CN111210054A - An optimal scheduling method for micro-energy network considering the uncertainty of direct load control - Google Patents

An optimal scheduling method for micro-energy network considering the uncertainty of direct load control Download PDF

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CN111210054A
CN111210054A CN201911332508.0A CN201911332508A CN111210054A CN 111210054 A CN111210054 A CN 111210054A CN 201911332508 A CN201911332508 A CN 201911332508A CN 111210054 A CN111210054 A CN 111210054A
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朱兰
牛培源
杨秋霖
姬星羽
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Shanghai University of Electric Power
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Abstract

本发明涉及一种考虑直接负荷控制不确定性的微能源网优化调度方法，包括以下步骤：步骤1：建立考虑不确定性的直接负荷控制模型；步骤2：获取微能源网的参数；步骤3：建立微能源网的功率平衡方程及设备的模型；步骤4：根据功率平衡方程和各设备的模型，建立考虑直接负荷控制不确定性和风、光出力不确定性的微能源网鲁棒优化调度模型；步骤5：将含有随机参数的鲁棒优化调度模型转化为确定性优化模型；步骤6：求解模型最优解，进行微能源网调度。与现有技术相比，本发明具有利于系统经济优化运行、防止优化结果过于乐观等优点。

Figure 201911332508

The invention relates to a method for optimizing and dispatching a micro-energy network considering the uncertainty of direct load control, comprising the following steps: step 1: establishing a direct load control model considering the uncertainty; step 2: acquiring parameters of the micro-energy network; step 3 : Establish the power balance equation of the micro-energy grid and the model of the equipment; Step 4: According to the power balance equation and the models of each equipment, establish a robust optimal scheduling of the micro-energy grid considering the uncertainty of direct load control and the uncertainty of wind and light output model; Step 5: Convert the robust optimal scheduling model with random parameters into a deterministic optimization model; Step 6: Solve the optimal solution of the model and perform micro-energy grid scheduling. Compared with the prior art, the present invention has the advantages of being beneficial to the economical optimization of the system, preventing the optimization result from being too optimistic, and the like.

Figure 201911332508

Description

Micro-energy network optimization scheduling method considering direct load control uncertainty
Technical Field
The invention relates to the field of micro energy network optimization scheduling, in particular to a micro energy network optimization scheduling method considering direct load control uncertainty.
Background
Direct Load Control (DLC) is an incentive type demand response, typically implemented for Thermal Control Loads (TCLs) with thermal storage capability among residential or small commercial users, such as air conditioners, electric water heaters, controlled loads that are disconnected from the system during an interruption period and reconnected to the system after the interruption period ends, forming a rebound load.
The existing micro energy network optimization scheduling method only considers the influence of the randomness of the output of renewable energy sources such as wind and light on the optimization scheduling result, less considers the direct load control to participate in the optimization scheduling, and does not consider the uncertainty of the controlled load participating in the direct load control. In the optimization operation process of the micro-energy network, due to the parameter deviation of the electric appliance at the user side, the prediction error of environmental factors and the cognitive difference of the participating users, the uncertainty of the participating users in the direct load control also needs to be considered.
The invention discloses a weak robustness optimal scheduling method for a micro energy network in Chinese patent publication No. CN108491977A, which can be suitable for optimal scheduling of the micro energy network under uncertain parameters and ensure safe operation of a system. However, the method does not consider the influence of uncertainty of controlled load power on optimization scheduling when a user participates in direct load control and participates in direct load control, and the adjustment between the optimality and the robustness of an optimization result is not flexible enough.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a micro energy network optimization scheduling method which is beneficial to the economic optimization operation of a system and prevents the optimization result from being too optimistic and considers the direct load control uncertainty.
The purpose of the invention can be realized by the following technical scheme:
a micro energy network optimization scheduling method considering direct load control uncertainty comprises the following steps:
step 1: establishing a direct load control model considering uncertainty;
step 2: acquiring parameters of the micro energy network;
and step 3: establishing a power balance equation of the micro energy network and a model of the equipment;
and 4, step 4: according to a power balance equation and models of all equipment, a micro energy network robust optimization scheduling model considering direct load control uncertainty and wind and light output uncertainty is established;
and 5: converting a robust optimization scheduling model containing random parameters into a deterministic optimization model;
step 6: and solving the optimal solution of the model, and scheduling the micro energy network.
Preferably, the expression of the direct load control model considering the uncertainty in step 1 is as follows:
Figure BDA0002330043960000021
wherein t is 1, 2., N is the total number of scheduling periods; pLD,tAnd PL,tRespectively a controlled afterload demand and an original demand at a time t; xiNumber of groups of controlled loads for period i; pC,iFor the power of each set of controlled loads during period i,
Figure BDA0002330043960000023
the random parameter represents that each group of controlled load power has uncertainty; d is the number of control periods of the controlled load; gt-d+1-iA compensation strategy coefficient for a rebound load; c. Ct+1-iD is equal to or less than 1 when t +1-i is equal to or less than 0 otherwise; a is0Max {1, t-d +1 }; when t is greater than or equal to d +1, a1＝max{1,t-d-h+1}，a2T-d, otherwise, a1＝0，a20; h is the compensation time interval number corresponding to the single-time interval control strategy.
Preferably, the parameters of the micro energy grid in step 2 include grid structure parameters, energy type, unit cost information, user power demand, and renewable energy output information.
More preferably, the grid structure comprises a power grid, a renewable power generator set, a micro-combustion engine, a natural gas grid, a gas boiler, an electric gas conversion device P2G, a waste heat boiler and an energy storage device; the renewable generating set comprises a wind generating set and a photovoltaic cell; the energy storage device comprises an electricity storage device, an air storage device and a heat storage device.
