A method and device for optimizing energy management of a regional integrated energy system

Abstract

The present invention provides a method and device for optimizing energy management in a regional integrated energy system. These methods obtain forecast information on the system's topology, load, and distributed generation power. Based on an energy management optimization model, the Monte Carlo method is used for sampling, combined with a genetic algorithm for solution, to determine the planned outputs of each of the cooling and heating power sources in the regional integrated energy system and the expected value of the energy management cost for the regional integrated energy system. This method significantly improves the confidence level in the planned outputs of each of the cooling and heating power sources in the regional integrated energy system and the expected value of the energy management cost for the regional integrated energy system. The method quantifies uncertainty through power forecast error samples, providing a reliable basis for improving the confidence level of energy management optimization results. The method can achieve coupled, complementary, and coordinated operation of multiple energy supply sources, such as cooling, heating, and electricity, within and outside a region under uncertain conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a method and device for optimizing energy management of a regional integrated energy system. Background Art

[0002] An integrated energy system is a new type of integrated energy system that utilizes advanced physical information technology and innovative management models to integrate multiple energy sources within a region, including coal, oil, natural gas, electricity, and thermal energy. This system achieves coordinated planning, optimized operation, collaborative management, interactive response, and mutual complementarity among these heterogeneous energy subsystems. While meeting the diverse energy needs within the system, it also aims to effectively improve energy efficiency and promote sustainable energy development.

[0003] In recent years, with the development of the economy and society, the demand for various forms of energy, including cooling, heating, electricity, and gas, has increased significantly in production and daily life. At the same time, the development and large-scale application of new energy technologies, such as photovoltaic power generation, wind power generation, micro-gas power generation, electric heating, and energy storage, have led to a continuous strengthening of the coupling between various energy subsystems.

[0004] Existing energy management for integrated energy systems generally focuses on improving the energy efficiency of individual devices or systems, and the scale and scope of energy supply are limited. Furthermore, constrained by traditional energy management and operational models, the various energy subsystems in integrated energy systems, such as coal, oil, gas, and electricity, are rarely coupled, operating independently, and fail to consider the coordinated optimization of multiple heterogeneous energy sources. Existing energy management for integrated energy systems is generally based on deterministic power generation and load forecasting information, failing to account for power forecast errors and uncertainties during system operation. This results in low confidence in energy management optimization results. Summary of the Invention

[0005] In order to overcome the deficiency of low confidence in energy management optimization results in the above-mentioned prior art, the present invention provides an energy management optimization method for a regional integrated energy system, which obtains the topology structure, load and distributed power generation prediction information of the regional integrated energy system; the topology structure, load and distributed power generation prediction information are input into a pre-constructed energy management optimization model, and based on the energy management optimization model, the Monte Carlo method is used for sampling, and the solution is performed in combination with the genetic algorithm to obtain the planned output of each cold and hot power source in the regional integrated energy system and the expected value of the energy management cost of the regional integrated energy system. The energy management optimization model considers the topology structure, load, distributed power generation prediction information and the planned output of each cold and hot power source in the regional integrated energy system, and is constructed with the goal of minimizing the energy management cost of the regional integrated energy system, thereby greatly improving the planned output of each cold and hot power source in the regional integrated energy system and the confidence of the regional integrated energy system.

[0006] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions:

[0007] In one aspect, the present invention provides a method for optimizing energy management of a regional integrated energy system, comprising:

[0008] Obtain the topology, load, and distributed generation power forecast information of regional integrated energy systems;

[0009] inputting the topology, load and distributed generation power forecast information into a pre-built energy management optimization model;

[0010] Based on the energy management optimization model, the Monte Carlo method is used for sampling and combined with the genetic algorithm to solve the problem, and the planned output of each of the cold and hot power sources in the regional integrated energy system and the expected value of the energy management cost of the regional integrated energy system are obtained;

[0011] The energy management optimization model takes into account the topology, load, distributed generation power forecast information and the planned output of each of the cold and hot power sources in the regional integrated energy system, and is constructed with the goal of minimizing the energy management cost of the regional integrated energy system.

[0012] Based on the energy management optimization model, the Monte Carlo method is used for sampling and solved in combination with the genetic algorithm to obtain the planned output of each of the cold and hot power sources in the regional integrated energy system and the expected value of the energy management cost of the regional integrated energy system, including:

[0013] Based on the random distribution of the historical power prediction error of distributed generation, the Monte Carlo sampling method is used to sample the current power prediction error of distributed generation to obtain the current power prediction error sample of distributed generation;

[0014] Based on the current power prediction error samples of the distributed generation, a genetic algorithm is used to solve the energy management optimization model to obtain the planned output of the cooling and heating power sources under each power prediction error sample;

[0015] The expected value of energy management cost of regional integrated energy system is determined based on the planned output of cooling and heating power sources under all power forecast error samples.

