CN109685332A - A kind of comprehensive energy multiagent balance of interest Optimization Scheduling and equipment - Google Patents

A kind of comprehensive energy multiagent balance of interest Optimization Scheduling and equipment Download PDF

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CN109685332A
CN109685332A CN201811489574.4A CN201811489574A CN109685332A CN 109685332 A CN109685332 A CN 109685332A CN 201811489574 A CN201811489574 A CN 201811489574A CN 109685332 A CN109685332 A CN 109685332A
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energy
moment
comprehensive energy
cost
interest
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CN201811489574.4A
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郇嘉嘉
曾诚玉
赵瑾
隋宇
余梦泽
张小辉
洪海峰
潘险险
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广东电网有限责任公司
广东电网有限责任公司电网规划研究中心
广东电网发展研究院有限责任公司
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0631Resource planning, allocation or scheduling for a business operation
    • G06Q10/06313Resource planning in a project environment
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0631Resource planning, allocation or scheduling for a business operation
    • G06Q10/06315Needs-based resource requirements planning or analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0637Strategic management or analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06Electricity, gas or water supply

Abstract

The application provides a kind of comprehensive energy multiagent balance of interest Optimization Scheduling and equipment, and wherein method includes: to obtain data and device attribute;Comprehensive energy demand response mathematical model is constructed, each subject goal function is established;It is solved to obtain integrated energy system optimal scheduling scheme by the non-dominated sorted genetic algorithm based on hyperplane.The application is according to the practical source of energy supply and device attribute in integrated energy system, construct comprehensive energy demand response mathematical model, and combine the demand responses technology such as storage, heat accumulation, establish each subject goal function, for the purpose of each interest subject equilibrium, it is solved using the non-dominated sorted genetic algorithm based on hyperplane, obtains the integrated energy system optimal scheduling scheme in the case of multiagent balance of interest.

Description

A kind of comprehensive energy multiagent balance of interest Optimization Scheduling and equipment

Technical field

This application involves electric power network technique field more particularly to a kind of comprehensive energy multiagent balance of interest Optimization Schedulings And equipment.

Background technique

The increasingly exhausted increasingly exacerbation with environmental pollution of traditional fossil energy is so that people open energy-consuming mode Begin to introspect.How to guarantee energy Green Development, promotes energy transition, improve energy utilization rate, be current urgent problem to be solved.

The comprehensive utilization of the various energy resources such as hot and cold in microgrid, electric, gas provides new approaches for energy development, Internet of Things with And the development of wide-area communication is to integrate the energy such as natural gas in region, electric energy, thermal energy, cold energy, is uniformly coordinated planning, optimization Energy supply is provided convenience.Integrated energy system (Integrated Energy System, IES) is on this basis with cold and hot Based on Electricity Federation produces unit (Combined Cooling Heating and Power, CCHP), pass through the step benefit to the energy With can effectively improve comprehensive energy utilization rate, reduce environmental pollution.And how integrated energy system guarantees efficiently to operate, and takes Certainly in how to the scheduling strategy of equipment each in system.

In integrated energy system, it is that user energizes that hot and cold, electric, gas, which is no longer as single energy flow, phase between various energy resources Mutual coupling influences each other and needs emphasis to consider the problems of as integrated energy system scheduling.

At present both at home and abroad for the research emphasis of integrated energy system Optimized Operation be by establish comprising CCHP, The comprehensive energy system of the equipment such as boiler, heat pump, Absorption Refrigerator, photovoltaic unit, blower, energy storage, with system operation cost Minimum target is solved using the methods of particle swarm algorithm, Multicriteria analysis and dual layer resist, to comprehensive energy system System is scheduled.

At present in the technology about integrated energy system Optimized Operation, place is not yet considered there are following: in actual conditions, Each equipment and be not belonging to same main body in integrated energy system and possessed, each main body tend not to for global optimum and sacrifice from Body interests;There are the discarded energies such as waste heat waste cold in integrated energy system, will further decrease system fortune if being recycled Row cost;Though comprehensive energy demand response technology has been mentioned, there has been no researchs at present takes into account comprehensive energy for this technology Among system call and project study;Electric car in peak load shifting, improves as Demand Side Response user important in microgrid Act on obvious in load curve, if considering electric car in integrated energy system scheduling, economy is perhaps more preferably.

