CN110429664B - Day-ahead economic dispatching method for combined cooling, heating and power micro-grid - Google Patents
Day-ahead economic dispatching method for combined cooling, heating and power micro-grid Download PDFInfo
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Abstract
The invention discloses a day-ahead economic dispatching method for a combined cooling, heating and power microgrid, and mainly solves the problem that the safety and the economy cannot be simultaneously considered in the prior optimization method for the combined cooling, heating and power microgrid CCHP-MG in the prior art. The method, bagThe method comprises the following steps: (S1) establishing a double-layer model for simulating the operation of the combined cooling heating and power microgrid CCHP-MG; (S2) according to the formula
Performing operation scheduling on a double-layer model of a combined cooling heating and power micro-grid CCHP-MG; (S3) performing optimized scheduling on the CCHP-MG double-layer model of the combined cooling, heating and power microgrid; (S4) generating C with column constraint&And (3) carrying out iterative solution on the double-layer model of the CCHP-MG of the combined cooling, heating and power micro-grid by the CG iterative algorithm. Through the scheme, the safety and economy integrated device achieves the purpose of safety and economy, and has high practical value and popularization value.
Description
Technical Field
The invention belongs to the technical field of micro-grids, and particularly relates to a day-ahead economic dispatching method for a combined cooling, heating and power micro-grid.
Background
In the electric power industry of China, a comprehensive energy system is greatly popularized due to high energy efficiency. Among them, a combined cooling heating and power system (CCHP) is a widely used comprehensive energy system. The CCHP is often deployed in a microgrid, and can efficiently meet three loads of cold, heat and electricity in the microgrid to form a combined cooling, heating and power microgrid (CCHP-MG, see attached figure 1). However, due to the different energy types involved and the strong uncertainty of microgrid loads and Renewable Energy (RES) units (wind generators, photovoltaic, etc.), the operation plan of CCHP-MG is often difficult to make.
The core part of the CCHP-MG operation plan is the day-ahead scheduling policy (DED). The DED is established one day before the dispatching day, and the plan content comprises output base points of all units in the CCHP-MG at different time periods of the dispatching day, start-stop states and an electric quantity trading plan with a main network of the power system. And on the basis of DED, fine control of the scheduling in-day unit can be performed, so that DED is the core foundation in the CCHP-MG operation plan.
Considering uncertainty of load and renewable energy, how to make DED for CCHP-MG involving multiple energy types is an important issue in the power industry.
In the conventional method, a dispatcher usually obtains the optimal DED without considering a prediction error according to the load and a predicted value of the RES. This method is called deterministic method (DO). DO has the advantage of being easy to implement, but uncertain day-ahead prediction errors are unavoidable, so DO-based DEDs (D-DEDs) tend to be too optimistic and not safe. In response to the deficiencies of D-DED, related scholars developed more advanced stochastic programming methods (SO). The basic idea is as follows: and the dispatcher obtains massive load samples and historical data of renewable energy output through a dispatching center or a local power grid, and further performs fitting processing on the sample data to finally obtain a probability density curve of the load and the renewable energy. The dispatcher simulates the prediction error based on the curve, and finally develops the DED (S-DED) considering different degrees of prediction error. Compared with the D-DED, the S-DED has stronger capability of adapting to prediction errors and better safety due to the consideration of the prediction errors in various scenes. However, the S-SED still has the following three problems to be improved: 1) the accuracy of the probability density curve cannot be guaranteed due to lack of related historical data; 2) the calculated amount is too high due to the mass scenes, and the feasibility is poor; 3) serious consequences caused by extreme scenes are not fully considered, and safety still needs to be improved. Based on the problem of SO, relevant scholars begin to research more advanced Robust Optimization (RO) theory and apply RO to the DED of the CCHP-MG to obtain the DED (R-DED) based on robustness. The R-DED is optimized without depending on massive historical data, and the adverse effect of an extreme scene is fully considered, so that the R-DED is small in calculated amount, strong in feasibility and excellent in safety. However, since the R-DED only considers extreme scenarios, the operation of CCHP-MG is too conservative and the economy is very poor.
Therefore, how to apply a more suitable optimization method to the operation planning of the CCHP-MG while considering safety and improving economic benefits is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to provide a day-ahead economic dispatching method for a combined cooling heating and power microgrid, and mainly solves the problem that the safety and the economy cannot be simultaneously considered in the prior optimization method for the combined cooling, heating and power microgrid CCHP-MG in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a day-ahead economic dispatching method for a combined cooling, heating and power micro-grid comprises the following steps:
(S1) establishing a double-layer model for simulating the operation of the combined cooling heating and power microgrid CCHP-MG;
(S2) performing operation scheduling on the double-layer model of the combined cooling heating and power micro-grid CCHP-MG according to the formula (1);
wherein, CDARepresents the day-ahead scheduling cost, CRTRepresents the real-time justification cost, HDARepresents the scheduling physics constraint equation before day, HRTRepresenting the real-time adjusted physical constraint equation, GDARepresenting the physical constraint inequality of day-ahead scheduling, GRTRepresenting a real-time adjustment physical constraint inequality, x representing a day-ahead scheduling scheme, y representing a real-time adjustment scheme, u representing an uncertainty parameter, and gamma representing an adjustable robust parameter;
(S3) performing optimized scheduling on the CCHP-MG double-layer model of the combined cooling, heating and power microgrid;
(S4) iterative solution is carried out on the double-layer model of the combined cooling heating and power micro-grid CCHP-MG by adopting a column constraint generation C & CG iterative algorithm.
