction. Before optimizing the scheduling of the combined cooling, heating and power integrated energy utilization system, the research object is first defined, and models are established for different types of loads in the combined cooling, heating and power integrated energy utilization system.

[0043] The integrated cooling, heating and power supply comprehensive energy utilization system proposed by the present invention includes wind power and photovoltaic power generation equipment, a conventional generator set and a standby generator set fueled by diesel, a gas turbine and a boiler fueled by natural gas, as well as an energy storage system, a waste heat recovery system, an absorption chiller and an air conditioner. Among them, the gas turbine not only generates electricity, but also generates heat through the waste heat recovery system, and the excess heat can be used for refrigeration by the absorption chiller. The user load is the cooling, heating and power load. There can also be power exchange between the system and the power grid, and the electricity price adopts a stepped electricity price.

[0044] Based on the above model, taking Figure 1 the integrated cooling, heating and power supply comprehensive energy utilization system shown as an example, the specific explanation of the system energy management optimization scheduling method is as Figure 2 shown, and this method includes:

[0045] Step 1: Obtain the predicted power generation of photovoltaic and wind power, the predicted cooling, heating and power loads of users, and the electricity price information during the optimization period.

[0046] Step 2: Take the time-of-use electricity price, wind power, predicted wind power generation and load during each optimization period as the inputs of the system, take the outputs of the gas turbine, conventional generator set, standby generator set and battery as decision variables, and obtain the optimal output of each generator set at each time through the PSO-CS joint search algorithm.

[0047] Step S21: For the optimization problem of the integrated cooling, heating and power supply comprehensive energy utilization system, taking the output of the gas turbine, conventional generator set, standby generator set and battery as decision variables, considering the time-of-use electricity price information and demand response information of the dispatching system, and then constructing an optimization scheduling model for the integrated cooling, heating and power supply comprehensive energy utilization system to obtain the dispatching strategy of the output equipment corresponding to each optimization period, including:

[0048] 1) Operation strategy:

[0049] The operation strategy of the integrated cooling, heating and power supply comprehensive energy utilization system of the present invention is that the wind power and photovoltaic power generation first satisfy the regional electricity demand, and the heat generated by the gas turbine is supplied to the regional heat demand through the waste heat recovery device. If there is still surplus heat, then the absorption chiller is used to provide cooling capacity for users.

[0050] 2) Objective function:

[0051] The objective of the present invention is to minimize the operation cost and environmental governance cost, including the operation cost of equipment, the governance cost of pollutants generated, and the fuel cost.

[0052]

[0053] In the formula, cost tot is the total daily operation cost, is the operating cost of the t device at time i, is the environmental governance cost of the t device at time i, is the fuel cost of the t device at time i.

[0054] 3) Constraints:

[0055] The constraints of the present invention include load constraint balance, output range limitation of each unit, and ramp power limitation of each unit.

[0056] The electrical load balance constraint is:

[0057]

[0058] In the formula, is the power consumption at time i, is the power generation of the gas turbine at time i, is the power generation of the wind turbine at time i, is the power generation of the photovoltaic power generation at time i, is the power of the conventional generator set at time i, is the power generation of the standby generator set at time i, is the air conditioner power at time i.

[0059] The heat balance constraint is:

[0060]

[0061] In the formula, is the heat load at time i, is the power of the gas turbine at time i, Rec is the waste heat recovery device efficiency, is the heat production power of the boiler at time i, is the heat absorption power of the absorption chiller at time i.

[0062] The cold balance constraint is:

[0063]

[0064] In the formula, is the cold load at time i, is the air conditioner cooling power at time i, is the cooling power of the absorption chiller at time i.

[0065] The power constraint of each unit is:

[0066] P t,min ≤P t i ≤P t,max

[0067] Wherein, P t i is the power generation power of the t device at the i-th moment, and P t,max is the maximum allowable power of the t device, and P t,min is the minimum operating power of the t device.

[0068] The ramp power constraints of each unit are as follows:

[0069] P t i -P t i-1 ≤P t,clm

[0070] Wherein, P t,clm is the maximum ramp power of the t device.

[0071] Step S22: Under the equality constraints and inequality constraints proposed above, use the PSO-CS multi-population joint search algorithm proposed by the present invention for solution. The description of this algorithm is as follows:

[0072] (1) The framework of the multi-population joint search algorithm is a two-layer optimization. The first layer is divided into multiple populations, and the particle swarm algorithm is used for optimization within each population. The optimal solution of each population after optimization is sent to the second-layer cuckoo algorithm as the initial nest position for the second optimization. The solution after the cuckoo algorithm optimization is then returned to each population in the first layer.

