WO2023103385A1 - 一种多能微网群自身及市场决策协同优化方法 - Google Patents

一种多能微网群自身及市场决策协同优化方法 Download PDF

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WO2023103385A1
WO2023103385A1 PCT/CN2022/106192 CN2022106192W WO2023103385A1 WO 2023103385 A1 WO2023103385 A1 WO 2023103385A1 CN 2022106192 W CN2022106192 W CN 2022106192W WO 2023103385 A1 WO2023103385 A1 WO 2023103385A1
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energy
memg
heat
period
power
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French (fr)
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杜凤青
刘婧
程凡
董真
潘爱强
王琛
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国网上海市电力公司
华东电力试验研究院有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Definitions

  • the invention relates to the technical field of multi-energy micro-grid group optimization, in particular to a method for collaborative optimization of multi-energy micro-grid group itself and market decision-making.
  • each MEMG has power grid, heat grid and gas grid respectively.
  • adjacent MEMGs are interconnected into a complete network, sharing energy with each other through the internal energy transmission network. Specifically, MEMGs with excess or insufficient energy are encouraged to first trade energy with the remaining MEMGs in the internal market rather than directly with the public grid.
  • the MEMG cluster itself cannot achieve internal energy balance, it can trade with the external centralized energy network when necessary, that is: when a MEMG generates surplus energy, it can first sell it to a nearby MEMG for internal energy balance. If there is any surplus, the remaining part will be sold back to the external power grid. Conversely, the insufficient part can purchase energy from the nearby MEMG first, and then purchase energy from the external grid.
  • the purpose of the present invention is to provide a multi-energy micro-grid group itself that improves the overall benefit, has a higher level of energy consumption inside the MEMG cluster, and achieves a balance between self-sufficiency and transaction sharing capabilities in order to overcome the above-mentioned defects in the prior art. Collaborative optimization method for market decision-making.
  • a multi-energy micro-grid group itself and a market decision-making collaborative optimization method includes:
  • Step 1 Construct the multi-energy micro-grid system model, including the multi-energy micro-grid lower model for MEMG internal optimization and the multi-energy micro-grid upper model for market decision optimization;
  • Step 2 Build a game model based on the master-slave game
  • Step 3 Solve the game model based on the two-layer MILP model, obtain the optimal game solution set, and output the collaborative optimization strategy.
  • the lower layer model of the multi-energy microgrid group takes the objective function of maximizing benefits within a certain period of time, specifically:
  • BeMEMG n is the benefit of the nth MEMG; a n,h is the marginal benefit coefficient of the nth MEMG in the period h; ele n,h , ht n,h , cl n,h are the nth MEMG’s Electricity, heat, and cooling loads in period h; COP is the cooling coefficient; els n,h and elb n,h are the electricity sales and purchase power of the nth MEMG in period h respectively; hts n,h and htb n,h are respectively is the heat sales and heat purchase power of the nth MEMG in the period h; M mt,n,h represents the gas consumption of the gas turbine of the nth MEMG in the period h; M gb,n,h is the gas volume consumed by the gas boiler ; Pres h , Preb h are the electricity sales and purchase prices of MEMG in period h respectively; Prhs n,h , Pr
  • the lower layer model of the multi-energy microgrid group is provided with equipment output constraints and energy balance constraints;
  • the equipment processing constraints include cogeneration unit output constraints, gas boiler output constraints, compression refrigerator output constraints, Battery output constraints, photovoltaic equipment output constraints, fan equipment output constraints, and thermal energy demand response constraints.
  • the output constraint of the cogeneration unit is specifically:
  • P mt,n,h is the gas turbine power generation power of the nth MEMG in the period h; M mt,n,h represents the gas consumption of the gas turbine of the nth MEMG in the period h; L ng represents the calorific value of natural gas; ⁇ mt Indicates the power generation efficiency of the gas turbine; P mt,n,min represents the minimum power of the gas turbine; P mt,n,max represents the maximum power of the gas turbine; P hc,n,h is the heat output of the heat exchanger of the nth MEMG in the period h; r mt is the heat-to-electricity ratio; ⁇ wh is the efficiency of waste heat boiler; ⁇ hc is the efficiency of heat exchange device;
  • Q gb,n,h is the output thermal power of the gas boiler of the nth MEMG in the h period; M gb,n,h is the gas volume consumed by the gas boiler; ⁇ gb is the efficiency of the gas boiler; Q gb, n,min is the minimum power of the gas boiler; Q gb,n,max is the maximum power of the gas boiler.
  • cl n,h is the cooling load of the nth MEMG in the h period;
  • COP is the cooling coefficient;
  • Co ec,n,h is the cooling power of the compression refrigerator;
  • Co ec,n,max is the upper limit of the cooling power ;
  • Es n,h is the battery storage capacity of the nth MEMG in h period; Bch n,h and Bdis n,h are charging and discharging power respectively; Nch and Ndis are charging and discharging efficiencies respectively; Es i (0) , Es i (24) are the initial power and final power of the battery for one day; E sn,max, E sn,min are the maximum and minimum remaining power of the battery system respectively; Pen ,h is the multi-energy microgrid n in h Bch n,max, Bdis n,max represent the maximum charge and discharge capacity of the battery respectively; kech n,h and kedis n,h are the charge and discharge of the multi-energy microgrid n in h period state.
  • a PV,n is the photovoltaic installation area of the nth MEMG;
  • a PV,n,max is the maximum installation area;
  • PV n,h is the photovoltaic power generation power of the nth MEMG;
  • Ii h is the multi-energy microgrid n in The unit irradiation amount in h period;
  • ⁇ PV is the efficiency of photovoltaic power generation.
  • P wt,n,h is the generating power of the fan
  • P wt,n,max is the maximum generating power
  • thermal energy demand response constraints are as follows:
  • ele n,h , ht n,h , cl n,h are the electricity, heat, and cooling loads of the nth MEMG in the period h respectively; Ele n,h,max , Ht n,h,max , Cl n, h, max are the upper limit of electricity, heat, and cooling loads of the nth MEMG in the h period; Ele n,h,min , Ht n,h,min , Cl n,h,min are the The lower limit of electricity, heat, and cooling loads in the time period; Ele n,h , Ht n,h , and Cl n,h are the initial forecast electricity, heat, and cooling loads, respectively, and p is the load reduction coefficient.
  • the energy balance constraint is specifically:
  • els n,h, elb n,h are the power sales and purchase power of the nth MEMG in the period h respectively; hts n,h and htb n,h are the heat sales and purchase power of the nth MEMG in the period h respectively Thermal power; P hx,n,h is the output of the heat exchange device of the multi-energy microgrid n during the period h; Q gb,n,h is the output of the gas boiler of the multi-energy microgrid n during the period h.
  • the objective function of the upper layer model of the multi-energy microgrid group is:
  • maxBeIA is the maximum benefit of the multi-energy micro-grid group in the period h;
  • PrEb h and PrEs h represent the electricity price of IA’s purchase and sale of electricity from the grid, respectively;
  • PrHb h and PrHs h represent the heat The heat price of its heat sale;
  • El h and Hl h respectively represent the total amount of electricity and heat transactions between the intermediary IA (Intermediary Agent) and the external network at a certain moment;
  • Els h and Elb h respectively represent the The total electricity sold and the total electricity purchased from the external network at a certain moment;
  • Hts h and Htb h respectively represent the total heat sold by IA to the external heating network and the total heat purchased from the external heating network at a certain moment.
  • the upper layer model of the multi-energy microgrid group is provided with energy transaction constraints, specifically:
  • el n,h is the net electric power of the nth MEMG in the period h; keb n,h and kes n,h are the 0-1 variables of the nth MEMG’s power purchase and sale respectively, and 1 means that it is in the electricity or electricity sales status; hkt n,h is the thermal power exchanged between the multi-energy microgrid n and the main network during the period h; khb n,h and khs n,h are the 0- 1 variable, 1 indicates that it is in the state of heat purchase or heat sale; Preb h and Pres h are the electricity prices MEMG buys from IA and sells back to IA respectively; among them, PrEb h and PrEs h represent IA’s purchase of electricity from the grid and its Electricity price for selling electricity ; similarly, Prhb h and Prhs h represent the heat price that MEMG buys from IA and sells back to IA; h is the net electric power of
  • the step 2 is specifically:
  • IA represents the leader of the game
  • ⁇ MEMG ⁇ represents the followers of the game
  • ⁇ Ame n,h ⁇ Amh n,h ⁇ represent the remaining power and heat of each MEMG respectively
  • ⁇ Pres h ⁇ , ⁇ Preb h ⁇ , ⁇ Prhs h ⁇ , ⁇ Prhb h ⁇ set represents the internal price strategy formulated by IA
  • ⁇ BeMEMG n, h ⁇ , ⁇ BeIA h ⁇ is the target benefit function of the transaction subject
  • the equilibrium point S of game F is:
  • the step 3 is specifically:
  • Step 3-1 Obtain the price of electricity purchase and sale and purchase and sale of heat formulated by the energy distribution network
  • Step 3-2 Each multi-energy microgrid performs self-optimization
  • Step 3-3 Each multi-energy microgrid uploads the margin and shortage of cooling, heating, and electric loads, as well as equipment output optimization, new energy power generation forecast, and load demand response range to the intermediary IA;
  • Step 3-4 The intermediary IA formulates the transaction price of electricity and heat in the multi-energy micro-grid group based on the information fed back by each multi-energy micro-grid, combined with the transaction price with the energy distribution network, with the goal of maximizing its own benefits;
  • Step 3-5 According to the transaction price set by the intermediary IA, each multi-energy microgrid can perform self-optimization, conduct demand response, adjust the demand for cooling, heating, and electric loads, and feed back the surplus and shortage information to the intermediary quotient IA;
  • Step 3-6 Determine whether the convergence condition is met, if yes, execute step 3-7, otherwise, return to step 3-4;
  • Step 3-7 Output the optimal game solution set.
  • the self-optimizing method of the multi-energy microgrid is:
  • each multi-energy micro-grid optimizes the output of equipment with the goal of maximizing its own benefits according to its own energy forecast data and the forecast data of cold, heat, and electricity used by the micro-grid, and optimizes the use of cold, heat, and electricity at the same time. Electricity load demand.
  • the present invention has the following beneficial effects:
  • the SGT-based method proposed by the multi-energy micro-grid group itself and the market decision-making collaborative optimization method in the present invention can effectively solve the problem of determining the internal transaction price of energy. Compared with the case where a single MEMG works alone, The cluster combination significantly improves the overall benefit of all MEMGs, with a 4.4% improvement when considering the benefit of IA on a typical day of study.
