CN112131712B - Multi-objective optimization method and system for multi-energy system on client side - Google Patents
Multi-objective optimization method and system for multi-energy system on client side Download PDFInfo
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Abstract
The invention discloses a multi-target optimization method and a multi-target optimization system for a client-side multi-energy system, which are used for acquiring attribute information of energy equipment in the client-side multi-energy system and acquiring forecast information of client electric load, cold and hot load and renewable energy output in the multi-energy system; constructing decision variables and constraint conditions of an optimization problem according to the acquired attribute information and the prediction information; respectively constructing each single objective function considering economy, energy conservation and environmental protection based on decision variables and constraint conditions; and setting preference weights of decision makers for the single objective functions, constructing a decision multi-objective optimization function, and solving to obtain a final optimal solution. The invention improves the linear weighted sum method to truly reflect the importance degree of each component; meanwhile, three major targets of economy, carbon emission and energy conservation are considered in the method, the method comprises a decision maker preference input module, a source-network-load-storage multi-energy optimization model and a linear optimization problem processing module, and the selection requirements of different decision makers on optimization targets can be met.
Description
Technical Field
The invention belongs to the technical field of client-side multi-energy systems, and particularly relates to a client-side multi-energy system multi-target optimization method considering preference of a decision maker.
Background
Compared with the traditional single energy system, the comprehensive energy system comprises a plurality of links such as sources, networks, loads and storages, covers various energy sources such as electricity, gas, cold and heat, and has strong energy coupling and complex operation mechanism, so that multi-energy collaborative optimization research needs to be carried out. The important purpose of the multi-energy collaborative optimization is to fully and reasonably utilize various energy sources and equipment operation capacity, reduce the comprehensive operation cost of a multi-energy system and realize the high-efficiency utilization and clean utilization of the energy sources. This is therefore a research process for multi-objective optimization.
Currently, multi-objective optimization for integrated energy systems focuses mainly on the following two aspects. On one hand, most researches focus on researching the multi-objective optimization problem by adopting a heuristic algorithm, such as a multi-objective particle swarm algorithm based on a multi-objective, a multi-objective optimization method based on a differential evolution algorithm, application of an artificial bee colony algorithm in multi-objective optimization, and multi-objective optimization application based on a combined algorithm. The heuristic algorithm has better solving capability for the nonlinear problem. On the other hand, the research of multi-objective optimization is also carried out from the perspective of the Pareto optimal solution set, for example, a genetic algorithm is utilized to solve an approximate Pareto optimal solution set, and the fast non-dominated solution sequencing is carried out according to the NSGA-II algorithm.
In summary of the current research situation, although the heuristic algorithm has the capability of solving the nonlinear problem, the dilemma of being locally optimal cannot be overcome due to the natural defect of random search. So that the heuristic algorithm is difficult to be put into practical online scheduling. And the multi-target Pareto optimal solution set is solved, the preference of a decision maker is not considered, and the method cannot be directly applied to practice. Since the decision maker needs a certain solution rather than a set of solutions. Linear programming algorithms and linear weighted sum methods can effectively solve the above problems. The modern linear programming algorithm essentially originates from the simplex method proposed in the last century, has the characteristics of high solving speed and stable result, and has the defect that the problem solving must be linear. The linear weighted sum method can make each component approach to the optimal value according to the importance degree, so as to provide a specific solution for a decision maker.
Disclosure of Invention
The invention aims to solve the defects in the prior art, provides the multi-energy system multi-target optimization method considering the preference of a decision maker, improves the linear weighting sum method to truly reflect the importance degree of each component, and can meet the selection requirements of different decision makers on optimization targets.
In order to achieve the technical purpose, the invention adopts the following technical scheme.
In one aspect, the invention provides a multi-objective optimization method for a multi-energy system at a client side, which comprises the following steps:
acquiring attribute information of energy equipment in a client-side multi-energy system, and acquiring forecast information of client electric load, cold and hot load and renewable energy output in the multi-energy system;
constructing decision variables and constraint conditions of an optimization problem according to the acquired attribute information and the prediction information; respectively constructing each single objective function considering economy, energy conservation and environmental protection based on decision variables and constraint conditions, and solving the optimal value of each single objective function;
and setting preference weights of decision makers for the optimal values of the single objective functions, constructing a decision multi-objective optimization function, and solving the decision multi-objective optimization function to obtain a final optimal solution.
