CN109523137B - Garden comprehensive energy optimization scheduling method considering building thermal load demand response - Google Patents
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Abstract
A campus comprehensive energy optimization scheduling method considering building thermal load demand response comprises the following steps: inputting the structure and parameters of the park comprehensive energy system according to the selected park comprehensive energy system; establishing a campus comprehensive energy system optimization scheduling model considering building heat load demand response characteristics; carrying out linear transformation on related nonlinear constraints in the optimal scheduling model of the park integrated energy system, establishing a mixed integer linear programming model and calling a corresponding solver to solve; and outputting a solving result, wherein the solving result comprises running cost, a host start-stop instruction, running conditions, energy supply power, energy supply instructions and power of an energy storage device, the temperature of water supplied by a centralized energy station, the indoor temperature of a building and the like. The invention fully considers the transmission loss and delay influence of a pipe network, the characteristic that the room temperature of the building can be adjusted in a comfortable range and the coupling relation between a concentrated energy station and an energy supply pipe network and between the energy supply network and the building load, can better realize the management of a demand side, and economically and reliably meets the energy demand of users.
Description
Technical Field
The invention relates to a campus comprehensive energy optimization scheduling method. In particular to a campus comprehensive energy optimization scheduling method considering building thermal load demand response.
Background
With the continuous aggravation of the problems of environmental pollution, energy exhaustion and the like, how to improve the energy utilization efficiency and construct a low-carbon energy structure become the topics of common attention all over the world, and the requirements on interconnection integration and complementary fusion of multiple types of energy are increasingly urgent. Park integrated energy systems (CIES) have come into play in this context. The park comprehensive energy system is a complex coupling system which integrates multiple energy sources such as electricity, cold, heat, gas and the like and is composed of a centralized energy supply network and distributed energy utilization terminals, and can realize high-efficiency utilization of energy sources and improve operation flexibility and economy of operation through collaborative optimization of an energy supply side, a transmission side and a demand side.
In the energy supply period, most of the park comprehensive energy systems generate air-conditioning cold/hot water for refrigeration/heating from a centralized energy station, the air-conditioning cold/hot water is conveyed to each building through a water supply pipeline, and then the load side fan coil system works to achieve the refrigeration effect; and the cold/hot water of the air conditioner after temperature change returns to the energy collecting station through a water return pipeline. At the moment, the system operates and couples a plurality of energy links of the energy station, the energy supply network and the building load, and the optimized scheduling not only needs to coordinate the operation of a plurality of energy supply/storage devices of the energy station in the energy station, but also needs to fully consider the influence of transmission loss and delay of the energy supply network and the coupling among the energy supply links; in addition, the building load is used as a demand side response resource, the indoor temperature of the building load can be adjusted within a comfortable range, the building load has certain virtual energy storage characteristics, and the economical efficiency of system operation can be improved through flexible management of energy storage and energy release.
At present, the research of the comprehensive energy system of the park mostly takes the building load as a fixed value to carry out the operation optimization of the centralized energy station. Because the influence of energy supply pipeline transmission is neglected, the difference between the scheduling plan and the actual operation condition is large, and the requirement of actual scheduling cannot be met; and under the time-of-use electricity price mechanism, the characteristic that the indoor temperature of the building can be adjusted within a comfortable range is not considered, and the economical efficiency is poor. Therefore, an optimal scheduling method which gives consideration to the loads of the centralized energy source station, the energy supply network and the building, considers the dynamic transmission characteristics of the loss and the delay of the pipe network and the virtual energy storage characteristics of the building is urgently needed, the operation of the energy supply side, the transmission side and the demand side is coordinated, and the energy utilization requirements of the comprehensive energy system of the park are economically and reliably met.
Disclosure of Invention
The invention aims to solve the technical problem of providing a campus comprehensive energy optimization scheduling method which can better realize the management of a demand side, economically and reliably meet the user energy demand and considers the building thermal load demand response.