More preferably, the step 3 specifically includes:
step 3-1: establishing a power balance equation comprising an electric power balance equation, a thermal power balance equation and an air power balance equation;
the electric power balance equation is concretely as follows:
Figure BDA0002330043960000022
wherein,
Figure BDA0002330043960000024
the output of the ith generator set in the time period t is I-3, I-1, 2 and 3 respectively represent three micro power supplies of wind, light and a micro combustion engine; pES,ch,tAnd PES,dis,tRespectively charging power and discharging power, P, of the electric storage deviceEB,tFor the power consumption of the electric boiler, P, during a period of time tP2G,tFor a period of t P2G power consumption of the device, PBGEX,tAnd PSGEX,tRespectively purchasing power from the main network and selling power to the main network for the micro energy network system in the t period;
the qi power balance equation is specifically as follows:
Figure BDA00023300439600000310
wherein G isg,tThe gas purchasing power of the micro energy network in the period of t, GP2G,tIs the gas generation power of P2G at t period GGS,ch,tAnd GGS,dis,tRespectively charging and discharging gas power G for gas storage equipmentL,tIn order to meet the natural gas demand of the user,
Figure BDA00023300439600000311
for gas consumption of micro-combustion engines, GGB,tThe gas consumption power of the gas boiler;
the thermal power balance equation is specifically as follows:
Figure BDA00023300439600000312
wherein Q isGB,tAnd QEB,tHeating powers of a gas boiler and an electric boiler in a period t respectively,
Figure BDA00023300439600000313
heating power for micro-combustion engine, QHS,ch,tAnd QHS,dis,tRespectively charging and discharging heat power, Q, of the heat storage equipmentL,tA thermal load demand for a period t;
step 3-2: establishing a model of energy conversion equipment, including a micro-gas turbine model, an electric gas conversion device model and a gas and heat boiler model;
the micro-combustion engine model expression is as follows:
Figure BDA0002330043960000031
in the formula:
Figure BDA0002330043960000035
and ηEHRespectively the power generation efficiency, the heating efficiency and the waste heat recovery efficiency of a waste heat boiler of the micro-gas turbine,
Figure BDA0002330043960000036
in order to achieve the heating power of the micro-combustion engine after passing through the waste heat boiler,
Figure BDA0002330043960000034
and
Figure BDA0002330043960000037
respectively the upper and lower limits of the output of the micro-combustion engine and the upper and lower limits of the climbing slope;
the electric gas conversion device model expression is as follows:
Figure BDA0002330043960000032
in the formula ηP2GIs the comprehensive energy conversion effect of the P2G deviceRate;
Figure BDA0002330043960000038
and
Figure BDA0002330043960000039
the upper and lower limits of the P2G device.
The expression of the gas boiler model is as follows:
Figure BDA0002330043960000033
in the formula ηGBFor the energy conversion efficiency, Q, of gas-fired boilersGB,maxAnd QGB,minRespectively the upper and lower limits of the output, delta Q, of the gas-fired boilerGB,max、ΔQGB,minThe upper limit and the lower limit of the climbing slope of the gas boiler are respectively set;
the electric boiler model expression is as follows:
Figure BDA0002330043960000041
in the formula ηEBFor the energy conversion efficiency of electric boilers, QEB,maxAnd QEB,minRespectively the upper and lower limits of the output, delta Q, of the electric boilerEB,maxAnd Δ QEB,minThe upper limit and the lower limit of the climbing slope of the electric boiler are respectively set;
step 3-3: establishing a model of energy storage equipment, including an electric energy storage equipment model, a thermal energy storage equipment model and a gas energy storage model;
the model expressions are as follows:
Figure BDA0002330043960000042
wherein E isS,tEnergy storage capacity for electricity, heat or gas for a period of t, ES,maxAnd ES,minRespectively the upper and lower limits of the energy storage capacity of electricity, heat or gas, tau is the self-loss rate of the stored energy of electricity, heat or gas, Pch,tAnd Pdis,tCharging and discharging power of electric, thermal or gas energy storage devices, respectively ηchAnd ηdisRespectively electric, thermal or pneumatic energy storage charge-discharge efficiency, Pch,max、Pch,min、Pdis,maxAnd Pdis,minRespectively are the upper and lower limits of the charge-discharge power of electricity, heat or gas energy storage;
the constraint formula of the energy storage equipment model is as follows:
ET＝E0
wherein E is0And ETEnergy storage capacity of electricity, heat or gas at the beginning and end of a scheduling period respectively;
step 3-4: according to the output information of the renewable energy, establishing the constraint of the model, specifically:
wind or light output constraints:
Figure BDA0002330043960000043
wherein,
Figure BDA0002330043960000045
and
Figure BDA0002330043960000046
respectively the minimum and maximum technical output of the i-th renewable generator set,
Figure BDA0002330043960000048
and
Figure BDA0002330043960000047
respectively representing the power of the fan unit and the photovoltaic cell, PW,tAnd PPV,tRespectively predicting power of wind power and photovoltaic power at a time t;
abandon wind and abandon the restraint formula of light:
Figure BDA0002330043960000044
wherein, piWAnd piPVThe maximum allowable wind-light abandoning ratio is obtained.
Preferably, the objective function of the micro energy grid robust optimization scheduling model in step 4 is as follows:
min C＝min(CE+CG+CD)
the calculation method of each item in the objective function specifically includes:
Figure BDA0002330043960000051
where Δ T is the optimized time interval, T is the total number of scheduling periods, CE、CGAnd CDRespectively compensating costs for electric energy interaction costs, natural gas purchase costs and controlled load interruption, PBGEX,tAnd PSGEX,tRespectively purchasing power from the main network and selling power to the main network for the micro energy network system in the t period CBE,tAnd CSE,tPrice of electricity purchased and sold, respectively, for micro-energy grid and main grid, CgasCost per unit of energy for purchasing natural gas, Gg,tThe gas purchasing power of the micro energy network in the period of t, Ccon,tThe price per unit of controlled load interruption is compensated for t periods.
Preferably, the specific steps of step 5 are:
step 5-1: constructing an uncertain set of random parameters of controlled load power, wind predicted power and light predicted power by using the (p, w) -norm uncertain set;
the expression of the (p, w) -norm uncertainty set is:
Figure BDA0002330043960000052
wherein, aiIs the i-th row random parameter of the coefficient matrix, aikIs aiThe k-th element of (a) the first,
Figure BDA0002330043960000056
Figure BDA0002330043960000057
is aiThe average value of the k-th element in (c),
Figure BDA0002330043960000058
and
Figure BDA0002330043960000059
are respectively the indexes of the fluctuation amount,
Figure BDA00023300439600000510
and
Figure BDA00023300439600000511
the deviation of the maximum/minimum value of the random parameter from the mean value, respectively, i.e.