Further, the constructed constraint conditions comprise equipment operation constraints and system balance constraints, the equipment operation constraints comprise gas generator and lithium bromide unit operation constraints, power grid constraints, energy storage battery constraints, ground source heat pump constraints, air source heat pump constraints and water energy storage tank constraints, and the system balance constraints comprise: electrical balance constraints, cold-hot balance constraints and gas balance constraints.
In a second aspect, the invention provides a multi-objective optimization system of a multi-energy system at a client side, which comprises an information acquisition module, a single objective function construction and solving module and a decision multi-objective optimization function construction and solving module;
the information acquisition module is used for acquiring attribute information of energy equipment in the client-side multi-energy system and acquiring forecast information of client electric load, cold and hot load and renewable energy output in the multi-energy system;
the single objective function construction and solving module is used for constructing decision variables and constraint conditions of an optimization problem according to the acquired attribute information and the prediction information; respectively constructing each single objective function considering economy, energy conservation and environmental protection based on decision variables and constraint conditions, and solving an optimal value of each single objective function;
the decision multi-objective optimization function constructing and solving module is used for setting preference weight of a decision maker for the optimal value of each single objective function, constructing a decision multi-objective optimization function, and solving the decision multi-objective optimization function to obtain a final optimal solution.
The present invention further provides a computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the steps of the method for optimizing the multi-target of the multi-energy system on the client side according to the above technical solution.
The beneficial technical effects are as follows:
the method comprises a linearization modeling process to meet the linear requirement of the algorithm, and improves the linear weighting sum method to enable the method to truly reflect the importance degree of each component; meanwhile, the method considers three targets of economy, carbon emission and energy conservation, comprises a decision maker preference input module, a source-network-load-storage multi-energy optimization model and a linear optimization problem processing module, and can meet the selection requirements of different decision makers on optimization targets.
Drawings
FIG. 1 is an overall architecture diagram of an embodiment of the present invention;
FIG. 2 shows the results of index normalization for different economic target weights in accordance with an embodiment of the present invention;
FIG. 3 shows the normalized result of the index of different environmental protection target weights according to the embodiment of the present invention;
fig. 4 shows the index normalization result of different energy-saving target weights according to the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The embodiment I provides a multi-objective optimization method for a client-side multi-energy system, which comprises the following steps: acquiring attribute information of energy equipment in a client-side multi-energy system, and acquiring forecast information of client electric load, cold and hot load and renewable energy output in the multi-energy system; constructing decision variables and constraint conditions of an optimization problem according to the acquired attribute information and the prediction information; respectively constructing each single objective function considering economy, energy conservation and environmental protection based on decision variables and constraint conditions; and setting preference weights of decision makers for the single objective functions, constructing a decision multi-objective optimization function, and solving the decision multi-objective optimization function to obtain a final optimal solution.
The embodiment specifically comprises the following steps:
1.1 firstly reading information of energy equipment, wherein the energy equipment comprises a gas generator, a power grid, a lithium bromide unit, a ground source heat pump, an air source heat pump, an energy storage battery and a water energy storage tank. In this embodiment, the obtaining of the attribute information of the power supply device, the cold and heat supply device, the power storage device, and the cold and heat storage device specifically includes (the parameters are shown in table 1):
