Energy optimization scheduling system based on double-end cooperation

Abstract

The invention discloses an energy optimization scheduling system based on double-end cooperation. A grit battery energy storage end utilizes grit to store energy and takes grit as an energy storage medium to carry out electricity and heat energy conversion, heat storage and energy supply; the compressed air energy storage end uses air as an energy storage medium, conversion of electric energy is achieved through circulating compression and expansion, and heat energy generated along with air compression is extracted and stored. The intelligent comprehensive energy management system realizes optimal scheduling of double-end energy storage and efficient utilization of the CCHP system to form a complementary operation mode; according to the method, gravel battery energy storage and compressed air energy storage are deeply matched, optimal allocation scheduling of the gravel battery energy storage and the compressed air energy storage is coordinated with the purposes of maximizing consumption of renewable energy sources during system operation, improving the robustness of the system and optimizing energy storage coordinated scheduling, and it is ensured that waste heat utilization and optimal allocation of energy storage resources are achieved through the CCHP system. According to the invention, a comprehensive energy complementation system integrating functions of power grid peak regulation, energy storage, efficient utilization of green energy, optimal configuration and the like can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy management systems with multi - energy complementarity, and relates to the optimal scheduling and optimal energy allocation of an integrated energy system with multi - energy coupling. Specifically, it relates to an energy optimal scheduling system based on dual - end collaboration. Background Art

[0002] With the growth of energy demand, the over - use of fossil energy has led to intensified environmental pollution and energy supply - demand contradictions. Improving energy utilization efficiency and optimizing the energy structure have become urgent needs. The Integrated Energy System (IES) is transforming from centralized to distributed by integrating clean and renewable energy, and its multi - energy complementarity and optimal operation have become key research directions. However, the volatility and uncertainty of renewable energy such as wind energy and solar energy require the assistance of energy storage and intelligent control technologies to improve the system's regulation ability and stability. In addition, although the IES has significant potential for energy efficiency improvement, its initial investment cost is relatively high, and optimal design and operation strategies are crucial for enhancing economic efficiency.

[0003] The global energy system is accelerating its upgrade towards diversification and intelligence, and China has also entered a new stage of building a modern energy system. As a micro - energy system for end - users, the IES integrates various energy forms such as electricity, heat, and gas to achieve multi - energy complementarity and efficient consumption of renewable energy. However, the energy conversion devices within the system have complex coupling relationships, and reasonable planning and optimization of the IES are the keys to improving system energy efficiency. The core of constructing the IES operation model lies in accurately describing the multi - energy input - output relationship and heterogeneous energy conversion mechanism, and it is necessary to comprehensively consider the operation characteristics and mutual coupling relationships of various devices. At the same time, optimizing the configuration of independent and coupled devices is a key technical means to promote the construction of a modern energy system and achieve efficient and clean utilization of energy. However, existing methods still have deficiencies in multi - energy coupling conversion modeling, construction timing, and multi - objective optimization, and further research is urgently needed.

[0004] In response to these challenges, this study establishes a multi - objective optimal scheduling model for the integrated energy system, focusing on studying the differences between different energy flows and the optimization paths of multi - energy coupling operation strategies. The integrated energy management system needs to be optimized from multiple dimensions such as technology, economy, and environment. Through technological innovation and system integration, it can achieve efficient, reliable, and clean utilization of energy to promote the development of the energy system towards intelligence and sustainability. Summary of the Invention

[0005] In view of the above problems, the object of the present invention is to propose an energy optimization scheduling system based on dual - end cooperation. Based on the multi - energy coupling characteristics of different power sources, the gravel battery energy storage and compressed air energy storage are deeply coordinated. The goal is to maximize the consumption of renewable energy in system operation, improve the robustness of the system, and optimize the coordinated scheduling of energy storage to ensure that the CCHP system can interact with the power grid. According to the power grid demand and energy price, the optimal allocation of waste heat utilization and energy storage resources is realized, aiming to create a comprehensive energy complementary system integrating functions such as power grid peak shaving, energy storage, efficient utilization of green energy, and optimal allocation.

[0006] Technical solution: The present invention discloses an energy optimization scheduling system based on dual - end cooperation, including an energy storage layer, a CCHP combined cooling, heating and power generation and waste heat recovery device. The energy storage layer is coupled by dual - end power sources, including gravel battery energy storage and compressed air energy storage. The compressed air energy storage stores energy during the low - demand period of electricity and releases energy during the peak period. The gravel battery energy storage makes up for the disadvantage of the long response time of the compressed air energy storage, and forms a complementary operation mode in combination with the coupling characteristics of dual - end energy storage. During high - electricity - price periods, the gravel battery energy storage is preferentially used for discharging. During low - electricity - price periods, the compressed air energy storage is started, and low - price electricity is used to compress air and store energy.

