Micro-grid energy management method based on load side flexible energy optimization

Abstract

The invention discloses a micro-grid energy management method based on load side flexible energy optimization. The method comprises the following steps: establishing a micro-grid source-grid-load energy production equipment model and an energy conversion equipment model; establishing a micro-grid source-grid-load energy aggregation model at an energy supply photovoltaic aggregator side; constructing an energy market optimization operation game model of the source-grid-load micro-grid by combining operation optimization targets and constraint conditions of multiple subjects in micro-grid source-grid-load energy management; and the feasibility of the optimization strategy model is proved through master-slave game equilibrium, and the obtained optimal solution of the energy management model strategy has economical efficiency and reliability in solving. The method aims at maximizing a non-cooperative energy supply side benefit function and a user side benefit function, promotes efficient utilization of comprehensive energy in a source-grid-load micro-grid, improves multi-party benefits of an industrial micro-grid energy market, provides a basis for further improving multi-energy coordinated operation, and has a wide application prospect. And the reliability of cooperative operation among multiple main bodies of the micro-grid is enhanced.

Description

Technical Field

[0001] The invention relates to a microgrid energy management method based on load-side flexible energy optimization, and belongs to the technical field of microgrid source-grid-load energy management. Background Art

[0002] A microgrid is a small, localized power system that usually includes distributed generation resources (such as solar photovoltaic, wind power, etc.), loads, and control equipment. With the development of renewable energy technologies and the diversification of energy demand, microgrids have shown great potential in improving energy efficiency, enhancing power supply reliability, and promoting sustainable development. Microgrid source-grid-load energy management achieves efficient, flexible, and reliable control of microgrids by comprehensively considering the dynamic relationship between power sources, grids, and loads.

[0003] Game strategy management of the microgrid source-grid-load energy network can significantly improve energy efficiency. Through real-time monitoring and optimized scheduling, renewable energy can be used to the maximum extent. It can also promote user participation and interaction. Users can adjust their electricity consumption behavior through the demand response mechanism, enhance the stable operation of the overall system, and improve the overall performance of the energy system.

[0004] At present, the main factors affecting the energy utilization in the typical industrial user energy system of the microgrid source-grid-load energy management strategy are:

[0005] (1) Inaccurate market simulation

[0006] Due to inaccurate market simulation, energy supply and demand forecasts often deviate, and traditional energy system game models usually only consider simple interactions between single masters and slaves, which cannot fully reflect the complexity of the actual energy market and the interactive relationship between multiple subjects, resulting in unreasonable resource allocation. Although this simplified processing is computationally simpler, it ignores the competition and cooperation behaviors of various participants in the market, resulting in inaccurate predictions and decisions of the model, which in turn affects the stability and economy of the entire energy system.

[0007] (2) Low energy conversion efficiency

[0008] Traditional energy system models often lack efficient energy conversion equipment and optimized control algorithms, resulting in low conversion efficiency between different forms of energy. In particular, the conversion between thermal energy, cold energy and electrical energy may lead to energy loss and resource waste due to the lack of optimized control strategies. In addition, the traditional scheduling ideas of "determining electricity by heat" and "determining heat by electricity" can no longer meet the needs of modern smart parks for efficient use of multiple energy sources. This not only affects the stability and reliability of the system, but also limits the effective consumption of clean energy and hinders the realization of sustainable development goals. Summary of the invention

[0009] The technical problem to be solved by the present invention is: to provide a microgrid energy management method based on load-side flexible energy optimization, taking into account the complexity and diversity of energy supply equipment and energy-consuming equipment contained in the source-grid-load microgrid, and modeling the source-grid-load microgrid with a double-layer Stackelberg model in a way of optimizing operation combined with game theory, which can flexibly adjust energy supply according to actual needs at different times, thereby improving energy utilization efficiency.

[0010] The present invention adopts the following technical solutions to solve the above technical problems:

[0011] A microgrid energy management method based on load-side flexible energy optimization includes the following steps:

[0012] Step 1, modeling the microgrid source-grid-load energy production equipment and energy conversion equipment to form a microgrid source-grid-load energy supply system; the energy production equipment includes a cogeneration unit and a gas boiler, and the energy conversion equipment includes an electric refrigerator, an absorption refrigerator, an electric boiler and a thermoelectric generator;

[0013] Step 2: Considering the impact of line loss on electricity purchase costs, an energy aggregation model of photovoltaic aggregators on the source-grid-load energy supply side of the microgrid is established;

[0014] Step 3: Based on steps 1 and 2, establish the operation optimization objectives and constraints of each subject in the microgrid source-grid-load energy management, and obtain the energy market optimization operation game model of the microgrid source-grid-load; among which, the subjects in the microgrid source-grid-load energy management include photovoltaic aggregators, energy aggregators and industrial parks;

[0015] Step 4. For the operation game model obtained in step 3, the energy aggregator is taken as the leader, the photovoltaic aggregator and the industrial park as the followers, and the master-slave game equilibrium is adopted to obtain the optimal solution of the operation game model, including the total amount of electricity aggregated by the photovoltaic aggregator from each producer, the electricity price sold by the photovoltaic aggregator, the elastic load of different types of energy involved in the conversion, and the selling price of different types of energy sold by the energy aggregator. Different types of energy include heat, electricity and cold energy.

