CN115310714A - Park energy optimization regulation and control method, regulation and control system and storage medium - Google Patents
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Abstract
The invention relates to the technical field of energy optimization, in particular to a park energy optimization regulation and control method, which comprises the following steps: respectively acquiring related power data of an energy system in an industrial park according to a preset period; constructing an energy optimization scheduling model of the industrial park based on the minimum operation cost and the maximum clean energy consumption; the clean energy consumption is the electric energy of a distributed photovoltaic power station, a distributed wind power plant and an energy storage system which are consumed by the load of a park; determining constraint conditions of the energy optimization scheduling model; and solving the energy optimization scheduling model by adopting a non-dominated sorting genetic algorithm containing an elite strategy according to the constraint conditions to obtain a Pareto optimal solution as energy optimization regulation.
Description
Technical Field
The invention relates to the technical field of energy optimization, in particular to a park energy optimization regulation and control method, a regulation and control system and a storage medium.
Background
At present, the phenomena of wind power abandonment and photovoltaic abandonment are serious, so that the problem of how to efficiently utilize clean energy is needed to be solved.
With the increasing power of the power market innovation, the demand of users on electric energy is increasing day by day, optimizing power utilization on an industrial park is a subject to be researched urgently, and establishing a modern energy system mainly based on new energy power generation and assisted by power grid power purchasing has positive significance for promoting new energy consumption and achieving the low-carbon goal. The industrial park integrates various clean energy sources and energy storage equipment, and can solve the problems of environmental protection, low carbon and economy under the condition of meeting the requirement of load power supply. The combined heat and power generation unit fully embodies the concept of energy cascade utilization, high-temperature heat energy is used for generating electricity, and low-temperature heat energy is used for supplying heat, so that the utilization efficiency of energy is improved. Solar energy and wind energy are taken as renewable energy sources, the development potential is huge, the power generation cost can be greatly reduced, however, the output of wind power and photovoltaic power has the characteristics of randomness and intermittence, therefore, an industrial park manager needs to prejudge the energy use condition in a future period of time, so that in order to continuously supply power to loads, the safe and stable operation of a system is ensured, and a reasonable energy supply mode needs to be searched to balance the supply and demand of maintenance systems, which is an urgent problem to be solved.
Disclosure of Invention
In view of the above problems, the invention provides a park energy optimization regulation and control method, which integrates power generation equipment, an energy storage system, loads and the like in an industrial park into an energy system, and constructs an industrial park energy optimization scheduling model, wherein the optimization model is beneficial to reduce the overall operation cost of the park, is beneficial to bringing good economic benefits and improving the production enthusiasm of enterprises. The method specifically comprises the following steps:
respectively collecting related power data of an energy system in a park according to a preset period;
constructing an energy optimization scheduling model of the industrial park based on the minimum operation cost and the maximum clean energy consumption; the clean energy consumption is the electric energy of a distributed photovoltaic power station, a distributed wind power plant and an energy storage system which are consumed by the load of a park;
determining constraint conditions of the energy optimization scheduling model;
and solving the energy optimization scheduling model by adopting a non-dominated sorting genetic algorithm containing an elite strategy according to the constraint conditions to obtain a Pareto optimal solution as energy optimization regulation.
Further, the energy optimization scheduling model is as follows:
in the formula: f. of 1 、f 2 Respectively the operation cost and the clean energy consumption of the industrial park; c g A cost to purchase electricity to the grid; c ess The energy storage system charge and discharge loss cost; c wp Operating costs for the power generation devices of the distributed wind power plant; c pv Operating costs for photovoltaic power generation units of a distributed photovoltaic power plant; c chp The running cost of the cogeneration system unit; p wpl 、P pvl 、P essl The power provided for the load of the park by the wind power plant, the photovoltaic power station and the energy storage system is respectively; t denotes an optimization period.
