CN112685879B - Multi-objective optimization method for regional electric heating interconnection energy system - Google Patents

Multi-objective optimization method for regional electric heating interconnection energy system Download PDF

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CN112685879B
CN112685879B CN202011484627.0A CN202011484627A CN112685879B CN 112685879 B CN112685879 B CN 112685879B CN 202011484627 A CN202011484627 A CN 202011484627A CN 112685879 B CN112685879 B CN 112685879B
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钟永洁
李玉平
常晓勇
王玉婷
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Nanjing SAC Automation Co Ltd
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Abstract

The invention discloses a multi-objective optimization method of a regional electric heating interconnection energy system, which comprises the steps of collecting regional electric heating interconnection energy system information; establishing a module unit model of the regional electric heating interconnection energy system according to the information of the regional electric heating interconnection energy system; establishing a multi-objective optimization scheduling model of the regional electric heating interconnected energy system according to the component unit model of the regional electric heating interconnected energy system, the preset energy balance constraint of the regional electric heating interconnected energy system and the preset multi-objective function; and solving a multi-objective optimization scheduling model of the regional electric heating interconnection energy system to obtain a multi-objective optimization scheduling result. According to the method, a multi-objective optimization scheduling model of the regional electric heating interconnected energy system is established according to the component unit model of the regional electric heating interconnected energy system, and multi-objective optimization of the regional electric heating interconnected energy system is achieved.

Description

Multi-objective optimization method for regional electric heating interconnection energy system
Technical Field
The invention relates to a multi-objective optimization method for a regional electric heating interconnection energy system, and belongs to the field of regional electric heating interconnection energy systems.
Background
Energy clean low-carbon transformation is a necessary trend of global energy development, energy transformation is socialized system engineering, the basic task is to construct a clean low-carbon novel energy system, and the fundamental approach is to re-electrify. From the production link, the electrification is embodied as large-scale development and utilization of new energy such as wind power, solar power generation and the like; from the consumption link, the electrification is embodied as deep replacement of fossil energy by electric energy. The construction of an interconnected energy system is accelerated, and the method is a necessary way for promoting the clean transformation of energy. Compared with the traditional power grid, the interconnected energy system is a configuration platform for promoting large-scale development and utilization of clean energy, is an innovative platform for supporting new technology, new state and new mode to emerge continuously, and is a market trading platform for realizing friendly interaction of different main bodies and meeting diversified energy requirements of users.
In a regional electric-heat interconnected energy system, the electric and heat capacities are large and far exceed the energy supply grades of common parks and cities. In the energy flow of the electric-heat interconnected energy system under multi-energy conversion, a hydraulic power plant, a nuclear power plant, a thermal power plant, renewable energy power generation and the like are used as main power supply sources, and a general regional main power generation form is included. In the aspect of heat supply, central heat supply is taken as a main part, and a heat storage boiler and an electric boiler are introduced, so that the flexibility of the operation of the electric and thermal interconnected energy system is improved. The heat storage device is introduced at the source side and the load side, so that the absorption of new energy is promoted, and the effects of time translation, peak clipping and valley filling and heat load fluctuation stabilization on the fluctuation of the new energy are achieved by utilizing the heat storage to collect renewable energy. Therefore, a multi-objective optimization method for the regional electric heating interconnected energy system is urgently needed, and theoretical guidance is provided for the regional electric heating interconnected energy system multi-scene operation mode scheduling, primary fossil energy conservation, new energy consumption promotion, carbon emission reduction and energy flow optimization.
Disclosure of Invention
The invention provides a multi-objective optimization method for a regional electric heating interconnection energy system, which solves the problems disclosed in the background technology.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a multi-objective optimization method for a regional electric heating interconnection energy system comprises the following steps,
collecting information of a regional electric heating interconnection energy system;
establishing a module unit model of the regional electric heating interconnection energy system according to the information of the regional electric heating interconnection energy system;
establishing a multi-objective optimization scheduling model of the regional electric heating interconnected energy system according to the component unit model of the regional electric heating interconnected energy system, the preset energy balance constraint of the regional electric heating interconnected energy system and the preset multi-objective function;
and solving a multi-objective optimization scheduling model of the regional electric heating interconnection energy system to obtain a multi-objective optimization scheduling result.
The information of the regional electric heating interconnected energy system comprises resource distribution and configuration of the regional electric heating interconnected energy system, an energy flow architecture, an operation scheduling mode, installed capacity data of various types of power supplies, typical solar wind power output characteristic data in winter, typical solar photovoltaic output characteristic data in winter, typical solar power load characteristic data in winter, typical solar heat load characteristic data in winter and upper and lower limit data of adjustable heat load.
The component unit models of the regional electric-thermal interconnection energy system comprise a gas power plant model, a nuclear power plant model, a hydraulic power plant model, a wind energy cluster field model, a solar energy cluster field model, a biomass power plant model, a thermal power plant model, an electric energy storage model, a pumped storage power station model, a heat storage type electric boiler model and a system input and output electric energy port model.
