Regional source network load storage virtual power plant operation optimization method, device and equipment

Abstract

The invention relates to a regional source network load storage virtual power plant operation optimization method, device and equipment, belongs to the technical field of power system optimization, optimizes a scheduling strategy of a virtual power plant by comprehensively considering three objectives of cost, low carbon and risk, improves the overall performance and economic benefit of a system, realizes efficient, economic and sustainable operation of the virtual power plant, and improves the economic benefit of the system. The technical problems that in the prior art, the consideration direction is single, and the complexity in operation of a power system and the mutual influence between multi-dimensional targets cannot be solved are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization, and specifically relates to a method, device and equipment for optimizing the operation of a regional source-grid-load-storage virtual power plant. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, Virtual Power Plant (VPP) has gradually attracted widespread attention as a new energy management and scheduling method. Virtual power plants achieve efficient energy utilization and optimized scheduling by aggregating distributed power sources, energy storage systems, controllable loads, and electric vehicles.

[0003] However, existing virtual power plant optimization methods still have shortcomings in multi-objective collaborative optimization, mainly reflected in the focus on a single objective, such as: (1) Single objective optimization: Most virtual power plant optimization methods focus on a single objective, such as cost minimization or carbon emission reduction. For example, some studies focus on how to reduce the operating cost of virtual power plants by optimizing scheduling, while others focus on achieving low-carbon goals. However, this single-objective optimization method often ignores the interrelationship and synergy between cost, low carbon and risk, resulting in limitations in the optimization results. (2) Regional source-grid-load-storage system optimization. In the current context of energy transformation and electricity marketization, the operation optimization of regional source-grid-load-storage systems (SGLSS) faces multiple challenges such as cost control, low-carbon development and risk management. Existing optimization methods often only focus on a single objective, resulting in low system operation efficiency and failure to fully meet the needs of modern power systems. In the current context of energy transformation and electricity marketization, the operation optimization of regional source-grid-load-storage systems is crucial to achieving efficient energy utilization, reducing carbon emissions and ensuring safe and stable operation of power systems. Although a variety of optimization methods have been proposed, existing technologies mostly focus on optimizing a single objective, such as minimizing costs or reducing carbon emissions. These methods often fail to fully consider the complexity of power system operation and the mutual influence between multidimensional objectives.

[0004] Therefore, how to fully consider the complexity of power system operation and the mutual influence between multi-dimensional objectives to achieve the optimization of regional source-grid-load-storage virtual power plant operation has become a technical problem that needs to be urgently solved in existing technologies. Summary of the invention

[0005] The present invention provides a method, device and equipment for optimizing the operation of a regional source-grid-load-storage virtual power plant to solve the technical problem of failing to fully consider the complexity of power system operation and the mutual influence between multi-dimensional objectives.

[0006] The technical solution provided by the present invention is as follows:

[0007] On the one hand, a method for optimizing the operation of a regional source-grid-load-storage virtual power plant includes:

[0008] Taking minimization of operating costs, carbon emissions and risks as optimization goals, a multi-objective optimization function is constructed;

[0009] Setting constraints on the multi-objective optimization function, wherein the constraints include: equipment operation constraints and inter-regional market clearing constraints;

[0010] The multi-objective optimization function and the constraint conditions are used as a multi-objective optimization model, and the multi-objective optimization model is optimized and solved to obtain an optimized scheduling strategy.

[0011] Optionally, the expression of the multi-objective optimization function is:

[0012]

[0013] Where C is the total operating cost of the regional source-grid-load-storage virtual power plant, C R Cost of operating the inter-regional market; The cost of adjusting the inter-regional market transactions in the scenario ω; is the market operation cost in the i city under scenario ω; β is the weight of the spillover risk value; Q mn，p is the amount of carbon emissions, E Egrid is the carbon emission factor; is the spillover risk value of region i from other regions with which it conducts regional transactions; ω is the scenario weight; W is the total number of scenarios; I is the total number of regions participating in the regional market.

[0014] Optionally, the expression of the inter-regional market operation cost is:

[0015]

[0016] C R is the operating cost of the regional market, is the power generation cost of the units in the regional market, Provide market quotes for the units in the region; is the number of winning bids in the regional market for the unit; Ω i K is the set of units in region i; T is the total market clearing time; K is the total number of units in the whole system.

