WO2022088067A1 - 分布式能源系统的优化方法、装置和计算机可读存储介质 - Google Patents

分布式能源系统的优化方法、装置和计算机可读存储介质 Download PDF

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WO2022088067A1
WO2022088067A1 PCT/CN2020/125388 CN2020125388W WO2022088067A1 WO 2022088067 A1 WO2022088067 A1 WO 2022088067A1 CN 2020125388 W CN2020125388 W CN 2020125388W WO 2022088067 A1 WO2022088067 A1 WO 2022088067A1
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uncontrolled
constraint
distributed energy
energy system
uncontrolled device
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PCT/CN2020/125388
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English (en)
French (fr)
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江宁
徐四清
赵爽
王德慧
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西门子股份公司
西门子（中国）有限公司
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Priority to CN202080106381.5A priority Critical patent/CN116529977A/zh
Priority to PCT/CN2020/125388 priority patent/WO2022088067A1/zh
Publication of WO2022088067A1 publication Critical patent/WO2022088067A1/zh

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers

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  • the present invention relates to the technical field of distributed energy system (Distributed Energy System, DES), and in particular, to an optimization method, device and computer-readable storage medium of a distributed energy system.
  • distributed Energy System Distributed Energy System
  • the traditional centralized energy supply system adopts large-capacity equipment and centralized production methods, and transmits various energy to many users within a large range through special transmission facilities (large power grid, large heat network, etc.).
  • special transmission facilities large power grid, large heat network, etc.
  • users have put forward further requirements for the economy, reliability and flexibility of energy systems.
  • the distributed energy system is directly oriented to users, producing and supplying energy locally according to the needs of users, and can meet the multiple goals of medium and small energy conversion and utilization.
  • the distributed energy system can make full use of their respective decentralized energy sources, combined with local loads, energy storage devices and related monitoring and protection devices, to form a robust and self-optimizing energy system.
  • the key factor that determines the economic performance and environmental performance of the distributed energy system is the design optimization of the distributed energy system.
  • the available resources and demand load of the microgrid can be predicted, and then according to the current technical situation, the network distribution, technology selection, capacity configuration and operation strategy of the microgrid function or energy-consuming system can be reasonably selected to make full use of different equipment.
  • the energy conversion and coupling relationship between them can achieve the purpose of optimal economic performance and environmental protection performance.
  • the current distributed energy system usually has multiple control entities, and there may be a situation in which the system operator can only control some energy devices in the network. Therefore, the current optimization methods ignore the non-adjustable characteristics of these uncontrolled devices, and the optimization effect is not good, which leads to many shortcomings such as system performance degradation, cost increase, and even failure to meet load requirements.
  • the embodiments of the present invention provide an optimization method, an apparatus and a computer-readable storage medium for a distributed energy system.
  • An optimization method for a distributed energy system comprising:
  • the existence of uncontrolled devices in the distributed energy system is fully considered, and the constraints of the uncontrolled devices are determined based on the rules of the uncontrolled devices.
  • the constraints of the uncontrolled devices are determined based on the rules of the uncontrolled devices.
  • the determining the constraint condition of the uncontrolled device based on the rule of the uncontrolled device includes:
  • the constraint conditions include:
  • the embodiment of the present invention realizes multiple constraint condition generation methods for uncontrolled devices based on time rules, and can easily constrain time rules for uncontrolled devices based on switching quantities and load quantities.
  • the determining the constraint condition of the uncontrolled device based on the rule of the uncontrolled device includes:
  • the constraint conditions include:
  • the embodiments of the present invention realize the generation of uncontrolled devices based on the constraints of the priority rules, and can easily constrain the priority rules of the uncontrolled devices based on the load amount and the switching value.
  • the determining the constraint condition of the uncontrolled device based on the rule of the uncontrolled device includes:
  • the constraints include:
  • the embodiment of the present invention realizes the generation of uncontrolled devices based on the constraint conditions of price rules, and can easily constrain price rules for uncontrolled devices based on the state of charge and charge and discharge flags.
  • the constraint condition matrix further includes constraints of the controlled equipment in the distributed energy system
  • the decision variable includes at least one of the following: the design power of the controlled equipment; the The decision switch of whether the controlled equipment is built; the real-time power of the controlled equipment in a typical operation period; the switch of whether the controlled equipment is started; the real-time power of the uncontrolled equipment in a typical operation period ; The switch value of whether the uncontrolled device is activated.
  • the constraint condition matrix of the embodiment of the present invention also includes the constraint conditions of the controlled device, and the decision variables are more convenient to manipulate.
  • An optimization device for a distributed energy system comprising:
  • An equipment determination module for determining uncontrolled equipment from a distributed energy system
  • condition determination module configured to determine the constraint condition of the uncontrolled device based on the rules of the uncontrolled device
  • an objective function determination module used for determining the decision variables of the distributed energy system, the constraint condition matrix including the constraint conditions, and the objective function related to the decision variables;
  • a decision variable determination module configured to determine the value of the decision variable when the constraint condition matrix is met and the objective function is an extreme value.
  • the existence of uncontrolled devices in the distributed energy system is fully considered, and the constraints of the uncontrolled devices are determined based on the rules of the uncontrolled devices.
  • the constraints of the uncontrolled devices are determined based on the rules of the uncontrolled devices.
  • condition determination module is configured to determine, when the rule of the uncontrolled device is that the uncontrolled device remains activated within the activation time period:
  • the embodiment of the present invention realizes multiple constraint condition generation methods for uncontrolled devices based on time rules, and can easily constrain time rules for uncontrolled devices based on switching quantities and load quantities.
  • condition determination module is configured to, when the rule of the uncontrolled device is the priority of the uncontrolled device relative to another device, determine: the load of the uncontrolled device is less than The inequality constraint equal to the maximum load of the uncontrolled device; the inequality constraint established based on the difference between the load of the uncontrolled device and the maximum load of the uncontrolled device and the switching value of the other device .
  • the embodiments of the present invention realize the generation of uncontrolled devices based on the constraints of the priority rules, and can easily constrain the priority rules of the uncontrolled devices based on the load amount and the switching value.
  • condition determination module when the rule of the uncontrolled device is that the activation state of the uncontrolled device is associated with the electricity price and the battery state of the uncontrolled device, determines: The inequality constraint that the state of charge of the uncontrolled device is less than or equal to the maximum state of charge of the uncontrolled device; the state of charge of the uncontrolled device is greater than or equal to the minimum state of charge of the uncontrolled device.
  • the inequality constraints established based on the current power price, the lower limit value of the power price and the allowable charging flag position; the inequality constraints established based on the current power price, the upper limit value of the power price and the allowable discharge flag position.
  • the embodiment of the present invention realizes the generation of uncontrolled devices based on the constraint conditions of price rules, and can easily constrain the price rules of uncontrolled devices based on the state of charge and charge and discharge flags.
  • the constraint condition matrix further includes constraints of the controlled equipment in the distributed energy system
  • the decision variable includes at least one of the following: the design power of the controlled equipment; the The decision switch of whether the controlled equipment is built; the real-time power of the controlled equipment in a typical operation period; the switch of whether the controlled equipment is started; the real-time power of the uncontrolled equipment in a typical operation period ; The switch value of whether the uncontrolled device is activated.
  • the constraint condition matrix of the embodiment of the present invention also includes the constraint conditions of the controlled device, and the decision variables are more convenient to manipulate.
  • An optimization device for a distributed energy system comprising a processor, a memory and a computer program stored on the memory and running on the processor, the computer program being executed by the processor to achieve any of the above The optimization method of the distributed energy system described in item.
  • the embodiment of the present invention realizes an optimization device of a distributed energy system with a processor-memory architecture, fully considers the existence of uncontrolled equipment in the distributed energy system, and determines its constraints based on the rules of uncontrolled equipment , by adding the constraints of uncontrolled equipment to the constraint matrix, the optimization effect of the distributed energy system can be improved, the system performance can be improved, the cost can be reduced and the load requirements can be met.
  • a computer-readable storage medium stores a computer program on the computer-readable storage medium, and when the computer program is executed by a processor, implements the method for optimizing a distributed energy system as described in any of the above.
  • the embodiment of the present invention proposes a computer-readable storage medium containing a computer program for executing an optimization method of a distributed energy system.
  • the optimization method fully considers the existence of uncontrolled equipment in the distributed energy system, and is based on the The rules of the controlled equipment determine its constraints. By adding the constraints of the uncontrolled equipment to the constraint matrix, the optimization effect of the distributed energy system can be improved, the system performance can be improved, the cost can be reduced and the load requirements can be met.
  • FIG. 1 is a flowchart of a method for optimizing a distributed energy system according to an embodiment of the present invention.
  • FIG. 2 is an exemplary schematic diagram of an optimization process of a distributed energy system according to an embodiment of the present invention.
  • FIG. 3 is an exemplary block diagram of an optimization apparatus of a distributed energy system according to an embodiment of the present invention.