The model of the wind energy cluster field is as follows,
Figure BDA0002839009570000031
wherein, Δ WT (t) is the difference value between the predicted maximum wind power output and the actual wind power output of the wind energy cluster field at the time t;
Figure BDA0002839009570000032
predicting the maximum wind power output for the unit characterization of the wind energy cluster field at the time t; c WT Installing capacity for wind energy cluster plants;
Figure BDA0002839009570000033
the actual wind power output of the wind energy cluster field at the time t is obtained; alpha is alpha WT The maximum abandoned wind power rate can be accepted for the wind energy cluster field; t is an optimized scheduling period;
the solar energy cluster field model is as follows,
Figure BDA0002839009570000034
wherein, Δ pv (t) is the difference between the predicted maximum photovoltaic output and the actual photovoltaic output of the solar energy collecting field at the time t;
Figure BDA0002839009570000035
predicting the maximum photovoltaic output for the unit characterization of the solar energy collecting field at the time t; c PV Installing capacity for the solar energy cluster field;
Figure BDA0002839009570000036
the actual photovoltaic output of the solar energy collecting field at the time t is obtained; alpha is alpha PV The maximum light rejection rate can be accepted for the solar energy collecting field;
the model of the thermal power plant is as follows,
Figure BDA0002839009570000037
wherein the content of the first and second substances,
Figure BDA0002839009570000038
the thermal output of the Nth type thermal power plant at the time t is obtained;
Figure BDA0002839009570000039
the heat output of the Nth type thermal power plant at the t +1 moment is obtained;
Figure BDA00028390095700000310
the electric output of the Nth type thermal power plant at the time t is obtained;
Figure BDA0002839009570000041
the thermoelectric ratio of the Nth type thermal power plant at the time t is shown;
Figure BDA0002839009570000042
the upper limit and the lower limit of the thermoelectric ratio of the Nth type thermal power plant are respectively set;
Figure BDA0002839009570000043
the upper limit and the lower limit of the electric output of the Nth type thermal power plant are respectively set;
Figure BDA0002839009570000044
the upper and lower climbing rates of the electric power of the thermal power plant are respectively; Δ t is an optimum adjustmentSimulating step length;
the electric energy storage model is that,
Figure BDA0002839009570000045
wherein, ES (T), ES (T +1) and ES (T + T) are respectively the energy storage energy of the electric energy storage at T, T +1 and T + T moments; chi shape ES The energy self-loss rate for storing energy for electricity;
Figure BDA0002839009570000046
charging power of the electric energy storage at t and t +1 moments respectively;
Figure BDA0002839009570000047
storing the discharge power of the electricity at t and t +1 moments;
Figure BDA0002839009570000048
upper and lower limits of charging power for the electrical energy storage, respectively;
Figure BDA0002839009570000049
the upper limit and the lower limit of the discharge power of the electric energy storage are respectively;
Figure BDA00028390095700000410
C ES the upper limit and the lower limit of the actual energy storage level of the electric energy storage are respectively set;
Figure BDA00028390095700000411
for charging power efficiency;
Figure BDA00028390095700000412
to discharge power efficiency;
the pumped storage power station model is
Figure BDA00028390095700000413
Wherein, HP (T), HP (T +1) and HP (T + T) are of the pumped storage power station at T, T +1 and T + T momentsUpper reservoir capacity; chi shape HP The energy self-loss rate of the pumped storage power station is obtained;
Figure BDA0002839009570000051
pumping power of the pumped storage power station at the time t and the time t + 1;
Figure BDA0002839009570000052
generating power of the pumped storage power station at the time t and the time t + 1;
Figure BDA0002839009570000053
respectively representing the upper limit and the lower limit of the pumping power of the pumped storage power station;
Figure BDA0002839009570000054
the upper limit and the lower limit of the generated power of the pumped storage power station are respectively set;
Figure BDA0002839009570000055
C HP the upper limit and the lower limit of the actual reservoir capacity of the pumped storage power station are respectively set;
Figure BDA0002839009570000056
the efficiency coefficient is set for the average water quantity to electric quantity when the pumped storage power station pumps water;
Figure BDA0002839009570000057
the efficiency coefficient is loaded and converted for the average water quantity to electric quantity when the pumped storage power station generates electricity;
the heat-storage electric boiler is modeled as follows,
Figure BDA0002839009570000058
wherein the content of the first and second substances,
Figure BDA0002839009570000059
outputting thermal power for the electric boiler at the time t;
Figure BDA00028390095700000510
inputting electric power for the electric boiler at the time t;
Figure BDA00028390095700000511
the electric heating efficiency of the electric boiler;
Figure BDA00028390095700000512
directly supplying thermal power to the electric boiler at the time t without passing through the heat storage tank; HST (T), HST (T +1) and HST (T + T) are the heat storage amount of the heat storage tank at T, T +1 and T + T moments; chi shape HST The energy self-loss rate of the heat storage tank is obtained;
Figure BDA00028390095700000513
the heat storage power of the heat storage tank at the time t and the time t +1 is obtained;
Figure BDA00028390095700000514
the heat release power of the heat storage tank at the time t and the time t +1 is obtained;
Figure BDA0002839009570000061
the upper limit and the lower limit of the heat storage power of the heat storage tank are respectively set;
Figure BDA0002839009570000062
the upper limit and the lower limit of the heat release power of the heat storage tank are respectively set;
Figure BDA0002839009570000063
C HST the upper limit and the lower limit of the heat storage capacity of the heat storage tank during actual work are respectively set;
Figure BDA0002839009570000064
the heat storage efficiency of the heat storage tank is improved;
Figure BDA0002839009570000065
the heat release efficiency of the heat storage tank is improved; AHL a (t) the actually participating adjustable heat load power of the regional electric heating interconnection energy system at the moment t; AHL is the maximum adjustable heat load power of the regional electric heating interconnection energy system;
the system incoming and outgoing power port models are,
Figure BDA0002839009570000066
wherein the content of the first and second substances,
Figure BDA0002839009570000067
respectively are state variables of the electric energy received by and sent out by the regional electric heating interconnected energy system at the moment t;
Figure BDA0002839009570000068
respectively is a state variable of the electric energy input and output of the regional electric heating interconnection energy system at the moment of t + 1; PCC in (t)、PCC out (t) electric power received by and sent out by the regional electric heating interconnection energy system at the moment t respectively;
Figure BDA0002839009570000069
the upper limit and the lower limit of the electric power received by the regional electric heating interconnection energy system are respectively set;
Figure BDA00028390095700000610
the upper limit and the lower limit of the electric power externally supplied by the regional electric heating interconnection energy system are respectively set;
Figure BDA00028390095700000611
respectively optimizing the maximum times of starting work of the incoming call tie and the outgoing call tie in the scheduling period;
Figure BDA0002839009570000071
the lower limit of the ratio of the input electric energy to the output electric energy in the optimized dispatching period is respectively;
the gas power plant model, the nuclear power plant model, the hydraulic power plant model, the biomass power plant model and the thermal power plant model adopt a unified formula as follows,
Figure BDA0002839009570000072
whereinThe type is the type of a power plant, and the type is GP, NP, HY, BP and CP which are respectively a gas power plant, a nuclear power plant, a hydraulic power plant, a biomass power plant and a thermal power plant; x type (t)、X type (t +1) is a working state variable of type at t and t +1 moments; p is type (t)、P type (t +1) is the electric output of type at t and t + 1;
Figure BDA0002839009570000073
P type respectively representing the upper limit and the lower limit of the electric output of type at the time t;
Figure BDA0002839009570000074
ΔP type the climbing speeds of the type and the type are respectively; n is a radical of hydrogen type And the maximum starting times of the type in the optimized scheduling period.
The preset energy balance constraint of the regional electric-thermal interconnection energy system comprises an electric energy balance constraint and a thermal energy balance constraint.