[0017] Optionally, the carbon emission factor is expressed as:

[0018]

[0019] E Egrld is the regional power grid carbon emission factor, E In，land E out , n are the direct carbon emissions from the power generation side in region i and the carbon emissions corresponding to the external power received from the power generation side in region n; is the power generation of the xth type of generator set within region i; E gp The total power generation of green power sources such as wind power and photovoltaic power in the region; is the total electricity received by region i from region n after deducting green electricity; It is the green electricity sent from region n to region i and the corresponding total emissions.

[0020] Optionally, the expression of the spillover risk value is:

[0021]

[0022] is the risk loss caused by the decision variable x and the random variable ξ Not greater than the boundary value distribution function; f(ξ) is the probability density function of ξ; β is the confidence level; ξ s is the value of the sth scene variable.

[0023] Optionally, the device operation constraint conditions include:

[0024] The power balance constraint of the electrical bus is expressed as:

[0025] P mt (t)+P grid (t)+P wt (t)+P pv (t) = P ess (t)+L(t),

[0026] Among them, P wt (t), P pv P(t) and L(t) are the power of wind power, photovoltaic power and flexible load in the tth Δt period respectively; mt (t), P grid (t), P ess (t) are the electric power generated by the micro gas turbine, the power purchased from the transmission grid, and the operating power of the energy storage system. The power is positive during charging.

[0027] Steam bus power balance constraint, expression is:

[0028] Q ac，steam (t)+Q ws，steam (t)+P pcr (t),

[0029] Among them, Q ac，steam (t), Q ws，steam(t), P pcr (t) are the thermal powers absorbed by the refrigerator, heat exchanger and heat storage device in the t-th Δt period respectively;

[0030] The operation constraints of the trigeneration unit are expressed as:

[0031]

[0032] Among them, P mtmln and P mtmax are the minimum power generation and maximum power generation that the micro gas turbine can provide under safe and stable operation; ΔP mtmax Q is the maximum change in power generated by the gas turbine between two adjacent Δt periods; ac，steam max , Q ws，steam max and Q steam，whb max are the maximum output powers of the absorption chiller, heat exchanger and waste heat boiler respectively;

[0033] The operation constraints of the thermal storage device are expressed as:

[0034]

[0035] Where K = 1, 2, 3, ..., T; Q steam，pcr max and P pcr max are the maximum heat release power and maximum heat storage power of the heat storage device respectively; η pcr is the energy conversion efficiency of the thermal storage device; SOH max is the maximum heat storage capacity of the heat storage device;

[0036] Energy storage system operation constraints:

[0037]

[0038] Where SOC(t) is the state of charge of the energy storage system in the tth Δt period; SOC min and SOC max They are the minimum state of charge and maximum state of charge of the energy storage system; SOC 0 and SOC τ are the initial charge state and final charge state of the energy storage system in a scheduling cycle respectively; P ess max is the maximum charging power of the energy storage system;

[0039] Flexible load operation constraints:

[0040] L min(t)≤L(t)≤L max (t)

[0041] Among them, L min (t) and L max (t) are the minimum power consumption and maximum power consumption of the flexible load in the tth Δt period respectively.

[0042] Optionally, the inter-regional market clearing constraints include:

[0043] Transmission power constraints of regional tie lines:

[0044]

[0045] in, It is the upper limit of transmission power of inter-regional interconnection lines. is the transmission power of inter-provincial interconnection lines, t is the market clearing time, l is the number of inter-regional interconnections, and L is the total number of inter-regional interconnections.

[0046] Regional market power balance constraints:

[0047]

[0048] In the formula, They are the electricity delivered to the electricity sales area and the electricity purchased by the electricity purchasing area; δ l is the line loss of the regional tie line l; Ω s ,Ω B They are respectively a collection of power selling areas and power purchasing areas; is the set of tie lines connected to region i; dual multiplier It is the clearing price in the market for the electricity sales area.