The technical scheme adopted by the invention is as follows: a campus comprehensive energy optimization scheduling method considering building thermal load demand response comprises the following steps:
1) Inputting electricity price information according to a selected park comprehensive energy system, reading predicted values of electricity load, cold/heat load and illumination intensity, and inputting equipment composition of a centralized energy station, equipment operation parameters, current stored energy of energy storage equipment, unit starting and stopping cost, a pipe network structure and parameters, building parameters, system scheduling intervals, heat medium parameters, building temperature parameters and upper and lower limit variables or parameters of outlet water temperature of the centralized energy station;
2) Establishing a campus comprehensive energy system optimization scheduling model considering the response characteristics of building thermal load demands according to the structure and parameters of the campus comprehensive energy system provided in the step 1), wherein the model comprises the following steps: setting the minimum sum of the operation cost and the unit start-stop cost of the park comprehensive energy system in one scheduling period as a target function, and respectively considering the operation constraint of a centralized energy station, the dynamic transmission constraint of a pipe network, the room temperature change constraint of a building, the coupling constraint of the centralized energy station and the pipe network, the coupling constraint of the pipe network and the building load, the supply and demand balance constraint of cold/heat load and the supply and demand balance constraint of electric load;
3) Carrying out linear transformation on relevant nonlinear constraints in the optimal scheduling model of the park comprehensive energy system obtained in the step 2), establishing a mixed integer linear programming model and calling a corresponding solver to solve;
4) And outputting the solving results of the step 3), including running cost, host start-stop instructions, running conditions, energy supply power, energy supply instructions and power of the energy storage device, water supply temperature of the centralized energy station, indoor temperature of the building and the like.
Setting the minimum sum of the operating cost and the unit starting and stopping cost of the park comprehensive energy system in one scheduling period as a target function, wherein the target function is expressed as follows:
in the formula, N T A total number of scheduling intervals for a scheduling period;
represents the price of the power purchased at the time t,
the power of the system and the external power grid tie line; Δ t is the system scheduling interval; e i The start-stop cost of different hosts is calculated; s is a device set, comprising: the system comprises a ground source heat pump, a conventional cold water host and a dual-working-condition host, wherein S is expressed as { HP, WC and DC };
the start-stop states of the jth ground source heat pump, the conventional cold water main engine and the dual-working-condition main engine at the moment t are respectively set,
a value of 1 indicates an operating state, and 0 indicates a shutdown state; omega HP 、Ω WC 、Ω DC Respectively a ground source heat pump, a conventional cold water main machine and a dual-working-condition main machine.
The building room temperature change constraint in the step 2) is expressed as:
-ΔT≤T i，t -T i，t - 1 ≤ΔT
T i，intitial ＝T i，end
in the formula, T i，t The indoor temperature of a building i at the moment t;
energy injection and energy loss of building i at time t, respectively; c air 、ρ air Air specific heat capacity and density respectively; v i The volume of the building i; t is out，t The outdoor temperature at time t; k i 、F i Respectively the average heat dissipation coefficient and the external surface area of the building i;
Trespectively an upper limit and a lower limit of the building temperature; delta T is the temperature ramp rate limit in the adjacent time period; t is aver，max 、T aver，min Respectively the upper limit and the lower limit of the average room temperature of the building; t is i，intitial 、T i，end Respectively building i initial temperature and terminal moment temperature.
The pipe network-building load coupling constraint in the step 2) is expressed as:
in the formula (I), the compound is shown in the specification,
injecting power into the building i for the pipeline at the time t;
respectively the temperature of cold water at an inlet and an outlet of a building i;
the temperatures of a water supply pipeline tail end node and a water return pipeline head end node which are connected with a building are respectively measured;
is a collection of pipes connected to a building i.
The invention relates to a campus comprehensive energy optimal scheduling method considering building heat load demand response, which aims to solve the problem of optimal scheduling of a campus comprehensive energy system, fully considers the transmission loss and delay influence of a pipe network, the characteristic that the room temperature of a building can be adjusted in a comfortable range and the coupling relation between a centralized energy station, an energy supply pipe network and a building load, plays the role of building load demand response characteristics on improving the system economy, establishes a unified optimal scheduling model of a campus comprehensive energy system source-network-load multi-energy link, and calls a relevant mathematical solver to solve through nonlinear constraint linear transformation to obtain a day-ahead scheduling plan, so that the management of a demand side can be better realized, and the energy demand of users can be economically and reliably met.
