CN114254494A - Multi-energy micro-grid group self and market decision collaborative optimization method - Google Patents

Abstract

The invention relates to a collaborative optimization method for self and market decision of a multi-energy microgrid group, which comprises the following steps: step 1: constructing a multi-energy micro-grid group system model, which comprises a multi-energy micro-grid group lower layer model for performing MEMG internal optimization and a multi-energy micro-grid group upper layer model for performing market decision optimization, and 2: constructing a game model based on a master-slave game, and step 3: and solving the game model based on the double-layer MILP model to obtain an optimal game solution set and output a collaborative optimization strategy. Compared with the prior art, the method has the advantages of improving the overall benefit, having higher energy consumption level in the MEMG cluster, obtaining the balance between the self-sufficient capability and the transaction sharing capability and the like.

Description

Multi-energy micro-grid group self and market decision collaborative optimization method

Technical Field

The invention relates to the technical field of multi-energy micro-grid group optimization, in particular to a multi-energy micro-grid group self and market decision cooperative optimization method.

Background

In a single multi-energy micro-grid MEMG, various distributed energy producers serve as suppliers, such as CHP (combined heat and power unit), photovoltaic, wind turbine, battery and the like, and comprehensive energy consumers serve as demanders. In each MEMG there is a grid, a heat supply network and a gas network, respectively, allowing for a combination of different energy types. In addition, on a self-sufficient basis, adjacent MEMGs are connected with each other to form a complete network, and energy is shared by the internal energy transmission network. Specifically, energy excess or deficiency MEMGs are encouraged to trade energy first with the remaining MEMGs in the interior market, rather than directly with the utility grid. However, if the MEMG cluster itself cannot achieve internal energy balancing, it can trade with external centralized energy networks if necessary, namely: when one MEMG generates residual energy, the residual energy can be sold to a nearby MEMG for internal energy balance, and if the residual energy exists, the residual part is sold back to an external power grid. Conversely, the deficit may be purchased from a nearby MEMG before purchasing energy from the external grid.

Then, self-optimization of the multi-energy microgrid and optimization of market decision in the transaction are involved in the transaction process, and related technologies for the self-optimization of the multi-energy microgrid and the optimization of the market decision are available in the prior art, but a method for performing collaborative optimization on the two optimizations is not available.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provide a multi-energy microgrid group and a market decision cooperative optimization method which have the advantages of improving the overall benefit, having higher energy consumption level in the MEMG cluster and obtaining the balance between self-sufficient capacity and transaction sharing capacity.

The purpose of the invention can be realized by the following technical scheme:

a multi-energy microgrid group and market decision collaborative optimization method comprises the following steps:

step 1: constructing a multi-energy micro-grid group system model, which comprises a multi-energy micro-grid group lower layer model for performing MEMG internal optimization and a multi-energy micro-grid group upper layer model for performing market decision optimization;

step 2: constructing a game model based on a master-slave game;

and step 3: and solving the game model based on the double-layer MILP model to obtain an optimal game solution set and output a collaborative optimization strategy.

Preferably, the lower-layer model of the multi-energy microgrid group realizes the maximization of the benefit in a certain time period as an objective function, and specifically comprises the following steps:

wherein, BeMEMG_nThe benefit for the nth MEMG; a is_n,hMarginal benefit coefficient of nth MEMG in h period; pres_h、Preb_hThe price of selling and purchasing electricity of the MEMG in the h period is respectively; prhs_n,h、Prhb_n,hRespectively the heat sale price and the heat purchase price of the MEMG in the h period; pg_mt、Pg_gbGas prices of a gas turbine and a gas boiler, respectively; c_btThe operation and maintenance coefficient of the storage battery.

More preferably, the lower layer model of the multi-energy microgrid group is provided with an equipment output constraint and an energy balance constraint; the equipment processing constraints comprise output constraints of a cogeneration unit, output constraints of a gas boiler, output constraints of a compression type refrigerator, output constraints of a storage battery, output constraints of photovoltaic equipment, output constraints of fan equipment and heat energy demand response constraints.

More preferably, the output constraint of the cogeneration unit is specifically as follows:

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

wherein, P_mt,n,hGas turbine generated power for the nth MEMG over a period of h; m_mt,n,hRepresenting the gas consumption of the gas turbine of the nth MEMG in the h period; eta_mtRepresenting the power generation efficiency of the gas turbine; p_hc,n,hIs the heat generation amount r of the heat exchanger of the nth MEMG in the h period_mtIs the thermoelectric ratio; the efficiency of the waste heat boiler; eta_hcEfficiency of the heat exchange device;

the output restriction of the gas boiler is specifically as follows:

Q_gb,n,h＝M_gb,n,hL_ngη_gb

Q_gb,n，min≤Q_gb,n,h≤Q_gb,n,max

wherein Q is_gb,n,hIs the output thermal power of the gas boiler of the nth MEMG during the h period; m_gb,n,hIs the volume of gas consumed by the gas boiler; eta_gbEfficiency of a gas boiler;

the output constraint of the compression type refrigerating machine is specifically as follows:

wherein, cl_n,hIs the cooling load of the nth MEMG during the h period; COP is the refrigeration coefficient; co_ec,n,hThe refrigeration power of the compression refrigerator; co_ec,n,maxThe upper limit of the refrigeration power;

the output constraint of the storage battery is specifically as follows:

wherein, Es_n,hIs the battery charge capacity of the nth MEMG during the h period; bch_n,h、Bdis_n,hRespectively charge power and discharge power; nch and Ndis are respectively charge and discharge efficiency;

the output constraint of the photovoltaic equipment is specifically as follows:

A_PV,n≤A_PV,n,max

0≤PV_n,h≤A_PV,nIi_hη_PV

wherein A is_PV,nA photovoltaic mounting area for an nth MEMG; a. the_PV,n,maxThe maximum installation area; PV (photovoltaic)_n,hPhotovoltaic power generation power for the nth MEMG;

the output constraint of the fan equipment is specifically as follows:

0≤P_wt,n,h≤P_wt,n,max

wherein, P_wt,n,hThe generated power of the fan; p_wt,n,maxIs the maximum generated power;

the thermal energy demand response constraint is specifically:

wherein ele_n,h、ht_n,h、cl_n,hThe electricity, heat and cold loads of the nth MEMG in the h period respectively; ele_n,h,max、Ht_n,h,max、Cl_n,h,maxUpper limits of electric, thermal and cooling loads of the nth MEMG in the h period respectively; ele_n,h,min、Ht_n,h,min、Cl_n,h,minLower limits of electric, thermal and cooling loads of the nth MEMG in the h period respectively; p is a load reduction coefficient.

More preferably, the energy balance constraint is specifically:

hts_n,h+ht_n,h＝htb_n,h+P_hx,n,h+Q_gb,n,h

wherein, els_n,h、elb_n,hRespectively selling electricity and purchasing electricity power of the nth MEMG in the h period; hts_n,h、htb_n,hThe heat selling power and the heat purchasing power of the nth MEMG in the h period are respectively.

Preferably, the objective function of the upper model of the multi-energy microgrid group is as follows:

wherein, El_h、Hl_hRespectively representing the total amount of electric and thermal transactions between the intermediary IA and the external network at a certain moment; els_hAnd Elb_hRespectively, the total electric quantity sold by the IA to the external network and the total electric quantity purchased from the external network at a certain moment; hts_hAnd Htb_hRespectively representing the total heat sold by the IA to the external heat net at a certain moment and the total heat purchased from the external heat net.

More preferably, the upper-layer model of the multi-energy microgrid group is provided with energy transaction constraints, and specifically comprises the following steps:

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

wherein el is_n,hNet electric power for the nth MEMG over a period of h; keb_n,h、kes_n,hRespectively is a 0-1 variable of electricity purchasing and selling of the nth MEMG, and a 1 represents that the power purchasing or selling state is in; khb_n,h、khs_n,hThe variables of 0-1 for the heat of purchase and the heat of sale of the nth MEMG respectively are 1, and the variables are in the heat of purchase or heat of sale state; preb_h、Pres_hThe electricity prices of the MEMG for buying and selling back to the IA are respectively; wherein, PrEb_hAnd PrEs_hRespectively represent the electricity prices of the IA for purchasing and selling electricity from and to the power grid; likewise, Prhb_hAnd Prhs_hRepresenting heat prices for the MEMG to buy and sell back to IA, respectively; PrHb_hAnd PrHs_hIndicating the heat prices at which IA purchases heat from and sells heat to external heat grids.

Preferably, the step 2 specifically comprises:

when the IA is an entity, aiming at earning an intermediate spread, the competitive interest relationship between the IA and the MEMG cluster may be described as a leader and multiple followers in the SGT, and may be described as:

wherein IA represents the leader of the game; { MEMG } represents a follower of the game; { Ame_n,h∪Amh_n,hRespectively representing the residual electric quantity and the residual heat energy of each MEMG; { Pres_h},{Preb_h},{Prhs_h},{Prhb_hThe set represents the internal price strategy made by IA, BeMEMG_n,h},{BeIA_hIs the target benefit function of the transaction body;

the balance points for game F are:

preferably, the step 3 specifically comprises:

step 3-1: acquiring the electricity and heat purchase prices set by the distribution network;

step 3-2: self-optimizing each multifunctional microgrid;

step 3-3: each multi-energy micro grid uploads the surplus and shortage of cold, heat and electric loads, equipment output optimization, new energy power generation prediction and a load demand response range to an intermediate quotient IA;

step 3-4: the intermediate agent IA establishes electricity and heat exchange price in the multi-energy micro-grid group according to the information fed back by each multi-energy micro-grid and by combining the transaction price with the energy distribution network and aiming at maximizing the benefit of the intermediate agent IA;

step 3-5: aiming at the transaction price formulated by the intermediary IA, each multi-energy microgrid is self-optimized, demand response is carried out, the demands for cold, heat and electric loads are adjusted, and the surplus and shortage information is fed back to the intermediary IA;

step 3-6: judging whether a convergence condition is reached, if so, executing the step 3-7, otherwise, returning to the step 3-4;

step 3-7: and outputting the optimal game solution set.

More preferably, the method for self-optimizing the multi-energy microgrid comprises the following steps:

aiming at the price of the energy distribution network, each multifunctional micro-grid optimizes the output of the equipment by taking the self benefit maximization as a target and simultaneously optimizes the load demands on cold, heat and electric energy according to the self energy prediction data and the prediction data of cold, heat and electricity for the micro-grid.

Compared with the prior art, the invention has the following beneficial effects:

firstly, improving the overall benefit: the multi-energy micro-grid group and the SGT-based method provided by the market decision collaborative optimization method can effectively solve the problem of determining the internal trading price of energy, compared with the situation that a single MEMG acts alone, the cluster combination can obviously improve the overall benefits of all the MEMGs, and the benefits of IA in typical days are considered to be improved by 4.4%.

Secondly, the energy consumption level inside the MEMG cluster is higher: according to the multi-energy micro-grid cluster and the market decision cooperative optimization method, the MEMG gives priority to the energy consumption in the multi-energy micro-grid cluster as much as possible, then the MEMG trades with an external energy network, and the internal energy self-consumption level is higher.

And thirdly, obtaining balance between self-sufficient capacity and transaction sharing capacity: the multi-energy micro-grid cluster self and market decision collaborative optimization method provides a mixed integer linear programming model for global optimization of the MEMG cluster, provides technical support for determination of energy trading prices in different scenes, proposes to share benefits with IA while seeking self-sufficient capacity and relatively stable energy requirements, and achieves balance between the self-sufficient capacity and the trading sharing capacity.

