CN115587668A - Distributed collaborative optimization scheduling method for multi-park integrated energy system - Google Patents
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Abstract
The invention provides a distributed collaborative optimization scheduling method for a multi-park integrated energy system, which is characterized in that a two-stage robust optimization scheduling model of the multi-park integrated energy system is constructed based on a deterministic model and in combination with source load uncertainty, interactive energy among parks is taken as a first-stage decision variable, a source load uncertainty set is brought into a second stage, and output of equipment in the parks and outsource energy purchase are taken as decision variables of the second stage; and (3) iteratively solving the two-stage robust model by adopting a nested CCG algorithm, and solving the problem of the second-stage double-layer mixed integer programming. And further carrying out cooperative optimization among different parks, splitting the problem into a social benefit maximization subproblem and a benefit distribution subproblem based on an asymmetric Nash bargaining theory and through related transformation, and solving by adopting a parallel ADMM method to obtain a final scheduling scheme. The invention can realize the balance of the economy and the robustness of operation of each park and also give consideration to the privacy and the benefits of each park.
Description
Technical Field
The invention belongs to the technical field of comprehensive energy systems, and relates to a distributed collaborative optimization scheduling method and system for a multi-park comprehensive energy system, in particular to a distributed collaborative optimization scheduling method and system for a park comprehensive energy system under the condition of uncertain source load.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Under the background of the era of energy crisis and environmental deterioration, an Integrated Energy System (IES) is coupled with various heterogeneous energy flows to realize flexible utilization and benefit improvement of energy, and the method is a powerful means for realizing energy transformation. According to the application and the geographical scale, the park-level comprehensive energy system can be well adapted to the characteristics of local resources and energy utilization, the resource allocation and energy utilization characteristics of different parks are different, and the multi-park comprehensive energy system is formed by mutual coordinated interconnected operation, so that the energy supply efficiency and the reliability can be effectively improved. However, the multi-campus integrated energy system has the characteristics of multi-main body and strong coupling, not only the conversion of multi-type energy flows in each campus needs to be considered, but also the multi-type energy interaction between the garages needs to be considered, so that great difficulty is brought to the development of optimized scheduling.
However, most of the current relevant research is deterministic optimal scheduling, the influence of uncertainty of source load on the system is ignored, and the original deterministic optimization is no longer applicable, so that not only the economy of the system needs to be considered, but also the robustness of the system needs to be considered, and the two are not available. Meanwhile, on the premise of both robustness and economy, the problem of reasonable benefit redistribution under the cooperation of a plurality of main bodies is rarely involved.
Disclosure of Invention
The invention provides a distributed collaborative optimization scheduling method for a multi-park integrated energy system to solve the problems, and the method can realize balance of the operation economy and robustness of the whole system and give consideration to the privacy and the benefits of various parks.
According to some embodiments, the invention adopts the following technical scheme:
a distributed collaborative optimization scheduling method for a multi-park integrated energy system comprises the following steps:
establishing a deterministic model of a multi-park comprehensive energy system, and applying constraint conditions;
determining a source load uncertain variable variation range in the park comprehensive energy system, and constructing an uncertain set for representation;
constructing a two-stage robust optimization scheduling model of the park comprehensive energy system based on a deterministic model and in combination with uncertainty of source load, taking interactive energy between parks as a first-stage decision variable, bringing a source load uncertainty set into a second stage, and taking output of equipment inside a park and external network purchase energy as decision variables of the second stage;
splitting the model into an outer layer main problem and a sub problem, splitting the outer layer sub problem into an inner layer main problem and an inner layer sub problem, iteratively solving the inner layer main problem and the inner layer sub problem until convergence is achieved, then iterating the outer layer main problem and the outer layer sub problem until a convergence condition is met, and calculating an optimal solution;
and carrying out cooperative optimization between different parks, splitting the parks into a social benefit maximization subproblem and a benefit distribution subproblem based on an asymmetric Nash bargaining theory, and solving the two subproblems to obtain a final scheduling scheme.
As an alternative embodiment, the specific process of establishing the multi-park integrated energy system deterministic model includes:
the deterministic model objective function is the minimum sum of the operation costs of all the parks, wherein the operation costs comprise the energy purchasing cost, the equipment operation and maintenance cost and the carbon transaction cost in an independent operation mode and the energy transaction cost and the network passing cost between the interconnected operation mode and other parks;
the constraints include device output constraints, energy balance constraints, and tie-line transmission power constraints.
As an alternative embodiment, the specific process of determining the source load uncertain variable variation range in the campus comprehensive energy system includes applying a box type uncertain set to describe wind power photovoltaics and cold and heat power loads, and the temporal correlation between the wind turbine and the photovoltaic is represented by a time correlation constraint model of pearson correlation coefficients.
As an alternative implementation mode, based on a deterministic model and in combination with uncertainty of source load, the first stage of the two-stage robust optimization scheduling model is the network passing cost of the garden, and the interaction power value between gardens is optimized; and in the second stage of the model, the optimal scheduling strategy and the worst scene in the park are solved on the basis of the interactive power value in the first stage, and when the interactive quantity in the first stage is 0, the model is an independent robust model of each park under the uncertain condition. Constraints include interactive power constraints, internal equality constraints, and non-negatives constraints that define internal decision variables.
