Micro-grid operation scheduling optimization method based on double-layer adaptive energy management system

Abstract

The invention discloses a micro-grid operation scheduling optimization method based on a double-layer adaptive energy management system, and the method proposes a double-layer adaptive energy management system based on a rolling horizon strategy, and achieves the optimization of short-term operation scheduling of a micro-grid. An upper planning layer is adopted for online operation scheduling, profit is maximized, meanwhile, near-real-time scheduling is executed by a lower layer, and correction measures generated by mismatching are predicted by minimizing input parameters. The upper planning layer utilizes the medium-term prediction information, considers market environment income / cost, peak electricity price, static and dynamic controllable loads and a battery energy storage system model, and solves through a mixed integer linear programming problem to obtain an initial scheduling plan of a controllable unit. And the lower scheduling layer operates on a shorter time step, an optimization algorithm is combined with power system simulation, network constraints are considered in real time, the deviation between the scheduling plan and the scheduling plan determined by the upper planning layer is minimized, and a real-time energy management strategy for stable and economic operation of the microgrid is realized.

Description

Technical Field

[0001] The invention relates to a microgrid operation dispatch optimization method based on a double-layer adaptive energy management system, and belongs to the technical field of microgrid operation dispatch. Background Art

[0002] In the context of today's energy transformation and sustainable development, microgrids, as an important part of smart grids, have an optimization strategy for operation and scheduling that is of great significance for improving energy efficiency, reducing costs, and reducing environmental impact. Microgrids usually include distributed energy resources (DER), energy storage systems, loads, and connections to the parent grid. They can operate independently or in parallel with the main grid. With the increasing penetration of renewable energy, such as the integration of photovoltaic (PV) systems, the operation of microgrids has become more complex because these energy sources are intermittent and uncertain. In addition, battery energy storage systems (BESS), as a key energy storage technology, play a vital role in balancing supply and demand, providing ancillary services, and enhancing system stability.

[0003] However, the operation and dispatch of microgrids faces many challenges, including how to effectively manage the volatility of renewable energy, how to optimize the use of energy storage equipment to extend its life, and how to maximize economic benefits while meeting technical constraints. Traditional dispatching methods often lack the ability to adapt to these uncertain factors and are difficult to achieve real-time optimization.

[0004] Existing microgrid energy management systems (EMS) have obvious limitations in dealing with the uncertainty of renewable energy, insufficient management of energy storage systems, and lack of real-time optimization capabilities. These systems often fail to fully consider the intermittent and unpredictable nature of distributed energy resources such as photovoltaic and wind power, resulting in challenges in supply and demand balance management. At the same time, existing EMS are also inadequate in dealing with the aging and performance degradation of battery energy storage systems (BESS), failing to effectively extend the service life of energy storage equipment and improve its efficiency. In addition, many systems lack a two-layer optimization structure, that is, the ability to combine long-term planning and short-term scheduling, which limits their adaptability to rapidly changing conditions. In terms of economic and technical constraints, existing systems often ignore the multiple constraints that need to be met simultaneously during the optimization process, resulting in the generated scheduling plan that may be infeasible or costly in actual operation. Existing EMS also fail to fully consider the characteristics and needs of different types of loads, limiting the flexibility of system scheduling. Effective coordination with the upper-level power grid is also generally lacking in existing systems, lacking optimization strategies in terms of electricity price changes, demand response, etc. As the scale of the system expands, existing optimization algorithms may find it difficult to provide solutions within a reasonable time, affecting the real-time performance of the system. Existing systems often do not incorporate equipment aging and maintenance costs into optimization models, resulting in underestimation of long-term operating costs. In terms of multi-objective optimization, many EMSs only focus on a single goal, such as minimizing costs or emissions, without achieving comprehensive optimization of economic benefits, environmental impacts, and system reliability.

[0005] Therefore, an advanced energy management system (EMS) is urgently needed to meet these challenges. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a microgrid operation scheduling optimization method based on a double-layer adaptive energy management system, propose a double-layer adaptive energy management system based on a rolling horizon strategy, and realize the optimization of microgrid short-term operation scheduling.