[0016] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0017] 1. The present invention establishes a microgrid source-grid-load energy production equipment and energy conversion equipment model; establishes a photovoltaic aggregator energy aggregation model on the source-grid-load energy supply side of the microgrid; combines the operation optimization goals and constraints of multiple subjects in the microgrid source-grid-load energy management to construct an energy market optimization operation game model for the source-grid-load microgrid; and proves the feasibility of the optimization strategy model through master-slave game equilibrium, so that the optimal solution of the energy management model strategy is effective and reliable.

[0018] 2. The present invention aims to maximize the benefits of the non-cooperative energy supply side and the user side benefit function, promote the efficient utilization of comprehensive energy in the source-grid-load microgrid, and enhance the interests of multiple parties in the industrial microgrid energy market, providing a basis for further improving the coordinated operation of multiple energy sources, thereby enhancing the reliability of cooperative operation among multiple subjects in the microgrid.

[0019] 3. The present invention introduces flexible load management, which enables the system to flexibly adjust energy supply according to actual needs at different times, thereby improving energy utilization efficiency. This flexible management method helps to reduce energy waste and improve the overall economy and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a game structure diagram between three target entities in the microgrid source-grid-load energy management of the present invention;

[0021] Figure 2 It is the structure diagram of the energy aggregation model of photovoltaic aggregators. DETAILED DESCRIPTION

[0022] 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.

[0023] The present invention proposes a modeling method for a microgrid source-grid-load energy management strategy with flexible energy optimization on the load side. The modeling method takes into account the complexity and diversity of energy supply equipment and energy consumption equipment contained in the source-grid-load microgrid, and models the source-grid-load microgrid with a double-layer Stackelberg model in a way of optimizing operation combined with game theory. The specific process is as follows:

[0024] (1) Establish a microgrid source-grid-load energy production equipment and energy conversion equipment model

[0025] Various energy supply equipment contained in the microgrid source-grid-load energy management model based on load-side flexible energy optimization is modeled to form the energy supply system of the source-grid-load microgrid; the mathematical models of energy production equipment and energy conversion equipment contained in the above-mentioned source-grid-load microgrid are specifically introduced below.

[0026] 1) Mathematical model of production capacity equipment

[0027] ①CHP unit model

[0028] When generating electrical power, the CHP unit (condensing system) generates high-temperature and high-pressure steam through the boiler and transmits it to the turbine, and then generates electricity by driving the turbine blades to rotate and do work. Therefore, the output power of the CHP unit will produce a certain output ratio of thermal energy output. Among them, since the relationship between electrical energy output and cost in the CHP condensing system usually includes marginal cost factors related to electrical energy output (such as temperature and pressure), fuel consumption, and fixed costs (including equipment depreciation and basic maintenance costs), they correspond to the nonlinear coefficient, linear coefficient, and constant term of the electrical energy output variable and the cost function. For thermal energy, the extraction of thermal energy mainly depends on fuel consumption and the operating cost of related equipment, and the impact on the overall cost of the system is relatively direct and linear. The electrical and thermal energy output functions of the CHP unit are as follows:

[0029]

[0030] In formula (1) and (2), is the output power of the CHP unit during period t, in KW; is the output thermal power of the CHP unit during period t, in KW; is the output power cost of the CHP unit in period t, in Yuan; is the output heat energy cost of the CHP unit in period t, in Yuan; are the cost coefficients of CHP power generation respectively; and is the cost coefficient of CHP heating.

[0031] ②Gas boiler model

[0032] A gas boiler is a device that uses natural gas, propane or butane as fuel to heat water or generate steam for industrial heat load or heating. The core components include burners, heat exchangers and flues. When the gas boiler is at low load, the proportion of heat dissipation loss will decrease as the load increases, but at high load, the heat dissipation loss may increase. Therefore, due to the marginal loss constraint of heat dissipation loss, the combustion efficiency of the gas boiler will not continue to increase. When the combustion efficiency reaches a certain value, its combustion efficiency reaches the maximum value. At this time, if the heat load continues to increase, although the fuel cost continues to increase, the combustion efficiency of the gas boiler will decrease. Therefore, the functional relationship between the cost of the gas boiler and the output heat energy variable includes a nonlinear part, a linear part and a constant part. The heat energy output function of the gas boiler is as follows:

[0033]

[0034] In formula (3), are the cost coefficients of heating by micro gas boilers; is the output power of the gas boiler during the period t, in KW; is the output electricity cost of the gas boiler during period t, in Yuan.