[0016] The construction of the energy management optimization model includes:

[0017] Determine the total power supply cost of pure power source units, the total heating cost of pure heat source units, and the total power supply and heating cost of cogeneration units respectively;

[0018] Determine the total cost of electricity purchased from the grid and the total cost of electricity connected to the grid for the regional integrated energy system;

[0019] Determining the objective function of the energy management optimization model based on the total power supply cost of the pure power source unit, the total heating cost of the pure heat source unit, the total power supply and heating cost of the cogeneration unit, the total cost of online electricity purchases and the total cost of online electricity of the regional integrated energy system;

[0020] The constraints of the energy management optimization model are determined based on the objective function.

[0021] The objective function is determined as follows:

[0022]

[0023] Where f is the energy management cost of the regional integrated energy system, E is the expected value operator, and n p is the number of pure power supply units, n c is the number of cogeneration units, n h is the number of pure heat source units, c ip is the unit power generation cost of the i-th pure power unit, c jp is the unit power generation cost of the jth cogeneration unit, c jh is the unit heating cost of the jth cogeneration unit, c kh is the unit heating cost of the kth pure heat source unit, c g is the unit price of electricity purchased from the Internet in the regional integrated energy system, c s is the unit price of on-grid electricity in regional integrated energy system, e ip is the power generation of the i-th pure power unit, e jp is the power generation of the jth cogeneration unit, e jh is the heat supply of the jth cogeneration unit, e kh The heat supply for the kth pure heat source unit, e g For the online purchase of electricity in regional integrated energy system, e s It is the grid-connected electricity of regional integrated energy system.

[0024] The energy management optimization model also includes constraints, which include heat balance constraints, power balance constraints, pure power unit output constraints, pure heat source unit output constraints, cogeneration unit operation constraints, battery energy storage system charge / discharge constraints, and thermal storage equipment heat absorption / release constraints.

[0025] The topology includes a gas system, an electric power system and a thermal system, and the thermal system includes a refrigeration system and a heating system;

[0026] The gas system provides gas to the power system and the heating system through the regional gas grid; the power system provides power to the cooling system and the heating system through the regional power grid.

[0027] The pure power supply unit includes a distributed photovoltaic power generation system and a distributed wind power generation system in the power system;

[0028] The pure heat source unit includes an electric boiler and a heat storage device in a heating system.

[0029] On the other hand, the present invention also provides a regional integrated energy system energy management optimization device, comprising:

[0030] An acquisition module is used to obtain the topology, load and distributed generation power forecast information of the regional integrated energy system;

[0031] An input module, configured to input the topology, load and distributed generation power forecast information into a pre-built energy management optimization model;

[0032] A solution module is used to perform sampling based on the energy management optimization model using the Monte Carlo method and solve it in combination with a genetic algorithm to obtain the planned output of each of the cold and hot power sources in the regional integrated energy system and the expected value of the energy management cost of the regional integrated energy system;

[0033] The energy management optimization model takes into account the topology, load, distributed generation power forecast information and the planned output of each of the cold and hot power sources in the regional integrated energy system, and is constructed with the goal of minimizing the energy management cost of the regional integrated energy system.

[0034] The solution module is specifically used for:

[0035] Based on the random distribution of the historical power prediction error of distributed generation, the Monte Carlo sampling method is used to sample the current power prediction error of distributed generation to obtain the current power prediction error sample of distributed generation;

[0036] Based on the current power prediction error samples of the distributed generation, a genetic algorithm is used to solve the energy management optimization model to obtain the planned output of the cooling and heating power sources under each power prediction error sample;

[0037] The expected value of energy management cost of regional integrated energy system is determined based on the planned output of cooling and heating power sources under all power forecast error samples.

[0038] The device further includes a modeling module, which is specifically configured to:

[0039] Determine the total power supply cost of pure power source units, the total heating cost of pure heat source units, and the total power supply and heating cost of cogeneration units respectively;

[0040] Determine the total cost of electricity purchased from the grid and the total cost of electricity connected to the grid for the regional integrated energy system;

[0041] Determining the objective function of the energy management optimization model based on the total power supply cost of the pure power source unit, the total heating cost of the pure heat source unit, the total power supply and heating cost of the cogeneration unit, the total cost of online electricity purchases and the total cost of online electricity of the regional integrated energy system;

[0042] The constraints of the energy management optimization model are determined based on the objective function.

