CN110619421A - Generalized demand response and energy storage combined optimization operation method - Google Patents
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Abstract
The invention discloses a generalized demand response and energy storage combined optimization operation method, which comprises the following steps: establishing a GDR characteristic model under IEGS; establishing a multivariate energy storage equipment model, establishing a GDR-multivariate energy storage combined optimization operation model and solving; in the invention, from the perspective of global economic benefit, in the optimized operation process of the comprehensive energy system, the combined operation condition of the GDR and the multi-element energy storage equipment is fully considered, and the optimized calculation analysis is carried out on the combined operation condition, so that a corresponding optimal operation scheduling strategy is made for IEGS operators.
Description
Technical Field
The embodiment of the invention relates to the technical field of comprehensive energy system operation optimization, in particular to a generalized demand response and energy storage combined optimization operation method.
Background
With the widespread application of electric power-to-gas technology and gas turbine units, the coupling between the power network and the natural gas network is continuously enhanced, and the integrated electrical-energy systems (IEGS) gradually come into the field of vision of people. The conventional single power demand side response also shifts to a Generalized Demand Response (GDR) including a plurality of energy types. The IEGS operator can guide the user to change the energy utilization behavior by signing a demand response contract with the user or independently making an electricity selling price, so that the load curve of the system is changed, and the whole system runs in a optimizing way.
Meanwhile, the diversified development of the energy storage technology and the perfection of an energy market mechanism provide a larger optimization space for an electric-gas coupled energy interconnection system. The combined use of multiple energy storage devices and generalized demand response technology by the IEGS operator will more beneficially drive the overall system to operate optimally.
However, in the past, when the IEGS optimization operation is researched, only a single optimization space considering the energy storage device or the demand response technology is used, the joint optimization operation problem under the interactive scene of the energy storage device and the demand response technology is rarely analyzed, or only the demand response is used as an integral variable to be analyzed, and the demand response is not considered in detail.
Disclosure of Invention
Therefore, the embodiment of the invention provides a generalized demand response and energy storage combined optimization operation method, which comprises the steps of establishing a GDR characteristic model; then, establishing energy storage models such as a power storage station, an air storage tank and the like according to the operating characteristics of the multi-element energy storage equipment under the IEGS; finally, a GDR-multi-energy-storage combined optimization operation strategy is provided with the maximum optimization target of the IEGS operation benefit, and a selling electricity price and energy storage time sequence curve of each node in the IEGS is worked out, so that the problem that in the prior art, GDR and multi-energy-storage equipment are jointly mobilized to cause adverse effect on the IEGS economic operation is solved.
In order to achieve the above object, the embodiment of the present invention discloses the following technical solutions:
a generalized demand response and energy storage combined optimization operation method comprises the following specific steps:
s100, establishing a GDR characteristic model under IEGS;
s200, establishing a multivariate energy storage equipment model;
and S300, establishing a GDR-multivariate energy storage combined optimization operation model and solving.
Further, in the step S100, a GDR characteristic model under IEGS is established, and the method includes:
by assuming that the natural gas price is constant, a relation between the real-time electricity price and the price-guided demand response is obtained:
wherein epsilon is the price demand elasticity coefficient responded by the user;is the reference electricity price;is the electricity price at time t;is a reference response quantity;is the demand response at time t;
the price type load comprises rigid load, reducible load, translatable load and replaceable load, and the expression is as follows:
the characteristics of rigid loads, reducible loads, translatable loads, and alternative loads are represented by the following equations:
wherein ,the price demand elastic coefficient for rigid loads;the load is transferred by the node i in the time period t, a positive value represents the energy used for transferring the other time periods to the time period, and a negative value represents the energy used for transferring the time period to the other time periods;andthe alternative electric load and the alternative natural gas load of the node i in the electric power network and the natural gas network respectively in the time period t take a positive value to indicate that the energy in other forms is replaced and supplied by the energy in other forms, and take a negative value to indicate that the energy in other forms is replaced and supplied by the energy in other forms; etaRThe constant heat value conversion coefficient between the electric energy and the natural gas energy is obtained.