[0035] 2) Mathematical model of energy conversion equipment

[0036] ①Electric refrigerator model

[0037] The refrigeration cycle of an electric refrigerator utilizes the phase change of the refrigerant from liquid to gas and then back to liquid, absorbing a large amount of heat from the area to be cooled and releasing it to the surrounding environment with relatively little energy input. The energy conversion function of the electric refrigerator is as follows:

[0038]

[0039] In formula (4), It is the power of the electric refrigerator's input electrical energy converted into cooling energy, in KW; is the input power of the electric refrigerator, in KW; k er is the cooling coefficient of the electric refrigerator.

[0040] ②Absorption Refrigeration Machine Model

[0041] Absorption chillers often use lithium bromide as the absorption solution. Lithium bromide absorption chillers provide cooling through a heat-driven cycle, using a mixed solution of lithium bromide and water, and do not require moving parts such as compressors in electric chillers. Absorption chillers start and stop quickly and easily, and the minimum cooling power output is negligible. The energy conversion function of a lithium bromide absorption chiller is as follows:

[0042]

[0043] In formula (5), It is the power of converting the input heat energy of lithium bromide absorption refrigerator into cold energy, in KW; is the input heat energy power of lithium bromide absorption refrigerator, unit KW; k ar It is the refrigeration coefficient of the lithium bromide absorption refrigerator.

[0044] ③Electric boiler model

[0045] Electric boilers are usually used to produce hot water or steam. They heat water or produce steam through resistive heating elements (such as electric heating wires or electric heating tubes). The performance of electric boilers can be measured by efficiency, that is, the proportion of input electrical energy converted into useful thermal energy. The energy conversion function of electric boilers is as follows:

[0046]

[0047] In formula (6), It is the power of the electric boiler’s input electrical energy converted into thermal energy, in KW; is the input power of the electric boiler, in KW; k eb It is the energy conversion efficiency coefficient of the electric boiler.

[0048] ④Thermoelectric generator model

[0049] A thermoelectric generator is a thermoelectric phenomenon. When there is a temperature difference between the two ends of two different materials, a voltage will be generated between the materials, which can be used to generate electricity. The efficiency of a thermoelectric generator is affected by factors such as material properties and temperature difference. Therefore, the energy conversion efficiency of a thermoelectric generator can usually be measured by efficiency, that is, the ratio of input thermal energy converted into electrical energy. The energy conversion function of a thermoelectric generator is as follows:

[0050]

[0051] In formula (7), It is the power of the thermoelectric generator that converts the input thermal energy into electrical energy, in KW; is the input thermal power of the thermoelectric generator, in KW; k teg is the energy conversion efficiency coefficient of the thermoelectric generator.

[0052] (2) Establishing the energy aggregation model of photovoltaic aggregators on the source-grid-load supply side of the microgrid

[0053] The present invention considers the impact of line loss on the cost of electricity purchase and incorporates line loss into the model, so that photovoltaic aggregators can calculate the cost more accurately when purchasing electricity and avoid additional losses caused by long-distance power transmission. This refined cost calculation helps to improve economic benefits and reduce operating costs.

[0054] The aggregation model of photovoltaic aggregator energy on the source-grid-load supply side of the microgrid is as follows Figure 2As shown in the figure, when PV aggregators purchase energy, they need to effectively collect and integrate the electricity produced by dispersed and low-density PV power generation users. Given that the locations of these distributed power generation users are relatively scattered, the transmission lines connected to the central node are relatively long, and the resulting power transmission losses have become a problem that cannot be ignored. When PV aggregators aggregate PV power, they usually give priority to purchasing energy from nearby dispersed small PV producers because the energy loss in the transmission lines will lead to additional electricity purchase costs. This priority selection strategy enables PV aggregators to have a significant priority aggregation effect on nearby PV energy producers. As the length of the transmission line increases, the aggregation benefit of PV aggregators gradually decreases, and the cost of unit aggregated PV power will also increase.

[0055] When purchasing electricity from photovoltaic producers, the cost function of the photovoltaic electricity aggregated by the photovoltaic aggregator is as follows:

[0056]

[0057] In formula (8), x pv ,y pv 、z pv are the cost coefficients of photovoltaic power aggregators, where x pv Affected by the aggregation benefits and the loss cost of the transmission line, y pv Affected by the startup cost of concentrated photovoltaic power, pv Affected by aggregate operating costs; is the cost of PA aggregation of the initial power in the tth period, in RMB; It is the total amount of electric energy aggregated by the PA at the beginning of the tth period, in KW.

[0058] (3) Establish the operation optimization objectives and constraints of multiple subjects in the microgrid source-grid-load energy management, and construct the energy market optimization operation game model of the source-grid-load microgrid

[0059] The present invention studies the microgrid source-grid-load energy management model and takes the economy and reliability into consideration during the modeling process. An economic optimal objective function is established for energy aggregators and photovoltaic aggregators, and an optimal objective function for the load end with multi-energy elastic load conversion is established for the industrial park, which has reliability, that is, the energy aggregator profit, photovoltaic aggregator profit and industrial park benefit function are maximized.