Further, the electricity purchasing cost C is carried out on the power grid g The calculation method of (2) is as follows:
in the formula: t is the electricity utilization period; t is an optimization period; Δ t is the time interval; m is a group of t The electricity purchase price of each electricity consumption time interval; p g,t Purchasing power to the power grid;
energy storage system charge-discharge loss cost C ess The calculation method of (2) is as follows:
in the formula: c. C ess A cost factor for energy storage system charge-discharge loss; p is essc ,t、P essd T is the charging and discharging power of the energy storage system; operating cost C of wind power generation plant wp The calculation method of (2) is as follows:
in the formula: c. C wp The electricity consumption cost of the factors of operation, maintenance and depreciation is taken into consideration for the wind power generation device; p wp,t The active power output of the wind power plant in the time period t is obtained;
operating cost C of photovoltaic power generation device pv Is calculated as follows：
In the formula: c. C pv The electricity consumption cost of the operation, maintenance and depreciation factors is taken into consideration for the photovoltaic power generation device; p pv,t The active power output of the photovoltaic power station in the time period t is obtained;
operating cost C of cogeneration unit chp The calculation method of (2) is as follows:
in the formula: a. b and c are power generation cost coefficients of the cogeneration unit; p chp The generated energy of the unit; q chp The heat supply amount of the unit; theta.theta. 0 Is a thermoelectric conversion coefficient.
Further, the constraint conditions of the industrial park energy optimization scheduling model are as follows:
(1) And (3) power balance constraint:
P g,t +P essl,t +P wpl,t +P pvl,t +P chp,t ＝P load,t
in the formula: p g,t Purchasing power to the power grid within a time period t; p is essl,t 、P wpl,t 、P pvl,t The power of the park load is supplied to the energy storage system, the wind power plant and the photovoltaic power station in the time period t respectively; p chp,t Generating capacity of the cogeneration unit at a time t; p load,t Load power for the park;
(2) Tie line power constraints:
P g,t ≤P g,max
(3) The generated energy constraint and the climbing power constraint of the cogeneration unit are as follows:
P chp,min ≤P chp,t ≤P chp ,max
-P chp,down ≤P chp,t -P chp,t-1 ≤P chp,up
in the formula：P chp,max 、P chp,min Respectively the upper limit and the lower limit of the output of the cogeneration unit; p is chp,up 、P chp,down Maximum power change rates of the unit during climbing up and down slopes are respectively set;
(4) And (3) energy storage system charge and discharge power constraint:
P essl,t ＝P essd,t η
P essc,t ≤P essc,max
P essd,t ≤P essd,max
in the formula: eta is AC-DC conversion efficiency;
(5) And (3) constraint of demand response:
ΔL t ≤ΔL max
in the formula: Δ L t Load variation before and after demand response in a period t; Δ L max The maximum load change allowed for the t period.
Meanwhile, the invention also provides a park energy optimization regulation and control system, which comprises,
the acquisition unit is used for respectively acquiring relevant power data of an energy system in the industrial park according to a preset period;
the model generation unit is used for constructing an energy optimization scheduling model of the industrial park based on the minimum operation cost and the maximum clean energy consumption; the clean energy consumption is the electric energy of a distributed photovoltaic power station, a distributed wind power plant and an energy storage system which are consumed by the load of a park;
the model optimization unit is used for determining constraint conditions of the energy optimization scheduling model;
and the model solving unit is used for solving the energy optimization scheduling model by adopting a non-dominated sorting genetic algorithm containing an elite strategy according to the constraint conditions to obtain a Pareto optimal solution as energy optimization regulation.
The invention also provides a computer storage medium having a computer program stored therein, which when executed by a processor implements the method of any one of the above.
Adopt above-mentioned technical scheme's beneficial effect to lie in: 1) The industrial park energy optimization regulation and control method increases the consideration of the consumption of clean energy, compares with the traditional industrial park power utilization mode, the electric energy generated by a wind power plant and a photovoltaic power station is preferentially consumed by the load in the park, the surplus electric energy is stored in the energy storage system, the energy storage system sells electricity to the power grid when the power grid price is higher, the electricity is purchased to the power grid when the power grid price is lower, the wind power abandonment and photovoltaic abandonment can be reduced, the consumption of clean energy is increased, and the whole operation cost of the industrial park is favorably reduced. 2) Through gathering the power consumption data information of industrial park in the optimization cycle, construct the energy optimization scheduling model of industrial park, can see the holistic running cost of garden and the trend of change of clean energy consumption in a period with the help of this model directly perceivedly, help the garden administrator to predict cost and clean energy consumption in the short term, recombine garden load demand and wind-powered electricity generation field, photovoltaic power plant, energy storage system, the cogeneration unit supplies with the power data of load, can also foresee the power consumption demand to the electric wire netting, in time adjust electric wire netting and purchase the electric quantity, the safety of each consumer in the guarantee garden, steady operation.