Figure BDA00023300439600000512
p1,iAs an indicator of robustness, JiIs the set of random parameters, | J, in the ith row of matrix AiL is JiThe number of middle elements;
the uncertain sets of the wind power prediction power random parameter, the photoelectric prediction power random parameter and the controlled load power random parameter are specifically as follows:
Figure BDA0002330043960000053
Figure BDA0002330043960000054
Figure BDA0002330043960000055
wherein, PC,t、PW,tAnd PPV,tRespectively a controlled load power, a wind power prediction power and a photoelectric prediction power,
Figure BDA0002330043960000064
and
Figure BDA0002330043960000065
for the desired value and deviation of the controlled load power t period,
Figure BDA0002330043960000066
and
Figure BDA0002330043960000067
respectively an upper limit and a lower limit of the controlled load power deviation,
Figure BDA0002330043960000068
and
Figure BDA0002330043960000069
respectively a predicted power expected value and an actual power deviation value of the wind turbine generator in a time period t,
Figure BDA00023300439600000610
and
Figure BDA00023300439600000611
respectively a predicted expected power value and an actual power deviation value of the photovoltaic cell in a period t,
Figure BDA00023300439600000612
and
Figure BDA00023300439600000613
respectively are the upper limit and the lower limit of the deviation of the power generated by the wind turbine generator,
Figure BDA00023300439600000616
respectively the upper limit and the lower limit of the deviation of the power generated by the photovoltaic cell;
Figure BDA00023300439600000614
and
Figure BDA00023300439600000615
respectively predicting fluctuation indexes of the power random parameters for the wind,
Figure BDA00023300439600000618
and
Figure BDA00023300439600000617
respectively predicting power randomness for lightAn index of the amount of fluctuation of the parameter,
Figure BDA00023300439600000619
and
Figure BDA00023300439600000620
respectively are fluctuation indexes of random parameters of the controlled load power;
step 5-2: transforming a constraint condition containing random parameters and an objective function, specifically:
setting a new objective function as Z, adding a constraint Z- (C)E+CG+CD) More than or equal to 0, namely:
Figure BDA0002330043960000061
the new objective function is:
min Z
step 5-3: and converting uncertainty constraint in the robust optimization model into certainty constraint according to the robust linear optimization theory under the (p, w) -norm uncertainty set.
According to the robust linear optimization theory under the (p, w) -norm uncertain set, by introducing a dual variable z1(t)、q1(t,k)、z2(t) and q2(t, k), the electric power balance equation can be converted into a robust peer-to-peer model, and the expression is as follows:
Figure BDA0002330043960000062
wherein p is1,t、p2,tIn order to be an indicator of the robustness,
Figure BDA00023300439600000621
for the fluctuation index of the random parameter of the controlled load power, by controlling p1,t、p2,tAnd
Figure BDA00023300439600000622
is adjusted between robustness and optimality.
Will be a formula
Figure BDA0002330043960000063
Converting into a robust peer-to-peer model, wherein the expression is specifically as follows:
Figure BDA0002330043960000071
wherein p is3In order to be an indicator of the robustness,
Figure BDA0002330043960000075
is an index of the amount of fluctuation, z3、q3,kThe new decision variables introduced for robust peer-to-peer transformation have no practical physical significance.
The wind or light output constraint formula and the wind or light abandon constraint formula also contain random parameters, and the robust equivalent formula after the robust equivalent transformation is specifically as follows:
Figure BDA0002330043960000072
Figure BDA0002330043960000073
wherein p is4And p5In order to be an indicator of the robustness,
Figure BDA0002330043960000076
and
Figure BDA0002330043960000077
predicting the fluctuation index of the power random parameter for the wind power,
Figure BDA0002330043960000078
and
Figure BDA0002330043960000079
predicting a power random parameter fluctuation amount index, z, for photovoltaic4,t、z5,t、q4,kAnd q is5,kFor robust pairAnd transforming the introduced new decision variables.
The robust peer-to-peer type of the wind/light abandoning constraint type is specifically as follows:
Figure BDA0002330043960000074
Figure BDA0002330043960000081
wherein p is6And p7In order to be an indicator of the robustness,
Figure BDA0002330043960000082
and
Figure BDA0002330043960000083
predicting the fluctuation index of the power random parameter for the wind power,
Figure BDA0002330043960000084
and
Figure BDA0002330043960000085
predicting a power random parameter fluctuation amount index, z, for photovoltaic6、z7、q6,tAnd q is7,tNew decision variables are introduced for robust peer-to-peer transformation.
Preferably, the optimal solution in step 6 is obtained by Lingo.
Compared with the prior art, the invention has the following advantages:
firstly, the invention brings the response of the demand side of the user side into the consideration range of the optimized scheduling, and the optimized result also shows that the direct load control can reduce the operation cost, thereby being beneficial to the economic optimized operation of the system.
The influence of the response uncertainty of the demand side and the uncertainty of the wind and light predicted output on the optimization result is considered at the same time, and the optimization result is prevented from being too optimistic.
And thirdly, controlling the relative value of the random parameter offset through a robustness index and controlling the size of a random parameter fluctuation interval through a fluctuation amount index by using the (p, w) -norm uncertain set used by the method. If the robustness index and the fluctuation index are too small, the operation cost is low, but the robustness is weak; if the setting is too large, the robustness is strong, but the cost is high. When the micro energy network is scheduled, a proper robustness index and a proper fluctuation index can be selected according to actual conditions, and adjustment is made between the optimality and the robustness of results.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic representation of a grid structure according to the present invention;
FIG. 3 is a diagram illustrating a typical winter solar wind, light output, air, electricity and heat load prediction in an embodiment of the present invention;
fig. 4 is a graph of controlled load for different robustness indicators 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 invention relates to a micro energy network optimization scheduling method considering direct load control uncertainty, which comprises the following steps as shown in figure 1:
step 1: establishing a direct load control model considering uncertainty;
step 2: acquiring parameters of the micro energy network;
and step 3: establishing a power balance equation of the micro energy network and a model of the equipment;
and 4, step 4: according to a power balance equation and models of all equipment, a micro energy network robust optimization scheduling model considering direct load control uncertainty and wind and light output uncertainty is established;
and 5: converting a robust optimization scheduling model containing random parameters into a deterministic optimization model;
step 6: and solving the optimal solution of the model through Lingo software to carry out micro energy network scheduling.