[0060] The target subjects of multi-agent operation optimization in microgrid source-grid-load energy management include energy aggregator (EA), industrial park (IP) and photovoltaic aggregator (PA). Figure 1 As shown. Step (3) runs the optimization objective and takes the benefit objective function of the three target subjects in the microgrid source-grid-load energy management as the optimal objective function strategy.

[0061] The benefit objective function of the energy aggregator among multiple agents in the microgrid source-grid-load energy management mainly includes the income from selling energy and the cost of aggregating energy:

[0062]

[0063] In formula (9), It represents the maximization of EA benefits of microgrid source-grid-load energy management; T is the total time period in a day, and a day is divided into 24 time periods; is the revenue from EA selling energy to IP users in the tth period, in RMB; is the cost of EA energy purchase and production in the tth period, unit yuan.

[0064] The calculation formula for the revenue from EA selling energy to IP users is as follows:

[0065]

[0066] In formula (10), They are the revenue from EA selling heat, cold and electricity to IP users in the tth period, in Yuan.

[0067] The formula for calculating the revenue of EA selling heat, cold and electricity to IP users is as follows:

[0068]

[0069] In formulas (11), (12), and (13), are the prices of heat, cold and electricity sold by EA to IP users in the tth period, in RMB; They are respectively the sales volume of heat, cold and electricity sold by EA to IP users in the tth period, in KW.

[0070] The cost calculation formula for EA aggregation and production of heat, cold and electricity is as follows:

[0071]

[0072] In formula (14), is the cost of EA purchasing and producing thermal energy in the tth period, in RMB; is the cost of EA purchasing and producing electric energy in the tth period, in RMB; is the cost of purchasing and producing cold energy by EA in the tth period, in Yuan.

[0073] The cost calculation formula for EA polymerization and heat production is as follows:

[0074]

[0075] In formula (15), are the cost coefficients of heating by micro gas boilers; are the cost coefficients of CHP fever, respectively; is the heat generation power of CHP in the tth period, in KW; It is the heat generating power of the gas boiler in the tth period, in KW.

[0076] The cost calculation formula for EA aggregation and production of electric energy is as follows:

[0077]

[0078] In formula (16), are the cost coefficients of CHP power generation respectively; is the power generated by CHP in the tth period, in KW; is the amount of electricity purchased by EA from the grid during the tth period, in KW; is the price of electricity purchased by EA from the grid during the tth period, in Yuan.

[0079] In the formula, the cost calculation formula for EA aggregation and production of cold energy is as follows:

[0080]

[0081] In formula (17), is the amount of aggregated electric energy purchased by EA from PA during the tth period, in KW; is the purchase amount of EA wind power in the tth period, unit KW; is the price of electricity purchased by EA from PA during the tth period, in RMB; is the price of wind power purchased by EA in the tth period, in RMB; k er is the cooling coefficient of the electric refrigerator.

[0082] The benefit objective function of the industrial park among multiple subjects in the microgrid source-grid-load energy management is the difference between the satisfaction function of industrial users using comprehensive energy and the cost of purchasing comprehensive energy:

[0083]

[0084] In formula (18), is the IP user side benefit function in the tth period; are functions of user satisfaction regarding purchasing heat, cooling and electricity from EA by IP users in the tth period, respectively.

[0085] Among them, the formula for IP user satisfaction is as follows:

[0086]

[0087] In formula (19), n is the category of different energy sources, namely h, e, and r; is the amount of n types of energy supplied by EA to PA, in KW; a 0n1 and b 0n1 are the preference coefficients of IP users for consuming n types of energy.

[0088] In the formula, the power load on the user side can be divided into non-interruptible rigid load and transferable load:

[0089]

[0090] In formula (20), is the non-adjustable load in the tth period, in KW; It is the elastic load involved in the conversion between multiple energy sources in the tth period, in KW.

[0091] The PA energy internal regulation conversion model mainly includes four energy conversion models: electricity to heat, electricity to cold, heat to electricity, and heat to cold. The formula of the PA comprehensive energy internal heat to electricity conversion model is as follows:

[0092]

[0093] In formula (21), is the amount of heat energy converted into electrical energy inside the PA at the tth moment, in KW; k is the amount of electrical energy converted from thermal energy inside the PA at the tth moment, in KW; h2e is the conversion efficiency of the internal heat energy of PA into electrical energy. Here, the heat-to-electricity device uses a thermoelectric generator, that is, k h2e =k teg .