As an alternative implementation mode, an improved nested CCG algorithm is adopted for solving, the model is divided into an outer layer main problem and a sub problem, the outer layer main problem is solved to obtain the transaction electric heat quantity of the garden interval, and the lower boundary of the algorithm is updated; the outer-layer sub-problem is a max-min double-layer optimization problem, the problem is a double-layer mixed integer linear programming problem, a conventional method for converting double layers into single layers fails, the CCG algorithm thought is used for reference, the outer-layer sub-problem is divided into inner-layer main and sub-problems, the inner-layer main and sub-problems are solved in an iterative mode until convergence is achieved, and therefore the solution of the max-min double-layer optimization problem is obtained.
As an alternative embodiment, the specific process of collaborative optimization among different parks comprises the steps that coupling among the parks is reflected on interconnection power and transaction price, and coupling variables are decoupled by introducing consistency variables.
The bargaining capability is quantified by constructing a contribution function through the transaction amount of the electrothermal energy, so that the total provided energy value and the total obtained energy value of each park during optimization are obtained, the contribution function of each park is constructed, the contribution function is added into a Nash bargaining expression, an asymmetric Nash bargaining profit distribution model of multiple parks is obtained, and the asymmetric Nash bargaining profit distribution model is equivalent to a social benefit maximization sub-problem and a benefit distribution sub-problem through relevant transformation. The social benefit maximization sub-problem target is the sum of the overall benefits of uncertain variables under the worst condition, namely the operation targets of all the parks, the coupling variable is only the interactive electric heating power value, each park is subjected to iterative optimization solution until the convergence condition is met, and the optimal scheduling plan and the interactive electric heating quantity of each park are obtained. The benefit distribution sub-problem aims at balancing and optimizing the benefits of each party, realizing reasonable distribution of cooperative benefits, obtaining the energy source interaction price of each park through iterative solution of each park, and finally determining the cost of each park.
A distributed collaborative optimization scheduling method for a multi-park integrated energy system comprises the following steps:
the deterministic model building module is configured to build a deterministic model of the multi-park integrated energy system and apply constraint conditions;
the uncertainty consideration module is configured to determine a source load uncertainty variable variation range in the park comprehensive energy system, and construct an uncertainty set for representation;
the optimization scheduling model building module is configured to build a two-stage robust optimization scheduling model of the park comprehensive energy system based on a deterministic model and in combination with source load uncertainty, and takes interactive energy among parks as a first-stage decision variable, brings a source load uncertainty set into a second stage, and takes output of equipment inside the park and outsource purchasing energy as a second-stage decision variable;
the solving module is configured to split the model into an outer layer main problem and a sub problem, split the outer layer sub problem into an inner layer main problem and an inner layer sub problem, iteratively solve the inner layer main problem and the inner layer sub problem until convergence is achieved, iterate the outer layer main problem and the outer layer sub problem until a convergence condition is met, and calculate an optimal solution;
and the collaborative optimization module is configured to perform collaborative optimization between different parks, split the parks into a social benefit maximization sub-problem and a benefit allocation sub-problem based on an asymmetric Nash bargaining theory, and solve the two sub-problems to obtain a final scheduling scheme.
A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to carry out the steps of the method.
A terminal device comprising a processor and a computer readable storage medium, the processor for implementing instructions; the computer readable storage medium is used for storing a plurality of instructions adapted to be loaded by a processor and to perform the steps of the method.
Compared with the prior art, the invention has the beneficial effects that:
the method solves the difficulty that the inner-layer subproblem cannot be directly converted into the single-layer subproblem when the inner-layer subproblem contains 0-1 variable by constructing a two-stage robust optimization model to deal with the uncertainty of source load and adopting a nested CCG algorithm to solve; for a model of multi-energy interconnection of electric heat among different parks, the model is divided into a social benefit maximization sub-problem and a benefit distribution sub-problem based on an asymmetric Nash bargaining theory, completely distributed solving can be realized, privacy and benefits of all participating main bodies are protected, and the balance of the economy and robustness of the whole system is realized.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a block diagram of a multi-park integrated energy system according to an embodiment of the present invention;
FIG. 2 is a flow chart of a nested CCG algorithm for solving a two-stage robust optimization of a park integrated energy system according to an embodiment of the present invention;
FIG. 3 is a flow chart of a distributed collaborative solution for a multi-campus integrated energy system in accordance with an embodiment of the present invention;
FIG. 4 is a graph illustrating an iterative solution of a distributed algorithm for each campus according to an embodiment of the present invention;
FIG. 5 is a graph of the power of the inter-site interaction electric heat in a campus in accordance with an embodiment of the present invention;
FIG. 6 is a diagram of the worst scenario for a campus of embodiments of the present invention;
FIG. 7 is a chart of electric heat energy prices for a campus transaction according to one embodiment of the present invention;
FIG. 8 is a schematic diagram of an electric energy scheduling strategy of the integrated energy system of each park according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a heat energy scheduling strategy of a multi-park integrated energy system according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
A multi-park integrated energy system distributed collaborative optimization scheduling method based on two-stage robustness is characterized in that the described multi-park integrated energy system is composed of three park-level integrated energy systems, electric energy and heat energy sharing transactions can be conducted among all parks, each park is composed of renewable energy sources, energy conversion equipment, energy storage equipment and external energy ports, and the overall architecture of the system is shown in figure 1. The optimal scheduling method of the embodiment realizes distributed solution under the condition of source load uncertainty, obtains the scheduling strategy of each park, gives consideration to the robustness and the economy of the model, and reasonably distributes the residual cooperation benefits of the main bodies of each park with multi-energy cooperative interaction.