The expression of the direct load control model considering uncertainty in the step 1 is as follows:
Figure BDA0002330043960000091
wherein t is 1, 2., N is the total number of scheduling periods; pLD,tAnd PL,tRespectively a controlled afterload demand and an original demand at a time t; xiNumber of groups of controlled loads for period i; pC,iFor the power of each set of controlled loads during period i,
Figure BDA0002330043960000093
the random parameter represents that each group of controlled load power has uncertainty; d is the number of control periods of the controlled load; gt-d+1-iA compensation strategy coefficient for a rebound load; c. Ct+1-iD is equal to or less than 1 when t +1-i is equal to or less than 0 otherwise; a is0Max {1, t-d +1 }; when t is greater than or equal to d +1, a1＝max{1,t-d-h+1}，a2T-d, otherwise, a1＝0，a20; h is the compensation time interval number corresponding to the single-time interval control strategy.
And the parameters of the micro energy network in the step 2 comprise grid structure parameters, energy types, unit cost information, user power requirements and renewable energy output information.
The grid structure considered in the present embodiment is shown in fig. 2, and includes a power grid, a renewable power generator set, a micro-combustion engine MT, a natural gas grid, a gas boiler GB, an electric boiler EB, an electric gas conversion device P2G, a waste heat boiler, and an energy storage device. The renewable power generation unit comprises a wind power generation unit WT and a photovoltaic cell PV, and the energy storage device comprises an energy storage device ES, an energy storage device GS and an energy storage device HS.
The step 3 specifically comprises the following steps:
step 3-1: establishing a power balance equation comprising an electric power balance equation, a thermal power balance equation and an air power balance equation;
the electric power balance equation is concretely as follows:
Figure BDA0002330043960000092
wherein,
Figure BDA0002330043960000104
the output of the ith generator set in the time period t is I-3, I-1, 2 and 3 respectively represent three micro power supplies of wind, light and a micro combustion engine; pES,ch,tAnd PES,dis,tRespectively charging power and discharging power, P, of the electric storage deviceEB,tFor the power consumption of the electric boiler, P, during a period of time tP2G,tFor a period of t P2G power consumption of the device, PBGEX,tAnd PSGEX,tRespectively purchasing power from the main network and selling power to the main network for the micro energy network system in the t period;
the gas power balance model specifically comprises the following steps:
Figure BDA00023300439600001011
wherein G isg,tThe gas purchasing power of the micro energy network in the period of t, GP2G,tIs the gas generation power of P2G at t period GGS,ch,tAnd GGS,dis,tRespectively charging and discharging gas power G for gas storage equipmentL,tIn order to meet the natural gas demand of the user,
Figure BDA00023300439600001012
for gas consumption of micro-combustion engines, GGB,tThe gas consumption power of the gas boiler;
the thermal power balance model specifically comprises:
Figure BDA00023300439600001013
wherein Q isGB,tAnd QEB,tHeating powers of a gas boiler and an electric boiler in a period t respectively,
Figure BDA00023300439600001014
heating power for micro-combustion engine, QHS,ch,tAnd QHS,dis,tRespectively charging and discharging heat power, Q, of the heat storage equipmentL,tA thermal load demand for a period t;
step 3-2: establishing a model of energy conversion equipment, including a micro-gas turbine model, an electric gas conversion device model and a gas and electric boiler model;
the micro-combustion engine model expression is as follows:
Figure BDA0002330043960000101
in the formula:
Figure BDA0002330043960000105
and
Figure BDA0002330043960000106
respectively the power generation efficiency, the heating efficiency and the waste heat recovery efficiency of a waste heat boiler of the micro-gas turbine,
Figure BDA0002330043960000107
in order to achieve the heating power of the micro-combustion engine after passing through the waste heat boiler,
Figure BDA0002330043960000108
the output upper limit and the output lower limit of the micro-combustion engine and the climbing upper limit and the climbing lower limit are set;
the electric gas conversion device model expression is as follows:
Figure BDA0002330043960000102
in the formula ηP2GThe overall energy conversion efficiency of the P2G plant;
Figure BDA0002330043960000109
and
Figure BDA00023300439600001010
the upper and lower limits of the P2G device.
The expression of the gas boiler model is as follows:
Figure BDA0002330043960000103
in the formula ηGBFor the energy conversion efficiency, Q, of gas-fired boilersGB,max、QGB,minRespectively the upper and lower limits of the output, delta Q, of the gas-fired boilerGB,max、ΔQGB,minThe upper limit and the lower limit of the climbing slope of the gas boiler are respectively set;
the electric boiler model expression is as follows:
Figure BDA0002330043960000111
in the formula ηEBFor the energy conversion efficiency of electric boilers, QEB,max、QEB,minRespectively the upper and lower limits of the output, delta Q, of the electric boilerEB,max、ΔQEB,minThe upper limit and the lower limit of the climbing slope of the electric boiler are respectively set;
step 3-3: establishing a model of energy storage equipment, including an electric energy storage equipment model, a thermal energy storage equipment model and a gas energy storage model;
the model expressions are as follows:
Figure BDA0002330043960000112
wherein E isS,tEnergy storage capacity for electricity, heat or gas for a period of t, ES,maxAnd ES,minRespectively the upper and lower limits of the energy storage capacity of electricity, heat or gas, tau is the self-loss rate of the stored energy of electricity, heat or gas, Pch,tAnd Pdis,tCharging and discharging power of electric, thermal or gas energy storage devices, respectively ηchAnd ηdisRespectively electric, thermal or gas energy storage charge-discharge efficiency, Pch,max、Pch,min、Pdis,maxAnd Pdis,minRespectively are the upper and lower limits of charge-discharge power of electric, thermal or gas energy storage;
the constraint formula of the energy storage equipment model is as follows:
ET＝E0
wherein E is0And ETEnergy storage capacity of electricity, heat or gas at the beginning and end of a scheduling period respectively;
step 3-4: according to the output information of the renewable energy, establishing the constraint of the model, specifically:
wind or light output constraints:
Figure BDA0002330043960000113
wherein,
Figure BDA0002330043960000114
and
Figure BDA0002330043960000115
respectively the minimum and maximum technical output of the i-th renewable generator set,
Figure BDA0002330043960000116
and
Figure BDA0002330043960000117
respectively representing the power of the fan unit and the photovoltaic cell, PW,tAnd PPV,tRespectively predicting power of wind power and photovoltaic power at a time t;
abandon wind and abandon the restraint formula of light:
Figure BDA0002330043960000121
wherein, piWAnd piPVThe maximum allowable wind-light abandoning ratio is obtained.
