A method for integrated energy allocation in microgrid areas

Abstract

This invention relates to a microgrid regional integrated energy optimization allocation method. Through multi-layer cyclic optimization, it rationally designs the type and capacity of newly constructed equipment, forming an allocation method guided by energy self-sufficiency, employing reasonable energy construction scale game theory, and adhering to the principle of full resource utilization. This method aims to achieve the highest system efficiency, lowest operation and maintenance costs, highest self-sufficiency rate, and minimum total life-cycle investment. The multi-layer optimization model includes an economic optimization model, a self-consistent optimization model, and an output fluctuation optimization model. The solutions output by each of these models are sequentially passed, and the solutions of each layer are passed progressively between the multi-layer optimization models until the last layer outputs the optimal solution or the predetermined number of iterations has been reached. This method, while alleviating regional power supply problems, reduces both the initial investment costs and subsequent operation and maintenance costs for energy-consuming units, and improves the efficiency of system resource allocation.

Description

Technical Field

[0001] This invention relates to the field of power, and more specifically to a method for optimizing the allocation of integrated energy resources in a microgrid area. Background Technology

[0002] China has a vast territory, with numerous service areas, stations, and villages located in remote regions such as plateaus, deserts, grasslands, and isolated islands. Due to constraints imposed by the natural environment, construction costs, and return on investment, effectively utilizing local natural resources such as wind and solar power, and constructing microgrid systems tailored to local conditions, is an effective way to ensure regional energy self-sufficiency and efficient utilization, achieving local prosperity and sustainable ecological improvement. However, the rationality of the configuration of energy sources and energy storage devices within a microgrid system directly affects the system's reliability, economic efficiency, and future sustainable development.

[0003] In recent years, scholars both domestically and internationally have conducted extensive research on the capacity optimization and configuration of microgrid systems. Some studies focus on single-objective optimization, such as minimizing system cost; others employ multi-objective optimization, solving for each sub-objective function separately, assigning weights to each sub-objective, and finally transforming the multi-objective optimization into a single-objective optimization using a linear weighted method. For the former, single-objective optimization considers relatively few factors and cannot adequately address the actual needs of different regions; while for the latter, the determination of weights inevitably involves subjectivity due to human intervention, affecting the accuracy of the optimization results.

[0004] When configuring energy source capacity for microgrids, multiple objectives must be comprehensively met. These include consideration of various constraints such as construction investment costs, operation and maintenance costs, energy balance, and site limitations. If capacity estimation is performed on a single-objective basis for each of these objectives, it becomes difficult to simultaneously ensure the matching of energy sources and loads within the microgrid and the long-term operation of the system. To maximize the reliability, sustainability, and scalability of local microgrid systems, practical and effective energy optimization methods should be adopted to develop new microgrid industries, models, and business formats with universal value.

[0005] In designing optimal energy configuration for microgrids, this invention considers the following factors:

[0006] (1) Energy source capacity configuration should adopt scientific and effective methods: If we blindly pursue energy security within the microgrid and build an excessively large scale of energy sources, it will not only increase investment costs, but also lead to problems such as resource waste or unstable energy use by users. Therefore, the energy source capacity configuration method should adopt scientific and effective deduction methods to minimize resource waste and negative impact on user energy use.

[0007] (2) Energy source capacity configuration fully considers natural resource endowment to achieve energy self-sufficiency: The energy source optimization configuration comprehensively evaluates the natural resource endowment and load energy consumption of the microgrid construction site. Based on the concept of green energy comprehensive self-sufficiency, it efficiently utilizes renewable energy sources such as wind and solar power, and maximizes the self-sufficiency of microgrid regional load energy consumption through asset energy conversion.

[0008] (3) Energy source capacity configuration needs to match microgrid energy consumption and investment capacity: While connecting to large-scale renewable energy and energy storage systems can increase the reliability of microgrid operation, it will inevitably involve high investment costs and subsequent operation and maintenance costs. For regional energy-consuming units, building related facilities independently will result in excessively high investment and operation and maintenance costs, which will easily increase the burden on energy-consuming units. Moreover, unreasonable facility capacity configuration will not only cause resource waste but also investment losses.