1. Multi-park comprehensive energy system certainty model
1.1 objective function
The objective function of the conventional deterministic model provided in this embodiment is the optimal overall cost, i.e. the minimum sum of the operating costs of each campus, and its expression is as follows:
in the formula, N is the number of the garden, F i CO To interconnect the costs of operating park i.
The operation cost of each park consists of energy purchasing cost, equipment operation and maintenance cost, carbon transaction cost and energy transaction cost and network passing cost with other parks under the condition of interconnected operation in an independent operation mode, and the expression of the operation cost is as follows:
in the formula, C i,buy For the energy purchase cost of the park i,
carbon transaction cost for park i, C i,om For the operating maintenance costs of the individual units in park i, C i,ex Energy trade cost and C for park i i,net For the passing-through fee of park i, F i NO For the independent operating costs of campus i.
The energy purchase cost is the sum of the electricity purchase cost of the park from an external power grid and the gas purchase cost of an external natural gas grid, and the expression of the energy purchase cost is as follows:
wherein,
respectively the electricity purchase price and the gas purchase price at the time t,
the power and gas purchasing power are respectively at the moment t.
The carbon trading comprises three parts of carbon emission quota, actual carbon emission and carbon emission right trading, and the emission source comprises an external grid power purchase, a gas turbine and a gas boiler.
In the formula,
in order to fix the carbon transaction price,
in order to meet the carbon emission quota for the day,
is the actual carbon emission on the day.
In the formula, λ e 、λ h Carbon quota coefficients of unit electricity production quantity and heat production quantity respectively, equivalent heat supply quantity emission of the gas turbine and the gas boiler is similar after the electricity quantity is converted into the heat quantity, and c eh Converting the electric quantity into heat quantity; epsilon e 、ε h The carbon emission coefficients per unit of electricity production and heat production, respectively.
The operation of each equipment in the garden can cause certain loss to the equipment and simultaneously needs to maintain the equipment, and the like, and the operation and maintenance cost of each equipment is converted into the operation and maintenance cost of each equipment in a unified way.
Wherein, K Trans 、K CHP 、K PV 、K WT 、K GB 、K EC 、K AC 、K BAT 、K TES An external network energy purchasing transformer, a gas turbine, a fan, a photovoltaic,The unit operation and maintenance cost of the gas boiler, the electric refrigerator, the absorption refrigerator, the storage battery and the heat storage device is unit/kW, and the power is based on the input end of the equipment.
The fees for the trading of campus i with other campuses include electric energy trading and thermal energy trading.
In the formula, omega i Is a collection of parks interconnected with park i,
and
the amount of electrical power and the amount of thermal power that park i expects to interact with interconnected park j at time t, a positive value indicating that park i purchases energy from park j, a negative value indicating that park i sells energy to park j,
and
respectively corresponding interactive electric heat energy prices. It should be noted that since the energy trading activity is only performed internally, the trading costs are mutually offset, and the total sum is 0, i.e., the internal balance, the trading costs can be disregarded when determining the overall cost.
The process of energy transmission between parks, namely passing through the network, needs to pay certain fees according to the transmission power amount and the transmission distance, and is shared by two trading parties,
in the formula,
and
unit net charge of mutual electric energy and heat energy of the garden i and the garden j at the moment t is related to the transmission distance between the gardens.
1.2 constraints
(1) And (3) equipment output constraint:
(a) Gas turbine
In the formula,
and
respectively the power generation and heat generation of the gas turbine in park i at the time t,
amount of natural gas consumed by the gas turbine at time t, eta i,CHP,e And η i,CHP,h Respectively the associated power and heat production efficiency.
And
respectively are the upper and lower limits of the power generation power of the gas turbine in the park i,
and
respectively the upper and lower limits of the heat production power of the gas turbine in the park i.
(b) Gas boiler
In the formula,
and
for the heat production power and the gas consumption power of the gas boiler in the park i at the moment t, eta i,GB,h In order to increase the heat generation efficiency of the gas boiler,
and
the maximum and minimum heat production power for the gas boiler in the park i are respectively.
(c) Electric refrigerator
In the formula,
and
the refrigeration power and the power consumption power of the electric refrigerator at the time t, eta i,EC The refrigeration coefficient of the electric refrigerator;
and
respectively, the minimum and maximum refrigeration power of the electric refrigerator.
(d) Absorption refrigerator
In the formula,
and
is the refrigeration coefficient, eta, of an absorption refrigerator i,AC In order to be the refrigeration coefficient of an absorption chiller,
and
respectively the minimum and maximum refrigeration power of the absorption refrigerator.
(e) Storage battery
In the formula,
energy storage of the accumulator at time t, delta i,e The self-loss coefficient of the storage battery,
and
charge and discharge powers, η, respectively, at time t i,e,ch And η i,e,dis The charging and discharging efficiencies of the battery are respectively,
and
respectively the minimum and maximum energy storage capacity of the battery,
and
the maximum powers of charging and discharging respectively,
and
is a flag bit for the status of charging,
and
the initial energy storage capacity and the final energy storage capacity of the storage battery are respectively.
(g) Heat storage device
In the formula,
the energy stored in the heat storage device at time t is delta i,h Is the self-loss factor of the thermal storage device,
and
the heat charging and discharging power at time t, eta respectively i,h,ch And η i,h,dis The charging and discharging efficiencies of the thermal storage device,
and
respectively the minimum and maximum energy storage capacity of the thermal storage device,
and
the maximum power for charging and discharging heat respectively,
and
is a flag bit of the state of charging,
and
the initial energy storage capacity and the final energy storage capacity of the heat storage device are respectively.