[0073] (2) Steps for dividing populations: The populations will be re-divided every certain number of iterations. When performing the population division operation, set the number x of populations, randomly select x particles as the centers of the populations, calculate the distances from the remaining particles to the population centers, and divide the particles into the populations corresponding to the population centers closest to themselves.

[0074] (3) Position of particles: The position of particles is the decision variable. The decision variable includes the magnitude of the output power of the selected device at each moment. In the present invention, the output powers of the gas turbine, conventional generator set, standby generator set, and energy storage system at each moment are taken as the decision variables.

[0075]

[0076] Where position i represents the position of the i-th particle, is the power of the gas turbine at the j-th moment in the i-th particle, is the power of the conventional generator set at the j-th moment in the i-th particle, is the power of the standby generator set at the j-th moment in the i-th particle, is the power of the storage battery at the j-th moment in the i-th particle.

[0077] (4) Particle fitness: The particle fitness is the total operating cost cost tot , and the calculation formula has been given.

[0078] (5) Particle swarm optimization velocity and position update formulas:

[0079] The velocity update formula of the particle is as follows

[0080] v i,k+1 = ω * v i,k + c 1 * rand.* (position i,best - position i,k ) + c 2 * rand.* (position best - position i,k )

[0081] + c 3 * rand.* (position sc - position i,k )

[0082] In the formula, v i,k is the velocity of the i-th particle at the k-th iteration, rand is a random number vector between 0 and 1, position i,best is the position of the historical optimal solution of the i-th particle, position best is the position of the current optimal particle, position sc is the position of the optimal particle in the population where the particle is located. ω, c 1 , c 2 , c 3 take 0.7, 1.5, 1.5, 1.5 in the present invention. If the updated particle velocity exceeds the allowable range, the upper or lower limit of the velocity is taken as the particle velocity.

[0083] The position update formula of the particle

[0084] position i,k+1 = position i,k + v i,k+1

[0085] Similar to the velocity update formula, if the particle position exceeds the allowable range, the upper or lower limit of the position is taken as the particle position.

[0086] (6) Cuckoo Search Algorithm: The cuckoo search algorithm mainly includes two steps: updating the position of the cuckoo's nest and reconstructing the cuckoo's nest. The position of the cuckoo's nest is updated using Levy flight. If the position of the cuckoo's nest after flight is better than the original position, the position of the cuckoo's nest is updated to the position after flight. Then, it is determined whether the cuckoo's nest needs to be reconstructed according to the probability. The Levy flight formula and the formula for reconstructing the cuckoo's nest are as follows.

[0087] X t+1 = X t + α.*Levy(β)

[0088] where X t is the position of the cuckoo's nest, α is the step size scaling factor, which is taken as 1 in the present invention, Levy(β) is the random path of Levy flight, and β is taken as 1.5 in the present invention.

[0089] The random path of Levy flight is:

[0090]

[0091] In the formula where u ~ N(0, σ 2 ), v ~ N(0,1),

[0092] The formula for reconstructing the cuckoo's nest is

[0093] X t+1 = X t + rand.*Heaviside(Pa - ε).*(X i - X j )

[0094] In the formula, Heaviside(x) is the step function, which is equal to 1 when x is greater than 0 and equal to 0 otherwise. X i , X j are the positions of any two cuckoo's nests. Pa is the probability of reconstructing the cuckoo's nest, and ε is a random number between 0 and 1.

[0095] Based on the above algorithm, the solution process of the present invention is as follows:

[0096] Step 1: Input the original data in the area, including the predicted cold, heat, and electricity loads, meteorological prediction data, parameters of each device, electricity price, natural gas price, and diesel price.

[0097] Step 2: Input the program sequence, including the total number of iterations, the total number of cuckoo iterations, the objective function, and generate the initial total population

[0098] Step 3: Let the iteration number i = 1 and start the iteration

[0099] Step 4: Determine whether the population division operation needs to be performed. If the population needs to be divided, go to Step 5; otherwise, go to Step 6.

[0100] Step 5: Divide the population. Randomly select x particles as the centers of the populations. Calculate the distances from the remaining particles to the population centers. Divide the particles into the populations corresponding to the population centers closest to them, and find the optimal particles within each population.

[0101] Step 6: Update the velocities and positions of the particles.

[0102] Step 7: Take the optimal particles of each population and the historical optimal particles as the initial cuckoo nest positions of the cuckoo algorithm. Set the iteration times k of the cuckoo algorithm to 1.

[0103] Step 8: Update the cuckoo nest positions.

[0104] Step 9: Determine whether the cuckoo nest positions need to be reconstructed according to the discovery probability.