  • the internal energy consumption level of the MEMG cluster is higher: the multi-energy micro-grid group itself and the market decision-making collaborative optimization method in the present invention make MEMG give priority to the energy consumption inside the multi-energy micro-grid cluster, and then conduct transactions with external energy networks , a higher level of internal energy self-consumption.
  • the multi-energy micro-grid group itself and the market decision-making collaborative optimization method in the present invention provide a mixed integer linear programming model for the global optimization of the MEMG cluster, and provide a solution for different scenarios
  • the determination of energy transaction prices provides technical support. While seeking self-sufficiency and relatively stable energy demand, it is recommended to share benefits with IA to achieve a balance between self-sufficiency and transaction sharing capabilities.
  • Fig. 1 is a schematic flow diagram of the multi-energy micro-grid group itself and the market decision-making collaborative optimization method in the present invention
  • Fig. 2 is a schematic diagram of the frame structure of the multi-energy microgrid group system in the embodiment of the present invention
  • Fig. 3 is the schematic flow chart of solving game model in the embodiment of the present invention.
  • FIG. 4 is a schematic diagram of the energy flow structure of MEMG in an embodiment of the present invention.
  • Fig. 5 is used solar radiation and wind speed data in the embodiment of the present invention.
  • FIG. 6 is a schematic diagram of electrical balance of a MEMG cluster in an embodiment of the present invention.
  • Figure 6(a), Figure 6(b) and Figure 6(c) represent MEMG1, MEMG2 and MEMG3 respectively;
  • Fig. 7 is a schematic diagram of the heat balance of the MEMG cluster in the embodiment of the present invention.
  • Figure 7(a), Figure 7(b) and Figure 7(c) represent MEMG1, MEMG2 and MEMG3 respectively;
  • Fig. 8 is the demand response analysis of MEMG cluster in the embodiment of the present invention.
  • Figure 8(a), Figure 8(b) and Figure 8(c) represent MEMG1, MEMG2 and MEMG3, respectively
  • Fig. 9 is a schematic diagram of the optimal price of internal energy exchange in the embodiment of the present invention.
  • Figure 9(a) is the price of electricity purchase and sale
  • Figure 9(b) is the price of heat purchase and sale
  • Figure 10 is the energy transaction volume between the external energy network and the MEMG cluster in the embodiment of the present invention.
  • Fig. 11 is the benefit per hour of each MEMG in a typical day in the embodiment of the present invention.
  • Figure 12 shows the hourly benefits of SGT-based IA on a typical day in an embodiment of the invention.
  • the framework structure of the multi-energy microgrid system is shown in Figure 2.
  • IA intermediary
  • IA is responsible for collecting necessary information, such as external energy prices, surplus/deficiency energy amounts of each MEMG, interest demand, etc., and determines internal energy transaction prices accordingly. This embodiment considers the following situations:
  • IA When IA handles the cash flow of energy transactions as an actual operator, IA's profit is based on the difference between the bidding price and the asking price of energy sharing within the cluster and energy transactions outside the cluster.
  • the price of energy transactions depends on the interests of the participants using the SGT (Stackelberg Game Theory) method.
  • This embodiment proposes a multi-energy microgrid group itself and a market decision-making collaborative optimization method, including:
  • Step 1 Construct the multi-energy micro-grid system model, including the multi-energy micro-grid lower model for MEMG internal optimization and the multi-energy micro-grid upper model for market decision optimization.
  • the SGT-based method will be used to deal with it.
  • IA as the real operator, is also an independent stakeholder.
  • the multi-energy microgrid cluster system model is a non-convex function, in order to solve this problem, it is necessary to implement multiple iterations between the upper and lower layers.
  • Step 2 Build a game model based on the master-slave game
  • Step 3 Solve the game model based on the two-layer MILP (Mixed Integer Linear Programming) model, obtain the optimal game solution set, and output the collaborative optimization strategy.
  • MILP Mated Integer Linear Programming
  • the objective function of each MEMG is to maximize the benefit within a certain period of time.
  • the income of each time period mainly depends on the energy fee paid by the user, the energy exchange income paid by the nearby MEMG, and the transaction income with the external energy network. Note that operating costs must be excluded from revenue, including primary energy costs, battery operating costs, etc.
  • the lower layer model of the multi-energy microgrid group takes the maximization of benefits within a certain period of time as the objective function, specifically:
  • BeMEMG n is the benefit of the nth MEMG
  • a n,h is the marginal benefit coefficient of the nth MEMG in the period h
  • ele n,h , ht n,h , cl n,h are the nth MEMG’s Electricity, heat, and cooling loads in the h period
  • COP is the cooling coefficient
  • els n,h and elb n,h are the power sales and purchases of the nth MEMG in the h period respectively
  • hts n,h and htb n,h are respectively is the heat sales and heat purchase power of the nth MEMG in the period h
  • M mt,n,h represents the gas consumption of the gas turbine of the nth MEMG in the period h
  • M gb,n,h is the gas volume consumed by the gas boiler
  • Pres h , Preb h are the electricity sales and purchase prices of MEMG in period h respectively
  • the lower model of the multi-energy microgrid group has equipment output constraints and energy balance constraints.
  • Equipment processing constraints include cogeneration unit output constraints, gas boiler output constraints, compression refrigerator output constraints, battery output constraints, photovoltaic equipment output constraints, fan Equipment output constraints and thermal energy demand response constraints.
  • the power generation of the combined heat and power unit is calculated by multiplying the consumed natural gas with the calorific value and its power generation efficiency. In addition, the power generation is limited by its allowable maximum and minimum values.
  • L ng is equal to 9.7(kWh)/m 3
  • L ng represents the calorific value of natural gas.
  • P mt,n,h is the power generation power of the gas turbine of the nth MEMG in the period h
  • M mt,n,h represents the gas consumption of the gas turbine of the nth MEMG in the period h
  • ⁇ mt represents the power generation efficiency of the gas turbine
  • P mt,n,min represent the minimum power of the gas turbine
  • P mt,n,max represent the maximum power of the gas turbine.
  • the heat recovered by the cogeneration unit and the final effective heat are equal to the power generation multiplied by the heat-to-electricity ratio, the efficiency of the heat exchange device, and the efficiency of the waste heat boiler, as shown below:
  • P hc,n,h is the heat output of the heat exchanger of the nth MEMG in the h period
  • r mt is the heat-to-electricity ratio
  • ⁇ wh is the efficiency of the waste heat boiler
  • ⁇ hc is the efficiency of the heat exchange device.
  • the heat generated by a gas boiler can be calculated by multiplying the gas consumption by the calorific value and power generation efficiency.
  • the output heat must be within the thermal capacity constraints.
  • the output constraints of gas boilers are as follows:
  • Q gb,n,h is the output thermal power of the gas boiler of the nth MEMG in the h period
  • M gb,n,h is the gas volume consumed by the gas boiler
  • ⁇ gb is the efficiency of the gas boiler
  • Q gb, n,min is the minimum power of the gas boiler
  • Q gb,n,max is the maximum power of the gas boiler.
  • the refrigeration power of the compression refrigerator must be greater than the cooling load peak demand divided by its performance coefficient, and less than the maximum power allowable value.
  • the output constraints of the compression refrigerator are as follows:
  • cl n,h is the cooling load of the nth MEMG in the h period
  • COP is the cooling coefficient
  • Co ec,n,h is the cooling power of the compression refrigerator
  • Co ec,n,max is the upper limit of the cooling power
  • Es i (0) and Es i (24) are the initial electric quantity and final electric quantity of a day in the storage battery set in this embodiment, which are 50% of the maximum charging and discharging capacity of the battery.
  • the battery output constraints are as follows:
  • Es n,h is the battery storage capacity of the nth MEMG in the h period
  • Bch n,h and Bdis n,h are the charging and discharging power respectively
  • Nch and Ndis are the charging and discharging efficiencies respectively
  • Es n,max , Es n,min are the maximum and minimum values of the remaining power of the battery system respectively
  • Pe n,h are the charge and discharge capacity of the battery of the multi-energy microgrid n in the period h
  • Bch n,max and Bdis n,max are the representative battery kech n,h and kedis n,h are the charging and discharging states of the multi-energy microgrid n in h period.
  • the installation area of photovoltaic units should not exceed the upper limit of the actual installation area of each producer and consumer, and the electricity generated should not exceed the rated power generation capacity.
  • the output constraints of photovoltaic equipment are specifically as follows:
  • a PV,n is the photovoltaic installation area of the nth MEMG
  • a PV,n,max is the maximum installation area
  • PV n,h is the photovoltaic power generation power of the nth MEMG
  • Ii h is the multi-energy microgrid n in The unit irradiance in h period
  • ⁇ PV is the efficiency of photovoltaic power generation.
  • P wt,n,h is the generating power of the fan
  • P wt,n,max is the maximum generating power
  • thermal demand response in addition to electricity, thermal demand response is also considered, and it is named Integrated DR (Demand Response).
  • Integrated DR Demand Response
  • MEMG has upper and lower limits for its cold, heat, and electrical loads. At the same time, the total amount of cooling, heating, and electricity of the day before and after optimization must remain unchanged. The constraints are as follows:
  • Ele n,h , ht n,h , cl n,h are the electricity, heat, and cooling loads of the nth MEMG in the period h respectively
  • Ele n,h,max , Ht n,h,max , Cl n, h, max are the upper limit of electricity, heat, and cooling loads of the nth MEMG in the period h respectively
  • Ele n,h,min , Ht n,h,min , Cl n,h,min are the upper limits of the nth MEMG in h
  • the lower limit of electricity, heat, and cooling loads in the time period Ele n,h , Ht n,h , and Cl n,h are the initial forecast electricity, heat, and cooling loads before the day, respectively
  • p is the load reduction coefficient.
  • the energy balance constraints for each MEMG are as follows, stating that the output energy must be equal to the input energy at each time period.
  • the specific energy balance constraints are:
  • els n,h and elb n,h are the power sales and purchase power of the nth MEMG in the period h respectively
  • hts n,h and htb n,h are the heat sales and purchase power of the nth MEMG in the period h respectively.
  • Thermal power, P hx,n,h is the output of the heat exchange device of the multi-energy microgrid n during the period h
  • Q gb,n,h is the output of the gas boiler of the multi-energy microgrid n during the period h.
  • IA took the responsibility and became the upper agent in the game.
