CN115099509A - Source-network load-storage multi-target cooperative regulation and control method and system - Google Patents
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Abstract
The invention belongs to the technical field of source network charge storage cooperative regulation and control, and provides a source network charge storage multi-target cooperative regulation and control method and a source network charge storage multi-target cooperative regulation and control system, wherein the method comprises the following steps: acquiring the predicted load of the comprehensive energy system in each planned time interval in the period to be regulated, and acquiring the output plan of each device in each planned time interval in the period to be regulated by adopting a multi-objective optimization algorithm and minimizing the operation optimization target under the energy balance constraint, the device output constraint and the energy interaction constraint; in a cycle to be regulated and controlled, dividing each plan time interval into a plurality of optimization time intervals, obtaining the actual load of each optimization time interval, and optimizing the output plan of each device in each optimization time interval under the device output constraint, climbing rate constraint, power grid constraint and natural gas pipe network constraint by taking the minimum deviation between the energy supply and the actual load after the output plan is executed as a target. And the source network load storage equipment of the comprehensive energy system is cooperatively regulated and controlled.
Description
Technical Field
The invention belongs to the technical field of source network charge storage cooperative regulation and control, and particularly relates to a source network charge storage multi-target cooperative regulation and control method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The source network and load storage cooperative interaction of the comprehensive energy system means that the dynamic balance capacity of the comprehensive energy system is improved more economically, efficiently and safely by a plurality of interaction forms among a power supply, a power network, a load and energy storage, and the cooperation is essentially an operation mode capable of realizing the maximum utilization of energy resources. The traditional power system operation control mode is that a power supply tracks load change to adjust, and no obvious interaction relation is formed. The source end, the network end, the stored energy and the load of the comprehensive energy system have flexible characteristics, so that comprehensive source network charge and storage interaction is formed, and various interaction modes such as source-source complementation, source network coordination, network charge interaction, source charge interaction, network storage interaction and the like are presented. The complex interaction mode brings great challenges to the cooperative regulation and control of the equipment in the comprehensive energy system.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a source network load storage multi-target cooperative regulation and control method and a source network load storage multi-target cooperative regulation and control system, which realize the cooperative regulation and control of source network load storage equipment of a comprehensive energy system through day-ahead optimization and day-in rolling optimization.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a source network load storage multi-target cooperative regulation and control method, which comprises the following steps:
acquiring the predicted load of the comprehensive energy system in each planning time interval in the cycle to be regulated, and acquiring the output plan of each device in each planning time interval in the cycle to be regulated by adopting a multi-objective optimization algorithm and minimizing an operation optimization target under the energy balance constraint, the device output constraint and the energy interaction constraint;
in a cycle to be regulated and controlled, dividing each plan time interval into a plurality of optimization time intervals, obtaining the actual load of each optimization time interval, and optimizing the output plan of each device in each optimization time interval under the device output constraint, climbing rate constraint, power grid constraint and natural gas pipe network constraint by taking the minimum deviation between the energy supply and the actual load after the output plan is executed as a target.
Further, the device processing constraints include energy production and conversion device output constraints, energy storage device constraints, thermal storage device constraints, and cold storage device constraints.
Further, the multi-objective optimization algorithm adopts a non-local sequencing genetic algorithm.
Further, the operational optimization objectives include an integrated energy utilization efficiency objective, a reliability objective, an economic objective, and an environmental protection objective.
Further, the energy balancing constraints include an electrical load balancing constraint, a thermal load balancing constraint, a cold load balancing constraint, and a natural gas load balancing constraint.
Further, the natural gas pipe network constraints include a node flow balance constraint and a gas flow pressure constraint.
Further, the equipment comprises energy storage equipment, heat storage equipment, cold accumulation equipment, a fan, photovoltaic power generation equipment, an air conditioner, a heat pump and equipment in a combined cooling heating and power system.
The second aspect of the invention provides a source-network load-storage multi-target cooperative regulation and control system, which comprises:
a day-ahead optimization module configured to: acquiring the predicted load of the comprehensive energy system in each planning time interval in the cycle to be regulated, and acquiring the output plan of each device in each planning time interval in the cycle to be regulated by adopting a multi-objective optimization algorithm and minimizing an operation optimization target under the energy balance constraint, the device output constraint and the energy interaction constraint;
a roll optimization module configured to: in a cycle to be regulated and controlled, dividing each plan time interval into a plurality of optimization time intervals, obtaining the actual load of each optimization time interval, and optimizing the output plan of each device in each optimization time interval under the device output constraint, climbing rate constraint, power grid constraint and natural gas pipe network constraint by taking the minimum deviation between the energy supply and the actual load after the output plan is executed as a target.
A third aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the source-network load-store multi-target cooperative regulation and control method as described above.
A fourth aspect of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps in the source-network-load-storage multi-target cooperative regulation and control method described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a source network load storage multi-target cooperative regulation and control method which realizes cooperative regulation and control of source network load storage equipment of a comprehensive energy system through day-ahead optimization and day-in rolling optimization.