basic information of the gas generator: maximum generating power P of gas generator e，max，GG Minimum generating power P of gas generator e，min，GG And the electric power generation efficiency eta e，GG ；
Basic information of the power grid: maximum power purchasing power P of power grid e，max，G Minimum electricity purchasing power P of power grid e，min，G ；
Basic information of the lithium bromide unit: thermoelectric ratio lambda of refrigerating thermal power by utilizing waste heat flue gas e2t，LBAC Maximum post-combustion refrigeration/thermal power P t，supmax，LBAC Minimum post-combustion cooling/heating power P t，supmin，LBAC And post-combustion refrigeration/thermal efficiency eta t，sup，LBAC ；
Basic information of the ground source heat pump: maximum refrigeration/heat power P of ground source heat pump t，max，GSHP Minimum cooling/heating power P t，min，GSHP Coefficient of performance of refrigeration/thermal t，GSHP ；
Basic information of air source heat pump: maximum refrigeration/thermal power P of air source heat pump t，max，ASHP Minimum refrigeration/heat power P of air source heat pump t，min，ASHP Coefficient of energy efficiency for refrigeration and heating t，ASHP ；
Basic information of the energy storage battery: including the current storage capacity C e，now，ESB Maximum storage capacity C e，max，ESB Minimum storage capacity C e，min，ESB Maximum charging power P e，chmax，ESB Maximum discharge power P e，dismax，ESB Minimum charging power P e，chmin，ESB Minimum discharge power P e，dismin，ESB Charging efficiency eta e，ch，EsB Discharge efficiency eta e，dis，ESB ；
Basic information of the water energy storage tank: current cold/hot storage state C t，now，WS Maximum cold storage/heat capacity C t，max，WS Minimum cold storage/heat capacity C t，min，WS Maximum cold/heat charging power P t，chmax，WS Maximum cooling/heating power P t，dismax，WS Minimum cold/heat charging power P t，chmin，Ws Minimum cold/heat discharge power P t，dismin，WS And the cooling/heating efficiency eta t，ch，WS And the cooling/heating efficiency eta t，dis，WS 。
Table 1 energy plant related parameters
| Serial number | Parameter name | Unit of | Value of | Serial number | Name of parameter | Unit | Value of |
| 1 | P e，max，GG | kW | 330 | 18 | C e，min，ESB | kWh | 40 |
| 2 | P e，min，GG | kW | 100 | 19 | P e，chmax，ESB | kW | 200 |
| 3 | η e，GG | 0.4 | 20 | P e，dismax，ESB | kW | 200 | |
| 4 | P e，max，G | kW | 660 | 21 | P e，chmin，ESB | kW | 20 |
| 5 | P e，min，G | kW | 0 | 22 | P e，dismin，ESB | kW | 20 |
| 6 | |
1 | 23 | η e，ch，ESB | 0.95 | ||
| 7 | P t，supmax，LBAC | kW | 376 | 24 | η e，dis，ESB | 0.95 | |
| 8 | P t，supmin，LBAC | kW | 75 | 25 | C t，now，WS | kWh | 393 |
| 9 | η t，sup，LBAC | 1.2 | 26 | C t，max，WS | kWh | 786 | |
| 10 | P t，max，GSHP | kW | 444 | 27 | C t，min，WS | kWh | 39 |
| 11 | P t，min，GSHP | kW | 90 | 28 | P t，chmax，WS | kW | 150 |
| 12 | COP t，GSHP | 4.5 | 29 | P t，dismax，WS | kW | 150 | |
| 13 | P t，max，ASHP | kW | 320 | 30 | P t，chmin，WS | kW | 30 |
| 14 | P t，min，ASHP | kW | 64 | 31 | P t，dismin，WS | kW | 30 |
| 15 | COP t，ASHP | 2.5 | 32 | η t，ch，WS | 0.9 | ||
| 16 | C e，now，ESB | kWh | 200 | 33 | η t，dis，WS | 0.9 | |
| 17 | C e，max，ESB | kWh | 400 |
1.2 reading the prediction information
The forecast information includes customer electrical load P e，L Cold/heat load P t，L And renewable energy output prediction information P in multi-energy system e，RES (in this example, the renewable energy source is only photovoltaic, so the output prediction information P of photovoltaic is read e，PV )。
2 building decision variables and constraint conditions
2.1 decision variables
The decision variables are control variables that can be regulated and controlled by each energy device (parameters are shown in table 2), and include: generating power set as gas generator
And the start-stop state of the gas generator
Power purchasing from the grid
And on-off state
Lithium bromide unit afterburning refrigeration/heat power
And post-combustion start-stop state
Refrigeration/heat power of ground source heat pump
And on-off state
Refrigeration heat power of air source heat pump
And start-stop state
Charging power of energy storage battery
State of charge of energy storage battery
And the discharge power of the energy storage cell
Discharge state of energy storage battery
Cold and heat charging power of water energy storage tank
Cold and hot charging state of water energy storage tank
Cooling and heating power of water energy storage tank
Cooling and heating state of water energy storage tank
P in the decision variables is a continuous variable, and b is a Boolean variable. The control time was set to 1 day with a unit time period of 1 hour, i.e. n =24.