[0043] The modeling module determines the objective function as follows:

[0044]

[0045] Where f is the energy management cost of the regional integrated energy system, E is the expected value operator, and n p is the number of pure power supply units, n c is the number of cogeneration units, n h is the number of pure heat source units, c ip is the unit power generation cost of the i-th pure power unit, c jp is the unit power generation cost of the jth cogeneration unit, c jh is the unit heating cost of the jth cogeneration unit, c kh is the unit heating cost of the kth pure heat source unit, c g is the unit price of electricity purchased from the Internet in the regional integrated energy system, c s is the unit price of on-grid electricity in regional integrated energy system, e ip is the power generation of the i-th pure power unit, e jp is the power generation of the jth cogeneration unit, e jh is the heat supply of the jth cogeneration unit, e kh The heat supply for the kth pure heat source unit, e g For the online purchase of electricity in regional integrated energy system, e s It is the grid-connected electricity of regional integrated energy system.

[0046] The modeling module is also used to determine constraints; the constraints include heat balance constraints, power balance constraints, pure power unit output constraints, pure heat source unit output constraints, cogeneration unit operation constraints, battery energy storage system charge / discharge constraints and thermal storage equipment heat absorption / release constraints.

[0047] The topology includes a gas system, an electric power system and a thermal system, and the thermal system includes a refrigeration system and a heating system;

[0048] The gas system provides gas to the power system and the heating system through the regional gas grid; the power system provides power to the cooling system and the heating system through the regional power grid.

[0049] The pure power supply unit includes a distributed photovoltaic power generation system and a distributed wind power generation system in the power system;

[0050] The pure heat source unit includes an electric boiler and a heat storage device in a heating system.

[0051] Compared with the closest existing technology, the technical solution provided by the present invention has the following beneficial effects:

[0052] In the energy management optimization method for a regional integrated energy system provided by the present invention, the topology structure, load and distributed generation power prediction information of the regional integrated energy system are obtained; the topology structure, load and distributed generation power prediction information are input into a pre-constructed energy management optimization model; based on the energy management optimization model, the Monte Carlo method is used for sampling, and the solution is performed in combination with the genetic algorithm to obtain the planned output of each cold and hot power source in the regional integrated energy system and the expected value of the energy management cost of the regional integrated energy system. The energy management optimization model considers the topology structure, load, distributed generation power prediction information and the planned output of each cold and hot power source in the regional integrated energy system, and is constructed with the goal of minimizing the energy management cost of the regional integrated energy system, thereby greatly improving the confidence level of the planned output of each cold and hot power source in the regional integrated energy system and the expected value of the energy management cost of the regional integrated energy system;

[0053] The present invention adopts the Monte Carlo sampling method to sample the distributed power generation power prediction error to obtain the power prediction error sample. The power prediction error sample is used to quantify the uncertainty factors, providing a reliable basis for improving the confidence of the energy management optimization results.

[0054] The technical solution provided by the present invention fully considers the coupling relationship and complementary characteristics of multiple heterogeneous energy sources, and can achieve the coupling, complementarity and coordinated operation of multiple energy supply sources such as cooling, heating and electricity within and outside the region under uncertain conditions;

[0055] The technical solution provided by the present invention is applicable to regional production and living energy demands represented by industrial parks, communities / blocks. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of the energy management optimization method for a regional integrated energy system according to an embodiment of the present invention;

[0057] Figure 2 It is a topological structure diagram of a regional integrated energy system in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present invention will be described in further detail below with reference to the accompanying drawings.

[0059] Example 1

[0060] Embodiment 1 of the present invention provides a method for optimizing energy management of a regional integrated energy system. The specific flow chart is as follows: Figure 1 As shown, the specific process is as follows:

[0061] S101: Obtaining the topology, load, and distributed generation power forecast information of the regional integrated energy system;

[0062] S102: Inputting the topology, load and distributed generation power forecast information into a pre-built energy management optimization model;

[0063] S103: Based on the energy management optimization model, a Monte Carlo method is used for sampling, and a genetic algorithm is used for solving the problem to obtain the planned output of each of the cooling and heating power sources in the regional integrated energy system and the expected value of the energy management cost of the regional integrated energy system;

[0064] The energy management optimization model takes into account the topology, load, distributed generation power forecast information, and the planned output of each cold and hot power source in the regional integrated energy system, and is constructed with the goal of minimizing the energy management cost of the regional integrated energy system.