[0007] The present invention adopts the following technical solutions to solve the above technical problems:

[0008] Microgrid operation dispatch optimization method based on two-layer adaptive energy management system, based on the rolling horizon strategy, a two-layer adaptive energy management system is designed to optimize the short-term operation dispatch of the microgrid, which specifically includes the following steps:

[0009] Step 1: collect the operation data of the microgrid in real time, including photovoltaic output, load demand and battery energy storage status; consider the efficiency and energy conversion of the battery energy storage system, establish the charging and discharging power model of the battery energy storage system, and further establish the energy storage status model of the battery energy storage system;

[0010] Step 2, establishing static and dynamic controllable load models;

[0011] Step 3: Based on steps 1 and 2, the upper planning layer is used for online preliminary scheduling, and an objective function with the goal of maximizing the total operating profit of the microgrid is established. The function is solved under the constraints of the market environment, peak electricity price, battery energy storage system and supply and demand balance to obtain a preliminary scheduling plan for the microgrid.

[0012] Step 4, perform near real-time scheduling at the lower layer, consider network constraints in real time, establish an objective function with the goal of minimizing the corrective measures generated by the predicted mismatch and making the real-time operation of the microgrid consistent with the preliminary scheduling plan of the upper planning layer, and solve it to obtain the real-time scheduling strategy of the microgrid.

[0013] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0014] 1. The dual-layer adaptive energy management system of the present invention significantly improves the adaptability and flexibility of the system by real-time monitoring and responding to fluctuations in renewable energy supply and changes in load demand in the microgrid, ensuring stable operation in a changing energy environment.

[0015] 2. By adopting a customized battery energy storage system (BESS) model, the present invention not only takes into account the dynamic performance of the energy storage equipment, but also pays special attention to the aging and performance degradation of the equipment, thereby effectively extending the service life of the energy storage equipment and significantly improving the operating efficiency and economic benefits of the energy storage system.

[0016] 3. The present invention combines a two-layer optimization structure of long-term planning and short-term scheduling, which can comprehensively consider the operation needs of the microgrid, realize the global and dynamic optimization of the microgrid operation, and improve the economy and reliability of the system.

[0017] 4. During the optimization process, the present invention fully considers and satisfies multiple constraints such as economy, technology and operability, ensuring the feasibility and cost-effectiveness of the generated scheduling plan in actual operation, while ensuring the stability and security of the system.

[0018] 5. The present invention can more effectively match supply and demand, reduce energy waste, and improve the overall efficiency and performance of the system by carefully managing and precisely regulating different types of loads.

[0019] 6. The present invention does not focus on a single goal, such as minimizing costs or emissions, but through a multi-objective optimization strategy, it takes into account economic benefits, environmental impacts and system reliability, thereby achieving a comprehensive improvement in the performance of the microgrid and providing strong technical support for the sustainable development of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1It is a flow chart of the microgrid operation dispatch optimization method based on a double-layer adaptive energy management system of the present invention. DETAILED DESCRIPTION

[0021] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0022] like Figure 1 As shown, the present invention develops a two-layer adaptive energy management system based on the rolling horizon strategy to optimize the short-term operation scheduling of the microgrid, ensure that the system can flexibly respond to the uncertainty of renewable energy supply, and proposes a microgrid operation scheduling optimization method based on the two-layer adaptive energy management system, which specifically includes:

[0023] S1. Define detailed models including battery energy storage system (BESS) model, static and dynamic controllable load models;

[0024] First, define the battery energy storage system (BESS) model:

[0025] Establish the charging and discharging power model of BESS, considering its efficiency and energy conversion:

[0026] P ch,a =η ch,a ·P ch_ac,a

[0027] P disch,a =η disch,a ·P disch_ac,a

[0028] Among them, P ch,a is the charging power of battery a, η ch,a is the charging efficiency of battery a, P ch_ac,a is the AC charging power of battery a; P disch,a is the discharge power of battery a; η disch,a is the discharge efficiency of battery a; P disch_ac,a is the AC discharge power of battery a.

[0029] The calculation of energy storage status and aging cost is introduced to ensure that the model can accurately reflect the long-term operation characteristics of BESS:

[0030]

[0031] C BESS_degradation =C cycle ·n cycles +C calendar ·Δi

[0032] Among them, E BEss,i+1 is the battery energy storage state at time i+1, E BESs,i is the battery energy storage state at time i, η E,a is the energy efficiency of battery a, P ch,a and P disch,a are charging and discharging power respectively, Δi is the time step; C BESS_degradation is the battery aging cost, C cycle is the cycle aging cost, n cycles is the number of charge and discharge cycles.