Summary of the invention

This application provides a kind of comprehensive energy multiagent balance of interest Optimization Scheduling and equipment, it is therefore intended that is directed to The deficiency of technology at present, formulates a kind of comprehensive energy Optimization Scheduling based on multiagent angle, and this method considers practical feelings Integrated energy system is divided into comprehensive energy service provider, renewable energy owner, electric car owner's three main bodies by condition, And comprehensively consider including the demand responses technology such as demand response, storage, heat accumulation, for the purpose of each interest subject equilibrium, formulate comprehensive Close each unit Optimized Operation strategy a few days ago in the energy.

In view of this, the application first aspect provides a kind of comprehensive energy multiagent balance of interest Optimization Scheduling, Include:

Obtain comprehensive energy service provider, renewable energy owner, electric car owner's three main bodies data and set Standby attribute;

Comprehensive energy demand response technical mathematics model is constructed, each subject goal function is established;

The data of three main bodies and device attribute are inputted into comprehensive energy demand response mathematical model, each subject goal letter Number, is solved by the non-dominated sorted genetic algorithm based on hyperplane, obtains integrated energy system optimal scheduling scheme.

Preferably, the building demand response mathematical model includes:

Construct comprehensive energy demand response mathematical model:

Wherein, i=1,2 ..., n;J=1,2 ..., m;ΔLiFor i class energy supply, HjFor j class energy demand response Amount;dijFor demand response coupling factor, indicate that the energy demand of j class responds the influence to i class energy resource supply.

Preferably, described to establish each subject goal function and include:

Establish the objective function of comprehensive energy service provider:

Wherein, NTFor day scheduling slot sum;cbIt (t) is t moment IESP superior power grid purchase electricity price; PpeIt (t) is t Moment IESP superior power grid power purchase power;C (t) is the new energy rate for incorporation into the power network that t moment IESP is formulated;PpvMIt (t) is t moment Power purchase power of the IESP to REO;ceve(t) the electric car charge/discharge electricity price formulated for t moment IESP;PeveIt (t) is t moment Electric car charge-discharge electric power, Peve(t) electric car electric discharge, P are indicated for timingeve(t) electric car charging is indicated when being negative; Cfuel(t) the combustion gas cost for being t moment IESP;CstIt (t) is t moment equipment start-stop cost;Cep(t) for t moment environmental pollution at This;For equipment O&M cost in t moment IESP;CHSIt (t) is t moment heat-storing device cost depletions;For t Moment electric storage device cost depletions, including battery life cost depletions and transmission loss cost.

Preferably, described to establish each subject goal function and include:

Establish the objective function of renewable energy owner:

Wherein, coutIt (t) is t moment REO superior power grid sale of electricity electricity price;PpvI(t) it is sold for t moment REO superior power grid Electrical power;For equipment O&M cost in t moment REO;For t moment electric storage device cost depletions in REO, packet Include battery life cost depletions and transmission loss cost.

Preferably, described to establish each subject goal function and include:

Establish the objective function of electric car owner:

Wherein, Δ p is the difference that electric car rate for incorporation into the power network and electric car unit power dispatch cost, and α rings for EVO The probability that should be dispatched.

Preferably, described to be solved by the non-dominated sorted genetic algorithm based on hyperplane, obtain comprehensive energy system System optimal scheduling scheme include:

For the purpose of each interest subject equilibrium, is solved, obtained more using the non-dominated sorted genetic algorithm based on hyperplane Integrated energy system optimal scheduling scheme under interest subject equilibrium situation.