TABLE 2 decision variable parameters
2.2 constraint Condition
The constraints include plant operating constraints and system balancing constraints. The equipment operation constraints comprise an upper and lower operating power limit constraint and an energy conversion constraint.
The gas generator and lithium bromide unit operating constraints are expressed as follows:
wherein q is gas Is the low calorific value of natural gas.
Is the gas consumption speed of the gas-fired generator,
the gas consumption speed of the lithium bromide unit is the same as that of the lithium bromide unit,
for cooling and heating lithium bromide unit
The grid constraints are as follows:
the energy storage cell is constrained as follows:
where the superscript i denotes the ith unit time period within the control time.
Indicates the ith unit timeThe state of charge of the segment energy storage battery,
for a minimum state of charge of the energy storage battery,
the maximum state of charge of the energy storage battery.
The charging and discharging power of the energy storage battery. Where charging is negative and discharging is positive.
The ground source heat pump constraints are expressed as follows:
wherein P is e，GSHP The power consumption of the ground source heat pump.
The air source heat pump constraints are expressed as follows:
wherein P is e，ASHP Is the power consumption of the air source heat pump.
The water energy storage tank is constrained as follows:
wherein
Shows the charging state of the water energy storage tank in the ith unit time period,
in order to be in the minimum state of charge energy,
in order to be in the maximum state of charge energy,
respectively is charge-discharge energy power and power consumption power, and lambda is a power consumption coefficient.
The system balance constraint comprises electric balance, cold and heat balance and gas balance.
The electrical balance is represented as:
wherein the content of the first and second substances,
in order to predict the power generation of the photovoltaic,
the electric quantity is purchased for the power grid,
in order to provide the electrical load to the consumer,
is the charge and discharge power of the energy storage battery,
is the power consumption of the ground source heat pump,
is the power consumption of the air source heat pump,
the consumed power of the water energy storage system is obtained. The right side of the equation consumes negative power and generates positive power.
The cold/heat balance is expressed as:
wherein
In order for the user to have a cold/hot load,
is the refrigeration heat power of the lithium bromide unit,
is the refrigeration heat power of the ground source heat pump,
is the cooling heat power of the air source heat pump,
the heat power for charging and discharging the water energy storage system.
The gas balance is expressed as:
wherein P is g，GS Is the total gas consumption speed of the gas,
is the gas consumption speed of the gas-fired generator,
the gas consumption speed of the lithium bromide unit.
3 multiple single objective functions
In the embodiment, three optimization targets are considered, namely economy, energy conservation and environmental protection. The calculation index of the economy is the operation cost of the system, including the electricity purchase cost and the gas purchase cost in the optimization time.
Wherein the content of the first and second substances,
time of use price, p, for the ith time period g The gas value is the gas value,
is the total gas consumption rate of the gas in the ith time period.
The calculation index of the energy saving performance is the quantity of the energy-saving standard coal purchased outside the system on the premise of meeting the load requirements of users. The outsourcing electric power conversion standard coal adopts equivalent conversion coefficient instead of equivalent conversion coefficient.
Wherein alpha is e2coal Conversion of equivalent power into standard coal factor, alpha g2coal The equivalent natural gas is converted into standard coal coefficient.
The calculation index of the environmental protection performance is that the amount of carbon dioxide emission is reduced by the purchased energy of the system on the premise of meeting the load demand of the user.
Wherein, the first and the second end of the pipe are connected with each other,
the carbon dioxide emission coefficient is converted into electric power,
the carbon dioxide emission coefficient is reduced for natural gas.
4 solving the optimal solution of the single target
And (3) respectively solving the minimum values of the three targets in the step (3) by using the decision variables and the constraint conditions constructed in the step (2). The constructed optimization problem is a mixed integer linear programming problem, and an open source solver is adoptedQuickly obtaining the optimal value of each target, and respectively marking the optimal values as
And
5 multiple target to single target
Regardless of the preference of the decision maker, a set of solutions, called Pareto (Pareto) fronts, is obtained when solving the multi-objective problem. In pareto frontiers, for a certain solution, no other solution within the feasible set can improve it. However, the actual problem often needs to obtain a definite answer, and it is not enough to obtain a solution set. Therefore, the preference of a decision maker needs to be added, and the multi-target problem is converted into a single-target problem to be solved.