[0049] In another aspect, a regional source-grid-load-storage virtual power plant operation optimization device comprises:

[0050] A building module is used to construct a multi-objective optimization function with minimization of operating costs, minimization of carbon emissions and minimization of risks as optimization objectives;

[0051] A setting module, used to set constraints on the multi-objective optimization function, wherein the constraints include: equipment operation constraints and inter-regional market clearing constraints;

[0052] The optimization module is used to optimize and solve the multi-objective optimization model using the multi-objective optimization function and the constraint conditions as a multi-objective optimization model to obtain an optimized scheduling strategy.

[0053] In yet another aspect, a regional source-grid-load-storage virtual power plant operation optimization device includes: a processor, and a memory connected to the processor;

[0054] The memory is used to store a computer program, and the computer program is used to at least execute any one of the above-mentioned methods for optimizing the operation of a regional source-grid-load-storage virtual power plant;

[0055] The processor is used to call and execute the computer program in the memory.

[0056] The beneficial effects of the present invention are:

[0057] The regional source-grid-load-storage virtual power plant operation optimization method, device and equipment provided in the embodiments of the present invention optimize the scheduling strategy of the virtual power plant by comprehensively considering the three goals of cost, low carbon and risk, improve the overall performance and economic benefits of the system, and realize the efficient, economical and sustainable operation of the virtual power plant, thereby solving the technical problems in the prior art of single consideration and inability to solve the complexity of power system operation and the mutual influence between multi-dimensional goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 A schematic flow chart of a method for optimizing the operation of a regional source-grid-load-storage virtual power plant provided by one embodiment of the present invention;

[0060] Figure 2 A schematic diagram of the structure of a regional source-grid-load-storage virtual power plant operation optimization device provided by one embodiment of the present invention;

[0061] Figure 3 A schematic diagram of the structure of a regional source-grid-load-storage virtual power plant operation optimization device provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0062] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0063] As described in the background technology, existing virtual power plant optimization methods still have shortcomings in multi-objective collaborative optimization, which is mainly reflected in the focus on a single objective, such as: (1) Single objective optimization: Most virtual power plant optimization methods mainly focus on a single objective, such as cost minimization or carbon emission reduction. For example, some studies focus on how to reduce the operating cost of virtual power plants by optimizing scheduling, while others focus on achieving low-carbon goals. However, this single-objective optimization method often ignores the interrelationship and synergy between cost, low carbon and risk, resulting in limitations in the optimization results. (2) Regional source-grid-load-storage system optimization. In the current context of energy transformation and electricity marketization, the operation optimization of regional source-grid-load-storage systems (SGLSS) faces multiple challenges such as cost control, low-carbon development and risk management. Existing optimization methods often only focus on a single objective, resulting in low system operation efficiency and failure to fully meet the needs of modern power systems. In the current context of energy transformation and electricity marketization, the operation optimization of regional source-grid-load-storage systems is crucial to achieving efficient energy utilization, reducing carbon emissions and ensuring safe and stable operation of power systems. Although a variety of optimization methods have been proposed, existing technologies mostly focus on optimizing a single objective, such as minimizing costs or reducing carbon emissions. These methods often fail to fully consider the complexity of power system operation and the mutual influence between multidimensional objectives.

[0064] Therefore, how to fully consider the complexity of power system operation and the mutual influence between multi-dimensional objectives to achieve the optimization of regional source-grid-load-storage virtual power plant operation has become a technical problem that needs to be urgently solved in existing technologies.

[0065] In addition, the prior art still has the following technical problems:

[0066] Inadequate uncertainty handling: The operating environment of virtual power plants is full of uncertainties, including the volatility of renewable energy, changes in market demand, and fluctuations in market prices. Existing optimization methods usually use relatively simple models or assumptions to deal with these uncertainties, and lack effective uncertainty quantification and management mechanisms. For example, when considering market fluctuations, some methods only deal with it through simple risk aversion models, but fail to fully consider the impact of various uncertainties on the optimization results.

[0067] Imperfect risk management: Risk is an important factor that cannot be ignored in the operation of virtual power plants. However, existing optimization methods often lack a systematic risk management strategy. Although some studies have introduced the concept of risk assessment, the quantification and control of risks are still not perfect in actual optimization models. For example, when considering the risk of equipment failure, there is a lack of detailed analysis and modeling of the probability and impact of different equipment failures.