The invention provides a source network charge storage multi-target cooperative regulation and control method, which adopts a multi-target optimization algorithm under the energy balance constraint, the equipment output constraint and the energy interaction constraint to enable output plans of all equipment in the day-ahead optimization to be more in line with requirements.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flow chart of a source-network load-storage multi-target cooperative regulation method according to an embodiment of the present invention;
fig. 2 is a graph comparing the optimization results before the day and the 7-cycle optimization results in the day according to the first embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
The embodiment provides a source network and load storage multi-target cooperative regulation and control method, which specifically comprises the following steps as shown in fig. 1:
According to the 'two high and three low' target of the integrated energy system, the operation optimization target of the integrated energy system comprises the following steps: an overall energy utilization efficiency goal, a reliability goal, an economic goal, and an environmental protection goal.
The target of the integrated energy utilization efficiency is a comprehensive index reflecting the energy consumption level and the effective utilization degree in the regional integrated energy system, and is a ratio of the energy effectively utilized in the system to the actually consumed energy, and may be expressed as η ═ effective utilized energy/supplied energy × 100 ═ 1-lost energy/supplied energy × 100%. The input energy in the electric-heating IES is electric energy from the power grid and energy from an energy network external to the system. The output energy is the load demand of the user. Furthermore, renewable energy sources reasonably utilized by the system itself, such as wind energy, light energy and air heat energy, do not belong to the energy input. However, the load that can be met by renewable energy sources is included in the energy output. The comprehensive energy utilization efficiency target is min 1/eta IES 。
In the formula: p e And P g Respectively representing the electric energy and the fuel gas input by an external pipe network; l is e And L h Represents the electrical and thermal load, respectively; s e And S h After taking into account the energy losses of the energy storage devices (batteries and other forms of energy storage),respectively representing the actually stored electric energy and heat energy; d e And D h After considering the energy release loss of the battery and other energy storage forms, the energy release respectively represents the actual energy release of electric energy and heat energy; lambda E ,λ G And λ H Energy coefficients representing electrical, gas and thermal energy; lambda [ alpha ] e ，λ h Is the energy coefficient of the electrical and thermal load. The energy coefficient of electric power is 1. The mathematical expressions for the thermal load and gas energy coefficients are as follows:
in the formula: t is a unit of 0 Represents ambient temperature; t represents the complete combustion temperature of the gas; t is h Representing the heat load temperature.
The reliability target can be defined as the proportion of the regional integrated energy system to purchase electricity to the large power grid and the proportion coefficient of the system power shortage, for the grid-connected regional comprehensive energy system, as a large power grid supports the power supply of the regional comprehensive energy system, certain power supply reliability can be achieved, but in order to reduce the dependence of the regional comprehensive energy system on a large power grid and improve the self-power supply capability of the system, and improve the utilization rate of renewable energy sources, pursue the extremely small goal of purchasing electric quantity from a large power grid by a system, namely the minimum proportion microgrid electricity purchasing proportion coefficient, but for the island type regional comprehensive energy system, the minimum proportion microgrid electricity purchasing proportion coefficient is influenced by the intermittence of new energy output, the power supply reliability is low, and a large-capacity standby power supply such as BESS and a diesel generator needs to be configured to meet the power supply requirement, so that the island type regional comprehensive energy system pursues the electricity shortage as the minimum target, namely the minimum electricity shortage proportionality coefficient. The power shortage of the system is a power supply reliability index commonly used by the power system. According to the correlation between the comprehensive energy system and the power system, the energy supply reliability index of the comprehensive energy system is constructed as follows: loss of Energy Supply (LESP), which represents the ratio of the Energy Supply gap of the system to the total Energy demand over a certain period of time.
In the formula: lesp (t) is the energy supply loss rate; p k (t) is the ratio of the energy deficit in the system to the total energy demand in the t-plan period,%; e demand,t The total energy demand in the system is kW in the t planning period; e supply,t And supplying energy demand in the system for the t planning time period, kW. Therefore, the reliability target is the energy supply loss rate.