The electric energy balance is constrained to be,
Figure BDA0002839009570000075
EL (t) characterizes actual electric load requirements of units of the regional electric heating interconnection energy system at the time t; c EL Predicting the power demand for optimizing the maximum of the electrical load in the scheduling period;
Figure BDA0002839009570000076
respectively storing the charging power of the electric energy at the moment t;
Figure BDA0002839009570000077
pumping power of the pumped storage power station at the moment t;
Figure BDA0002839009570000078
inputting electric power for the electric boiler at the time t; PCC in (t)、PCC out (t) electric power received by and sent out by the regional electric heating interconnection energy system at the moment t respectively; p is type (t) is the electric output of type at the time t, the type is the type of a power plant, and the type is GP, NP, HY, BP and CP are respectively a gas power plant, a nuclear power plant, a hydraulic power plant, a biomass power plant and a thermal power plant;
Figure BDA0002839009570000081
the actual wind power output of the wind energy cluster field at the time t is obtained;
Figure BDA0002839009570000082
the actual photovoltaic output of the solar energy collecting field at the time t is obtained;
Figure BDA0002839009570000083
the electric output of the Nth type thermal power plant at the time t is obtained;
Figure BDA0002839009570000084
storing the discharge power of the electricity at the time t;
Figure BDA0002839009570000085
generating power of the pumped storage power station at the time t;
the thermal energy balance is constrained to be,
Figure BDA0002839009570000086
HL (t) characterizes the actual heat load demand of the unit of the regional electric heating interconnection energy system at the time t; c HL Maximum predicted power requirements for thermal loads within the optimal scheduling period;
Figure BDA0002839009570000087
the thermal output of the Nth type thermal power plant at the time t is obtained; the AHL is the maximum adjustable heat load power of a regional electric heating interconnection energy system.
The preset multi-objective functions comprise an energy-saving optimized dispatching objective function, a consumption-type optimized dispatching objective function and an emission-reduction optimized dispatching objective function; the energy-saving optimized dispatching objective function aims at reducing primary fossil energy consumption; a consumption type optimization scheduling objective function aims at promoting new energy consumption; the emission reduction type optimized dispatching objective function aims at reducing the carbon emission of the regional electric heating interconnection energy system.
The energy-saving type optimized scheduling objective function is,
Figure BDA0002839009570000088
the FFC is an energy-saving optimized scheduling objective function; p CP (t)、
Figure BDA0002839009570000089
P GP (t) the electric output of the thermal power plant, the Nth-class thermal power plant and the gas power plant at the moment t respectively; eta e,CP 、η e,CHP 、η e,GP The power generation efficiency of a thermal power plant, a thermal power plant and a gas power plant respectively; eta grid The electric energy transmission efficiency of the regional power grid is shown, and T is an optimized dispatching cycle;
the consumption-type optimal scheduling objective function is,
Figure BDA0002839009570000091
wherein ARREN is a consumption type optimized scheduling objective function; delta WT (t) is the difference value of the predicted maximum wind power output and the actual wind power output of the wind energy cluster field at the time t; Δ pv (t) is the difference between the predicted maximum photovoltaic contribution and the actual photovoltaic contribution of the solar energy collecting field at time t;
Figure BDA0002839009570000092
predicting the maximum wind power output for the unit characterization of the wind energy cluster field at the time t;
Figure BDA0002839009570000093
predicting the maximum photovoltaic output for the unit characterization of the solar energy collecting field at the time t; c WT Installing capacity for wind energy cluster plants; c PV The installed capacity of the solar energy cluster field;
the reduced form optimal scheduling objective function is,
Figure BDA0002839009570000094
wherein CAREM is an emission reduction type optimized scheduling objective function; p BP (t) the electric output of the biomass power plant at the moment t;
Figure BDA0002839009570000095
the actual wind power output of the wind energy cluster field at the moment t is obtained;
Figure BDA0002839009570000096
the actual photovoltaic output of the solar energy collecting field at the time t is obtained; gamma ray CP 、γ CHP 、γ GP 、γ BP 、γ PV 、γ WT The unit output carbon emission factors of a thermal power plant, a gas power plant, a biomass power plant, a solar energy cluster field and a wind energy cluster field are respectively.
The multi-objective optimization scheduling model of the regional electric heating interconnection energy system is as follows,
Figure BDA0002839009570000101
s.t.λ FFC +λ ARREN +λ CAREM ＝1
the muliobj is an objective function of the multi-objective optimization scheduling model; the FFC is an energy-saving optimized scheduling objective function; ARREN is a consumption type optimized scheduling objective function; CAREM is an emission reduction type optimized scheduling objective function; FFC obj 、ARREN obj 、CAREM obj Respectively obtaining optimal values of an energy-saving optimized dispatching objective function, a digestion optimized dispatching objective function and an emission reduction optimized dispatching objective function; lambda [ alpha ] FFC 、λ ARREN 、λ CAREM And weights corresponding to the energy-saving optimized dispatching objective function, the absorption optimized dispatching objective function and the emission-reduction optimized dispatching objective function are respectively.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a method of multi-objective optimization of a regional electric heat interconnected energy system.
The invention achieves the following beneficial effects: 1. according to the method, a multi-objective optimization scheduling model of the regional electric-heating interconnected energy system is established according to a component unit model of the regional electric-heating interconnected energy system, so that multi-objective optimization of the regional electric-heating interconnected energy system is realized; 2. the regional electric-heating interconnection energy system can simplify and analyze the complicated coupling relation, energy exchange and energy flow of the region from the viewpoint of electric heating energy balance, can effectively save primary fossil energy, promote new energy consumption and reduce carbon emission; 3. the method can provide support for the key technology of the optimal scheduling of the multi-energy flow, provide reference for solving the problem of collaborative optimization of energy supply and energy utilization, provide a solution for improving new energy acceptance, meeting energy utilization requirements and promoting energy supply quality, and provide theoretical and practical guidance for planning construction and comprehensive popularization of the energy internet multi-energy flow system.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block diagram of an embodiment of the present invention;
FIG. 3 is a graph of typical regional winter daily electrical load characteristics;
FIG. 4 is a plot of a regional winter typical daily heat load signature;
fig. 5 is a graph of typical solar wind power (WT) versus Photovoltaic (PV) signature for a regional winter season.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, a multi-objective optimization method for a district electric-heat interconnection energy system includes the following steps:
step 1, collecting information of a regional electric heating interconnection energy system.
The regional electric heating interconnected energy system is a regional electric heating interconnected energy system considering adjustable heat load, and the specific information comprises resource distribution and configuration, an energy flow architecture, an operation scheduling mode, various types of power installed capacity data, winter typical solar wind power output characteristic data, winter typical solar photovoltaic output characteristic data, winter typical solar electric load characteristic data, winter typical solar heat load characteristic data, adjustable heat load upper and lower limit data and the like of the regional electric heating interconnected energy system.
And 2, establishing a module unit model of the regional electric heating interconnection energy system according to the information of the regional electric heating interconnection energy system.
The main component unit models of the regional electric-thermal interconnection energy system comprise a gas power plant model, a nuclear power plant model, a hydraulic power plant model, a wind energy cluster field model, a solar cluster field model, a biomass power plant model, a thermal power plant model, an electric energy storage model, a pumped storage power station model, a heat storage type electric boiler model and a system input and output electric energy port model.