Further, in the step S200, a multivariate energy storage device model is established, and the method includes:
establishing a storage battery model:
wherein ,andrespectively representing the charging power and the discharging power of the energy storage battery;andrespectively representing the charging efficiency and the discharging efficiency of the energy storage battery;taking 1 to represent charging and discharging for 0-1 variable representing charging state and discharging state at time t;representing the residual electric energy of the energy storage battery at the time t; delta represents the self-discharge rate of the energy storage battery;
establishing a gas storage tank model:
wherein ,represents the effective storage volume of the storage tank;representing the geometric volume of the gas storage tank; p is a radical ofhigh and plowRespectively representing absolute pressures at the highest and lowest operating conditions; p is a radical of0Representing engineering standard pressure;andrespectively representing 0-1 variables of an inflation state and a deflation state at the time t, and taking 1 to represent inflation and deflation;andrespectively showing the charging power and the discharging power of the air storage tank.
Further, the steps of establishing and solving the GDR-multivariate energy storage joint optimization operation model in step S300 are as follows:
step S301, establishing a GDR-multivariate energy storage combined optimization operation model, which comprises the following steps:
establishing an objective function by taking the maximum operation benefit of the IEGS system as an optimization target;
formulating constraint conditions for constraining the objective function;
step S302, solving the joint optimization operation model, wherein the method comprises the following steps: the joint optimization operation model is based on a Yalmip platform in a Matlab environment, and a mature business solver Cplex is called to solve.
Further, in step S301, an objective function is formulated with the maximum operational benefit of the IEGS system as an optimization objective, and the expression is as follows:
max F＝F1+F2+F3+F4；
wherein ：
wherein, F1 is the energy acquisition cost; f2 is equipment running cost; f3 is GDR implementation benefit; f4 is energy storage operating cost;andrespectively the electricity purchasing power and the gas purchasing power purchased from a superior energy supplier;andrespectively the electricity purchase price and the gas purchase price purchased from a superior energy supplier;andthe price of electricity sold and the price of gas sold are respectively for IEGS operators; omega is a set formed by system nodes; t is a scheduling period which is 24 h;the IEGS operator has the output power of a conventional generator set;andthe unit power operating costs of the wind power generation set, the conventional generator set, the gas turbine and the electric gas conversion equipment owned by the IEGS operator respectively;andelectrical load and gas load for respectively signing an incentive type response of a demand response contract;andthe unit power compensation cost of the electric load and the gas load of the excitation type response respectively;andthe operating costs of the electricity storage equipment and the gas storage equipment respectively;
further, the step S301 formulates a constraint condition for constraining the objective function, which includes:
i) constraint of equality
In IEGS, energy load power balancing constraints include:
ii) inequality constraint
The GDR quantity constraints include:
wherein ,andrespectively a dead zone threshold and a saturation zone threshold of the electricity price at a node i in a time period t;
the operational constraints of the battery include:
the operational constraints of the air reservoir include:
the embodiment of the invention has the following advantages:
from the perspective of global economic benefits, in the optimized operation process of the comprehensive energy system, the combined operation condition of the GDR and the multi-element energy storage equipment is fully considered, and optimized calculation analysis is performed on the combined operation condition, so that a corresponding optimal operation scheduling strategy is made for IEGS operators.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
The structures, ratios, sizes, and the like shown in the present specification are only used for matching with the contents disclosed in the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions that the present invention can be implemented, so that the present invention has no technical significance, and any structural modifications, changes in the ratio relationship, or adjustments of the sizes, without affecting the effects and the achievable by the present invention, should still fall within the range that the technical contents disclosed in the present invention can cover.
FIG. 1 is a schematic diagram of a system scheduling of an IEGS carrier according to an embodiment of the present invention;
FIG. 2 is a graph showing a comparison of selling electricity prices in mode b and mode d according to the embodiment of the present invention;
fig. 3 is a diagram illustrating the operation of the multiple energy storage device in mode d according to the embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. When a component is referred to as being "disposed on" another component, it can be directly on the other component or intervening components may also be present.
Furthermore, the terms "long", "short", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of describing the present invention, but do not indicate or imply that the referred devices or elements must have the specific orientations, be configured to operate in the specific orientations, and thus are not to be construed as limitations of the present invention.