Drawings
FIG. 1 is a schematic flow chart of a park energy optimization regulation and control method in the present invention;
FIG. 2 is a schematic diagram of an energy supply and supply integrated energy system of an industrial park according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the NSGA-II algorithm flow in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1 to 3, the present invention provides an industrial park energy optimization regulation method considering operation cost and clean energy consumption, including the following steps: step 1: an energy system integrating energy supply and energy supply in an industrial park is planned, and is shown in figure 1. The energy system comprises a cogeneration system, a distributed photovoltaic power station, a distributed wind power plant and an energy storage system.
In the normal operation process of the industrial park, electric energy provided by clean energy is preferentially consumed, when photovoltaic power generation and wind power generation are enough to meet the industrial load demand, surplus electric energy is stored in an energy storage system, and the stored electric energy can be sold to a power grid when the electricity price is high according to the situation; when photovoltaic power generation and wind power generation are insufficient, electric energy in the energy storage system can be released to supply a load, and if the load demand cannot be met, electricity needs to be purchased from a power grid;
and 2, step: constructing an industrial park energy optimization scheduling model based on an industrial park energy supply and energy supply integrated energy system;
in the formula: f. of 1 、f 2 Respectively the operation cost and the clean energy consumption of the industrial park; c g A cost for purchasing electricity to the grid; c ess The energy storage system charge-discharge loss cost; c wp The operating cost of the wind power generation device; c pv The operating cost of the photovoltaic power generation device; c chp The running cost of the cogeneration unit; p wpl 、P pvl 、P essl The power provided for the load of the park by the wind power plant, the photovoltaic power station and the energy storage system is respectively provided;
purchase of electricity to the grid charge C g The calculation method of (2) is as follows:
in the formula: t is the electricity utilization time period; t is an optimization period; Δ t is the time interval; m t The electricity purchase price of each electricity consumption time interval; p g,t Purchasing to the grid for industrial loadsElectrical power;
energy storage system charge-discharge loss cost C ess The calculation method of (2) is as follows:
in the formula: c. C ess A cost factor of the energy storage system charge-discharge loss; p essc,t 、P essd,t Charging and discharging power of the energy storage system;
operating cost of wind power generation plant C wp The calculation method of (2) is as follows:
in the formula: c. C wp The electricity consumption cost of factors such as operation, maintenance, depreciation and the like is taken into consideration for the wind power generation device; p wp,t Active power output of the wind power plant in a time period t is obtained;
operating cost C of photovoltaic power generation device pv The calculation method of (2) is as follows:
in the formula: c. C pv The electricity consumption cost of the factors such as operation, maintenance, depreciation and the like is taken into consideration for the photovoltaic power generation device; p pv,t The active power output of the photovoltaic power station in the time period t is obtained;
operating cost C of cogeneration unit chp The calculation method of (2) is as follows:
in the formula: a. b and c are power generation cost coefficients of the cogeneration unit; p chp The generated energy of the unit; q chp The heat supply amount of the unit; theta.theta. 0 Is a thermoelectric conversion coefficient;
and step 3: establishing a constraint condition of an industrial park energy optimization scheduling model;
(1) And power balance constraint:
P g,t +P essl,t +P wpl,t +P pvl,t +P chp,t ＝P load,t
in the formula: p g,t Purchasing power to the power grid in the t time period; p is essl,t 、P wpl,t 、P pvl,t The power of the park load is supplied to the energy storage system, the wind power plant and the photovoltaic power station in the time period t respectively; p chp,t Generating capacity of the cogeneration unit at a time t; p is load,t Load power for the park;
(2) Tie line power constraint:
P g,t ≤P g,max
(3) The method comprises the following steps of (1) generating capacity constraint and climbing power constraint of a cogeneration unit:
P chp,min ≤P chp,t ≤P chp,max
-P chp,down ≤P chp,t -P chp,t-1 ≤P chp,up
in the formula: p chp,max 、P chp,min Respectively the upper limit and the lower limit of the output of the cogeneration unit; p chp,up 、P chp,down Maximum power change rates of the unit when climbing up and down slopes are respectively set;
(4) And (3) energy storage system charge and discharge power constraint:
P essl,t ＝P essd,t η
P essc,t ≤P essc,max
P essd,t ≤P essd,max
in the formula: eta is AC-DC conversion efficiency;
(5) And (3) constraint of demand response:
ΔL t ≤ΔL max
in the formula: Δ L t Load variation before and after demand response in a period t; Δ L max The maximum load variation allowed for the t period;
and 4, step 4: and solving the industrial park energy optimization scheduling model by adopting a non-dominated sorting genetic algorithm (NSGA-II) containing an elite strategy, and obtaining a Pareto optimal solution as shown in figure 2, wherein the Pareto optimal solution comprises the operation cost and the clean energy consumption of the industrial park.