The economic objective refers to minimizing the operation cost in the operation process of the regional comprehensive energy system, the economic operation cost mainly comprises electricity purchase cost, heat purchase cost, natural gas cost, equipment operation maintenance cost, equipment depreciation cost and equipment start-stop cost, the natural gas cost is divided into two parts, namely the cost generated by directly using natural gas by a user and the cost generated by using natural gas as fuel of energy supply equipment such as CCHP (combined cycle power supply), a gas boiler and the like, the depreciation cost of the equipment only considers the depreciation cost of an energy storage system in the comprehensive energy system at present, and the depreciation cost of other energy supply equipment is not considered at first. The objective of IES operational optimization is to minimize system operating costs by properly arranging the output plan of each controllable unit to meet distributed power operating and grid constraints, i.e., the objective of integrated energy system economic regulation is to minimize energy costs. The economic operation cost mainly comprises electricity purchasing cost, heat purchasing cost, natural gas cost, equipment operation and maintenance cost, equipment depreciation cost and equipment start-stop cost. The objective function is as follows:
F 1 ＝minC op ＝C op-G +C op-H +C op-NG +C op-M +C op-D +C op-SS
in the formula, C op Represents the system operating cost, dollars; c op-G Representing the electricity purchase cost, yuan; c op-H Represents the cost of heat purchase, Yuan; c op-NG Represents the cost of purchasing natural gas, dollars; c op-M Representing the operation and maintenance cost of the equipment; c op-D Represents the depreciation cost of the equipment; c op-SS And the start-stop cost and the unit of the equipment are represented.
(1) Cost of purchasing electricity from power grid
In the formula (I), the compound is shown in the specification,
representing the electricity purchase price, element, of the planning period t;
representing the power purchasing power, kW, of a planning time interval t in the period to be regulated;
a price, dollar, representing the electricity sold at the planned time period t;
power sold in kW, which represents the planned time period t; t represents a regulation cycle.
(2) Cost of purchasing heat
In the formula (I), the compound is shown in the specification,
represents the heat purchase price, Yuan;
indicating the heat purchasing power, kW, for the t planning period; t represents the regulation period.
(3) Cost of natural gas
In the formula, delta CCHP Represents the operating cost of the heat energy supply module; p CCHP (t) represents the demand response gas price, dollars/m 3 ；
Indicating natural gas price, yuan/m 3 ；LHV NG Expressing the lower heating value of natural gas, kWh/m 3 ；P NG_st (t) represents the power of the natural gas storage system, kW; a is NG_st Represents the power cost of the natural gas storage system, dollars/kW; Δ t represents the duration of one planned time period t.
(4) Cost of equipment operation and maintenance
In the formula, C i Represents the operation and maintenance cost of the ith distributed generation, yuan/kW; p is i (t) represents the output power of the distributed generation, kW, for the t planning period.
(5) Cost of depreciation of equipment
The depreciation cost of the equipment mainly refers to the depreciation cost of the stored energy. The number of times of charge and discharge, the depth of discharge and the rate of discharge are all important factors influencing the length of the life cycle of energy storage, and according to the experimental data of a National Renewable Energy Laboratory (NREL), an accumulated damage model of a battery, namely, each discharge event can be converted into effective ampere hours. If the cumulative number of active ampere-hours reaches the total effective throughput, the battery is deemed to be unusable and must be replaced with an appropriately sized battery. The effective total throughput can be described, inter alia, by the following equation:
Γ R ＝L R D R C R
the charge-discharge time per unit time can be converted into equivalent ampere-hours considering the depth of discharge:
therefore, the equivalent ampere hour is used for calculation under the premise of considering the energy storage charging depth, and the aging cost of charging and discharging in unit time of energy storage can be specifically expressed as follows:
in the formula, L R Representing the cycle times of the stored energy under the rated discharge depth and the rated discharge current; d R Represents the rated depth of discharge; c R The rated capacity, Ah, at rated discharge current is shown; d A Representing the actual depth of discharge; d is a radical of act,t Representing the number of ampere-hours under equivalent discharge current per unit time; d is a radical of eff,t Represents the number of ampere hours under actual discharge current per unit time (planned time period t); u. of 0 And u 1 Representing the fitting parameters; c A Representing the actual capacity; c bat Representing initial investment cost of energy storage; alt represents the total charge-discharge time length from the beginning of the energy storage putting into use to the planned time period t in the period to be regulated.
(6) Cost of starting and stopping equipment
In the formula, C SS,i Representing the starting and stopping cost of the equipment i in unit planning time interval;
representing the starting and stopping states of the equipment i in a planning time period t;
representing the starting and stopping states of the device i in a planning time period t-1;
a value of (1) indicates a shutdown state and a value of (0) indicates a startup state.
The environmental protection target corresponds to the carbon emission level and the air pollutant emission level, mainly comprises the environmental loss caused by energy production pollutants, the non-environmental loss caused by the environmental loss and the pollution discharge fee collected by related departments, and can also realize the target of reducing the pollutant emission by improving the utilization level of renewable energy and reducing the light abandoning rate of wind abandon. Environmental protection goals include:
(1) carbon emissions
A combined cooling heating and power system and a gas boiler which take natural gas as fuel are important power and heat supply units for researching the system and are also important sources of pollutant emission. The effective reduction of carbon emission will not only play a significant role in environmental protection, but also be beneficial to the long-term operation of the park. The calculation formula is as follows:
carbon dioxide is a main factor causing global warming, and controlling the emission of carbon dioxide is of great significance for reducing the greenhouse effect. In the above formula, the first and second carbon atoms are,
is the total carbon emission of the integrated energy system, P grid And f gas Respectively representing the electric quantity purchased from the distribution network of the integrated energy system and the gas quantity purchased from the external gas network in the planning time interval t,
and
respectively representing the carbon dioxide conversion coefficient per unit electricity and the carbon dioxide conversion coefficient per unit gas.