Constructing a multi-objective function vector as follows:
f＝[f 1 ，f 2 ，f 3 ] T
a decision maker preference vector is constructed. The decision maker preference module is input from the outside and sets the weight values of different targets according to the attention degree of the decision maker to each target.
α＝[α 1 ，α 2 ，α 3 ]
Wherein alpha is 1 +α 2 +α 3 ＝1。
The component weighting sum method can well give consideration to the influence of different targets, and commonly used linear weighting sum methods, square weighting sum methods, alpha-methods, statistical weighting sum methods and the like. The linear weighted sum method can make each component approach to the optimal value according to the importance degree, and the function is expressed as follows:
if the optimal values of the two targets are too different, the method has the problem that the influence of the targets cannot be accurately reflected. Therefore, in other embodiments, the magnitude difference between objective function values can be eliminatedThe policy multi-objective optimization function is expressed as follows:
6 solving an optimization problem
And (5) constructing an optimization problem by using the decision variables and the constraint conditions constructed in the step (2) and the objective function constructed in the step (5), wherein the problem is a mixed integer linear programming problem, and an open source solver is adopted to obtain the optimal solution of the decision variables and each single target value.
7 compare and select
And (3) repeating the steps 5-6 by changing the weight coefficient preferred by the decision maker to obtain a plurality of groups of different decision variable solutions and objective function values, and selecting an optimal operation scheme after comprehensive consideration. A method for changing the weight values of the other two targets with the main change target is adopted, and various combined results are tested. The ratio of each target value to the optimal value under each weight ratio is plotted as a graph, as shown in fig. 2 to 4, so that the direct comparison effect of the schemes preferred by different decision makers can be obtained.
Correspondingly, the invention further provides a multi-objective optimization system of the multi-energy system at the client side, which is characterized by comprising an information acquisition module, a single objective function construction and solving module and a decision multi-objective optimization function construction and solving module;
the information acquisition module is used for acquiring attribute information of energy equipment in the client-side multi-energy system and acquiring forecast information of client electric load, cold and hot load and renewable energy output in the multi-energy system;
the single objective function construction and solving module is used for constructing decision variables and constraint conditions of an optimization problem according to the acquired attribute information and the prediction information; respectively constructing each single objective function considering economy, energy conservation and environmental protection based on decision variables and constraint conditions, and solving the optimal value of each single objective function;
the decision multi-objective optimization function constructing and solving module is used for setting preference weight of a decision maker for the optimal value of each single objective function, constructing a decision multi-objective optimization function, and solving the decision multi-objective optimization function to obtain a final optimal solution.
It should be noted that in this embodiment, the implementation of each module corresponds to the above method, and this embodiment is not described again.
The invention provides a multi-energy system multi-target optimization method considering the preference of a decision maker. The optimization method comprises a linear modeling process to meet the linear requirement of the algorithm, and the linear weighting sum method is improved to truly reflect the importance degree of each component. Specifically, the method considers three major targets of economy, carbon emission and energy conservation, comprises a decision maker preference input module, a source-network-load-storage multi-energy optimization model and a linear optimization problem processing module, and can meet the selection requirements of different decision makers on optimization targets.