[0007] Furthermore, the gravel battery energy storage is connected to each other through a gravel heating device and a high - temperature gravel storage heat - insulation silo device. The gravel heating device uses the heat collection of the photovoltaic power generation solar panel to heat the gravel, and stores the heated gravel in the high - temperature gravel storage heat - insulation silo device. Then, the high - temperature gravel storage heat - insulation silo device conducts heat - insulation and heat - preservation treatment on the high - temperature gravel to realize energy storage;

[0008] The compressed air energy storage uses air as the energy storage medium, and realizes the conversion of electric energy through cyclic compression and expansion. The heat energy generated during air compression is extracted and stored. Excess electric energy is used to drive a compressor to inhale and compress the air in the environment into high - temperature and high - pressure air, converting electric energy into internal energy to complete the energy input process. Then, the heat - exchanged low - temperature air is stored in the gas storage unit through the compression - side heat exchanger, and the heated high - temperature heat - exchange medium is stored in the heat storage unit, separating the internal energy into heat energy and potential energy to complete the decoupling of compressed heat energy and pressure potential energy.

[0009] Furthermore, the CCHP combined cooling, heating and power generation and waste heat recovery device includes a turbine generator device, a heating device, an absorption refrigeration device and a waste heat recovery device;

[0010] The turbine generator device receives the high - temperature and high - pressure air energy of the gravel battery energy storage and the compressed air energy storage, and flows through the turbine through a heat exchanger. The air flow impacts the turbine blades to drive the generator to generate electric energy;

[0011] The heating equipment receives hot air, flows through the plate heat exchanger, and is then transported to the user end through the heating network to meet the heating demand of the users.

[0012] The absorption refrigeration equipment receives hot air for refrigeration operation.

[0013] The waste heat recovery equipment recovers the waste heat from the power generation, heating, refrigeration processes and the energy storage layer. On the one hand, it is used for the waste heat recycling within the CCHP system, and on the other hand, it is used for the external energy storage layer utilization. The gravel battery energy storage utilizes the waste heat for thermal management and is used to heat compressed air.

[0014] Furthermore, the intelligent integrated energy optimization management system further includes a regulation unit. The regulation unit solves the Pareto solution set by using the robust augmented e-constraint method and combines the comprehensive weight and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimization algorithm to achieve the coordinated cooperation between the CCHP combined cooling, heating and power generation, the waste heat recovery equipment, the gravel battery energy storage and the compressed air energy storage, and realizes the optimal energy scheduling. Specifically as follows:

[0015] Step 1: Construct a multi-objective optimization model for the energy optimization scheduling system, comprehensively considering multiple objectives such as maximizing the consumption of renewable energy, the robustness of the system, and optimizing the coordinated scheduling of energy storage.

[0016] (1) To maximize the consumption of renewable energy, the objective function J1 is the degree of mismatch between the supply and demand of renewable energy:

[0017]

[0018] In the formula: R e (t), R h (t), R c (t) respectively represent the supply of renewable energy for electricity, heat, and cold (MW); S e (t) is the charging / discharging power of the battery energy storage (MW), S h (t) is the charging / discharging heat power of the thermal energy storage (MW), S c (t) is the charging / discharging cooling power of the cold energy storage (MW);

[0019] (2) The robustness of the system. The objective function J2 measures the stability of the power output of the entire system. By calculating the variation range of the power generation power and the energy storage charging / discharging power, the target power fluctuation is optimized to reduce the drastic change of the power between different time steps of the system:

[0020]

[0021] In the formula: P gen (t) is the output of the power generation equipment (MW), S g (t) is the charging / discharging heat power of the gravel energy storage (MW), S a(t) Air compression energy storage charge / discharge power (MW);

[0022] (3) Optimize energy storage scheduling to ensure reasonable load distribution between gravel battery energy storage and air compression energy storage:

[0023]

[0024] In the formula: λ a is the gravel energy storage power penalty coefficient, λ g is the air compression energy storage power penalty coefficient, used to control its usage ratio;

[0025] Step 2: Design constraint conditions;

[0026] Step 2.1 Supply-demand balance

[0027] P gen (t) + S e (t) + R e (t) = D e (t)

[0028] P heat (t) + S h (t) + R h (t) = D h (t)

[0029] P cool (t) + S c (t) + R c (t) = D c (t)

[0030] In the formula: P gen (t), S e (t), R e (t), D e (t) are the power outputs of power generation equipment (MW), the charge / discharge power of battery energy storage (MW), the supply of renewable energy for electricity, and the electricity demand respectively; P heat (t), S h (t), R h (t), D h (t) are the power outputs of heat supply equipment (MW), the charge / discharge power of heat energy storage (MW), the supply of renewable energy for heat, and the heat demand respectively; P cool (t), S c (t), R c (t), D c (t) are the power outputs of absorption refrigeration equipment (MW), the charge / discharge power of cold energy storage (MW), the supply of renewable energy for cold, and the cold energy demand respectively;