Drawings
FIG. 1 is a flow chart of a campus complex energy optimization scheduling method of the present invention that considers building thermal load demand response;
FIG. 2 is a diagram of a centralized energy station energy supply architecture;
FIG. 3 is a block diagram of a system power supply network;
FIG. 4 is a graph of building stored energy power considering only the building thermal load demand response characteristics;
FIG. 5 is a graph of building energy storage power in consideration of both pipe network dynamic transmission characteristics and building thermal load demand response characteristics;
FIG. 6 is a diagram of building room temperature changes under a scheduling strategy without considering pipe network dynamic transmission characteristics and building thermal load demand response characteristics;
FIG. 7 is a graph of building room temperature change under a dispatch strategy that considers only building thermal load demand response characteristics;
FIG. 8 is a diagram of the room temperature change of a building under a scheduling strategy only considering the dynamic transmission characteristics of a pipe network;
fig. 9 is a diagram of the change of the room temperature of the building under the scheduling strategy considering both the dynamic transmission characteristic of the pipe network and the thermal load demand response characteristic of the building.
Detailed Description
The invention provides a park comprehensive energy resource optimization scheduling method considering building thermal load demand response, which is described in detail in the following with reference to embodiments and drawings.
As shown in fig. 1, the method for optimal scheduling of park integrated energy considering building thermal load demand response of the present invention includes the following steps:
1) Inputting electricity price information according to a selected park comprehensive energy system, reading predicted values of electric load, cold/heat load and illumination intensity, and inputting equipment composition, equipment operation parameters, current stored energy of energy storage equipment, unit start-stop cost, pipe network structure and parameters, building parameters, system scheduling intervals, heating medium parameters, building temperature parameters and upper and lower limit variables or parameters of water temperature at the outlet of the concentrated energy station;
2) Establishing a campus comprehensive energy system optimization scheduling model considering the response characteristics of building thermal load demands according to the structure and parameters of the campus comprehensive energy system provided in the step 1), wherein the model comprises the following steps: setting the minimum sum of the operating cost and the unit starting and stopping cost of the park integrated energy system in a scheduling period as a target function, and respectively considering the operation constraint of a centralized energy station, the dynamic transmission constraint of a pipe network, the room temperature change constraint of a building, the coupling constraint of the centralized energy station and the pipe network, the coupling constraint of the pipe network and the building load, the supply and demand balance constraint of cold/heat load and the supply and demand balance constraint of electric load; wherein the content of the first and second substances,
(1) The minimum sum of the operation cost and the unit starting and stopping cost in a scheduling period of the park comprehensive energy system is set as a target function and is expressed as follows:
in the formula, N T The total scheduling interval number of one scheduling period;
represents the price of the power purchased at the time t,
power for the system and external grid tie lines; Δ t is the system scheduling interval; e i The start-stop cost of different hosts; s is a device set, comprising: the system comprises a ground source heat pump, a conventional cold water host and a dual-working-condition host, wherein S is expressed as { HP, WC and DC };
starting and stopping states of the jth ground source heat pump, the conventional cold water main machine and the dual-working-condition main machine at the moment t respectively; omega HP 、Ω WC 、Ω DC Respectively a ground source heat pump, a conventional cold water main machine and a dual-working-condition main machine.
A model-dependent binary variable of 1 represents the power mode/device in the on/off state and 0 represents the off/off state, the same applies below.
(2) The operation constraint of the centralized energy station comprises the following steps:
(2.1) operation constraint of the ground source heat pump unit:
in the formula (I), the compound is shown in the specification,
supplying cold power to the ith ground source heat pump at the moment t; n is a radical of hydrogen HP The number of the ground source heat pump main machines is;
respectively the minimum and maximum refrigeration power of the heat pump host;
the power consumed by the heat pump unit at the moment t;
is the ith heat pump coefficient of performance (COP); p is a radical of HP，CWP And P HP，CP And rated power of the refrigerating water pump and the cooling water pump are respectively interlocked for the heat pump host.