As will be appreciated by one skilled in the art, 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 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (7)
1. The multi-target optimization method for the multi-energy system on the client side is characterized by comprising the following steps of:
acquiring attribute information of energy equipment in a client-side multi-energy system, and acquiring forecast information of client electric load, cold and hot load and renewable energy output in the multi-energy system;
constructing decision variables and constraint conditions of an optimization problem according to the acquired attribute information and the prediction information; respectively constructing each single objective function considering economy, energy conservation and environmental protection based on decision variables and constraint conditions, and solving the optimal value of each single objective function;
setting preference weights of decision makers for the optimal values of the single objective functions, constructing a decision multi-objective optimization function, and solving the decision multi-objective optimization function to obtain a final optimal solution;
the constructed constraint conditions comprise equipment operation constraint and system balance constraint, the equipment operation constraint comprises gas generator and lithium bromide unit operation constraint, power grid constraint, energy storage battery constraint, ground source heat pump constraint, air source heat pump constraint and water energy storage tank constraint, and the system balance constraint comprises: electrical balance constraints, cold-hot balance constraints and gas balance constraints;
the equipment operation constraints specifically include:
(1) the gas generator and lithium bromide unit operating constraints are expressed as follows:
wherein q is gas Is natural gas low calorific value, P e，max，GG Is the maximum power generation power, P, of the gas generator e，min，GG The minimum generating power of the gas generator is obtained;
is the gas consumption speed, eta, of the gas-fired generator e，GG Generating efficiency for the gas generator; p t，supmin，LBAC The minimum cold heating power of the cold and heat supply equipment is provided;
P t，supmax，LBAC the maximum refrigeration and heating power of the afterburning combustion is utilized;
the gas consumption speed of the lithium bromide unit; eta t，sup，LBAC The heating efficiency is the afterburning refrigeration;
the refrigeration heat power of the lithium bromide unit; lambda [ alpha ] e2t，LBAC The thermoelectric ratio is the refrigeration thermal power of the waste heat flue gas;
(2) the grid constraints are as follows:
P e，max，G for maximum purchase power, P, of the grid e，min，G The minimum electricity purchasing power of the power grid is obtained;
(3) the energy storage cell constraints are as follows:
wherein the superscript i represents the ith unit time period within the control time;
indicating the state of charge of the energy storage battery for the ith unit time period,
is the minimum state of charge of the energy storage battery,
the maximum charge state of the energy storage battery is obtained;
the charging and discharging power of the energy storage battery; wherein the charging is negative and the discharging is positive, P e，chmax，ESB A maximum charging power; state of charge of energy storage battery
P e，chmin，ESB Storing the minimum charging power for energy;
charging power for the energy storage battery;
the discharge state of the energy storage battery is set;
is the discharge power of the energy storage battery; p e，chmax，ESB For storing maximum charging power, P e，dismax，ESB Storing the maximum discharge power; p e，chmin，ESB Storing the minimum charging power for energy; p is e，dismin，ESB Storing the minimum discharge power; eta e，ch，ESB Charging efficiency for energy storage; eta e，dis，ESB The energy storage discharge efficiency is obtained; c e，max，ESB The maximum energy storage capacity is stored;
(4) ground source heat pump constraints are expressed as follows:
wherein
The state is the starting and stopping state of the ground source heat pump; p is t，min，GSHP The minimum refrigeration and heating power of the ground source heat pump is achieved; p t，max，GSHP The ground source heat pump has the maximum refrigerating and heating power;
is the power consumption of the ground source heat pump,
is the cooling heat power of the air source heat pump,
the heat power for charging and discharging the water energy storage system; p is e，GSHP The power consumption of the ground source heat pump;
the refrigeration heat power of the ground source heat pump; COP (coefficient of Performance) t，GSHP The energy efficiency coefficient of the refrigeration and heating of the ground source heat pump is obtained;
(5) the air source heat pump constraint is expressed as follows:
wherein P is e，ASHP The power consumption of the air source heat pump;
(6) the water accumulator tank is constrained as follows:
wherein P is t，chmin，WS The minimum cold charging and heat charging power of the water energy storage tank; p t，chmax，WS The water energy storage tank has the maximum cold charging and heat charging power;
the cold and heat charging power of the water energy storage tank is provided;
the cold and hot charging state of the water energy storage tank is realized;
the cooling and heating power of the water energy storage tank is adopted;
the cold and hot state of the water energy storage tank is set; p t，dismax，WS The maximum cooling and heat release power of the water energy storage tank is achieved; p is t，dismin，WS Minimum cooling and heat release power for the water energy storage tank; eta t，ch，WS Efficiency of cold charging and heat charging for water energy storage tank eta t，dis，WS The heat release efficiency of the water energy storage tank is improved; c t，max，WS The maximum cold and heat storage capacity of the water energy storage tank is set;
showing the charging state of the water energy storage tank in the ith unit time period,
in order to be in a state of minimum energy loading,
in order to be in the maximum state of charge energy,
in order to charge and discharge the energy-saving power,
λ is the power consumption coefficient;
the decision variables in the above constraints include: generated power of gas generator
Generating power starting and stopping state of gas generator
Power purchasing from the grid
Start-stop state of power purchase from power grid
Lithium bromide unit afterburning refrigeration and heating power
Post-combustion start-stop state
P in the decision variables is a continuous variable, and b is a Boolean variable.