  • FIG. 4 is an exemplary structural diagram of an optimization device of a distributed energy system according to an embodiment of the present invention.
  • FIG. 5 is an exemplary structural block diagram of an optimization apparatus for a distributed energy system with a memory-processor architecture according to an embodiment of the present invention.
  • the main control body such as the system operator
  • the main control body can fully control the control authority of all energy equipment, and the uncontrollable factors beyond the control of the main control body are ignored.
  • the current distributed energy system usually has multiple control entities, and there may be situations where the main control entity can only control some energy devices in the network, and other control entities except the main control entity can control other energy devices. . Therefore, the current optimization method ignores the non-adjustable characteristics of these uncontrolled devices that are not controlled by the main control body, resulting in poor optimization results, resulting in many shortcomings such as system performance degradation, cost increase, and even failure to meet load requirements.
  • FIG. 1 is a flowchart of a method for optimizing a distributed energy system according to an embodiment of the present invention.
  • the approach shown in FIG. 1 is preferably performed by the main control entity of the distributed energy system (such as the system operator).
  • the method includes:
  • Step 101 Determine uncontrolled devices from the distributed energy system.
  • the controlled devices that can be controlled by the main control entity and the uncontrolled devices that are not controlled by the main control entity are determined.
  • the uncontrolled device may be controlled by its own control subject (such as the device manufacturer).
  • the main control entity may be implemented as a central control node in a central location, such as a system operator of a distributed energy system.
  • the control subject of the uncontrolled device itself can be implemented as an edge control node such as a device manufacturer.
  • the uncontrolled equipment can be automatically determined by the main control entity based on the model data of the distributed energy system, or the uncontrolled equipment can be determined manually.
  • the master control entity determines the uncontrolled device based on the device attributes in the model data.
  • uncontrolled equipment can be implemented as energy conversion equipment that does not need to be piecewise linearized, such as boilers, steam turbines, etc., provided with heat exchangers, cables or pipes, etc.
  • the uncontrolled device may also be implemented as an energy conversion device that needs to be piecewise linearized, such as gas, gas engine, absorption heat pump or compression heat pump, and so on.
  • Step 102 Determine the constraint condition of the uncontrolled device based on the rule of the uncontrolled device.
  • the rules of the uncontrolled device are the user control logic of the uncontrolled device.
  • rules for uncontrolled devices include time-based rules, priority-based rules, and price-based rules.
  • the time-based rules may include that the controlling body of the uncontrolled equipment (such as the manufacturer) starts or shuts down the equipment according to a fixed time, or changes the operating load according to a predetermined operating curve.
  • the optimization algorithm for example, mixed integer linear programming algorithm
  • the on-off and load analog curves of the uncontrolled equipment can be directly given in the input, and used as the uncontrollable input.
  • the constraint condition when the rule for the uncontrolled device is that the uncontrolled device remains activated during the activation time period, includes: in a discrete time point region corresponding to the activation time period , the equation constraints on the amount of load on the uncontrolled equipment.
  • the constraint condition when the rule for the uncontrolled device is that the uncontrolled device remains activated during the activation time period, includes: in a discrete time point region corresponding to the activation time period Within the constraints of the equation that the switching value of the uncontrolled device is equal to the startup value, in the discrete time point region that does not correspond to the startup time period, the switching value of the uncontrolled device is equal to the shutdown value, etc. and an inequality constraint that the load of the uncontrolled equipment is not equal to zero within the discrete time point region corresponding to the start-up time period.
  • Priority-based rules may include a control subject (such as a manufacturer) of an uncontrolled device to start or shut down the device according to a fixed priority order.
  • the constraint condition includes: the load of the uncontrolled device is less than or equal to the uncontrolled device The inequality constraint of the maximum load of the controlled equipment; the inequality constraint established based on the difference between the load of the uncontrolled equipment and the maximum load of the uncontrolled equipment and the switching value of the other equipment
  • device 2 is started; after device 2 is started and reaches full load, device 3 is started.
  • the price-based rules usually include the control subject (such as the manufacturer) of the uncontrolled equipment to decide whether to start or shut down the uncontrollable equipment according to the price of energy consumption.
  • the constraints include: the uncontrolled device The inequality constraint that the state of charge (SOC) of the controlled device is less than or equal to the maximum state of charge of the uncontrolled device; the state of charge of the uncontrolled device is greater than or equal to the minimum state of charge of the uncontrolled device.
  • SOC state of charge
  • Pri is the current electricity price
  • Prlow is the lower limit of the electricity price (0.3 yuan/kWh);
  • Prhigh is the upper limit of the electricity price (0.8 yuan/kWh);
  • SOCmax is the maximum state of charge of the energy storage battery;
  • SOCmin is the The minimum state of charge of the energy storage battery;
  • SOCi is the current state of charge of the energy storage battery;
  • Ydischg is the allowable discharge flag. When Ydischg is equal to 1, the uncontrolled device is allowed to discharge, and when Ydischg is equal to 0, the uncontrolled device is not allowed. Discharge;
  • Ychg is the allowable charging flag. When Ychg is equal to 1, the uncontrolled device is allowed to charge, and when Ychg is equal to 0, the uncontrolled device is not allowed to charge;
  • C is a preset very large constant value, which can be at the level of millions .
  • Step 103 Determine decision variables of the distributed energy system, a constraint condition matrix including the constraint conditions, and an objective function related to the decision variables.
  • the constraint condition matrix further includes the constraint conditions of the controlled equipment in the distributed energy system
  • the decision variables include at least one of the following: the design power of the controlled equipment; the decision switching value of whether the controlled equipment is constructed or not ; The real-time power of the controlled device in a typical operating cycle; the switch value of whether the controlled device is activated; the real-time power of the uncontrolled device in a typical operating cycle; whether the uncontrolled device is activated Switch,. Therefore, the constraint condition matrix includes both the constraint conditions of the uncontrolled device determined in step 102 and the constraint conditions of the controlled device. Wherein, the corresponding manner of setting the constraint condition of the controlled device is a mature technology, which is not repeated in the embodiments of the present invention.
  • Step 104 Determine the value of the decision variable when the constraint condition matrix is met and the objective function is an extreme value.
  • the objective function comprises a function related to economic cost.
  • a mixed integer linear programming (MILP) method can be used to determine the value of the decision variable when the constraint condition matrix is met and the objective function is an extreme value.
  • MILP mixed integer linear programming
  • an exact algorithm and a heuristic algorithm can be used to solve the mixed integer linear programming model to obtain the value of the decision variable when each constraint condition in the respective constraint condition matrix is met and the objective function is an extreme value.
  • the precise algorithm includes branch and bound method, column generation method, etc.
  • heuristic algorithm includes genetic algorithm, ant colony algorithm, particle swarm algorithm, simulated annealing algorithm, and so on.
  • the existence of uncontrolled devices in the distributed energy system is fully considered, and the constraints of the uncontrolled devices are determined based on the rules of the uncontrolled devices.
  • the adaptive scope of the optimal design of the distributed energy system is increased, which can improve the optimization effect of the distributed energy system, improve the system performance, reduce the cost and meet the load requirements.
  • the system model of a distributed energy system usually includes:
  • the energy parent pipe represents a collection of pipes in which a certain energy form can flow freely, such as electricity, green electricity, high temperature heat, low temperature heat, cold, natural gas, etc.
  • energy flows back and forth with negligible losses.
  • each energy parent tube it can have its own input and output point (grid point (gd)), corresponding to its own input and output price curve.
  • the energy main pipe mainly corresponds to the same type of energy pipes that are physically connected in the same factory area.
  • Energy conversion equipment can convert one kind of energy into another one or more, such as boilers, steam turbines, etc., and even heat exchangers, cables, pipes, etc. Energy conversion equipment has its own input medium, output medium, conversion efficiency, corresponding mechanism model and so on.
  • Piecewise linearized energy conversion device (converter_piece wise-linear, cvpwl).
  • Piecewise linearized energy conversion equipment is a subclass of energy conversion equipment, and its conversion efficiency has a nonlinear relationship with load, which needs to be represented by piecewise linearization.
  • Such as gas turbines, gas engines, absorption heat pumps, compression heat pumps, etc. piecewise linearized energy conversion equipment can have its own input medium, output medium, conversion efficiency, and corresponding mechanism models.
  • Uncontrollable energy input equipment refers to the uncontrollable energy input equipment in the system, mainly renewable energy equipment, such as wind power, solar photovoltaic, solar thermal and so on. Uncontrollable energy input devices have their own output medium, a given capacity load curve, etc.
  • Uncontrollable energy input device refers to the uncontrollable energy output device in the system, mainly energy load, such as heat load, cooling load, etc. Uncontrollable energy input devices have their own output medium, a given energy load curve, etc.
  • the energy storage device refers to the device that can control the input energy in some time periods and output energy in other time periods, such as pumped water storage, electric heat storage, ice cold storage and so on.