[0031] Step 2.2: Dynamic equations of gravel battery energy storage and compressed air energy storage

[0032] E g (t + 1)= E g (t)+ η g S g (t)

[0033] E a (t + 1)= E a (t)+ η a S a (t)

[0034] Where: E g (t) is the current stored energy of the gravel energy storage, η g is the charge-discharge efficiency of the gravel energy storage; E a (t) is the current stored energy (MWh) of the air compression energy storage, η a reflects the efficiency of the air compression energy storage, considering compression-expansion losses; η ∈ [0, 1];

[0035] Step 2.3: Energy storage power and capacity constraints;

[0036]

[0037] 0 ≤ E g (t) ≤ C g , 0 ≤ E a (t) ≤ C a

[0038] Where: S max represents the rated maximum charge-discharge power of the corresponding device, C g , C a represent the maximum energy storage capacity (MWh) of the gravel energy storage and the air compression energy storage.

[0039] Furthermore, the Pareto solution set of the robust augmented e-constraint method is solved, and the comprehensive weight and the technique for order preference by similarity to an ideal solution (TOPSIS) optimization algorithm are combined for processing, as follows:

[0040] Step 3.1: Taking J1 as the main objective and J2, J3 as the secondary objectives, set the ε threshold: J2 ≤ 2, J3 ≤ 3, and 2, 3 are traversed within a reasonable range to form the Pareto front;

[0041] Step 3.2: Add augmented variables, and the objective function is transformed into:

[0042]

[0043] Where: M is the penalty coefficient, 2, 3 are the ε constraint values, ensuring the correct optimization direction. For the obtained solution set, the duplicate solutions and non-frontier solutions are removed to obtain the final four-dimensional Pareto solution set;

[0044] Step 3.3: TOPSIS approximation ideal solution sorting

[0045] (1) Calculate the positive ideal solution PIS and negative ideal solution NIS:

[0046] PIS = maxJ i , NIS = minJ i

[0047] Where: PIS is the positive ideal solution; NIS is the negative ideal solution; J i is the i-th objective function

[0048] (2) Calculate the Euclidean distance:

[0049]

[0050] Where: is the Euclidean distance between the i-th objective function value and the positive ideal solution; S i - is the Euclidean distance between the i-th objective function value and the negative ideal solution

[0051] (3) Calculate the TOPSIS evaluation value:

[0052]

[0053] Where: the TOPSIS evaluation value, C i represents the closeness of the i-th objective function to the positive and negative ideal solutions; According to the value of C i to sort the evaluation schemes, the larger the value of C i , the better the comprehensive evaluation result;

[0054] Step 3.4: Select the optimal Pareto solution as the final scheduling strategy.

[0055] Beneficial effects:

[0056] 1. The present invention integrates different energy forms and can be extended to research and promotion in areas such as electricity, heat energy, natural gas, etc., to achieve efficient utilization and complementarity of energy. Firstly, it can improve energy utilization efficiency from the energy storage layer, increase the consumption capacity of renewable energy, and enhance the reliability and flexibility of energy supply. According to means such as energy storage and demand response, it can balance the intermittency and uncertainty of renewable energy and increase its proportion in the energy structure. Secondly, it realizes backup and switching between different energies at the distribution layer, reduces the risk of interruption of single energy supply, and enhances the resilience of the entire system.

[0057] 2. The present invention adopts a Pareto solution set solving method using the robust augmented e-constraint method, and combines the comprehensive weight and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) optimization algorithm to achieve the maximum consumption of renewable energy in multi-energy sources, improve the robustness of the system, and optimize the coordinated scheduling of energy storage to achieve the highest energy efficiency. The integrated energy system can flexibly adjust the production and consumption of energy according to actual demands and energy supply situations to adapt to changing market and environmental conditions, participate in market regulation through demand response programs, and enhance the reliability of the entire power grid at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a schematic structural diagram of the system of the present invention;

[0059] Figure 2 is a flowchart of the algorithm of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] The following further elaborates on the specific technical solutions of the present invention with specific examples.

[0061] As Figure 1 shown, the energy optimization scheduling system based on dual-terminal collaboration disclosed by the present invention integrates a gravel battery energy storage, a compressed air energy storage, and a combined cooling, heating and power (CCHP) cogeneration and waste heat recovery device into an intelligent integrated energy optimization management system, and utilizes the dual-terminal power coupling characteristics to achieve optimal distribution of waste heat utilization and energy storage resources.