(2) Junctor transmission power constraints
(a) External network energy purchasing constraint
In the formula,
and
respectively purchasing power and gas power from the outside in the park i at the time t,
and
respectively purchasing the maximum power of electricity and gas from the outside for the park i.
(b) Inter-park interaction power constraints
In the formula,
and
when the value of the electric energy and the thermal energy interacted between the park i and the park j is positive, the park i buys energy from the park j, when the value of the electric energy and the thermal energy is negative, the park i sells energy to the park j, the electric heat energy interaction amount of the main body at the opposite end is opposite,
and
the trade prices of the electric heat energy which are correspondingly interacted, the electricity and heat exchange prices in the garden are equal,
and
the maximum amount of interaction between the electric energy and the heat energy between the park i and the park j.
(3) Energy balance constraint:
in the formula,
and
respectively, electric, thermal and cold load of park i at time t, wherein
The amount of power trading performed for campus i with other external campuses,
the amount of thermal energy traded for campus i with other parks outside.
Therefore, a deterministic low-carbon scheduling model of the multi-park integrated energy system is established, however, source load prediction data has randomness, and optimal scheduling under the uncertain condition needs to be considered, so that the system has stronger robustness.
2. Two-stage robust optimization scheduling model of multi-park comprehensive energy system
2.1 source-to-load uncertainty set
The output and the load of the renewable energy in the system have large uncertainty, and the influence of the uncertainty of the source load on the system is difficult to reflect. The uncertainty of source load is calculated, the box-type uncertain set is applied to describe wind power photovoltaic and cold and heat power loads, and the specific expression is as follows:
wherein D is i For the uncertain parameter set of the campus i,
and
respectively the predicted output and the actual output of renewable energy sources, namely wind power and photovoltaic variables of the park i,
and
respectively a predicted value and an actual value of the cooling, heating and power load,
and
are the auxiliary variables respectively, and are the auxiliary variables,
power range in which source load fluctuates up and down, gamma i,d And describing the uncertainty degree of the source load for the uncertainty value, and obtaining the proportions of different uncertainties according to the total number of the uncertainty time periods.
Generally, a certain correlation exists between a wind turbine and a photovoltaic in time, and a time correlation constraint model of pearson correlation coefficients is as follows:
in the formula,
respectively the change flags of the deviations of the adjacent time periods,
the total uncertainty of the time correlation is represented by the fact that the smaller the value of the total uncertainty represents the larger the time correlation is, and the lower the robustness conservation is.
2.2 two-stage robust optimization model and solution for park comprehensive energy system
For the established multi-park deterministic model, a multi-park two-stage robust optimization model is established by combining uncertainty of source load, and an objective function expression of the multi-park deterministic model is as follows:
wherein it is a two-stage robust optimization model for each campus, i.e. F i CO The system target is the sum of targets of various parks under the condition of uncertain source load, and a park i is taken as an example for explanation. The first stage of the two-stage robust model of the park is the network passing cost of the park, and the interaction power value between the parks is optimized; the second stage of the model is based on the interactive power value of the first stage, the optimal scheduling strategy and the worst scene in the park are solved, and when the interactive quantity of the first stage is 0, the model is an independent robust model of each park under the uncertain condition, namely F i NO 。
For convenience of description, the two-stage robust optimization model of the campus is simplified into a matrix form:
s.t.Cy i ≤g i
Ax i +Bd i +Dy i ＝c i
Ex i +Fz i ≤h i
x i ≥0
in the formula, y i Deciding variables, namely electric heat interaction quantity, for the first stage of the park i; d is a radical of i Uncertain parameters including wind power, photovoltaic and cooling, heating and power loads; x is a radical of a fluorine atom i And the decision variables of the second stage represent the energy scheduling strategy of the park. The first term is constrained to be the interactive power constraint, the second term is the internal equality constraint, and the third term is the associated inequality constraint, where z i The variable is 0-1 and represents the charge and discharge state of the stored energy, and the last inequality constraint limits the non-negativity of the internal decision variable.
2.3 two-stage robust optimization problem solving based on nested CCG Algorithm
The two-stage robust optimization model of the park is a min-max-min three-layer optimization problem and is difficult to directly solve. Solving by adopting an improved nested CCG algorithm, and splitting the model into an outer layer main problem and a sub problem, wherein the expression of the outer layer main problem is as follows:
mina T y i +γ i
s.t.Cy i ≤g i
wherein ξ i The maximum value of the objective function of the second stage is represented by the introduced auxiliary variable; k is the number of iterations in the sequence,
for the second stage decision variables corresponding to the first iteration,
the worst uncertain variable solved for the sub-problem in the first iteration is a fixed value in the main problem. The transaction electric heat quantity of the garden interval is obtained by solving the main problem, and the lower bound LB of the algorithm is updated out ＝a T y i +γ。
The outer-layer sub-problem is a max-min double-layer optimization problem which is a mixed integer linear programming problem, the outer-layer sub-problem is divided into inner-layer main and sub-problems by using the traditional CCG algorithm thought as reference, the inner-layer main and sub-problems are solved iteratively until convergence, and therefore the solution of the max-min double-layer optimization problem is obtained, and the specific expression is as follows:
the solving model of the inner layer main problem is as follows:
maxξ i
wherein ξ i For the introduced auxiliary variables, the optimal cutting target value of the inner layer subproblem is represented, k is all the current iteration times and corresponds to k groups of 0-1 variable scenes,
for the decision variable value passed to the first stage of the second stage,
is the decision variable of the r-th iteration of the inner layer main problem,
obtaining an optimal solution for the inner layer subproblem in the r iteration;
and
is a dual variable, an
For the complementary relaxation conditions in the formula, due to the presence of the nonlinear term, the large M method is used for equivalent linearization, and the transformation form is as follows:
wherein, M is a large positive number,
auxiliary variables of 0-1.