[0009] Based on the above considerations, this invention proposes a multi-objective cyclic optimization method adapted to microgrid energy source quantification, which can be used for comprehensive energy optimization in microgrid areas and has strong regional applicability. Summary of the Invention

[0010] This invention, based on the natural resources and load characteristics of existing microgrid construction areas, and while ensuring regional energy reliability, rationally designs the types and capacities of newly constructed equipment through a multi-layered cyclical optimization configuration method. This results in a configuration method guided by energy self-sufficiency, employing a reasonable game theory approach to energy construction scale, and adhering to the principle of full resource utilization. The goal is to achieve the highest system efficiency, lowest operation and maintenance costs, highest self-sufficiency rate, and minimum total life-cycle investment. This method alleviates regional power supply problems, reduces both the initial investment costs and subsequent operation and maintenance costs for energy-consuming units, and improves the efficiency of system resource allocation.

[0011] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0012] A microgrid regional integrated energy optimization allocation method includes a multi-layer optimization model, each with a different optimization objective. Each optimization model includes an optimization objective, decision variables, and constraints. The solutions of each optimization model are passed sequentially between the multi-layer optimization models in a progressive manner. If the solution output by the last optimization model is not the optimal solution of that optimization model, the solution output by the last optimization model is fed back to the first optimization model, and the first optimization model is solved again. The solution is solved layer by layer in the above manner in a progressive loop until the last optimization model outputs the optimal solution or the set number of iterations has been reached. Then, the solution of the last optimization model is output and the loop ends.

[0013] The multi-level optimization model includes at least an economic optimization model, a self-consistent optimization model, and an output fluctuation optimization model. The solutions output by the economic optimization model, the self-consistent optimization model, and the output fluctuation optimization model are passed on in sequence. The economic optimization model aims to optimize the economic efficiency of the microgrid system, the self-consistent optimization model aims to maximize the self-consistency rate of the microgrid system, and the output fluctuation optimization model aims to minimize the daily output fluctuation rate of the microgrid system.

[0014] Furthermore, the objective function of the economic optimization model is to minimize the overall investment and operation and maintenance cost of the microgrid system, the decision variables of the economic optimization model are the configurable capacity of energy and energy storage within the microgrid system, and the constraints of the economic optimization model are investment cost constraints and construction land constraints.

[0015] The objective function of the self-consistent optimization model is to maximize the self-consistency rate of the microgrid system. The decision variable of the self-consistent optimization model is the actual output capacity of the clean energy source within the microgrid system. The constraints of the self-consistent optimization model are the output constraints and installed capacity constraints of the economic optimization model.

[0016] The objective function of the power output fluctuation optimization model is to minimize the daily power output fluctuation rate within 8760 hours of microgrid system production simulation. The decision variables of the power output fluctuation optimization model are the energy source and energy storage generation capacity within the microgrid system. The constraints of the power output fluctuation optimization model are the output constraints of the self-consistent optimization model, as well as the power output balance constraints of the microgrid, the charging and discharging power of the energy storage system, and its own capacity constraints.

[0017] Furthermore, the optimization objective of the economic optimization model is as follows:

[0018]

[0019] In the above formula, f cos This is a function that minimizes the investment and maintenance costs throughout the entire lifecycle of a microgrid system.

[0020] f inv A function to minimize the investment cost of building a microgrid system;

[0021] f m This function minimizes the maintenance costs of the microgrid system in its later stages of use.

[0022] f w,inv f pv,inv f es,inv f o,inv These are the investment cost functions for wind power, photovoltaic power, energy storage, and other power energy construction, respectively.

[0023] P i The installed capacity of the energy source for the microgrid system is expressed in kW.

[0024] w, pv, es, and o refer to wind power, photovoltaic power, energy storage, and other electrical energy sources, respectively.

[0025] c i,inv The decision variable is the initial investment cost, expressed in yuan / kW.

[0026] r is the discount rate;

[0027] T i The calendar life of the project comprised of decision-making entities, in years;

[0028] f w,m f pv,m f es,m f o,m These are the construction, use, and operation / maintenance cost functions for wind power, photovoltaic power, energy storage, and other power energy sources, respectively.

[0029] c i,m To estimate the operation and maintenance costs for the decision-making body over the project's calendar life, the unit is RMB / kW;

[0030] The decision variables of the economic optimization model are as follows:

[0031]

[0032] In the above formula, P w P pv P es P o These represent the installed capacity of wind power, photovoltaic power, energy storage, and other power energy sources, respectively, in kW.

[0033] S w S pv S es S o These are the collections of installed capacity for wind power, photovoltaic power, energy storage, and other power energy sources, respectively.

[0034] P wmin P wmax These are the lower and upper limits of wind power installed capacity, respectively, in kW;

[0035] P pvmin P pvmax These represent the lower and upper limits of photovoltaic installed capacity, respectively, in kW;

[0036] P esmin P esmax These are the lower and upper limits of energy storage installed capacity, respectively, in kW;

[0037] P omin P omax These are the lower and upper limits of the installed capacity of other power energy sources, respectively, in kW;

[0038] The investment cost constraint condition of the economic optimization model is as follows:

[0039]

[0040] In the above formula, C max This represents the maximum allowable total investment cost for a microgrid system.