[0094] The formula of the PA comprehensive energy internal electricity-to-heat exchange model is as follows:

[0095]

[0096] In formula (22), is the amount of electrical energy converted into heat energy inside the PA at the tth moment, in KW; k is the amount of heat energy converted from electrical energy inside the PA at the tth moment, in KW; e2his the conversion efficiency of the internal electrical energy of PA into thermal energy. Here, the electrical-to-heat equipment uses an electric boiler, i.e., k e2h =k eb The formula of the PA comprehensive energy internal electricity-to-cooling conversion model is as follows:

[0097]

[0098] In formula (23), is the amount of electrical energy converted into cooling energy inside the PA at the tth moment, in KW; k is the amount of cold energy converted from electrical energy inside the PA at the tth moment, in KW; e2r is the conversion efficiency of the internal electrical energy of the PA into cold energy. Here, the electrical-to-cold device uses an electric refrigerator, i.e., k e2r =k er The formula of the PA comprehensive energy internal heat-to-cold conversion model is as follows:

[0099]

[0100] In formula (24), is the amount of heat energy converted into cold energy inside the PA at the tth moment, in KW; k is the amount of cold energy converted from heat energy inside PA at the tth moment, in KW; h2r is the conversion efficiency of the internal heat energy of PA into cold energy. Here, the heat-to-cold device uses an absorption refrigerator, i.e., k h2r =k ar .

[0101] The benefit objective function of the photovoltaic aggregator among multiple entities in the microgrid source-grid-load energy management is the difference between the electricity sales revenue and the electricity purchase cost:

[0102]

[0103] In formula (25), is the revenue of PA in the tth period, unit yuan.

[0104] The constraints include power constraints, equipment capacity constraints and price constraints. The power constraints include electric power constraints, thermal power constraints, cooling power balance constraints, elastic electric load equation constraints, elastic thermal load equation constraints, and elastic cooling load equation constraints. The equipment capacity constraints include CHP unit capacity constraints, gas boiler capacity constraints, electric chiller capacity constraints, absorption chiller capacity constraints, electric boiler capacity constraints, and thermoelectric generator capacity constraints. The price constraints include photovoltaic aggregator price constraints and energy aggregator price constraints.

[0105] The calculation formula of the electric power constraint function is as follows:

[0106]

[0107] The calculation formula of thermal power constraint function is as follows:

[0108]

[0109] The calculation formula of the cooling power constraint function is as follows:

[0110]

[0111] The elastic load equality constraint formula is as follows:

[0112]

[0113] The capacity constraint formula of CHP unit is as follows:

[0114]

[0115] In formula (32) and (33), The minimum output power of the CHP unit, in KW; The maximum output power of the CHP unit, in KW; The minimum thermal power output of the CHP unit, in KW; It is the maximum thermal power output of the CHP unit, in KW.

[0116] The gas boiler capacity constraint formula is as follows:

[0117]

[0118] In formula (34), The minimum thermal power output of the gas boiler; unit KW; It is the maximum thermal power output of the gas boiler, in KW.

[0119] The capacity constraint formula of the electric refrigerator is as follows:

[0120]

[0121] In formula (35), The minimum cooling power output of the electric refrigerator, in KW; It is the maximum cooling power output of the electric refrigerator, in KW.

[0122] The capacity constraint formula of absorption chiller is as follows:

[0123]

[0124] In formula (36), The minimum cooling power output of the absorption chiller, in KW; It is the maximum cooling power output of the absorption chiller, in KW.

[0125] The capacity constraint formula of electric boiler is as follows:

[0126]

[0127] In formula (37), The minimum thermal power output of the electric boiler, in KW; It is the maximum thermal power output of the electric boiler, in KW.

[0128] The capacity constraint formula of the thermoelectric generator is as follows:

[0129]

[0130] In formula (38), The minimum output power of the thermoelectric generator, in KW; The maximum electrical power output of the thermoelectric generator, in KW.

[0131] The price constraint formula for PV aggregators is as follows:

[0132]

[0133] In formula (39) and (40), The minimum purchase price of photovoltaic power for photovoltaic aggregators, in yuan; The maximum purchase price of photovoltaic power purchased by photovoltaic aggregators, in yuan; The minimum electricity price for photovoltaic aggregators to sell electricity, in yuan; The maximum electricity price that a photovoltaic aggregator can sell electricity at, in yuan.

[0134] The price constraint formula for energy aggregators is as follows:

[0135]

[0136] In formula (41), The minimum selling price of n types of energy sold by energy aggregators, in RMB; The maximum selling price of n types of energy sold by energy aggregators, in yuan.

[0137] (4) The feasibility of the optimization strategy model is proved through the master-slave game equilibrium, and the optimal solution of the energy management model strategy is obtained, which is economical and reliable in the solution.

[0138] In the Stackelberg game, when all followers choose their best response strategy based on the leader's strategy, and the leader cannot further improve its own benefits by unilaterally changing its strategy when it expects the followers to take the best response, the game reaches the Stackelberg equilibrium. To be the equilibrium solution of the cold energy game, it needs to satisfy:

[0139]

[0140] In the Stackelberg equilibrium, no participant can improve their own benefits by unilaterally changing their strategy. The only condition for the existence of this equilibrium is that the master-slave game model must satisfy a specific theorem.

[0141] Theorem 1: In a game with multiple participants, if:

[0142] 1) The strategy set of the leader and the follower is a non-empty, closed, bounded, convex set in Euclidean space;

[0143] 2) For the leader’s best strategy, followers have a unique best countermeasure;

[0144] 3) For each follower’s optimal strategy, the leader has a unique optimal countermeasure.