  • the energy storage device can have its own input medium, output medium, conversion efficiency, corresponding mechanism model, etc.
  • Piecewise linear optimized energy storage device (storage_piece wise-linear, stpwl) is a subclass of energy storage device.
  • Piecewise linear optimized energy storage devices represent devices that can be controlled to input energy in some time periods and output energy in other time periods, and their input and output costs have nonlinear characteristics. Such as power battery and so on.
  • the piecewise linear optimized energy storage device can have its own input medium, output medium, conversion efficiency, corresponding mechanism model, etc.
  • Variable construction during optimization includes:
  • X CXload+CYload+Xload+Yload; where X represents the optimization vector; Xload represents the continuous optimization variable; Yload represents the shaping optimization variable; CXload represents the capacity continuous optimization variable; CYload represents the switch variable of whether the unit is built.
  • Xload Xcv+Xcvpwl+Xrn+Xgd+Xst+Xstpwl; where X represents the power (input or output) of a specific device at each moment; cv represents an energy conversion device; cvpwl represents a piecewise linearized energy conversion device; rn represents renewable energy devices; st represents energy storage devices; stpwl represents piecewise linearized energy storage devices.
  • Yload Ycv+Ycvpwl+Yrn+Ygd+Yst+Ystpwl; where Y represents the switching state of a specific device at each moment; cv represents energy conversion equipment; cvpwl represents piecewise linearized energy conversion equipment; n represents renewable energy Energy device; st represents energy storage device; stpwl represents piecewise linearized energy storage device.
  • CXload CXcv+CXcvpwl+CXrn+CXgd+CXst+CXstpwl; where CX represents the design power (input or output) of a specific device; cv represents energy conversion equipment; cvpwl represents piecewise linearized energy conversion equipment; rn represents Renewable energy device; st represents energy storage device; stpwl represents piecewise linearized energy storage device.
  • CYload CYcv+Ycvpwl+Yrn+Ygd+Yst+Ystpwl; where CY represents the switch variable of whether a specific equipment is built; cv represents energy conversion equipment; cvpwl represents piecewise linearized energy conversion equipment; rn represents renewable energy device; st represents energy storage device; stpwl represents piecewise linearized energy storage device.
  • Cost Capital_Cost+Operation_Cost+Fuel_Cost+Grid_Cost. Cost is the total cost; Capital_Cost is the construction investment; Operation_Cost is the operation cost; Fuel_Cost is the fuel cost; Grid_Cost is the grid cost.
  • Capital_cost ⁇ Capitalcost_cv+ ⁇ Capitalcost_cvpwl+ ⁇ Capitalcost_rn+ ⁇ Capitalcost_st+ ⁇ Capitalcost_st_pwl.
  • Capital_cost is the construction investment
  • Capitalcost_cv is the conversion equipment construction investment
  • Capitalcost_cvpwl is the non-linear conversion equipment construction investment
  • Capitalcost_st is the energy storage equipment construction investment
  • Capitalcost_st_pwl is the nonlinear energy storage equipment construction investment.
  • Operation_cost ⁇ Operationcost_cv+ ⁇ Operationcost_cvpwl+ ⁇ Operationcost_rn+ ⁇ Operationcost_st_keep+ ⁇ Operationcost_st_keep_pwl+ ⁇ Operationcost_st_in+ ⁇ Operationcost_st_out.
  • Operation_cost is the total operating cost
  • Operationcost_cv is the operating cost of the conversion equipment
  • Operationcost_cvpwl is the operating cost of the nonlinear conversion equipment
  • Operationcost_rn is the operating cost of the renewable energy equipment
  • Operationcost_st_in is the charging cost of the energy storage device
  • Operationcost_st_out is the output cost of the energy storage device.
  • Energy_cost is the energy cost
  • energycost_buy is the energy purchase cost
  • energycost_sell is the energy selling cost.
  • the equation constraints in the constraint matrix may include equation constraints for the energy balance of the controlled equipment and the load amount of the uncontrolled equipment.
  • the energy balance includes the energy balance, renewable energy balance, predetermined load balance and energy storage balance of each energy parent at each time point.
  • Inequality constraints in the constraints matrix can include maximum/minimum load constraints for controlled and uncontrolled devices, energy storage power limits and rate-of-change constraints, time rule constraints for uncontrolled devices, priority specification constraints, and price specification constraints ,and many more.
  • FIG. 2 is an exemplary schematic diagram of an optimization process of a distributed energy system according to an embodiment of the present invention.
  • data collection 20 is performed first, then parameter setting 21 is performed, optimization calculation 22 is performed, and result processing 23 is performed finally.
  • the peripheral adjustment setting 29 is performed taking into account the environment/price curve 47 and the investment/operating cost 48 .
  • a model 30 needs to be performed.
  • the models established in the process of establishing the model 30 include: a buy/sell model 49 , an energy conversion model 50 , an energy storage model 51 , a renewable energy model 52 , a load model 53 and a controllable demand side model 54 .
  • a constraint matrix 31 also needs to be established. Constraint matrix 31 includes constraints for uncontrolled devices and constraints for controlled devices. In the optimization calculation 22, it is also necessary to set the boundary conditions 32 and call the optimization algorithm 33.
  • result output 34 In result processing 23, result output 34, economic calculation 35, and sensitivity analysis 36 are performed.
  • the resulting output 34 includes technology selection/pipeline layout 59 , capacity configuration 60 and optimal operation strategy 61 .
  • the embodiment of the present invention also proposes an optimization device for a distributed energy system.
  • FIG. 3 is an exemplary block diagram of an optimization apparatus of a distributed energy system according to an embodiment of the present invention.
  • the optimization device 300 of the distributed energy system includes:
  • a device determining module 301 configured to determine uncontrolled devices from the distributed energy system
  • condition determination module 302 configured to determine the constraint condition of the uncontrolled device based on the rule of the uncontrolled device
  • an objective function determination module 303 configured to determine a decision variable of the distributed energy system, a constraint condition matrix including the constraint condition, and an objective function related to the decision variable;
  • the decision variable determination module 304 is configured to determine the value of the decision variable when the constraint condition matrix is met and the objective function is an extreme value.
  • condition determination module 302 is configured to, when the rule of the uncontrolled device is that the uncontrolled device remains activated within the activation time period, determine: at a discrete time corresponding to the activation time period In the region of time points, the equation constraint on the load of the uncontrolled device; or, in the region of discrete time points corresponding to the startup time period, the switching value of the uncontrolled device is equal to the startup value
  • condition determination module 302 is configured to, when the rule of the uncontrolled device is the priority of the uncontrolled device relative to another device, determine: the load of the uncontrolled device is less than or equal to The inequality constraint condition of the maximum load of the uncontrolled device; the inequality constraint condition established based on the difference between the load of the uncontrolled device and the maximum load of the uncontrolled device and the switching value of the other device.
  • condition determination module 302 when the rule of the uncontrolled device is that the activation state of the uncontrolled device is associated with the electricity price and the battery state of the uncontrolled device, determines: the The inequality constraint that the state of charge of the uncontrolled device is less than or equal to the maximum state of charge of the uncontrolled device; the state of charge of the uncontrolled device is greater than or equal to the minimum state of charge of the uncontrolled device Inequality constraints; inequality constraints established based on the current electricity price, lower limit value of electricity price and allowable charging flag; inequality constraints established based on current electricity price, upper limit value of electricity price and allowable discharge flag.
  • the constraint condition matrix further includes constraints of the controlled equipment in the distributed energy system, and the decision variables include the power of the controlled equipment, the switching value of the controlled equipment, the uncontrolled equipment. The power of the controlled device and the switching value of the uncontrolled device.
  • FIG. 4 is an exemplary structural diagram of an optimization device of a distributed energy system according to an embodiment of the present invention.
  • the optimization device 70 of the distributed energy system includes:
  • a human-machine interface module 71 for receiving optimization tasks
  • a processor 73 respectively coupled to the human-machine interface module 71 and the database 72 via a bus 74, is configured to: after receiving the optimization task, determine the uncontrolled equipment from the distributed energy system based on the model data ; Determine the constraints of the uncontrolled device based on the rules of the uncontrolled device; determine the decision variables of the distributed energy system, the constraint matrix containing the constraints, and the objective function related to the decision variables; Determine the value of the decision variable when the constraint condition matrix is satisfied and the objective function is an extreme value.
  • the embodiments of the present invention also provide an optimization device for a distributed energy system with a memory-processor architecture.
  • FIG. 5 is an exemplary structural block diagram of an optimization apparatus for a distributed energy system with a memory-processor architecture according to an embodiment of the present invention.
  • the optimization apparatus 500 of the distributed energy system includes a processor 501 , a memory 502 and a computer program stored in the memory 502 and executable on the processor 501 , and the computer program is executed by the processor 501 When realizing the optimization method of the distributed energy system described in any one of the above.