(2.1) conventional water chilling unit operation constraint:
in the formula (I), the compound is shown in the specification,
supplying cooling power to the ith conventional cold water main machine at the moment t; n is a radical of WC The number of the conventional cold water main machines is counted;
are respectively a constantRegulating the lower limit and the upper limit of the refrigeration power of the cold water main machine;
consuming power for a conventional water chilling unit at time t;
is the coefficient of performance of the conventional cold water main engine; p WC，CWP 、p WC，CP And p WC，CT The rated power of the interlocking chilled water pump, the cooling water pump and the open cooling tower of the conventional cold water main machine are respectively.
(2.3) operation constraint of the ice storage system:
in the formula (I), the compound is shown in the specification,
the refrigeration power of the ice cold storage system and the ice storage tank at the moment t;
the refrigeration and ice making powers of the ith double-working-condition host at the moment t are respectively;
the lower limit and the upper limit of the refrigeration power of the dual-working-condition main machine are set;
the lower limit and the upper limit of ice making power of the dual-working-condition main machine are set;
the operation mode of refrigeration and ice making of the ith dual-working-condition host at the moment t;
a refrigeration and ice-making operation mode of the double-working-condition unit at the moment t;
cold energy is stored in the ice storage tank at the moment t;W IT 、
the lower limit and the upper limit of the cold quantity stored in the ice storage tank are set; epsilon IT The self-cooling rate of the ice storage tank is obtained; Δ t is a scheduling interval;
the upper limit of the cold discharge power of the ice storage tank is set;
the upper limit of the refrigeration power of a single refrigeration water pump is set;
starting and stopping a freezing water pump of the ith ice storage system at the moment t;
the power consumption of the ice storage system is t moment;
coefficient of performance of refrigeration and ice making for dual-working condition main machine, P EP 、P DC，CP 、P DC，CT 、P IS，CWP Respectively the rated power of the glycol solution pump, the cooling water pump, the open cooling tower and the freezing water pump.
(3) The dynamic transmission constraint of the pipe network is expressed as
In the formula, m i，t The water flow of the ith pipeline at the time t;
respectively the head end temperature and the tail end temperature of the ith pipeline at the time t; x belongs to { s, r), wherein s and r respectively represent a water supply pipeline and a water return pipeline;
is the mixing temperature of the crossing node i at time t;
the pipeline sets are respectively a tail end node and a head end node in the water supply/return network, which are nodes i, namely inflow and outflow nodes; n is i，t The minimum scheduling interval for the cold water flowing into the pipeline i at the time t to flow out of the pipeline for the first time; s i，t 、R i，t The mass of cold water flowing into the pipeline i is t to t + n and t to t + n-1 respectively; rho, A i 、L i Are respectively emptyAdjusting the density of cold water and the cross sectional area and length of the ith pipeline; q. q of i，t A weighting coefficient is delayed for the ith pipeline at the time t; lambda i Heat transfer coefficient per unit length of pipe; c w Is the specific heat capacity of water; t is a Is the ambient temperature. The pipeline transmission delay diagram is shown in figure 4.
(4) The building room temperature change constraint is represented as:
-ΔT≤T i，t -T i，t - 1 ≤ΔT (31)
T i，intitial ＝T i，end (33)
in the formula, T i，t The indoor temperature of the building i at the moment t;
energy injection and energy loss of building i at time t, respectively; c air 、ρ air Air specific heat capacity and density respectively; v i Is the volume of building i; t is out，t Is the outdoor temperature at time t; k is i 、F i Respectively the average heat dissipation coefficient and the external surface area of the building i;
Trespectively an upper limit and a lower limit of the building temperature; delta T is the temperature ramp rate limit in adjacent time periodsPreparing; t is aver，max 、T aver，min Respectively the upper limit and the lower limit of the average room temperature of the building; t is i，intitial 、T i，end Respectively building i initial temperature and terminal moment temperature.