[0041] C w C pv C es C o The unit cost for wind power, photovoltaic, energy storage, and other power energy sources is respectively, expressed in yuan / kW;

[0042] The land constraints for the construction of the economic optimization model are as follows:

[0043]

[0044] In the above formula, S max The maximum permitted construction land area for the microgrid system construction zone;

[0045] S w S pv S es S o These are the land area per unit capacity for wind power, photovoltaic power, energy storage, and other electrical energy sources, respectively, in m². 2 / kW;

[0046] The optimization objective of the self-consistent optimization model is as follows:

[0047]

[0048] In the above formula, f self_consistent For the function that maximizes the energy self-sufficiency of the microgrid system;

[0049] P wr P pvr P or These represent the actual output power of wind power, photovoltaic power, and other electrical energy sources, respectively, in kW.

[0050] T w_year T pv_year T o_year These are the annual effective power generation utilization hours for wind power, photovoltaic power, and other power energy sources, respectively, in hours.

[0051] P load The annual average load power is expressed in kW.

[0052] The decision variables of the self-consistent optimization model are as follows:

[0053]

[0054] In the above formula, η w η pv η o These are the output efficiencies of wind power, photovoltaic power, and other power energy sources, respectively, less than 1, and dimensionless;

[0055] The optimization objective of the power output fluctuation optimization model is as follows:

[0056]

[0057] In the above formula, F is the function that minimizes the daily power fluctuation rate of the microgrid system over an 8760-hour production simulation period;

[0058] P w (d,t), P pv (d,t), P es (d,t), P o (d,t) represent the power output of wind power, photovoltaic power, energy storage, and other electrical energy sources at hour t on day d, respectively, in kW;

[0059] P avg (d) represents the average daily power output at hour t on day d, without considering energy storage regulation, in kW;

[0060] The decision variables of the power output fluctuation optimization model are as follows:

[0061]

[0062] In the above formula, η w,m η pv,m η es,m η o,m The values ​​are the real-time output efficiency per unit power of wind power, photovoltaic power, energy storage, and other electrical energy sources at hour t on day d, respectively, and are dimensionless.

[0063] Under the output balance constraint, the power demand for energy storage charging and discharging in the power output fluctuation optimization model is as follows:

[0064]

[0065] In the above formula, ΔP(d,t) represents the charging and discharging power required by energy storage to meet the supply and demand imbalance between the source and the load in the production process simulation, with the unit being kW; where "+" represents charging and "-" represents discharging.

[0066] P load (d,t) represents the real-time energy demand of the load at hour t on day d, in kW;

[0067] PG (d,t) represents the total output power of wind power, photovoltaic power, and other electrical energy sources at hour t on day d, in kW;

[0068] The chargeable and dischargeable power of energy storage theory is as follows:

[0069]

[0070] In the above formula, P esn Rated charging and discharging power for energy storage, in kW;

[0071] The real-time available capacity of energy storage theory is as follows:

[0072]

[0073] In the above formula, E es (d,t) represents the real-time available capacity of energy storage in theory on day d and hour t.

[0074] Furthermore, the upper limit of the installed capacity of wind power, photovoltaic, energy storage, and other power energy sources in Formula 2 shall be determined with reference to the following formula:

[0075]

[0076] In the above formula, C wmax S wmax These are the wind power installed capacity limits under investment constraints and land constraints, respectively, in kW;

[0077] C pvmax S pvmax These are the upper limits for photovoltaic installations under investment constraints and land constraints, respectively, in kW;

[0078] C esmax S esmax These are the upper limits for energy storage installations under investment constraints and land constraints, respectively, in kW;

[0079] C omax S omax These represent the upper limits of installed capacity for other power energy sources, respectively, based on investment and land constraints, in kW.

[0080] Furthermore, when passing optimization results between adjacent optimization models, if the optimization model at the upper level has a solution, the output result is passed to the optimization model at the lower level. The optimization model at the lower level calculates the optimization objective function of its own level based on the optimization result of the optimization model at the upper level. If the optimization model at the upper level has no solution, the optimization objective function of its own level needs to be recalculated until the corresponding solution of the optimization model at its own level is calculated.

[0081] Furthermore, when the solution output by the last layer optimization model is passed to the first layer optimization model, if the first layer optimization model cannot calculate its own solution based on the result passed by the last layer optimization model, then the last layer optimization model needs to recalculate a new solution and then pass the new solution to the first layer optimization model.