Obtaining the value of the uncertain variable by solving the main problem of the inner layer
Updating upper bound UB of inner-layer sub-problem in = theta, and the obtained uncertain scene is brought into the inner sub-problem, and a concrete solving model is as follows:
minb T x i
Ex i +Fz i ≤h i
x i ≥0,z i ∈{0,1}
inner layerSolving the main problem by the sub-problem SPs to obtain the second stage optimization problem under the uncertain scene, and obtaining the optimal solution about the energy storage state
And decision variables
And update the lower bound
If UB in -LB in ≤ε in It means that the algorithm converges and stops iteration, and updates the lower bound of the target value of the outer sub-problem
Otherwise, the iterative solution of the main and sub problems is repeatedly carried out. When the algorithm converges and obtains the optimal solution
Then, the worst scene in the second stage problem is obtained, the subsequent steps are consistent with the basic CCG algorithm, the iteration of the outer layer main and sub problems is carried out, and the upper and lower boundaries are continuously approximated until UB is met out -LB out ≤ε out 。
The solving flow chart of the nested CCG algorithm is shown in FIG. 2.
3 multi-park distributed collaborative optimization solution based on two-stage robust
3.1 decoupling of coupling amount
Coupling between the circle sections is realized on the basis of interconnection power and transaction price, coupling variables are decoupled by introducing consistent variables, and the expression of the introduced consistent variables is as follows:
wherein,
and
respectively are consistency variables of the interactive electric energy and the interactive heat energy between the park i and the park j, and as the interactive power has the characteristic of positive buying and negative selling, a negative sign is added at the opposite end;
and
respectively are consistency variables of the electric energy transaction price and the heat energy transaction price.
3.2 distributed collaborative solving based on Nash bargaining multicenter
The embodiment quantifies the bargaining capability by constructing a contribution function through the transaction amount of the electrothermal energy. Firstly, the total energy supply value of each park during optimization is obtained
And total gained energy value
The specific expression is as follows:
then, constructing a contribution function of each park, wherein the specific expression is as follows:
wherein,
and
respectively providing and receiving the maximum value of the electric heating energy for each park. The bargaining value is always not negative, and under the condition of no energy sharing, the bargaining value is 0, namely no benefit distribution link exists.
And finally, adding the contribution function into a Nash bargaining expression to obtain an asymmetric Nash bargaining profit distribution model of the multi-campus, wherein the expression is as follows:
wherein, F i CO In order to participate in the utility function of the cooperative game,
for bargaining strategies, N is the number of entities participating in the bargaining, i.e. the total number of parks, F i NO In order to participate in the benefits of the main bodies before cooperation, namely negotiation breaking points in the cooperative game, the promotion constraint of the benefits of the main bodies before and after cooperation needs to be met.
The following equation is satisfied when the above equation takes the maximum value:
since the total transaction cost satisfies an internal balance, i.e.
So that transaction costs can be omitted. Order to
By substituting into the equivalent expression and taking the logarithm of it, the objective function can be transformed into:
due to F i NO The cost of operating independently for participating subjects can be considered a known quantity, while due to the strictly monotonic increasing nature of the natural logarithm function,
always greater than 0, the original formula can be equivalently expressed and transformed, as follows:
the above formula is a sub-problem of social benefit maximization, the target is the sum of the overall benefits of uncertain variables under the worst condition, namely the operation targets of all parks, and the coupling variable is only the interactive electric heating power value. The iterative optimization solving process for the social benefit maximization subproblem park i is as follows:
wherein the objective function consists of a two-stage robust model of the campus and a penalty term for a coupling variable, k is an iteration number,
and
respectively a dual variable, rho, corresponding to the coupling constraint of the interactive electric energy and the thermal energy at the kth iteration 1 And the penalty factor is the problem of optimal overall benefit.
The termination condition of iteration can also be independently judged by each park, and the solving of the subproblems of each park needs to meet the requirements of the original residual error and the dual residual error:
and continuously carrying out iterative solution on the subproblems until the convergence precision is met, and obtaining the optimized dispatching plan and the interactive electric heat quantity of each park. After obtaining the result of the optimal overall benefit, taking logarithm of the original asymmetric target function formula to obtain a yield distribution subproblem:
in the formula, the decision variable is the electric heating price of the transaction, and the problem can be solved by adopting a distributed ADMM method, so that the reasonable distribution of the transaction residual value of each park is realized. The iterative solution process is as follows:
wherein,
decision variables for assigning sub-problems to the proceeds, including trade electricity prices and trade heat prices, ρ 2 And assigning a penalty factor corresponding to the sub-problem of the income, wherein the iteration termination condition is consistent with the sub-problem of the social benefit maximization.
The overall solution process for multi-campus collaboration is shown in figure 3.