Drawings

Fig. 1 is a schematic flow chart of the multi-energy microgrid group and a market decision collaborative optimization method in the present invention;

fig. 2 is a schematic diagram of a framework structure of a multi-energy microgrid group system according to an embodiment of the present invention;

FIG. 3 is a schematic flow chart of solving a game model according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of an energy flow structure of the MEMG in the embodiment of the present invention;

FIG. 5 is solar irradiance and wind speed data used in an embodiment of the present invention;

fig. 6 is an electrical balance diagram of the MEMG cluster in the embodiment of the present invention;

wherein fig. 6(a), fig. 6(b) and fig. 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;

wherein fig. 7(a), fig. 7(b) and fig. 7(c) represent MEMG1, MEMG2 and MEMG3, respectively;

FIG. 8 is a demand response analysis of an MEMG cluster in an embodiment of the present invention;

wherein, fig. 8(a), fig. 8(b) and fig. 8(c) represent MEMG1, MEMG2 and MEMG3, respectively

FIG. 9 is a schematic diagram of the optimal price of the internal energy exchange in an embodiment of the invention;

wherein, fig. 9(a) is the electricity purchase price and fig. 9(b) is the heat purchase price;

fig. 10 shows the energy trading volume between the external energy network and the MEMG cluster in the embodiment of the present invention;

FIG. 11 is a graph of the hourly benefits of each MEMG on a typical day for an embodiment of the present invention;

fig. 12 is a graph of the hourly benefit of the SGT-based IA on a typical day in an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.

The framework structure of the multi-energy microgrid cluster system is shown in fig. 2, and a reasonable transaction mode is required when internal energy transactions between MEMGs in a cluster and transactions with an external energy network are processed. Here, unlike peer-to-peer energy trading, a broker-based trading model is employed, with the IA (broker) acting as an independent entity, ensuring private flow of information and fair allocation of resources. While the exchanged energy flows through the established transport infrastructure, all participants are trading directly with the IA. The IA is responsible for collecting necessary information such as external energy prices, remaining/insufficient energy amounts of each MEMG, interest demand, etc., and determining internal energy trading prices based thereon. The present embodiment considers the following cases:

when the IA handles cash flows for energy trading as a real operator, the profits of the IA are based on the spread of bids and asks for prices of the cluster internal energy sharing and the cluster external energy trading. The energy transaction price depends on the interest game played by the participants by adopting an SGT (Stackelberg game theory) method.

The embodiment provides a collaborative optimization method for self and market decision of a multi-energy microgrid group, which comprises the following steps:

step 1: and constructing a multi-energy micro-grid group system model, which comprises a multi-energy micro-grid group lower layer model for performing MEMG internal optimization and a multi-energy micro-grid group upper layer model for performing market decision optimization. For the optimization of the energy trading price determination associated with the previous level, it will be handled with SGT based approach, with IA as the real operator and also as an independent benefit agent. In addition, it should be noted that since it 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: constructing a game model based on the master-slave game,

and step 3: and solving the game model based on the double-layer MILP model to obtain an optimal game solution set and output a collaborative optimization strategy.

The objective function of each MEMG is to maximize the benefit over a period of time. For each MEMG, the revenue for each period depends primarily on the energy cost paid by the user, the energy exchange revenue paid by the nearby MEMGs, and the revenue of the transaction with the external energy network. Note that the operating costs must be driven off from the revenue, including primary energy costs, battery operating costs, etc. The lower-layer model of the multi-energy micro-grid group realizes the maximization of benefits in a certain time period as an objective function, and specifically comprises the following steps:

wherein, BeMEMG_nFor the benefit of the nth MEMG, a_n,hMarginal benefit coefficient for nth MEMG in h period, Pres_h、Preb_hThe price of selling and purchasing electricity of the MEMG in the h period is Prhs_n,h、Prhb_n,hRespectively the heat sale price and the heat purchase price of the MEMG in the h period, Pg_mt、Pg_gbGas prices, C, of gas turbines and gas boilers, respectively_btThe operation and maintenance coefficient of the storage battery.

The lower-layer model of the multi-energy micro-grid group is provided with equipment output constraint and energy balance constraint, and the equipment processing constraint comprises cogeneration unit output constraint, gas boiler output constraint, compression type refrigerator output constraint, storage battery output constraint, photovoltaic equipment output constraint, fan equipment output constraint and heat energy demand response constraint.

(1) Combined heat and power generating unit

The power generation amount of the cogeneration unit is calculated by multiplying consumed natural gas by the calorific value and the power generation efficiency thereof, and in addition, the power generation amount is limited by the maximum value and the minimum value allowed for it. Where L is_ngEqual to 9.7(kWh)/m³。

The output constraint of the cogeneration unit is specifically as follows:

P_mt,n,h＝M_mt,n,hL_ngη_mt

P_mt,n,min≤P_mt,n,h≤P_mt,n,max

wherein, P_mt,n,hPower generation of gas turbine for nth MEMG during h period, M_mt,n,hRepresenting the gas consumption of the gas turbine, η, of the nth MEMG during h_mtIndicating the power generation efficiency of the gas turbine.

In addition, the heat recovered by the cogeneration unit and the final effective heat are equal to the generated energy multiplied by the heat-electricity ratio, the heat exchange device efficiency and the exhaust-heat boiler efficiency, as follows:

P_hc,n,h＝P_mt,n,hr_mtη_whη_hc

wherein, P_hc,n,hIs the heat generation amount r of the heat exchanger of the nth MEMG in the h period_mtIs the thermoelectric ratio, which is the efficiency, eta, of the waste heat boiler_hcIs the efficiency of the heat exchange device.