The wind energy cluster field model is as follows:
Figure BDA0002839009570000121
wherein, Δ WT (t) is the difference value between the predicted maximum wind power output and the actual wind power output of the wind energy cluster field at the time t, namely the abandoned wind power quantity;
Figure BDA0002839009570000122
predicting the maximum wind power output for the unit characterization of the wind energy cluster field at the time t; c WT Installing capacity for wind energy cluster plants;
Figure BDA0002839009570000123
the actual wind power output of the wind energy cluster field at the time t is obtained; alpha (alpha) ("alpha") WT The maximum abandoned wind power rate can be accepted for the wind energy cluster field; and T is an optimized scheduling period.
The solar energy collecting field model is as follows:
Figure BDA0002839009570000124
wherein, Δ pv (t) is the difference between the predicted maximum photovoltaic output and the actual photovoltaic output of the solar energy collecting field at the time t, i.e. the discarded light power;
Figure BDA0002839009570000125
predicting the maximum photovoltaic output for the unit characterization of the solar energy collecting field at the time t; c PV The installed capacity of the solar energy cluster field;
Figure BDA0002839009570000126
the actual photovoltaic output of the solar energy collecting field at the time t is obtained; alpha is alpha PV The maximum light rejection rate is acceptable for the solar energy cluster field.
The thermal power plant model is as follows:
Figure BDA0002839009570000127
wherein the content of the first and second substances,
Figure BDA0002839009570000128
the thermal output of the Nth type thermal power plant at the time t is obtained;
Figure BDA0002839009570000129
the thermal output of the Nth type thermal power plant at the t +1 moment is obtained;
Figure BDA0002839009570000131
the electric output of the Nth type thermal power plant at the time t is obtained;
Figure BDA0002839009570000132
the thermoelectric ratio of the Nth type thermal power plant at the time t is shown;
Figure BDA0002839009570000133
the upper limit and the lower limit of the thermoelectric ratio of the Nth type thermal power plant are respectively set;
Figure BDA0002839009570000134
the upper limit and the lower limit of the electric output of the Nth type thermal power plant are respectively set;
Figure BDA0002839009570000135
the upper and lower climbing rates of the electric power of the thermal power plant are respectively; and delta t is the optimized scheduling simulation step length.
The electric energy storage model is as follows:
Figure BDA0002839009570000136
wherein, ES (T), ES (T +1) and ES (T + T) are respectively the energy storage energy of the electric energy storage at T, T +1 and T + T moments; chi-type food processing machine ES The energy self-loss rate for storing energy for electricity;
Figure BDA0002839009570000137
charging power of the electric energy storage at t and t +1 moments respectively;
Figure BDA0002839009570000138
storing the discharge power of the electricity at t and t +1 moments;
Figure BDA0002839009570000139
upper and lower limits of charging power for the electrical energy storage, respectively;
Figure BDA00028390095700001310
the upper limit and the lower limit of the discharge power of the electric energy storage are respectively;
Figure BDA00028390095700001311
C ES the upper limit and the lower limit of the actual energy storage level of the electric energy storage are respectively set;
Figure BDA00028390095700001312
for charging power efficiency;
Figure BDA00028390095700001313
to discharge power efficiency.
The pumped storage power station model is as follows:
Figure BDA0002839009570000141
the HP (T), HP (T +1) and HP (T + T) are the upper reservoir capacity of the pumped storage power station at the time of T, T +1 and T + T; chi shape HP The energy self-loss rate of the pumped storage power station is obtained;
Figure BDA0002839009570000142
pumping power of the pumped storage power station at the time t and the time t + 1;
Figure BDA0002839009570000143
generating power of the pumped storage power station at the time t and the time t + 1;
Figure BDA0002839009570000144
respectively representing the upper limit and the lower limit of the pumping power of the pumped storage power station;
Figure BDA0002839009570000145
the upper limit and the lower limit of the generated power of the pumped storage power station are respectively set;
Figure BDA0002839009570000146
C HP the upper limit and the lower limit of the actual reservoir capacity of the pumped storage power station are respectively set;
Figure BDA0002839009570000147
the efficiency coefficient is set for the average water quantity to electric quantity when the pumped storage power station pumps water;
Figure BDA0002839009570000148
and the efficiency coefficient is changed for the average water quantity to electric quantity when the pumped storage power station generates electricity.
The heat storage electric boiler model is as follows:
Figure BDA0002839009570000149
wherein the content of the first and second substances,
Figure BDA0002839009570000151
outputting thermal power for the electric boiler at the time t;
Figure BDA0002839009570000152
inputting electric power for the electric boiler at the time t;
Figure BDA0002839009570000153
the electric heating efficiency of the electric boiler;
Figure BDA0002839009570000154
directly supplying thermal power to the electric boiler at the time t without passing through the heat storage tank; HST (T), HST (T +1) and HST (T + T) are the heat storage amount of the heat storage tank at T, T +1 and T + T moments; chi shape HST The energy self-loss rate of the heat storage tank is obtained;
Figure BDA0002839009570000155
the heat storage power of the heat storage tank at the time t and the time t +1 is obtained;
Figure BDA0002839009570000156
the heat release power of the heat storage tank at the time t and the time t +1 is obtained;
Figure BDA0002839009570000157
the upper limit and the lower limit of the heat storage power of the heat storage tank are respectively set;
Figure BDA0002839009570000158
the upper limit and the lower limit of the heat release power of the heat storage tank are respectively set;
Figure BDA0002839009570000159
C HST the upper limit and the lower limit of the heat storage capacity of the heat storage tank during actual work are respectively set;
Figure BDA00028390095700001510
the heat storage efficiency of the heat storage tank is improved;
Figure BDA00028390095700001511
the heat release efficiency of the heat storage tank is improved; AHL a (t) the actually participating adjustable heat load power of the regional electric heating interconnection energy system at the moment t; the AHL is the maximum adjustable heat load power of a regional electric heating interconnection energy system.
The system input and output electric energy port model is as follows:
Figure BDA00028390095700001512
wherein the content of the first and second substances,
Figure BDA00028390095700001513
the state variables of the electric energy input and output of the regional electric heating interconnected energy system at the moment t respectively take the values of 0 or 1, wherein,
Figure BDA0002839009570000161
a value of 1 indicates that the system receives electrical energy at time t,
Figure BDA0002839009570000162
a value of 0 indicates that the system is not subjected to electrical energy at time t,
Figure BDA0002839009570000163
the value 1 represents that the system sends out electric energy at the moment t,
Figure BDA0002839009570000164
the value 0 indicates that the system does not send out electric energy at the moment t;
Figure BDA0002839009570000165
respectively are state variables of the electric energy input and output by the regional electric heating interconnected energy system at the moment of t + 1; PCC in (t)、PCC out (t) electric power input and output by the regional electric heating interconnected energy system at the moment t respectively;
Figure BDA0002839009570000166
are respectively a regional electric heating elementThe upper limit and the lower limit of the electric power received by the energy connection system;
Figure BDA0002839009570000167
the upper limit and the lower limit of the electric power externally supplied by the regional electric heating interconnection energy system are respectively set;
Figure BDA0002839009570000168
respectively optimizing the maximum times of starting work of the incoming call tie and the outgoing call tie in the scheduling period;
Figure BDA0002839009570000169
the lower limit of the ratio of the received electric energy to the delivered electric energy in the optimized dispatching period is respectively, and the value of the lower limit does not exceed 1.