[0145] Then the equilibrium solution of the Stackelberg game model exists and is unique.

[0146] Theorem 2: If the objective function of the leader and the objective function of the follower are both second-order continuously differentiable, and the Hessian matrix formed by their second-order partial derivatives is a negative definite matrix, then there is a unique optimal solution.

[0147] prove:

[0148] 1) According to formula (1) to formula (41), the strategy set of each participant is a non-empty, closed, bounded convex set in Euclidean space.

[0149] 2) First, find the objective function of IP (18) with respect to The first and second partial derivatives of :

[0150]

[0151] Since the coefficient of IP user satisfaction is positive, that is:

[0152]

[0153] It can be seen that the Hessian matrix formed by the second-order partial derivative of the objective function is negative definite, so it satisfies Theorem 2 and thus satisfies Condition 2 of Theorem 1. Setting the first-order partial derivative to 0 gives the best strategy for the follower after the IP game:

[0154]

[0155] Find the objective function of PA (25) with respect to The first and second partial derivatives of :

[0156]

[0157] Since the coefficient of the PA photovoltaic concentration function is positive, that is:

[0158]

[0159] It can be seen that the Hessian matrix formed by the second-order partial derivative of the objective function is negative definite, so it satisfies Theorem 2 and thus satisfies Condition 2 of Theorem 1. Setting the first-order partial derivative to 0 gives the best strategy for the follower after the PA game:

[0160]

[0161] From the above proof, it can be seen that when the purchase and sale prices of electricity of EA are given, both the follower PA and the IP user have the best strategy, that is, the only optimal solution.

[0162] 3) Prove that when the follower's strategy is given, the leader also has the best countermeasure, that is, the only optimal solution.

[0163] Substitute the best strategy of the followers into the above formula and find the objective function of the leader (9) about The first and second partial derivatives of :

[0164]

[0165] Because b 0r1 , k er and x pv All are positive, so the eigenvalues ​​of the Hessian matrix formed by the second-order partial derivative of the objective function of the leader EA are all negative, so it is negatively definite, satisfying Theorem 2 and thus condition 3 of Theorem 1. Let the first-order partial derivative be 0 to obtain the best countermeasures after the leader's game:

[0166]

[0167] Similarly, when playing the game for thermal energy and electrical energy respectively, there is a unique optimal solution for the leader, and the proof is similar to the above process.

[0168] The best strategy after the heat game between leaders and followers:

[0169]

[0170] The best strategy after the power game between the leader and the follower:

[0171]

[0172]

[0173] In summary, it can be proved that the three energy game models of double-layer master-slave in microgrid energy management in this patent all have unique Stackelberg equilibrium.

[0174] 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 energy management method based on load-side flexible energy optimization are implemented.

[0175] 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 energy management method based on load-side flexible energy optimization.

[0176] 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.

[0177] 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 1 A device that provides the functions specified in a block or multiple blocks.

[0178] 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.

[0179] 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.

[0180] 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 energy management method based on load-side flexible energy optimization, characterized in that: The steps include: Step 1, modeling the microgrid source-grid-load energy production equipment and energy conversion equipment to form a microgrid source-grid-load energy supply system; the energy production equipment includes a cogeneration unit and a gas boiler, and the energy conversion equipment includes an electric refrigerator, an absorption refrigerator, an electric boiler and a thermoelectric generator; Step 2: Considering the impact of line loss on electricity purchase costs, an energy aggregation model of photovoltaic aggregators on the source-grid-load energy supply side of the microgrid is established; Step 3: Based on steps 1 and 2, establish the operation optimization objectives and constraints of each subject in the microgrid source-grid-load energy management, and obtain the energy market optimization operation game model of the microgrid source-grid-load; among which, the subjects in the microgrid source-grid-load energy management include photovoltaic aggregators, energy aggregators and industrial parks; Step 4. For the operation game model obtained in step 3, the energy aggregator is taken as the leader, the photovoltaic aggregator and the industrial park as the followers, and the master-slave game equilibrium is adopted to obtain the optimal solution of the operation game model, including the total amount of electricity aggregated by the photovoltaic aggregator from each producer, the electricity price sold by the photovoltaic aggregator, the elastic load of different types of energy involved in the conversion, and the selling price of different types of energy sold by the energy aggregator. Different types of energy include heat, electricity and cold energy.