(2) Pollutant discharge
In the capacity planning stage of the regional integrated energy system, the system pollutant emission and the environmental cost are also one of the factors of important consideration. The pollutant emission of the system mainly comes from outsourcing coal power, a CCHP unit and a GB unit.
In the formula, Q pollution Is the pollutant discharge amount, kg; alpha is alpha k Is the emission coefficient of the pollutant k in the power generation process; p grid (t) is the grid input power, kW; p in,NG (t) denotes natural gas input power, kW; beta is a k Is the emission coefficient of pollutant k in natural gas combustion, HV gas Indicating a low heating value of natural gas.
Wherein the energy balance constraints include:
(1) electrical load balancing constraints
P grid-buy (t)+P WT (t)+P PV (t)+P CCHP (t)+P EES-dis (t)＝P grid-sell (t)+P EES-char (t)+P load (t)
In the formula, P grid-buy (t) the power of the electric quantity purchased by the comprehensive energy system from the power grid is kW; p is grid-sell (t) is the power when the comprehensive energy system sells electricity to the power grid, kW; p WT (t) is the output power of the wind power generation (fan), kW; p pv (t) is the output power of the distributed photovoltaic power generation equipment, kW; p is CCHP (t) is CCHP electrical output power, kW; p EES-dis (t) is the discharge power of the battery, kW; p EES-char (t) is the charging power of the battery, kW; p is load (t) represents an electrical load within the integrated energy system; the CCHP comprises a gas power generation device, a gas boiler, an electric boiler, a refrigerating unit and the like.
(2) Thermal load balancing constraints
H h_grid (t)+H HP (t)+H AC (t)+H CCHP (t)+H h_re (t)＝H load (t)+H h_st (t)
In the formula, H h_grid (t) is the heat exchange power between the heating company and the integrated energy system, kW; h HP (t) is the heat pump output power, kW; h AC (t) is the output power of the air conditioner, kW; h CCHP (t) is CCHP heat output power, kW; h h_re (t) is a heat storage system (heat storage tank)) Power to release heat, kW; h load (t) heat load in the integrated energy system, kW; h h_st (t) is the power of the heat stored in the heat storage system, kW;
(3) cold load balancing constraints
L HP (t)+L AC (t)+L CCHP (t)+L l_re (t)＝L load (t)+L l_st (t)
In the formula, L HP (t) power of the heat pump output cooling load, L CCHP (t) is the power of CCHP output cooling load, kW; l is AC (t) is the power of the air conditioner output cooling load, kW; l is a radical of an alcohol h_re (t) power of cooling load released by a cold accumulation system, kW; l is a radical of an alcohol load (t) is the cooling load in the integrated energy system, kW; l is h_st (t) is the power of the cold load stored by the cold accumulation system, kW;
(4) natural gas load balance constraint
In the formula, P ng_grid (t) is the power of natural gas supplied by the natural gas grid to the integrated energy system, kW; p ng_st (t) is the power, kW, released by the gas storage system in the integrated energy system;
the power generation efficiency of gas power generation in CCHP,%; p ng_life And (t) is the gas load for residents in the regional comprehensive energy system, kW.
Wherein the device output constraints comprise:
(1) energy production and conversion equipment output constraints
The energy production and energy conversion equipment in the integrated energy system mainly comprises: fans, photovoltaics, gas boilers, electric boilers, air conditioners and the like. The output constraints are as follows:
0≤P PV ≤P PV_max
0≤P WT ≤P r
P heat_min ≤P heat ≤P heat_max
Q EB_min ≤Q EB ≤Q EB_max
Q er_min ≤Q er ≤Q er_max
in the formula, P WT 、P PV 、P heat 、Q EB And Q er Respectively the output of a fan, a photovoltaic, a gas boiler, an electric boiler and an air conditioner, P pv_max Is the upper output limit of the photovoltaic; p r Is the upper limit of the output of the fan; p is heat_min And P heat_max Respectively, the upper limit and the lower limit of the output of the gas boiler; q EB_max And Q EB_min Respectively, the upper limit and the lower limit of the output of the electric boiler; q er_max And Q er_min The upper and lower limit output of the air conditioner.