Thus, a multi-park distributed cooperative optimization model based on two-stage robustness is established, a parallel ADMM algorithm and a nested CCG algorithm are adopted for solving, and solving of problems inside each park in the algorithm is achieved through a CPLEX solver and a MOSEK solver. In order to verify the advantages of performing multi-energy interconnection in a multi-park comprehensive energy system and considering source load uncertainty and income promotion and distribution under interconnection of parks, the following four scenes are specially set for analysis:
scene one: and source load uncertainty is calculated, electric heating combined operation is carried out in each park, and distributed solution is carried out.
Scene two: and (4) source load uncertainty is calculated, electric heating combined operation of various parks is carried out, and centralized solution is carried out.
Scene three: and (4) not considering source load uncertainty, performing electric heating combined operation in each park, and performing distributed solution.
Scene four: and the uncertainty of the source load is considered, and each park independently operates and independently solves.
The overall optimized scheduling results of the system under different scenarios are shown in table 1.
TABLE 1 Overall optimized scheduling results under different scenarios
(1) Analysis of results of different solution methods
Comparing the optimization results of the first scene and the second scene, the overall system cost obtained by the centralized method is basically the same as that obtained by the distributed method, the cost of the distributed method is slightly higher than that of the centralized method by 2.25 yuan, the error is only 0.0026%, and the accuracy requirement of solution is met. By taking the scene one as an example for analysis, the energy purchasing cost is a main cost item, the percentage of the energy purchasing cost is 94.47%, and the percentage of the network passing cost and the operation and maintenance cost is 0.24% and 6.60%. As a carbon transaction mechanism is introduced, the system preferentially uses natural gas to meet the load requirement, the environmental protection requirement of the system is ensured, the carbon emission is 90.31 tons, the carbon transaction cost is a negative value, and the total cost is reduced by 1.31 percent.
The centralized method needs to collect all data in each park, while the distributed method only needs interaction power information between parks, and considers the differentiated benefit appeal and privacy protection of each subject. The distributed algorithm can meet the requirements of accuracy and privacy, the algorithm is more reasonable, the iterative solving process of the social benefit maximization sub-problem of the algorithm is shown in figure 4, and the obtained electric heat energy interaction power condition of each garden interval is shown in figure 5.
As can be seen from FIG. 4, each park in the initial stage of iteration is optimized mainly with the cost item as a target, and the coupling constraint between parks is ignored to a certain extent. Along with the increase of the iteration times, the importance of the coupling constraint penalty item is gradually improved, the interaction power is promoted to meet the coupling constraint, the cost target value of each park is gradually converged, and the requirement of convergence is met in 76 times of iteration. The total cost of the distributed solution also converges, consistent with the results obtained with the centralized approach. The trend of the cost value of each park is different, and the game process among the parks is reflected.
As can be seen from fig. 5, the parks can cooperate with each other through the electric and thermal transactions, and the sale and purchase of the electric and thermal can be realized at different time intervals. The park 1 has higher load in the daytime and lower load at night, and simultaneously has higher wind power output at night, energy is purchased from the parks 2 and 3 in the daytime, and energy is sold at night; the trends of the interactive electric energy of the parks 2 and 3 are basically consistent and are opposite to the trend of the park 1, the daily heat load of the park 2 is large, heat can be purchased from other parks, and the rest of the parks all use the optimal energy to perform the energy interaction between the parks. The sum of the interactive quantities of all the time periods of the 3 kinds of energy is 0, and the requirement of internal energy balance is met.
(2) Whether to consider source load uncertainty result analysis
In order to verify the effectiveness of the constructed two-stage robust optimization model, the source load data and the load loss amount of the scene one and the scene three are compared and analyzed. The source load worst scenario of each park is obtained by solving the scenario one, and taking the result of the park 1 as an example, the two-stage robust scheduling worst scenario is shown in fig. 6, wherein (a), (b), (c) and (d) are scenarios of electrical load, thermal load, cold load and photovoltaic, respectively. The method comprises the steps of processing source load parameters by adopting a box-type uncertain set, selecting uncertain values of the source load parameters to influence optimization results to a great extent, randomly extracting 300 scenes in a change interval of uncertain variables, and calculating a mean value of total load loss under each scene to serve as an evaluation index to represent system risks, wherein system operation cost and load loss under different uncertainty proportions are shown in a table 2.
The optimization results of the scene one and the scene three are compared with the worst source load scene, so that the source load uncertainty is considered, the total system cost is obviously improved, and the system standby capacity is improved and the reliability is enhanced mainly because the system purchases energy from the outside. The photovoltaic power generation system is minimum when the photovoltaic power generation amount is high in 10-15 time periods, and each load is maximum at the peak of price and energy consumption, so that the photovoltaic power generation system accords with the connotation of the worst scene.
TABLE 2 sensitivity test results for uncertainty ratio
As can be seen from table 2, when the uncertainty ratio is 0, the results of the two-stage robust optimization and the deterministic optimization are consistent. With the increase of the uncertainty ratio, the source load conditions in more time intervals can be considered in the day-ahead scheduling, the operation cost is increased, the risk is reduced, the robustness is stronger, and the scheduling scheme is more conservative. When the uncertainty ratio is 1, the load loss amount is 0, and the scheduling scheme is most conservative at this time. High reliability brings high cost investment, but high risk can bring high return, in actual scheduling, a decision maker should comprehensively consider economy and robustness, decide according to actual conditions, and set a reasonable uncertainty value to cope with uncertainty of source load.