[0065] Based on the energy management optimization model, the Monte Carlo method is used for sampling and combined with the genetic algorithm to solve the problem. The planned output of each cooling and heating power source in the regional integrated energy system and the expected value of the energy management cost of the regional integrated energy system are obtained, including:

[0066] Based on the random distribution of the historical power prediction error of distributed generation, the Monte Carlo sampling method is used to sample the current power prediction error of distributed generation to obtain the current power prediction error sample of distributed generation;

[0067] Based on the current power forecast error samples of distributed generation, a genetic algorithm is used to solve the energy management optimization model to obtain the planned output of cooling and heating power sources under each power forecast error sample.

[0068] The expected value of energy management cost of regional integrated energy system is determined based on the planned output of cooling and heating power sources under all power forecast error samples.

[0069] Construction of energy management optimization model, including:

[0070] Determine the total power supply cost of pure power source units, the total heating cost of pure heat source units, and the total power supply and heating cost of cogeneration units respectively;

[0071] Determine the total cost of electricity purchased from the grid and the total cost of electricity connected to the grid for the regional integrated energy system;

[0072] The objective function of the energy management optimization model is determined based on the total power supply cost of pure power units, the total heating cost of pure heat source units, the total power supply and heating cost of cogeneration units, the total cost of online electricity purchases and the total cost of online electricity in the regional integrated energy system;

[0073] The constraints of the energy management optimization model are determined based on the objective function.

[0074] The objective function is determined as follows:

[0075]

[0076] Where f is the energy management cost of the regional integrated energy system, E is the expected value operator, and n p is the number of pure power supply units, n c is the number of cogeneration units, n h is the number of pure heat source units, c ip is the unit power generation cost of the i-th pure power unit, c jp is the unit power generation cost of the jth cogeneration unit, c jh is the unit heating cost of the jth cogeneration unit, c kh is the unit heating cost of the kth pure heat source unit, c g is the unit price of electricity purchased from the Internet in the regional integrated energy system, c s is the unit price of on-grid electricity in regional integrated energy system, e ip is the power generation of the i-th pure power unit, e jp is the power generation of the jth cogeneration unit, e jh is the heat supply of the jth cogeneration unit, e kh The heat supply for the kth pure heat source unit, e g For the online purchase of electricity in regional integrated energy system, e s It is the grid-connected electricity of regional integrated energy system.

[0077] The energy management optimization model also includes constraints, including heat balance constraints, power balance constraints, pure power unit output constraints, pure heat source unit output constraints, combined heat and power unit operation constraints, battery energy storage system charge / discharge constraints, and thermal storage equipment heat absorption / release constraints. The specific constraints are as follows:

[0078] (1) Heat balance constraint

[0079]

[0080] In the above formula, E h is the heat load demand, E hl is the energy loss of the district heating network, ES hIt is the heat storage capacity of the heat storage equipment (heat absorption is positive, heat release is negative).

[0081] (2) Power balance constraints

[0082]

[0083] In the above formula, E p is the electrical load demand; E pl is the energy loss of the regional power grid; ES p It is the storage capacity of the battery energy storage system (charging is positive and discharging is negative).

[0084] (3) Pure power output constraints

[0085]

[0086] In the above formula, p i is the output of the i-th pure power unit; are the upper and lower limits of the output of the i-th pure power unit.

[0087] (4) Pure heat source output constraint

[0088]

[0089] In the above formula, h k is the output of the kth pure power unit; are the upper and lower limits of the output of the kth pure power unit.

[0090] (5) Cogeneration operation constraints

[0091]

[0092] In the above formula, p j is the power output of the jth cogeneration unit; is the upper and lower limits of the power output of the jth cogeneration unit; h j The heating output of the jth cogeneration unit; are the upper and lower limits of the heating output of the jth cogeneration unit.

[0093] (6) Energy storage system charging / discharging constraints

[0094]

[0095] In the above formula, p es Battery charge / discharge power; The upper and lower limits of charging / discharging power for battery energy storage facilities; ESp is the battery energy status; The upper and lower limits of battery energy storage space.

[0096] (7) Heat storage equipment heat absorption / release constraints

[0097]

[0098] In the above formula, h es Provides operating power for thermal storage facilities; The upper and lower limits of heat absorption / release power of thermal energy storage facilities; ES h It is a state of thermal energy storage; The upper and lower limits of thermal energy storage space.