Further, the step (S3) of performing optimized scheduling on the CCHP-MG dual-layer model includes a first-layer model and a second-layer model, where the first-layer model is a day-ahead scheduling process for determining the unit start-stop combination and the basic output only under the condition of an uncertainty prediction value, and the second-layer model is a real-time scheduling process for performing real-time regulation and control on the unit output based on the first-layer model decision under the worst uncertainty condition.
Specifically, the column constraint generation C & CG iterative algorithm in the step (S4) decomposes the CCHP-MG two-layer model into a main problem MP and a sub-problem SP, where the main problem MP is a mixed integer linear programming problem that can be directly solved, and the sub-problem SP is a two-layer non-convex problem that cannot be directly solved.
Compared with the prior art, the invention has the following beneficial effects:
the CCHP-MG of the combined cooling heating and power micro-grid provides a feasible day-ahead scheduling decision framework with adjustable capacity. On the premise of considering the internal uncertainty of the CCHP-MG, the Adjustable Robust Optimization (ARO) can provide a day-ahead scheduling strategy (A-DED) which gives consideration to economy and safety, and further lay a foundation for the safe and economical operation of the CCHP-MG. In addition, the A-DED can flexibly provide strategies with different safety according to the operation budget. Compared with the traditional D-DED and S-DED, the A-DED can process prediction errors and does not depend on massive scenes, so that the calculation burden is small, and the feasibility is strong. Most importantly, the A-DED can flexibly adjust the safety and the economy according to the running budget of the CCHP-MG, balance between the A-DED and the CCHP-MG is achieved, and a choice with both safety and economy is provided for formulating a day-ahead scheduling plan of the CCHP-MG.
Drawings
FIG. 1 is a flow chart of the system of the present invention.
Fig. 2 is a flow chart of the column constraint generation C & CG iterative algorithm of the present invention.
Fig. 3 is a schematic diagram of the installation and use of the microgrid of the invention.
Detailed Description
The present invention is further illustrated by the following figures and examples, which include, but are not limited to, the following examples.
Examples
As shown in fig. 1, a day-ahead economic dispatching method for a combined cooling, heating and power microgrid comprises the following steps:
(S1) establishing a double-layer model for simulating the operation of the combined cooling heating and power microgrid CCHP-MG;
(S2) performing operation scheduling on the double-layer model of the combined cooling heating and power micro-grid CCHP-MG according to the formula (1);
wherein, CDARepresents the day-ahead scheduling cost, CRTRepresents the real-time justification cost, HDARepresents the scheduling physics constraint equation before day, HRTRepresenting the real-time adjusted physical constraint equation, GDARepresenting the physical constraint inequality of day-ahead scheduling, GRTRepresenting a real-time adjustment physical constraint inequality, x representing a day-ahead scheduling scheme, y representing a real-time adjustment scheme, u representing an uncertainty parameter (uncertainty parameters in CCHP-MG mainly include but are not limited to wind power, photovoltaic, electric vehicles and the like, and are uniformly described by u for convenient description), and Γ representing an adjustable robust parameter;
(S3) performing optimized scheduling on the CCHP-MG double-layer model of the combined cooling, heating and power microgrid, and the method comprises a first layer model and a second layer model, wherein the first layer model is a day-ahead scheduling process for determining the start-stop combination and the basic output of the unit only under the condition of uncertainty prediction values, the second layer model is a real-time scheduling process for performing real-time regulation and control on the output of the unit based on the decision of the first layer model under the worst uncertainty, and as the first layer model and the second layer model need to be solved simultaneously, but a mutual influence relationship exists between the two layers of models, a column constraint is introduced to generate a C & CG iterative algorithm for solving the double-layer model.
(S4) decomposing the double-layer model of the combined cooling heating and power generation micro-grid CCHP-MG into a main problem MP (formula (2)) and a sub problem SP (formula (3)) by adopting a column constraint generation C & CG iterative algorithm, wherein the main problem MP is a mixed integer linear programming problem which can be directly solved, and the sub problem SP is a double-layer non-convex problem which cannot be directly solved.
The main question MP and the sub-question SP are respectively represented as:
wherein C represents a cost coefficient matrix of a day-ahead scheduling scheme, Θ represents an auxiliary variable, D represents a cost coefficient matrix of a real-time adjustment scheme, E represents a random variable coefficient matrix, u represents the above-mentioned uncertainty parameter, and A, G, B, h, C, D, i, E, F, G all represent coefficient matrices.
The sub-problem SP is therefore deduced as a single-layer problem using strong dual theory:
wherein ξ denotes auxiliary variables, α, β and gamma denote dual variables,
denote the transpose of u, i, x, C, j, E, respectively.