(3) Benefit distribution result analysis
As can be seen from the comparison of the scene one and the scene four, compared with the independent operation, the total cost of the interconnected operation is reduced by 3583.02 yuan, and the cost is reduced by 4.1%. Each garden is through the cooperation of electric heat interconnection, and the purchase energy of follow outside electric wire netting and natural gas net reduces, and the carbon emission reduces, therefore purchases the energy cost and the carbon transaction cost reduces, and the equipment utilization in each garden has further been promoted in the interconnection of multipotency, therefore its operation maintenance cost rises to some extent. Compared with the independent operation, the energy interaction between the parks needs to pay certain net-passing cost, but the numerical value is small, all the cost is integrated, and the total cost is reduced. Therefore, the parks cooperate with each other and are standby, and the overall energy utilization level can be improved. The inter-campus electric heat trading prices obtained by the benefit distribution subproblem for each campus are shown in fig. 7, and the asymmetric nash bargained benefit distribution results are shown in table 3.
As can be seen from fig. 7, the bargained price of the electric energy for trading in the garden area is lower than the price of the external power grid in each period, and the bargained price of the heat energy is at a lower level. Therefore, when the internal energy of each park is insufficient, the energy can be purchased from the parks with other sufficient energy at first, and then the energy can be purchased to the superior energy network, so that both trading parties can obtain better benefits from the energy, and the reduction of the running cost is realized. Therefore, each park can determine the transaction cost according to the obtained interactive energy power and the price, and the benefit distribution of each park is realized to obtain the final cost.
Table 3 multi-campus interconnection benefits allocation units: yuan-Yuan (Chinese character)
As can be seen from Table 3, the trade cost of each park is positive or negative, and the sum of the trade costs is 0, which meets the requirement of internal balance of energy trade. The energy trading cost for campus 1 is negative, indicating that it sells more energy to other parks and its final cost is reduced. The energy trading cost of campus 2 is positive, indicating that it is buying more energy from other parks, and its total cost is rising, as is park 3. Because the price of the trading energy is lower than the energy purchased by the external network, the scheduling strategy of the parks is further optimized by the multi-energy interaction and cooperation among the parks, the final cost of each park is lower than the cost of independent operation, the cost reduction proportion is respectively 7.6%, 4.1% and 1.2%, and each park can actively participate in cooperation. The reduced cost is distributed according to the comprehensive contribution degree of the transaction electric energy and the heat energy, compared with the cost reduction of each park of Nash bargaining in a standard form, the method can consistently reduce the cost
The method embodies the action difference of each park in cooperation and realizes fair and reasonable distribution of benefits.
Taking the scenario one as an example, the scheduling strategy of the integrated energy systems in each park is obtained through specific analysis and solution, and the rationality of the obtained result is further verified.
Fig. 8 shows an electric energy scheduling policy in a third scenario, where (a), (b), and (c) correspond to parks 1, 2, and 3, respectively. At any time in the dispatching cycle, each park meets the balance of supply and demand of electric energy, the electric load of each park is mainly met by renewable energy sources and a gas turbine, and the insufficient part is met by park interaction electric energy, large power grid electricity purchasing and a storage battery. The optimized dispatching strategy is mainly influenced by time-of-use electricity price, when the electricity purchase of a large power grid of each park is concentrated in the electricity price valley, the storage battery is charged in the period, and the storage battery is discharged at the electricity price peak and the energy consumption peak, so that the good peak clipping and valley filling effects are achieved. Taking the park 1 as an example, when the electric load value and the electricity price are lower in the time periods of 0-00 and 23-00; the load level is higher in the period from 11 to 00; the load level is low and the electricity price is high in the 18. The scheduling policy analysis for other parks is similar to the above analysis and will not be described herein again.
Fig. 9 shows a thermal energy scheduling strategy in scenario three, where (a), (b), and (c) correspond to parks 1, 2, and 3, respectively. At any time in the dispatching cycle, each park meets the requirement of heat energy supply and demand balance, and the heat load of each park is provided by the gas turbine, the gas boiler, the heat storage device and the interactive heat energy among the parks. In campus 1, 1: the time period is 00-6, and the time period is 23-24, wherein the heat load is mainly met by a gas boiler, the heat storage device releases certain heat energy, and the load level is low at the time and can sell heat to other parks; 7-00-22, thermal energy is mainly provided by the gas turbine, there are some interactive thermal behaviors in combination with other campus energy usage differences, and provides thermal energy to the absorption chiller. The scheduling strategies for other time periods and parks 2, 3 are the same as the above analysis, and are not described herein again
The cold load energy supply form of each garden is comparatively simple, and based on the time of use of electricity price, the cold load is all satisfied by the absorption refrigerator when the electricity price is peak, and then all satisfied by the electric refrigerator at section at ordinary times and valley period, consequently no longer to the independent analysis of the cold energy scheduling plan of each garden.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A distributed collaborative optimization scheduling method for a multi-park integrated energy system is characterized by comprising the following steps:
establishing a multi-park comprehensive energy system deterministic model and applying constraint conditions;
determining a source load uncertain variable variation range in the park comprehensive energy system, and constructing an uncertain set for representation;
based on a deterministic model, combining uncertainty of source load, constructing a two-stage robust optimization scheduling model of the park comprehensive energy system, taking interactive energy among parks as a first-stage decision variable, bringing a source load uncertainty set into a second stage, and taking output of equipment inside the park and outsourced network purchase energy as a second-stage decision variable;
splitting the model into an outer layer main problem and a sub problem, splitting the outer layer sub problem into an inner layer main problem and an inner layer sub problem, iteratively solving the inner layer main problem and the inner layer sub problem until convergence, then iterating the outer layer main problem and the outer layer sub problem until a convergence condition is met, and calculating an optimal solution;
and carrying out cooperative optimization between different parks, splitting the parks into a social benefit maximization subproblem and a benefit distribution subproblem based on an asymmetric Nash bargaining theory, and solving the two subproblems to obtain a final scheduling scheme.