Preferably, it is described by the non-dominated sorted genetic algorithm based on hyperplane solved in establish the step of hyperplane Suddenly include:

Step 1: sought from new group R all targets minimum value, the as ideal point of objective function is denoted as

Step 2: objective function is converted as the following formula:

Step 3: Function Extreme Value point after converting is calculated using ASF function;

In formula: M is objective function number, wiFor weight coefficient, and

Step 4: choosing extreme point of the minimal solution in ASF as the dimension, be denoted as ai, the extreme point of all dimensions A hyperplane is constructed, and objective function is normalized as the following formula in hyperplane;

The application second aspect provides a kind of comprehensive energy multiagent balance of interest Optimized Operation equipment, the equipment packet Include processor and memory:

Said program code is transferred to the processor for storing program code by the memory;

The processor is used for mostly main according to the comprehensive energy of the above-mentioned first aspect of instruction execution in said program code Body balance of interest Optimization Scheduling.

The application third aspect provides a kind of computer readable storage medium, and the computer readable storage medium is used for Program code is stored, said program code is used to execute the comprehensive energy multiagent balance of interest Optimized Operation of above-mentioned first aspect Method.

As can be seen from the above technical solutions, the application has the following advantages:

The application provides a kind of comprehensive energy multiagent balance of interest Optimization Scheduling and equipment, and wherein method includes: Obtain comprehensive energy service provider, renewable energy owner, electric car owner's three main bodies data and device attribute;Structure The demand response mathematical model for building meter and comprehensive energy demand response establishes each subject goal function;By the data of three main bodies Demand response mathematical model, each subject goal function that meter and comprehensive energy demand response are inputted with device attribute, by being based on The non-dominated sorted genetic algorithm of hyperplane is solved, and integrated energy system optimal scheduling scheme is obtained.The application is according to comprehensive Close the practical source of energy supply and device attribute in energy resource system, be classified as comprehensive energy service provider, renewable energy owner, Electric car owner's three main bodies construct comprehensive energy demand response mathematical model, and combine the demand responses such as storage, heat accumulation Technology establishes each subject goal function, for the purpose of each interest subject equilibrium, using the non-dominated ranking heredity based on hyperplane Algorithm solves, and obtains the integrated energy system optimal scheduling scheme in the case of multiagent balance of interest.

Detailed description of the invention

It in ord to more clearly illustrate embodiments of the present application, below will be to required use in embodiment or description of the prior art Attached drawing be briefly described, it should be apparent that, the drawings in the following description are only some examples of the present application, for this For the those of ordinary skill of field, without any creative labor, it can also be obtained according to these attached drawings other Attached drawing.

Fig. 1 is comprehensive energy multiagent figure in the embodiment of the present application;

Fig. 2 is a kind of one embodiment of comprehensive energy multiagent balance of interest Optimization Scheduling provided by the present application Schematic diagram;

Fig. 3 is in a kind of one embodiment of comprehensive energy multiagent balance of interest Optimization Scheduling provided by the present application The flow chart of non-dominated sorted genetic algorithm based on hyperplane;

Fig. 4 is in an a kind of application examples of comprehensive energy multiagent balance of interest Optimization Scheduling provided by the present application Thermoelectricity load and photovoltaic power curve figure;

Fig. 5 is in an a kind of application examples of comprehensive energy multiagent balance of interest Optimization Scheduling provided by the present application The three-dimensional forward position pareto figure;

Fig. 6 is in an a kind of application examples of comprehensive energy multiagent balance of interest Optimization Scheduling provided by the present application IESP electrical power power output distribution map;

Fig. 7 is in an a kind of application examples of comprehensive energy multiagent balance of interest Optimization Scheduling provided by the present application IESP thermal power power output distribution map;

Fig. 8 is in an a kind of application examples of comprehensive energy multiagent balance of interest Optimization Scheduling provided by the present application REO power output distribution situation figure.

Specific embodiment

This application provides a kind of comprehensive energy multiagent balance of interest Optimization Scheduling and equipment, it is therefore intended that is directed to The deficiency of technology at present, formulates a kind of comprehensive energy Optimization Scheduling based on multiagent angle, and this method considers practical feelings Integrated energy system is divided into comprehensive energy service provider, renewable energy owner, electric car owner's three main bodies by condition, And comprehensively consider including technologies such as comprehensive energy demand response, storage, heat accumulations, for the purpose of each interest subject equilibrium, formulate comprehensive Close each unit Optimized Operation strategy a few days ago in the energy.