(2) Energy storage device (energy storage battery) restraint
The constraints of the energy storage battery mainly comprise charge and discharge power constraints, charge state constraints and balance constraints of the initial state and the final state of the equipment, and the specific constraints are as follows:
wherein SOC (t) and SOC (t) 0 ) Respectively representing the energy storage battery at t and t 0 A remaining capacity for a planned time period; delta represents the self-discharge rate,%/h, of the energy storage battery; SOC min And SOC max Minimum and maximum constraints for residual capacity, respectively; p is ch_e,max And P dis_e,max Maximum charge and discharge power, respectively, soc (t) representing the remaining capacity of the energy storage battery over the t scheduled time period; p bat (t) represents the battery load power of the energy storage battery during the t planning period, P ch_e (t) and P dis_e (t) charging and discharging power, η, of the energy storage battery during a t-plan period, respectively ch_e And η dis_e The conversion efficiency of the energy storage battery during charging and discharging is respectively.
(3) Restraint of heat storage tank
In the formula, Q TS (t) represents the remaining heat of the heat storage tank for the t-plan period; mu.s hloss The heat dissipation loss rate of the heat storage tank is represented; q TS (t 0 ) Representing an initial planning period t 0 The heat storage amount of the heat storage tank;
denotes t to t 0 The heat charging amount of the heat storage tank between planned time periods;
represents t to t 0 Heat release for the planned time period;
is the ratio of the maximum allowable stored heat to the stored heat capacity;
the ratio of the minimum allowable heat storage amount to the heat storage capacity; c TS Is the heat storage capacity; eta ch_h And η dis_h The conversion efficiency of the heat storage tank during heat charging and heat discharging is respectively.
(4) Ice storage tank restraint
CES min ≤CES(t)≤CES max
In the formula, CES max And CES min Maximum and minimum constraints on ice storage tank capacity, Q, respectively cesin,min And Q cesin,max Minimum and maximum values of ice storage capacity, Q, respectively cesout,min And Q cesout,max Minimum and maximum values of ice melting power, CES (t), Q cesin (t) and Q cesout (t) represents the capacity of the ice storage tank, the ice storage power and the ice melting power, respectively, during the planned time period t.
In the regional integrated energy system, considering the interaction relationship between the system and the outside and the safety between the system and the energy network, the energy exchange power between the integrated energy system and the external network must be kept within a certain range, and the energy interaction constraint is as follows:
P e_min ≤|P e_grid (t)|≤P e_max
P g_min ≤|P g_network (t)|≤P g_max
in the formula, P e_grid (t) is the electric quantity exchange power between the electric network and the comprehensive energy system in the planned time period t, P g_network (t) power for supplying natural gas to the integrated energy system from the natural gas grid for a planned time period t, P e_min The minimum electric quantity exchange power between the power grid and the comprehensive energy system is kW; p e_max The maximum electric quantity exchange power between the power grid and the comprehensive energy system is kW; p g_min A minimum power, kW, to supply natural gas to the integrated energy system for the natural gas grid; p is g_max The maximum power, kW, of natural gas is supplied to the integrated energy system for the natural gas grid.
Wherein, the multi-objective optimization Algorithm adopts a Non-local sequencing Genetic Algorithm (NSGA-II).
The multi-main-body active adjustment of the comprehensive energy system is realized by adjusting the output state of each energy device on the basis of the cooperative optimization of the comprehensive energy system so as to ensure that the devices are in temperature coincidence with the operating state variables on the basis of ensuring the safe operation of the devices. The objective of the multi-initiative optimization of the integrated energy system is to make the plant state variables consistent with predetermined variables, for example, in a heating system, the system return water temperature should be kept to meet the set return water temperature requirement (or, the heat supply amount of the heating system should meet the actual heat load).
The integrated energy system includes 3 subsystems: the electric system comprises Photovoltaic (PV), Wind Turbine (WT), storage Battery (BESS) and electric network; the cold system comprises a ground source Heat Pump (HP), an air conditioner, a refrigerating unit (a dual-working-condition refrigerating unit (AC) and a conventional refrigerating unit (EC)) and an ice storage tank (IS); the heat system comprises a ground source Heat Pump (HP), an air conditioner, an Electric Boiler (EB), a Gas Boiler (GB) and a heat storage Tank (TS). In the period to be regulated and controlled, if the output plan of each device (including an energy storage device (battery), a heat storage device (heat storage tank), a cold storage device (ice storage tank), a fan, a photovoltaic power generation device, a CCHP, an air conditioner and a heat pump) in each planned time interval obtained in step 1 is based on, the output plan is carried out on each device in each planned time interval, and the energy supply of each subsystem (including the power supply amount of an electric system in the comprehensive energy system, the cooling amount of a cold system and the heating amount of a warm system) can be obtained.
The minimum deviation between the energy supply and the actual load after the execution of the output plan is taken as a target, that is, the minimum deviation between the energy supply and the actual load of each subsystem is ensured, so that the objective function of the comprehensive energy system, which is actively optimized by multiple subjects, can be expressed as follows:
minF＝|X YS -X t |
in the formula, X YS For the actual load (i.e. the user's demand for energy supply to the subsystems), X t The state variable (energy supply) value of each subsystem in the optimization time period t after the output plan is executed (namely the state variable value of each subsystem in the planning time period belonging to the optimization time period t after the output plan is executed).