[0082] The comprehensive energy optimization allocation method proposed in this invention adopts a multi-layered optimization objective cyclical transmission architecture. Through multiple cyclical transmissions and iterative solutions, a globally optimal solution that takes into account multiple optimization objectives is obtained, thereby selecting the optimal source and storage capacity that meet the optimization objectives. This method satisfies the energy demand problem of loads within microgrids and ensures the reliability and economy of system construction, operation and maintenance, and energy use. This allocation method can be modified to meet different optimization objectives according to specific project requirements. This allocation method is also applicable to other optimization scenarios such as objective classification and process control, and has strong universality and technical promotion value.

[0083] The integrated energy optimization allocation method proposed in this invention, based on the optimization objective of minimizing construction and operation and maintenance costs, allocates the optimal construction capacity of wind, solar, storage, and other energy sources. This allocation method effectively considers the user's own interests, determines the source based on the load, avoids blind construction, rationally plans investment and operation and maintenance expenditure costs, and the optimization objective can be adjusted according to specific realities, making it highly adaptable to actual engineering projects. This gives the proposed allocation method strong economic promotion value.

[0084] The integrated energy optimization configuration method proposed in this invention is not only suitable for independent microgrid systems in medium and low density areas, but also applicable to engineering designs such as integrated energy systems and virtual power plants in developed regions with flexible power supply policies. The reasonable energy source configuration method can maximize cost reduction and improve energy utilization while ensuring reliable load energy use. The optimization objectives in this configuration method can be adjusted according to the microgrid assembly area, and have strong application value in various scenarios. Attached Figure Description

[0085] Figure 1 This is a schematic diagram of the multi-layer target optimization loop structure in the configuration method of the present invention;

[0086] Figure 2 The diagram below illustrates the loop structure based on the three-layer optimization model in the configuration method given in the embodiment.

[0087] Figure 3 The flowchart of the configuration method based on the three-layer optimization model is given as an example. Detailed Implementation

[0088] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0089] This embodiment discloses a microgrid regional integrated energy optimization allocation method. It uses the key architecture of the microgrid system and key elements within the system to guide the selection of decision variables in the optimization allocation, thereby determining constraints and defining optimization objectives. The optimization allocation method of this invention employs a multi-layer optimization model, constructing the basic components, architecture, and iterative operation mode of the multi-layer optimization iterative model. In the design of each layer of the optimization model, the purpose, optimization objective, decision variables, and constraints of each layer are given.

[0090] The microgrid system mentioned in this embodiment includes "source", "storage", and "load". Taking the power source as an example, the energy source includes wind power, photovoltaic, small hydropower, fuel cells, etc. Taking the energy storage device as an example, the energy storage device includes lithium phosphate, lead-carbon, flow hydride and other electrochemical energy storage batteries. The "load" is the carrier of energy consumption in the microgrid area, which is divided according to the scenario. The main scenarios include remote area stations, western service areas, villages, scientific research institutes, border outposts, isolated islands, etc.

[0091] The optimal configuration method is the core of energy source saturation in a microgrid system. This invention designs a multi-layer optimal configuration method where each layer's optimization model includes an optimization objective, decision variables, and constraints. The solutions of each layer's optimization models are passed sequentially in a progressive manner: the optimal solution determined by the first layer is passed down; the second layer determines its optimal solution based on the output of the first layer and continues to pass it down to the next layer, and so on until the last layer is reached; the last layer determines its optimal solution and feeds the result back to the first layer; the first layer combines the feedback from the last layer to re-optimize its optimal solution, and this process is repeated iteratively to obtain the optimal solution and corresponding optimal value for each layer until the iteration ends. The last layer outputs its optimal solution or has reached the set number of iterations, at which point it outputs its solution and the loop ends.

[0092] The aforementioned multi-level optimization model loop structure is as follows: Figure 1 As shown, theoretically there is no upper limit to the optimization target, but based on computing power and the primary focus of the microgrid area, this embodiment takes... Figure 2The following explanation uses a three-layer optimization model structure as an example. When determining the optimization objectives for each layer, the main considerations for constructing new energy sources in microgrid areas are taken into account. For remote areas, energy security is the primary prerequisite, and energy-related economic indicators are mainly considered. Based on this, the optimization configuration objectives of this invention include minimizing the investment and maintenance costs of the microgrid system, achieving power balance, and minimizing daily power fluctuation. Considering the continuous increase in new energy penetration, the optimization configuration objectives also include maximizing energy self-consistency. That is, the three-layer optimization models given in this embodiment are the economic optimization model, the self-consistent optimization model, and the power fluctuation optimization model. The solutions output by each of these models are passed sequentially. The economic optimization model aims for optimal economic efficiency of the microgrid system; the self-consistent optimization model aims for the highest self-consistency rate of the microgrid system; and the power fluctuation optimization model aims for the lowest daily power fluctuation of the microgrid system.