[0062] The compressed air energy storage has the characteristics of large-scale and long-term energy storage, and low cost. It can store energy during the low valley of power demand and release energy during the peak period. The gravel battery energy storage has a fast response speed, high energy conversion efficiency, and high control performance, and has short-term and fast load regulation, which can make up for the disadvantage of the long response time of the compressed air system. Combining the energy storage coupling characteristics of the two forms a complementary operation mode. During high electricity price periods, the battery energy storage system is preferentially used for discharging to reduce the grid power purchase cost. During low electricity price periods, the compressed air energy storage system is started to compress air with low-price electricity and store energy, achieving efficient utilization and optimal allocation of energy, and improving the reliability and flexibility of energy supply.

[0063] The gravel battery energy storage in the energy storage layer is interconnected through a gravel heating device and a high-temperature gravel storage heat insulation silo device. The gravel heating device heats the sand using the heat collection of a photovoltaic solar panel, etc., and stores the heated sand in the high-temperature gravel storage heat insulation silo device. Then, the high-temperature gravel storage heat insulation silo device performs heat insulation treatment on the high-temperature sand to achieve energy storage.

[0064] Compressed air energy storage in the energy storage layer uses air as the energy storage medium and realizes the conversion of electric energy through cyclic compression and expansion. The heat energy generated during air compression is extracted and stored. Excess electric energy is used to drive a compressor to draw in and compress air from the environment into high-temperature and high-pressure air, converting the electric energy into internal energy to complete the energy input process. Then, the cooled air after heat exchange is stored in the gas storage unit through the compression-side heat exchanger, and the heated high-temperature heat exchange medium is stored in the heat storage unit, separating the internal energy into heat energy and potential energy to complete the decoupling of compressed heat energy and pressure potential energy.

[0065] The CCHP (Combined Cooling, Heating and Power) and waste heat recovery equipment includes a turbine generator equipment, a heating equipment, an absorption refrigeration equipment, and a waste heat recovery equipment. These equipment are combined to achieve cascaded utilization of energy.

[0066] The turbine generator equipment receives the high-temperature and high-pressure air energy from the gravel battery energy storage and the compressed air energy storage, and flows through the turbine through a heat exchanger. The air flow impacts the turbine blades to drive the generator to generate electric energy.

[0067] The heating equipment receives the hot air and flows through a plate heat exchanger, and then is transported to the user end through the heating network to meet the heating demand of the users.

[0068] The absorption refrigeration equipment receives the hot air for refrigeration operation. The heat energy drives the refrigeration equipment through an absorption refrigerator, and uses the absorption and release of the absorbent to the refrigerant to achieve the refrigeration cycle.

[0069] The waste heat recovery equipment: recovers the waste heat during power generation, heating, refrigeration and in the energy storage layer. On the one hand, it is used for the waste heat recycling within the CCHP system, and on the other hand, it is used for the external energy storage device utilization. The gravel battery uses the waste heat for thermal management to reduce the cooling demand, improve the service life and efficiency of the battery. Secondly, it is used to heat the air in the compressed air energy storage system, etc., to improve the overall energy recycling of the system and provide additional energy for the system, reducing carbon emissions.

[0070] The intelligent integrated energy optimization management system also includes a regulation unit. The regulation unit uses the Pareto solution set of the robust augmented e-constraint method to solve, and combines the comprehensive weight and the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) optimization algorithm to realize the coordinated cooperation between the CCHP combined cooling, heating and power and waste heat recovery equipment and the gravel battery energy storage and the compressed air energy storage, and achieve the optimal energy scheduling.

[0071] Step 1: Considering multiple objectives such as maximizing the consumption of renewable energy during the construction period, improving the robustness of the system, and optimizing the energy storage coordinated scheduling, construct the following objective function:

[0072] (1) Maximize energy consumption, reduce the curtailment of wind, light and heat, and improve the utilization rate of renewable energy to ensure that renewable energy can be consumed to the greatest extent:

[0073]

[0074] In the formula: the objective function J1 is the degree of mismatch between the supply and demand of renewable energy, R e (t), R h (t), R c (t) respectively represent the renewable energy supplies of electricity, heat, and cold; S e (t) is the charge / discharge power (MW) of the battery energy storage, S h (t) is the charge / discharge heat power (MW) of the thermal energy storage, S c (t) is the charge / discharge cooling power (MW) of the cold energy storage. J1, as the objective function, quantifies the degree of mismatch between the supply and demand of renewable energy. Its physical meaning is to measure the sum of the squares of the total deviation between supply and demand over the entire time interval t. The more matched the supply and demand are, the smaller the value of the objective function, which means maximizing the consumption of renewable energy.

[0075] (2) Enhance the system robustness (reduce power fluctuations), reduce the power fluctuations of the energy storage system between different time steps, reduce the losses caused by frequent charging and discharging to the equipment, and improve the overall robustness.