(1) And (3) equipment output constraint:
in the formula, P i,t,max And P i,t,min The maximum value and the minimum value of the output of the non-adjustable equipment i (such as a fan and a photovoltaic); p is f,t,max And P f,t,m i n Is an effective regulation and control interval of the adjustable and controllable unit f; p i,t The output of the uncontrollable equipment in the optimized time t is obtained; p f,t The output of the unit f can be regulated.
(2) And (3) climbing rate constraint: when the integrated energy system issues a regulation plan to the distributed energy devices, the following constraints exist:
in the formula, P i,t And P i,t-1 The device power of the current optimization time period t and the last optimization time period;
and
the maximum power the device is allowed to rise and fall per unit time, respectively.
(3) And (3) power grid constraint: the power flow constraint in the power grid comprises transmission power, circuit admittance and the like, and the specific relation is as follows:
in the formula (I), the compound is shown in the specification,
is the transmission power of the transmission line l at the time of the t optimization; l represents a set of all transmission lines; b l Is the circuit admittance of the transmission line;
and
phases of a starting node and a terminating node of the power transmission line l at the t optimization period respectively; m is a set value; y is le Is a 0-1 variable of the line operating state; p l max Is the upper limit of the transmission power of line l.
(3) Natural gas pipe network restraint: to ensure safe operation of the natural gas pipeline network, the constraints include a node flow balance constraint and a gas flow pressure constraint.
And node flow balance constraint: the flow balance constraint of the natural gas node mainly comprises the flow injected into the network by the ith node, the flow of the upstream and downstream nodes and the natural gas load constraint of the ith node. Their specific relationship is as follows:
in the formula, F t,i Is the flow injected into the natural gas network node i at the time of t optimization; j and k are nodes upstream and downstream of node i; f t,j And F t,k The flow rates of the nodes j and k in the t optimization period are respectively; f 2,t,i Is the natural gas load at node i during the t-optimization period; f t,ij Is the transmission flow of the pipeline ij at the time of t optimization;
is the transmission flow limit for pipe ij.
The gas flow pressure constraints mainly include gas flow direction, gas pressure amplitude, delivery flow extrema, gas power and gas heat value. The relationship is as follows:
in the formula, gamma t,ij Is a symbolic variable representing the direction of flow of the air stream; k ij Is the transmission coefficient of the pipe ij; u shape t,i Is the pressure amplitude at node i; u shape t,j Is the pressure amplitude at node j;
and
respectively, the upper and lower limits of the pressure amplitude at node i.
In this embodiment, taking a certain park as an example, the established three-stage operation optimization model of the integrated energy system simulates a regulation and control plan of the park. The energy supply of the park mainly uses electricity as a core, uses fuel gas as auxiliary energy, and fully utilizes renewable energy sources such as wind, light, geothermal energy and the like to realize the energy supply of the park. The energy supply mode of the ground source heat pump for efficient refrigeration in winter and efficient heating in summer is realized by depending on the characteristic of constant temperature of ground source water all the year round. The storage battery has the functions of stabilizing the fluctuation of renewable energy sources, clipping peaks and filling valleys and responding to peak and valley electricity prices in an energy system; the ice storage tank and the heat storage tank mainly respond to night double-storage electricity price, and the electricity price only carries out cold storage and heat storage on the double-working-condition refrigerating unit and the electric boiler equipment. The park is provided with large-scale information processing equipment, so that the requirement on the stability of energy supply of a cooling system is high. The method selects summer typical days as research objects, performs operation optimization regulation and control on the distributed energy equipment of the comprehensive energy system in the future day by taking hours as precision, performs cycle optimization every 1 hour, and adopts a trigger mechanism for monitoring optimization. Aiming at two scenes of normal operation and burst operation, the comparative analysis of the circular optimization, the monitoring optimization and the traditional day-ahead optimization mode is respectively realized.
Considering that renewable energy equipment in the park has strong dependence on environmental factors, a curve of the change of the illumination intensity and the wind speed of the weather bureau on the day and an electricity, heat and cold energy demand curve of the park can be obtained, the cold load and the electricity load of the park in summer account for a large proportion and are concentrated in the daytime for a long time, and the cold load integrally presents obvious peak-valley difference. The heat load and the electric load are distributed more stably and uniformly, and the heat load requirement is smaller.
In the operation of the comprehensive energy system, the external energy price and the carbon emission directly influence the operation optimization effect, and the natural gas price adopts 1.6 RMB/m 3 The discharge coefficient is 1.43kg/m 3 The conventional electricity purchasing price of the park follows the time-of-use electricity price, and the heat storage and ice storage electricity prices follow the double-storage electricity price.
According to the three-stage operation optimization process of the comprehensive energy system, the efficiency curve, the output characteristic, the use cost and the like of the distributed energy equipment are fully considered, the construction condition of the park comprehensive energy system is combined, and key parameters of the distributed energy equipment are shown in the table 1.