[0099] The topological structure of regional integrated energy system is as follows: Figure 2 As shown, using distributed photovoltaic, wind power, and gas cogeneration as the main power / heat source, supplemented by energy-saving equipment (heat storage equipment, etc.) and electric energy replacement equipment (electric boilers, etc.), it can meet the supply and consumption needs of various energy forms such as cold / heat, electricity, and gas in the region. It is suitable for areas with complete natural gas and heat pipelines, a high proportion of power generation, and a high degree of integration between power grids, gas grids, and heat grids.

[0100] The topological structure of the regional integrated energy system includes gas system, power system and thermal system. The thermal system includes cooling system and heating system.

[0101] The gas system provides gas to the power system and heating system through the regional gas grid; the power system provides electricity to the cooling system and heating system through the regional power grid.

[0102] Part of the electricity of regional integrated energy comes from distributed energy sources such as distributed photovoltaic power generation systems and distributed wind power generation systems, and the other part comes from the external power grid.

[0103] The power loads that the power system needs to supply include refrigeration equipment (centralized refrigeration units, decentralized air-conditioning loads, etc.), heating equipment (electric boilers, heat storage equipment, etc.) and conventional power loads other than refrigeration / heating.

[0104] The thermal system includes electric boilers, heat storage equipment, pressure pumps, and regional heating networks;

[0105] The cooling system includes centralized refrigeration units, decentralized air conditioning loads, ground source heat pumps, and district cooling networks;

[0106] The gas system mainly consists of a gas network, compressors, cogeneration units, industrial, commercial and residential loads, etc. The gas source can be a local gas station or delivered by an external gas network.

[0107] The power and gas systems are primarily coupled through combined heat and power (CHP) units and compressors. The CHP units generate electricity from gas and transmit it to the power system; at the same time, the power system powers the compressors in the gas system. The power and thermal systems are primarily coupled through CHP units and cooling / heating equipment. The CHP units supply heat to the thermal system while also supplying power to the power system; the power system also supplies power to the cooling / heating equipment.

[0108] Pure power generation units include distributed photovoltaic power generation systems and distributed wind power generation systems in the power system;

[0109] Pure heat source units include electric boilers and heat storage equipment in the heating system.

[0110] The cold and hot power sources in the regional integrated energy system are cold source, heat source and power source. The cold source includes ground source heat pumps, centralized refrigeration units and decentralized air-conditioning equipment. The heat source includes cogeneration units, electric boilers and heat storage equipment. The power source includes distributed photovoltaic power generation systems, distributed wind power generation systems and battery energy storage systems.

[0111] Example 2

[0112] Based on the same inventive concept, Example 2 of the present invention further provides a regional integrated energy system energy management optimization device. The functions of each component are described in detail below:

[0113] An acquisition module is used to obtain the topology, load and distributed generation power forecast information of the regional integrated energy system;

[0114] An input module for inputting topology, load and distributed generation power forecast information into a pre-built energy management optimization model;

[0115] The solution module is used to obtain the planned output of each of the cooling and heating power sources in the regional integrated energy system and the expected value of the energy management cost of the regional integrated energy system based on the energy management optimization model, using the Monte Carlo method for sampling and combining it with the genetic algorithm for solution;

[0116] The energy management optimization model takes into account the topology, load, distributed generation power forecast information, and the planned output of each cold and hot power source in the regional integrated energy system, and is constructed with the goal of minimizing the energy management cost of the regional integrated energy system.

[0117] The solver module is specifically used for:

[0118] Based on the random distribution of the historical power prediction error of distributed generation, the Monte Carlo sampling method is used to sample the current power prediction error of distributed generation to obtain the current power prediction error sample of distributed generation;

[0119] Based on the current power forecast error samples of distributed generation, a genetic algorithm is used to solve the energy management optimization model to obtain the planned output of cooling and heating power sources under each power forecast error sample.

[0120] The expected value of energy management cost of regional integrated energy system is determined based on the planned output of cooling and heating power sources under all power forecast error samples.

[0121] The apparatus provided in Example 2 of the present invention further includes a modeling module, which is specifically configured to:

[0122] Determine the total power supply cost of pure power source units, the total heating cost of pure heat source units, and the total power supply and heating cost of cogeneration units respectively;

[0123] Determine the total cost of electricity purchased from the grid and the total cost of electricity connected to the grid for the regional integrated energy system;

[0124] The objective function of the energy management optimization model is determined based on the total power supply cost of pure power units, the total heating cost of pure heat source units, the total power supply and heating cost of cogeneration units, the total cost of online electricity purchases and the total cost of online electricity in the regional integrated energy system;

[0125] The constraints of the energy management optimization model are determined based on the objective function.