[0076]

[0077] In the formula: P gen (t) is the output power (MW) of the power generation equipment, S g (t) is the charge / discharge heat power (MW) of the gravel energy storage, S a (t) is the charge / discharge power (MW) of the compressed air energy storage.

[0078] The objective function J2 measures the stability of the power output of the entire system. By calculating the change amplitude (the absolute difference between adjacent moments) of the power generation power and the energy storage charge / discharge power, the target power fluctuation is optimized, and the drastic power change of the system between different time steps is reduced; J2 makes the system more stable by restricting the power fluctuation, reduces the equipment losses, and thus improves the overall robustness.

[0079] (3) Optimize the energy storage scheduling to ensure a reasonable distribution of the load between the gravel battery energy storage and the compressed air energy storage, prevent a single energy storage system from bearing too large a load, and thus improve the operating efficiency and lifespan of the entire system.

[0080]

[0081] In the formula: λ a is the power penalty coefficient of the gravel energy storage, λ g is the power penalty coefficient of the compressed air energy storage, which is used to control its usage ratio.

[0082] The objective function J3 is mainly used to optimize the energy storage scheduling, making the load distribution of different energy storage systems more reasonable, preventing a certain energy storage system from overloading, and improving the efficiency and lifespan of the overall system.

[0083] Both the objective function J1 and the objective function J3 involve the square term of power. The square of power is usually used to measure energy consumption, system losses, or to constrain the scheduling range. In the objective function J1, the square term reflects the energy loss of the energy storage system, that is, a larger power output corresponds to higher losses. In the objective function J3, the square term is used to balance the usage ratio of different energy storage systems, avoiding a single energy storage system from taking on an excessive power load, and more accurately depicting the optimization of power distribution. In the objective function J2, to quantify the power volatility, the amplitude of power change between adjacent moments, the absolute value is used instead of the square because the square term would emphasize large fluctuations more, while the absolute value can treat all fluctuations equally, not overly punishing large fluctuations, and at the same time ensuring reasonable constraints on all fluctuations, which is more suitable for measuring the robustness of the overall system.

[0084] Step 2: Design the constraints

[0085] 2.1 Supply-demand balance

[0086] P gen (t)+S e (t)+R e (t) = D e (t)

[0087] P heat (t)+S h (t)+R h (t) = D h (t)

[0088] P cool (t)+S c (t)+R c (t) = D c (t)

[0089] In the formula: P gen (t), S e (t), R e (t), D e (t) are the power output of the power generation equipment (MW), the charge / discharge power of the battery energy storage (MW), the supply of renewable energy for electricity, and the electricity demand respectively; P heat (t), S h (t), R h (t), D h (t) are the power output of the heating equipment (MW), the charge / discharge power of the thermal energy storage (MW), the supply of renewable energy for heat, and the heat demand respectively; P cool (t), S c(t), R c (t), D c (t) are the output of the absorption refrigeration equipment (MW), the charging / discharging power of the cold energy storage (MW), the cold renewable energy supply, and the cold energy demand respectively;

[0090] 2.2 Dynamic equations of sand and air compression energy storage

[0091] E g (t + 1) = E g (t) + η g S g (t)

[0092] E a (t + 1) = E a (t) + η a S a (t)

[0093] Where: E g (t) is the current stored energy of the sand energy storage, η g is the charge-discharge efficiency of the sand energy storage; E a (t) is the current stored energy of the air compression energy storage (MWh), η a reflects the efficiency of the air compression energy storage, considering compression-expansion losses; η ∈ [0, 1].

[0094] 2.3 Energy storage power and capacity constraints

[0095]

[0096] 0 ≤ E g (t) ≤ C g , 0 ≤ E a (t) ≤ C a

[0097] Where: S max represents the rated maximum charge-discharge power of the corresponding equipment, C g , C a represent the maximum energy storage capacities (MWh) of the sand energy storage and the air compression energy storage.

[0098] Step 3: Multi-objective optimization by the robust augmented constraint method (AUGMECON + TOPSIS)

[0099] For the above multi-objective robust optimization model, the present invention uses AUGMECON to generate the Pareto optimal solution set, considers uncertainty factors to enhance the system robustness, comprehensively considers multiple objectives such as maximizing renewable energy consumption during the construction period, enhancing system robustness, and optimizing energy storage coordinated scheduling. It avoids invalid iterations through the information of the slack variables of the constrained objective function, thereby improving the solution speed. However, it requires that the objective function coefficients must be integers and the lowest point of the Pareto set must be known.

[0100] 3.1 Taking the maximization of renewable energy consumption during the construction period comprehensively considered by the established integrated energy management system as the main objective J1, and J2 and J3 as constraints, set the ε threshold:

[0101] J2 ≤ 2, J3 ≤ 3

[0102] Where: 2 and 3 are traversed within a reasonable range to form the Pareto front.