  • the memory 502 can be specifically implemented as various storage media such as Electrically Erasable Programmable Read-Only Memory (EEPROM), Flash memory (Flash memory), Programmable Program Read-Only Memory (PROM).
  • the processor 501 may be implemented to include one or more central processing units or one or more field programmable gate arrays, wherein the field programmable gate arrays integrate one or more central processing unit cores.
  • the central processing unit or the central processing unit core may be implemented as a CPU or an MCU or a DSP or the like.
  • a module can be implemented by multiple modules, and the functions of multiple modules can also be implemented by the same module. These modules can be located in the same device. , or in a different device.
  • the hardware modules in various embodiments may be implemented mechanically or electronically.
  • a hardware module may include specially designed permanent circuits or logic devices (eg, special purpose processors, such as FPGAs or ASICs) for performing specific operations.
  • Hardware modules may also include programmable logic devices or circuits (eg, including general-purpose processors or other programmable processors) temporarily configured by software for performing particular operations.
  • programmable logic devices or circuits eg, including general-purpose processors or other programmable processors
  • temporarily configured circuit for example, configured by software
  • the present invention also provides a machine-readable storage medium storing instructions for causing a machine to perform the method as described herein.
  • a system or device equipped with a storage medium on which software program codes for realizing the functions of any one of the above-described embodiments are stored, and make the computer (or CPU or MPU of the system or device) ) to read and execute the program code stored in the storage medium.
  • a part or all of the actual operation can also be completed by an operating system or the like operating on the computer based on the instructions of the program code.
  • the program code read out from the storage medium can also be written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion unit connected to the computer, and then the instructions based on the program code cause the device to be installed in the computer.
  • the CPU on the expansion board or the expansion unit or the like performs part and all of the actual operations, thereby realizing the functions of any one of the above-mentioned embodiments.
  • Embodiments of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (eg, CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), Magnetic tapes, non-volatile memory cards and ROMs.
  • the program code may be downloaded from a server computer or cloud over a communications network.

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Abstract

一种分布式能源系统的优化方法（100）、装置和计算机可读存储介质。方法包括：从分布式能源系统中确定出不受控设备（101）；基于所述不受控设备的规则确定所述不受控设备的约束条件（102）；确定所述分布式能源系统的决策变量、包含所述约束条件的约束条件矩阵以及与所述决策变量相关的目标函数（103）；确定符合所述约束条件矩阵且所述目标函数为极值时的、所述决策变量的值（104）。考虑到分布式能源系统中不受控设备的存在，基于不受控设备的规则确定其约束条件，可以提升优化效果，提高系统性能、降低成本和满足负荷要求。

Description

分布式能源系统的优化方法、装置和计算机可读存储介质 技术领域
本发明涉及分布式能源系统(Distributed Energy System，DES)技术领域，特别是涉及分布式能源系统的优化方法、装置和计算机可读存储介质。
背景技术
传统的集中式供能系统采用大容量设备和集中生产方式，通过专门的输送设施(大电网、大热网等)将各种能量输送给较大范围内的众多用户。随着光伏、风电、天然气热电联产等分布式电源的广泛应用，用户对于能源系统的经济型、可靠性和灵活性等提出了进一步的要求。
分布式能源系统直接面向用户，按用户需求就地生产和供应能量，可满足多重目标的中、小型能量转换利用。分布式能源系统作为一种新型的能源网络化供应与管理系统，能够充分利用各自分散能源，结合本地负荷、储能装置及相关监控和保护装置，构成鲁棒性及自优化特性的能源系统。在设计阶段中，决定分布式能源系统经济性能及环保性能的关键因素，是分布式能源系统的设计优化工作。通过优化，可以对微网可用资源和需求负荷进行预测，然后根据当前技术情况，合理选择微网功能或用能系统的网络分布、技术选型、容量配置和运行策略，以达到充分利用不同设备间的能量转换与耦合关系，实现经济性能及环保性能最优的目的。
在现有技术的分布式能源系统的优化过程中，假定系统运营商能够全面掌控所有能源设备的控制权限。比如，在中国专利申请号201210355526.2和201610890423.4中，均认定系统运营商可以掌控并自由调配所有部件，并在此基础上进行优化。
然而，目前的分布式能源系统通常具有多个控制主体，可能存在系统运营商只能控制网络中部分能源设备的情况。因此，目前的优化方法忽略了这些不受控设备的不可调节特性，优化效果不佳，从而导致系统性能下降、成本上升，甚至无法满足负荷要求等诸多缺点。
发明内容
本发明实施方式提出分布式能源系统的优化方法、装置和计算机可读存储介质。
本发明实施方式的技术方案如下：
一种分布式能源系统的优化方法，包括：
从分布式能源系统中确定出不受控设备；
基于所述不受控设备的规则确定所述不受控设备的约束条件；
确定所述分布式能源系统的决策变量、包含所述约束条件的约束条件矩阵以及与所述决策变量相关的目标函数；
确定符合所述约束条件矩阵且所述目标函数为极值时的、所述决策变量的值。