The gas power plant model, the nuclear power plant model, the hydraulic power plant model, the biomass power plant model and the thermal power plant model adopt a unified formula as follows:
Figure BDA00028390095700001610
the type is a type of a power plant, and the types are GP, NP, HY, BP and CP which are respectively a gas power plant, a nuclear power plant, a hydraulic power plant, a biomass power plant and a thermal power plant; x type (t)、X type (t +1) is the working state variable of type at t and t +1 moments; p is type (t)、P type (t +1) is the electric output of type at t and t + 1;
Figure BDA00028390095700001611
P type respectively representing the upper limit and the lower limit of the electric output of type at the time t;
Figure BDA00028390095700001612
ΔP type the climbing speeds of the type and the type are respectively; n is a radical of type And the maximum starting times of the type in the optimized scheduling period.
And 3, establishing a multi-objective optimization scheduling model of the regional electric-heating interconnected energy system according to the module unit model of the regional electric-heating interconnected energy system, the preset energy balance constraint of the regional electric-heating interconnected energy system and the preset multi-objective function.
The preset energy balance constraint of the regional electric heating interconnection energy system comprises an electric energy balance constraint and a thermal energy balance constraint.
The electric energy balance constraint is as follows:
Figure BDA0002839009570000171
EL (t) characterizes actual electric load requirements for units of the regional electric heating interconnection energy system at the time t; c EL Predicting the power demand for optimizing the maximum of the electrical load in the scheduling period;
Figure BDA0002839009570000172
respectively storing the charging power of the electric energy at the moment t;
Figure BDA0002839009570000173
pumping power of the pumped storage power station at the moment t;
Figure BDA0002839009570000174
inputting electric power for the electric boiler at the time t; PCC (program-controlled computer) in (t)、PCC out (t) electric power received by and sent out by the regional electric heating interconnection energy system at the moment t respectively; p type (t) the electric output of type at t moment, wherein the type is the type of a power plant, and the type is GP, NP, HY, BP and CP which are respectively a gas power plant, a nuclear power plant, a hydraulic power plant, a biomass power plant and a thermal power plant;
Figure BDA0002839009570000175
the actual wind power output of the wind energy cluster field at the time t is obtained;
Figure BDA0002839009570000176
actual photovoltaic output of the solar energy collecting field at the moment t is obtained;
Figure BDA0002839009570000177
is heat of the Nth classThe electric output of the power plant at the moment t;
Figure BDA0002839009570000178
storing the discharge power of the electricity at the time t;
Figure BDA0002839009570000179
and generating power of the pumped storage power station at the moment t.
The thermal energy balance constraint is:
Figure BDA00028390095700001710
HL (t) characterizes the actual heat load demand of the unit of the regional electric heating interconnection energy system at the time t; c HL Maximum predicted power requirements for thermal loads within the optimal scheduling period;
Figure BDA00028390095700001711
the thermal output of the Nth type thermal power plant at the time t is obtained; the AHL is the maximum adjustable heat load power of a regional electric heating interconnection energy system.
The preset multi-objective functions comprise an energy-saving optimized dispatching objective function, a consumption-type optimized dispatching objective function and an emission-reduction optimized dispatching objective function; the energy-saving optimized dispatching objective function aims at reducing primary fossil energy consumption; a consumption type optimization scheduling objective function aims at promoting new energy consumption; the emission reduction type optimized dispatching objective function aims at reducing the carbon emission of the regional electric heating interconnection energy system.
The energy-saving target mainly considers reducing the consumption of primary fossil energy, and the mode of consuming the fossil energy mainly comprises the coal consumption of a thermal power plant, the coal consumption of a thermal power plant and the fuel gas consumption of a gas power plant; the energy-saving optimized scheduling objective function is as follows:
Figure BDA0002839009570000181
the FFC is an energy-saving optimized scheduling objective function; p CP (t)、
Figure BDA0002839009570000182
P GP (t) the electric output of the thermal power plant, the Nth thermal power plant and the gas power plant at the moment t respectively; eta e,CP 、η e,CHP 、η e,GP The power generation efficiency of a thermal power plant, a thermal power plant and a gas power plant respectively; eta grid And T is the optimized dispatching cycle.
The absorption type target mainly considers promoting the absorption of new energy, mainly comprises the absorption of wind power and the absorption of photoelectricity, namely, the light rate of abandoned wind is reduced as much as possible; the consumption type optimal scheduling objective function is as follows:
Figure BDA0002839009570000183
wherein ARREN is a consumption type optimized scheduling objective function; delta WT (t) is the difference value of the predicted maximum wind power output and the actual wind power output of the wind energy cluster field at the time t; Δ pv (t) is the difference between the predicted maximum photovoltaic contribution and the actual photovoltaic contribution of the solar energy collecting field at time t;
Figure BDA0002839009570000184
predicting the maximum wind power output for the unit characterization of the wind energy cluster field at the time t;
Figure BDA0002839009570000191
predicting the maximum photovoltaic output for the unit characterization of the solar energy collecting field at the time t; c WT Installing capacity for wind energy cluster plants; c PV The solar energy collecting and field installing capacity is achieved.
The emission reduction target mainly considers the reduction of the carbon emission of the system, the carbon emission sources mainly include the carbon emission brought by coal in a thermal power plant and a thermal power plant, the carbon emission brought by gas in a gas power plant, and the carbon emission brought by the combustion of biomass fuel in a biomass power plant, so that the consumption of new energy is promoted, and the equivalent reduction of the emission brought by fossil energy consumption is realized; the emission reduction type optimized scheduling objective function is as follows:
Figure BDA0002839009570000192
wherein CAREM is an emission reduction type optimized scheduling objective function; p BP (t) is the electric output of the biomass power plant at the moment t;
Figure BDA0002839009570000193
the actual wind power output of the wind energy cluster field at the time t is obtained;
Figure BDA0002839009570000194
the actual photovoltaic output of the solar energy collecting field at the time t is obtained; gamma ray CP 、γ CHP 、γ GP 、γ BP 、γ PV 、γ WT The unit output carbon emission factors of a thermal power plant, a gas power plant, a biomass power plant, a solar energy cluster field and a wind energy cluster field are respectively.