The formula (4) is a single-layer non-convex problem, and a Big-M linearization method is adopted to process bilinear terms in the formula (4)
Deriving equation (4) as equation (5):
wherein u isupRepresents the upper bound of the interval, udownRepresents the lower bound of the interval, upreIndicates intra-interval prediction value, ξ+Denotes a positive value of ξ, ξ-Indicating a negative value of ξ, M indicating a sufficiently large positive number,
all represent 0-1 auxiliary variables.
Equation (5) is the final derived form of the sub-problem SP, which is a straightforward solution for the MILP problem. And solving the optimization problem under the condition of considering the mutual influence relationship between the first layer model and the second layer model, and iteratively solving the main problem MP (formula (2)) and the sub problem SP (formula (5)) by adopting a C & CG iterative algorithm.
The specific flow of the C & CG algorithm is shown in fig. 2:
first, the main question MP is initialized, specifically set as formula (6):
wherein UB represents an iteration upper bound, LB represents an iteration lower bound, epsilon represents an iteration interval, and k represents the iteration number.
Secondly, solving the main problem through a formula (7) and updating parameters,
wherein the content of the first and second substances,
representing a day-ahead scheduling scheme cost factor,
represents the optimal day-ahead scheduling scheme solved by the main problem in the k +1 iterations,
and (4) representing the objective function value obtained by the neutron problem in the k +1 iteration.
Thirdly, solving the objective function of the sub-problem involved in the formula (7), and updating the parameters through the formula (8),
UB＝cTx* k+1+R(u* k+1) (8)
wherein R represents a coefficient matrix of random variables,
and (4) representing the worst random variable obtained by the neutron problem in the k +1 iteration.
Fourthly, judging according to the UB-LB not more than epsilon, if yes, extracting the knotIf so, ending the algorithm; if not, generating a new variable yk+1,uk+1Generating a new constraint condition formula (9), meanwhile, after k is updated to k +1, importing the second step, and continuously executing the solving process;
wherein u isk+1Representing the initial harsh scene used in k +1 iterations, yk+1Denotes u in k +1 iterationsk+1Corresponding real-time adjustment scheme, η denotes auxiliary variables, a, B, B, D, f, IuEach represents a coefficient matrix.
Through the steps, a day-ahead scheduling decision plan with both conservation and safety can be finally obtained.
Fig. 3 is a schematic diagram of a microgrid in use according to the present invention, when electric energy is distributed for use, selective power supply of a power supply mode can be performed on wiring according to actual requirements of a power generation end (a power grid, a photovoltaic, a fan, an electric energy storage, a fuel cell, and a gas turbine) at a front end and a load end (an electric load, a thermal load, and a cold load) at a rear end, and a selection of both safety and economy can be provided by using a microgrid distribution principle.
The above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, but all changes that can be made by applying the principles of the present invention and performing non-inventive work on the basis of the principles shall fall within the scope of the present invention.
Claims (2)
1. A day-ahead economic dispatching method for a combined cooling heating and power micro-grid is characterized by comprising the following steps:
(S1) establishing a double-layer model for simulating the operation of the combined cooling heating and power microgrid CCHP-MG;
(S2) performing operation scheduling on the double-layer model of the combined cooling heating and power micro-grid CCHP-MG according to the formula (1);
wherein, CDARepresents the day-ahead scheduling cost, CRTRepresents the real-time justification cost, HDARepresents the scheduling physics constraint equation before day, HRTRepresenting the real-time adjusted physical constraint equation, GDARepresenting the physical constraint inequality of day-ahead scheduling, GRTRepresenting a real-time adjustment physical constraint inequality, x representing a day-ahead scheduling scheme, y representing a real-time adjustment scheme, u representing an uncertainty parameter, and gamma representing an adjustable robust parameter;
(S3) performing optimized scheduling on the CCHP-MG double-layer model of the combined cooling, heating and power microgrid;
(S4) a column constraint generation C & CG iterative algorithm is adopted to solve a main problem MP of a formula (2) and a sub problem SP of the formula (3) in an iterative manner on the double-layer model of the combined cooling, heating and power micro-grid CCHP-MG, wherein the main problem MP is a mixed integer linear programming problem which can be directly solved, and the sub problem SP is a double-layer non-convex problem which cannot be directly solved;
the main question MP and the sub-question SP are respectively represented as:
wherein C represents a cost coefficient matrix of a day-ahead scheduling scheme, Θ represents an auxiliary variable, D represents a cost coefficient matrix of a real-time adjustment scheme, E represents a random variable coefficient matrix, u represents the above-mentioned uncertainty parameter, and A, G, B, h, C, D, i, E, F, G all represent coefficient matrices.
2. The day-ahead economic dispatching method for the combined cooling heating and power micro-grid according to claim 1, wherein the step (S3) of optimally dispatching the CCHP-MG double-layer model comprises a first-layer model and a second-layer model, the first-layer model is a day-ahead dispatching process for deciding the unit start-stop combination and the basic output only under the condition of uncertainty predicted values, and the second-layer model is a real-time dispatching process for deciding the real-time regulation and control of the unit output based on the first-layer model under the worst uncertainty.
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