可见，在本发明实施方式中，充分考虑到分布式能源系统中不受控设备的存在，并基于不受控设备的规则确定其约束条件，通过将不受控设备的约束条件增加到约束条件矩阵中，可以提升分布式能源系统的优化效果，提高系统性能、降低成本和满足负荷要求。
在一个实施方式中，所述基于不受控设备的规则确定所述不受控设备的约束条件包括：
当所述不受控设备的规则为所述不受控设备在启动时间段内保持启动时，所述约束条件包括：
在对应于所述启动时间段的离散时间点区域内，关于所述不受控设备的负荷量的等式约束条件；或
在对应于所述启动时间段的离散时间点区域内，所述不受控设备的开关量等于启动值的等式约束条件、在不对应于所述启动时间段的离散时间点区域内，所述不受控设备的开关量等于关闭值的等式约束条件以及在对应于所述启动时间段的离散时间点区域内，所述不受控设备的负荷量不等于零的不等式约束条件。
因此，本发明实施方式实现了不受控设备基于时间规则的多种约束条件生成方式，可以方便地基于开关量和负荷量约束出不受控设备的时间规则。
在一个实施方式中，所述基于不受控设备的规则确定所述不受控设备的约束条件包括：
当所述不受控设备的规则为所述不受控设备相对于另外设备的优先级时，所述约束条件包括：
所述不受控设备的负荷小于等于所述不受控设备的最大负荷的不等式约束条件；
基于所述不受控设备的负荷与所述不受控设备的最大负荷的差与所述另外设备的开关量所建立的不等式约束条件。
因此，本发明实施方式实现了不受控设备基于优先级规则的约束条件生成，可以方便地基于负荷量和开关量约束出不受控设备的优先级规则。
在一个实施方式中，所述基于不受控设备的规则确定所述不受控设备的约束条件包括：
当所述不受控设备的规则为所述不受控设备的启动状态与电力价格和该不受控设备的电池状态相关联时，所述约束条件包括：
所述不受控设备的荷电状态小于等于所述不受控设备的最大荷电状态的不等式约束条件；
所述不受控设备的荷电状态大于等于所述不受控设备的最小荷电状态的不等式约束条件；
基于当前电力价格、电力价格下限值和允许充电标志位所建立的不等式约束条件；
基于当前电力价格、电力价格上限值和允许放电标志位所建立的不等式约束条件。
因此，本发明实施方式实现了不受控设备基于价格规则的约束条件生成，可以方便地基于荷电状态和充放电标志位约束出不受控设备的价格规则。
在一个实施方式中，其中所述约束条件矩阵还包括所述分布式能源系统中的受控设备的约束条件，所述决策变量包括下列中的至少一个：所述受控设备的设计功率；所述受控设备是否建设的决策开关量；所述受控设备在典型运行周期内的实时功率；所述受控设备是否启动的开关量；所述不受控设备在典型运行 周期内的实时功率；所述不受控设备是否启动的开关量。
因此，本发明实施方式的约束条件矩阵还包含受控设备的约束条件，而且决策变量更加便于操控。
一种分布式能源系统的优化装置，包括：
设备确定模块，用于从分布式能源系统中确定出不受控设备；
条件确定模块，用于基于所述不受控设备的规则确定所述不受控设备的约束条件；
目标函数确定模块，用于确定所述分布式能源系统的决策变量、包含所述约束条件的约束条件矩阵以及与所述决策变量相关的目标函数；
决策变量确定模块，用于确定符合所述约束条件矩阵且所述目标函数为极值时的、所述决策变量的值。
可见，在本发明实施方式中，充分考虑到分布式能源系统中不受控设备的存在，并基于不受控设备的规则确定其约束条件，通过将不受控设备的约束条件增加到约束条件矩阵中，可以提升分布式能源系统的优化效果，提高系统性能、降低成本和满足负荷要求。
在一个实施方式中，所述条件确定模块，用于当所述不受控设备的规则为所述不受控设备在启动时间段内保持启动时，确定：
在对应于所述启动时间段的离散时间点区域内，关于所述不受控设备的负荷量的等式约束条件；或
在对应于所述启动时间段的离散时间点区域内，所述不受控设备的开关量等于启动值的等式约束条件、在不对应于所述启动时间段的离散时间点区域内，所述不受控设备的开关量等于关闭值的等式约束条件以及在对应于所述启动时间段的离散时间点区域内，所述不受控设备的负荷量不等于零的不等式约束条件。
因此，本发明实施方式实现了不受控设备基于时间规则的多种约束条件生成方式，可以方便地基于开关量和负荷量约束出不受控设备的时间规则。
在一个实施方式中，所述条件确定模块，用于当所述不受控设备的规则为所述不受控设备相对于另外设备的优先级时，确定：所述不受控设备的负荷小于等于所述不受控设备的最大负荷的不等式约束条件；基于所述不受控设备的负荷与所述不受控设备的最大负荷的差与所述另外设备的开关量所建立的不等式约束条件。
因此，本发明实施方式实现了不受控设备基于优先级规则的约束条件生成，可以方便地基于负荷量和开关量约束出不受控设备的优先级规则。
在一个实施方式中，所述条件确定模块，当所述不受控设备的规则为所述不受控设备的启动状态与电力价格和该不受控设备的电池状态相关联时，确定：所述不受控设备的荷电状态小于等于所述不受控设备的最大荷电状态的不等式约束条件；所述不受控设备的荷电状态大于等于所述不受控设备的最小荷电状态的不等式约束条件；基于当前电力价格、电力价格下限值和允许充电标志位所建立的不等式约束条件；基于当前电力价格、电力价格上限值和允许放电标志位所建立的不等式约束条件。
因此，本发明实施方式实现了不受控设备基于价格规则的约束条件生成，可以方便地基于荷电状态和 充放电标志位约束出不受控设备的价格规则。
在一个实施方式中，其中所述约束条件矩阵还包括所述分布式能源系统中的受控设备的约束条件，所述决策变量包括下列中的至少一个：所述受控设备的设计功率；所述受控设备是否建设的决策开关量；所述受控设备在典型运行周期内的实时功率；所述受控设备是否启动的开关量；所述不受控设备在典型运行周期内的实时功率；所述不受控设备是否启动的开关量。
因此，本发明实施方式的约束条件矩阵还包含受控设备的约束条件，而且决策变量更加便于操控。
一种分布式能源系统的优化装置，包括处理器、存储器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述计算机程序被所述处理器执行时实现如上任一项所述的分布式能源系统的优化方法。
可见，本发明实施方式实现了具有处理器-存储器架构的分布式能源系统的优化装置，充分考虑到分布式能源系统中不受控设备的存在，并基于不受控设备的规则确定其约束条件，通过将不受控设备的约束条件增加到约束条件矩阵中，可以提升分布式能源系统的优化效果，提高系统性能、降低成本和满足负荷要求。
一种计算机可读存储介质，所述计算机可读存储介质上存储计算机程序，所述计算机程序被处理器执行时实现如上任一项所述的分布式能源系统的优化方法。
可见，本发明实施方式提出了包含用于被执行分布式能源系统的优化方法的计算机程序的计算机可读存储介质，优化方法充分考虑到分布式能源系统中不受控设备的存在，并基于不受控设备的规则确定其约束条件，通过将不受控设备的约束条件增加到约束条件矩阵中，可以提升分布式能源系统的优化效果，提高系统性能、降低成本和满足负荷要求。
附图说明
图1为本发明实施方式的分布式能源系统的优化方法的流程图。
图2为本发明实施方式的分布式能源系统的优化过程的示范性示意图。
图3为本发明实施方式的分布式能源系统的优化装置的示范性模块图。
图4为本发明实施方式的分布式能源系统的优化装置的示范性结构图。
图5为本发明实施方式的具有存储器-处理器架构的分布式能源系统的优化装置的示范性结构框图。
其中，附图标记如下：
标号 含义
100 分布式能源系统的优化方法
101～104 步骤
20 资料收集
21 参数设定
22 优化计算
23 结果处理
24 文献调研
25 现场调研
26 优化目标设定
27 负荷预测
28 设备性能预测
29 周边条件
30 建立模型
31 建立约束矩阵
32 设定边界条件
33 调用优化算法
34 结果输出
35 经济性核算
36 敏感性分析
37 政策信息
38 发展规划
39 能源需求
40 可用资源
41 重点企业/用户
42 经济性
43 环保性
44 负荷需求曲线
45 可再生供能曲线
46 效率曲线
47 环境/价格曲线
48 投资/运行成本
49 买入/卖出模型
50 能源转化模型
51 储能模型
52 可再生能源模型
53 负荷模型
54 可控需求侧模型
55 能量分形式平衡
56 能量时空平衡
57 设备运行约束
58 多主体设备运行约束
59 技术选择/管网布局
60 容量配置
61 最优运行策略
70 分布式能源系统的优化装置
71 人机接口模块
72 数据库
73 处理器
74 总线
300 分布式能源系统的优化装置
301 设备确定模块
302 条件确定模块
303 目标函数确定模块
304 决策变量确定模块
500 分布式能源系统的优化装置
501 处理器
502 存储器
具体实施方式
为了使本发明的技术方案及优点更加清楚明白，以下结合附图及实施方式，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施方式仅仅用以阐述性说明本发明，并不用于限定本发明的保护范围。
为了描述上的简洁和直观，下文通过描述若干代表性的实施方式来对本发明的方案进行阐述。实施方式中大量的细节仅用于帮助理解本发明的方案。但是很明显，本发明的技术方案实现时可以不局限于这些细节。为了避免不必要地模糊了本发明的方案，一些实施方式没有进行细致地描述，而是仅给出了框架。下文中，“包括”是指“包括但不限于”，“根据……”是指“至少根据……，但不限于仅根据……”。由于汉语的语言习惯，下文中没有特别指出一个成分的数量时，意味着该成分可以是一个也可以是多个，或可理解为至少一个。