The multi-objective optimization scheduling model of the regional electric heating interconnected energy system is as follows:
Figure BDA0002839009570000195
s.t.λ FFC +λ ARREN +λ CAREM ＝1
wherein, muliobj is an objective function of the multi-objective optimization scheduling model; the FFC is an energy-saving optimized scheduling objective function; ARREN is a consumption type optimized scheduling objective function; CAREM is an emission reduction type optimized scheduling objective function; FFC obj 、ARREN obj 、CAREM obj Respectively obtaining optimal values of an energy-saving optimized dispatching objective function, a consumption-type optimized dispatching objective function and an emission-reduction optimized dispatching objective function; lambda FFC 、λ ARREN 、λ CAREM And weights corresponding to the energy-saving optimized dispatching objective function, the absorption optimized dispatching objective function and the emission-reduction optimized dispatching objective function are respectively set.
Step 4, solving a multi-objective optimization scheduling model of the regional electric heating interconnection energy system to obtain a multi-objective optimization scheduling result; and outputting information such as a multi-objective optimized scheduling result, a single-objective optimized scheduling result, the difference of the optimized scheduling results with or without adjustable heat load, the wind and light abandoning condition in typical days in winter, the output characteristic of the thermal storage electric boiler in typical days in winter and the like to a scheduling side.
According to the method, a multi-objective optimization scheduling model of the regional electric-heating interconnected energy system is established according to a component unit model of the regional electric-heating interconnected energy system, so that multi-objective optimization of the regional electric-heating interconnected energy system is realized; according to the method, the regional electric-heating interconnection energy system can simplify and analyze the complicated coupling relation, energy exchange and energy flow of the region from the viewpoint of electric heating energy balance, can effectively save primary fossil energy, promote new energy consumption and reduce carbon emission; the method can provide support for the key technology of the optimal scheduling of the multi-energy flow, can provide reference for solving the problem of collaborative optimization of energy supply and energy utilization, can provide a solution for improving new energy acceptance, meeting energy utilization requirements and promoting energy supply quality, and can provide theoretical and practical guidance for planning construction and comprehensive popularization of the energy internet multi-energy flow system.
Based on the method, the typical winter days in 2018 in a certain northern area are taken as research objects, the simulation step length is set to be 1 hour, and the optimized scheduling operation period is set to be 24 hours. The example structure and energy flow relationship of the above method in the example are shown in fig. 2.
In fig. 2, the fuel resource source of the gas power plant is natural gas, the fuel resource source of the nuclear power plant is nuclear fuel, the energy resource of the hydraulic power plant is hydroenergy, the energy resource of the wind energy cluster field is wind energy, the energy resource of the solar energy cluster field is solar energy, the fuel resource source of the biomass power plant is biomass, the fuel resource source of the thermal power plant is coal, and the fuel resource source of the thermal power plant is coal; injecting electric energy generated by a gas power plant, a nuclear power plant, a hydraulic power plant, a wind energy cluster field, a solar energy cluster field, a biomass power plant, a thermal power plant and a thermal power plant into a regional power grid; the pumped storage power station and the electric energy storage are connected into a regional power grid, and can absorb electric energy from the regional power grid and inject the electric energy into the regional power grid; the regional power grid is provided with two interfaces with the outside, one is an incoming electric energy port, the other is an outgoing electric energy port, and the ports are all connected into the regional power grid; the heat energy source of the heat load requirement mainly comprises heat supply of a thermal power plant and heat supply of a heat accumulation type electric boiler, wherein the heat accumulation type electric boiler heat supply amount is the upper limit of the adjustable heat load; the electrical load demands electrical energy from the regional grid.
The main parameter setting of the above method example is as follows: installed capacity of coal electricity is 3058.70 ten thousand kilowatts; the capacity of the heat supply machine assembling machine is 2470.00 ten thousand kilowatts; the installed capacity of the water and electricity is 179.30 ten thousand kilowatts; the installed capacity of the pumped storage power station is 120.00 ten thousand kilowatts; the installed capacity of the nuclear power is 447.50 ten thousand kilowatts; the installed capacity of the biomass is 30.80 ten thousand kilowatts; the installed gas-electricity capacity is 18.50 ten thousand kilowatts; the installed capacity of the electric energy storage is 0; the installed capacity of wind power is 760.70 ten thousand kilowatts; the photoelectric installed capacity is 302.00 ten thousand kilowatts; maximum electrical load 4052.41 ten thousand kilowatts; the capacity of the delivered electric energy is 300.00 ten thousand kilowatts; the power capacity is 3556.19 ten thousand kilowatts. A typical daily electrical load characteristic curve in winter in the area 2018 is shown in fig. 3, a typical daily thermal load characteristic curve in winter in the area 2018 is shown in fig. 4, and a typical daily electrical power (WT) and Photovoltaic (PV) characteristic curve in winter in the area 2018 is shown in fig. 5.
Analyzing a multi-objective optimization result: selecting typical 2018 years for multi-objective optimization research of a regional electric-heating interconnected energy system, selecting a typical winter day as an optimized scheduling cycle in the 2018 years, respectively considering configured thermoelectric decoupling capacity and non-configured thermoelectric decoupling capacity in optimized scheduling analysis of the 2018 years for comparative analysis, and respectively simulating optimized operation conditions of the system under the conditions of an energy-saving optimized target (F1), a consumption optimized target (F2), an emission-reducing optimized target (F3) and comprehensive multi-objective, wherein the optimized result of the regional electric-heating interconnected energy system is shown in table 1.
TABLE 1 results of optimization of regional electric-thermal interconnection energy system
Figure BDA0002839009570000211
Figure BDA0002839009570000221
According to table 1, the optimization of a single factor is pursued by a single target, on the basis of realizing the balance of electric heating energy of a regional electric-thermal interconnection energy system, the benefit of the single target is maximized, for example, the primary fossil energy consumption F1 is minimized, the wind and light abandoning rate F2 is minimized, the carbon emission F3 is minimized, and the benefit of other targets is necessarily sacrificed while the benefit of the single target is maximized, so that the benefits of each target need to be comprehensively considered, namely, the objectives are comprehensively considered, the conflict and contradiction of the benefits between the objectives are balanced, the multi-target optimization is considered, and the multi-target optimization is found in table 1, the benefits of each single target are considered, a balance result with the maximum comprehensive benefit is obtained, and the multi-target optimization operation mode is a better choice.
The optimization result shows that the adjustable heat load can obviously influence the optimization scheduling result, the heat storage electric boiler is configured on the same basis, the consumption of primary fossil energy can be reduced, the consumption of new energy is promoted, and the carbon emission is reduced, and the effect is more obvious when the capacity of the adjustable heat load is larger.
The optimization results in the embodiment show that the method is effective, feasible and reasonable, can provide theoretical guidance and reference for modeling of a regional electric-thermal interconnection energy system, multi-mode operation scheduling and the like, has important reference significance for source-network-load-storage coordination optimization, multi-target scheduling and system flexibility analysis, and has important basic values for promoting energy internet multi-energy complementary utilization, enhancing resource coordination and coordination, realizing economic and efficient operation, improving multi-energy flow scheduling flexibility, and exerting regulation and optimization potential and analysis.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a method of multi-objective optimization of a regional electric heat interconnected energy system.