  • the income of IA depends on the energy transaction volume and transaction price with the external centralized network and MEMG cluster.
  • the objective function of the upper model of the multi-energy microgrid cluster is:
  • maxBeIA is the maximum benefit of the multi-energy micro-grid group in the period h
  • PrEb h and PrEs h represent the electricity price of IA’s purchase and sale of electricity from the grid, respectively
  • PrHb h and PrHs h represent the electricity price of IA’s purchase of heat from the external heating network and The heat price of its heat sales
  • El h and Hl h respectively represent the total amount of electricity and heat transactions between the middleman IA and the external network at a certain moment
  • Els h and Elb h respectively refer to the total electricity sold by IA to the external network and the total electricity purchased from the external network at a certain moment
  • Hts h and Htb h respectively represent the total heat sold by IA to the external heating network and the total heat purchased from the external heating network at a certain moment.
  • the upper model of the multi-energy microgrid group has energy transaction constraints, specifically:
  • el n,h is the net electric power of the nth MEMG in the period h
  • keb n,h and kes n,h are the 0-1 variables of the nth MEMG’s electricity purchase and sale respectively
  • 1 indicates that it is in the Electricity or electricity sales status
  • hkt n,h is the heat exchange power between the multi-energy microgrid n and the main network during h period
  • khb n,h and khs n,h are the 0- 1 variable
  • 1 indicates that it is in the state of heat purchase or heat sale
  • Preb h and Pres h are the electricity prices that MEMG buys from IA and sells back to IA respectively
  • PrEb h and PrEs h represent the electricity prices that IA buys from the grid and sells to it.
  • Prhb h and Prhs h represent the heat price that MEMG buys from IA and sells back to IA
  • PrHb h and PrHs h represent the heat price that IA buys heat from and sells heat to the external heat network
  • El h is the net electric power of the multi-energy microgrid group in the period h
  • Hl h is the net thermal power of the multi-energy microgrid group in the period h.
  • Step 2 is specifically:
  • IA represents the leader of the game
  • ⁇ MEMG ⁇ represents the follower of the game
  • ⁇ Ame n,h ⁇ Amh n,h ⁇ respectively represent the remaining power and heat of each MEMG
  • ⁇ Pres h ⁇ , ⁇ Preb h ⁇ , ⁇ Prhs h ⁇ , ⁇ Prhb h ⁇ set represents the internal price strategy formulated by IA
  • ⁇ BeMEMG n,h ⁇ , ⁇ BeIA h ⁇ is the target benefit function of the transaction subject.
  • the equilibrium point S of game F is:
  • each follower aims to maximize its own operating costs. It is worth noting that MEMG cannot buy and sell electricity/heat at the same time.
  • the load demand range of MEMG is as follows. "max” is the upper limit of load demand allowed after demand response, and “min” is the lower limit of load demand allowed after demand response.
  • the optimal power load demand is obtained as:
  • the optimal load can be described as:
  • the optimal electricity load demand is max(ele n,h +cl n,h /COP). Otherwise, if Preb h ⁇ Preb n,h,max , the optimal load demand is (PV n,h +P wt,n,h +P mt,n,h -Pen ,h ). That is, the load is related to the price and has an upper limit and a lower limit.
  • the optimal heat load demand is obtained by taking the partial derivative with respect to heat load.
  • the corresponding heat load demand range, heat purchase price range and heat sale price range can be obtained, the formula is as follows:
  • Prhb h [Prhb n,h, min ,Prhb n,h,max ]
  • Prhs h [Prhs n,h,min ,Prhs n,h,max ]
  • Step 3 is specifically:
  • This embodiment proposes a two-layer MILP model, and its method framework is shown in FIG. 3 .
  • This embodiment adopts the method based on SGT to solve this problem: consider the benefit of IA.
  • SGT is a special non-cooperative game in which there is a hierarchical relationship between players, divided into leaders and followers. In SGT, each player is selfish and aims to maximize his own interests. Leaders can impose their strategies on followers. The solution to this game is called a Stackelberg equilibrium.
  • the IA and each MEMG are considered as distinct entities with separate goals to minimize running costs, which can be described as single-leader and multi-follower, respectively.
  • Step 3-1 Obtain the price of electricity purchase and sale and purchase and sale of heat formulated by the energy distribution network
  • Step 3-2 Each multi-energy microgrid performs self-optimization
  • Step 3-3 Each multi-energy microgrid uploads the margin and shortage of cooling, heating, and electric loads, as well as equipment output optimization, new energy power generation forecast, and load demand response range to the intermediary IA,
  • Step 3-4 The intermediary IA formulates the transaction price of electricity and heat in the multi-energy micro-grid group based on the information fed back by each multi-energy micro-grid, combined with the transaction price with the energy distribution network, with the goal of maximizing its own benefits,
  • Step 3-5 According to the transaction price set by the intermediary IA, each multi-energy microgrid can perform self-optimization, conduct demand response, adjust the demand for cooling, heating, and electric loads, and feed back the surplus and shortage information to the intermediary Business IA,
  • Step 3-6 Determine whether the convergence condition is met, if yes, execute step 3-7, otherwise, return to step 3-4,
  • Step 3-7 Output the optimal game solution set.
  • the self-optimizing method of the multi-energy microgrid is:
  • each multi-energy micro-grid optimizes the output of equipment with the goal of maximizing its own benefits according to its own energy forecast data and the forecast data of cold, heat, and electricity used by the micro-grid, and optimizes the use of cold, heat, and electricity at the same time. Electricity load demand.
  • this embodiment will consider MEMGs of three regional building types, namely MEMG1, MEMG2 and MEMG3, representing hotels, residential buildings and commercial buildings, respectively. It is assumed that there are both schedulable distributed generators and non-schedulable distributed generators in the network MEMG.
  • Each MEMG contains battery energy storage system, controllable distributed generators, renewable distributed generators and end-user loads. If the mutually generated energy cannot achieve self-sufficiency of MEMG, the nearby MEMG or centralized energy system will provide electrical and thermal energy to the defective part through IA, and vice versa.
  • the load requirements of each MEMG will be introduced in detail below.
  • Table 1 lists various technical assumptions, including the technical efficiency of the energy equipment used. At the same time, it should be noted that the adjustment range of the controllable multiple loads is set to 20% of the load forecast curve, and the energy storage operation cost coefficient is 0.02 yuan/kWh.
  • Table 2 shows the device capacity used in each MEMG. Generally speaking, the photovoltaic, WT and cogeneration units of MEMG2 have the highest capacity, while the storage battery capacity of the three parks is set to be the same.
  • the energy price category can be divided into two parts: the price of internal energy transactions using IA and the price of energy transactions using public energy networks according to the optimization results, as shown in Table 3. Due to the time-of-use electricity price mechanism, the electricity price set by the grid at different times is different. In addition, to promote the adoption of combined heat and power units, natural gas prices can enjoy preferential treatment, while auxiliary boilers cannot enjoy the same price discount. In addition, for repurchasing energy prices, especially heat supply prices, its determination method often varies from case to case, and is highly dependent on negotiations among relevant stakeholders. The repurchase price here is determined by reference.
  • Figure 5 shows the solar irradiance and wind speed data used in this application example.
  • the two sets of data can complement each other within a day.
  • the peak solar radiation is 0.55kW/m2 at 1:00 in the daytime, and the peak wind speed is as high as 7.31m/s at 11:00 and 12:00 at night.
  • FIG. 6 and Figure 7 show the energy exchange in the cell based on the SGT method and considering the IA benefit, MEMG1, MEMG2 and MEMG3 power balance. It can be found that in the cluster, MEMG2 usually acts as a power provider, while MEMG1 and MEMG3 both act as electricity providers. This is mainly due to the higher capacity of regional generators adopted by MEMG2, and the lower capacity of regional generators adopted by MEMG1 and MEMG3. That is, the energy exchange between neighbors can only be realized when there is a large difference in energy supply and demand among MEMGs.
  • MEMG2 uses a combined heat and power unit, from an economic point of view, because its operating cost is higher than that of photovoltaic and wind power systems, it only works for a few hours.
  • both MEMG1 and MEMG3 cogeneration units are in daily operation. This is because during this time period, the demand for electricity and heat is relatively high, and cogeneration can generate electricity and power at the same time, and its economic performance is better.
  • each MEMG prefers to purchase electricity directly from an external public energy network rather than produce it itself due to relatively low electricity prices at night.
  • MEMG3 has the largest difference in nighttime and daytime loads, cogeneration units and internal exchange energy can meet its daily electricity needs due to its relatively low renewable energy installation, while wind power can meet its nighttime energy needs.
  • the thermal balance of the optimized three MEMGs Compared with electricity consumption, the operation strategy is relatively simple, and the heat demand is mainly provided by cogeneration units, gas boilers and heat exchange units or a combination of the two.
  • MEMG1 acts as a hot buyer, while both MEMG2 and MEMG3 act as hot sellers.
  • the exchange part accounts for very little of the total heat requirement. This is because in summer, the total heat demand is lower relative to the electricity demand, which also includes electricity consumption for cooling the load, so most of MEMG1's heat demand can be provided by the combined heat and power unit and its own gas boiler.
  • FIG 8 shows the load transfer and transfer situation before and after optimization. It can be found that the loads of the three MEMGs vary greatly compared with the initial values. In general, due to simultaneous optimization of load response and energy prices, each MEMG tends to reduce its own load demand when energy prices are high, and vice versa. That is to say, each MEMG hopes to maximize its own interests in each time period through dynamic price and load interaction.
  • Figure 9 derives the basis for the use of SGT in optimal energy transaction prices. It can be found that the internal energy transaction price set by the IA proposed in this paper is between the sale price and the repurchase price set by the external power grid. Among them, the energy sales price set by the external energy network is the highest, followed by the internal sales price, the internal repurchase price and the external energy network repurchase price. In terms of electricity prices, it is obvious that the transaction prices of the IA and the distribution network are basically the same during the hours of 0:00-6:00 and 22:00-24:00. This is because during this period, the repurchase price of electricity sales in the distribution network is relatively low, and the profit margin of IA is limited. And during 10:00-22:00, the difference is relatively large.
  • the external heat distribution network sets the highest thermal energy sales price, followed by the internal thermal energy sales price, the internal thermal energy repurchase price and the external heat distribution network heat energy repurchase price. Furthermore, it can be concluded that during 0:00-10:00 and 19:00-24:00, due to the absence of heat exchange between multiple MEMGs, the internal thermal energy transaction price is optimized to coincide with the external thermal energy price.