(5) The constraint of the centralized energy station-pipe network coupling is expressed as
In the formula (I), the compound is shown in the specification,
supplying energy power to the concentrated energy source station at the moment t;
respectively setting the return water temperature and the water supply temperature of the air conditioner cold water of the centralized energy station at the time t;
respectively the temperature of a water supply pipeline head end node and a water return pipeline tail end node connected with the centralized energy station; t is ces，min 、T ces，max Respectively setting the lower limit and the upper limit of the temperature of cold water of an air conditioner at the outlet of the centralized energy station; omega ces Is a collection of pipes connected to a centralized energy station.
(6) The pipe network-building load coupling constraint is expressed as:
in the formula (I), the compound is shown in the specification,
injecting power into the Chinese character 'ji' for the pipeline at the moment t;
respectively the temperature of cold water at an inlet and an outlet of a building i;
the temperatures of a water supply pipeline tail end node and a water return pipeline head end node which are connected with a building are respectively measured;
is a collection of pipes connected to a building i.
(7) The cold/heat load supply and demand balance constraint is expressed as
(8) The constraint of the balance of the supply and the demand of the electrical load is expressed as
In the formula (I), the compound is shown in the specification,
for the photovoltaic system output power and the tie line power at the time t respectively,
for the maximum allowed power value of the tie-line,
the system electrical load is time t.
3) Carrying out linear transformation on relevant nonlinear constraints in the optimal scheduling model of the park comprehensive energy system obtained in the step 2), establishing a mixed integer linear programming model and calling a corresponding solver to solve;
for the formula (2), (5), (9) and (10), after expansion, multiplier terms of binary variables and continuous variables exist, and for the formula (1), multiplier terms of binary variables and binary variables exist, and the nonlinear terms are linearized by introducing auxiliary variables and constraints. After linearization, this optimization problem translates into a mixed integer linear programming problem:
(1) For a nonlinear term Ur, where U is a binary variable, r is a continuous positive variable, and the upper limit of r is
An auxiliary variable R can be introduced to replace the non-linear term, and R satisfies the following constraint:
(2) For the non-linear term U 1 U 2 Wherein U is 1 、U 2 For binary variables, an auxiliary binary variable U is introduced to replace the non-linear term, and U satisfies the following constraint:
4) And outputting the solving results of the step 3), including running cost, host start-stop instructions, running conditions, energy supply power, energy supply instructions and power of the energy storage device, water supply temperature of the centralized energy station, indoor temperature of the building and the like.
The invention establishes a park comprehensive energy optimization scheduling method considering building thermal load demand response, and solves the problem by adopting a relevant solver after linearization to obtain a system operation scheme in a scheduling period.
For the embodiment, firstly, the electricity price information is input, and the system has an electricity load predicted value, a cold load predicted value and an illumination intensity predicted value in a scheduling period; and then inputting initial values of variables or parameters such as the composition of centralized energy station equipment, equipment operation parameters, the current cold storage capacity of cold storage equipment, the start-stop cost of a unit, pipe network parameters, heat medium parameters, building temperature parameters, upper and lower limits of outlet water temperature of the centralized energy station and the like. In the system, an external power grid and a photovoltaic system meet the power demand; the centralized energy station generates air conditioner cold water which is conveyed to each building through an energy supply pipeline, and the fan coil pipe meets the cooling demand. The centralized energy source station comprises: 3 ground source heat pumps, 2 conventional cold water main machines and a group of ice storage subsystems (two double-working-condition main machines and one ice storage tank). The cooling structure of the centralized energy station is shown in figure 2, the detailed parameters are shown in table 1, the structure and the parameters of the system energy supply network are shown in figures 3 and 2, and the building parameters are shown in table 3. Initial values of cold capacity of ice storage are all 0; the starting costs of the ground source pump, the conventional cold water main engine and the dual-working-condition main engine are respectively 40.0 yuan/time, 120.0 yuan/time and 120.0 yuan/time; the system scheduling interval is 30min; the density and specific heat capacity of cold water of the air conditioner are respectively 1000kg/m 3 4.2 kJ/(kg ℃ C.); the air density and the specific heat capacity are respectively 1.2kg/m 3 1.007 kJ/(kg ℃ C.); the lower limit and the upper limit of the temperature of cold water of an air conditioner at the outlet of the centralized energy station are respectively 5 ℃ and 8 ℃; setting the lower limit and the upper limit of the building temperature to be 19 ℃ and 25 ℃, the standard temperature to be 22 ℃, the upper limit of the building temperature change of the adjacent dispatching interval to be 2 ℃, and the lower limit and the upper limit of the daily average temperature to be 21 ℃ and 23 ℃ respectively; peak power price 1.35/kWh (8-00-11, 18, 00-23), valley power price 0.47/kWh (00-7, 23-00.