The NSGA-II algorithm comprises the following specific solving steps:
step 4.1: encoding chromosomes, randomly initializing a parent population P 0 ；
The decision variables of the chromosome codes comprise the operation cost and the consumption of clean energy in the industrial park, and a parent population P with the number of individuals N is randomly generated 0 ；
Step 4.2: father group P i Generating subgroup Q through crossing and mutation i ；
Step 4.3: combining the father individuals and the offspring individuals, and performing rapid non-dominated sorting;
the specific operation of the fast non-dominated sorting method is as follows:
(1) Finding out all individuals in the population which are not dominated by other individuals, and storing the individuals in the current set F 1 In (1), giving F 1 All individuals in the sequence i are identical rank ＝1；
(2) Removal of F from the entire population 1 After the individuals are searched, all the individuals which are not dominated by other individuals in the rest groups are found out and stored in a set F 2 In (1), giving F 2 All individuals in the sequence i are identical rank ＝2；
(3) Repeating steps (1) and (2) above until all individuals are ranked;
step 4.4: adopting an elite selection strategy to keep the optimal individuals in the current evolution process;
the specific process of the elite selection strategy is as follows: respectively finding out individuals with highest fitness and lowest fitness in the current population, and if the fitness of the optimal individual in the current population is higher than the fitness of the historical optimal individual, taking the optimal individual in the current population as a new optimal individual; if the fitness of the optimal individual in the current population is lower than that of the historical optimal individual, replacing the current worst individual with the current optimal individual;
step 4.5: computing a non-dominating set F t Crowding distance of middle individuals;
the calculation formula of the crowding distance is as follows:
in the formula (f) m (i + 1) and f m (i-1) the mth objective function values of the (i + 1) th and (i-1) th individuals of the non-dominating set respectively,
and
respectively the maximum value and the minimum value of the mth objective function; setting the crowding distance between the minimum individual and the maximum individual with the function value as ∞;
step 4.6: selecting and generating a new parent population;
according to non-dominating set F k Sequentially putting new parent population from low to high until a certain level of non-dominating set F t When the population size exceeds N, F is added t The individuals in the system are sorted from big to small according to the crowdedness, and are sequentially filled into a new father group until the population size reaches N;
step 4.7: increasing iteration times and repeating the steps 4.2-4.6 until the iteration times reach the maximum algebra;
step 4.8: and obtaining a Pareto optimal solution.
And when the iteration times reach the maximum algebra, outputting a Pareto optimal solution, wherein the Pareto optimal solution comprises the minimum operation cost of the industrial park and the maximum consumption of clean energy.
The application also provides a park energy optimization regulation and control system, which comprises,
the acquisition unit is used for respectively acquiring relevant power data of an energy system in the industrial park according to a preset period;
the model generation unit is used for constructing an energy optimization scheduling model of the industrial park based on the minimum operation cost and the maximum clean energy consumption; the clean energy consumption is the electric energy of a distributed photovoltaic power station, a distributed wind power plant and an energy storage system which are consumed by the load of a park;
the model optimization unit is used for determining constraint conditions of the energy optimization scheduling model;
and the model solving unit is used for solving the energy optimization scheduling model by adopting a non-dominated sorting genetic algorithm containing an elite strategy according to the constraint conditions to obtain a Pareto optimal solution as energy optimization regulation.