TABLE 1 park Equipment parameters
And considering the influence of the operation condition and the load factor of the equipment on the operation efficiency, and setting dynamic efficiency curves for the heat pump, the double-condition refrigerating unit, the conventional refrigerating unit and the gas boiler. The technical characteristics of different types of equipment, different manufacturers and different types of equipment have great difference, great influence is generated on the operation optimization of the comprehensive energy system, and different pipe network parameters and grid structures can also influence the overall energy supply effect of the networks and the systems such as cold, heat, electricity, gas and the like. Therefore, it is necessary to set up the regulation instruction cycle of each optimization stage of the integrated energy system for different equipment and network transmission characteristics.
TABLE 2 instruction optimization cycle for each device
The optimization objective of this embodiment is to optimize the total operating cost and carbon emission, and the optimization cycle of each control stage is set in combination with the requirements of different control stages on the time scale due to the difference in technical characteristics of different devices. In the day-ahead optimization regulation and control stage, the output level of the equipment is mainly optimized to ensure the satisfaction degree of user energy under the target guidance, and the requirement on the optimization time scale is not fine. Therefore, the optimal regulation and control period for optimizing each energy system in the day ahead is 1 h. The rolling optimization stage mainly solves the problems of system load prediction errors and energy supply deviation caused by equipment output change, so that the output state of partial equipment needs to be adjusted in the adjustment stage, the day-ahead regulation and control plan is improved, and the requirement on the regulation and control time scale is relatively fine. And setting the optimization regulation and control period of the day-ahead optimized cold and heat energy system to be 30min by combining the response time of each device and the transmission state of each energy flow network.
Regulating and optimizing results in the day ahead: under the current conventional system operation energy-saving environment, according to the comprehensive energy system three-stage operation optimization model constructed in the text, the daily economy and environmental protection are taken as double energy-saving optimization design targets, and the energy-saving optimization is carried out on the internal equipment structure of the system, so that the energy-saving output of the system is greatly improved. The trend of daily economy and environmental protection varies with the operating strategy of the plant. Because the two optimization targets are mutually exclusive, the operation optimization can not output the only optimal solution, but is expressed in a Pareto solution set form. As shown in fig. 2, the Pareto solution set is composed of 50 sets of feasible solutions, wherein when the daily operation cost is low, the overall environmental protection performance of the system is poor, the carbon emission is high, and conversely, the carbon emission is low.
And (3) rolling an optimization result: on the basis of determining a day-ahead optimization regulation and control scheme, re-optimization and scheme updating are carried out on an intra-day operation scheme according to the influence of actual load on a subsequent regulation and control scheme in a cycle interval of every 30 minutes, and the influence on economic cost and a system carbon emission evaluation result is considered due to the deviation between the energy supply condition and the actual load condition of the operation scheme, so that in order to ensure that the user energy load is met, a fitting goodness index of an operation daily energy curve and an energy supply curve is additionally introduced, and the day-ahead optimization result is compared with 7 cycle optimization results in the day, as shown in a table 3 and a figure 2.
TABLE 3 comparison of the optimization results before the day with the optimization results of 7 cycles in the day
It can be seen from fig. 2 that the economic cost and carbon emission of the operation of the comprehensive energy system mainly show a descending trend, the economic cost and carbon emission are reduced by the all-day cycle optimization 9943.71 yuan and 430.50kg, which respectively account for 11.77% and 10.65% of the day-ahead optimization, the goodness of fit of the system energy supply curve and the user energy demand curve shows an ascending trend, the all-day cycle optimization is improved by 3.83%, and the goodness of fit of the all-day cycle optimization accounts for 3.86%. When the circulation optimization is carried out for 0-2 times, the economic cost and the carbon emission do not change greatly, even the first circulation has a rising trend, and the goodness of fit of an energy supply and demand curve of the system is improved mainly by sacrificing the economic cost and the carbon emission; in the 3-5 times of cyclic optimization process, the energy supply characteristic of high fitting goodness at the early stage is ensured, and the reduction in the economic cost and the carbon emission is obvious; in the 6-7 circulation process, as the occurring time is longer and longer, the future regulation and control optimization space is smaller, so that the change tends to be stable in all aspects, and finally the optimal system operation is achieved.
Example two
The embodiment provides a source network and load storage multi-target cooperative regulation and control system, which specifically comprises the following modules:
a day-ahead optimization module configured to: acquiring the predicted load of the comprehensive energy system in each planning time interval in the cycle to be regulated, and acquiring the output plan of each device in each planning time interval in the cycle to be regulated by adopting a multi-objective optimization algorithm and minimizing an operation optimization target under the energy balance constraint, the device output constraint and the energy interaction constraint;
a roll optimization module configured to: in a cycle to be regulated and controlled, dividing each plan time interval into a plurality of optimization time intervals, obtaining the actual load of each optimization time interval, and optimizing the output plan of each device in each optimization time interval under the device output constraint, the climbing rate constraint, the power grid constraint and the natural gas pipe network constraint by taking the minimum deviation between the energy supply and the actual load after the output plan is executed as a target.