To enable present invention purpose, feature, advantage more obvious and understandable, below in conjunction with the application Attached drawing in embodiment, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that disclosed below Embodiment be only some embodiments of the present application, and not all embodiment.Based on the embodiment in the application, this field Those of ordinary skill's all other embodiment obtained without making creative work belongs to the application protection Range.

It should be understood that the application is applied to the case where comprehensive energy multiagent, referring to Fig. 1, Fig. 1 is in the embodiment of the present application Comprehensive energy multiagent figure, as shown in Figure 1, including comprehensive energy service provider, renewable energy owner, electric car in Fig. 1 Owner's three main bodies, comprehensive dispatching of power netwoks situation carry out microgrid self-energy balance.

Firstly, the part noun or term that occur during the embodiment of the present application is described are suitable for as follows It explains:

Non-dominated sorted genetic algorithm based on hyperplane: Hyperplane based Non-dominated Sorting Genetic Algorithm, NSGA-III.

Referring to Fig. 2, an a kind of reality of comprehensive energy multiagent balance of interest Optimization Scheduling provided by the present application Apply example, comprising:

101, obtain comprehensive energy service provider, renewable energy owner, electric car owner's three main bodies data And device attribute;

It should be noted that may determine that it energizes practical source according to the data of three main bodies and device attribute;

Wherein, comprehensive energy service provider is responsible for user and provides the energy such as cool and thermal power, possess cogeneration units, boiler, The powering devices such as electric refrigerating machine, lithium bromide absorption unit, and from renewable energy owner and higher level's power grid power purchase, electronic vapour Vehicle can also carry out electric energy with it and interact, and reach integrated energy system self-energy balance jointly, and formulate according to energy balance situation To the electricity price at times of renewable energy owner's power purchase and the day part charge and discharge electricity price of automobile user.

Wherein, renewable energy owner possesses renewable energy power generation equipment and its matched energy storage device;It can basis Comprehensive energy service provider is to the purchase electricity price of renewable energy owner and selling for renewable energy owner's superior power grid Electric electricity pricing energy funds circulating plan.

Wherein, electric car owner is big as randomness in comprehensive energy, the user of substantial amounts, can be according to Che Xi Used and day part charge and discharge electricity price determines whether to participate in scheduling.

102, comprehensive energy demand response mathematical model is constructed, each subject goal function is established;

Comprehensive energy demand response mathematical model forms mathematical relationship after models coupling real data, is calculating target letter Play calculating in number;

Each subject goal function includes: the target letter of the objective function of comprehensive energy service provider, renewable energy owner It counts, the objective function of electric car owner, for the Solve problems of multiple objective function, the present invention passes through based on the non-of hyperplane Dominated Sorting Genetic Algorithm is solved;

103, the data of three main bodies and device attribute are inputted into comprehensive energy demand response mathematical model, each subject goal Function is solved by the non-dominated sorted genetic algorithm based on hyperplane, obtains integrated energy system optimal scheduling scheme.

Further, building comprehensive energy demand response mathematical model includes:

Construct comprehensive energy demand response mathematical model:

Wherein, i=1,2 ..., n;J=1,2 ..., m;ΔLiFor i class energy supply, HjFor j class energy demand response Amount;dijFor demand response coupling factor, indicate that the energy demand of j class responds the influence to i class energy resource supply.

Further, establishing each subject goal function includes:

Establish the objective function of comprehensive energy service provider:

Wherein, NTFor day scheduling slot sum;cbIt (t) is t moment IESP superior power grid purchase electricity price; PpeIt (t) is t Moment IESP superior power grid power purchase power;C (t) is the new energy rate for incorporation into the power network that t moment IESP is formulated;PpvMIt (t) is t moment Power purchase power of the IESP to REO;ceve(t) the electric car charge/discharge electricity price formulated for t moment IESP;PeveIt (t) is t moment Electric car charge-discharge electric power, Peve(t) electric car electric discharge, P are indicated for timingeve(t) electric car charging is indicated when being negative; Cfuel(t) the combustion gas cost for being t moment IESP;CstIt (t) is t moment equipment start-stop cost;Cep(t) for t moment environmental pollution at This;For equipment O&M cost in t moment IESP;CHSIt (t) is t moment heat-storing device cost depletions;For t Moment electric storage device cost depletions, including battery life cost depletions and transmission loss cost.