As an improvement of the scheme, the energy optimization scheduling model generated in the model generation unit is as follows:
in the formula: f. of 1 、f 2 Respectively the operation cost and the clean energy consumption of the industrial park; c g A cost to purchase electricity to the grid; c ess The energy storage system charge and discharge loss cost; c wp Operating costs for the power generation devices of the distributed wind power plant; c pv Operating costs for photovoltaic power generation units of a distributed photovoltaic power plant; c chp The running cost of the cogeneration system unit; p wpl 、P pvl 、P essl The power provided for the load of the park by the wind power plant, the photovoltaic power station and the energy storage system is respectively; t denotes an optimization period.
As an improvement of the scheme, in the energy optimization scheduling model, the electricity purchasing cost C is added to the power grid g The calculation method of (2) is as follows:
in the formula: t is the electricity utilization time period; t is an optimization period; Δ t is the time interval; m t The electricity purchase price of each electricity consumption time interval; p is g,t Purchasing power from the power grid;
energy storage system charge-discharge loss cost C ess The calculation method of (2) is as follows:
in the formula: c. C ess A cost factor of the energy storage system charge-discharge loss; p essc,t 、P essd,t Charging and discharging power of the energy storage system;
operating cost C of wind power generation plant wp The calculation method of (2) is as follows:
in the formula: c. C wp The electricity consumption cost of the factors of operation, maintenance and depreciation is taken into consideration for the wind power generation device; p wp,t The active power output of the wind power plant in the time period t is obtained;
operating cost C of photovoltaic power generation device pv The calculation method of (2) is as follows:
in the formula: c. C pv The electricity consumption cost of the operation, maintenance and depreciation factors is taken into consideration for the photovoltaic power generation device; p is pv,t The active power output of the photovoltaic power station in the time period t is obtained;
operating cost C of cogeneration unit chp The calculation method of (2) is as follows:
in the formula: a. b and c are power generation cost coefficients of the cogeneration unit; p is chp The generated energy of the unit; q chp The heat supply amount of the unit; theta 0 Is a thermoelectric conversion coefficient.
As an improvement of the scheme, energy optimization scheduling model constraint conditions set in the model optimization unit are as follows:
(1) And power balance constraint:
P g,t +P essl,t +P wpl,t +P pvl,t +P chp,t ＝P load,t
in the formula: p g,t Purchasing power to the power grid within a time period t; p is essl,t 、P wpl,t 、P pvl,t The power of the park load is supplied to the energy storage system, the wind power plant and the photovoltaic power station in the time period t respectively; p chp,t Generating capacity of the cogeneration unit at a time period t; p load,t Load power for the park;
(2) Tie line power constraints:
P g,t ≤P g,max
(3) The method comprises the following steps of (1) generating capacity constraint and climbing power constraint of a cogeneration unit:
P chp,min ≤P chp,t ≤P chp,max
-P chp,down ≤P chp,t -P chp,t-1 ≤P chp,up
in the formula: p chp,max 、P chp,min Respectively the upper limit and the lower limit of the output of the cogeneration unit; p chp,up 、P chp,down Maximum power change rates of the unit during climbing up and down slopes are respectively set;
(4) And (3) energy storage system charge and discharge power constraint:
P essl,t ＝P essd,t η
P essc,t ≤P essc,max
P essd,t ≤P essd,max
in the formula: eta is AC-DC conversion efficiency;
(5) And (3) constraint of demand response:
ΔL t ≤ΔL max
in the formula,. DELTA.L t Load variation before and after demand response in a period t; Δ L max The maximum load change allowed for the t period.
The above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be apparent to those skilled in the art that the embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer.
Claims (9)
1. A park energy optimization regulation and control method is characterized by comprising the following steps:
respectively acquiring related power data of an energy system in an industrial park according to a preset period;
constructing an energy optimization scheduling model of the industrial park based on the minimum operation cost and the maximum clean energy consumption; the clean energy consumption is the electric energy of a distributed photovoltaic power station, a distributed wind power plant and an energy storage system which are consumed by the load of a park;
determining constraint conditions of the energy optimization scheduling model;
and solving the energy optimization scheduling model by adopting a non-dominated sorting genetic algorithm containing an elite strategy according to the constraint conditions to obtain a Pareto optimal solution as energy optimization regulation.