(2) Gas boiler

The heat generated by the gas boiler can be calculated by multiplying the gas consumption by the heating value and the power generation efficiency. In addition, the output heat must be within capacity constraints. The output restriction of the gas boiler is specifically as follows:

Q_gb,n,h＝M_gb,n,hL_ngη_gb

Q_gb,n，min≤Q_gb,n,h≤Q_gb,n,max

wherein Q is_gb,n,hIs the output thermal power of the gas boiler of the nth MEMG during the h period, M_gb,n,hIs the volume of gas consumed by the gas boiler, eta_gbIs the efficiency of a gas boiler.

(3) Compression type refrigerating machine

The refrigeration power of a compression refrigerator must be greater than the peak demand of the refrigeration load divided by its coefficient of performance and less than the maximum power allowed. The output constraint of the compression type refrigerating machine is specifically as follows:

wherein, cl_n,hTo be the cooling load of the nth MEMG in the h period, COP is the coefficient of refrigeration, Co_ec,n,hFor the refrigerating power of compression-type refrigerators, Co_ec,n,maxIn order to be the upper limit of the cooling power,

(4) battery restraint

The charging and discharging of the battery can not be carried out simultaneously, and simultaneously, the maximum value of the charging and discharging power can not be exceeded. Es_i(0)、Es_i(24) The initial charge amount and the final charge amount for one day in the battery set in the present embodiment are 50% of the maximum capacity. The output constraint of the storage battery is specifically as follows:

wherein, Es_n,hIs the storage battery capacity of the nth MEMG in the h period, Bch_n,h、Bdis_n,hCharge and discharge power, Nch and Ndis charge and discharge efficiency,

(5) photovoltaic device

The installation area of the photovoltaic unit should not exceed the upper limit of the actual installation area of each producer and consumer, the generated electric quantity should not exceed the rated power generation capacity, and the output constraint of the photovoltaic equipment is specifically as follows:

A_PV,n≤A_PV,n,max

0≤PV_n,h≤A_PV,nIi_hη_PV

wherein A is_PV,nIs the photovoltaic mounting area of the nth MEMG, A_PV,n,maxTo maximize the mounting area, PV_n,hFor the photovoltaic power generation of the nth MEMG,

(6) fan blower

Similar to the PV unit, the actual output power of the wind turbine must also comply with the allowable output range, and the constraints of the wind turbine equipment output are specifically:

0≤P_wt,n,h≤P_wt,n,max

wherein, P_wt,n,hIs the generated power of the fan, P_wt,n,maxIn order to maximize the power generated by the generator,

(7) demand response

In this embodiment, in addition to electricity, the demand response of thermal energy is considered and named as integrated DR. The MEMG is used as a multi-energy flow coupling collecting and dispersing body, and the cold load, the heat load and the electric load of the MEMG have upper limit and lower limit. Meanwhile, the total amount of cold, heat and electricity in the day before and after optimization must be kept constant. The constraints are as follows:

wherein ele_n,h、ht_n,h、cl_n,hElectrical, thermal and cooling load of the nth MEMG in the h period, Ele_n,h,max、Ht_n,h,max、Cl_n,h,maxUpper limits of electric, thermal and cooling loads of the nth MEMG in the h period, Ele_n,h,min、Ht_n,h,min、Cl_n,h,minLower limits of the electric, thermal and cooling loads of the nth MEMG in the h period, respectively, and p is a load reduction coefficient.

The energy balance of each MEMG is constrained as follows, indicating that the energy output must equal the energy input during each time period. Here, the compression refrigerator is adopted to meet the cooling load demand, and is powered by electric energy, and the energy balance constraint is specifically as follows:

hts_n,h+ht_n,h＝htb_n,h+P_hx,n,h+Q_gb,n,h

wherein, els_n,h、elb_n,hRespectively the power sold and purchased by the nth MEMG in the h period, hts_n,h、htb_n,hThe heat selling power and the heat purchasing power of the nth MEMG in the h period are respectively.

The IA assumes responsibility and becomes the upper-level agent in the game. The income of IA depends on the energy trading volume with the external centralized network and the MEMG cluster, and the trading price, and the objective function of the upper layer model of the multi-energy microgrid cluster is as follows:

wherein, El_h、Hl_hRespectively representing the total amount of electric and thermal transactions between the intermediary IA and the external network at a certain moment，Els_hAnd Elb_hRespectively, the total amount of electricity sold by IA to the external network and the total amount of electricity purchased from the external network at a certain moment, Hts_hAnd Htb_hRespectively representing the total heat sold by the IA to the external heat net at a certain moment and the total heat purchased from the external heat net.

The upper-layer model of the multi-energy micro-grid group is provided with energy transaction constraints, and the energy transaction constraints are specifically as follows:

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

wherein el is_n,hNet electric power for the nth MEMG during h period, keb_n,h、kes_n,hA variable of 0-1 respectively for electricity purchase and sale of the nth MEMG, a value of 1 indicates that the MEMG is in an electricity purchase or sale state, khb_n,h、khs_n,hThe variation 0-1 of the heat of purchase and sale of the nth MEMG respectively, 1 indicates that the product is in the heat of purchase or sale state, Preb_h、Pres_hThe price of electricity purchased from MEMG to IA and sold back to IA, respectively, wherein PrEb_hAnd PrEs_hRespectively representing the electricity prices of the IA for purchasing and selling electricity from and to the power grid, and likewise, Prhb_hAnd Prhs_hPrHb represents the heat price for purchase and sale of MEMG to IA and back to IA, respectively_hAnd PrHs_hIndicating the heat prices at which IA purchases heat from and sells heat to external heat grids.