[0093] Considering that the optimization configuration objective of this embodiment is a multi-layered architecture, and that there are entangled relationships between the objectives, the interests of multiple objectives need to be considered simultaneously. Therefore, the optimization configuration method presented in this embodiment, while complying with the decision-making of each layer, also retains the relative autonomy of each layer. Figure 2 Taking the three-layer optimization model shown below as an example, the design of each layer of the optimization model will be explained one by one below.

[0094] The economic optimization model is used as the first-level optimization model. Its objective function is to minimize the overall investment and operation and maintenance cost of the microgrid system. The decision variables of the economic optimization model are the configurable capacity of energy and energy storage within the microgrid system. The constraints of the economic optimization model are investment cost constraints and construction land constraints.

[0095] The optimization objective of the economic optimization model is as follows:

[0096]

[0097] In the above formula, f cos This is a function that minimizes the investment and maintenance costs throughout the entire lifecycle of a microgrid system.

[0098] f inv A function to minimize the investment cost of building a microgrid system;

[0099] f m This function minimizes the maintenance costs of the microgrid system in its later stages of use.

[0100] f w,inv f pv,inv f es,inv f o,inv These are the investment cost functions for wind power, photovoltaic power, energy storage, and other power energy construction, respectively.

[0101] P iThe installed capacity of the energy source for the microgrid system is expressed in kW.

[0102] w, pv, es, and o refer to wind power, photovoltaic power, energy storage, and other electrical energy sources, respectively.

[0103] c i,inv The decision variable is the initial investment cost, expressed in yuan / kW.

[0104] r is the discount rate;

[0105] T i The calendar life of the project comprised of decision-making entities, in years;

[0106] f w,m f pv,m f es,m f o,m These are the construction, use, and operation / maintenance cost functions for wind power, photovoltaic power, energy storage, and other power energy sources, respectively.

[0107] c i,m The estimated operation and maintenance costs for the decision-making entity over the project's calendar life are expressed in yuan / kW.

[0108] The decision variables in an economic optimization model refer to the strategies that the decision-making entity can adjust during the optimization process. For the decision-making entity in an economic optimization model, this refers to the construction capacity of wind power, photovoltaic power, energy storage, and other power energy sources. The decision variables are as follows:

[0109]

[0110] In the above formula, P w P pv P es P o These represent the installed capacity of wind power, photovoltaic power, energy storage, and other power energy sources, respectively, in kW.

[0111] S w S pv S es S o These are the collections of installed capacity for wind power, photovoltaic power, energy storage, and other power energy sources, respectively.

[0112] P wmin P wmax These are the lower and upper limits of wind power installed capacity, respectively, in kW;

[0113] P pvmin P pvmax These represent the lower and upper limits of photovoltaic installed capacity, respectively, in kW;

[0114] P esmin Pesmax These are the lower and upper limits of energy storage installed capacity, respectively, in kW;

[0115] P omin P omax These represent the lower and upper limits of the installed capacity of other power energy sources, respectively, in kW.

[0116] The upper limit values ​​for the installed capacity of wind power, photovoltaic, energy storage, and other power energy sources in Formula 2 above are determined with reference to the following formula:

[0117]

[0118] In the above formula, C wmax S wmax These are the wind power installed capacity limits under investment constraints and land constraints, respectively, in kW;

[0119] C pvmax S pvmax These are the upper limits for photovoltaic installations under investment constraints and land constraints, respectively, in kW;

[0120] C esmax S esmax These are the upper limits for energy storage installations under investment constraints and land constraints, respectively, in kW;

[0121] C omax S omax These represent the upper limits of installed capacity for other power energy sources, respectively, based on investment and land constraints, in kW.

[0122] The investment cost constraint of the economic optimization model is as follows:

[0123]

[0124] In the above formula, C max This represents the maximum allowable total investment cost for a microgrid system.

[0125] C w C pv C es C o The unit cost for wind power, photovoltaic, energy storage, and other power energy sources is respectively, expressed in yuan / kW;

[0126] The land constraints for the construction of the economic optimization model are as follows:

[0127]

[0128] In the above formula, S max The maximum permitted construction land area for the microgrid system construction zone;

[0129] Sw S pv S es S o These are the land area per unit capacity for wind power, photovoltaic power, energy storage, and other electrical energy sources, respectively, in m². 2 / kW.