[0033] Then build static and dynamically controllable load models:

[0034] The static controllable load model takes into account the fact that the load can be reduced at different levels and defines the corresponding reduction cost model:

[0035] P static,i =(1-δ static,i )·P base,i

[0036] Among them, P static,i is the static load power at time i, δ static,i is the load reduction factor, P base,i is the base load power.

[0037] The dynamically controllable load model takes into account that the load can be adjusted within a continuous range and defines the adjustment cost:

[0038] P dynamic,i =(1-δ dynamic,i )·P base,i

[0039] Among them, P dynamic,i is the dynamic load power at time i, δ dynamic,i is the dynamic load adjustment coefficient, P base,i is the base load power.

[0040] S2, adopts the upper planning layer to perform online operation scheduling, through the solution of mixed integer linear programming (MILP) problem, with the goal of maximizing profit while satisfying the constraints of market environment, peak electricity price and static and dynamic controllable load models;

[0041] First, define the objective function to maximize the total operating profit of the microgrid, including the cost of purchasing electricity from the grid and the income from selling electricity to the grid, while considering the operating costs of the battery energy storage system and dispatchable generation resources:

[0042]

[0043] Among them, c gridout,i and c grid in,i Represent the prices of electricity sold and purchased by the microgrid from the large grid at time i, P grid out,i and P grid in,i is the power sold and purchased from the microgrid to the large grid at time i. BESS,a and C DG,a Represent the operating costs of the battery a energy storage system and the dispatchable power generation resources respectively.

[0044] Considering the impact of market environment and peak electricity price on microgrid operation profit, the peak electricity price cost or benefit W of time period i is calculated by defining the following function: peak,i :

[0045]

[0046] Among them, c peak price is the peak electricity price, P peak,i is the peak power at time i, P peak,hist,i is the historical peak power, c grid out,i the price at which electricity is sold to the grid;

[0047] When P peak,i ≥P peak,hist,i When , it means that the peak power of the current period is not lower than the historical peak power, and the microgrid needs to pay an additional peak power price cost c peak price ·(P peak,hist,i -P peak,i ).

[0048] When P peak,i <P peak,hist,i When , it means that the peak power of the current period is lower than the historical peak power, and the microgrid earns revenue by selling electricity to the grid, and the revenue is -c grid out,i ·(P peak,i -P peak,hist,i ).

[0049] When it is predicted that electricity demand during a certain period will be lower than the historical peak, the microgrid can plan to increase electricity sales during that period. If it is predicted that electricity demand will be higher than the historical peak, the microgrid can reduce power purchases or increase local power generation to avoid paying high peak power price costs.

[0050] Set constraints for the battery energy storage system to operate within a safe and efficient operating range:

[0051] E BESS,min ≤EBESS,i ≤E BESS,max

[0052] Among them, E BESS,min and E BESS,max are the minimum and maximum energy storage states of the battery energy storage system at time i, respectively.

[0053] Ensure that the microgrid can meet the load demand at any time and introduce supply and demand balance constraints:

[0054]

[0055] Among them, P PV is the power generation of the photovoltaic system. i is the total demand at time i.

[0056] Use the optimization solver CPLEX, input the above objective function and constraints, solve the MILP problem, and obtain the optimal scheduling strategy.

[0057] Generate and implement dispatch strategies. Based on the optimal solution obtained, generate dispatch strategies for microgrids, including charging and discharging plans for battery energy storage systems, power generation plans for dispatchable power generation resources, and power trading strategies with the power grid.

[0058] S3, the lower layer performs near real-time dispatch, which takes into account network constraints in real time and minimizes the corrective measures generated by mismatches in input parameter predictions through a collaborative simulation approach combined with optimization algorithms and power system simulation tools;

[0059] First, it is clear that the goal of the lower-level optimization is to minimize the corrective measures generated by the mismatch of input parameter predictions, ensuring that the real-time operation of the microgrid is consistent with the dispatch plan of the upper planning layer:

[0060]

[0061] in, and They are the power dispatch plans of the power grid, BESS and dispatchable generation resources given by the upper planning layer; and It is the real-time power value calculated during the lower-level optimization process.

[0062] Collect microgrid operating data in real time, including PV output, load demand, and battery storage status, and update the prediction model to reflect the latest operating conditions.

[0063] The optimization algorithm is combined with power system simulation tools to create a virtual environment that can simulate the operation of the microgrid in real time. In the co-simulation environment, network constraints such as line capacity limitations, voltage stability and power factor requirements are considered in real time to ensure that the operation of the microgrid does not exceed the safety and stability range of the power grid.