The step 2 specifically comprises the following steps:

when the IA is an entity, aiming at earning an intermediate spread, the competitive interest relationship between the IA and the MEMG cluster may be described as a leader and multiple followers in the SGT, and may be described as:

wherein IA stands for the leader of the game, { MEMG } stands for the follower of the game, { Ame_n,h∪Amh_n,hRespectively representing the remaining capacity and the remaining thermal energy of each MEMG, { Pres_h},{Preb_h},{Prhs_h},{Prhb_hThe set represents the internal price strategy made by IA, BeMEMG_n,h},{BeIA_hIs the target benefit function of the transaction body.

The balance points for game F are:

under this condition, neither IA nor MEMG can unilaterally change pricing and energy operating strategies to gain higher benefits.

In the lower tier, each follower aims to maximize its own operating costs, notably, MEMGs cannot buy or sell electricity/heat simultaneously.

Other constraints must also be respected simultaneously for the determination of the internal transaction price. The following analysis is carried out using electricity as an example:

when a MEMG is the electricity purchaser, the shortage portion it purchases from IA can be described as:

in this case, the load demand range of the MEMG is as follows. "Max" is the upper limit of allowable load demand after demand response.

The optimal power load demand is obtained by calculating the first-order partial derivative:

while the optimal load can be described as:

available electricity price range:

as can be seen from the above formula, for any one electricity purchasing MEMG in the group, the transaction price corresponds to a specific range, which is simply referred to as:

Preb_h∈[Preb_n,h，min,Preb_n,h，max]

it should be noted that when the electricity price is Preb_h≤Preb_n,h，minWithin range, the optimal electrical load requirement is max (ele)_n,h+cl_n,h/COP). Otherwise, if Preb_h≥Preb_n,h,maxThen the optimal load demand is (PV)_n,h+P_wt,n,h+P_mt,n,h-Pe_n,h). I.e. the load is price dependent, with an upper and lower limit.

Similarly, when the MEMG is used as an electricity vendor, the optimal range of the power load demand and the corresponding electricity price range can be derived as follows:

can be simplified as follows:

Pres_h∈[Pres_n,h,min,Pres_n,h,max]

also, for thermal energy, the optimal thermal load demand is obtained by the partial derivative of the thermal load. Corresponding heat load demand ranges, heat purchase price ranges and heat sale price ranges can be obtained, and the formula is as follows:

P_hx,n,h+Q_gb,n,h≤ht_n,h≤max(ht_n,h)

may be abbreviated as:

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

may be abbreviated as:

Prhs_h∈[Prhs_n,h,min,Prhs_n,h,max]

from the above analysis, it can be seen that the utility of IA is a piecewise function:

{PrEs_h≤Pres_h≤Preb_h≤PrEb_h,PrHs_h≤Prhs_h≤Prhb_h≤PrHb_h}

the step 3 specifically comprises the following steps:

this example presents a two-layer MILP model whose method framework is shown in FIG. 3. The present embodiment employs an SGT-based approach to solve this problem, taking into account the benefits of IA. SGT is a special non-cooperative game, with players having a hierarchical relationship, divided into leaders and followers. In the SGT, each player is selfish, in order to maximize his or her own interest. The leader can impose their policy on the follower. The solution for this game is called Stackelberg equalization. In the present embodiment, the IA and each MEMG are considered to be distinct entities with separate objectives to minimize operating costs, which can be described as a single leader and multiple followers, respectively.

Step 3-1: acquiring the electricity and heat purchase prices set by the distribution network,

step 3-2: each multi-energy microgrid is self-optimized,

step 3-3: each multi-energy microgrid uploads the surplus and shortage of cold, heat and electric loads, equipment output optimization, new energy power generation prediction and load demand response range to an intermediate quotient IA,

step 3-4: the intermediary IA formulates the electricity and heat exchange price in the multi-energy microgrid group according to the information fed back by each multi-energy microgrid, in combination with the transaction price of the energy distribution network and with the aim of maximizing the benefit of the intermediary IA,

step 3-5: aiming at the transaction price formulated by the intermediary IA, each multi-energy microgrid carries out self-optimization, carries out demand response, adjusts the demands for cold, heat and electric loads, feeds back the surplus and shortage information to the intermediary IA,

step 3-6: judging whether the convergence condition is reached, if so, executing the step 3-7, otherwise, returning to the step 3-4,

step 3-7: and outputting the optimal game solution set.

The method for self-optimizing the multi-energy microgrid comprises the following steps:

aiming at the price of the energy distribution network, each multifunctional micro-grid optimizes the output of the equipment by taking the self benefit maximization as a target and simultaneously optimizes the load demands on cold, heat and electric energy according to the self energy prediction data and the prediction data of cold, heat and electricity for the micro-grid.

A specific application example is provided below:

as shown in fig. 4, the present embodiment will consider three regional building types of MEMGs, namely MEMG1, MEMG2, and MEMG3, representing hotel, residential, and commercial buildings, respectively. It is assumed that there are both schedulable and non-schedulable distributed generators in the network MEMG. Each MEMG includes a battery energy storage system, a controllable distributed generator, a renewable distributed generator, and an end-user load. If the mutual generation of energy does not enable the self-sufficiency of the MEMGs, a nearby MEMG or concentrated energy system will provide electrical and thermal energy to the defect portion via the IA, and vice versa. In analyzing the DR optimization results, the load requirements of each MEMG will be described in detail below.

Table 1 lists various technical assumptions, including the technical efficiency of the energy device used. Meanwhile, the adjustment range of the controllable multiple loads is set to be 20% of the load prediction curve, and the energy storage operation cost coefficient is 0.02 yuan/kWh. On the other hand, table 2 shows the equipment capacity used in each MEMG. Generally, the capacity of the photovoltaic, WT and cogeneration units of the MEMG2 is highest, while the cell capacity settings are the same for the three parks.