[0130] The self-consistent optimization model is used as the second-level optimization model. The objective function of the self-consistent optimization model is to maximize the self-consistency rate of the microgrid system. The decision variable of the self-consistent optimization model is the actual output capacity of the clean energy source in the microgrid system. The constraints of the self-consistent optimization model are the output constraints and installed capacity constraints of the economic optimization model.

[0131] The optimization objective of the self-consistent optimization model is as follows:

[0132]

[0133] In the above formula, f self_consistent For the function that maximizes the energy self-sufficiency of the microgrid system;

[0134] P wr P pvr P or These represent the actual output power of wind power, photovoltaic power, and other electrical energy sources, respectively, in kW.

[0135] T w_year T pv_year T o_year These are the annual effective power generation utilization hours for wind power, photovoltaic power, and other power energy sources, respectively, in hours.

[0136] P load The annual average power of the load is expressed in kW.

[0137] The decision-making entities in the self-consistent optimization model are the output capacity of wind power, photovoltaic power, and other power energy sources. The decision variables are as follows:

[0138]

[0139] In the above formula, η w η pv η o These represent the output efficiencies of wind power, photovoltaic power, and other power energy sources, respectively, and are less than 1 and dimensionless.

[0140] The constraints of the self-consistent optimization model are subject to the output constraints of the first-level optimization model, and depend on the investment and construction capacity of wind power, photovoltaic power, and other power energy sources.

[0141] The power output fluctuation optimization model is used as the third-level optimization model. The objective function of the power output fluctuation optimization model is to minimize the daily power output fluctuation rate within 8760 hours of microgrid system production simulation. The decision variables of the power output fluctuation optimization model are the energy source and energy storage generation capacity within the microgrid system. The constraints of the power output fluctuation optimization model are the output constraints of the self-consistent optimization model, as well as the power output balance constraints of the microgrid, the charging and discharging power of the energy storage system, and its own capacity constraints.

[0142] Among them, when designing the optimization objective of the power output fluctuation optimization model, the optimization objective is to minimize the daily power output fluctuation rate within the microgrid system, that is, to maximize the power output of wind power, photovoltaics, and other power energy sources, and minimize the available power output of energy storage, as shown in the following formula:

[0143]

[0144] In the above formula, F is the function that minimizes the daily power fluctuation rate of the microgrid system over an 8760-hour production simulation period;

[0145] P w (d,t), P pv (d,t), P es (d,t), P o (d,t) represent the power output of wind power, photovoltaic power, energy storage, and other electrical energy sources at hour t on day d, respectively, in kW;

[0146] P avg (d) represents the average daily power output at hour t on day d, without considering energy storage regulation, in kW.

[0147] The decision-making entity of the power output fluctuation optimization model is the power output of the installed wind power, photovoltaic, energy storage, and other electrical energy sources at hour t on day d. The decision variables are as follows:

[0148]

[0149] In the above formula, η w,m η pv,m η es,m η o,m The values ​​are the real-time output efficiency per unit power of wind power, photovoltaic power, energy storage, and other electrical energy sources at hour t on day d, respectively, and are dimensionless.

[0150] In addition to being constrained by the second-level optimization model, the output fluctuation optimization model is also constrained by the output balance constraint, energy storage charging and discharging power, and its own capacity during the 8760-hour production simulation. Under the output balance constraint, the energy storage charging and discharging power requirements are as follows:

[0151]

[0152] In the above formula, ΔP(d,t) represents the charging and discharging power required by energy storage to meet the supply and demand imbalance between the source and the load in the production process simulation, with the unit being kW; where "+" represents charging and "-" represents discharging.

[0153] P load (d,t) represents the real-time energy demand of the load at hour t on day d, in kW;

[0154] P G (d,t) represents the total output power of wind power, photovoltaic power, and other electrical energy sources at hour t on day d, in kW;

[0155] The chargeable and dischargeable power of energy storage theory is as follows:

[0156]

[0157] In the above formula, P esn Rated charging and discharging power for energy storage, in kW;

[0158] The real-time available capacity of energy storage theory is as follows:

[0159]

[0160] In the above formula, E es (d,t) represents the real-time available capacity of energy storage in theory on day d and hour t.