The objective function of the micro energy network robust optimization scheduling model in the step 4 is as follows:
min C＝min(CE+CG+CD)
the calculation method of each item in the objective function specifically includes:
Figure BDA0002330043960000122
where Δ T is the optimized time interval, T is the total number of scheduling periods, CE、CGAnd CDRespectively compensating costs for electric energy interaction costs, natural gas purchase costs and controlled load interruption, PBGEX,tAnd PSGEX,tRespectively purchasing power from the main network and selling power to the main network for the micro energy network system in the t period CBE,tAnd CSE,tPrice of electricity purchased and sold, respectively, for micro-energy grid and main grid, CgasCost per unit of energy for purchasing natural gas, Gg,tThe gas purchasing power of the micro energy network in the period of t, Ccon,tThe price per unit of controlled load interruption is compensated for t periods.
The specific steps of the step 5 are as follows:
step 5-1: constructing an uncertain set of random parameters of controlled load power, wind predicted power and light predicted power by using the (p, w) -norm uncertain set;
the expression of the (p, w) -norm uncertainty set is:
Figure BDA0002330043960000123
wherein, aiIs the i-th row random parameter of the coefficient matrix, aikIs aiThe k-th element of (a) the first,
Figure BDA0002330043960000124
Figure BDA0002330043960000125
is aiThe average value of the k-th element in (c),
Figure BDA0002330043960000126
and
Figure BDA0002330043960000127
are respectively the indexes of the fluctuation amount,
Figure BDA0002330043960000128
and
Figure BDA0002330043960000129
the deviation of the maximum/minimum value of the random parameter from the mean value, respectively, i.e.
Figure BDA00023300439600001210
p1,iAs an indicator of robustness, JiIs the set of random parameters, | J, in the ith row of matrix AiL is JiNumber of middle elements, variable αikBy a robustness indicator p1,iDetermining when the robustness indicator p1,i＝|JiIn | the robustness of the optimization result is strongest, p1,iThe smaller, the less robust, when p1,iWhen 0, the model becomes a deterministic model.
The uncertain sets of the controlled load power random parameter, the wind power prediction power random parameter and the photoelectric prediction power random parameter are specifically as follows:
Figure BDA0002330043960000131
Figure BDA0002330043960000132
Figure BDA0002330043960000133
wherein, PC,t、PW,tAnd PPV,tRespectively a controlled load power, a wind power prediction power and a photoelectric prediction power,
Figure BDA0002330043960000135
and
Figure BDA0002330043960000136
for the desired value and deviation of the controlled load power t period,
Figure BDA0002330043960000137
and
Figure BDA0002330043960000138
respectively an upper limit and a lower limit of the controlled load power deviation,
Figure BDA0002330043960000139
and
Figure BDA00023300439600001310
respectively a predicted power expected value and an actual power deviation value of the wind turbine generator in a time period t,
Figure BDA00023300439600001311
and
Figure BDA00023300439600001312
respectively a predicted expected power value and an actual power deviation value of the photovoltaic cell in a period t,
Figure BDA00023300439600001313
and
Figure BDA00023300439600001314
respectively are the upper limit and the lower limit of the deviation of the power generated by the wind turbine generator,
Figure BDA00023300439600001319
respectively the upper limit and the lower limit of the deviation of the power generated by the photovoltaic cell;
Figure BDA00023300439600001315
and
Figure BDA00023300439600001316
respectively predicting fluctuation indexes of the power random parameters for the wind,
Figure BDA00023300439600001320
and
Figure BDA00023300439600001321
respectively as fluctuation indexes of random parameters of the light prediction power,
Figure BDA00023300439600001317
and
Figure BDA00023300439600001318
respectively are fluctuation indexes of random parameters of the controlled load power;
step 5-2: transforming a constraint condition containing random parameters and an objective function, specifically:
setting a new objective function as Z, adding a constraint Z- (C)E+CG+CD) More than or equal to 0, namely:
Figure BDA0002330043960000134
the new objective function is:
min Z
step 5-3: converting uncertainty constraint in the robust optimization model into certainty constraint according to the robust linear optimization theory under the (p, w) -norm uncertainty set,
according to the robust linear optimization theory under the (p, w) -norm uncertain set, by introducing a dual variable z1(t)、q1(t,k)、z2(t) and q2(t, k), the electric power balance equation can be converted into a robust peer-to-peer model, and the expression is as follows:
Figure BDA0002330043960000141
wherein p is1,t、p2,tIn order to be an indicator of the robustness,
Figure BDA0002330043960000145
for the fluctuation index, p can be controlled1,t、p2,tAnd
Figure BDA0002330043960000146
is adjusted between robustness and optimality.
In the same way, the formula can also be used
Figure BDA0002330043960000142
Converting into a robust peer-to-peer model, wherein the expression is specifically as follows:
Figure BDA0002330043960000143
wherein p is3In order to be an indicator of the robustness,
Figure BDA0002330043960000147
is an index of the amount of fluctuation, z3、q3,kThe new decision variables introduced for robust peer-to-peer transformation have no practical physical significance.
In addition, the wind or light output constraint and the wind or light curtailment constraint include random parameters, and robust equivalent transformation can be performed in the same manner.
The robust equivalence of the wind/light output constraint is specifically:
Figure BDA0002330043960000144
Figure BDA0002330043960000151
wherein p is4、p5In order to be an indicator of the robustness,
Figure BDA0002330043960000155
predicting the fluctuation index of the power random parameter for the wind power,
Figure BDA0002330043960000156
predicting a power random parameter fluctuation amount index, z, for photovoltaic4,t、z5,t、q4,k、q5,kThe new decision variables introduced for robust peer-to-peer transformation have no practical physical significance.
The robust peer-to-peer type of the wind/light abandoning constraint type is specifically as follows:
Figure BDA0002330043960000152
Figure BDA0002330043960000153
wherein p is6、p7In order to be an indicator of the robustness,
Figure BDA0002330043960000157
predicting the fluctuation index of the power random parameter for the wind power,
Figure BDA0002330043960000158
predicting a power random parameter fluctuation amount index, z, for photovoltaic6、z7、q6,t、q7,tThe new decision variables introduced for robust peer-to-peer transformation have no practical physical significance.