TABLE 1 Equipment parameters

TABLE 2 capacity of force devices

Generally, the energy price category can be divided into two parts, i.e., the price for internal energy trading using IA and the price for energy trading using public energy network according to the optimization result, as shown in Table 3. Due to the adoption of a time-of-use electricity price mechanism, the electricity prices established by the power grid at different time intervals are different. Furthermore, to facilitate adoption of cogeneration units, the price of natural gas can be enjoyed with preferential treatment, while the auxiliary boiler cannot be enjoyed with the same price discount. Furthermore, the way in which the prices of energy for buyback, in particular heating, are determined is often case-by-case highly dependent on negotiations between the relevant stakeholders. Where the buyback price is determined with reference.

TABLE 3 energy prices

Fig. 5 is a graph of solar irradiance and wind speed data as used in this application example. In general, the two sets of data complement each other in one day, with the peak solar irradiance at 1:00 during the day being 0.55kW/m2, and the peak wind speeds at 11:00 and 12:00 during the night being as high as 7.31 m/s.

By utilizing the proposed mixed integer linear programming model, individual optimization and IA benefit-considered cluster optimization can be deduced without considering internal energy transaction price determination, time-sharing energy operation strategy, economic performance and load demand response of each scene. Here, a typical day of the summer season was chosen for detailed analysis for simplicity.

(1) Optimal operation strategy of each MEMG

By using the proposed optimization model, an hourly optimal operation strategy can be obtained, taking MEMG1, MEMG2 and MEMG3 as examples, and fig. 6 and 7 show the electric quantity balance conditions of MEMG1, MEMG2 and MEMG3 when energy exchange in a cell is carried out based on an SGT method and considering IA benefits. It can be seen that in a cluster, MEMG2 generally acts as a power provider, while MEMG1 and MEMG3 both act as power providers. This is mainly due to the higher zone generator capacity employed by MEMG2 and the lower zone generator capacity employed by MEMG1 and MEMG 3. That is, energy exchange between neighborhoods can be realized only when there is a large difference in energy supply and demand among the MEMGs. In addition, the MEMG2 uses a cogeneration unit, but from an economic perspective, it operates only for hours because of its relatively high operating cost compared to photovoltaic and wind power systems. In addition, the MEMG1 and the MEMG3 cogeneration units can be found to be in daily operation, because in the time period, the electricity and heat demands are relatively high, and the cogeneration can generate electricity and electricity at the same time, so that the economic performance is better. In contrast, because nighttime electricity prices are relatively low, individual MEMGs are more inclined to purchase electricity directly from external public energy networks, rather than producing themselves. The MEMG3 has the largest load difference between night and day, the cogeneration unit and the internal exchange energy can meet the daily power demand because the renewable energy is relatively low, and the wind power generation can meet the night energy demand.

On the other hand, the heat balance of the three MEMGs after optimization. Compared with the electricity utilization situation, the operation strategy is relatively simple, and the heat demand is mainly provided by a cogeneration unit, a gas boiler and a heat exchange unit or a combination of the cogeneration unit, the gas boiler and the heat exchange unit. Typically, the MEMG1 acts as a hot buyer, while both MEMG2 and MEMG3 act as hot sellers. For the MEMG1, unlike the case of electricity, the exchange portion accounts for little of the total heat demand. This is because in summer the total heat demand is low relative to the power demand, which also includes power consumption for cooling the load, so most of the heat demand of the MEMG1 can be provided by the cogeneration unit and its own gas boiler. Furthermore, for MEMG2 and MEMG3, the waste heat generated by the cogeneration unit after self-satisfaction will be sold to IA for additional benefit. Notably, the sum of the remaining portions provided by MEMG2 and MEMG3 is higher than the insufficient portion of MEMG1, indicating that the remaining portions are sold to external heat networks.

(2) Demand response analysis

FIG. 8 illustrates load shifting and shifting before and after optimization. It can be seen that the load variation of all three MEMGs is large compared to the initial value. In general, each MEMG tends to reduce its load demand when the energy price is high, and vice versa, since the load response and the energy price are optimized simultaneously. That is, each MEMG wishes to maximize its own profit at each time period through dynamic price and load interaction.

(3) Simulation result of energy trading price

Fig. 9 derives the basic approach to using SGT for optimal energy trading prices. It can be seen that the internal energy trading prices set by the IA proposed herein are between the selling prices and the buyback prices set by the external grid. Wherein, the energy selling price set by the external energy network is the highest, and then the internal selling price, the internal buyback price and the external energy network buyback price are set. In terms of electricity prices, it is evident that the trading prices of IA and distribution networks are substantially the same during periods 0:00-6:00 and 22:00-24: 00. This is because, during this time, the electricity-selling buyback price of the distribution grid is relatively low, and the profit margin of the IA is limited. And the difference is larger during the period of 10:00-22: 00. In the aspect of heat energy, generally the same as electric energy, the external heat distribution network sets the highest heat energy selling price, and then the internal heat energy selling price, the internal heat energy purchasing price and the external heat distribution network heat energy purchasing price are set. Furthermore, it can be concluded that during 0:00-10:00 and 19:00-24:00, the internal heat energy trading price is in agreement with the external heat energy price optimization due to the absence of heat exchange between multiple MEMGs. During the period from 11:00 to 18:00, the internal heat energy repurchase price is higher than the heat energy trading price set by the external heat distribution network, and meanwhile, the internal heat energy selling price is lower than the external heat energy selling price, so that the MEMG can be promoted to exchange residual/insufficient energy with neighborhoods firstly and trade energy with external suppliers, and the trading mode can increase the stability and flexibility of the whole energy system. Furthermore, it is necessary to point out that the prices established by the IAs vary slightly in different scenarios in order to encourage the MEMGs to exchange energy internally, rather than directly with an external energy network.