2. The park energy optimization and regulation method according to claim 1, characterized in that: the energy optimization scheduling model comprises the following steps:
in the formula: f. of 1 、f 2 Respectively the operation cost and the clean energy consumption of the industrial park; c g A cost to purchase electricity to the grid; c ess The energy storage system charge and discharge loss cost; c wp Operating costs for the power generation devices of the distributed wind power plant; c pv Operating costs for photovoltaic power generation units of distributed photovoltaic power stations; c chp The running cost of the cogeneration system unit; p is wpl 、P pvl 、P essl The power provided for the load of the park by the wind power plant, the photovoltaic power station and the energy storage system is respectively; t denotes an optimization period.
3. The park energy optimization and regulation method according to claim 2, characterized in that:
purchase of electricity to the grid charge C g The calculation method of (2) is as follows:
in the formula: t is the electricity utilization time period; t is an optimization period; Δ t is the time interval; m t The electricity purchase price of each electricity consumption time interval; p is g,t Purchasing power from the power grid;
charge and discharge loss charge of energy storage system C ess The calculation method of (2) is as follows:
in the formula: c. C ess A cost factor of the energy storage system charge-discharge loss; p is essc,t 、P essd,t Charging and discharging power of the energy storage system;
operating cost C of wind power generation plant wp The calculation method of (2) is as follows:
in the formula: c. C wp The electricity consumption cost of the factors of operation, maintenance and depreciation is taken into consideration for the wind power generation device; p wp,t The active power output of the wind power plant in the time period t is obtained;
operating cost C of photovoltaic power generation device pv The calculation method of (2) is as follows:
in the formula: c. C pv The electricity consumption cost of the operation, maintenance and depreciation factors is taken into consideration for the photovoltaic power generation device; p pv,t Active power output of the photovoltaic power station in a time period t is obtained;
operating cost C of cogeneration unit chp The calculation method of (2) is as follows:
in the formula: a. b and c are power generation cost coefficients of the cogeneration unit; p chp The generated energy of the unit; q chp The heat supply amount of the unit; theta 0 Is a thermoelectric conversion coefficient.
4. The park energy optimization and regulation method according to claim 1, characterized in that: the energy optimization scheduling model has the following constraint conditions:
(1) And power balance constraint:
P g,t +P essl,t +P wpl,t +P pvl,t +P chp,t ＝P load,t
in the formula: p g,t Purchasing power to the power grid within a time period t; p is essl,t 、P wpl,t 、P pvl,t The power of the park load is supplied to the energy storage system, the wind power plant and the photovoltaic power station in the time period t respectively; p chp,t Generating capacity of the cogeneration unit at a time t; p load,t Load power for the park;
(2) Tie line power constraint:
P g,t ≤P g,max
(3) The method comprises the following steps of (1) generating capacity constraint and climbing power constraint of a cogeneration unit:
P chp,min ≤P chp,t ≤P chp,max
-P chp,down ≤P chp,t -P chp,t-1 ≤P chp,up
in the formula: p chp,max 、P chp,min Respectively the upper limit and the lower limit of the output of the cogeneration unit; p chp,up 、P chp,down Maximum power change rates of the unit during climbing up and down slopes are respectively set;
(4) And (3) energy storage system charge and discharge power constraint:
P essl,t ＝P essd,t η
P essc,t ≤P essc,max
P essd,t ≤P essd,max
in the formula: eta is AC-DC conversion efficiency;
(5) And (3) constraint of demand response:
ΔL t ≤ΔL max
in the formula,. DELTA.L t Load variation before and after demand response in a period t; Δ L max The maximum load change allowed for the t period.
5. The utility model provides a regulation and control system is optimized to garden energy which characterized in that: comprises the steps of (a) preparing a substrate,
the acquisition unit is used for respectively acquiring relevant power data of an energy system in the industrial park according to a preset period;
the model generation unit is used for constructing an energy optimization scheduling model of the industrial park based on the minimum operation cost and the maximum clean energy consumption; the clean energy consumption amount refers to electric energy of a distributed photovoltaic power station, a distributed wind power station and an energy storage system which are consumed by the load of a park;
the model optimization unit is used for determining constraint conditions of the energy optimization scheduling model;
and the model solving unit is used for solving the energy optimization scheduling model by adopting a non-dominated sorting genetic algorithm containing an elite strategy according to the constraint conditions to obtain a Pareto optimal solution as energy optimization regulation.