[0161] When optimizing the allocation of integrated energy in a microgrid area according to the three-layer optimization model given above, such as... Figure 3 As shown, when passing optimization results between adjacent optimization models, if the upper-level optimization model has a solution, the output result is passed to the lower-level optimization model, which then calculates its own objective function based on the upper-level model's result. If the upper-level optimization model has no solution, it means that the optimization result is not suitable for the lower-level objective function, and the objective function needs to be recalculated until a solution is found. When the solution output by the last-level optimization model is passed to the first-level optimization model, if the first-level optimization model cannot calculate its own solution based on the result passed from the last-level model, it needs to recalculate a new solution and then pass it to the first-level model, continuing until the decision variables can be deduced and the solution is passed down to the next level, until the set number of iterations is reached.

[0162] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A microgrid regional integrated energy optimization configuration method, characterized in that: The multi-layer optimization model comprises multiple layers of optimization models, each layer of optimization model has different optimization objectives, each layer of optimization model comprises optimization objectives, decision variables and constraint conditions, and the solutions of each layer of optimization model are sequentially transmitted in a progressive manner between the multiple layers of optimization models, the solution output by the last layer of optimization model is fed back to the first layer of optimization model if the solution is not the optimal solution of the last layer of optimization model, the first layer of optimization model is solved again, and the progressive cycle is solved layer by layer in the above manner until the optimal solution of the last layer of optimization model is output or the set number of iteration cycles is reached, and then the solution of the last layer of optimization model is output and the cycle is ended; The multi-layer optimization model comprises at least an economic optimization model, a self-consistent optimization model and a power fluctuation optimization model, and the solutions output by the economic optimization model, the self-consistent optimization model and the power fluctuation optimization model are sequentially transmitted; the economic optimization model takes the economic optimization of the micro-grid system as the optimization objective, the self-consistent optimization model takes the highest self-consistent rate of the micro-grid system as the optimization objective, and the power fluctuation optimization model takes the lowest daily power fluctuation rate of the micro-grid system as the optimization objective; The optimization objective of the self-consistent optimization model is as follows: (Formula 6) In the above formula, f self_consistent is the maximization function of the energy self-consistency rate for the microgrid system; P wr , P pvr , P or Pwind, Ppv, Pother are the actual available power of wind power, photovoltaic power and other power energy sources, respectively, in kW. T w_year , T pv_year , T o_year Annual effective generating utilization hours of wind power, photovoltaic power, and other power energy sources, respectively, in units of h P load Load annual average power in kW.

2. The microgrid regional integrated energy optimization configuration method according to claim 1, characterized in that: The optimization objective function of the economic optimization model is the lowest overall investment operation cost of the micro-grid system, the decision variable of the economic optimization model is the configurable capacity of energy and energy storage in the micro-grid system, and the constraint condition of the economic optimization model is the investment cost constraint and the construction land constraint; The optimization objective function of the self-consistent optimization model is the highest self-consistent rate of the micro-grid system, the decision variable of the self-consistent optimization model is the actual output capacity of clean energy sources in the micro-grid system, and the constraint condition of the self-consistent optimization model is constrained by the output of the economic optimization model and the installed capacity constraint; The optimization objective function of the power fluctuation optimization model is the lowest daily power fluctuation rate of the micro-grid system in the production simulation of 8760 hours, the decision variable of the power fluctuation optimization model is the energy source and energy storage generation capacity in the micro-grid system, and the constraint condition of the power fluctuation optimization model is constrained by the output of the self-consistent optimization model and the micro-grid power balance constraint, the energy storage charging and discharging power and its own capacity constraint.