It should be noted that, each module in the present embodiment corresponds to each step in the first embodiment one to one, and the specific implementation process is the same, which is not described again here.
EXAMPLE III
The embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the program implements the steps in the source-network load-store multi-target cooperative regulation and control method according to the first embodiment.
Example four
The embodiment provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the steps in the source-network-load-storage multi-target cooperative regulation and control method according to the embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A source-network charge-storage multi-target cooperative regulation and control method is characterized by comprising the following steps:
acquiring the predicted load of the comprehensive energy system in each planned time interval in the period to be regulated, and acquiring the output plan of each device in each planned time interval in the period to be regulated by adopting a multi-objective optimization algorithm and minimizing the operation optimization target under the energy balance constraint, the device output constraint and the energy interaction constraint;
in a cycle to be regulated and controlled, dividing each plan time interval into a plurality of optimization time intervals, obtaining the actual load of each optimization time interval, and optimizing the output plan of each device in each optimization time interval under the device output constraint, climbing rate constraint, power grid constraint and natural gas pipe network constraint by taking the minimum deviation between the energy supply and the actual load after the output plan is executed as a target.
2. The source-network charge-storage multi-target cooperative regulation and control method of claim 1, wherein the equipment processing constraints comprise energy production and conversion equipment output constraints, energy storage equipment constraints, heat storage equipment constraints and cold storage equipment constraints.
3. The source-network charge-storage multi-target cooperative regulation and control method as claimed in claim 1, wherein the multi-target optimization algorithm adopts a non-local sequencing genetic algorithm.
4. The source-network-charge-storage multi-target cooperative regulation and control method as claimed in claim 1, wherein the operation optimization target comprises a comprehensive energy utilization efficiency target, a reliability target, an economic target and an environmental protection target.
5. The source-grid-charge-storage multi-target cooperative regulation and control method of claim 1, wherein the energy balance constraint comprises an electrical load balance constraint, a thermal load balance constraint, a cold load balance constraint and a natural gas load balance constraint.
6. The source-network-charge-storage multi-target cooperative regulation and control method of claim 1, wherein the natural gas pipe network constraints comprise node flow balance constraints and gas flow pressure constraints.
7. The source-grid-charge-storage multi-target cooperative regulation and control method as claimed in claim 1, wherein the equipment comprises equipment in energy storage equipment, heat storage equipment, cold storage equipment, a fan, photovoltaic power generation equipment, an air conditioner, a heat pump and a combined cooling, heating and power system.
8. A source-network charge-storage multi-target cooperative regulation and control system is characterized by comprising:
a day-ahead optimization module configured to: acquiring the predicted load of the comprehensive energy system in each planned time interval in the period to be regulated, and acquiring the output plan of each device in each planned time interval in the period to be regulated by adopting a multi-objective optimization algorithm and minimizing the operation optimization target under the energy balance constraint, the device output constraint and the energy interaction constraint;
a scroll optimization module configured to: in a cycle to be regulated and controlled, dividing each plan time interval into a plurality of optimization time intervals, obtaining the actual load of each optimization time interval, and optimizing the output plan of each device in each optimization time interval under the device output constraint, the climbing rate constraint, the power grid constraint and the natural gas pipe network constraint by taking the minimum deviation between the energy supply and the actual load after the output plan is executed as a target.
9. A computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the steps in a network-source load-storage multi-target cooperative regulation and control method according to any one of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the steps of the method for source-network-charge-storage multi-target cooperative regulation and control as claimed in any one of claims 1-7.
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| CN116613821A (en) * | 2023-07-13 | 2023-08-18 | 国网浙江省电力有限公司宁波供电公司 | Multi-energy cooperative operation method, operation platform, equipment and storage medium |
| CN117293926A (en) * | 2023-11-22 | 2023-12-26 | 中能智新科技产业发展有限公司 | Real-time scheduling method and device for source network charge storage integrated platform resources |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116613821A (en) * | 2023-07-13 | 2023-08-18 | 国网浙江省电力有限公司宁波供电公司 | Multi-energy cooperative operation method, operation platform, equipment and storage medium |
| CN116613821B (en) * | 2023-07-13 | 2023-10-24 | 国网浙江省电力有限公司宁波供电公司 | Multi-energy cooperative operation method, operation platform, equipment and storage medium |
| CN117293926A (en) * | 2023-11-22 | 2023-12-26 | 中能智新科技产业发展有限公司 | Real-time scheduling method and device for source network charge storage integrated platform resources |
| CN117293926B (en) * | 2023-11-22 | 2024-03-12 | 中能智新科技产业发展有限公司 | Real-time scheduling method and device for source network charge storage integrated platform resources |
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