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
Example one
As shown in fig. 1 to 3, the invention discloses a generalized demand response and energy storage joint optimization operation method, which comprises the following specific steps:
s100, establishing a GDR characteristic model under IEGS;
s200, establishing a multivariate energy storage equipment model;
and S300, establishing a GDR-multivariate energy storage combined optimization operation model and solving.
Further, in the step S100, a GDR characteristic model under IEGS is established, and the method includes:
the IEGS operator makes electricity selling prices in real time, and by assuming that the natural gas price is constant, a relation between the real-time electricity prices and the price-guided demand response quantity is obtained:
wherein epsilon is the price demand elasticity coefficient responded by the user;is the reference electricity price;is the electricity price at time t;is a reference response quantity;is the demand response at time t;
the price type load comprises rigid load, reducible load, translatable load and replaceable load, and the expression is as follows:
the characteristics of rigid loads, reducible loads, translatable loads, and alternative loads are represented by the following equations:
wherein ,the price demand elastic coefficient for rigid loads;the load is transferred by the node i in the time period t, a positive value represents the energy used for transferring the other time periods to the time period, and a negative value represents the energy used for transferring the time period to the other time periods;andthe alternative electric load and the alternative natural gas load of the node i in the electric power network and the natural gas network respectively in the time period t take a positive value to indicate that the energy in other forms is replaced and supplied by the energy in other forms, and take a negative value to indicate that the energy in other forms is replaced and supplied by the energy in other forms; etaRThe constant heat value conversion coefficient between the electric energy and the natural gas energy is obtained.
Further, in the step S200, a multivariate energy storage device model is established, and the method includes:
the method comprises the following steps that a storage battery model is established, the storage battery needs to be charged with electric energy or released with electric energy in real time, but consumption exists in the electric energy transmission and conversion process, and a physical model of the method can be expressed as follows:
wherein ,andrespectively representing the charging power and the discharging power of the energy storage battery;andrespectively representing the charging efficiency and the discharging efficiency of the energy storage battery;taking 1 to represent charging and discharging for 0-1 variable representing charging state and discharging state at time t;representing the residual electric energy of the energy storage battery at the time t; delta represents the self-discharge rate of the energy storage battery;
the establishment of a gas storage tank model and a gas storage tank are main storage equipment units of a natural gas system, and because gas is easy to compress, natural gas is filled and released by controlling the gas pressure of the gas storage tank, and the physical model can be expressed as follows:
wherein ,represents the effective storage volume of the storage tank;representing the geometric volume of the gas storage tank; p is a radical ofhigh and plowRespectively representing absolute pressures at the highest and lowest operating conditions; p is a radical of0Representing engineering standard pressure;andrespectively representing 0-1 variables of an inflation state and a deflation state at the time t, and taking 1 to represent inflation and deflation;andrespectively showing the charging power and the discharging power of the air storage tank.
Further, the steps of establishing and solving the GDR-multivariate energy storage joint optimization operation model in step S300 are as follows:
step S301, establishing a GDR-multivariate energy storage combined optimization operation model, which comprises the following steps:
comprehensively considering the characteristics of source-load interaction and multi-element energy storage equipment under the IEGS, and establishing an objective function by taking the maximum operation benefit of the IEGS system as an optimization target;
formulating constraint conditions for constraining the objective function so as to ensure the safe and stable operation of the system;
step S302, solving the joint optimization operation model: the optimization model provided by the invention is a typical 0-1 mixed integer linear programming problem, the decision variables are the selling price of electricity at each moment and the SOC of the multi-element energy storage equipment at each moment, and the joint optimization operation model is based on a Yalmip platform in a Matlab environment and calls a mature commercial solver Cplex to solve, so that a global optimal solution is obtained.