  • the internal heat repurchase price is higher than the heat transaction price set by the external heat distribution network, and at the same time, the internal heat sales price is lower than the external heat sales price, which can promote MEMG, first exchange with neighbors Surplus/deficiency energy, and then trade energy with external suppliers, this trading method will increase the stability and flexibility of the entire energy system. Furthermore, it is worth pointing out that in order to encourage the exchange of energy within the MEMG cluster instead of exchanging energy directly with the external energy network, the prices formulated by the IA vary slightly under different scenarios.
  • Scenario 1 Each MEMG performs self-optimization as an independent individual, without IA participating in management and monitoring, and directly trades with the external energy network, which is set as IOP.
  • Scenario 2 The energy collaborative coupling of each MEMG is managed and supervised by IA, and the method of this paper is used to formulate the transaction price and trade within the cluster, which is set as SGT.
  • Figure 10 shows the energy transaction volume between users in the cluster and the entire MEMG cluster in two scenarios.
  • the energy purchase and energy repurchase of the cluster can be significantly lower than the total energy transaction volume directly with the external energy grid, especially in the SGT-based scenario. That is to say, from the perspective of the overall energy, the proposed three MEMGs exchange a large amount of energy with each other through union, and balance the surplus and deficiency of energy within the cluster.
  • the situation is different when focusing on electricity and heat separately.
  • the conclusions drawn are consistent with the trend of total energy transactions in all scenarios; for thermal energy, whether it is the repurchase part or the repurchase part, the thermal energy transaction volume between the MEMG cluster and the external heat distribution network in the SGT-based scenario is less than IOP (independent optimization) scenarios.
  • IOP independent optimization
  • Figure 11 shows the hourly benefit optimization of each MEMG under 2 scenarios in a typical day.
  • all MEMGs gain more benefit during the day than at night.
  • the combination of MEMG can deduce the highest benefit of the whole cluster, the results are not constant at all times.
  • the benefit of independent optimization (IOP) is significantly higher than that of SGT mode under the scenario of considering IA benefit. This is because the object of optimization is to maximize the benefit of the whole day, not every hour.
  • Figure 12 shows the derived hourly returns over a typical day when considering the profits of IA using an SGT-based approach.
  • SGT SGT-based approach
  • Table 4 shows the benefits of each stakeholder (including all MEMGs and IAs) on a typical day. It can be found that, generally speaking, MEMG 2 has the highest profit, followed by MEMG 1 and MEMG 3, which is highly dependent on the amount of energy remaining that can bring additional benefits. Again, compared to the case where all MEMGs individually optimize IOP, the benefit of each MEMG is increased by the energy exchange between them, regardless of whether the benefit of IA is considered. When considering the benefit of IA, the profit of MEMG1, MEMG2, MEMG3 and the whole MEMG cluster increased by 1.77%, 1.17%, 2.71%, 2.92%, respectively.

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Abstract

本发明涉及一种多能微网群自身及市场决策协同优化方法，协同优化方法包括：步骤1：构建多能微网群系统模型，包括用于进行MEMG内部优化的多能微网群下层模型以及用于进行市场决策优化的多能微网群上层模型，步骤2：基于主从博弈构建博弈模型，步骤3：基于双层MILP模型求解博弈模型，获得最优博弈解集，输出协同优化策略。与现有技术相比，本发明具有提高总体效益、MEMG集群内部能量消耗水平更高、获得自给能力与交易共享能力之间的平衡等优点。

Description

一种多能微网群自身及市场决策协同优化方法 技术领域
本发明涉及多能微网群优化技术领域，尤其是涉及一种多能微网群自身及市场决策协同优化方法。
背景技术
在单个多能微网MEMG(Multi-energy Microgrid)中，各种分布式能源生产者作为供方，如CHP(热电联产机组Combined Heat andPower)、光伏、风机、电池等，综合能源消费者作为需求方。考虑到不同能源类型的组合，每个MEMG中分别有电网、热网和气网。此外，在自给自足的基础上，相邻的MEMG相互连接成一个完整的网络，通过内部能量传输网络相互分享能量。具体而言，鼓励能源过剩或不足的MEMG，首先与内部市场中其余MEMG进行能源交易，而不是直接与公共电网进行交易。然而，如果MEMG集群本身不能实现内部能量平衡，则在必要时可以与外部集中能量网络进行交易，即：当一个MEMG产生剩余能量时，可以先卖给附近的MEMG进行内部能量平衡，如果还有剩余，则将剩余部分卖回外部电网。反之，不足部分可以先从附近的MEMG购买能量，然后再从外部电网购买能量。
然后在进行交易的过程中涉及多能微网的自优化以及交易中市场决策的优化，现有技术中有分别针对多能微网自优化和市场决策优化的相关技术，但并未有一种可以对两种优化进行协同优化的方法。
发明内容
本发明的目的就是为了克服上述现有技术存在的缺陷而提供一种提高总体效益、MEMG集群内部能量消耗水平更高、获得自给能力与交易共享能力之间的平衡的多能微网群自身及市场决策协同优化方法。
本发明的目的可以通过以下技术方案来实现：
一种多能微网群自身及市场决策协同优化方法，所述的协同优化方法包括：
步骤1：构建多能微网群系统模型，包括用于进行MEMG内部优化的多能微网群下层模型以及用于进行市场决策优化的多能微网群上层模型；
步骤2：基于主从博弈构建博弈模型；
步骤3：基于双层MILP模型求解博弈模型，获得最优博弈解集，输出协同优化策略。
优选地，所述的多能微网群下层模型以某时间段内实现效益最大化为目标函数，具体为：
Figure PCTCN2022106192-appb-000001
其中，BeMEMG n为第n个MEMG的效益；a n,h为第n个MEMG在h时段的边际效益系数；ele n,h、ht n,h、cl n,h分别为第n个MEMG在h时段的电、热、冷负荷；COP是制冷系数；els n,h、elb n,h分别为第n个MEMG在h时段的售电、购电功率；hts n,h、htb n,h分别为第n个MEMG在h时段的售热、购热功率；M mt,n,h表示第n个MEMG在h时段的燃气轮机的耗气量；M gb,n,h是燃气锅炉所消耗的燃气体积；Pres h、Preb h分别为在h时段 MEMG的售电、购电价格；Prhs n,h、Prhb n,h分别为在h时段MEMG的售热、购热价格；Pg mt、Pg gb分别为燃气轮机与燃气锅炉的燃气价格；C bt为蓄电池的运维系数；Bch n,h、Bdis n,h分别为充电、放电功率。