6. The park energy optimization and regulation system of claim 5, wherein: the energy optimization scheduling model generated in the model generation unit is as follows:
in the formula: f. of 1 、f 2 Respectively the operation cost and the clean energy consumption of the industrial park; c g A cost for purchasing electricity to the grid; c ess The energy storage system charge-discharge loss cost; c wp Power generation device for distributed wind power plantSetting the running cost; c pv Operating costs for photovoltaic power generation units of a distributed photovoltaic power plant; c chp The running cost of the cogeneration system unit; p is wpl 、P pvl 、P essl The power provided for the load of the park by the wind power plant, the photovoltaic power station and the energy storage system is respectively provided; t denotes an optimization period.
7. The park energy optimization and regulation system of claim 6, wherein: in the energy optimization scheduling model, the electricity purchasing cost C is given to the power grid g The calculation method of (2) is as follows:
in the formula: t is the electricity utilization time period; t is an optimization period; Δ t is the time interval; m t The electricity price is the electricity purchase price of each electricity utilization period; p g,t Purchasing power to the power grid;
charge and discharge loss charge of energy storage system C ess The calculation method of (2) is as follows:
in the formula: c. C ess A cost factor of the energy storage system charge-discharge loss; p essc,t 、P essd,t Charging and discharging power of the energy storage system;
operating cost C of wind power generation plant wp The calculation method of (2) is as follows:
in the formula: c. C wp The electricity consumption cost of the factors of operation, maintenance and depreciation is taken into consideration for the wind power generation device; p wp,t The active power output of the wind power plant in the time period t is obtained;
operating cost C of photovoltaic power generation device pv Is calculated byThe method comprises the following steps:
in the formula: c. C pv The electricity consumption cost of the operation, maintenance and depreciation factors is taken into account for the photovoltaic power generation device; p pv,t The active power output of the photovoltaic power station in the time period t is obtained;
operating cost C of cogeneration unit chp The calculation method of (2) is as follows:
in the formula: a. b and c are power generation cost coefficients of the cogeneration unit; p chp The generated energy of the unit; q chp The heat supply amount of the unit; theta 0 Is a thermoelectric conversion coefficient.
8. The park energy optimization and regulation system of claim 5, wherein: the energy optimization scheduling model constraint conditions set in the model optimization unit are as follows:
(1) And power balance constraint:
P g，t +P essl，t +P wpl，t +P pvl，t +P chp，t ＝P load，t
in the formula: p g，t Purchasing power to the power grid within a time period t; p essl，t 、P wpl，t 、P pvl，t The power of the park load is supplied to the energy storage system, the wind power plant and the photovoltaic power station in the time period t respectively; p chp，t Generating capacity of the cogeneration unit at a time t; p load，t Load power for the park;
(2) Tie line power constraint:
P g，t ≤P g，max
(3) The method comprises the following steps of (1) generating capacity constraint and climbing power constraint of a cogeneration unit:
P chp，min ≤P chp，t ≤P chp，max
-P chp，down ≤P chp，t -P chp，t-1 ≤P chp，up
in the formula: p chp，max 、P chp，min Respectively the upper limit and the lower limit of the output of the cogeneration unit; p chp，up 、P chp，down Maximum power change rates of the unit when climbing up and down slopes are respectively set;
(4) And (3) energy storage system charge and discharge power constraint:
P essl，t ＝P essd，t η
P escc，t ≤P essc，max
P essd，t ≤P essd，max
in the formula: eta is AC-DC conversion efficiency;
(5) And (3) constraint of demand response:
ΔL t ≤ΔL max
in the formula,. DELTA.L t Load variation before and after demand response in a period t; Δ L max The maximum load change allowed for the t period.
9. A computer storage medium, characterized in that a computer program is stored in the computer readable storage medium, which computer program, when being executed by a processor, carries out the method according to any one of claims 1 to 4.
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