3. The microgrid regional integrated energy optimization configuration method according to claim 2, characterized in that: The optimization objective of the economic optimization model is as follows: (Formula 1) In the above formula, f cos is the minimum function of the life cycle investment and operation cost of the microgrid system. f inv Minimize the function of the investment cost for the microgrid system construction; f m minimize the operation and maintenance cost of the microgrid system in the later stage f w,inv 、 f pv,inv 、 f es,inv 、 f o,inv respectively are the wind power, photovoltaic, energy storage, other electric power energy construction investment cost functions; P i The construction installed capacity of the micro-grid system energy source is in kW. w, pv, es and o respectively represent wind power, photovoltaic, energy storage and other electric power energy; c i,inv The investment cost of the decision variable is in yuan per kW. r is the discount rate; T i Calendar life of the project in years for the decision maker to make decisions. f w,m , f pv,m , f es,m , f o,m respectively are the wind power, photovoltaic, energy storage, other electric power energy construction use / operation and maintenance cost functions; c i,m To estimate the decision-making subject's use of operation and maintenance costs within the project calendar life, in yuan / kW; The decision variable of the economic optimization model is as follows: (Formula 2) In the above formula, P w , P pv , P es , P o respectively, the installed capacity of wind power, photovoltaic, energy storage, other power energy sources, unit: kW; G w , G pv , G es , G o are respectively the construction installed capacity set of wind power, photovoltaic, energy storage, other electric power energy sources; P wmin 、 P wmax respectively the lower and upper limits of the wind power installed capacity in kW. P pvmin 、 P pvmax Pminand Pmaxare the lower and upper limits of the installed photovoltaic capacity, respectively, in kW. P esmin , P esmax are the lower and upper limits of the energy storage installed capacity, respectively, in kW; P omin , P omax are lower and upper limits of the installed capacity of other power energy sources, respectively, in kW; The upper limit of the installed capacity of wind power, photovoltaic, energy storage and other electric power energy sources in the formula 2 is determined with reference to the following formula: (Formula 3) In the above formula, C wmax , S wmax are the upper limits of wind power installed capacity under investment constraint and land constraint, respectively, in kW. C pvmax , S pvmax The upper limit of photovoltaic installed capacity under investment constraint and land constraint, respectively, in kW C esmax 、 S esmax The upper limit of energy storage capacity, in kW, under investment constraint and land constraint, respectively. C omax , S omax Upper limit of other power energy source installation, unit: kW The investment cost constraint condition of the economic optimization model is as follows: (Formula 4) In the above formula, C max is the upper limit of the total investment cost allowed for the microgrid system; C w , C pv , C es , C o respectively the unit cost of wind power, photovoltaic, energy storage, other power energy source capacity, unit: yuan / kW; The construction land constraint condition of the economic optimization model is as follows: (Formula 5) In the above formula, S max is the upper limit of the construction land area allowed for the micro-grid system construction region; S w 、 S pv 、 S es 、 S o respectively the unit capacity footprint area of wind power, photovoltaic, energy storage, other power energy sources, unit m 2 / kW; The decision variable of the self-consistent optimization model is as follows: (Formula 7) In the above formula, η w , η pv , η o are the output efficiencies of wind power, photovoltaic power, and other power energy sources, respectively, less than 1 and dimensionless. The optimization objective of the power fluctuation optimization model is as follows: (Formula 8) In the above formula, F producing a simulation of the minimum function of the 8760 h daily output fluctuation for the microgrid system; P w ( d, t )、 P pv ( d, t )、 P es ( d, t )、 P o ( d, t ) respectively represent the output power of wind power, photovoltaic, energy storage, and other power energy sources at the dth day and tth hour, with the unit of kW. P avg ( d ) is the daily average output power of the dth day and the tth hour without considering the energy storage adjustment, in units of kW; The decision variable of the power fluctuation optimization model is as follows: (Formula 9) In the above formula, η w,m , η pv,m , η es,m , η o,m are respectively the real-time output efficiency of wind power, photovoltaic, energy storage, and other power energy sources per unit power in the tth hour of dth day, dimensionless. The energy storage charging and discharging power demand of the power fluctuation optimization model under the power balance constraint is as follows: (Formula 10) In the above formula, Δ P ( d, t ) is the charge and discharge power required to balance the supply and demand between the source and the load in the production process simulation, in kW; where "+" indicates charging and "-" indicates discharging. P load ( d, t ) is the real-time energy demand power of the load at the dth day and the tth hour, with the unit of kW; P G ( d, t ) is the total output power of wind power, photovoltaic power, other power energy sources in the tth hour of the dth day, with the unit of kW; The theoretical charging and discharging power of energy storage is as follows: (Formula 11) In the above formula, P esn P is the rated charge and discharge power of the energy storage, in kW. The real-time available capacity of energy storage is as follows: (Formula 12) In the above formula, E es ( d, t d, t ) is the real-time available capacity on the dth day and the th hour of the energy storage theory.

4. The microgrid regional integrated energy optimization configuration method of claim 1, wherein: When the optimization results are transmitted between the adjacent layers of the optimization model, if the optimization model in the upper layer has a solution, the output result is transmitted to the optimization model in the lower layer, and the optimization model in the lower layer calculates the optimization objective function of the current layer according to the optimization result of the optimization model in the upper layer; if the optimization model in the upper layer has no solution, the optimization objective function of the current layer needs to be recalculated until the corresponding solution of the optimization model in the current layer is calculated.

5. The microgrid regional integrated energy optimization configuration method according to claim 4, characterized in that: When the solution output by the optimization model in the last layer is transmitted to the optimization model in the first layer, if the optimization model in the first layer cannot calculate the solution of the optimization model in the first layer according to the result transmitted by the optimization model in the last layer, the optimization model in the last layer needs to calculate a new solution and then transmit the new solution to the optimization model in the first layer.

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