更加优选地，所述的多能微网群下层模型设有设备出力约束和能量平衡约束；所述的设备处理约束包括热电联产机组出力约束、燃气锅炉出力约束、压缩式制冷机出力约束、蓄电池出力约束、光伏设备出力约束、风机设备出力约束和热能需求响应约束。
更加优选地，所述的热电联产机组出力约束具体为：
P mt,n,h＝M mt,n,hL ngη mt
P mt,n,min≤P mt,n,h≤P mt,n,max
P hc,n,h＝P mt,n,hr mtη whη hc
其中，P mt,n,h为第n个MEMG在h时段的燃气轮机发电功率；M mt,n,h表示第n个MEMG在h时段的燃气轮机的耗气量；L ng表示天然气热值；η mt表示燃气轮机的发电效率；P mt,n,min表示燃气轮机最小功率；P mt,n,max表示燃气轮机最大功率；P hc,n,h是第n个MEMG在h时段的热交换器的产热量；r mt是热电比；η wh为余热锅炉的效率；η hc为换热装置的效率；
燃气锅炉出力约束具体为：
Q gb,n,h＝M gb,n,hL ngη gb
Q gb,n，min≤Q gb,n,h≤Q gb,n,max
其中，Q gb,n,h是第n个MEMG在h时段的燃气锅炉的输出热功率；M gb,n,h是燃气锅炉所消耗的燃气体积；η gb为燃气锅炉的效率；Q gb,n,min为燃气锅炉最小功率；Q gb,n,max为燃气锅炉最大功率。
压缩式制冷机出力约束具体为：
Figure PCTCN2022106192-appb-000002
其中，cl n,h为是第n个MEMG在h时段的冷负荷；COP是制冷系数；Co ec,n,h为压缩式制冷机的制冷功率；Co ec,n,max为制冷功率的上限；
蓄电池出力约束具体为：
Figure PCTCN2022106192-appb-000003
Figure PCTCN2022106192-appb-000004
其中，Es n,h是第n个MEMG在h时段的蓄电池储电量；Bch n,h、Bdis n,h分别为充电、放电功率；Nch、Ndis 分别为充电、放电效率；Es i(0)、Es i(24)为蓄电池中一天的初始电量和最终电量；E sn,max、E sn,min分别为蓄电池系统剩余电量的最大和最小值；Pe n,h为多能微网n在h时段的蓄电池的充、放电量；Bch n,max、Bdis n,max分别为代表蓄电池的最大充电、放电量；kech n,h、kedis n,h为多能微网n在h时段的充放电状态。
光伏设备出力约束具体为：
A PV,n≤A PV,n,max
0≤PV n,h≤A PV,nIi hη PV
其中，A PV,n为第n个MEMG的光伏安装面积；A PV,n,max为最大安装面积；PV n,h为第n个MEMG的光伏发电功率；Ii h为多能微网n在h时段的单位辐照量；η PV为光伏发电效率。
风机设备出力约束具体为：
0≤P wt,n,h≤P wt,n,max
其中，P wt,n,h为风机的发电功率；P wt,n,max为最大发电功率；
热能需求响应约束具体为：
Figure PCTCN2022106192-appb-000005
Figure PCTCN2022106192-appb-000006
其中，ele n,h、ht n,h、cl n,h分别为第n个MEMG在h时段的电、热、冷负荷；Ele n,h,max、Ht n,h,max、Cl n,h,max分别为第n个MEMG在h时段的电、热、冷负荷的上限；Ele n,h,min、Ht n,h,min、Cl n,h,min分别为第n个MEMG在h时段的电、热、冷负荷的下限；Ele n,h、Ht n,h、Cl n,h分别为日前初始预测电、热、冷负荷，p为负荷削减系数。
更加优选地，所述的能量平衡约束具体为：
Figure PCTCN2022106192-appb-000007
hts n,h+ht n,h＝htb n,h+P hx,n,h+Q gb,n,h
其中，els n,h、elb n,h分别为第n个MEMG在h时段的售电、购电功率；hts n,h、htb n,h分别为第n个MEMG在h时段的售热、购热功率；P hx,n,h为多能微网n在h时段的换热装置出力大小；Q gb,n,h为多能微网n在h时段的燃气锅炉出力。
优选地，所述的多能微网群上层模型的目标函数为：
Figure PCTCN2022106192-appb-000008
其中，maxBeIA为多能微网群在h时段的最大效益；PrEb h和PrEs h分别代表IA从电网购电和向其售电的电价； PrHb h和PrHs h表示IA从外部热网购热和向其售热的热价；El h、Hl h分别表示中间商IA(Intermediary Agent)在某一时刻与外部网络之间的电和热交易总量；Els h和Elb h分别是指IA向外部网络出售的总电量和某一时刻从外部网络购买的总电量；Hts h和Htb h分别表示IA在某一时刻向外部热网出售的总热量和从外部热网购买的总热量。
更加优选地，所述的多能微网群上层模型设有能量交易约束，具体为：
el n,h＝elb n,h-els n,h
0≤elb n,h≤elb n,maxkeb n,h
0≤els n,h≤els n,maxkes n,h
keb n,h+kes n,h≤1
hkt n,h＝htb n,h-hts n,h
0≤htb n,h≤htb n,maxkhb n,h
0≤hts n,h≤hts n,maxkhs n,h
khb n,h+khs n,h≤1
keb n,h、kes n,h、khb n,h、khs n,h∈{0，1}
PrEs h≤Pres h≤Preb h≤PrEb h
PrHs h≤Prhs h≤Prhb h≤PrHb h
Figure PCTCN2022106192-appb-000009
Figure PCTCN2022106192-appb-000010
其中，el n,h为第n个MEMG在h时段的净电功率；keb n,h、kes n,h分别为第n个MEMG的购电、售电的0-1变量，为1表示处于购电或售电状态；hkt n,h为多能微网n在h时段与主网交换热功率；khb n,h、khs n,h分别为第n个MEMG的购热、售热的0-1变量，为1表示处于购热或售热状态；Preb h、Pres h分别为MEMG向IA购买和卖回给IA的电价；其中，PrEb h和PrEs h分别代表IA从电网购电和向其售电的电价；同样，Prhb h和Prhs h表示分别为MEMG向IA购买和卖回给IA的热价；PrHb h和PrHs h表示IA从外部热网购热和向其售热的热价；El h为多能微网群在h时段的净电功率；Hl h为多能微网群在h时段的净热功率。
优选地，所述的步骤2具体为：
当IA作为实体，以赚取中间差价为目标时，IA与MEMG集群之间的竞争利益关系可以描述为SGT中的领导者和多个跟随者，可以描述为：
Figure PCTCN2022106192-appb-000011
其中，IA代表博弈的领导者；{MEMG}代表博弈的追随者；{Ame n,h∪Amh n,h}分别表示每个MEMG的剩 余电量和剩余热能；{Pres h},{Preb h},{Prhs h},{Prhb h}集合表示IA制定的内部价格策略，{BeMEMG n,h},{BeIA h}是交易主体的目标效益函数；
博弈F的均衡点S为：
Figure PCTCN2022106192-appb-000012
优选地，所述的步骤3具体为：
步骤3-1：获取配能网制定的购售电和购售热价格；
步骤3-2：各多能微网进行自优化；
步骤3-3：各多能微网将冷、热、电负荷的余量和缺量以及设备出力优化、新能源发电预测和负荷需求响应范围上传至中间商IA；
步骤3-4：中间商IA根据各多能微网反馈的信息，结合与配能网的交易价格，以自身效益最大化为目标，制定多能微网群内的电、热交易价格；
步骤3-5：针对中间商IA制定的交易价格，各多能微网以进行自优化，进行需求响应，调整对冷、热、电负荷的需求，并将余量和缺量信息反馈给中间商IA；
步骤3-6：判断是否达到收敛条件，若是，则执行步骤3-7，否则，返回步骤3-4；
步骤3-7：输出最优博弈解集。
更加优选地，所述多能微网进行自优化的方法为：
各多能微网针对配能网的价格，并根据自身能源预测数据及微网用冷、热、电的预测数据，以自身效益最大化为为目标优化设备出力，同时优化对冷、热、电能的负荷需求。
与现有技术相比，本发明具有以下有益效果：
一、提高总体效益：本发明中的多能微网群自身及市场决策协同优化方法提出的基于SGT的方法可以有效地解决能源内部交易价格的确定问题，与单个MEMG单独作用的情况相比，集群组合可明显提高所有MEMG的总体效益，考虑IA在研究典型日的效益时提高了4.4％。
二、MEMG集群内部能量消耗水平更高：本发明中的多能微网群自身及市场决策协同优化方法使得MEMG尽量优先考虑多能微网集群内部的能源消耗，然后再与外部能源网络进行交易，内部能量自消耗水平更高。
三、获得自给能力与交易共享能力之间的平衡：本发明中的多能微网群自身及市场决策协同优化方法为MEMG集群的全局优化提供了一个混合整数线性规划模型，并为不同场景下能源交易价格的确定提供了技术支持，在寻求自给能力和相对稳定的能源需求的同时，建议与IA分享利益，达到自给能力与交易共享能力之间的平衡。
附图说明
图1为本发明中多能微网群自身及市场决策协同优化方法的流程示意图；
图2为本发明实施例中多能微网群系统的框架结构示意图；
图3为本发明实施例中求解博弈模型的流程示意图；
图4为本发明实施例中MEMG的能量流结构示意图；
图5为本发明实施例中所使用的太阳辐照和风速数据；
图6为本发明实施例中MEMG集群的电平衡示意图；
其中，图6(a)、图6(b)和图6(c)分别代表MEMG1、MEMG2和MEMG3；
图7为本发明实施例中MEMG集群的热平衡示意图；
其中，图7(a)、图7(b)和图7(c)分别代表MEMG1、MEMG2和MEMG3；
图8为本发明实施例中MEMG集群的需求响应分析；
其中，图8(a)、图8(b)和图8(c)分别代表MEMG1、MEMG2和MEMG3
图9为本发明实施例中内部能量交换的最优价格示意图；
其中，图9(a)为购售电价格，图9(b)为购售热价格；
图10为本发明实施例中外部能源网与MEMG集群的能源交易量；
图11为本发明实施例中在典型日下各MEMG每小时的效益；
图12为本发明实施例中在典型日下基于SGT的IA的每小时效益。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明的一部分实施例，而不是全部实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都应属于本发明保护的范围。
多能微网群系统的框架结构如图2所示，在处理集群内MEMG之间的内部能源交易和与外部能源网络的交易时，需要合理的交易模式。这里不同于对等能源交易，采用了基于中间商的交易模式，IA(中间商)作为一个独立的实体，确保信息的私有流动和资源的公平分配。尽管交换的能源通过已建立的传输基础设施流动，但所有参与者都直接与IA进行交易。IA负责收集必要的信息，如外部能源价格、各MEMG的剩余/不足能源量、利息需求等，并据此确定内部能源交易价格。本实施例考虑如下情况：
当IA作为实际运营商处理能源交易现金流时，IA的利润基于集群内部能源共享和集群外部能源交易的竞价和要价的差价。能源交易价格依赖于参与者采用SGT(Stackelberg博弈理论)方法进行的利益博弈。
本实施例提出一种多能微网群自身及市场决策协同优化方法，包括：