In this embodiment, taking the micro energy grid structure in fig. 2 as an example, the micro energy grid operation parameters are shown in table 1, and the typical winter solar wind power generation, gas, electricity and heat load prediction is shown in fig. 3. The total number of the selected scheduling time is 24 hours, the unit scheduling time is 15 minutes, and the controlled load is controlled within a period of 06: 00-24: 00. The average value of the power prediction of each group of controlled loads participating in direct load control is 5kW, the compensation coefficient of the DLC of the single-period control strategy is 222, namely compensation is carried out in three periods, and the compensation power of each period is twice the reduction power of the DLC. And selecting the wind and light prediction error fluctuation proportion and the controlled load power proportion to be +/-20%.
TABLE 1
Figure BDA0002330043960000154
Figure BDA0002330043960000161
The robustness indexes of the wind and light prediction random output parameters are all 75% of the maximum value, the fluctuation index is 1, the robustness indexes of the controlled load random variables are 0%, 25%, 50%, 75% and 100%, and the fluctuation index is 0.5, the controlled load curves under different robustness indexes are obtained and are shown in the graph 4, and meanwhile, the situation that the micro-energy network does not contain DLC is considered, and the operation cost of the micro-energy network is obtained and is shown in the table 2.
TABLE 2
Figure BDA0002330043960000162
As can be seen from fig. 4, the robustness index of the controlled load power random parameter is increased from 0 to 75%, and as the uncertainty of the controlled load random parameter is increased, the degree of participation of the direct load control in scheduling is reduced, and the load shedding power is reduced. When the robustness index is continuously increased from 75% to 100%, the controlled load curve has no obvious change, because the robustness index limits the total deviation amount between the predicted value and the actual value of the controlled load power in the whole scheduling period, when the robustness index is increased, the influence of the randomness of the controlled load power on the optimization result is increased, and the controlled load power is reduced. Since the direct load control mainly acts on the load peak time, the optimization result of the controlled load group number in other time is 0, and the influence of changing the robustness index on the optimization result is relatively small in these time. Therefore, after the robustness index of the random variable of the controlled load power is increased to a certain degree, the robustness index is continuously increased without influencing the controlled load power.
As can be seen from table 2, the microgrid running cost is the largest when no DLC is included, and thus, the DLC participates in scheduling to reduce the running cost of the system. As the robustness indicator increases from 0 to 75%, the total cost is positively correlated with the robustness indicator. Due to the output limit of micro-combustion engines, gas boilers and other equipment and the gas purchasing quantity limit of systems, the change of the gas purchasing cost is not obvious. The power interaction cost is obviously increased because the degree of DLC participation in scheduling is reduced as the robustness index is increased, the peak load regulation cannot be effectively carried out, the power interaction cost is increased, and even if the DLC compensation cost is reduced as the robustness index is increased, the total operation cost of the system is still increased. The total cost does not change significantly as the robustness indicator increases from 75% to 100%, which is consistent with the results reflected in fig. 4.
After the uncertainty of the controlled load power participating in the direct load control is considered by the optimized scheduling method in the embodiment, the uncertainty is not considered, the degree of the direct load control participating in the scheduling is reduced, the power of load reduction is reduced, the electric energy interaction cost is increased, even if the power of load reduction is reduced, the direct load control compensation cost required to be paid to a user by a system is also reduced, and compared with the uncertainty not considered, the total running cost of the micro-energy network is still increased. That is, the power interaction cost is increased to a greater extent than the DLC compensation cost is reduced, taking into account the uncertainty, resulting in an increase in the total cost of the micro power grid. Therefore, the influence of the direct load control uncertainty on the optimization result is considered when the micro energy network is scheduled, and the method can be utilized to select a proper robustness index and a fluctuation index according to the actual situation for the uncertainty of the controlled load participating in the DLC and the uncertainty of the wind and light prediction output, and adjust the optimality and the robustness of the result.
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 (8)

1. A micro energy network optimization scheduling method considering uncertainty of direct load control is characterized by comprising the following steps:
step 1: establishing a direct load control model considering uncertainty;
step 2: acquiring parameters of the micro energy network;
and step 3: establishing a power balance equation of the micro energy network and a model of the equipment;
and 4, step 4: according to a power balance equation and models of all equipment, a micro energy network robust optimization scheduling model considering direct load control uncertainty and wind and light output uncertainty is established;
and 5: converting a robust optimization scheduling model containing random parameters into a deterministic optimization model;
step 6: and solving the optimal solution of the model, and scheduling the micro energy network.
2. The micro energy grid optimization scheduling method considering uncertainty of direct load control according to claim 1, wherein in step 1, the expression of the direct load control model considering uncertainty is:
Figure FDA0002330043950000011
wherein t is 1, 2., N is the total number of scheduling periods; pLD,tAnd PL,tRespectively a controlled afterload demand and an original demand at a time t; xiNumber of groups of controlled loads for period i; pC,iFor the power of each set of controlled loads during period i,
Figure FDA0002330043950000012
the random parameter represents that each group of controlled load power has uncertainty; d is the number of control periods of the controlled load; gt-d+1-iA compensation strategy coefficient for a rebound load; c. Ct+1-iD is equal to or less than 1 when t +1-i is equal to or less than 0 otherwise; a is0Max {1, t-d +1 }; when t is greater than or equal to d +1, a1＝max{1,t-d-h+1}，a2T-d, otherwise, a1＝0，a20; h is the compensation time interval number corresponding to the single-time interval control strategy.
3. The method as claimed in claim 1, wherein the parameters of the micro energy grid in step 2 include grid structure parameters, energy type, unit cost information, user power demand, and renewable energy output information.
4. The micro energy grid optimized dispatching method considering the uncertainty of the direct load control according to claim 3, characterized in that the grid structure comprises a power grid, a renewable power generator set, a micro gas turbine, a natural gas grid, a gas boiler, an electric gas conversion device P2G, a waste heat boiler and an energy storage device; the renewable generating set comprises a wind generating set and a photovoltaic cell; the energy storage device comprises an electricity storage device, an air storage device and a heat storage device.