Comparing the operating costs of systems adopting different scheduling strategies, and the result is shown in a table 4, wherein a traditional economic scheduling strategy is adopted in the strategy 1 (namely the dynamic transmission characteristic of a pipeline and the response characteristic of the thermal load demand of a building are not considered), the dynamic transmission characteristic of a pipe network is not considered in the strategy 2, the dynamic transmission characteristic of the pipe network is not considered in the strategy 3, and the dynamic transmission characteristic of the pipe network is not considered in the strategy 3; and the strategy 4 simultaneously considers the dynamic transmission characteristic of the pipe network and the building thermal load demand response characteristic. The building energy storage curves of the strategy 2 and the strategy 4 are respectively shown in the figure 4 and the figure 5. The influence of different scheduling strategies on the actual operation of the system is mainly reflected on the change of the indoor temperature of the building on the load side, and the change of the indoor temperature of the building when different scheduling strategies are executed is shown in fig. 6-9.
The computer hardware environment for executing the optimized calculation is Intel (R) Xeon (R) CPU E5-2603, the dominant frequency is 1.60GHz, and the internal memory is 8GB; the software environment is a Windows 10 operating system.
Compared with the system operation cost under different scheduling strategies, the system operation cost can be seen that after the building heat load demand response is considered under the time-of-use electricity price mechanism, the building load energy storage at the valley price and the energy release at the non-valley price (see fig. 4 and 5) can be greatly reduced, and the operation cost can be greatly reduced, and the operation economy can be improved. After the energy supply network is considered, the operation cost is properly increased due to the influence of the transmission loss of the energy supply network, but the optimized scheduling result is more suitable for practical application due to the fact that the transmission process of the network in practical operation is considered.
Compared with the indoor temperature change curves of the building under different scheduling strategies, the indoor temperature of the building under the strategy (strategy 1 and strategy 2) without considering the dynamic transmission characteristics of the pipeline can fluctuate rapidly at adjacent moments in actual operation, the room temperature of the building in the strategy 2 deviates from a comfortable temperature interval (19-25 ℃), and the comfort of a user is greatly influenced by the phenomena; after the dynamic transmission characteristics of the pipe network are fully considered, the indoor temperature of the building in the strategy 3 is always maintained at the standard temperature (see fig. 8) in actual operation, and the indoor temperature in the strategy 4 is slowly changed in a comfort interval (see fig. 9).
In conclusion, the optimal scheduling method for the comprehensive energy of the garden in consideration of the building thermal load demand response fully considers the transmission loss and delay influence of a pipe network and the demand response characteristic of adjustable building indoor temperature, is close to the operation of an actual system, can better realize the management of a demand side, and economically and reliably meets the user energy demand.