步骤1：构建多能微网群系统模型，包括用于进行MEMG内部优化的多能微网群下层模型以及用于进行市场决策优化的多能微网群上层模型。对于与上一级相关的能源交易价格确定优化，将采用基于SGT的方法来处理，IA作为真实操作者，也是独立的利益主体。此外，需要指出的是，由于多能微网集群系统模型是一个非凸函数，为了解决这个问题，需要实现上下层之间的多次迭代。
步骤2：基于主从博弈构建博弈模型，
步骤3：基于双层MILP(Mixed Integer Linear Programming)模型求解博弈模型，获得最优博弈解集，输出协同优化策略。
每个MEMG的目标函数都是在一定时间段内实现效益最大化。对于各MEMG而言，各时段的收益主要取决于用户支付的能源费用、附近MEMG支付的能源交换收益以及与外部能源网络的交易收益。注意到，运营成本必须从收益中剔除，包括一次能源成本、电池运营成本等。多能微网群下层模型以某时间段内实现效益最大化为目标函数，具体为：
Figure PCTCN2022106192-appb-000013
其中，BeMEMG n为第n个MEMG的效益，a n,h为第n个MEMG在h时段的边际效益系数，ele n,h、ht n,h、cl n,h分别为第n个MEMG在h时段的电、热、冷负荷，COP是制冷系数，els n,h、elb n,h分别为第n个MEMG在h时段的售电、购电功率，hts n,h、htb n,h分别为第n个MEMG在h时段的售热、购热功率，M mt,n,h表示第n个MEMG在h时段的燃气轮机的耗气量，M gb,n,h是燃气锅炉所消耗的燃气体积，Pres h、Preb h分别为在h时段MEMG的售电、购电价格，Prhs n,h、Prhb n,h分别为在h时段MEMG的售热、购热价格，Pg mt、Pg gb分别为燃气轮机与燃气锅炉的燃气价格，C bt为蓄电池的运维系数，Bch n,h、Bdis n,h分别为充电、放电功率。
多能微网群下层模型设有设备出力约束和能量平衡约束，设备处理约束包括热电联产机组出力约束、燃气锅炉出力约束、压缩式制冷机出力约束、蓄电池出力约束、光伏设备出力约束、风机设备出力约束和热能需求响应约束。
(1)热电联产机组
热电联产机组的发电量由消耗的天然气与热值及其发电效率相乘计算，此外，发电量受其允许的最大值和最小值的限制。这里L ng等于9.7(kWh)/m 3，L ng表示天然气热值。
热电联产机组出力约束具体为：
P mt,n,h＝M mt,n,hL ngη mt
P mt,n,min≤P mt,n,h≤P mt,n,max
其中，P mt,n,h为第n个MEMG在h时段的燃气轮机发电功率，M mt,n,h表示第n个MEMG在h时段的燃气轮机的耗气量，η mt表示燃气轮机的发电效率，P mt,n,min表示燃气轮机最小功率，P mt,n,max表示燃气轮机最大功率。
另外，热电联产机组回收的热量和最终有效热量等于发电量乘以热电比、换热装置效率和余热锅炉效率，如下所示：
P hc,n,h＝P mt,n,hr mtη whη hc
其中，P hc,n,h是第n个MEMG在h时段的热交换器的产热量，r mt是热电比，η wh为余热锅炉的效率，η hc为换热装置的效率。
(2)燃气锅炉
燃气锅炉产生的热量可以用燃气消耗量乘以热值和发电效率来计算。另外，输出热量必须在热容量约束范围内。燃气锅炉出力约束具体为：
Q gb,n,h＝M gb,n,hL ngη gb
Q gb,n，min≤Q gb,n,h≤Q gb,n,max
其中，Q gb,n,h是第n个MEMG在h时段的燃气锅炉的输出热功率，M gb,n,h是燃气锅炉所消耗的燃气体积，η gb为燃气锅炉的效率，Q gb,n,min为燃气锅炉最小功率，Q gb,n,max为燃气锅炉最大功率。
(3)压缩式制冷机
压缩制冷机的制冷功率必须大于冷负荷峰值需求除以其性能系数，且小于最大功率允许值。压缩式制冷机出力约束具体为：
Figure PCTCN2022106192-appb-000014
其中，cl n,h为是第n个MEMG在h时段的冷负荷，COP是制冷系数，Co ec,n,h为压缩式制冷机的制冷功率，Co ec,n,max为制冷功率的上限，
(4)蓄电池约束
蓄电池的充放电不能同时进行，同时也不能超过充、放电功率最大值。Es i(0)、Es i(24)为本实施例中设定的蓄电池中一天的初始电量和最终电量，为蓄电池最大充放电容量的50％。蓄电池出力约束具体为：
Figure PCTCN2022106192-appb-000015
Figure PCTCN2022106192-appb-000016
其中，Es n,h是第n个MEMG在h时段的蓄电池储电量，Bch n,h、Bdis n,h分别为充电、放电功率，Nch、Ndis分别为充电、放电效率，Es n,max、Es n,min分别为蓄电池系统剩余电量的最大和最小值，Pe n,h为多能微网n在h时段的蓄电池的充、放电量，Bch n,max、Bdis n,max分别为代表蓄电池的最大充电、放电量；kech n,h、kedis n,h为多能微网n在h时段的充放电状态。
(5)光伏设备
光伏机组的安装面积不应超过每个生产者和消费者实际安装面积的上限，所产生的电量不应超过额定发电容量，光伏设备出力约束具体为：
A PV,n≤A PV,n,max
0≤PV n,h≤A PV,nIi hη PV
其中，A PV,n为第n个MEMG的光伏安装面积，A PV,n,max为最大安装面积，PV n,h为第n个MEMG的光伏发电功率，Ii h为多能微网n在h时段的单位辐照量，η PV为光伏发电效率。
(6)风机
与PV(Photovoltaic)单元类似，风机的实际输出功率也必须服从允许输出范围，风机设备出力约束具体为：
0≤P wt,n,h≤P wt,n,max
其中，P wt,n,h为风机的发电功率，P wt,n,max为最大发电功率，
(7)需求响应
在本实施例中，除电外，还考虑了热能的需求响应，并将其命名为集成DR(Demand Response)。MEMG作为多能量流耦合的集散体，其冷、热、电负载均有上限和下限。同时，优化前后一天的冷、热、电总量必须保持不 变。约束条件如下：
Figure PCTCN2022106192-appb-000017
Figure PCTCN2022106192-appb-000018
其中，ele n,h、ht n,h、cl n,h分别为第n个MEMG在h时段的电、热、冷负荷，Ele n,h,max、Ht n,h,max、Cl n,h,max分别为第n个MEMG在h时段的电、热、冷负荷的上限，Ele n,h,min、Ht n,h,min、Cl n,h,min分别为第n个MEMG在h时段的电、热、冷负荷的下限，Ele n,h、Ht n,h、Cl n,h分别为日前初始预测电、热、冷负荷，p为负荷削减系数。
每个MEMG的能量平衡约束如下，说明在每个时间段输出的能量必须等于输入的能量。这里，自采用压缩式制冷机满足冷负荷需求，并由电能提供动力，能量平衡约束具体为：
Figure PCTCN2022106192-appb-000019
其中，els n,h、elb n,h分别为第n个MEMG在h时段的售电、购电功率，hts n,h、htb n,h分别为第n个MEMG在h时段的售热、购热功率，P hx,n,h为多能微网n在h时段的换热装置出力大小，Q gb,n,h为多能微网n在h时段的燃气锅炉出力。
IA承担了责任，成为博弈中的上层代理商。IA的收益取决于与外部集中网络和MEMG集群的能源交易量，以及交易价格，多能微网群上层模型的目标函数为：
Figure PCTCN2022106192-appb-000020
其中，maxBeIA为多能微网群在h时段的最大效益，PrEb h和PrEs h分别代表IA从电网购电和向其售电的电价，PrHb h和PrHs h表示IA从外部热网购热和向其售热的热价，El h、Hl h分别表示中间商IA在某一时刻与外部网络之间的电和热交易总量，Els h和Elb h分别是指IA向外部网络出售的总电量和某一时刻从外部网络购买的总电量，Hts h和Htb h分别表示IA在某一时刻向外部热网出售的总热量和从外部热网购买的总热量。
多能微网群上层模型设有能量交易约束，具体为：
el n,h＝elb n,h-els n,h
0≤elb n,h≤elb n,maxkeb n,h
0≤els n,h≤els n,maxkes n,h
keb n,h+kes n,h≤1
hkt n,h＝htb n,h-hts n,h
0≤htb n,h≤htb n,maxkhb n,h
0≤hts n,h≤hts n,maxkhs n,h
khb n,h+khs n,h≤1
keb n,h、kes n,h、khb n,h、khs n,h∈{0，1}
PrEs h≤Pres h≤Preb h≤PrEb h
PrHs h≤Prhs h≤Prhb h≤PrHb h
Figure PCTCN2022106192-appb-000021
Figure PCTCN2022106192-appb-000022
其中，el n,h为第n个MEMG在h时段的净电功率，keb n,h、kes n,h分别为第n个MEMG的购电、售电的0-1变量，为1表示处于购电或售电状态，hkt n,h为多能微网n在h时段与主网交换热功率，khb n,h、khs n,h分别为第n个MEMG的购热、售热的0-1变量，为1表示处于购热或售热状态，Preb h、Pres h分别为MEMG向IA购买和卖回给IA的电价，其中，PrEb h和PrEs h分别代表IA从电网购电和向其售电的电价，同样，Prhb h和Prhs h表示分别为MEMG向IA购买和卖回给IA的热价，PrHb h和PrHs h表示IA从外部热网购热和向其售热的热价，El h为多能微网群在h时段的净电功率，Hl h为多能微网群在h时段的净热功率。
步骤2具体为：
当IA作为实体，以赚取中间差价为目标时，IA与MEMG集群之间的竞争利益关系可以描述为SGT中的领导者和多个跟随者，可以描述为：
Figure PCTCN2022106192-appb-000023
其中，IA代表博弈的领导者，{MEMG}代表博弈的追随者，{Ame n,h∪Amh n,h}分别表示每个MEMG的剩余电量和剩余热能，{Pres h},{Preb h},{Prhs h},{Prhb h}集合表示IA制定的内部价格策略，{BeMEMG n,h},{BeIA h}是交易主体的目标效益函数。
博弈F的均衡点S为：
Figure PCTCN2022106192-appb-000024
在此条件下，IA和MEMG均不能单方面改变定价和能源运行策略以获得更高的利益。
在下层中，每个跟随者以最大化自身运营成本为目标，值得注意的是，MEMG不能同时买卖电/热。
对于内部交易价格的确定，还必须同时遵守其他约束条件。下面以电为例进行分析：
当一个MEMG作为购电者时，它从IA购买的缺量部分可以描述为：
Figure PCTCN2022106192-appb-000025
在这种情况下，MEMG的负载需求范围如下所示。“max”为需求响应后允许的负载需求上限，“min”为需求响应后允许的负载需求下限。
Figure PCTCN2022106192-appb-000026
通过计算一阶偏导数，得到最优电力负荷需求为：
Figure PCTCN2022106192-appb-000027
同时最优负荷可描述为：
Figure PCTCN2022106192-appb-000028
可得电价范围：
Figure PCTCN2022106192-appb-000029
由上式可知，对于组中任意一个购电者MEMG，其交易价格对应于一个特定的范围，简称为：
Preb h∈[Preb n,h，min,Preb n,h，max]