Further, establishing each subject goal function includes:

Establish the objective function of renewable energy owner:

Wherein, coutIt (t) is t moment REO superior power grid sale of electricity electricity price;PpvI(t) it is sold for t moment REO superior power grid Electrical power;For equipment O&M cost in t moment REO;For t moment electric storage device cost depletions in REO, packet Include battery life cost depletions and transmission loss cost.

Further, establishing each subject goal function includes:

Establish the objective function of electric car owner:

Wherein, Δ p is the difference that electric car rate for incorporation into the power network and electric car unit power dispatch cost, and α rings for EVO The probability that should be dispatched.

Further, it is solved by the non-dominated sorted genetic algorithm based on hyperplane, obtains integrated energy system Optimal scheduling scheme includes:

For the purpose of each interest subject equilibrium, is solved, obtained more using the non-dominated sorted genetic algorithm based on hyperplane Integrated energy system optimal scheduling scheme under interest subject equilibrium situation.

It should be noted that being solved before obtaining optimal pareto using the non-dominated sorted genetic algorithm based on hyperplane It can be solved by the non-dominated sorted genetic algorithm as shown in Figure 3 based on hyperplane along solution:

Wherein, a crucial step is how to construct hyperplane, can be divided into following a few small steps:

Step 1: sought from new group R all targets minimum value, the as ideal point of objective function is denoted as

Step 2: objective function is converted as the following formula:

Step 3: Function Extreme Value point after converting is calculated using ASF function;

In formula: M is objective function number, wiFor weight coefficient, and

Step 4: choosing extreme point of the minimal solution in ASF as the dimension, be denoted as ai, the extreme point of all dimensions A hyperplane is constructed, and objective function is normalized as the following formula in hyperplane;

It is solved by the non-dominated sorted genetic algorithm based on hyperplane, obtains the synthesis in the case of multiagent balance of interest Energy resource system optimal scheduling scheme.

Illustrate the technical solution of the application below with reference to concrete application example:

By taking some smart grid Demonstration Garden of China as an example, typical day thermoelectricity load and photovoltaic power curve are as schemed in garden Shown in 4, garden cogeneration units carry out heat supply using " electricity determining by heat " mode, and garden new energy is configured with 2 based on photovoltaic The distributed photovoltaic unit of × 20KW, garden refrigeration duty are translated into electric load consideration based on air conditioner load herein.Emulation With 15 minutes for a time interval, it was divided into 96 periods for one day.Get peak valley usually section garden to power grid purchase electricity price And photovoltaic is to the data such as table 1 of power grid sale of electricity electricity price, wherein peak period: 8:00-11:00,18:00-22:00;Usually section 11: 00-18:00,22:00-23:00;Paddy period 00:00-8:00,23:00-24:00.

NSGA-III algorithm is programmed using MATLAB, more to the Itellectualized uptown comprehensive energy based on NSGA-III algorithm Main body optimizes scheduling and calculates, and the population scale of NSGA-III is set as 50, and the number of iterations 100, crossover probability 0.8 becomes Different probability is that 0.2, pareto optimum individual coefficient is 0.3.

1 tou power price table of table

Table 1Time-of-use price

The forward position pareto based on NSGA-III is as shown in Figure 5.As shown in Figure 5, the economic benefit of REO increases, then IESP Operating cost increase therewith, the decline of the economic benefit of EVO;And the economic benefit of EVO increases, the operating cost of IESP also increases Add.Embody the interest game of each main body.

IESP each unit is contributed, and distribution situation is as shown in Figure 6, Figure 7, and REO power output distribution is as shown in Figure 8.