[0105] Step 10: Determine whether the cuckoo optimization algorithm has ended. If it has not ended, jump back to Step 8. If it has ended, return the cuckoo optimization result to the population and update the historical optimal particles. Jump back to Step 4.

[0106] Step 11: After the iteration ends, the optimal particle is the optimal solution. Output the optimal solution to obtain the output conditions of each distributed generating unit at the optimal time.

[0107] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An optimal scheduling method for a combined cooling, heating and power integrated energy utilization system, characterized in that: It includes the following steps: S1. Obtain the predicted power generation of photovoltaic and wind power, the predicted cooling, heating and power loads of users, and electricity price information during the optimization period; S2. Use the time-of-use electricity price, wind power, predicted wind power generation, and load of each optimization period as the inputs of the system. The system includes wind power and photovoltaic power generation equipment, a conventional generator set fueled by diesel and a standby generator set, a gas turbine and a boiler fueled by natural gas, as well as an energy storage system, a waste heat recovery system, an absorption chiller and an air conditioner; among them, the gas turbine not only generates electricity, but also can generate heat through the waste heat recovery system, and the excess heat can be used for refrigeration by the absorption chiller; the user load is the cooling, heating and power load; power exchange can also be carried out between the system and the power grid, and the electricity price adopts a stepped electricity price. Taking the gas turbine, conventional generator set, standby generator set, and battery output as decision variables, the optimal output of each generator set at each time period is obtained through the PSO-CS joint search algorithm; In the step S2, the PSO-CS joint search algorithm is as follows: The lower layer of the algorithm is divided into multiple communities. In each community, the PSO algorithm update formula is used, and then the optimization result is sent to the upper layer for CS algorithm optimization, and the optimized result is returned to the upper layer; the velocity and position update formulas of the PSO algorithm are as follows: v i,k+1 = ω * v i,k + c 1 * rand.*(position i,best - position i,k ) + c 2 * rand.*(position best - postion i,k +c 3 *rand.*(position sc -position i,k ) position i,k+1 = position i,k + v i,k+1 where v i,k is the velocity of the i-th particle at the k-th iteration, rand is a random number vector between 0 and 1, and position i,best is the position of the historical best solution of the i-th particle, and position best is the position of the current best particle, and position sc is the position of the best particle in the population where the particle is located; ω, c 1 , c 2 , c 3 take 0.7, 1.5, 1.5, 1.5 in the present invention; if the updated particle velocity exceeds the allowable range, the upper or lower limit of the velocity is taken as the particle velocity; similar to the velocity update formula, if the particle position exceeds the allowable range, the upper or lower limit of the position is taken as the particle position; The update formula of the CS algorithm is as follows: The CS algorithm mainly includes two steps: updating the nest position and reconstructing the nest; the nest position is updated by levy flight. If the nest position after flight is better than the original position, the nest position is updated to the position after flight, and then it is determined whether to reconstruct the nest according to the probability; The Levy flight formula and the nest reconstruction formula are as follows: X t+1 = X t + α * Levy(β) where X t is the position of the bird's nest, α is the step size scaling factor, which is taken as 1 in the present invention, Levy(β) is the random path of Levy flight, and β is taken as 1.5 in the present invention; The levy flight random path is: wherein where \(u\sim N(0,\sigma 2 ^2)\), \(v\sim N(0,1)\), The nest reconstruction formula is X t+1 = X t + rand.*Heaviside(Pa - ε).*(X i - X j ) where Heaviside(x) is a step function that equals 1 when x > 0 and 0 otherwise, X i and X j are any two nest positions; Pa is the nest reconstruction probability, and ε is a random number between 0 and 1.

2. According to the optimal scheduling method for a combined cooling, heating and power integrated energy utilization system described in claim 1, characterized in that: In the step S2, for the optimization problem of the combined cooling, heating and power integrated energy utilization system, taking the output of the gas turbine, conventional generator set, standby generator set, and battery as decision variables, considering the time-of-use electricity price information and demand response information of the scheduling system, and then constructing an optimal scheduling model for the combined cooling, heating and power integrated energy utilization system. The optimal scheduling model for the combined cooling, heating and power integrated energy utilization system includes: Taking the operating cost and environmental governance cost as the objective, and taking power balance and the power limits of each output unit as the constraint conditions, optimize the equipment scheduling strategy to construct an optimal scheduling model for the combined cooling, heating and power integrated energy utilization system; the objective function is as follows: where cost tot is the total daily operating cost, is the operating cost of equipment t at time i, is the environmental governance cost of equipment t at time i, is the fuel cost of equipment t at time i.

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