申请人发现：在现有技术的分布式能源系统的优化过程中，都是假定主控制主体(比如系统运营商)能够全面掌控所有能源设备的控制权限，而忽视了主控制主体无法控制的不受控设备的存在。实际上，目 前的分布式能源系统通常具有多个控制主体，可能存在主控制主体只能控制网络中的部分能源设备，而除了主控制主体之外的其他控制主体可以控制另外的能源设备的情况。因此，目前的优化方法忽略了这些不受主控制主体控制的不受控设备的不可调节特性，导致优化效果不佳，从而导致系统性能下降、成本上升，甚至无法满足负荷要求等诸多缺点。
申请人发现了上述技术问题，并提出相应的解决方案。
图1为本发明实施方式的分布式能源系统的优化方法的流程图。图1所示方式优选由分布式能源系统的主控制实体(比如系统运行商)执行。
如图1所示，该方法包括：
步骤101：从分布式能源系统中确定出不受控设备。
在这里，基于分布式能源系统中的各个设备的受控情况，确定出可以受主控制实体控制的受控设备，以及不受主控制实体控制的不受控设备。其中，不受控设备可以受到自身的控制主体(比如设备厂家)的控制。主控制实体可以实施为分布式能源系统的系统运行商等处于中心位置的中心控制节点。不受控设备自身的控制主体可以实施为设备厂家等边缘控制节点。在这里，可以由主控制实体基于分布式能源系统的模型数据自动确定出不受控设备，也可以由人工确定出不受控设备。比如，主控制实体基于模型数据中的设备属性确定出不受控设备。
举例，不受控设备可以实施为不需要被分段线性化的能源转换设备，比如锅炉、汽轮机等，设置换热器、电缆或管道等。再举例，不受控设备还可以实施为需要被分段线性化的能源转换设备，比如燃气、燃气发动机、吸收式热泵或压缩式热泵，等等。
以上示范性描述了不受控设备的典型实例，本领域技术人员可以意识到，这种描述仅是示范性的，并不用于限定本发明实施方式的保护范围。
步骤102：基于所述不受控设备的规则确定所述不受控设备的约束条件。
不受控设备的规则即为不受控设备的用户控制逻辑。通常而言，不受控设备的规则包括基于时间的规则、基于优先级的规则和基于价格的规则。
(1)、基于时间的规则
基于时间的规则可以包括不受控设备的控制主体(比如厂家)按照固定的时间启动或关闭设备，或按照预定的运行曲线改变运行负荷。在这里，可以在优化算法(比如，混合整数线性规划算法)中，直接在输入中给定该不受控设备的开关量以及负荷模拟量的曲线，将其作为不可控输入。
在一个实施方式中，当所述不受控设备的规则为所述不受控设备在启动时间段内保持启动时，所述约束条件包括：在对应于所述启动时间段的离散时间点区域内，关于所述不受控设备的负荷量的等式约束条件。
在一个实施方式中，当所述不受控设备的规则为所述不受控设备在启动时间段内保持启动时，所述约 束条件包括：在对应于所述启动时间段的离散时间点区域内，所述不受控设备的开关量等于启动值的等式约束条件、在不对应于所述启动时间段的离散时间点区域内，所述不受控设备的开关量等于关闭值的等式约束条件以及在对应于所述启动时间段的离散时间点区域内，所述不受控设备的负荷量不等于零的不等式约束条件。
以上示范性描述了基于时间的规则时的约束条件实例，本领域技术人员可以意识到，这种描述仅是示范性的，并不用于限定本发明实施方式的保护范围。
(2)、基于优先级的规则
基于优先级的规则，可以包括不受控设备的控制主体(比如厂家)按照固定的优先顺序启动或关闭设备。
在一个实施方式中，当所述不受控设备的规则为所述不受控设备相对于另外设备的优先级时，所述约束条件包括：所述不受控设备的负荷小于等于所述不受控设备的最大负荷的不等式约束条件；基于所述不受控设备的负荷与所述不受控设备的最大负荷的差与所述另外设备的开关量所建立的不等式约束条件
比如，在设备1启动并到达满负荷后，才开始启动设备2；在设备2启动并到达满负荷后，才开始启动设备3。
因此，可以在约束矩阵中增加如下约束：
约束1：-(L1-L1max)*C+S2<＝1；
约束2：-(L1-L1max)*C-S2<＝-1；
约束3：L1-L1max<＝0；
约束4：-(L2-L2max)*C+S3<＝1；
约束5：-(L2-L2max)*C-S3<＝-1；
约束6：L1-L1max<＝0。
其中：L1为设备1的当前负荷；L1max为设备1的最大负荷；L2为设备2的当前负荷；L2max为设备2的最大负荷；S2为设备2的开关量，当S2等于1时设备2启动，当S2等于0时设备2关闭；S3为设备3的开关量，当S3等于1时设备3启动，当S3等于0时设备3关闭；C为预设的非常大的常数值，可以为百万级别。
以上示范性描述了基于优先级的规则时的约束条件实例，本领域技术人员可以意识到，这种描述仅是示范性的，并不用于限定本发明实施方式的保护范围。
(3)、基于价格的规则
基于价格的规则，通常包括不受控设备的控制主体(比如厂家)按照用能的价格来决定是否启动或关闭不可控设备。
在一个实施方式中，当所述不受控设备的规则为所述不受控设备的启动状态与电力价格和该不受控设 备的电池状态相关联时，所述约束条件包括：所述不受控设备的荷电状态(SOC)小于等于所述不受控设备的最大荷电状态的不等式约束条件；所述不受控设备的荷电状态大于等于所述不受控设备的最小荷电状态的不等式约束条件；基于当前电力价格、电力价格下限值和允许充电标志位所建立的不等式约束条件；基于当前电力价格、电力价格上限值和允许放电标志位所建立的不等式约束条件。
比如，假定当网电价格低于0.3元/千瓦时(Kwh)且不受控设备的储能电池未充满时允许充电；当网电价格高于0.8元/千瓦时且不受控设备的储能电池有余电时允许放电。那么，在约束矩阵中增加如下约束条件：
约束1：(SOCi-SOCmax)<＝0；
约束2：(Pri-Prlow)+Ychg*C<＝C；
约束3：-(Pri-Prlow)-Ychg*C<＝0；
约束4：SOCmin-SOCi<＝0；
约束5：(Pri-Prhigh)+Ydischg*C<＝0；
约束6：-(Pri-Prhigh)+(Ydischg-1)*C<＝0；
其中：Pri是当前电价；Prlow是电价的下限值(0.3元/千瓦时)；Prhigh是电价的上限值(0.8元/千瓦时)；SOCmax为储能电池的最大荷电状态；SOCmin是储能电池的最小荷电状态；SOCi是储能电池的当前荷电状态；Ydischg是允许放电标志位，其中Ydischg等于1时不受控设备允许放电，当Ydischg等于0时不受控设备不允许放电；Ychg是允许充电标志位，其中Ychg等于1时不受控设备允许充电，当Ychg等于0时不受控设备不允许充电；C为预设的非常大的常数值，可以为百万级别。
以上示范性描述了基于价格的规则的约束条件实例，本领域技术人员可以意识到，这种描述仅是示范性的，并不用于限定本发明实施方式的保护范围。
步骤103：确定所述分布式能源系统的决策变量、包含所述约束条件的约束条件矩阵以及与所述决策变量相关的目标函数。
在这里，约束条件矩阵还包括分布式能源系统中的受控设备的约束条件，决策变量包括下列中的至少一个：所述受控设备的设计功率；所述受控设备是否建设的决策开关量；所述受控设备在典型运行周期内的实时功率；所述受控设备是否启动的开关量；所述不受控设备在典型运行周期内的实时功率；所述不受控设备是否启动的开关量、。因此，约束条件矩阵中既包含在步骤102中所确定的不受控设备的约束条件，还包括受控设备的约束条件。其中，设定受控设备的约束条件的相应方式为成熟技术，本发明实施方式不再赘述。
步骤104：确定符合所述约束条件矩阵且所述目标函数为极值时的、所述决策变量的值。
优选地，目标函数包含与经济成本相关的函数。在一个实施方式中，可以利用混合整数线性规划(Mixed-integer linear programming，MILP)方法，确定符合所述约束条件矩阵且所述目标函数为极值时的、 所述决策变量的值。比如，可以采用精确算法和启发式算法对混合整数线性规划模型进行求解算法，以得到符合各自约束条件矩阵中的各个约束条件且所述目标函数为极值时的决策变量的值。其中精确算法包括分支定界法、列生成法，等等，启发式算法包括遗传算法、蚁群算法、粒子群算法、模拟退火算法，等等。
可见，在本发明实施方式中，充分考虑到分布式能源系统中不受控设备的存在，并基于不受控设备的规则确定其约束条件，通过将不受控设备的约束条件增加到约束条件矩阵中，增大了分布式能源系统优化设计的适应范围，可以提升分布式能源系统的优化效果，提高系统性能、降低成本和满足负荷要求。
下面对本发明实施方式的具体过程进行详细说明。
分布式能源系统的系统模型通常包括：
(1)、能源母管：
能源母管表示某种能量形式可以自由流动的管道集合，能量形式包含诸如电，绿色电力，高温热，低温热，冷、天然气等。在每个能源母管中，能量可以忽略损耗地来回流动。在每个能源母管中，可以有自己的输入输出点(grid point(gd))，对应有自己的输入输出价格曲线。能源母管主要对应在同一厂区内，物理上联通的同类能源管道。
(2)、能源转换设备(cv)：
能源转换设备可以将一种能量转换为另外的一种或多种，如锅炉、汽轮机等，甚至换热器、电缆、管道等。能源转换设备有自己的输入介质、输出介质、转换效率、相应的机理模型等。分段线性化能源转换设备(converter_piece wise-linear，cvpwl)。分段线性化能源转换设备是能源转换设备的子类，其转换效率与负荷成非线性关系，需要用分段线性化方式来表示。如燃机、燃气发动机、吸收式热泵、压缩式热泵等，分段线性化能源转换设备可以有自己的输入介质、输出介质、转换效率、相应的机理模型等。
(3)、不可控能源输入设备(rn)：
不可控能源输入设备指系统中的不可控制的能源输入设备，主要为可再生能源设备，如风电、太阳能光伏、太阳能光热等。不可控能源输入设备有自己的输出介质、给定的产能负荷曲线等
(4)、不可控能源输出设备(ld)：
不可控能源输入设备指系统中的不可控制的能源输出设备，主要为能源负荷，如热负荷、冷负荷等。不可控能源输入设备有自己的输出介质、给定的用能负荷曲线等。