[0126] The modeling module determines the objective function as follows:

[0127]

[0128] Where f is the energy management cost of the regional integrated energy system, E is the expected value operator, and n p is the number of pure power supply units, n c is the number of cogeneration units, n h is the number of pure heat source units, c ip is the unit power generation cost of the i-th pure power unit, c jp is the unit power generation cost of the jth cogeneration unit, c jh is the unit heating cost of the jth cogeneration unit, c kh is the unit heating cost of the kth pure heat source unit, c g is the unit price of electricity purchased from the Internet in the regional integrated energy system, c s is the unit price of on-grid electricity in regional integrated energy system, e ip is the power generation of the i-th pure power unit, e jp is the power generation of the jth cogeneration unit, e jh is the heat supply of the jth cogeneration unit, e khThe heat supply for the kth pure heat source unit, e g For the online purchase of electricity in regional integrated energy system, e s It is the grid-connected electricity of regional integrated energy system.

[0129] The modeling module is also used to determine constraints; these constraints include heat balance constraints, electricity balance constraints, pure power unit output constraints, pure heat source unit output constraints, cogeneration unit operation constraints, battery energy storage system charge / discharge constraints, and thermal storage equipment heat absorption / release constraints.

[0130] The topology includes gas system, power system and thermal system. The thermal system includes cooling system and heating system.

[0131] The gas system provides gas to the power system and heating system through the regional gas grid; the power system provides electricity to the cooling system and heating system through the regional power grid.

[0132] Pure power generation units include distributed photovoltaic power generation systems and distributed wind power generation systems in the power system;

[0133] Pure heat source units include electric boilers and heat storage equipment in the heating system.

[0134] Example 3

[0135] Example 3 of the present invention takes the integrated energy system of an industrial park as an example, wherein the power system includes: distributed photovoltaic 20MW and urban power grid power supply, supplemented by energy storage batteries 5MW / 5MWh; the thermal system includes: a gas turbine-based cogeneration system with a rated capacity of 5MW, a power generation efficiency of 27.1%, a power-to-heat ratio of 0.64, and a minimum technical output of 30%; supplemented by a set of thermal storage equipment with a maximum thermal storage and release power of 2MW and a thermal storage capacity of 2MWh; the industrial gas price is 2.75￥ / m 3 Each cubic meter of commercial-quality natural gas burned produces 38MJ (approximately 10.6kW·h) of energy. The cooling system includes: a ground-source heat pump with a total cooling capacity of 3600kW and an energy efficiency ratio of 4.4 (meaning that for every kWh of energy consumed by the ground-source heat pump, users receive over 4.4kWh of cooling capacity); a baseload refrigeration unit with a cooling capacity of 3200kW and an energy efficiency ratio of 3.3; and distributed air conditioning equipment with an energy efficiency ratio of 2.8. The energy storage system has an initial SOC of 20%. The system's cooling, heating, and electrical loads, as well as photovoltaic output characteristics, from 0:00 to 24:00 on a typical day in the park are shown in Table 1:

[0136] Table 1

[0137] time Cooling load heat load Other electrical loads Photovoltaic output 1 6.00 3.36 23.93 0.00 2 6.00 1.92 20.78 0.00 3 6.00 4.44 18.90 0.00 4 6.00 2.16 17.63 0.00 5 6.00 2.04 17.03 0.00 6 12.00 3.84 16.80 0.00 7 17.40 3.60 16.73 0.00 8 18.00 4.68 17.33 0.78 9 17.88 4.56 18.75 2.54 10 18.24 2.64 21.15 10.16 11 18.60 3.12 24.38 12.20 12 19.20 5.04 25.73 12.71 13 19.80 4.80 25.88 18.63 14 20.40 2.33 25.65 17.45 15 20.16 2.40 25.05 19.14 16 19.80 2.52 25.13 17.79 17 19.20 2.47 25.28 12.71 18 18.96 2.52 25.35 5.08 19 18.24 2.50 26.03 2.54 20 17.94 3.24 28.80 0.00 21 16.80 1.20 32.85 0.00 22 14.40 4.08 32.33 0.00 23 13.20 1.44 30.60 0.00 24 12.00 1.32 27.00 0.00

[0138] Assume that the day-ahead photovoltaic power generation forecast error follows a normal distribution N(μ,σ), where μ = 0 and σ = 0.85. Based on the probability distribution function, 100 random samples of the forecast error are taken, and corresponding photovoltaic power generation time-series output scenarios are established that take the forecast error into account.