5. The micro energy grid optimal scheduling method considering the uncertainty of the direct load control according to claim 4, wherein the step 3 specifically comprises:
step 3-1: establishing a power balance equation comprising an electric power balance equation, a thermal power balance equation and an air power balance equation;
the electric power balance equation is concretely as follows:
Figure FDA0002330043950000021
wherein,
Figure FDA0002330043950000022
the output of the ith generator set in the time period t is I-3, I-1, 2 and 3 respectively represent three micro power supplies of wind, light and a micro combustion engine; pES,ch,tAnd PES,dis,tRespectively charging power and discharging power, P, of the electric storage deviceEB,tFor the power consumption of the electric boiler, P, during a period of time tP2G,tFor a period of t P2G power consumption of the device, PBGEX,tAnd PSGEX,tRespectively purchasing power from the main network and selling power to the main network for the micro energy network system in the t period;
the qi power balance equation is specifically as follows:
Figure FDA0002330043950000023
wherein G isg,tThe gas purchasing power of the micro energy network in the period of t, GP2G,tIs the gas generation power of P2G at t period GGS,ch,tAnd GGS,dis,tRespectively charging and discharging gas power G for gas storage equipmentL,tIn order to meet the natural gas demand of the user,
Figure FDA0002330043950000024
for gas consumption of micro-combustion engines, GGB,tThe gas consumption power of the gas boiler;
the thermal power balance equation is specifically as follows:
Figure FDA0002330043950000025
wherein Q isGB,tAnd QEB,tHeating powers of a gas boiler and an electric boiler in a period t respectively,
Figure FDA0002330043950000026
heating power for micro-combustion engine, QHS,ch,tAnd QHS,dis,tRespectively charging and discharging heat power, Q, of the heat storage equipmentL,tA thermal load demand for a period t;
step 3-2: establishing a model of energy conversion equipment, including a micro-gas turbine model, an electric gas conversion device model and a gas and heat boiler model;
the micro-combustion engine model expression is as follows:
Figure FDA0002330043950000027
in the formula:
Figure FDA0002330043950000028
and ηEHRespectively the power generation efficiency, the heating efficiency and the waste heat recovery efficiency of a waste heat boiler of the micro-gas turbine,
Figure FDA0002330043950000029
in order to achieve the heating power of the micro-combustion engine after passing through the waste heat boiler,
Figure FDA00023300439500000210
and
Figure FDA00023300439500000211
respectively the upper and lower limits of the output of the micro-combustion engine and the upper and lower limits of the climbing slope;
the electric gas conversion device model expression is as follows:
Figure FDA00023300439500000212
in the formula ηP2GThe overall energy conversion efficiency of the P2G plant;
Figure FDA0002330043950000031
and
Figure FDA0002330043950000032
the upper and lower limits of the P2G device.
The expression of the gas boiler model is as follows:
Figure FDA0002330043950000033
in the formula ηGBFor the energy conversion efficiency, Q, of gas-fired boilersGB,maxAnd QGB,minRespectively the upper and lower limits of the output, delta Q, of the gas-fired boilerGB,max、ΔQGB,minThe upper limit and the lower limit of the climbing slope of the gas boiler are respectively set;
the electric boiler model expression is as follows:
Figure FDA0002330043950000034
in the formula ηEBFor the energy conversion efficiency of electric boilers, QEB,maxAnd QEB,minRespectively the upper and lower limits of the output, delta Q, of the electric boilerEB,maxAnd Δ QEB,minThe upper limit and the lower limit of the climbing slope of the electric boiler are respectively set;
step 3-3: establishing a model of energy storage equipment, including an electric energy storage equipment model, a thermal energy storage equipment model and a gas energy storage model;
the model expressions are as follows:
Figure FDA0002330043950000035
wherein E isS,tEnergy storage capacity for electricity, heat or gas for a period of t, ES,maxAnd ES,minRespectively the upper and lower limits of the energy storage capacity of electricity, heat or gas, tau is the self-loss rate of the stored energy of electricity, heat or gas, Pch,tAnd Pdis,tCharging and discharging power of electric, thermal or gas energy storage devices, respectively ηchAnd ηdisRespectively electric, thermal or pneumatic energy storage charge-discharge efficiency, Pch,max、Pch,min、Pdis,maxAnd Pdis,minRespectively are the upper and lower limits of the charge-discharge power of electricity, heat or gas energy storage;
the constraint formula of the energy storage equipment model is as follows:
ET＝E0
wherein E is0And ETEnergy storage capacity of electricity, heat or gas at the beginning and end of a scheduling period respectively;
step 3-4: according to the output information of the renewable energy, establishing the constraint of the model, specifically:
wind or light output constraints:
Figure FDA0002330043950000041
wherein,
Figure FDA0002330043950000042
and
Figure FDA0002330043950000043
respectively the minimum and maximum technical output of the i-th renewable generator set,
Figure FDA0002330043950000044
and
Figure FDA0002330043950000045
respectively representing the power of the fan unit and the photovoltaic cell, PW,tAnd PPV,tRespectively predicting power of wind power and photovoltaic power at a time t;
abandon wind and abandon the restraint formula of light:
Figure FDA0002330043950000046
wherein, piWAnd piPVThe maximum allowable wind-light abandoning ratio is obtained.
6. The method for optimizing and scheduling a micro energy network considering uncertainty of direct load control according to claim 1, wherein the objective function of the robust optimizing and scheduling model of the micro energy network in the step 4 is as follows:
min C＝min(CE+CG+CD)
the calculation method of each item in the objective function specifically includes:
Figure FDA0002330043950000047
where Δ T is the optimized time interval, T is the total number of scheduling periods, CE、CGAnd CDRespectively compensating costs for electric energy interaction costs, natural gas purchase costs and controlled load interruption, PBGEX,tAnd PSGEX,tRespectively purchasing power from the main network and selling power to the main network for the micro energy network system in the t period CBE,tAnd CSE,tPrice of electricity purchased and sold, respectively, for micro-energy grid and main grid, CgasCost per unit of energy for purchasing natural gas, Gg,tThe gas purchasing power of the micro energy network in the period of t, Ccon,tThe price per unit of controlled load interruption is compensated for t periods.