[0103] 3.2 By adding augmented variables, AUGMECON reduces redundant calculations by adding additional variables, ensures the uniform distribution of Pareto front points, improves efficiency, and the objective function is transformed into:

[0104]

[0105] Where: M is the penalty coefficient, 2 and 3 are the ε constraint values, ensuring the correct optimization direction. For the obtained solution set, eliminate duplicate solutions and non-frontier solutions to obtain the final four-dimensional Pareto solution set.

[0106] The goal of multi-objective optimization is to select the renewable energy consumption as the main index, and the robustness and energy storage coordinated scheduling as the secondary indexes. Through integrating multiple objectives, the system can achieve optimal operation. Although the main objective is selected, the secondary function indexes should also be considered to achieve the overall optimum of the system. The main objective is minimized because the index of quantifying renewable energy consumption is characterized by the degree of mismatch between renewable energy supply and demand. The smaller the function value J1, the higher the matching degree, that is, the greater the renewable energy consumption. Use the robust augmented constraint multi-objective optimization algorithm to perform the main integration and solution of the above three functions.

[0107] 3.3 TOPSIS approximation ideal solution ranking

[0108] (1) Calculate the positive ideal solution PIS and the negative ideal solution NIS

[0109] PIS = max J i , NIS = min J i

[0110] (2) Calculate the Euclidean distance

[0111]

[0112] (3) Calculate the TOPSIS evaluation value

[0113]

[0114] According to the value of C i Sort the evaluation schemes according to the magnitude of the value. The larger the value, the better the comprehensive evaluation result.

[0115] (4) Select the optimal Pareto solution as the final scheduling strategy.

[0116] 1. The optimal Pareto solution is not a single solution, but a set of solution sets that satisfy the optimal trade-off relationship. This set of solution sets ensures that it is impossible to improve another goal without sacrificing a certain goal to achieve the coordinated optimization of the energy storage system and improve the energy utilization efficiency. The final scheduling scheme is selected through the Pareto front and TOPSIS evaluation. These solutions achieve a balance among the three goals of maximizing renewable energy consumption, minimizing system power fluctuations, and optimizing energy storage scheduling. Specifically, the optimal Pareto solution corresponds to the optimal power distribution scheme under a certain time period t: that is, a certain Pareto solution satisfies the optimal trade-off among energy consumption, power fluctuation, and energy storage optimization.

[0117] 2. Regulation parameters: P gen (t) Output power of power generation equipment MW (adjust the output of different power sources), R e (t), R h (t), R c (t) Absorbed power of renewable energy MW (monitor the access of renewable energy such as wind power and photovoltaic power), S e (t) S h (t) S c (t) Charge / discharge power of gravel energy storage and compressed air energy storage MW (adjust the discharge strategy of gravel and air energy storage), energy storage power penalty coefficient λ a λ g (Optimize the charge and discharge priority of energy storage equipment)

[0118] 3. Final regulation strategy

[0119] Combining the optimal Pareto solution and the corresponding regulation parameters, the optimal energy storage regulation strategy is:

[0120] Real-time optimize the power distribution of energy storage equipment. When there is high renewable energy generation, increase the charging power of gravel energy storage / compressed air energy storage. When the power demand is at a peak, increase the discharge power of gravel energy storage / compressed air energy storage to ensure that the energy storage equipment operates within a safe range.

[0121] Reduce power fluctuations, enhance system robustness, avoid large-scale charge and discharge of energy storage devices, and smoothly regulate power; limit the rate of change of generator power through intelligent control.

[0122] Maximize the consumption of renewable energy. By optimizing the scheduling of the input power of wind energy and solar energy, adjust the charging time window of gravel energy storage and compressed air energy storage to reduce curtailment of wind and solar power.

[0123] To maximize the consumption of renewable energy, improve system robustness, and optimize the coordinated scheduling of energy storage, gravel energy storage and air compression energy storage should adopt a dynamic complementary scheduling strategy. Specifically: gravel energy storage accounts for 55%-65% of the tasks, mainly used for long-term thermal energy storage, supporting the CCHP waste heat recovery system, improving the comprehensive energy utilization efficiency, providing thermal buffering in winter heating and summer absorption refrigeration, and reducing the power burden. While air compression energy storage accounts for 35%-45% of the tasks. During peak electricity consumption periods, drive the generator by quickly releasing compressed air. Use low-cost electricity to compress air at night or during peak renewable energy periods to achieve peak shaving and valley filling. When the grid load fluctuates, air compression energy storage can provide power compensation within milliseconds, enhancing the emergency response of system robustness. The optimized strategy of "gravel energy storage + air compression energy storage" at both ends can ensure the maximum consumption of renewable energy while improving the stability and economy of the energy storage system.