2. The method for multi-objective optimization of a client-side multi-energy system according to claim 1, wherein the system balance constraints specifically include:
(1) the electrical balance is represented as:
wherein the content of the first and second substances,
in order to predict the power generation amount of the photovoltaic,
purchasing electric quantity for the power grid; the right side of the equation is negative in power consumption and positive in power generation;
in order to provide the electrical load to the consumer,
is the charging and discharging power of the energy storage battery,
is the power consumption of the ground source heat pump,
is the power consumption of the air source heat pump,
the consumed power of the water energy storage system;
(2) the cold-heat balance is expressed as:
wherein
A user cold/heat load;
is the refrigeration heat power of the lithium bromide unit,
is the refrigeration heat power of the ground source heat pump,
is the refrigeration heat power of the air source heat pump,
the heat power for charging and discharging the water energy storage system;
(3) the gas balance is expressed as:
wherein P is g，GS The total gas consumption rate is the gas consumption rate;
is the gas consumption speed of the gas-fired generator,
the gas consumption speed of the lithium bromide unit.
3. The multi-objective optimization method for the client-side multi-energy system according to claim 1, wherein the respectively constructing of each single objective function considering economy, energy conservation and environmental protection based on decision variables and constraint conditions specifically comprises:
the objective function considering economy is expressed as follows:
wherein the content of the first and second substances,
time of use price, p, for the ith time period g For gas prices, the optimization problem formed by the objective function has the optimal value of
The objective function considering the energy saving property is expressed as follows:
wherein alpha is e2coal Conversion of equivalent power into standard coal factor, alpha g2coal For equivalent natural gas to standard coal coefficient, the optimal value of the optimization problem formed by the objective function is
The objective function considering environmental protection is expressed as follows:
wherein the content of the first and second substances,
the carbon dioxide emission coefficient is converted into electric power,
the optimal value of the optimization problem formed by the objective function is that the coefficient of the emission of carbon dioxide for natural gas is reduced to be
4. The method of multi-objective optimization for a client-side multi-energy system according to claim 1, wherein the decision multi-objective optimization function is expressed as follows:
wherein f is 1 To take into account the economic objective function, f 2 To take into account the objective function of energy saving, f 3 An objective function considering environmental protection; alpha is alpha 1 To take account of the economic objective function f 1 The weight of (a) is determined,
α 2 to take into account the objective function f of energy saving 2 The weight of (a) is calculated,
α 3 to take into account the environmental protection of the objective function f 3 Weight of (a), a 1 +α 2 +α 3 The value of =1,n takes 3.
5. The method of multi-objective optimization for a client-side multi-energy system according to claim 1, wherein the decision multi-objective optimization function is expressed as follows:
wherein f is 1 To take into account the economic objective function, f 2 To take into account the objective function of energy saving, f 3 An objective function considering environmental protection;
to take into account the target optimum value for economy,
to take into account the target optimum value for energy saving,
a target optimum value considering environmental protection;
α 1 to take account of the economic objective function f 1 Weight of (a), a 2 To take into account the objective function f of energy saving 2 Weight of (a), a 3 To take into account the objective function f of environmental protection 3 Weight of (a), a 1 +α 2 +α 3 The value of =1,n takes 3.