TABLE 1 centralized energy station architecture and parameters
TABLE 2 energy supply network parameter information
TABLE 3 building parameter information
TABLE 4 comparison of operating costs for different scheduling strategies
Claims (4)
1. A campus comprehensive energy optimization scheduling method considering building thermal load demand response is characterized by comprising the following steps:
1) Inputting electricity price information according to a selected park comprehensive energy system, reading predicted values of electricity load, cold/heat load and illumination intensity, and inputting equipment composition of a centralized energy station, equipment operation parameters, current stored energy of energy storage equipment, unit starting and stopping cost, a pipe network structure and parameters, building parameters, system scheduling intervals, heat medium parameters, building temperature parameters and upper and lower limit variables or parameters of outlet water temperature of the centralized energy station;
2) Establishing a campus comprehensive energy system optimization scheduling model considering the response characteristics of building thermal load demands according to the structure and parameters of the campus comprehensive energy system provided in the step 1), wherein the model comprises the following steps: setting the minimum sum of the operation cost and the unit start-stop cost of the park comprehensive energy system in one scheduling period as a target function, and respectively considering the operation constraint of a centralized energy station, the dynamic transmission constraint of a pipe network, the room temperature change constraint of a building, the coupling constraint of the centralized energy station and the pipe network, the coupling constraint of the pipe network and the building load, the supply and demand balance constraint of cold/heat load and the supply and demand balance constraint of electric load;
3) Carrying out linear transformation on relevant nonlinear constraints in the optimal scheduling model of the park comprehensive energy system obtained in the step 2), establishing a mixed integer linear programming model and calling a corresponding solver to solve;
4) And outputting the solving result of the step 3), which comprises running cost, a host start-stop instruction, running conditions, energy supply power, an energy supply instruction and power of an energy storage device, the temperature of water supplied by a centralized energy station and the indoor temperature of a building.
2. The optimal scheduling method of the comprehensive energy resource of the garden in consideration of the building thermal load demand response as claimed in claim 1, wherein the minimum sum of the operation cost and the unit start-stop cost in one scheduling period of the comprehensive energy resource system of the garden in the step 2) is set as an objective function, and is expressed as:
in the formula, N T The total scheduling interval number of one scheduling period;
represents the price of the electricity purchased at the time t,
for power of system and external grid tie-line(ii) a Δ t is the system scheduling interval; e i The start-stop cost of different hosts; s is a device set, comprising: the system comprises a ground source heat pump, a conventional cold water host and a dual-working-condition host, wherein S is expressed as { HP, WC and DC };
respectively starting and stopping states of the jth ground source heat pump, the conventional cold water host and the dual-working-condition host at the moment t,
a value of 1 indicates an operating state, and 0 indicates a shutdown state; omega HP 、Ω WC 、Ω DC Respectively a ground source heat pump, a conventional cold water main machine and a dual-working-condition main machine.
3. The method for the optimal scheduling of the integrated energy resources in the campus based on the consideration of the building thermal load demand response as claimed in claim 1, wherein the building room temperature variation constraint in step 2) is expressed as:
-ΔT≤T i,t -T i,t-1 ≤ΔT
T i,intitial ＝T i,end
in the formula, T i,t Is at t timeCarving building i indoor temperature;
respectively energy injection and energy loss of building i at the time t; c air 、ρ air Air specific heat capacity and density respectively; v i Is the volume of building i; t is out,t Is the outdoor temperature at time t; k is i 、F i Respectively the average heat dissipation coefficient and the external surface area of the building i;
Trespectively an upper limit and a lower limit of the building temperature; delta T is the temperature ramp rate limit in the adjacent time period; t is a unit of aver,max 、T aver,min Respectively the upper limit and the lower limit of the average room temperature of the building; t is i,intitial 、T i,end Respectively the initial temperature and the terminal moment temperature of the building i; n is a radical of T The total scheduling interval number of one scheduling period; Δ t is the system scheduling interval.
4. The optimal scheduling method for comprehensive energy resources in a garden taking account of the response of the building thermal load demand as claimed in claim 1, wherein the pipe network-building load coupling constraint of step 2) is expressed as:
in the formula (I), the compound is shown in the specification,
at time tInjecting power from the pipeline to the building i;
respectively the temperature of cold water at an inlet and an outlet of a building i at the moment t;
respectively measuring the temperature of a water supply pipeline tail end node and a water return pipeline head end node connected with the building at the time t;
is a collection of pipes connected to a building i.
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| CN110619110B (en) * | 2019-09-18 | 2023-04-07 | 南京师范大学 | Coordinated operation optimization method for comprehensive energy system with heat pump |
| CN111415030B (en) * | 2019-11-19 | 2022-11-29 | 广西电网有限责任公司 | Building energy system comprehensive demand response optimization method considering energy utilization comfort level |
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| CN112050273B (en) * | 2020-08-05 | 2021-10-22 | 河海大学 | Hot water pipe network virtual energy storage control method based on quasi-dynamic time delay characteristic |
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