Multiagent balance of interest Optimized Operation is the results show that REO divides in peak Pinggu period to the optimal sale of electricity electricity price of microgrid Not are as follows: 0.8688 yuan/KWh, 0.5157 yuan/KWh and 0.1574/KWh, the corresponding power purchase for being above microgrid superior power grid are electric Valence can be calculated at this time so that consumption is maximized inside renewable energy by Fig. 8 lower than photovoltaic to the corresponding sale of electricity electricity price of power grid Photovoltaic consumption rate is up to 93.69% inside microgrid.And as shown in Figure 6 at the photovoltaic sufficient moment, the electricity supply of IESP is main next From in CHP and to REO power purchase, the purchase of electricity of superior power grid is almost 0, effectively reduces the operating cost of IESP. Energy-storage system is configured in REO, selection is discharged in load boom period, and the load valley phase stores the extra electricity of photovoltaic system, is reduced The abandoning light rate of REO;And the time shift characteristic of energy-storage system, so that REO was remained in the peak period in the insufficient situation of solar energy (18:00-21:00) improves the economic benefit of REO to IESP sale of electricity.

In integrated energy system, in the electric load peak period, electric load differs larger with thermic load, since CHP is operated in Under " electricity determining by heat " mode, to meet electric load balance, the thermal energy power output of CHP needs to increase, if will lead to heat without heat-storing device Energy is discarded.The thermal energy that heat-storing device is recycled can be used for freezing, it can also be used to carry out heat supply at remaining moment, it is negative to reduce electric heating The joint of lotus trough period CHP is contributed, and combustion gas cost is reduced.

Boiler heat supplying power consumption causes electric load curve to increase, but comprehensive energy demand response, energy storage device and Under the collective effect of electric car, compared with reduction, the variance of load curve is also only the peak-valley difference of new load curve 37.84, much smaller than the 94.69 of original loads.And it will be appreciated from fig. 6 that raising of the load curve in 11:00-13:00 is conducive to photovoltaic The power output of unit dissolves.

Electric car participates in the time of scheduling between 19:00- next day 6:00, and after Optimized Operation, EVO selection exists The electricity price peak period discharges, and electricity price trough period charges, and reaches net profit maximization, and EVO electric discharge electricity price is 0.8981 Member/KWh is less than IESP superior power grid purchase electricity price, reduces the operating cost of IESP.

It is to a kind of implementation of comprehensive energy multiagent balance of interest Optimization Scheduling provided by the present application above Example is described in detail, below will be to a kind of comprehensive energy multiagent balance of interest Optimized Operation equipment provided by the present application One embodiment is described in detail.

A kind of one embodiment of comprehensive energy multiagent balance of interest Optimized Operation equipment provided by the present application, it is described to set Standby includes processor and memory:

Said program code is transferred to the processor for storing program code by the memory;

The processor is used for the comprehensive energy multiagent according to instruction execution above-described embodiment in said program code Balance of interest Optimization Scheduling.

A kind of one embodiment of computer readable storage medium provided by the present application, the computer readable storage medium For storing program code, the comprehensive energy multiagent balance of interest optimization that said program code is used to execute above-described embodiment is adjusted Degree method.

The above, above embodiments are only to illustrate the technical solution of the application, rather than its limitations;Although referring to before Embodiment is stated the application is described in detail, those skilled in the art should understand that: it still can be to preceding Technical solution documented by each embodiment is stated to modify or equivalent replacement of some of the technical features;And these It modifies or replaces, the spirit and scope of each embodiment technical solution of the application that it does not separate the essence of the corresponding technical solution.

Claims (8)