(4) Total amount of energy trades for the entire cluster

Two comparison scenarios are set in this embodiment:

scene 1: each MEMG is used as an independent individual to perform self-optimization, no IA participates in management and monitoring, and the MEMG directly transacts with an external energy network and is set as IOP.

Scene 2: the energy of each MEMG is cooperatively coupled, is uniformly managed and supervised by IA, and adopts the method to make trade price trade in the cluster, and the trade price is set as SGT.

Fig. 10 shows the energy trading volume between the users in the cluster and the whole MEMG cluster under 2 scenarios. Overall, it can be seen that compared to the benefits when each MEMG is optimized individually, the cluster can be significantly lower in both energy purchase and energy repurchase parts than the total energy traffic directly with external energy networks, especially in SGT-based scenarios. That is, from the overall energy perspective, the proposed three MEMGs exchange a large amount of energy with each other by combining, balancing the surplus and deficit of energy within the cluster. It is noted, however, that the situation is different when electricity and heat are of concern, respectively. For electric energy, the obtained conclusion is consistent with the total energy trading trend in all scenes; for heat energy, whether the purchase part or the repurchase part is the energy purchase part, the heat energy trading volume of the MEMG cluster and the external heat distribution network under the SGT-based scene is smaller than that under the IOP (independent optimization) scene. Under the SGT-based scenario, because the benefits of the MEMG cluster and external energy network transaction are necessarily shared with IA, and the obtained price difference of purchased and sold energy is large, in order to obtain the maximum benefit of the MEMG, each MEMG can balance the energy supply and demand through internal exchange as much as possible.

(5) Revenue analysis of participants under different scenarios

Fig. 11 shows the hourly benefit optimization for each MEMG for 2 scenarios in a typical day. Generally, all MEMGs receive more benefit during the day than during the night. Furthermore, it can be concluded that although in most cases the combination of MEMGs can deduce the highest benefit of the whole cluster, considering the benefits of IA, the results are not constant at all times. For example, the benefit of Independent Optimization (IOP) is significantly higher than in SGT mode over the 8:00-9:00 time period, considering IA benefits scenarios. This is because the goal of optimization is to maximize the benefit of an entire day, rather than every hour.

Fig. 12 shows the hour revenue derived during a typical day when the profit of IA is considered using the SGT based approach. In summary, by using the SGT method, the benefits of the leader-IA and the plurality of follower-MEMGs can be balanced. Obviously, the IA benefit is greater than 0, the higher the better, only when the MEMG is in energy exchange with the external grid. In addition, IA can gain higher profits because peak hours of energy demand are during the day, and the amount of energy exchanged during this time is the greatest, especially during the 11:00-18:00 time. Whereas during periods 1:00,7:00-10:00,22:00-24:00, the IA benefit is equal to 0, since each MEMG can achieve energy autonomy without external assistance.

Table 4 shows the revenue for each stakeholder (including all MEMGs and IAs) over a typical day. It can be seen that in general terms, MEMG2 is most profitable, MEMG1 times, MEMG3 times, which is highly dependent on the amount of energy remaining to bring additional revenue. Again, the benefit of each MEMG is increased by the exchange of energy between them, whether or not the benefit of IA is considered, compared to the case where all MEMGs individually optimize IOP. When considering the benefits of IA, the profit increases for MEMG1, MEMG2, MEMG3 and the whole MEMG cluster by 1.77%, 1.17%, 2.71%, 2.92%, respectively.

TABLE 4 typical day benefit under two scenarios

While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A multi-energy microgrid group and market decision collaborative optimization method is characterized by comprising the following steps:

step 1: constructing a multi-energy micro-grid group system model, which comprises a multi-energy micro-grid group lower layer model for performing MEMG internal optimization and a multi-energy micro-grid group upper layer model for performing market decision optimization;

step 2: constructing a game model based on a master-slave game;

and step 3: and solving the game model based on the double-layer MILP model to obtain an optimal game solution set and output a collaborative optimization strategy.

2. The method of claim 1, wherein the objective function of the multi-energy microgrid group lower model for maximizing the benefits within a certain time period is specifically:

wherein, BeMEMG_nThe benefit for the nth MEMG; a is_n,hMarginal benefit coefficient of nth MEMG in h period; pres_h、Preb_hThe price of selling and purchasing electricity of the MEMG in the h period is respectively; prhs_n,h、Prhb_n,hRespectively the heat sale price and the heat purchase price of the MEMG in the h period; pg_mt、Pg_gbGas prices of a gas turbine and a gas boiler, respectively; c_btThe operation and maintenance coefficient of the storage battery.

3. The method of claim 2, wherein the lower model of the multi-energy microgrid group is provided with an equipment output constraint and an energy balance constraint; the equipment processing constraints comprise output constraints of a cogeneration unit, output constraints of a gas boiler, output constraints of a compression type refrigerator, output constraints of a storage battery, output constraints of photovoltaic equipment, output constraints of fan equipment and heat energy demand response constraints.

4. The method of claim 3, wherein the output constraints of the cogeneration units are specifically as follows:

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

wherein, P_mt,n,hGas turbine generated power for the nth MEMG over a period of h; m_mt,n,hRepresenting the gas consumption of the gas turbine of the nth MEMG in the h period; eta_mtRepresenting the power generation efficiency of the gas turbine; p_hc,n,hIs the heat generation amount r of the heat exchanger of the nth MEMG in the h period_mtIs the thermoelectric ratio; the efficiency of the waste heat boiler; eta_hcEfficiency of the heat exchange device;

the output restriction of the gas boiler is specifically as follows:

Q_gb,n,h＝M_gb,n,hL_ngη_gb

Q_gb,n，min≤Q_gb,n,h≤Q_gb,n,max

wherein Q is_gb,n,hIs the output thermal power of the gas boiler of the nth MEMG during the h period; m_gb,n,hIs the volume of gas consumed by the gas boiler; eta_gbEfficiency of a gas boiler;

the output constraint of the compression type refrigerating machine is specifically as follows:

wherein, cl_n,hIs the cooling load of the nth MEMG during the h period; COP is the refrigeration coefficient; co_ec,n,hThe refrigeration power of the compression refrigerator; co_ec,n,maxThe upper limit of the refrigeration power;

the output constraint of the storage battery is specifically as follows:

wherein, Es_n,hIs the battery charge capacity of the nth MEMG during the h period; bch_n,h、Bdis_n,hRespectively charge power and discharge power; nch and Ndis are respectively charge and discharge efficiency;

the output constraint of the photovoltaic equipment is specifically as follows:

A_PV,n≤A_PV,n,max

0≤PV_n,h≤A_PV,nIi_hη_PV

wherein A is_PV,nA photovoltaic mounting area for an nth MEMG; a. the_PV,n,maxThe maximum installation area; PV (photovoltaic)_n,hPhotovoltaic power generation power for the nth MEMG;

the output constraint of the fan equipment is specifically as follows:

0≤P_wt,n,h≤P_wt,n,max

wherein, P_wt,n,hThe generated power of the fan; p_wt,n,maxIs the maximum generated power;

the thermal energy demand response constraint is specifically:

wherein ele_n,h、ht_n,h、cl_n,hThe electricity, heat and cold loads of the nth MEMG in the h period respectively; ele_n,h,max、Ht_n,h,max、Cl_n,h,maxUpper limits of electric, thermal and cooling loads of the nth MEMG in the h period respectively; ele_n,h,min、Ht_n,h,min、Cl_n,h,minLower limits of electric, thermal and cooling loads of the nth MEMG in the h period respectively; p is a load reduction coefficient.

5. The method of claim 3, wherein the energy balance constraints are specifically:

hts_n,h+ht_n,h＝htb_n,h+P_hx,n,h+Q_gb,n,h

wherein, els_n,h、elb_n,hRespectively selling electricity and purchasing electricity power of the nth MEMG in the h period; hts_n,h、htb_n,hThe heat selling power and the heat purchasing power of the nth MEMG in the h period are respectively.

6. The method of claim 1, wherein an objective function of the upper model of the multi-energy microgrid group is as follows:

wherein, El_h、Hl_hRespectively representing the total amount of electric and thermal transactions between the intermediary IA and the external network at a certain moment; els_hAnd Elb_hRespectively, the total electric quantity sold by the IA to the external network and the total electric quantity purchased from the external network at a certain moment; hts_hAnd Htb_hRespectively representing the total heat sold by the IA to the external heat net at a certain moment and the total heat purchased from the external heat net.

7. The method of claim 6, wherein the upper model of the multi-energy microgrid group is provided with energy trading constraints, and specifically comprises:

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

wherein el is_n,hNet electric power for the nth MEMG over a period of h; keb_n,h、kes_n,hRespectively is a 0-1 variable of electricity purchasing and selling of the nth MEMG, and a 1 represents that the power purchasing or selling state is in; khb_n,h、khs_n,hThe variables of 0-1 for the heat of purchase and the heat of sale of the nth MEMG respectively are 1, and the variables are in the heat of purchase or heat of sale state; preb_h、Pres_hThe electricity prices of the MEMG for buying and selling back to the IA are respectively; wherein, PrEb_hAnd PrEs_hRespectively represent the electricity prices of the IA for purchasing and selling electricity from and to the power grid; all in oneLike, Prhb_hAnd Prhs_hRepresenting heat prices for the MEMG to buy and sell back to IA, respectively; PrHb_hAnd PrHs_hIndicating the heat prices at which IA purchases heat from and sells heat to external heat grids.

8. The method of claim 1, wherein the step 2 specifically comprises:

when the IA is an entity, aiming at earning an intermediate spread, the competitive interest relationship between the IA and the MEMG cluster may be described as a leader and multiple followers in the SGT, and may be described as:

wherein IA represents the leader of the game; { MEMG } represents a follower of the game; { Ame_n,h∪Amh_n,hRespectively representing the residual electric quantity and the residual heat energy of each MEMG; { Pres_h},{Preb_h},{Prhs_h},{Prhb_hThe set represents the internal price strategy made by IA, BeMEMG_n,h},{BeIA_hIs the target benefit function of the transaction body;

the balance points for game F are:

9. the method of claim 1, wherein the step 3 specifically comprises:

step 3-1: acquiring the electricity and heat purchase prices set by the distribution network;

step 3-2: self-optimizing each multifunctional microgrid;

step 3-3: each multi-energy micro grid uploads the surplus and shortage of cold, heat and electric loads, equipment output optimization, new energy power generation prediction and a load demand response range to an intermediate quotient IA;

step 3-4: the intermediate agent IA establishes electricity and heat exchange price in the multi-energy micro-grid group according to the information fed back by each multi-energy micro-grid and by combining the transaction price with the energy distribution network and aiming at maximizing the benefit of the intermediate agent IA;

step 3-5: aiming at the transaction price formulated by the intermediary IA, each multi-energy microgrid is self-optimized, demand response is carried out, the demands for cold, heat and electric loads are adjusted, and the surplus and shortage information is fed back to the intermediary IA;

step 3-6: judging whether a convergence condition is reached, if so, executing the step 3-7, otherwise, returning to the step 3-4;

step 3-7: and outputting the optimal game solution set.

10. The method of claim 9, wherein the method for self-optimizing the multi-energy microgrid group and the market decision comprises:

aiming at the price of the energy distribution network, each multifunctional micro-grid optimizes the output of the equipment by taking the self benefit maximization as a target and simultaneously optimizes the load demands on cold, heat and electric energy according to the self energy prediction data and the prediction data of cold, heat and electricity for the micro-grid.

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