6. The multi-objective optimization system of the multi-energy system at the client side is characterized by comprising an information acquisition module, a single objective function construction and solving module and a decision multi-objective optimization function construction and solving module;
the information acquisition module is used for acquiring attribute information of energy equipment in the client-side multi-energy system and acquiring forecast information of client electric load, cold and hot load and renewable energy output in the multi-energy system;
the single objective function construction and solving module is used for constructing decision variables and constraint conditions of an optimization problem according to the acquired attribute information and the prediction information; respectively constructing each single objective function considering economy, energy conservation and environmental protection based on decision variables and constraint conditions, and solving the optimal value of each single objective function;
the decision multi-objective optimization function constructing and solving module is used for setting decision maker preference weights for the optimal values of the single objective functions, then constructing a decision multi-objective optimization function, and solving the decision multi-objective optimization function to obtain a final optimal solution;
the constructed constraint conditions comprise equipment operation constraint and system balance constraint, the equipment operation constraint comprises gas generator and lithium bromide unit operation constraint, power grid constraint, energy storage battery constraint, ground source heat pump constraint, air source heat pump constraint and water energy storage tank constraint, and the system balance constraint comprises: electrical balance constraints, cold-hot balance constraints and gas balance constraints;
the equipment operation constraints specifically include:
(1) the gas generator and lithium bromide unit operating constraints are expressed as follows:
wherein q is gas Is natural gas low calorific value, P e，max，GG Is the maximum power generation power, P, of the gas generator e，min，GG The minimum generating power of the gas generator;
is the gas consumption speed, eta, of the gas-fired generator e，GG Generating efficiency for the gas generator; p t，supmin，LBAC The minimum cold heating power of the cold and heat supply equipment is provided;
P t，supmax，LBAC the maximum refrigeration and heating power of the afterburning combustion is utilized;
the gas consumption speed of the lithium bromide unit; eta t，sup，LBAC The heating efficiency is the afterburning refrigeration;
the refrigeration heat power of the lithium bromide unit; lambda [ alpha ] e2t，LBAC The thermoelectric ratio is the refrigeration thermal power of the waste heat flue gas;
(2) the grid constraints are as follows:
P e，max，G for maximum power purchase, P, of the grid e，min，G The minimum electricity purchasing power of the power grid is obtained;
(3) the energy storage cell constraints are as follows:
wherein the superscript i represents the ith unit time period within the control time;
indicating the state of charge of the energy storage battery for the ith unit time period,
is the minimum state of charge of the energy storage battery,
the maximum charge state of the energy storage battery is obtained;
the charging and discharging power of the energy storage battery; wherein the charging is negative and the discharging is positive, P e，chmax，ESB A maximum charging power; state of charge of energy storage battery
P e，chmin，ESB Storing the minimum charging power for energy;
to storeCharging power of the battery;
the discharge state of the energy storage battery is set;
is the discharge power of the energy storage battery; p e，chmax，ESB For storing maximum charging power, P e，dismax，ESB Storing the maximum discharge power; p e，chmin，ESB Storing the minimum charging power for energy; p is e，dismin，ESB Storing the minimum discharge power; eta e，ch，ESB Charging efficiency for energy storage; eta e，dis，ESB The energy storage discharge efficiency is obtained; c e，max，ESB The maximum energy storage capacity is stored;
(4) ground source heat pump constraints are expressed as follows:
wherein
The state is the starting and stopping state of the ground source heat pump; p t，min，GSHP The minimum refrigeration and heating power of the ground source heat pump is achieved; p is t，max，GSHP The ground source heat pump has the maximum refrigerating and heating power;
is the power consumption of the ground source heat pump,
is the refrigeration heat power of the air source heat pump,
the heat power for charging and discharging the water energy storage system; p e，GSHP The power consumption of the ground source heat pump;
the cooling heat power of the ground source heat pump; COP t，GSHP The energy efficiency coefficient of the ground source heat pump for refrigeration and heating;
(5) the air source heat pump constraint is expressed as follows:
wherein P is e，ASHP The power consumption of the air source heat pump;
(6) the water accumulator tank is constrained as follows:
wherein P is t，chmin，WS The minimum cold charging and heat charging power of the water energy storage tank; p is t，chmax，WS The water energy storage tank has the maximum cold charging and heat charging power;
cold and hot charging power for the water energy storage tank;
the cold and hot charging state of the water energy storage tank is realized;
the heat and cold power of the water energy storage tank;
the cold and hot state of the water energy storage tank is set; p t，dismax，WS The maximum cooling and heat release power of the water energy storage tank is achieved; p t，dismin，WS Minimum cooling and heat release power for the water energy storage tank; eta t，ch，WS Efficiency of cold charging and heat charging for water energy storage tank eta t，dis，WS The heat release efficiency of the water energy storage tank is improved; c t，max，WS The maximum cold and heat storage capacity of the water energy storage tank is set;
showing the charging state of the water energy storage tank in the ith unit time period,
in order to be in the minimum state of charge energy,
in order to be in the maximum energy-loaded state,
in order to charge and discharge the energy power,
is power consumption power, and lambda is power consumption coefficient;
the decision variables in the above constraints include: generated power of gas generator
Generating power starting and stopping state of gas generator
Power purchasing from the grid
Start-stop state for purchasing power from power grid
Lithium bromide unit afterburning refrigeration and heating power
Post-combustion start-stop state
P in the decision variables is a continuous variable, and b is a Boolean variable.
7. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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