2. The microgrid energy management method based on load-side flexible energy optimization according to claim 1 is characterized in that: The modeling process in step 1 is as follows: The output functions of the electric and thermal energy of the cogeneration unit are as follows: In the formula, is the output electricity cost of the cogeneration unit during period t, is the output heat energy cost of the cogeneration unit during period t, is the output power of the cogeneration unit during period t, is the output thermal power of the cogeneration unit during period t, are the cost coefficients of combined heat and power generation, and Cost coefficient for heat generation of cogeneration unit; The heat energy output function of the gas boiler is as follows: In the formula, is the output electricity cost of the gas boiler during period t, is the output power of the gas boiler during the period t, are the cost coefficients of gas boiler heating respectively; The energy conversion function of the electric refrigerator is as follows: In the formula, The power of the electric refrigerator's input electrical energy converted into cold energy. is the input power of the electric refrigerator, k er is the cooling coefficient of the electric refrigerator; The energy conversion function of the absorption refrigerator is as follows: In the formula, The power of the absorption chiller that converts the input heat energy into cold energy. is the input heat power of the absorption refrigerator, k ar is the refrigeration coefficient of the absorption chiller; The energy conversion function of the electric boiler is as follows: In the formula, The power of the electric boiler's input electrical energy converted into thermal energy. is the input power of the electric boiler, k eb is the energy conversion efficiency coefficient of the electric boiler; The energy conversion function of the thermoelectric generator is as follows: In the formula, The power of the thermoelectric generator that converts the input thermal energy into electrical energy. is the input thermal power of the thermoelectric generator, k teg is the energy conversion efficiency coefficient of the thermoelectric generator.

3. The microgrid energy management method based on load-side flexible energy optimization according to claim 1 is characterized in that: In step 2, the energy aggregation model of the photovoltaic aggregator, that is, the cost function relationship of the photovoltaic power aggregated by the photovoltaic aggregator is as follows: In the formula, is the cost of aggregating electricity from each photovoltaic producer by the photovoltaic aggregator in the tth period, is the total amount of electricity aggregated by the photovoltaic aggregator from each photovoltaic producer in the tth period, x pv ,y pv 、z pv are the cost coefficients of photovoltaic power aggregators, where x pv Affected by the aggregation benefits and transmission line loss costs, y pv Affected by the startup cost of concentrated photovoltaic power, pv Affected by aggregate operating costs.

4. The microgrid energy management method based on load-side flexible energy optimization according to claim 2 is characterized in that: In step 3, the operation optimization goal of each subject is to make the benefit objective function of each subject the optimal objective function; wherein, The energy aggregator's benefit objective function includes the revenue from selling energy and the cost of aggregating energy: In the formula, represents the maximization of the energy aggregator's profit in the microgrid source-grid-load energy management, T is the total time period of the day, is the revenue from energy aggregators selling energy to industrial park users during the tth period, is the cost of energy purchase and production by the energy aggregator in period t; in, The calculation formula is as follows: In the formula, are the revenues of energy aggregators from selling heat, cold and electricity to industrial park users in the tth period, are the prices of heat, cold and electricity sold by energy aggregators to industrial park users in the tth period, are the sales of heat, cold and electricity sold by the energy aggregator to the users of the industrial park in the tth period respectively; The calculation formula is as follows: In the formula, is the cost of purchasing and producing thermal energy by the energy aggregator in the tth period, is the cost of purchasing and producing electric energy by the energy aggregator in the tth period, is the cost of purchasing and producing cold energy by the energy aggregator during the tth period; is the amount of electricity purchased by the energy aggregator from the grid during the tth period, is the price at which the energy aggregator purchases electricity from the grid during the tth period; is the price at which the energy aggregator purchases electricity from the PV aggregator during the tth period, k er is the refrigeration coefficient of the electric refrigerator, is the amount of aggregated electricity purchased by the energy aggregator from the PV aggregator during the tth period, is the price at which the energy aggregator purchases wind power in the tth period, is the amount of wind power purchased by the energy aggregator in the tth period; The benefit objective function of the industrial park is the difference between the satisfaction of industrial users in using comprehensive energy and the cost of purchasing comprehensive energy: In the formula, It means that the user-side benefit function of the industrial park is maximized in the tth period; are the user satisfaction of purchasing heat, cold and electricity from the energy aggregator on the user side of the industrial park in the tth period respectively; The calculation formula of user satisfaction is as follows: In the formula, n is the category of different energy sources, n = h, r, e; is the amount of energy supplied by the energy aggregator to the photovoltaic aggregator of the nth category, a 0n1 and b 0n1 are the preference coefficients of industrial park users for consuming the nth type of energy, is the load that cannot be adjusted in the tth period, is the elastic load of the nth type of energy participating in the conversion in the tth period; The benefit objective function of a photovoltaic aggregator is the difference between the electricity sales revenue and the electricity purchase cost: In the formula, represents the maximization of the revenue of the photovoltaic aggregator in the tth period, is the cost of aggregating the electricity of each photovoltaic producer by the photovoltaic aggregator in the tth period; The internal energy conversion models of photovoltaic aggregators include four energy conversion models: electricity to heat, electricity to cold, heat to electricity, and heat to cold. Among them, the formula of the heat to electricity conversion model is as follows: In the formula, is the amount of heat energy converted into electrical energy inside the photovoltaic aggregator at time t, is the amount of electrical energy converted from thermal energy inside the photovoltaic aggregate at the tth moment, k h2e is the conversion efficiency of the internal thermal energy of the photovoltaic aggregator into electrical energy. Here, the thermal-to-electricity device uses a thermoelectric generator, that is, k h2e =k teg ; The formula of the electric-to-heat exchange model is as follows: In the formula, is the amount of electrical energy converted into thermal energy inside the PV aggregator at time t, is the amount of heat energy converted from electrical energy inside the photovoltaic aggregate at the tth moment, k e2h is the conversion efficiency of the internal electrical energy of the photovoltaic aggregator into thermal energy. Here, the electrical-to-heat equipment uses an electric boiler, i.e., k e2h =k eb ; The electric-to-cold conversion model formula is as follows: In the formula, is the amount of electrical energy converted into cold energy inside the photovoltaic aggregator at the tth moment, is the amount of cold energy converted from electrical energy inside the photovoltaic aggregator at the tth moment, k e2r is the conversion efficiency of the internal electrical energy of the photovoltaic aggregator into cold energy. Here, the electrical-to-cold equipment uses an electric refrigerator, i.e., k e2r =k er ; The heat-to-cold conversion model formula is as follows: In the formula, is the amount of heat energy converted into cold energy inside the photovoltaic aggregate at the tth moment, is the amount of cold energy converted from thermal energy inside the photovoltaic aggregate at the tth moment, k h2r is the conversion efficiency of the internal heat energy of the photovoltaic aggregator into cold energy. Here, the heat-to-cold device uses an absorption refrigerator, i.e., k h2r =k ar .