[0124] The optimization results of AUGMECON+TOPSIS show that to maximize the consumption of renewable energy, improve system robustness, and optimize the coordinated scheduling of energy storage, gravel battery energy storage and air compression energy storage should adopt a dynamic complementary scheduling strategy. Specifically: gravel energy storage accounts for 55%-65% of the tasks, mainly used for long-term thermal energy storage, supporting CCHP combined cooling, heating and power and waste heat recovery equipment, improving the comprehensive energy utilization efficiency, providing thermal buffering in winter heating and summer absorption refrigeration, and reducing the power burden. While air compression energy storage should account for 35%-45% of the tasks. During peak electricity consumption periods, drive the generator by quickly releasing compressed air. Use low-cost electricity to compress air at night or during peak renewable energy periods to achieve peak shaving and valley filling; when the grid load fluctuates, air compression energy storage can provide power compensation within milliseconds, enhancing the emergency response of system robustness. The optimized strategy of "gravel battery energy storage + air compression energy storage" at both ends can ensure the maximum consumption of renewable energy while improving the stability and economy of the energy storage system.

[0125] In summary, AUGMECON can ensure that the solution set obtained is the global optimal Pareto solution set for maximizing the consumption of renewable energy, improving system robustness, and optimizing the coordinated scheduling of energy storage in the constructed integrated energy management system, and the solution time is greatly reduced, having advantages in solution quality and solution efficiency; the proposed Pareto solution set optimization strategy can also reliably optimize the configuration results that the system emphasizes.

Claims

1. An energy optimization scheduling system based on dual - end collaboration, characterized in that, It includes an energy storage layer, a CCHP (Combined Cooling, Heating and Power) system with waste heat recovery equipment. The energy storage layer is coupled by a dual - end power supply and includes gravel battery energy storage and compressed air energy storage. The compressed air energy storage stores energy during the low - demand period of electricity and releases energy during the peak period. The gravel battery energy storage makes up for the shortcoming of the long response time of the compressed air energy storage, and forms a complementary operation mode in combination with the characteristics of dual - end energy storage coupling. During the high - electricity - price period, the gravel battery energy storage is preferentially used for discharging. During the low - electricity - price period, the compressed air energy storage is started, and low - price electricity is used to compress air and store energy.

2. The energy optimization scheduling system based on dual - end collaboration according to claim 1, wherein, The gravel battery energy storage is interconnected through a gravel heating device and a high - temperature gravel storage and heat - insulation silo device. The gravel heating device uses the heat collection of a photovoltaic solar panel to heat the gravel, and stores the heated gravel in the high - temperature gravel storage and heat - insulation silo device. Then, the high - temperature gravel storage and heat - insulation silo device conducts heat - insulation and heat - preservation treatment on the high - temperature gravel to achieve energy storage. The compressed air energy storage uses air as the energy storage medium and realizes the conversion of electrical energy through cyclic compression and expansion. The heat energy generated during air compression is extracted and stored. Excess electrical energy is used to drive a compressor to inhale and compress the ambient air into high - temperature and high - pressure air, converting electrical energy into internal energy to complete the energy input process. Then, through the compression - side heat exchanger, the heat - exchanged low - temperature air is stored in the gas storage unit, and the heated high - temperature heat - exchange medium is stored in the heat storage unit, separating the internal energy into heat energy and potential energy to complete the decoupling of compressed heat energy and pressure potential energy.

3. The energy optimization scheduling system based on dual - end collaboration according to claim 1, wherein, The CCHP cold - heat - power cogeneration and waste - heat recovery equipment includes a turbine - generator equipment, a heating equipment, an absorption refrigeration equipment, and a waste - heat recovery equipment. The turbine - generator equipment receives the high - temperature and high - pressure air energy of the gravel battery energy storage and the compressed air energy storage, and the air flow passes through the turbine through a heat exchanger. The air flow impacts the turbine blades to drive the generator to generate electricity. The heating equipment receives the hot air and flows through a plate heat exchanger and then is transported to the user side through a heating network to meet the heating demand of users. The absorption refrigeration equipment receives the hot air for refrigeration operation. The waste - heat recovery equipment recovers the waste heat from the power generation, heating, refrigeration processes and the energy storage layer. On the one hand, it is used for the waste - heat recycling within the CCHP system, and on the other hand, it is used for the external energy storage layer. The gravel battery energy storage uses the waste heat for thermal management and to heat the compressed air.