[0068] Insufficient multi-energy collaborative optimization: With the continuous integration of various energy forms, the energy structure of virtual power plants has become more complex. However, existing optimization methods still have shortcomings in multi-energy collaborative optimization. For example, in electric-thermal coupled virtual power plants, existing technologies often ignore the energy conversion relationship between cogeneration units and electrical conversion equipment. In addition, when considering the collaborative optimization of new energy and traditional energy, there is also a lack of effective models and algorithms to achieve efficient integration and optimal scheduling of multiple energy sources.

[0069] Therefore, the existing virtual power plant optimization methods have many shortcomings in multi-objective collaborative optimization, uncertainty processing, risk management and multi-energy collaborative optimization, and it is difficult to meet the needs of efficient, economical and sustainable operation of virtual power plants in complex environments.

[0070] Based on this, the embodiments of the present invention provide a method, device and equipment for optimizing the operation of a regional source-grid-load-storage virtual power plant. By comprehensively considering the three objectives of cost, low carbon and risk, the scheduling strategy of the virtual power plant is optimized, the overall performance and economic benefits of the system are improved, and the efficient, economical and sustainable operation of the virtual power plant is achieved. This solves the technical problems in the prior art that the single consideration direction and the inability to solve the complexity of the power system operation and the mutual influence between multi-dimensional objectives are solved.

[0071] Figure 1 A flow chart of a method for optimizing the operation of a regional source-grid-load-storage virtual power plant is provided in accordance with an embodiment of the present invention. Figure 1 As shown, the method provided by the embodiment of the present invention may include the following steps:

[0072] S101. Construct a multi-objective optimization function with minimization of operating costs, carbon emissions and risks as optimization goals.

[0073] S102: setting constraints for the multi-objective optimization function, wherein the constraints include: equipment operation constraints and inter-regional market clearing constraints.

[0074] S103: Taking the multi-objective optimization function and the constraint conditions as a multi-objective optimization model, optimizing and solving the multi-objective optimization model to obtain an optimized scheduling strategy.

[0075] The operating cost is the operating cost of the inter-regional market. The objective of minimizing the operating cost of the inter-regional market takes into account the power generation cost, transmission loss and energy storage cost; the objective of minimizing carbon emissions takes into account the carbon dioxide emissions generated by consumption; the objective of minimizing the value at risk takes into account the risk of supply and demand imbalance, equipment failure risk and market volatility risk. The objective function of the coordinated clearing of the inter-regional two-level spot market is to minimize the sum of the operating cost of the regional market, the value at risk, and the carbon emission cost.

[0076] It can be understood that by adopting the technical solution provided in the embodiment of the present invention, the scheduling strategy of the virtual power plant is optimized by comprehensively considering the three objectives of cost, low carbon and risk, the overall performance and economic benefits of the system are improved, and the efficient, economical and sustainable operation of the virtual power plant is achieved, which solves the technical problems in the prior art that the single consideration direction and the inability to solve the complexity of the power system operation and the mutual influence between multi-dimensional objectives are solved.

[0077] In some embodiments, the expression of the multi-objective optimization function is:

[0078]

[0079] Where C is the total operating cost of the regional source-grid-load-storage virtual power plant, C R Cost of operating the inter-regional market; The cost of adjusting the inter-regional market transactions in the scenario ω; is the market operation cost in the i city under scenario ω; β is the weight of the spillover risk value; Q mn，p is the amount of carbon emissions, E Egrld is the carbon emission factor; is the spillover risk value of region i from other regions with which it conducts regional transactions; ω is the scenario weight; W is the total number of scenarios; I is the total number of regions participating in the regional market.

[0080] When constructing a multi-objective optimization function, you can first construct an operating cost function, a carbon emission function, and a risk assessment function, and then integrate them into a multi-objective optimization function.

[0081] Among them, the constructed operating cost function can be

[0082]

[0083] is the operating cost of the market in region i, is the power generation cost of the units in the market in this region, Provide market quotes for the units in the region; is the number of winning bids in the regional market for the unit; Ω i K is the set of units in region i; T is the total market clearing time; K is the total number of units in the whole system.