Further, in step S301, an objective function is formulated with the maximum operational benefit of the IEGS system as an optimization objective, and the expression is as follows:
max F＝F1+F2+F3+F4；
the system operation income is the operation benefit generated by the IEGS operator at the energy purchase side and the end user side due to the joint optimization operation of the GDR and the multi-element energy storage equipment, wherein: f1 is energy acquisition cost; f2 is equipment running cost; f3 is GDR implementation benefit; f4 is energy storage operating cost; wherein:
wherein ,andrespectively the electricity purchasing power and the gas purchasing power purchased from a superior energy supplier;andrespectively the electricity purchase price and the gas purchase price purchased from a superior energy supplier;andthe price of electricity sold and the price of gas sold are respectively for IEGS operators; omega is a set formed by system nodes; t is a scheduling period which is 24 h;the IEGS operator has the output power of a conventional generator set; andthe unit power operating costs of the wind power generation set, the conventional generator set, the gas turbine and the electric gas conversion equipment owned by the IEGS operator respectively;andelectrical load and gas load for respectively signing an incentive type response of a demand response contract;andthe unit power compensation cost of the electric load and the gas load of the excitation type response respectively;andthe operating costs of the electricity storage equipment and the gas storage equipment respectively;
further, the step S301 formulates a constraint condition for constraining the objective function, which includes:
i) constraint of equality
In IEGS, energy load power balancing constraints include:
ii) inequality constraint
Actually, the IEGS operator can only implement GDR effectively within a certain electricity price interval, and considering the adjustable potential of each characteristic load, the GDR quantity constraints include:
wherein ,andrespectively a dead zone threshold and a saturation zone threshold of the electricity price at a node i in a time period t;
the energy storage device has certain physical and technical constraints of the energy storage device, and the operation constraints of the storage battery comprise:
accordingly, the operational constraints of the air tanks include:
and (3) benefit verification:
taking an actual regional IEGS as an example, an IEGS operator is set to provide energy services for 6 communities. The invention sets 4 system operation modes, which are respectively as follows:
(a) the system operation mode is implemented without multi-element energy storage and GDR;
(b) GDR only implemented system run mode;
(c) the system operation mode only has multivariate energy storage;
(d) and a GDR-multi-element energy storage combined system operation mode.
And respectively calculating the 4 system operation modes, and analyzing and comparing results to verify the effect of the invention.
The total revenue of the IEGS operator in these 4 operation modes respectively during the scheduling operation period is: 3.19, 3.5, 3.31 and 4.07 ten thousand yuan. As can be seen, mode (d) > mode (b) > mode (c) > mode (a), where mode (d) yields significantly better than the other three modes.
To further illustrate the effect of the joint optimization, the calculation results of the mode (b) and the mode (d) are selected for comparative analysis, as shown in fig. 2 and fig. 3. The electricity rate level of the mode (d) is more stable than that of the mode (b), which will also be beneficial to the production life of the end user. Further analysis, during the low energy consumption period (e.g. 1-5 period) of the load, the power price of the mode (d) is higher than that of the mode (b) because the energy storage device stores energy and part of the energy comes from the characteristic operation of TL and RL in the GDR, the power price is lower so that the user is less prone to gas, and the joint optimization enables the operator to meet the multi-energy user to a greater extent in the scheduling period. Accordingly, during peak load energy usage periods (e.g., 17-20 hours), mode (d) may have a lower power price than mode (b) because the energy storage devices are now discharging energy and some additional energy comes from the characteristics of TL and RL in the GDR, where higher power prices may make the users more favorable to gas, and the joint optimization may enable the operator to meet the multi-energy users to a greater extent during the scheduling period.
TABLE 1 IEGS in-Equipment information
TABLE 2 energy price parameters
| Energy purchase price | Base sales price | |
| Electric power | 0.48 | 0.5 |
| Natural gas | 2.8 | 2.9 |
In conclusion, compared with the prior art, the GDR-multivariate energy storage joint optimization operation strategy provided by the invention can increase the income of IEGS operators and also create better energy utilization environment for regional IEGS.
The above embodiments are merely to illustrate the technical solutions of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (6)
1. A generalized demand response and energy storage combined optimization operation method is characterized by comprising the following steps:
s100, establishing a generalized demand response characteristic model under an electrical comprehensive energy system;
s200, establishing a multivariate energy storage equipment model;
and S300, establishing a generalized demand response multivariate energy storage combined optimization operation model and solving.