(5)、储能设备(st)：
储能设备表示系统中可以控制的在某些时间段输入能量，在另一些时间段输出能量的设备，如抽水蓄能、电储热、冰蓄冷等等。储能设备可以有自己的输入介质、输出介质、转换效率、相应的机理模型等。分段线性优化储能设备(storage_piece wise-linear，stpwl)是储能设备的子类。分段线性优化储能设备表示系统中可以控制的在某些时间段输入能量，在另一些时间段输出能量的设备，其输入输出成本具有非线性特性。如动力电池等等。分段线性优化储能设备可以有自己的输入介质、输出介质、转换效率、相应的机 理模型等。
优化过程中的变量构造包括：
(1)X＝CXload+CYload+Xload+Yload；其中X表示优化向量；Xload表示连续优化变量；Yload表示整形优化变量；CXload表示容量连续优化变量；CYload表示机组是否兴建的开关变量。
(2)Xload＝Xcv+Xcvpwl+Xrn+Xgd+Xst+Xstpwl；其中X表示特定设备在各个时刻的功率(输入或输出)；cv表示能源转化设备；cvpwl表示分段线性化的能源转化设备；rn表示可再生能源设备；st表示储能设备；stpwl表示分段线性化的储能设备。
(3)Yload＝Ycv+Ycvpwl+Yrn+Ygd+Yst+Ystpwl；其中Y表示特定设备在各个时刻的开关状态；cv表示能源转化设备；cvpwl表示分段线性化的能源转化设备；n表示可再生能源设备；st表示储能设备；stpwl表示分段线性化的储能设备。
(4)CXload＝CXcv+CXcvpwl+CXrn+CXgd+CXst+CXstpwl；其中CX表示特定设备的设计功率(输入或输出)；cv表示能源转化设备；cvpwl表示分段线性化的能源转化设备；rn表示可再生能源设备；st表示储能设备；stpwl表示分段线性化的储能设备。
(5)CYload＝CYcv+Ycvpwl+Yrn+Ygd+Yst+Ystpwl；其中CY表示特定设备是否兴建的开关变量；cv表示能源转化设备；cvpwl表示分段线性化的能源转化设备；rn表示可再生能源设备；st表示储能设备；stpwl表示分段线性化的储能设备。
对于成本计算：Cost＝Capital_Cost+Operation_Cost+Fuel_Cost+Grid_Cost。Cost为总成本；Capital_Cost为建设投资；Operation_Cost为运行成本；Fuel_Cost为燃料成本；Grid_Cost为电网成本。
对于每一个设备：Capital_cost＝ΣCapitalcost_cv+ΣCapitalcost_cvpwl+ΣCapitalcost_rn+ΣCapitalcost_st+ΣCapitalcost_st_pwl。其中：Capital_cost为建设投资；Capitalcost_cv为转换设备建设投资；Capitalcost_cvpwl为非线性转换设备建设投资；Capitalcost_st为储能设备建设投资；Capitalcost_st_pwl为非线性储能设备建设投资。
对于每一个设备且对于每一个时间点：
Operation_cost＝ΣOperationcost_cv+ΣOperationcost_cvpwl+ΣOperationcost_rn+ΣOperationcost_st_keep+ΣOperationcost_st_keep_pwl+ΣOperationcost_st_in+ΣOperationcost_st_out。其中：Operation_cost为总运行成本；Operationcost_cv为转换设备运行成本；Operationcost_cvpwl为非线性转换设备运行成本；Operationcost_rn为可再生能源设备运行成本；Operationcost_st_keep为储能设备保持成本；Operationcost_st_keep_pwl为非线性储能设备保持成本；Operationcost_st_in为储能设备充入成本；Operationcost_st_out为储能设备输出成本。
对于每一个时间点且对于每一个能量母管：Energy_cost＝Σenergycost_buy-Σenergycost_sell。其中：Energy_cost为能量成本；energycost_buy为能量采购成本；energycost_sell为能量出售成本。
约束条件矩阵中的等式约束可以包括受控设备的能量平衡和不受控设备的负荷量的等式约束条件。能量平衡包括每一个能量母管在每一个时间点的能量平衡、可再生能源平衡、预定负荷平衡和储能平衡。约束条件矩阵中的不等式约束可以包括受控设备和不受控设备的最大/最小负荷约束、储能功率限制和变化率限制、不受控设备的时间规则约束、优先权规格约束和价格规格约束，等等。然后，将构建好的目标函数与约束矩阵输入MILP优化求解器，解得的解向量即为确定的决策变量的值。
图2为本发明实施方式的分布式能源系统的优化过程的示范性示意图。
在优化过程中，首先执行资料收集20，然后执行参数设定21，再执行优化计算22，最后执行结果处理23。
在资料收集20中，需要对政策信息37、发展规划38、能源需求39、可用资源40和重点企业/用户执行文献调研24和现场调研25。
在参数设定21中，需要考虑经济性42和环保性43执行优化目标设定26，考虑负荷需求曲线44和可再生供能曲线45执行负荷预测27，考虑效率曲线46执行设备性能预测28，考虑环境/价格曲线47和投资/运行成本48执行周边调节设定29。
在优化计算22中，需要执行建立模型30。建立模型30过程中所建立的模型包含：买入/卖出模型49、能源转化模型50、储能模型51、可再生能源模型52、负荷模型53和可控需求侧模型54。在优化计算22中，还需要建立约束矩阵31。约束矩阵31包括不受控设备的约束条件和受控设备的约束条件。在优化计算22中，还需要设定边界条件32和调用优化算法33。
在结果处理23中，执行结果输出34、经济性核算35和敏感性分析36。其中结果输出34包括技术选择/管网布局59、容量配置60和最优运行策略61。
基于上述描述，本发明实施方式还提出了分布式能源系统的优化装置。
图3为本发明实施方式的分布式能源系统的优化装置的示范性模块图。
如图3所示，分布式能源系统的优化装置300包括：
设备确定模块301，用于从分布式能源系统中确定出不受控设备；
条件确定模块302，用于基于所述不受控设备的规则确定所述不受控设备的约束条件；
目标函数确定模块303，用于确定所述分布式能源系统的决策变量、包含所述约束条件的约束条件矩阵以及与所述决策变量相关的目标函数；
决策变量确定模块304，用于确定符合所述约束条件矩阵且所述目标函数为极值时的、所述决策变量的值。
在一个实施方式中，条件确定模块302，用于当所述不受控设备的规则为所述不受控设备在启动时间段内保持启动时，确定：在对应于所述启动时间段的离散时间点区域内，关于所述不受控设备的负荷量的等式约束条件；或，在对应于所述启动时间段的离散时间点区域内，所述不受控设备的开关量等于启动值 的等式约束条件、在不对应于所述启动时间段的离散时间点区域内，所述不受控设备的开关量等于关闭值的等式约束条件以及在对应于所述启动时间段的离散时间点区域内，所述不受控设备的负荷量不等于零的不等式约束条件。
在一个实施方式中，条件确定模块302，用于当所述不受控设备的规则为所述不受控设备相对于另外设备的优先级时，确定：所述不受控设备的负荷小于等于所述不受控设备的最大负荷的不等式约束条件；基于所述不受控设备的负荷与所述不受控设备的最大负荷的差与所述另外设备的开关量所建立的不等式约束条件。
在一个实施方式中，条件确定模块302，当所述不受控设备的规则为所述不受控设备的启动状态与电力价格和该不受控设备的电池状态相关联时，确定：所述不受控设备的荷电状态小于等于所述不受控设备的最大荷电状态的不等式约束条件；所述不受控设备的荷电状态大于等于所述不受控设备的最小荷电状态的不等式约束条件；基于当前电力价格、电力价格下限值和允许充电标志位所建立的不等式约束条件；基于当前电力价格、电力价格上限值和允许放电标志位所建立的不等式约束条件。
在一个实施方式中，其中约束条件矩阵还包括分布式能源系统中的受控设备的约束条件，所述决策变量包括所述受控设备的功率、所述受控设备的开关量、所述不受控设备的功率和所述不受控设备的开关量。
图4为本发明实施方式的分布式能源系统的优化装置的示范性结构图。
如图4所示，分布式能源系统的优化装置70包括：
人机接口模块71，用于接收优化任务；
数据库72，用于保存分布式能源系统的模型数据；
处理器73，经由总线74与所述人机接口模块71与所述数据库72分别耦合，被配置用于：在接收到优化任务后，基于模型数据从分布式能源系统中确定出不受控设备；基于不受控设备的规则确定所述不受控设备的约束条件；确定所述分布式能源系统的决策变量、包含所述约束条件的约束条件矩阵以及与所述决策变量相关的目标函数；确定符合所述约束条件矩阵且所述目标函数为极值时的、所述决策变量的值。
基于上述描述，本发明实施方式还提出了具有存储器-处理器架构的分布式能源系统的优化装置。
图5为本发明实施方式的具有存储器-处理器架构的分布式能源系统的优化装置的示范性结构框图。
如图5所示，分布式能源系统的优化装置500包括处理器501、存储器502及存储在存储器502上并可在处理器501上运行的计算机程序，所述计算机程序被所述处理器501执行时实现如上任一项所述分布式能源系统的优化方法。其中，存储器502具体可以实施为电可擦可编程只读存储器(EEPROM)、快闪存储器(Flash memory)、可编程程序只读存储器(PROM)等多种存储介质。处理器501可以实施为包括一或多个中央处理器或一或多个现场可编程门阵列，其中现场可编程门阵列集成一或多个中央处理器核。具体地，中央处理器或中央处理器核可以实施为CPU或MCU或DSP等等。