[0084] When constructing the carbon emission function, the carbon emission factor can be constructed, and the expression is:

[0085]

[0086] E Egrld is the regional power grid carbon emission factor, E ln，l and E out, n are the direct carbon emissions from the power generation side in region i and the carbon emissions corresponding to the external power received from the power generation side in region n; is the power generation of the xth type of generator set within region i; E gp It is the total power generation of green power sources such as wind power and photovoltaic power in the region; taking into account the structural elements of cross-regional power transmission and transmission, it fully reflects the emission reduction benefits of green power in cross-regional transactions. is the total electricity received by region i from region n after deducting green electricity; It is the green electricity sent from region n to region i and the corresponding total emissions.

[0087] CVaR is developed on the basis of value at risk (VaR), which means that under a certain confidence level, the risk loss is greater than the expected value of VaR loss. CvaR can measure the high risk loss caused by low probability scenarios. The expression of the spillover risk value is:

[0088]

[0089] is the risk loss caused by the decision variable x and the random variable ξ Not greater than the boundary value distribution function; f(ξ) is the probability density function of ξ; β is the confidence level; ξ s is the value of the sth scene variable.

[0090] In some real-time examples, the device operation constraints include:

[0091] The power balance constraint of the electrical bus is expressed as:

[0092] P mt (t)+P grid (t)+P wt (t)+P pv (t) = P ess (t)+L(t),

[0093] Among them, P wt (t), P pv P(t) and L(t) are the power of wind power, photovoltaic power and flexible load in the tth Δt period respectively; mt (t), P grid (t), P ess (t) are the electric power generated by the micro gas turbine, the power purchased from the transmission grid, and the operating power of the energy storage system. The power is positive during charging.

[0094] Steam bus power balance constraint, expression is:

[0095] Qac，steam (t)+Q ws，steam (t)+P pcr (t),

[0096] Among them, Q ac，steam (t), Q ws，steam (t), P pcr (t) are the thermal powers absorbed by the refrigerator, heat exchanger and heat storage device in the t-th Δt period respectively;

[0097] The operation constraints of the trigeneration unit are expressed as:

[0098]

[0099] Among them, P mtmin and P mtmax are the minimum power generation and maximum power generation that the micro gas turbine can provide under safe and stable operation; ΔP mtmax Q is the maximum change in power generated by the gas turbine between two adjacent Δt periods; ac，steam max , Q ws，steam max and Q steam，whb max are the maximum output powers of the absorption chiller, heat exchanger and waste heat boiler respectively;

[0100] The operation constraints of the thermal storage device are expressed as:

[0101]

[0102] Where K = 1, 2, 3, ..., T; Q steam，pcr max and P pcr max are the maximum heat release power and maximum heat storage power of the heat storage device respectively; η pcr is the energy conversion efficiency of the thermal storage device; SOH max is the maximum heat storage capacity of the heat storage device;

[0103] Energy storage system operation constraints:

[0104]

[0105] Where SOC(t) is the state of charge of the energy storage system in the tth Δt period; SOC min and SOC max They are the minimum state of charge and maximum state of charge of the energy storage system; SOC 0 and SOC T are the initial charge state and final charge state of the energy storage system in a scheduling cycle respectively; Pess max is the maximum charging power of the energy storage system;

[0106] Flexible load operation constraints:

[0107] L min (t)≤L(t)≤L max (t)

[0108] Among them, L min (t) and L max (t) are the minimum power consumption and maximum power consumption of the flexible load in the tth Δt period respectively.

[0109] In some embodiments, the inter-regional market clearing constraints include:

[0110] Transmission power constraints of regional tie lines:

[0111]

[0112] in, It is the upper limit of transmission power of inter-regional interconnection lines. is the transmission power of inter-provincial interconnection lines, t is the market clearing time, l is the number of inter-regional interconnections, and L is the total number of inter-regional interconnections.

[0113] Regional market power balance constraints:

[0114]

[0115] In the formula, They are the electricity delivered to the electricity sales area and the electricity purchased by the electricity purchasing area; δ l is the line loss of the regional tie line l; Ω S ,Ω B They are respectively a collection of power selling areas and power purchasing areas; is the set of tie lines connected to region i; dual multiplier The clearing price of the electricity sales area in the market

[0116] The embodiment of the present invention provides a method for optimizing the operation of a regional source-grid-load-storage virtual power plant. By comprehensively considering the three objectives of cost, low carbon and risk, the scheduling strategy of the virtual power plant is optimized, the overall performance and economic benefits of the system are improved, and the efficient, economical and sustainable operation of the virtual power plant is achieved. The technical problems in the prior art that the consideration direction is single and the complexity of the power system operation and the mutual influence between multi-dimensional objectives are not resolved can be solved.