4. The energy optimization scheduling system based on dual - end collaboration according to claim 3, characterized in that The intelligent integrated energy optimization management system also includes a regulation unit. The regulation unit uses the Pareto solution set of the robust augmented e - constraint method to solve, and combines the comprehensive weight and the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) optimization algorithm to realize the coordinated cooperation between the CCHP cold - heat - power cogeneration and waste - heat recovery equipment, the gravel battery energy storage, and the compressed air energy storage, and achieve the optimal energy scheduling. Specifically as follows: Step 1: Construct a multi - objective optimization model for the energy optimization scheduling system, comprehensively considering multiple objectives such as maximizing the consumption of renewable energy, the robustness of the system, and optimizing the coordinated scheduling of energy storage. (1) To maximize the consumption of renewable energy, the objective function J1 is the degree of mismatch between the supply and demand of renewable energy: Where: R e (t), R h (t), R c (t) respectively represent the electrical, thermal, and cold renewable energy supplies (MW); S e (t) is the battery energy storage charge / discharge power (MW), S h (t) is the thermal energy storage charge / heat release power (MW), S c (t) is the cold energy storage charge / cold release power (MW); (2) Robustness of the system. The objective function J2 measures the stability of the power output of the entire system. By calculating the variation ranges of the power generation power and the charge-discharge power of the energy storage, the target power fluctuation is optimized to reduce the drastic power change of the system between different time steps: Where: P gen (t) Output of power generation equipment (MW), S g (t) Charge / discharge power of gravel energy storage (MW), S a (t) Charge / discharge power of air compression energy storage (MW); (3) Optimize the energy storage scheduling to ensure a reasonable load distribution between the gravel battery energy storage and the compressed air energy storage: where: λ a is the power penalty coefficient of gravel energy storage, and λ g is the power penalty coefficient of air compression energy storage, which is used to control its usage ratio; Step 2: Design the constraint conditions; Step 2.1 Supply-demand balance P gen (t) + S e (t) + R e (t) = D e (t) P heat (t) + S h (t) + R h (t) = D h (t) P cool (t) + S c (t) + R c (t) = D c (t) Where: P gen (t), S e (t), R e (t), D e (t) are the power output of the power generation equipment (MW), the charge / discharge power of the battery energy storage (MW), the supply of renewable energy for electricity, and the electricity demand, respectively; P heat (t), S h (t), R h (t), D h (t) are the power output of the heat supply equipment (MW), the charge / discharge power of the heat energy storage (MW), the supply of renewable energy for heat, and the heat demand, respectively; P cool (t), S c (t), R c (t), D c (t) are the power output of the absorption refrigeration equipment (MW), the charge / discharge power of the cold energy storage (MW), the supply of renewable energy for cold, and the cold demand, respectively; Step 2.2: Dynamic equations of the gravel battery energy storage and the compressed air energy storage E g (t + 1)= E g (t)+ η g S g (t) E a (t + 1)= E a (t)+ η a S a (t) Where: E g (t) is the current stored energy of the gravel energy storage, and η g is the charge-discharge efficiency of the gravel energy storage; E a (t) is the current stored energy (MWh) of the air compression energy storage, and η a reflects the efficiency of the air compression energy storage, considering compression-expansion losses; η ∈ [0, 1]; Step 2.3: Constraints on the energy storage power and capacity; 0 ≤ E g (t) ≤ C g , 0 ≤ E a (t) ≤ C a Where: S max represents the rated maximum charge-discharge power of the corresponding device, C g , C a represent the maximum energy storage capacity (MWh) of gravel energy storage and air compression energy storage.

5. The energy optimization scheduling system based on dual - end collaboration according to claim 4, wherein Solve the Pareto solution set using the robust augmented e-constraint method, and process it in combination with the comprehensive weight and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) optimization algorithm as follows: Step 3.1: Taking J1 as the main objective and J2 and J3 as secondary objectives, set the ε threshold: J2 ≤ 2, J3 ≤ 3, 2,3 Traverse within a reasonable range to form the Pareto front; Step 3.2: Add augmented variables, and the objective function is transformed into: where: M is the penalty coefficient, 2,3 is the ε constraint value to ensure the correct optimization direction. For the obtained solution set, repeated solutions and non-frontier solutions are eliminated to obtain the final four-dimensional Pareto solution set; Step 3.3: TOPSIS approximation to the ideal solution ranking (1) Calculate the positive ideal solution (PIS) and the negative ideal solution (NIS): PIS = maxJ i , NIS = minJ i where: PIS is the positive ideal solution; NIS is the negative ideal solution; J i is the i-th objective function (2) Calculate the Euclidean distance: where: is the Euclidean distance between the $i$-th objective function value and the positive ideal solution; is the Euclidean distance between the $i$-th objective function value and the negative ideal solution (3) Calculate the TOPSIS evaluation value: Where: TOPSIS evaluation value, C i represents the proximity of the i-th objective function to the positive and negative ideal solutions; based on C i values, the ranking of the evaluation schemes is achieved. The larger the C i value, the better the comprehensive evaluation result; Step 3.4: Select the optimal Pareto solution as the final scheduling strategy.

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