[0139] The industrial park adopts time-of-use electricity prices based on the purchase price of electricity from the urban power grid and the on-grid price of surplus electricity according to the peak and off-peak periods, giving full play to the leverage effect of price, mobilizing users to consciously adjust production and participate in demand-side responses such as peak shaving and valley filling, and balanced energy consumption. The time-of-use on-grid purchase and sale prices are shown in Table 2:

[0140] Table 2

[0141]

[0142]

[0143] Based on the above configuration parameters and typical characteristics, the planned output of the cooling and heating power sources for each power forecast error sample was determined. The expected energy management cost of the regional integrated energy system was also determined based on the planned output of the cooling and heating power sources for all power forecast error samples. The expected values ​​for the cooling, heating, and power scheduling scheme are shown in Table 3. Under this scheduling scheme, the expected energy cost of the regional integrated energy system is 332,500 yuan.

[0144] Table 3

[0145]

[0146]

[0147] For the convenience of description, the various parts of the above-mentioned device are divided into various modules or units according to their functions and described separately. Of course, when implementing this application, the functions of each module or unit can be implemented in the same or multiple software or hardware.

[0148] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0149] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 steps in the process. 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.

[0150] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0151] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Ordinary technicians in the relevant field can still modify or replace the specific implementation methods of the present invention with equivalents by referring to the above embodiments. Any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention are within the scope of protection of the present invention to be approved.

Claims

1. A regional integrated energy system energy management optimization method, characterized in that: include: Obtain the topology, load, and distributed generation power forecast information of regional integrated energy systems; inputting the topology, load and distributed generation power forecast information into a pre-built energy management optimization model; Based on the energy management optimization model, the Monte Carlo method is used for sampling and combined with the genetic algorithm to solve the problem, and the planned output of each of the cold and hot power sources in the regional integrated energy system and the expected value of the energy management cost of the regional integrated energy system are obtained; The energy management optimization model takes into account the topology, load, distributed generation power forecast information, and the planned output of each of the cooling and heating power sources in the regional integrated energy system, and is constructed with the goal of minimizing the energy management cost of the regional integrated energy system. Based on the energy management optimization model, the Monte Carlo method is used for sampling and solved in combination with the genetic algorithm to obtain the planned output of each of the cold and hot power sources in the regional integrated energy system and the expected value of the energy management cost of the regional integrated energy system, including: Based on the random distribution of the historical power prediction error of distributed generation, the Monte Carlo sampling method is used to sample the current power prediction error of distributed generation to obtain the current power prediction error sample of distributed generation; Based on the current power prediction error samples of the distributed generation, a genetic algorithm is used to solve the energy management optimization model to obtain the planned output of the cooling and heating power sources under each power prediction error sample; The expected value of energy management cost of regional integrated energy system is determined based on the planned output of cooling and heating power sources under all power forecast error samples. The construction of the energy management optimization model includes: Determine the total power supply cost of pure power source units, the total heating cost of pure heat source units, and the total power supply and heating cost of cogeneration units respectively; Determine the total cost of electricity purchased from the grid and the total cost of electricity connected to the grid for the regional integrated energy system; Determining the objective function of the energy management optimization model based on the total power supply cost of the pure power source unit, the total heating cost of the pure heat source unit, the total power supply and heating cost of the cogeneration unit, the total cost of online electricity purchases and the total cost of online electricity of the regional integrated energy system; determining constraints of an energy management optimization model based on the objective function; The constraints include heat balance constraints, electricity balance constraints, pure power source unit output constraints, pure heat source unit output constraints, cogeneration unit operation constraints, battery energy storage system charging and discharging constraints, and thermal storage equipment heat absorption and release constraints.

2. The regional integrated energy system energy management optimization method according to claim 1, characterized in that: The objective function is determined as follows: Where f is the energy management cost of the regional integrated energy system, E is the expected value operator, and n p is the number of pure power supply units, n c is the number of cogeneration units, n h is the number of pure heat source units, c ip is the unit power generation cost of the i-th pure power unit, c jp is the unit power generation cost of the jth cogeneration unit, c jh is the unit heating cost of the jth cogeneration unit, c kh is the unit heating cost of the kth pure heat source unit, c g is the unit price of electricity purchased from the Internet in the regional integrated energy system, c s is the unit price of on-grid electricity in regional integrated energy system, e ip is the power generation of the i-th pure power unit, e jp is the power generation of the jth cogeneration unit, e jh is the heat supply of the jth cogeneration unit, e kh The heat supply for the kth pure heat source unit, e g For the online purchase of electricity in regional integrated energy system, e s It is the grid-connected electricity of regional integrated energy system.