[0117] In the process of solving the model, multi-objective optimization algorithms such as genetic algorithms and particle swarm optimization can be used to solve the above objective functions and constraints. These algorithms use iterative optimization to find the optimal solution or approximate optimal solution that meets all constraints, so as to achieve the coordinated optimization of cost, low carbon and risk.

[0118] Monte Carlo simulation or robust optimization methods are introduced to deal with uncertainties in system operation. For example, a large number of random scenarios are generated through Monte Carlo simulation to simulate the volatility of renewable energy and the uncertainty of market prices, and then optimization calculations are performed under these scenarios to obtain a robust scheduling strategy.

[0119] In order to further explain the technical solution of the present invention, the present invention also provides a specific embodiment:

[0120] Data collection and analysis: Collect operating data of distributed power sources, energy storage equipment, controllable loads and other resources in the area, including power generation, energy storage capacity, load demand, etc.; analyze the fluctuation characteristics of renewable energy, changing trends in market prices, etc.

[0121] Model construction and solution: Based on the collected data, construct the objective functions of cost, low carbon and risk, as well as the corresponding constraints; use genetic algorithms to solve the optimization problem and obtain the optimal scheduling strategy.

[0122] Strategy implementation and evaluation: Apply the optimized dispatch strategy to the actual operation of the virtual power plant, monitor the operating status of the system, and evaluate the optimization effect, such as cost reduction, carbon emission reduction, and system reliability improvement.

[0123] Specific case analysis: Take two areas in area A as examples to explore the practical application of the present invention. These two areas are typical in the central and western parts of area A. Their differences in energy composition, economic development and geographical characteristics provide good conditions for demonstrating the effects of the present invention.

[0124] In the current clearing model, the spillover risk values ​​faced by power transmission areas 1 and 2 are 268,800 yuan and 475,600 yuan respectively, while the spillover risk values ​​faced by power purchasing areas 3 and 4 are lower, only 200,000 yuan and 400,000 yuan. This shows that since the units in the power transmission areas participate in the regional market, they are more susceptible to external market risks, while the units in the power purchasing areas are mainly responsible for the balance of supply and demand in the region and do not participate in the regional market, so they are less affected by external market risks.

[0125] After implementing the two-level market clearing model considering spillover risk proposed in the present invention, the spillover risk values ​​faced by regions 1 and 4 decreased by 42,900 yuan and 36,300 yuan respectively. Although the spillover risk values ​​of the power purchasing regions increased, the market operating costs of the four regions decreased. In the existing clearing model, low-cost units are given priority to win bids in the regional market, resulting in a reduction in the remaining capacity of the participating regional markets, making the regional market operating costs higher than the model proposed in the present invention. In the regional virtual power plant, the optimization method of the present invention can achieve a 10% cost reduction, a 15% reduction in carbon emissions, and a 5% improvement in system reliability. In addition, the present invention also takes into account the principle of minimizing carbon emission costs and total social costs, and the cost comparison results of the optimized power system in area A are significant.

[0126] Therefore, the method of the present invention is applied to optimize the source-grid-load-storage system in area A, achieving multiple goals of cost reduction, carbon emission reduction and risk control.

[0127] The technical solution of the present invention includes:

[0128] Multi-objective optimization model construction: Construct a multi-objective optimization model that includes three objectives: cost minimization, carbon emission minimization, and risk minimization.

[0129] Cost minimization: Consider the cost of purchasing electricity, the cost of using the energy storage system, and the cost of purchasing gas for the combined heat and power unit.

[0130] Low carbon emissions: Optimize the operating costs of wind power and photovoltaic power, and consider the carbon emission factors of regional power grids.

[0131] Risk management: Inflexible risk measurement based on conditional value at risk (CVaR).

[0132] System operation constraints: including electrical bus power balance, steam bus power balance, trigeneration unit operation constraints, etc.

[0133] Flexible load response decision model: Establish a simplified response decision model with the goal of maximizing the electricity satisfaction of flexible load users.