需要注意的是，当电价在Preb h≤Preb n,h，min范围内时，最优用电负荷需求为max(ele n,h+cl n,h/COP)。否则，如果Preb h≥Preb n,h,max，则最优负载需求是(PV n,h+P wt,n,h+P mt,n,h-Pe n,h)。即负荷与价格有关，有上限和下限。
同样，MEMG作为售电商时，其电力负荷需求的最优范围和相应的电价范围可推导为：
Figure PCTCN2022106192-appb-000030
Figure PCTCN2022106192-appb-000031
可简化为：
Pres h∈[Pres n,h,min,Pres n,h,max]
同样，对于热能，通过对热负荷的偏导数得到最优热负荷需求。可以得到相应的热负荷需求范围、购热价格范围和售热价格范围，公式如下：
P hx,n,h+Q gb,n,h≤ht n,h≤max(ht n,h)
Figure PCTCN2022106192-appb-000032
可以缩写为：
Prhb h∈[Prhb n,h，min,Prhb n,h,max]
min(ht n,h)≤ht n,h≤P hx,n,h+Q gb,n,h
Figure PCTCN2022106192-appb-000033
可以缩写为：
Prhs h∈[Prhs n,h,min,Prhs n,h,max]
从上面的分析可以看出，IA的效用是分段函数：
{PrEs h≤Pres h≤Preb h≤PrEb h,PrHs h≤Prhs h≤Prhb h≤PrHb h}
步骤3具体为：
本实施例提出一个双层MILP模型，其方法框架如图3所示。本实施例采用基于SGT方法来解决该问题:考虑IA的效益。SGT是一种特殊的非合作游戏，玩家之间存在着等级关系，分为领导者和追随者。在SGT中，每个玩家都是自私的，目的是最大化自己的利益。领导者能够把他们的策略强加给追随者。这个博弈的解被称为Stackelberg均衡。在本实施例中，IA和每个MEMG被认为是具有单独目标的不同实体，以使运行成本最小化，可以分别描述为单领导者和多追随者。
步骤3-1：获取配能网制定的购售电和购售热价格，
步骤3-2：各多能微网进行自优化，
步骤3-3：各多能微网将冷、热、电负荷的余量和缺量以及设备出力优化、新能源发电预测和负荷需求响应范围上传至中间商IA，
步骤3-4：中间商IA根据各多能微网反馈的信息，结合与配能网的交易价格，以自身效益最大化为目标，制定多能微网群内的电、热交易价格，
步骤3-5：针对中间商IA制定的交易价格，各多能微网以进行自优化，进行需求响应，调整对冷、热、电负荷的需求，并将余量和缺量信息反馈给中间商IA，
步骤3-6：判断是否达到收敛条件，若是，则执行步骤3-7，否则，返回步骤3-4，
步骤3-7：输出最优博弈解集。
所述多能微网进行自优化的方法为：
各多能微网针对配能网的价格，并根据自身能源预测数据及微网用冷、热、电的预测数据，以自身效益最大化为为目标优化设备出力，同时优化对冷、热、电能的负荷需求。
下面提供一个具体的应用例：
如图4所示，本实施例将考虑三个区域建筑类型的MEMG，即MEMG1、MEMG2和MEMG3，分别代表酒店、住宅和商业建筑。假设网络MEMG中既存在可调度的分布式发电机，也存在不可调度的分布式发电机。每个MEMG包含电池储能系统、可控分布式发电机、可再生分布式发电机和终端用户负载。如果相互产生的能量不能实现MEMG的自给自足，则附近的MEMG或集中能源系统将通过IA为缺陷部分提供电能和热能，反之亦然。在分析DR优化结果时，下面将详细介绍各MEMG的负载需求。
表1列出了各种技术假设，包括所使用的能源设备的技术效率。同时需要注意的是，可控多重负荷的调节范围设为负荷预测曲线的20％，储能运行成本系数为0.02元/kWh。另一方面，表2显示了在每个MEMG中使用的设备容量。一般来说，MEMG2的光伏、WT和热电联产单元的容量最高，而三个园区的蓄电池容量设置相同。
表1设备参数
设备 参数
燃气轮机 30％
热电比 1.47
燃气锅炉 90％
余热锅炉 80％
换热器 90％
蓄电池充电 95％
蓄电池放电 95％
蓄电池储能范围 20％-100％
制冷系数 4.7
光伏 14％
表2出力设备容量
MEMG PV/kW WT/kW 燃气轮机/kW 燃气锅炉/kW 蓄电池/kWh 制冷机/kW
1 100 100 100 150 100 100
2 650 550 200 120 100 80
3 100 100 100 120 100 120
一般而言，能源价格类别可分为两部分:根据优化结果，利用IA进行内部能源交易的价格，以及利用公共能源网络进行能源交易的价格，如表3所示。由于采用分时电价机制，电网在不同时段制定的电价是不同的。此外，为促进热电联产机组的采用，天然气价格可享受优惠待遇，而辅助锅炉不能享受相同的价格折扣。此外，对于回购能源价格，特别是供热价格，其确定方式往往因不同案例而异，高度依赖于相关利益相关者之间的谈判。这里回购价格参照来确定。
表3能源价格
Figure PCTCN2022106192-appb-000034
图5为本应用例所使用的太阳辐照和风速数据。一般来说，两组数据在一天内可以相互补充，太阳辐照峰值在白天的1:00，为0.55kW/m2，风速峰值在夜间的11:00和12:00，高达7.31m/s。
利用所提出的混合整数线性规划模型，可以在不考虑内部能源交易价格确定、分时能源运行策略、经济性能以及各场景负荷需求响应的情况下，推导出个体优化、考虑IA效益的集群优化。在这里，为了简单起见，选择了夏季的一个典型的一天进行详细分析。
(1)各MEMG最优运行策略
利用所提出的优化模型可以得到小时最优运行策略，以MEMG1、MEMG2和MEMG3为例，图6和图7给出 了基于SGT方法并考虑IA效益的小区内能量交换时，MEMG1、MEMG2和MEMG3的电量平衡情况。可以发现，在集群中，MEMG2通常作为供电商，而MEMG1和MEMG3都作为需电商。这主要是由于MEMG2采用的区域发电机容量较高，MEMG1和MEMG3采用的区域发电机容量较低。也就是说，只有在各MEMG中存在较大的能源供需差异时，邻里之间的能源交换才能实现。此外，MEMG2虽然采用了热电联产机组，但从经济角度看，由于其运行成本相对光伏和风电系统较高，因此仅工作数小时。此外，可以发现MEMG1和MEMG3热电联产机组均在日常运行，这是因为在此时间段内，电、热需求相对较高，热电联产可以同时发电和发电，其经济性能较好。相比之下，由于夜间电价相对较低，各个MEMG更倾向于直接从外部公共能源网络购买电力，而不是自己生产。MEMG3夜间和日间负荷差异最大，热电联产机组和内部交换能由于其可再生能源安装相对较低，可以满足其日常电力需求，而风力发电则满足其夜间能源需求。
另一方面，优化后的三种MEMG的热平衡。与用电情况相比，运行策略相对简单，热需求主要由热电联产机组、燃气锅炉和热交换机组或两者组合分别提供。通常来说，MEMG1充当热买家，而MEMG2和MEMG3都充当热卖家。对于MEMG1，不同于电力的情况，交换部分占总热需求很少。这是因为在夏季，总热需求相对于电力需求较低，电力需求也包含了用于冷却负荷的电力消耗，因此MEMG1的大部分热需求可以由热电联产机组和自身的燃气锅炉提供。此外，对于MEMG2和MEMG3，热电联产机组在自我满足后产生的余热将出售给IA以获得额外利益。值得注意的是，MEMG2和MEMG3提供的剩余部分之和高于MEMG1的不足部分，说明剩余的部分卖给了外部热网。
(2)需求响应分析
图8为优化前后的负荷转移和转移情况。可以发现，与初始值相比，三种MEMG的负载变化都很大。一般情况下，由于负荷响应和能源价格同时优化，每个MEMG在能源价格高时倾向于降低自身的负荷需求，反之亦然。也就是说，每个MEMG都希望通过动态的价格和负荷交互，在每个时间段实现自身利益的最大化。
(3)能源交易价格的仿真结果
图9推导出了最优能源交易价格时采用SGT的基础方法。可以发现,本文提出的IA制定的内部能源交易价格介于外部电网制定的出售价格和回购价格之间。其中，外部能源网设定的能源销售价格最高，其次是内部销售价格、内部回购价格和外部能源网回购价格。在电价方面，在0:00-6:00和22:00-24:00时段，IA和配电网的交易价格基本一致，这一点很明显。这是因为在此期间，配电网的售电回购价格相对较低，IA的利润空间有限。而在10:00-22:00期间，差异比较大。热能方面，一般与电能一样，外部配热网制定热能销售价格最高，其次是内部热能销售价格、内部热能回购价格和外部配热网热能回购价格。此外，可以得出结论，在0:00-10:00和19:00-24:00期间，由于多个MEMG之间不存在热交换，内部热能交易价格与外部热能价格优化一致。而在11:00-18:00期间，内部热能回购价格高于外部配热网制定的热能交易价格，同时，内部热能销售价格低于外部热能销售价格，这样可以促进MEMG，首先与邻里交换剩余/不足能源，再与外部供应商交易能源，该交易方式将增加整个能源系统的稳定性和灵活性。此外，有必要指出，为了鼓励MEMG集群内部交换能源，而不是直接与外部能源网络交换能源，IA在不同情景下制定的价格略有变化。
(4)整个集群的能源交易总量
本实施例设定两种比较场景：
场景1：各MEMG作为独立个体进行自优化，无IA参与管理、监控，直接与外部能源网路交易，设为IOP。
场景2：各MEMG的能源协同耦合，由IA统一管理监督，并采用本文方法制定交易价格在集群内交易，设为SGT。
图10给出了2种场景下集群内用户与整个MEMG集群之间的能源交易量。总体上可以发现，与各MEMG单 独优化时的效益相比，集群在能源购买和能源回购部分都能明显低于直接与外部能源网的能源总交易量，尤其在基于SGT的场景下。也就是说，从整个能量方面来看，所提出的三个MEMG通过联合，相互交换了大量的能量，在集群内部平衡了能量的富余部分和不足部分。然而，值得注意的是，当分别关注电和热时，情况是不同的。对于电能，得出的结论与所有场景下的总能源交易趋势一致；对于热能，无论是购回部分还是回购部分，基于SGT的场景下MEMG集群与外部配热网的热能交易量都小于IOP(独立优化)场景。在基于SGT的情景下，由于MEMG集群与外部能源网络交易的收益必然与IA共享，且得到的购、售能源价格差异较大，为获得自身最大效益，各MEMG将尽可能通过内部交换平衡能源供需。
(5)不同场景下各参与者收益分析
图11显示了典型一天中2种场景下每个MEMG的小时效益优化情况。一般来说，所有MEMG在白天比在夜间获得更多的效益。此外，可以得出结论，尽管在大多数情况下，考虑IA的利益，MEMG的组合可以推导出整个集群的最高利益，但结果并非在所有时间都是不变的。例如，在8:00-9:00时间段内，考虑IA效益的场景下，独立优化(IOP)的效益明显高于SGT模式。这是因为优化的对象是最大化一整天的利益，而不是每一个小时。
图12显示了采用基于SGT的方法考虑IA的利润时，在典型的一天中推导出的小时收益。综上所述，通过采用SGT方法，可以使领导者-IA和多个跟随者-MEMGs的效益得到平衡。显然，只有当MEMG与外电网存在能量交换时，IA的效益大于0，越高越好。另外，由于能源需求的高峰期是在白天，所以这段时间的能量交换量最大，特别是11:00-18:00这段时间，IA可以获得更高的利润。而在1:00,7:00-10:00,22:00-24:00时段，IA的效益等于0，因为每个MEMG都可以在没有外界帮助的情况下实现能量自给。
表4显示了每个利益相关者(包括所有MEMG和IA)在一个典型的日子里的收益。可以发现，一般来说，MEMG2的利润最高，MEMG 1次之，MEMG 3次之，这高度依赖于能带来额外收益的能量剩余量。再次，与所有MEMG各自优化IOP的情况相比，无论是否考虑IA的效益，每个MEMG的效益都是通过它们之间的能量交换而增加的。当考虑IA的利益时，MEMG1、MEMG2、MEMG3和整个MEMG集群的利润分别增加了1.77％、1.17％、2.71％、2.92％。