2. The combined optimization operation method of generalized demand response and energy storage according to claim 1, wherein the step S100 of establishing a generalized demand response characteristic model under an electrical integrated energy system comprises:
assuming that the natural gas price is constant, the relation between the real-time electricity price and the price-guided demand response quantity is obtained as follows:
wherein ,εiA price demand elasticity coefficient for user response;is the reference electricity price;is the electricity price at time t;is a reference response quantity;is the demand response at time t;
the price type load comprises rigid load, reducible load, translatable load and replaceable load, and the expression is as follows:
the characteristics of rigid loads, reducible loads, translatable loads, and alternative loads are represented by the following equations:
wherein ,the price demand elastic coefficient for rigid loads;transferring load for node i during time period t;andrespectively replacing the electric load and the natural gas load of a node i in the electric power network and the natural gas network in a time period t; etaRThe constant heat value conversion coefficient between the electric energy and the natural gas energy is obtained.
3. The method of claim 1, wherein the step S200 of building the multivariate energy storage device model comprises:
establishing a storage battery model:
wherein ,andrespectively representing the charging power and the discharging power of the energy storage battery;andrespectively representing the charging efficiency and the discharging efficiency of the energy storage battery;taking 1 to represent charging and discharging for 0-1 variable representing charging state and discharging state at time t;representing the residual electric energy of the energy storage battery at the time t; delta represents the self-discharge rate of the energy storage battery;
establishing a gas storage tank model:
wherein ,represents the effective storage volume of the storage tank;representing the geometric volume of the gas storage tank; p is a radical ofhigh and plowRespectively representing absolute pressures at the highest and lowest operating conditions; p is a radical of0Representing engineering standard pressure;andrespectively representing 0-1 variables of an inflation state and a deflation state at the time t, and taking 1 to represent inflation and deflation;andrespectively showing the charging power and the discharging power of the air storage tank.
4. The joint optimization operation method of generalized demand response and energy storage according to claim 1, wherein the step of establishing and solving the joint optimization operation model of generalized demand response multivariate energy storage in step S300 is as follows:
step S301, establishing a joint optimization operation model of generalized demand response multivariate energy storage, which comprises the following steps:
establishing an objective function by taking the maximum operation benefit of the electricity-gas comprehensive energy system as an optimization target;
formulating constraint conditions for constraining the objective function;
step S302, solving the joint optimization operation model, wherein the method comprises the following steps: the joint optimization operation model is based on a Yalmip platform in a Matlab environment, and a mature business solver Cplex is called to solve.
5. The combined optimization operation method of generalized demand response and energy storage according to claim 4, wherein in step S301, an objective function is formulated with the maximum operation benefit of the electrical integrated energy system as an optimization objective, and the expression is as follows:
max F＝F1+F2+F3+F4；
wherein ：
wherein, F1 is the energy acquisition cost; f2 is equipment running cost; f3 is GDR implementation benefit; f4 is energy storage operating cost;andrespectively the electricity purchasing power and the gas purchasing power purchased from a superior energy supplier;andrespectively the electricity purchase price and the gas purchase price purchased from a superior energy supplier;andthe price of electricity sold and the price of gas sold are respectively for IEGS operators; omega is a set formed by system nodes; t is a scheduling period which is 24 h;the output power of a conventional generator set is owned by an electrical comprehensive energy system operator;andthe unit power operating costs of the wind power generation set, the conventional generator set, the gas turbine and the electric gas conversion equipment owned by the IEGS operator respectively;andelectrical load and gas load for respectively signing an incentive type response of a demand response contract;andthe unit power compensation cost of the electric load and the gas load of the excitation type response respectively;andthe operating costs of the electricity storage equipment and the gas storage equipment respectively.
6. The method of claim 4, wherein the step S301 of formulating constraints for constraining the objective function comprises:
i) and equality constraint, wherein in the electric comprehensive energy system, the energy load power balance constraint comprises the following steps:
ii) inequality constraints, the generalized demand-response constraints comprising:
wherein ,andrespectively a dead zone threshold and a saturation zone threshold of the electricity price at a node i in a time period t;
the operational constraints of the battery include:
the operational constraints of the air reservoir include:
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