[0064] A genetic optimization algorithm (GA) is selected and applied in a collaborative simulation environment to find the optimal solution that minimizes the corrective measures. Real-time data and network constraints are input into the optimization algorithm to solve the underlying optimization problem and obtain the real-time scheduling strategy of each component of the microgrid.

[0065] According to the solution of the optimization algorithm, real-time dispatching instructions are generated to guide the real-time operation of the battery energy storage system, dispatchable power generation resources and loads in the microgrid. The generated real-time dispatching instructions are sent to each control unit of the microgrid, and their execution is monitored in real time to ensure the stable and efficient operation of the microgrid.

[0066] The short-term operation dispatch results of the microgrid are tested to verify the economic and environmental impact of the developed optimization framework under different operation modes, ensuring the effectiveness and practicality of the microgrid optimization configuration.

[0067] Define different modes of the test microgrid, such as grid-connected mode and island mode, and set corresponding parameters, such as load demand, renewable energy supply and electricity price information. Use the developed optimization framework to perform short-term operation scheduling of the microgrid, perform upper-level planning and lower-level real-time scheduling, and minimize costs:

[0068]

[0069] Among them, C DG is the operating cost of dispatchable power generation resources; when a microgrid participates in a demand response program, it needs to pay additional costs or obtain additional benefits to adjust the load in response to changes in electricity prices. This part of the cost or benefit can be expressed as:

[0070] In simulation or actual operation, the operation data of the microgrid, including power production, consumption, energy storage status and economic indicators, are collected and used to evaluate the performance of the optimization framework. The total operation benefit C of the microgrid is calculated. total , including electricity purchase costs, electricity sales revenue, energy storage system operation and maintenance costs, and the use of renewable energy:

[0071]

[0072] Among them, C RE The operating cost of renewable energy (such as solar energy and wind energy).

[0073] To evaluate the environmental impact of microgrid operations, greenhouse gas emissions and pollutant emissions are mainly calculated. The following formula is achieved by analyzing the carbon footprint and pollutant emission coefficient of power generation resources:

[0074]

[0075] Among them, E CO2,a and E p,a are the emission coefficients of carbon dioxide and other pollutants from power generation resources, respectively.

[0076] Conduct sensitivity analysis on key parameters to evaluate the impact of changes in these parameters on the economic and environmental performance of the microgrid. Compare the test results with the existing system or traditional operation strategy to verify the effectiveness of the optimization framework. Based on the test results, further adjust and optimize the operation strategy.

[0077] Based on the same inventive concept, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the aforementioned microgrid operation and scheduling optimization method based on a two-layer adaptive energy management system are implemented.

[0078] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the aforementioned microgrid operation and scheduling optimization method based on a two-layer adaptive energy management system.

[0079] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0081] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0083] The above embodiments are only for illustrating the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A microgrid operation dispatch optimization method based on a two-layer adaptive energy management system, characterized in that: Based on the rolling horizon strategy, a two-layer adaptive energy management system is designed to optimize the short-term operation and scheduling of microgrids, which includes the following steps: Step 1: collect the operation data of the microgrid in real time, including photovoltaic output, load demand and battery energy storage status; consider the efficiency and energy conversion of the battery energy storage system, establish the charging and discharging power model of the battery energy storage system, and further establish the energy storage status model of the battery energy storage system; Step 2, establishing static and dynamic controllable load models; Step 3: Based on steps 1 and 2, the upper planning layer is used for online preliminary scheduling, and an objective function with the goal of maximizing the total operating profit of the microgrid is established. The function is solved under the constraints of the market environment, peak electricity price, battery energy storage system and supply and demand balance to obtain a preliminary scheduling plan for the microgrid. Step 4, perform near real-time scheduling at the lower layer, consider network constraints in real time, establish an objective function with the goal of minimizing the corrective measures generated by the predicted mismatch and making the real-time operation of the microgrid consistent with the preliminary scheduling plan of the upper planning layer, and solve it to obtain the real-time scheduling strategy of the microgrid.