1. a kind of comprehensive energy multiagent balance of interest Optimization Scheduling characterized by comprising
Obtain comprehensive energy service provider, renewable energy owner, the data of electric car owner's three main bodies and equipment category Property;
Comprehensive energy demand response mathematical model is constructed, each subject goal function is established;
The data of three main bodies and device attribute are inputted into comprehensive energy demand response mathematical model, each subject goal function, are led to It crosses the non-dominated sorted genetic algorithm based on hyperplane to be solved, obtains integrated energy system optimal scheduling scheme.
2. a kind of comprehensive energy multiagent balance of interest Optimization Scheduling according to claim 1, which is characterized in that institute Stating building comprehensive energy demand response mathematical model includes:
Construct comprehensive energy demand response mathematical model:
Wherein, i=1,2 ..., n;J=1,2 ..., m;ΔLiFor i class energy supply, HjFor j class energy demand response quautity;dij For demand response coupling factor, indicate that the energy demand of j class responds the influence to i class energy resource supply.
3. a kind of comprehensive energy multiagent balance of interest Optimization Scheduling according to claim 1, which is characterized in that institute It states and establishes each subject goal function and include:
Establish the objective function of comprehensive energy service provider:
Wherein, NTFor day scheduling slot sum;cbIt (t) is t moment IESP superior power grid purchase electricity price;PpeIt (t) is t moment IESP superior power grid power purchase power;C (t) is the new energy rate for incorporation into the power network that t moment IESP is formulated;PpvMIt (t) is t moment IESP To the power purchase power of REO;ceve(t) the electric car charge/discharge electricity price formulated for t moment IESP;Peve(t) electronic for t moment Automobile charge-discharge electric power, Peve(t) electric car electric discharge, P are indicated for timingeve(t) electric car charging is indicated when being negative;Cfuel (t) the combustion gas cost for being t moment IESP;CstIt (t) is t moment equipment start-stop cost;CepIt (t) is t moment environmental pollution cost;For equipment O&M cost in t moment IESP;CHSIt (t) is t moment heat-storing device cost depletions;For t moment storage Electric installation cost depletions, including battery life cost depletions and transmission loss cost.
4. a kind of comprehensive energy multiagent balance of interest Optimization Scheduling according to claim 1, which is characterized in that institute It states and establishes each subject goal function and include:
Establish the objective function of renewable energy owner:
Wherein, coutIt (t) is t moment REO superior power grid sale of electricity electricity price;PpvIIt (t) is t moment REO superior power grid sale of electricity function Rate;For equipment O&M cost in t moment REO;For t moment electric storage device cost depletions in REO, including electricity Pond life consumption cost and transmission loss cost.
5. a kind of comprehensive energy multiagent balance of interest Optimization Scheduling according to claim 1, which is characterized in that institute It states and establishes each subject goal function and include:
Establish the objective function of electric car owner:
Wherein, Δ p is the difference that electric car rate for incorporation into the power network and electric car unit power dispatch cost, and α is EVO response scheduling Probability.
6. a kind of comprehensive energy multiagent balance of interest Optimization Scheduling according to claim 1, which is characterized in that institute State by the non-dominated sorted genetic algorithm based on hyperplane solved in establish hyperplane the step of include:
Step 1: sought from new group R all targets minimum value, the as ideal point of objective function is denoted as
Step 2: objective function is converted as the following formula:
Step 3: Function Extreme Value point after converting is calculated using ASF function;
In formula: M is objective function number, wiFor weight coefficient, and
Step 4: choosing extreme point of the minimal solution in ASF as the dimension, be denoted as ai, the extreme point of all dimensions can construct One hyperplane, and objective function is normalized as the following formula in hyperplane;
7. a kind of comprehensive energy multiagent balance of interest Optimized Operation equipment, which is characterized in that the equipment include processor with And memory:
Said program code is transferred to the processor for storing program code by the memory;
The processor is used for according to the instruction execution comprehensive energy described in any one of claims 1-6 in said program code Multiagent balance of interest Optimization Scheduling.
8. a kind of computer readable storage medium, which is characterized in that the computer readable storage medium is for storing program generation Code, said program code require the described in any item comprehensive energy multiagent balance of interest Optimized Operations of 1-6 for perform claim Method.
CN201811489574.4A 2018-12-06 2018-12-06 A kind of comprehensive energy multiagent balance of interest Optimization Scheduling and equipment CN109685332A (en)

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CN106408131A (en) * 2016-09-30 2017-02-15 安徽工程大学 Photovoltaic microgrid multi-target scheduling method based on demand-side management
CN107967533A (en) * 2017-11-08 2018-04-27 国网冀北电力有限公司 A kind of meter and distributed generation resource and the load forecasting method of Demand Side Response
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Publication number Priority date Publication date Assignee Title
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