[0134] Coordinated clearing of spot markets between regions: minimizing the cost of regional market operation, the cost of regional market transaction adjustment, and the sum of regional market operation cost and spillover risk value.

[0135] Compared with the prior art, the present invention has the following beneficial effects:

[0136] A multi-objective balance of cost, low carbon and risk has been achieved, improving the economic benefits and environmental friendliness of system operation.

[0137] By adjusting the weights, we can flexibly respond to optimization needs under different policies and market environments.

[0138] It enhances the system's ability to resist risks and improves the safety and reliability of the power system.

[0139] Decision support tools are provided to help decision makers better understand and evaluate optimization results.

[0140] Based on a general inventive concept, an embodiment of the present invention also provides a regional source-grid-load-storage virtual power plant operation optimization device for implementing the above method.

[0141] Figure 2 A schematic diagram of a regional source-grid-load-storage virtual power plant operation optimization device provided by an embodiment of the present invention, see Figure 2 , the device provided by the embodiment of the present invention may include:

[0142] A construction module 21 is used to construct a multi-objective optimization function with minimization of operating costs, minimization of carbon emissions and minimization of risks as optimization objectives;

[0143] A setting module 22, used to set constraints on the multi-objective optimization function, wherein the constraints include: equipment operation constraints and inter-regional market clearing constraints;

[0144] The optimization module 23 is used to use the multi-objective optimization function and the constraint conditions as a multi-objective optimization model, and optimize and solve the multi-objective optimization model to obtain an optimized scheduling strategy.

[0145] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0146] Based on a general inventive concept, an embodiment of the present invention also provides a regional source-grid-load-storage virtual power plant operation optimization device.

[0147] Figure 3 A schematic diagram of a regional source-grid-load-storage virtual power plant operation optimization device structure provided by an embodiment of the present invention, please refer to Figure 3 , an embodiment of the present invention provides a regional source-grid-load-storage virtual power plant operation optimization device, including: a processor 31, and a memory 32 connected to the processor.

[0148] The memory 32 is used to store a computer program, and the computer program is used at least for the regional source-grid-load-storage virtual power plant operation optimization method recorded in any one of the above embodiments;

[0149] The processor 31 is used to call and execute the computer program in the memory.

[0150] Based on a general inventive concept, an embodiment of the present invention further provides a storage medium.

[0151] A storage medium stores a computer program, which, when executed by a processor, implements each step of the above-mentioned regional source-grid-load-storage virtual power plant operation optimization method.

[0152] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0153] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0154] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0155] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0156] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0157] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0158] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0159] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0160] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0161] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for optimizing the operation of a regional source-grid-load-storage virtual power plant, characterized in that: include: Taking minimization of operating costs, carbon emissions and risks as optimization goals, a multi-objective optimization function is constructed; Setting constraints on the multi-objective optimization function, wherein the constraints include: equipment operation constraints and inter-regional market clearing constraints; The multi-objective optimization function and the constraint conditions are used as a multi-objective optimization model, and the multi-objective optimization model is optimized and solved to obtain an optimized scheduling strategy.

2. The method according to claim 1, characterized in that The expression of the multi-objective optimization function is: Where C is the total operating cost of the regional source-grid-load-storage virtual power plant, C R Cost of operating the inter-regional market; The cost of adjusting the inter-regional market transactions in the scenario ω; is the market operation cost in the i city under scenario ω; β is the weight of the spillover risk value; Q mn，p is the amount of carbon emissions, E Egrld is the carbon emission factor; is the spillover risk value of region i from other regions with which it conducts regional transactions; ω is the scenario weight; W is the total number of scenarios; I is the total number of regions participating in the regional market.

3. The method according to claim 2, characterized in that The expression of the inter-regional market operation cost is: C R is the operating cost of the regional market, is the power generation cost of the units in the regional market, Provide market quotes for the units in the region; is the number of winning bids in the regional market of the unit; Ω i K is the set of units in region i; T is the total market clearing time; K is the total number of units in the whole system.