需要说明的是，上述各流程和各结构图中不是所有的步骤和模块都是必须的，可以根据实际的需要忽 略某些步骤或模块。各步骤的执行顺序不是固定的，可以根据需要进行调整。各模块的划分仅仅是为了便于描述采用的功能上的划分，实际实现时，一个模块可以分由多个模块实现，多个模块的功能也可以由同一个模块实现，这些模块可以位于同一个设备中，也可以位于不同的设备中。各实施方式中的硬件模块可以以机械方式或电子方式实现。例如，一个硬件模块可以包括专门设计的永久性电路或逻辑器件(如专用处理器，如FPGA或ASIC)用于完成特定的操作。硬件模块也可以包括由软件临时配置的可编程逻辑器件或电路(如包括通用处理器或其它可编程处理器)用于执行特定操作。至于具体采用机械方式，或是采用专用的永久性电路，或是采用临时配置的电路(如由软件进行配置)来实现硬件模块，可以根据成本和时间上的考虑来决定。
本发明还提供了一种机器可读的存储介质，存储用于使一机器执行如本申请所述方法的指令。具体地，可以提供配有存储介质的系统或者装置，在该存储介质上存储着实现上述实施例中任一实施方式的功能的软件程序代码，且使该系统或者装置的计算机(或CPU或MPU)读出并执行存储在存储介质中的程序代码。此外，还可以通过基于程序代码的指令使计算机上操作的操作系统等来完成部分或者全部的实际操作。还可以将从存储介质读出的程序代码写到插入计算机内的扩展板中所设置的存储器中或者写到与计算机相连接的扩展单元中设置的存储器中，随后基于程序代码的指令使安装在扩展板或者扩展单元上的CPU等来执行部分和全部实际操作，从而实现上述实施方式中任一实施方式的功能。用于提供程序代码的存储介质实施方式包括软盘、硬盘、磁光盘、光盘(如CD-ROM、CD-R、CD-RW、DVD-ROM、DVD-RAM、DVD-RW、DVD+RW)、磁带、非易失性存储卡和ROM。可选择地，可以由通信网络从服务器计算机或云上下载程序代码。
在本文中，“示意性”表示“充当实例、例子或说明”，不应将在本文中被描述为“示意性”的任何图示、实施方式解释为一种更优选的或更具优点的技术方案。为使图面简洁，各图中的只示意性地表示出了与本发明相关部分，而并不代表其作为产品的实际结构。另外，以使图面简洁便于理解，在有些图中具有相同结构或功能的部件，仅示意性地绘示了其中的一个，或仅标出了其中的一个。在本文中，“一个”并不表示将本发明相关部分的数量限制为“仅此一个”，并且“一个”不表示排除本发明相关部分的数量“多于一个”的情形。在本文中，“上”、“下”、“前”、“后”、“左”、“右”、“内”、“外”等仅用于表示相关部分之间的相对位置关系，而非限定这些相关部分的绝对位置。
以上所述，仅为本发明的较佳实施例而已，并非用于限定本发明的保护范围。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (12)

  1. 一种分布式能源系统的优化方法(100)，其特征在于，包括：
    从分布式能源系统中确定出不受控设备(101)；
    基于所述不受控设备的规则确定所述不受控设备的约束条件(102)；
    确定所述分布式能源系统的决策变量、包含所述约束条件的约束条件矩阵以及与所述决策变量相关的目标函数(103)；
    确定符合所述约束条件矩阵且所述目标函数为极值时的、所述决策变量的值(104)。
  2. 根据权利要求1所述的分布式能源系统的优化方法(100)，其特征在于，
    所述基于不受控设备的规则确定所述不受控设备的约束条件(102)包括：
    当所述不受控设备的规则为所述不受控设备在启动时间段内保持启动时，所述约束条件包括：
    在对应于所述启动时间段的离散时间点区域内，关于所述不受控设备的负荷量的等式约束条件；或
    在对应于所述启动时间段的离散时间点区域内，所述不受控设备的开关量等于启动值的等式约束条件、在不对应于所述启动时间段的离散时间点区域内，所述不受控设备的开关量等于关闭值的等式约束条件以及在对应于所述启动时间段的离散时间点区域内，所述不受控设备的负荷量不等于零的不等式约束条件。
  3. 根据权利要求1所述的分布式能源系统的优化方法(100)，其特征在于，
    所述基于不受控设备的规则确定所述不受控设备的约束条件(102)包括：
    当所述不受控设备的规则为所述不受控设备相对于另外设备的优先级时，所述约束条件包括：
    所述不受控设备的负荷小于等于所述不受控设备的最大负荷的不等式约束条件；
    基于所述不受控设备的负荷与所述不受控设备的最大负荷的差与所述另外设备的开关量所建立的不等式约束条件。
  4. 根据权利要求1所述的分布式能源系统的优化方法(100)，其特征在于，
    所述基于不受控设备的规则确定所述不受控设备的约束条件(102)包括：
    当所述不受控设备的规则为所述不受控设备的启动状态与电力价格和该不受控设备的电池状态相关联时，所述约束条件包括：
    所述不受控设备的荷电状态小于等于所述不受控设备的最大荷电状态的不等式约束条件；
    所述不受控设备的荷电状态大于等于所述不受控设备的最小荷电状态的不等式约束条件；
    基于当前电力价格、电力价格下限值和允许充电标志位所建立的不等式约束条件；
    基于当前电力价格、电力价格上限值和允许放电标志位所建立的不等式约束条件。
  5. 根据权利要求1-4中任一项所述的分布式能源系统的优化方法(100)，其特征在于，
    其中所述约束条件矩阵还包括所述分布式能源系统中的受控设备的约束条件，所述决策变量包括下列中的至少一个：
    所述受控设备的设计功率；所述受控设备是否建设的决策开关量；所述受控设备在典型运行周期内的 实时功率；所述受控设备是否启动的开关量；所述不受控设备在典型运行周期内的实时功率；所述不受控设备是否启动的开关量。
  6. 一种分布式能源系统的优化装置(300)，其特征在于，包括：
    设备确定模块(301)，用于从分布式能源系统中确定出不受控设备；
    条件确定模块(302)，用于基于所述不受控设备的规则确定所述不受控设备的约束条件；
    目标函数确定模块(303)，用于确定所述分布式能源系统的决策变量、包含所述约束条件的约束条件矩阵以及与所述决策变量相关的目标函数；
    决策变量确定模块(304)，用于确定符合所述约束条件矩阵且所述目标函数为极值时的、所述决策变量的值。
  7. 根据权利要求6所述的分布式能源系统的优化装置(300)，其特征在于，
    所述条件确定模块(302)，用于当所述不受控设备的规则为所述不受控设备在启动时间段内保持启动时，确定：
    在对应于所述启动时间段的离散时间点区域内，关于所述不受控设备的负荷量的等式约束条件；或
    在对应于所述启动时间段的离散时间点区域内，所述不受控设备的开关量等于启动值的等式约束条件、在不对应于所述启动时间段的离散时间点区域内，所述不受控设备的开关量等于关闭值的等式约束条件以及在对应于所述启动时间段的离散时间点区域内，所述不受控设备的负荷量不等于零的不等式约束条件。
  8. 根据权利要求6所述的分布式能源系统的优化装置(300)，其特征在于，
    所述条件确定模块(302)，用于当所述不受控设备的规则为所述不受控设备相对于另外设备的优先级时，确定：所述不受控设备的负荷小于等于所述不受控设备的最大负荷的不等式约束条件；基于所述不受控设备的负荷与所述不受控设备的最大负荷的差与所述另外设备的开关量所建立的不等式约束条件。
  9. 根据权利要求6所述的分布式能源系统的优化装置(300)，其特征在于，
    所述条件确定模块(302)，当所述不受控设备的规则为所述不受控设备的启动状态与电力价格和该不受控设备的电池状态相关联时，确定：所述不受控设备的荷电状态小于等于所述不受控设备的最大荷电状态的不等式约束条件；所述不受控设备的荷电状态大于等于所述不受控设备的最小荷电状态的不等式约束条件；基于当前电力价格、电力价格下限值和允许充电标志位所建立的不等式约束条件；基于当前电力价格、电力价格上限值和允许放电标志位所建立的不等式约束条件。
  10. 根据权利要求6-9中任一项所述的分布式能源系统的优化装置(300)，其特征在于，
    其中所述约束条件矩阵还包括所述分布式能源系统中的受控设备的约束条件，所述决策变量包括下列中的至少一个：
    所述受控设备的设计功率；所述受控设备是否建设的决策开关量；所述受控设备在典型运行周期内的实时功率；所述受控设备是否启动的开关量；所述不受控设备在典型运行周期内的实时功率；所述不受控 设备是否启动的开关量。
  11. 一种分布式能源系统的优化装置(500)，其特征在于，包括处理器(501)、存储器(502)及存储在所述存储器(502)上并可在所述处理器(501)上运行的计算机程序，所述计算机程序被所述处理器(501)执行时实现如权利要求1至5中任一项所述的分布式能源系统的优化方法(100)。
  12. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储计算机程序，所述计算机程序被处理器执行时实现如权利要求1至5中任一项所述的分布式能源系统的优化方法(100)。
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CN117810980B (zh) * 2023-12-28 2024-06-04 南京理工大学 基于储能系统自由交易的光储虚拟网格博弈优化方法

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