表4两种场景下典型日的效益
Figure PCTCN2022106192-appb-000035
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到各种等效的修改或替换，这些修改或替换都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以权利要求的保护范围为准。

Claims (10)

  1. 一种多能微网群自身及市场决策协同优化方法，其特征在于，所述的协同优化方法包括：
    步骤1：构建多能微网群系统模型，包括用于进行MEMG内部优化的多能微网群下层模型以及用于进行市场决策优化的多能微网群上层模型；
    步骤2：基于主从博弈构建博弈模型；
    步骤3：基于双层MILP模型求解博弈模型，获得最优博弈解集，输出协同优化策略。
  2. 根据权利要求1所述的一种多能微网群自身及市场决策协同优化方法，其特征在于，所述的多能微网群下层模型以某时间段内实现效益最大化为目标函数，具体为：
    Figure PCTCN2022106192-appb-100001
    其中，BeMEMG n为第n个MEMG的效益；a n,h为第n个MEMG在h时段的边际效益系数；ele n,h、ht n,h、cl n,h分别为第n个MEMG在h时段的电、热、冷负荷；COP是制冷系数；els n,h、elb n,h分别为第n个MEMG在h时段的售电、购电功率；hts n,h、htb n,h分别为第n个MEMG在h时段的售热、购热功率；M mt,n,h表示第n个MEMG在h时段的燃气轮机的耗气量；M gb,n,h是燃气锅炉所消耗的燃气体积；Pres h、Preb h分别为在h时段MEMG的售电、购电价格；Prhs n,h、Prhb n,h分别为在h时段MEMG的售热、购热价格；Pg mt、Pg gb分别为燃气轮机与燃气锅炉的燃气价格；C bt为蓄电池的运维系数；Bch n,h、Bdis n,h分别为充电、放电功率。
  3. 根据权利要求2所述的一种多能微网群自身及市场决策协同优化方法，其特征在于，所述的多能微网群下层模型设有设备出力约束和能量平衡约束；所述的设备处理约束包括热电联产机组出力约束、燃气锅炉出力约束、压缩式制冷机出力约束、蓄电池出力约束、光伏设备出力约束、风机设备出力约束和热能需求响应约束。
  4. 根据权利要求3所述的一种多能微网群自身及市场决策协同优化方法，其特征在于，所述的热电联产机组出力约束具体为：
    P mt,n,h＝M mt,n,hL ngη mt
    P mt,n,min≤P mt,n,h≤P mt,n,max
    P hc,n,h＝P mt,n,hr mtη whη hc
    其中，P mt,n,h为第n个MEMG在h时段的燃气轮机发电功率；M mt,n,h表示第n个MEMG在h时段的燃气轮机的耗气量；L ng表示天然气热值；η mt表示燃气轮机的发电效率；，P mt,n,min表示燃气轮机最小功率；P mt,n,max表示燃气轮机最大功率；P hc,n,h是第n个MEMG在h时段的热交换器的产热量；r mt是热电比；η wh为余热锅炉的效率；η hc为换热装置的效率；
    燃气锅炉出力约束具体为：
    Q gb,n,h＝M gb,n,hL ngη gb
    Q gb,n，min≤Q gb,n,h≤Q gb,n,max
    其中，Q gb,n,h是第n个MEMG在h时段的燃气锅炉的输出热功率；M gb,n,h是燃气锅炉所消耗的燃气体积；η gb为燃气锅炉的效率；Q gb,n,min为燃气锅炉最小功率；Q gb,n,max为燃气锅炉最大功率；
    压缩式制冷机出力约束具体为：
    Figure PCTCN2022106192-appb-100002
    其中，cl n,h为是第n个MEMG在h时段的冷负荷；COP是制冷系数；Co ec,n,h为压缩式制冷机的制冷功率；Co ec,n,max为制冷功率的上限；
    蓄电池出力约束具体为：
    Figure PCTCN2022106192-appb-100003
    Figure PCTCN2022106192-appb-100004
    其中，Es n,h是第n个MEMG在h时段的蓄电池储电量；Bch n,h、Bdis n,h分别为充电、放电功率；Nch、Ndis分别为充电、放电效率；Es i(0)、Es i(24)为蓄电池中一天的初始电量和最终电量；Es n,max、Es n,min分别为蓄电池系统剩余电量的最大和最小值；Pe n,h为多能微网n在h时段的蓄电池的充、放电量；Bch n,max、Bdis n,max分别代表蓄电池的最大充电、放电量；kech n,h、kedis n,h为多能微网n在h时段的充放电状态。
    光伏设备出力约束具体为：
    A PV,n≤A PV,n,max
    0≤PV n,h≤A PV,nIi hη PV
    其中，A PV,n为第n个MEMG的光伏安装面积；A PV,n,max为最大安装面积；PV n,h为第n个MEMG的光伏发电功率；li h为多能微网n在h时段的单位辐照量；η PV为光伏发电效率；
    风机设备出力约束具体为：
    0≤P wt,n,h≤P wt,n,max
    其中，P wt,n,h为风机的发电功率；P wt,n,max为最大发电功率；
    热能需求响应约束具体为：
    Figure PCTCN2022106192-appb-100005
    Figure PCTCN2022106192-appb-100006
    其中，ele n,h、ht n,h、cl n,h分别为第n个MEMG在h时段的电、热、冷负荷；Ele n,h,max、Ht n,h,max、Cl n,h,max分别为第n个MEMG在h时段的电、热、冷负荷的上限；Ele n,h,min、Ht n,h,min、Cl n,h,min分别为第n个MEMG在h时段的电、热、冷负荷的下限；Ele n,h、Ht n,h、Cl n,h分别为日前初始预测电、热、冷负荷；p为负荷削减系数。
  5. 根据权利要求3所述的一种多能微网群自身及市场决策协同优化方法，其特征在于，所述的能量平衡约束具体为：
    Figure PCTCN2022106192-appb-100007
    hts n,h+ht n,h＝htb n,h+P hx,n,h+Q gb,n,h
    其中，els n,h、elb n,h分别为第n个MEMG在h时段的售电、购电功率；hts n,h、htb n,h分别为第n个MEMG在h时段的售热、购热功率；P hx,n,h为多能微网n在h时段的换热装置出力大小；Q gb,n,h为多能微网n在h时段的燃气锅炉出力。
  6. 根据权利要求1所述的一种多能微网群自身及市场决策协同优化方法，其特征在于，所述的多能微网群上层模型的目标函数为：
    Figure PCTCN2022106192-appb-100008
    其中，maxBeIA为多能微网群在h时段的最大效益；PrEb h和PrEs h分别代表IA从电网购电和向其售电的电价；PrHb h和PrHs h表示IA从外部热网购热和向其售热的热价；El h、Hl h分别表示中间商IA在某一时刻与外部网络之间的电和热交易总量；Els h和Elb h分别是指IA向外部网络出售的总电量和某一时刻从外部网络购买的总电量；Hts h和Htb h分别表示IA在某一时刻向外部热网出售的总热量和从外部热网购买的总热量。
  7. 根据权利要求6所述的一种多能微网群自身及市场决策协同优化方法，其特征在于，所述的多能微网群上层模型设有能量交易约束，具体为：
    el n,h＝elb n,h-els n,h
    0≤elb n,h≤elb n,maxkeb n,h
    0≤els n,h≤els n,maxkes n,h
    keb n,h+kes n,h≤1
    hkt n,h＝htb n,h-hts n,h
    0≤htb n,h≤htb n,maxkhb n,h
    0≤hts n,h≤hts n,maxkhs n,h
    khb n,h+khs n,h≤1
    keb n,h、kes n,h、khb n,h、khs n,h∈{0，1}
    PrEs h≤Pres h≤Preb h≤PrEb h
    PrHs h≤Prhs h≤Prhb h≤PrHb h
    Figure PCTCN2022106192-appb-100009
    Figure PCTCN2022106192-appb-100010
    其中，el n,h为第n个MEMG在h时段的净电功率；keb n,h、kes n,h分别为第n个MEMG的购电、售电的0-1变量，为1表示处于购电或售电状态；hkt n,h为多能微网n在h时段与主网交换热功率；khb n,h、khs n,h分别为第n个MEMG的购热、售热的0-1变量，为1表示处于购热或售热状态；Preb h、Pres h分别为MEMG向IA购买和卖回给IA的电价；其中，PrEb h和PrEs h分别代表IA从电网购电和向其售电的电价；同样，Prhb h和Prhs h表示分别为MEMG向IA购买和卖回给IA的热价；PrHb h和PrHs h表示IA从外部热网购热和向其售热的热价；El h为多能微网群在h时段的净电功率；Hl h为多能微网群在h时段的净热功率。
  8. 根据权利要求1所述的一种多能微网群自身及市场决策协同优化方法，其特征在于，所述的步骤2具体为：
    当IA作为实体，以赚取中间差价为目标时，IA与MEMG集群之间的竞争利益关系可以描述为SGT中的领导者和多个跟随者，可以描述为：
    Figure PCTCN2022106192-appb-100011
    其中，IA代表博弈的领导者；{MEMG}代表博弈的追随者；{Ame n,h∪Amh n,h}分别表示每个MEMG的剩余电量和剩余热能；{Pres h},{Preb h},{Prhs h},{Prhb h}集合表示IA制定的内部价格策略，{BeMEMG n,h},{BeIA h}是交易主体的目标效益函数；
    博弈F的均衡点S为：
    Figure PCTCN2022106192-appb-100012
  9. 根据权利要求1所述的一种多能微网群自身及市场决策协同优化方法，其特征在于，所述的步骤3具体为：
    步骤3-1：获取配能网制定的购售电和购售热价格；
    步骤3-2：各多能微网进行自优化；
    步骤3-3：各多能微网将冷、热、电负荷的余量和缺量以及设备出力优化、新能源发电预测和负荷需求响应范 围上传至中间商IA；
    步骤3-4：中间商IA根据各多能微网反馈的信息，结合与配能网的交易价格，以自身效益最大化为目标，制定多能微网群内的电、热交易价格；
    步骤3-5：针对中间商IA制定的交易价格，各多能微网以进行自优化，进行需求响应，调整对冷、热、电负荷的需求，并将余量和缺量信息反馈给中间商IA；
    步骤3-6：判断是否达到收敛条件，若是，则执行步骤3-7，否则，返回步骤3-4；
    步骤3-7：输出最优博弈解集。
  10. 根据权利要求9所述的一种多能微网群自身及市场决策协同优化方法，其特征在于，所述多能微网进行自优化的方法为：
    各多能微网针对配能网的价格，并根据自身能源预测数据及微网用冷、热、电的预测数据，以自身效益最大化为为目标优化设备出力，同时优化对冷、热、电能的负荷需求。
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