4. The method according to claim 2, characterized in that: The carbon emission factor is expressed as: E Egrid is the regional power grid carbon emission factor, E ln，l and E out，n are the direct carbon emissions from the power generation side in region i and the carbon emissions from the external power consumption on the power generation side in region n; is the power generation of the xth type of generator set within the area l; E gp The total power generation of green power sources such as wind power and photovoltaic power in the region; is the total electricity received by region i from region n after deducting green electricity; It is the green electricity sent from region n to region l and the corresponding total emissions.

5. The method according to claim 2, characterized in that: The expression of the spillover risk value is: The risk loss θ(x, ξ) caused by the decision variable x and the random variable ξ is not greater than the boundary value distribution function; f(ξ) is the probability density function of ξ; β is the confidence level; ξ s is the value of the sth scene variable.

6. The method according to claim 1, characterized in that The equipment operation constraints include: The power balance constraint of the electrical bus is expressed as: P mt (t)+P grid (t)+P wt (t)+P pv (t)=P ess (t)+L(t) Among them, P wt (t), P pv P(t) and L(t) are the power of wind power, photovoltaic power and flexible load in the tth Δt period respectively; mt (t), P grid (t), P ess (t) are the power generated by the micro gas turbine, the power purchased by the transmission grid, and the operating power of the energy storage system; Steam bus power balance constraint, expression is: Q ac，sbeam (t)+Q ws，steam (t)+P pcr (t), Among them, Q ac，steam (t), Q ws，steam (t), P pcr (t) are the thermal powers absorbed by the refrigerator, heat exchanger and heat storage device in the t-th Δt period respectively; The operation constraints of the trigeneration unit are expressed as: Among them, P mtmin and P mtmax are the minimum power generation and maximum power generation that the micro gas turbine can provide under safe and stable operation; ΔP mtmax Q is the maximum change in power generated by the gas turbine between two adjacent Δt periods; ac，steammax , Q ws，steammax and Q steam，whbmax are the maximum output powers of the absorption chiller, heat exchanger and waste heat boiler respectively; The operation constraints of the thermal storage device are expressed as: Where K = 1, 2, 3, ..., T; Q steam，pcrmax and P pcrmax are the maximum heat release power and maximum heat storage power of the heat storage device respectively; η pcr is the energy conversion efficiency of the thermal storage device; SOH max is the maximum heat storage capacity of the heat storage device; Energy storage system operation constraints: Where SOC(t) is the state of charge of the energy storage system in the tth Δt period; SOC min and SOC max are the minimum state of charge and maximum state of charge of the energy storage system respectively; SOC0 and SOC T are the initial charge state and final charge state of the energy storage system in a scheduling cycle respectively; P essmax is the maximum charging power of the energy storage system; Flexible load operation constraints: L min (t)≤L(t)≤L max (t) Among them, L min (t) and L max (t) are the minimum power consumption and maximum power consumption of the flexible load in the tth Δt period respectively.

7. The method according to claim 1, characterized in that The constraints on inter-regional market clearing include: Transmission power constraints of regional tie lines: in, The upper limit of the transmission power of the inter-regional interconnection lines; is the transmission power of inter-provincial tie lines, t is the market clearing time, l is the number of inter-regional ties, and L is the total number of inter-regional ties; Regional market power balance constraints: In the formula, They are the electricity delivered to the electricity sales area and the electricity purchased by the electricity purchasing area; δ l is the line loss of the regional tie line l; Ω S ,Ω B They are respectively a collection of power selling areas and power purchasing areas; is the set of tie lines connected to region i; dual multiplier It is the clearing price in the market for the electricity sales area.

8. A regional source-grid-load-storage virtual power plant operation optimization device, characterized in that: include: A building module is used to construct a multi-objective optimization function with minimization of operating costs, minimization of carbon emissions and minimization of risks as optimization objectives; A setting module, used for setting constraints on the multi-objective optimization function, wherein the constraints include: equipment operation constraints and inter-regional market clearing constraints; The optimization module is used to optimize and solve the multi-objective optimization model using the multi-objective optimization function and the constraint conditions as a multi-objective optimization model to obtain an optimized scheduling strategy.

9. A regional source-grid-load-storage virtual power plant operation optimization device, characterized in that: include: A processor, and a memory connected to the processor; The memory is used to store a computer program, and the computer program is used at least to execute the regional source-grid-load-storage virtual power plant operation optimization method according to any one of claims 1 to 7; The processor is used to call and execute the computer program in the memory.

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