7. The method for optimizing and scheduling a micro energy grid considering uncertainty of direct load control according to claim 1, wherein the specific steps of the step 5 are as follows:
step 5-1: constructing an uncertain set of random parameters of controlled load power, wind predicted power and light predicted power by using the (p, w) -norm uncertain set;
the expression of the (p, w) -norm uncertainty set is:
Figure FDA0002330043950000048
wherein, aiIs the i-th row random parameter of the coefficient matrix, aikIs aiThe k-th element of (a) the first,
Figure FDA0002330043950000049
Figure FDA0002330043950000051
is aiThe average value of the k-th element in (c),
Figure FDA0002330043950000052
and
Figure FDA0002330043950000053
are respectively the indexes of the fluctuation amount,
Figure FDA0002330043950000054
and
Figure FDA0002330043950000055
the deviation of the maximum/minimum value of the random parameter from the mean value, respectively, i.e.
Figure FDA0002330043950000056
p1,iAs an indicator of robustness, JiIs the set of random parameters, | J, in the ith row of matrix AiL is JiThe number of middle elements;
the uncertain sets of the wind power prediction power random parameter, the photoelectric prediction power random parameter and the controlled load power random parameter are specifically as follows:
Figure FDA0002330043950000057
Figure FDA0002330043950000058
Figure FDA0002330043950000059
wherein, PC,t、PW,tAnd PPV,tRespectively a controlled load power, a wind power prediction power and a photoelectric prediction power,
Figure FDA00023300439500000510
and
Figure FDA00023300439500000511
for the desired value and deviation of the controlled load power t period,
Figure FDA00023300439500000512
and
Figure FDA00023300439500000513
respectively an upper limit and a lower limit of the controlled load power deviation,
Figure FDA00023300439500000514
and
Figure FDA00023300439500000515
respectively a predicted power expected value and an actual power deviation value of the wind turbine generator in a time period t,
Figure FDA00023300439500000516
and
Figure FDA00023300439500000517
predicted power of photovoltaic cell in t periodThe expected value and the actual power deviation value,
Figure FDA00023300439500000518
and
Figure FDA00023300439500000519
respectively are the upper limit and the lower limit of the deviation of the power generated by the wind turbine generator,
Figure FDA00023300439500000520
respectively the upper limit and the lower limit of the deviation of the power generated by the photovoltaic cell;
Figure FDA00023300439500000521
and
Figure FDA00023300439500000522
respectively predicting fluctuation indexes of the power random parameters for the wind,
Figure FDA00023300439500000523
and
Figure FDA00023300439500000524
respectively as fluctuation indexes of random parameters of the light prediction power,
Figure FDA00023300439500000525
and
Figure FDA00023300439500000526
respectively are fluctuation indexes of random parameters of the controlled load power;
step 5-2: transforming a constraint condition containing random parameters and an objective function, specifically:
setting a new objective function as Z, adding a constraint Z- (C)E+CG+CD) More than or equal to 0, namely:
Figure FDA00023300439500000527
the new objective function is:
min Z
step 5-3: and converting uncertainty constraint in the robust optimization model into certainty constraint according to the robust linear optimization theory under the (p, w) -norm uncertainty set.
According to the robust linear optimization theory under the (p, w) -norm uncertain set, by introducing a dual variable z1(t)、q1(t,k)、z2(t) and q2(t, k), the electric power balance equation can be converted into a robust peer-to-peer model, and the expression is as follows:
Figure FDA0002330043950000061
wherein p is1,t、p2,tIn order to be an indicator of the robustness,
Figure FDA0002330043950000062
for the fluctuation index of the random parameter of the controlled load power, by controlling p1,t、p2,tAnd
Figure FDA0002330043950000063
is adjusted between robustness and optimality.
Will be a formula
Figure FDA0002330043950000064
Converting into a robust peer-to-peer model, wherein the expression is specifically as follows:
Figure FDA0002330043950000065
wherein p is3In order to be an indicator of the robustness,
Figure FDA0002330043950000066
is an index of the amount of fluctuation, z3、q3,kNew decision variables introduced for robust peer-to-peer transformation, noneThe actual physical meaning.
The wind or light output constraint formula and the wind or light abandon constraint formula also contain random parameters, and the robust equivalent formula after the robust equivalent transformation is specifically as follows:
Figure FDA0002330043950000067
Figure FDA0002330043950000071
wherein p is4And p5In order to be an indicator of the robustness,
Figure FDA0002330043950000072
and
Figure FDA0002330043950000073
predicting the fluctuation index of the power random parameter for the wind power,
Figure FDA0002330043950000074
and
Figure FDA0002330043950000075
predicting a power random parameter fluctuation amount index, z, for photovoltaic4,t、z5,t、q4,kAnd q is5,kNew decision variables are introduced for robust peer-to-peer transformation.
The robust peer-to-peer type of the wind/light abandoning constraint type is specifically as follows:
Figure FDA0002330043950000076
Figure FDA0002330043950000077
wherein p is6And p7In order to be an indicator of the robustness,
Figure FDA0002330043950000078
and
Figure FDA0002330043950000079
predicting the fluctuation index of the power random parameter for the wind power,
Figure FDA00023300439500000710
and
Figure FDA00023300439500000711
predicting a power random parameter fluctuation amount index, z, for photovoltaic6、z7、q6,tAnd q is7,tNew decision variables are introduced for robust peer-to-peer transformation.
8. The micro energy grid optimization scheduling method considering uncertainty of direct load control according to claim 1, wherein the optimal solution in the step 6 is obtained by Lingo.
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CN112821451A (en) * 2021-01-11 2021-05-18 国网福建省电力有限公司泉州供电公司 Distributed photovoltaic access coping method for smart town power distribution network based on demand side management and energy storage
CN112821451B (en) * 2021-01-11 2023-05-09 国网福建省电力有限公司泉州供电公司 Urban power distribution network photovoltaic access response method based on demand side management and energy storage
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CN113793052A (en) * 2021-09-23 2021-12-14 长沙理工大学 A Robust Optimal Scheduling Method for Regional Integrated Energy System
CN114596008A (en) * 2022-05-09 2022-06-07 支付宝(杭州)信息技术有限公司 Robust optimization method for resource allocation decision considering prediction uncertainty
CN117172389A (en) * 2023-11-01 2023-12-05 山东建筑大学 Regional comprehensive energy optimization operation method and system considering wind-light uncertainty
CN117172389B (en) * 2023-11-01 2024-02-02 山东建筑大学 Regional comprehensive energy optimization operation method and system considering wind-light uncertainty

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