A computing device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing a method for multi-objective optimization of a regional electric interconnect energy system.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (7)

1. A multi-objective optimization method for a regional electric heating interconnection energy system is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
collecting information of a regional electric heating interconnection energy system;
establishing a module unit model of the regional electric heating interconnection energy system according to the information of the regional electric heating interconnection energy system;
establishing a multi-objective optimization scheduling model of the regional electric-thermal interconnection energy system according to the component unit model of the regional electric-thermal interconnection energy system, the preset energy balance constraint of the regional electric-thermal interconnection energy system and the preset multi-objective function, wherein,
the preset multi-objective functions comprise an energy-saving optimized dispatching objective function, a consumption optimized dispatching objective function and an emission-reducing optimized dispatching objective function, the energy-saving optimized dispatching objective function aims at reducing primary fossil energy consumption, the consumption optimized dispatching objective function aims at promoting new energy consumption, the emission-reducing optimized dispatching objective function aims at reducing carbon emission of a regional electric-heating interconnected energy system,
the energy-saving optimized scheduling objective function is as follows,
Figure FDA0003665517760000011
wherein, the FFC is an energy-saving optimized scheduling objective function, P CP (t)、
Figure FDA0003665517760000012
P GP (t) the electric output, eta, of the thermal power plant, the Nth-class thermal power plant and the gas power plant at the moment t e,CP 、η e,CHP 、η e,GP The power generation efficiency, eta, of thermal power plants, and gas power plants grid The electric energy transmission efficiency of the regional power grid, T is an optimized dispatching cycle,
the objective function of the subtractive optimization scheduling is,
Figure FDA0003665517760000021
wherein ARREN is a vanishing type optimized dispatching objective function, delta WT (t) is the difference value between the predicted maximum wind power output and the actual wind power output of the wind energy cluster field at the time t, delta PV (t) is the difference value between the predicted maximum photovoltaic output and the actual photovoltaic output of the solar energy cluster field at the time t,
Figure FDA0003665517760000022
predicting the maximum wind power output for the unit characterization of the wind energy cluster field at the time t,
Figure FDA0003665517760000023
predicting maximum photovoltaic output, C, for unit characterization of a solar energy collection field at time t WT Installed capacity for wind energy cluster, C PV In order to install the capacity of the solar energy cluster,
the reduced form optimal scheduling objective function is,
Figure FDA0003665517760000024
wherein CAREM is an emission reduction type optimized scheduling objective function, P BP (t) is the electrical output of the biomass power plant at time t,
Figure FDA0003665517760000025
for the actual wind power output of the wind energy cluster field at the time t,
Figure FDA0003665517760000026
is the actual photovoltaic output, gamma, of the solar energy collecting field at the moment t CP 、γ CHP 、γ GP 、γ BP 、γ PV 、γ WT Respectively are carbon emission factors of unit output of a thermal power plant, a gas power plant, a biomass power plant, a solar energy cluster field and a wind energy cluster field,
the multi-objective optimization scheduling model of the regional electric heating interconnection energy system is as follows,
Figure FDA0003665517760000027
s.t.λ FFC +λ ARREN +λ CAREM ＝1
the method comprises the following steps that (1) muliobj is an objective function of a multi-objective optimization scheduling model, and FFC is an energy-saving optimization scheduling objective function; ARREN is a consumption type optimized scheduling objective function, CAREM is an emission reduction type optimized scheduling objective function, FFC obj 、ARREN obj 、CAREM obj Respectively are the optimal values of an energy-saving type optimized dispatching objective function, a consumption type optimized dispatching objective function and an emission reduction type optimized dispatching objective function, lambda FFC 、λ ARREN 、λ CAREM Weights corresponding to the energy-saving type optimized dispatching objective function, the absorption type optimized dispatching objective function and the emission reduction type optimized dispatching objective function are respectively set;
and solving a multi-objective optimization scheduling model of the regional electric heating interconnection energy system to obtain a multi-objective optimization scheduling result.
2. The multi-objective optimization method for a district electric heat interconnection energy system according to claim 1, characterized in that: the information of the regional electric heating interconnected energy system comprises resource distribution and configuration of the regional electric heating interconnected energy system, an energy flow architecture, an operation scheduling mode, installed capacity data of various types of power supplies, typical solar wind power output characteristic data in winter, typical solar photovoltaic output characteristic data in winter, typical solar power load characteristic data in winter, typical solar heat load characteristic data in winter and upper and lower limit data of adjustable heat load.
3. The multi-objective optimization method for a district electric heat interconnection energy system according to claim 1, characterized in that: the component unit models of the regional electric-thermal interconnection energy system comprise a gas power plant model, a nuclear power plant model, a hydraulic power plant model, a wind energy cluster field model, a solar energy cluster field model, a biomass power plant model, a thermal power plant model, an electric energy storage model, a pumped storage power station model, a heat storage type electric boiler model and a system input and output electric energy port model.