3. The regional integrated energy system energy management optimization method according to claim 1, characterized in that: The topology includes a gas system, an electric power system and a thermal system, and the thermal system includes a refrigeration system and a heating system; The gas system provides gas to the power system and the heating system through the regional gas grid; the power system provides power to the cooling system and the heating system through the regional power grid.

4. The regional integrated energy system energy management optimization method according to claim 3, characterized in that: The pure power supply unit includes a distributed photovoltaic power generation system and a distributed wind power generation system in the power system; The pure heat source unit includes an electric boiler and a heat storage device in a heating system.

5. A regional integrated energy system energy management optimization device, characterized in that: include: An acquisition module is used to obtain the topology, load and distributed generation power forecast information of the regional integrated energy system; An input module, configured to input the topology, load and distributed generation power forecast information into a pre-built energy management optimization model; A solution module is used to perform sampling based on the energy management optimization model using the Monte Carlo method and solve it in combination with a genetic algorithm to obtain the planned output of each of the cold and hot power sources in the regional integrated energy system and the expected value of the energy management cost of the regional integrated energy system; The energy management optimization model takes into account the topology, load, distributed generation power forecast information, and the planned output of each of the cooling and heating power sources in the regional integrated energy system, and is constructed with the goal of minimizing the energy management cost of the regional integrated energy system. The solution module is specifically used for: Based on the random distribution of the historical power prediction error of distributed generation, the Monte Carlo sampling method is used to sample the current power prediction error of distributed generation to obtain the current power prediction error sample of distributed generation; Based on the current power prediction error samples of the distributed generation, a genetic algorithm is used to solve the energy management optimization model to obtain the planned output of the cooling and heating power sources under each power prediction error sample; The expected value of energy management cost of regional integrated energy system is determined based on the planned output of cooling and heating power sources under all power forecast error samples. The device further includes a modeling module, which is specifically configured to: Determine the total power supply cost of pure power source units, the total heating cost of pure heat source units, and the total power supply and heating cost of cogeneration units respectively; Determine the total cost of electricity purchased from the grid and the total cost of electricity connected to the grid for the regional integrated energy system; Determining the objective function of the energy management optimization model based on the total power supply cost of the pure power source unit, the total heating cost of the pure heat source unit, the total power supply and heating cost of the cogeneration unit, the total cost of online electricity purchases and the total cost of online electricity of the regional integrated energy system; determining constraints of an energy management optimization model based on the objective function; The modeling module is also used to determine constraints; the constraints include heat balance constraints, power balance constraints, pure power unit output constraints, pure heat source unit output constraints, cogeneration unit operation constraints, battery energy storage system charging and discharging constraints, and thermal storage equipment heat absorption and release constraints.

6. The regional integrated energy system energy management optimization device according to claim 5, characterized in that: The modeling module determines the objective function as follows: Where f is the energy management cost of the regional integrated energy system, E is the expected value operator, and n p is the number of pure power supply units, n c is the number of cogeneration units, n h is the number of pure heat source units, c ip is the unit power generation cost of the i-th pure power unit, c jp is the unit power generation cost of the jth cogeneration unit, c jh is the unit heating cost of the jth cogeneration unit, c kh is the unit heating cost of the kth pure heat source unit, c g is the unit price of electricity purchased from the Internet in the regional integrated energy system, c s is the unit price of on-grid electricity in regional integrated energy system, e ip is the power generation of the i-th pure power unit, e jp is the power generation of the jth cogeneration unit, e jh is the heat supply of the jth cogeneration unit, e kh The heat supply for the kth pure heat source unit, e g For the online purchase of electricity in regional integrated energy system, e s It is the grid-connected electricity of regional integrated energy system.

7. The regional integrated energy system energy management optimization device according to claim 5, characterized in that: The topology includes a gas system, an electric power system and a thermal system, and the thermal system includes a refrigeration system and a heating system; The gas system provides gas to the power system and the heating system through the regional gas grid; the power system provides power to the cooling system and the heating system through the regional power grid.

8. The regional integrated energy system energy management optimization device according to claim 5, characterized in that: The pure power supply unit includes a distributed photovoltaic power generation system and a distributed wind power generation system in the power system; The pure heat source unit includes an electric boiler and a heat storage device in a heating system.

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