2. The microgrid operation scheduling optimization method based on a two-layer adaptive energy management system according to claim 1 is characterized in that: In step 1, the charging and discharging power model of the battery energy storage system is as follows: P.S ch，a η ch,a ·P ch_ac，a P.S disch,a η disch,a ·P disch_ac，a Among them, P ch，a , η ch,a , P ch_ac，a are the charging power, charging efficiency and AC charging power of battery a respectively; P disch,a , η disch,a , P disch_ac，a are the discharge power, discharge efficiency and AC discharge power of battery a; The energy storage state model of the battery energy storage system is as follows: C BESS_degradation =C cycle ·n cycles +C calendar ·Δi Among them, E BESS,i+1 、E BESS，i are the battery energy storage states of the battery energy storage system at time i+1 and i, respectively, E,a is the energy efficiency of battery a, Δi is the time step, C BESS_degradation is the battery aging cost, C cycle is the cycle aging cost, n cycles is the number of charge and discharge cycles, C calendar is the calendar aging cost.

3. The microgrid operation dispatch optimization method based on a two-layer adaptive energy management system according to claim 1 is characterized in that: In step 2, considering that the load can be reduced at different levels, the static controllable load model is as follows: P static，i =(1-d static，i )·P base，i Considering that the load can be adjusted within a continuous range, the dynamic controllable load model is as follows: P dynamic，i =(1-d dynamic，i )·P base，i Among them, P static，i is the static load power at time i, δ static，i is the load reduction factor, P base，i is the basic load power, P dynamic，i is the dynamic load power at time i, δ dynamic，i is the dynamic load adjustment factor.

4. The microgrid operation scheduling optimization method based on a two-layer adaptive energy management system according to claim 1 is characterized in that: The specific process of step 3 is as follows: Considering the cost of purchasing electricity from the grid and the income from selling electricity to the grid, as well as the operating costs of the battery energy storage system and dispatchable power generation resources, the following objective function is established with the goal of maximizing the total operating profit of the microgrid: Among them, c gridout，i 、c gridin，i Represent the prices of electricity sold and purchased by the microgrid from the large grid at time i, P gridout，i , P gridin，i are the power sold and purchased by the microgrid from the large grid at time i, C BESS，a and C DG，a Represent the operating costs of the battery a energy storage system and dispatchable power generation resources, C BESS_degradation is the aging cost of the battery, To schedule the time for operation, Assemble for battery system; Considering the impact of market environment and peak electricity price on microgrid operation profit, the peak electricity price cost or benefit W at time i is defined as peak，i : Among them, c peak price is the peak electricity price, P peak，i is the peak power at time i, P peak，hist，i is the historical peak power, cgridout,i is the price at which the microgrid sells electricity to the large grid at time i; When P peak，i ≥P peak，hist，i When the microgrid needs to pay an additional peak electricity price cost c peak price ·(P peak，hist，i -P peak，i ); when P peak，i <P peak，hist，i When the microgrid sells electricity to the large grid, it earns revenue. grid out，i ·(P peak，i -P peak，hist，i ); Battery energy storage system constraints: AND BESS，min ≤E BESS，i ≤E BESS，max Among them, E BESS，min and E BESS，max are the minimum and maximum energy storage states of the battery energy storage system at time i, E BESS，i is the battery energy storage state of the battery energy storage system at time i; Supply and demand balance constraints: Among them, P disch，a is the discharge power of battery a, P PV is the power generation of the photovoltaic system, P static，i , P dynamic，i are the static and dynamic load powers at time i, respectively, D i is the total demand at time i; The optimization solver CPLEX is used to solve the above mixed integer linear programming problem and generate a preliminary scheduling plan for the microgrid, including the charging and discharging plan of the battery energy storage system, the generation plan of the dispatchable power generation resources, and the power trading strategy with the large power grid.

5. The microgrid operation dispatch optimization method based on a two-layer adaptive energy management system according to claim 1 is characterized in that: The specific process of step 4 is as follows: The objective function of the lower layer optimization is as follows: in, and are the power dispatch plans for microgrids, battery energy storage systems, and dispatchable power generation resources given by the upper planning layer, and are the real-time power values ​​calculated in the lower-layer optimization process, and are the operation scheduling time sets; Network constraints, including line capacity limitations, voltage stability, and power factor requirements, are considered in real time to ensure that the operation of the microgrid does not exceed the safety and stability range of the grid. The genetic optimization algorithm SA is used to solve the problem and obtain the real-time dispatching strategy of the microgrid.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the steps of the microgrid operation scheduling optimization method based on a two-layer adaptive energy management system as described in any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the microgrid operation scheduling optimization method based on a two-layer adaptive energy management system as described in any one of claims 1 to 5 are implemented.

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