4. The multi-objective optimization method for a district electric heat interconnection energy system according to claim 3, characterized in that:
the model of the wind energy cluster field is as follows,
Figure FDA0003665517760000041
wherein alpha is WT The maximum abandoned wind power rate can be accepted for the wind energy cluster field;
the solar energy cluster field model is as follows,
Figure FDA0003665517760000042
wherein alpha is PV The maximum light rejection rate can be accepted for the solar energy collecting field;
the model of the thermal power plant is as follows,
Figure FDA0003665517760000043
wherein the content of the first and second substances,
Figure FDA0003665517760000044
is of the Nth classThermal output of the thermal power plant at the time t;
Figure FDA0003665517760000045
the heat output of the Nth type thermal power plant at the t +1 moment is obtained;
Figure FDA0003665517760000046
the thermoelectric ratio of the Nth type thermal power plant at the time t is obtained;
Figure FDA0003665517760000047
the upper limit and the lower limit of the thermoelectric ratio of the Nth type thermal power plant are respectively set;
Figure FDA0003665517760000048
the upper limit and the lower limit of the electric output of the Nth type thermal power plant are respectively;
Figure FDA0003665517760000049
the upper and lower climbing rates of the electric power of the thermal power plant are respectively; delta t is an optimized scheduling simulation step length;
the electric energy storage model is that,
Figure FDA0003665517760000051
wherein, ES (T), ES (T +1) and ES (T + T) are respectively the energy storage energy of the electric energy storage at T, T +1 and T + T moments; chi shape ES The energy self-loss rate of the stored energy is the electricity;
Figure FDA0003665517760000052
charging power of the electric energy storage at t and t +1 moments respectively;
Figure FDA0003665517760000053
storing the discharge power of the electricity at t and t +1 moments;
Figure FDA0003665517760000054
charging power up and down for electric energy storage respectivelyLimiting;
Figure FDA0003665517760000055
the upper limit and the lower limit of the discharge power of the electric energy storage are respectively;
Figure FDA0003665517760000056
C ES the upper limit and the lower limit of the actual energy storage level of the electric energy storage are respectively set;
Figure FDA0003665517760000057
for charging power efficiency;
Figure FDA0003665517760000058
to discharge power efficiency;
the pumped storage power station model is
Figure FDA0003665517760000059
The HP (T), HP (T +1) and HP (T + T) are the upper reservoir capacity of the pumped storage power station at the time of T, T +1 and T + T; chi-type food processing machine HP The energy self-loss rate of the pumped storage power station is obtained;
Figure FDA00036655177600000510
pumping power of the pumped storage power station at the time t and the time t + 1;
Figure FDA00036655177600000511
generating power of the pumped storage power station at the time t and the time t + 1;
Figure FDA00036655177600000512
respectively representing the upper limit and the lower limit of the pumping power of the pumped storage power station;
Figure FDA0003665517760000061
the upper limit and the lower limit of the generated power of the pumped storage power station are respectively set;
Figure FDA0003665517760000062
C HP the upper limit and the lower limit of the actual reservoir capacity of the pumped storage power station are respectively set;
Figure FDA0003665517760000063
the efficiency coefficient is set for the average water quantity to electric quantity when the pumped storage power station pumps water;
Figure FDA0003665517760000064
the efficiency coefficient is loaded and converted for the average water quantity to electric quantity when the pumped storage power station generates electricity;
the heat-storage electric boiler is modeled as follows,
Figure FDA0003665517760000065
wherein the content of the first and second substances,
Figure FDA0003665517760000066
outputting thermal power for the electric boiler at the time t;
Figure FDA0003665517760000067
inputting electric power for the electric boiler at the time t;
Figure FDA0003665517760000068
the electric heating efficiency of the electric boiler;
Figure FDA0003665517760000069
directly supplying thermal power to the electric boiler at the time t without passing through the heat storage tank; HST (T), HST (T +1) and HST (T + T) are the heat storage amount of the heat storage tank at T, T +1 and T + T moments; chi shape HST The energy self-loss rate of the heat storage tank is obtained;
Figure FDA00036655177600000610
the heat storage power of the heat storage tank at the time t and the time t +1 is obtained;
Figure FDA00036655177600000611
the heat release power of the heat storage tank at t and t +1 moments;
Figure FDA00036655177600000612
the upper limit and the lower limit of the heat storage power of the heat storage tank are respectively set;
Figure FDA00036655177600000613
the upper limit and the lower limit of the heat release power of the heat storage tank are respectively set;
Figure FDA00036655177600000614
C HST the upper limit and the lower limit of the heat storage capacity of the heat storage tank during actual work are respectively set;
Figure FDA00036655177600000615
the heat storage efficiency of the heat storage tank is improved;
Figure FDA00036655177600000616
the heat release efficiency of the heat storage tank is improved; AHL a (t) the actually participating adjustable heat load power of the regional electric heating interconnection energy system at the moment t; AHL is the maximum adjustable heat load power of the regional electric heating interconnection energy system;
the system input and output electric energy port model is,
Figure FDA0003665517760000071
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003665517760000072
respectively is a state variable of receiving and sending electric energy by the regional electric heating interconnection energy system at the moment t;
Figure FDA0003665517760000073
are respectively regional electric heating interconnectionThe energy system receives and sends out state variables of electric energy at the moment of t + 1; PCC (program-controlled computer) in (t)、PCC out (t) electric power received by and sent out by the regional electric heating interconnection energy system at the moment t respectively;
Figure FDA0003665517760000074
the upper limit and the lower limit of the electric power received by the regional electric heating interconnection energy system are respectively set;
Figure FDA0003665517760000075
the upper limit and the lower limit of the electric power externally supplied by the regional electric heating interconnection energy system are respectively set;
Figure FDA0003665517760000076
respectively optimizing the maximum times of starting work of the incoming call tie line and the outgoing call tie line in the scheduling period;
Figure FDA0003665517760000077
the lower limit of the ratio of the input electric energy to the output electric energy in the optimized dispatching period is respectively;
the gas power plant model, the nuclear power plant model, the hydraulic power plant model, the biomass power plant model and the thermal power plant model adopt a unified formula as follows,
Figure FDA0003665517760000081
the type is a type of a power plant, and the type is GP, NP, HY, BP and CP are respectively a gas power plant, a nuclear power plant, a hydraulic power plant, a biomass power plant and a thermal power plant; x type (t)、X type (t +1) is the working state variable of type at t and t +1 moments; p type (t)、P type (t +1) is the electric output of type at t and t +1 moments;
Figure FDA0003665517760000082
P type respectively representing the upper limit and the lower limit of the electric output of type at the time t;
Figure FDA0003665517760000083
ΔP type the climbing speeds are the upper climbing speed and the lower climbing speed of type respectively; n is a radical of type And the maximum starting times of the type in the optimized scheduling period.
5. The multi-objective optimization method for a district electric heat interconnection energy system according to claim 1, characterized in that: the preset energy balance constraint of the regional electric heating interconnection energy system comprises an electric energy balance constraint and a thermal energy balance constraint.
6. The multi-objective optimization method for a district electric heat interconnection energy system according to claim 5, characterized in that:
the electric energy balance is constrained to be,
Figure FDA0003665517760000084
EL (t) characterizes actual electric load requirements for units of the regional electric heating interconnection energy system at the time t; c EL Predicting the power demand for optimizing the maximum of the electrical load in the scheduling period;
Figure FDA0003665517760000085
respectively storing the charging power of the electric energy at the moment t;
Figure FDA0003665517760000086
pumping power of the pumped storage power station at the moment t;
Figure FDA0003665517760000087
inputting electric power for the electric boiler at the time t; PCC (program-controlled computer) in (t)、PCC out (t) electric power received by and sent out by the regional electric heating interconnection energy system at the moment t respectively; p type (t) is the electric output of type at t moment, type is the type of power plant, type is GP, NP, HY, BP, CP are gas power plant, nuclear power plant, hydroelectric power plant respectivelyBiomass power plants, thermal power plants;
Figure FDA0003665517760000091
storing the discharge power of the electricity at the time t;
Figure FDA0003665517760000092
generating power of the pumped storage power station at the time t;
the thermal energy balance is constrained to be,
Figure FDA0003665517760000093
HL (t) characterizes the actual heat load demand of the unit of the regional electric heating interconnection energy system at the time t; c HL Maximum predicted power requirements for thermal loads within the optimal scheduling period;
Figure FDA0003665517760000094
the thermal output of the Nth type thermal power plant at the time t is obtained; the AHL is the maximum adjustable heat load power of a regional electric heating interconnection energy system.
7. A computer readable storage medium storing one or more programs, wherein: the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods of claims 1-6.
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