5. The microgrid energy management method based on load-side flexible energy optimization according to claim 4 is characterized in that: In step 3, the constraints include power constraints, equipment capacity constraints and price constraints, wherein the power constraints include electric power constraints, thermal power constraints, cold power balance constraints, elastic electric load equation constraints, elastic thermal load equation constraints and elastic cold load equation constraints; the equipment capacity constraints include cogeneration unit capacity constraints, gas boiler capacity constraints, electric refrigerator capacity constraints, absorption refrigerator capacity constraints, electric boiler capacity constraints and thermoelectric generator capacity constraints; the price constraints include photovoltaic aggregator price constraints and energy aggregator price constraints; The electric power constraint formula is as follows: The thermal power constraint formula is as follows: The cold power balance constraint formula is as follows: The elastic electrical, heating and cooling load equation constraint formulas are as follows: The capacity constraint formula of the cogeneration unit is as follows: In the formula, are the minimum and maximum electric power output of the cogeneration unit, are the minimum and maximum thermal power output of the cogeneration unit respectively; The gas boiler capacity constraint formula is as follows: In the formula, They are the minimum and maximum thermal power output of the gas boiler respectively; The capacity constraint formula of the electric refrigerator is as follows: In the formula, is the cooling power output by the electric refrigerator in the tth period, are the minimum and maximum cooling power output by the electric refrigerator respectively; The capacity constraint formula of absorption chiller is as follows: In the formula, is the cooling power output by the absorption chiller in the tth period, are the minimum and maximum cooling power output by the absorption chiller respectively; The capacity constraint formula of electric boiler is as follows: In the formula, is the thermal power output of the electric boiler in the tth period, are the minimum and maximum thermal power output of the electric boiler respectively; The capacity constraint formula of the thermoelectric generator is as follows: In the formula, is the electric power output by the thermoelectric generator in the tth period, are the minimum and maximum electrical power output of the thermoelectric generator respectively; The price constraint formula for PV aggregators is as follows: In the formula, is the purchase price of PV power purchased by the PV aggregator in the tth period, are the minimum and maximum purchase prices for photovoltaic power from photovoltaic aggregators. are the minimum and maximum electricity selling prices for PV aggregators to sell electricity; The price constraint formula for energy aggregators is as follows: In the formula, is the selling price of the nth type of energy sold by the energy aggregator in the tth period, They are the minimum and maximum selling prices for the nth type of energy sold by energy aggregators.

6. The microgrid energy management method based on load-side flexible energy optimization according to claim 5 is characterized in that: In step 4, The best strategies after the cold energy game between the leader and the follower are as follows: In the formula, are the optimal total value of the electricity of each photovoltaic producer aggregated by the photovoltaic aggregator in the tth period, the optimal elastic cooling load participating in the conversion between multiple energy sources, the optimal price of cooling energy sold by the energy aggregator to the industrial park, and the optimal price of electricity sold by the photovoltaic aggregator to the energy aggregator. pv ,y pv are the cost coefficients for aggregating PV power by PV aggregators, respectively; The best strategies after the heat game between the leader and the follower are as follows: In the formula, They are the optimal elastic electric energy load participating in the conversion between multiple energy sources in the tth period and the optimal price of thermal energy sold by energy aggregators to industrial parks; The best strategy after the power game between the leader and the follower is as follows: In the formula, They are the optimal elastic thermal load participating in the conversion between multiple energy sources in the tth period, the optimal price of electricity sold by the energy aggregator to the industrial park, is the demand for purchased grid electricity in period t.

7. 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 energy management method based on load-side flexible energy optimization are implemented as described in any one of claims 1 to 6.

8. 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 energy management method based on load-side flexible energy optimization are implemented as described in any one of claims 1 to 6.

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