2. The distributed collaborative optimal scheduling method for the multi-park integrated energy system according to claim 1, wherein the specific process of establishing the deterministic model of the multi-park integrated energy system includes:
the deterministic model is an objective function with the minimum sum of the operating costs of all parks, wherein the operating costs comprise the energy purchasing cost, the equipment operation and maintenance cost and the carbon transaction cost in the independent operation mode and the energy transaction cost and the network passing cost between other parks in the interconnected operation mode.
3. The method according to claim 1, wherein the constraints of the deterministic model include equipment output constraints, energy balance constraints, and tie line transmission power constraints.
4. The distributed collaborative optimization scheduling method for the multi-campus integrated energy system according to claim 1, wherein the specific process of determining the source load uncertain variable variation range in the campus integrated energy system includes applying box type uncertain sets to describe wind power photovoltaics and cold and thermal power loads, and the temporal correlation between the wind turbine and the photovoltaics is represented by a temporal correlation constraint model of pearson correlation coefficients.
5. The distributed collaborative optimization scheduling method for the multi-park integrated energy system according to claim 1, wherein based on a deterministic model, in combination with uncertainty of source load, a first stage of a two-stage robust optimization scheduling model optimizes interaction power values between parks for the passing-through cost of parks; the second stage of the model is based on the interactive power value of the first stage, the optimal scheduling strategy and the worst scene in the park are solved, and when the interactive quantity of the first stage is 0, the model is an independent robust model of each park under the uncertain condition; constraints include interactive power constraints, internal equality constraints, and non-negatives constraints that define internal decision variables.
6. The distributed collaborative optimization scheduling method for the multi-park integrated energy system according to claim 1, is characterized in that a modified nested CCG algorithm is adopted for solving, a model is split into an outer-layer main problem and a sub-problem, the outer-layer main problem is solved to obtain the transaction electricity heat quantity of a park interval, and the lower bound of the algorithm is updated;
the outer-layer sub-problem is a max-min double-layer optimization problem, the problem is a double-layer mixed integer linear programming problem, a conventional method for converting double layers into single layers fails, the CCG algorithm thought is used for reference, the outer-layer sub-problem is divided into inner-layer main and sub-problems, the inner-layer main and sub-problems are solved in an iterative mode until convergence is achieved, and therefore the solution of the max-min double-layer optimization problem is obtained.
7. The distributed collaborative optimization scheduling method for the multi-campus integrated energy system according to claim 1, wherein a specific process of collaborative optimization among different campuses includes that coupling among the campuses is reflected on interconnection power and transaction price, and a consistency variable is introduced to decouple a coupling variable;
the bargaining capability is quantified by constructing a contribution function through the transaction amount of the electric and thermal energy to obtain a total provided energy value and a total obtained energy value of each park during optimization, constructing the contribution function of each park, adding the contribution function into a Nash bargaining expression to obtain an asymmetric Nash bargaining profit distribution model of the multiple parks, and equivalently forming a social benefit maximization sub-problem and a benefit distribution sub-problem through related transformation; the social benefit maximization sub-problem target is the sum of the overall benefits of uncertain variables under the worst condition, namely the operation targets of all the parks, the coupling variable is only the interactive electric heating power value, each park is subjected to iterative optimization solution until the convergence condition is met, and the optimal scheduling plan and the interactive electric heating quantity of each park are obtained; and the interest distribution subproblem aims at balancing and optimizing the interests of all parties, realizing reasonable distribution of cooperative gains, obtaining the energy interaction price of each park through iterative solution of each park, and finally determining the cost of each park.
8. A multi-park comprehensive energy system distributed collaborative optimization scheduling system is characterized by comprising:
the deterministic model building module is configured to build a deterministic model of the multi-park integrated energy system and apply constraint conditions;
the uncertainty consideration module is configured to determine a source load uncertainty variable variation range in the park comprehensive energy system, and construct an uncertainty set for representation;
the optimization scheduling model building module is configured to build a two-stage robust optimization scheduling model of the park comprehensive energy system based on a deterministic model and in combination with uncertainty of source load, take interactive energy between parks as a first-stage decision variable, bring a source load uncertainty set into a second stage, and take output of equipment inside a park and outsource purchasing energy as decision variables of the second stage;
the solving module is configured to split the model into an outer layer main problem and a sub problem, split the outer layer sub problem into an inner layer main problem and an inner layer sub problem, iteratively solve the inner layer main problem and the inner layer sub problem until convergence is achieved, iterate the outer layer main problem and the outer layer sub problem until a convergence condition is met, and calculate an optimal solution;
and the collaborative optimization module is configured to perform collaborative optimization between different parks, split the parks into a social benefit maximization sub-problem and a benefit allocation sub-problem based on an asymmetric Nash bargaining theory, and solve the two sub-problems to obtain a final scheduling scheme.
9. A computer-readable storage medium having stored thereon instructions adapted to be loaded by a processor of a terminal device and to perform the steps of the method of any of claims 1-7.
10. A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; a computer readable storage medium for storing a plurality of instructions adapted to be loaded by a processor and to perform the steps of the method of any one of claims 1 to 7.
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