Robust and random combined micro energy network multi-granularity coordinated regulation method for service area

Abstract

The application provides a robust and random combined service area micro energy network multi-granularity collaborative regulation method. The method comprises the following steps: establishing a fine-grained model and a coarse-grained model of a service area micro energy network; using the fine-grained model to perform robust optimization scheduling design of the service area micro energy network; using the coarse-grained model to perform random optimization scheduling design of the service area micro energy network; combining the robust optimization scheduling design and the random optimization scheduling design of the service area micro energy network under a rolling optimization framework, using the robust optimization and the random optimization in adjacent periods and distant periods of rolling optimization respectively, and obtaining a multi-granularity source-load-storage collaborative scheduling scheme combined with robust optimization and random optimization. The application constructs a fine-grained model of distributed resources of each link of a source-load-storage of a service area micro energy network, and combines the two-granularity models of scheduling resources of each link of the source-load-storage of the service area micro energy network according to the optimization calculation demand to be used for operation optimization of the micro energy network.

Description

Technical Field

[0001] This invention relates to the field of micro-energy control technology, and in particular to a multi-granularity collaborative control method for service area micro-energy networks that combines robustness and randomness. Background Technology

[0002] The International Energy Agency (IEA) points out that the transportation sector accounts for approximately 25% of global carbon emissions and 33% of global energy consumption. Therefore, in addition to vigorously developing electric vehicles and hydrogen fuel cell vehicles, constructing clean and low-carbon transportation infrastructure and converting transportation assets into energy resources is crucial for increasing the share of renewable energy in energy consumption and promoting coordinated decarbonization across sectors. In highway transportation, service areas are often located far from energy hubs. Developing a self-generated and self-consumed distributed renewable energy model based on the local resource endowment of these service areas can not only reduce the load demand in areas without grid connections but also accelerate carbon emission reduction. Microgrids, as a special type of distributed energy system, can integrate various renewable energy sources, energy storage, distributed power sources, local loads, and control units, perfectly meeting the operational needs of energy systems in highway service areas.

[0003] One existing demand-response-involved, stochastic & adjustable robust hybrid day-ahead dispatching model for wind power consumption employs a combined optimization approach integrating robust optimization and stochastic optimization, with a focus on determining the "optimal transition time." Considering the characteristic that wind power forecasts are more accurate the closer to the operating point, this model combines the advantages of stochastic and adjustable robust optimization methods to propose a day-ahead dispatching model with demand response participation, where stochastic and adjustable robust optimization methods are jointly constructed. By determining the optimal transition time, the model effectively combines stochastic and adjustable robust optimization, highlighting the advantages of robustness in long-term dispatching and economic efficiency in short-term dispatching, thus achieving source-load interaction.

[0004] Existing research on energy systems in highway service areas mostly focuses on the charging stations, lacking research on the complete energy system of highway service areas. This fails to fully improve the operational efficiency of the energy system in service areas and their ability to absorb renewable energy.

[0005] Existing research considers different energy system compositions and has established different types of energy unit models. Due to the nonlinear and discontinuous characteristics of energy units and their operating links, overly simplified modeling methods, such as energy router models, cannot accurately depict the operating state of energy units. Overly refined modeling methods will increase the solution burden and cannot meet the high efficiency requirements of online optimization and service area energy system operation.

[0006] Existing single robust optimization methods can lead to overly conservative optimization results, while stochastic optimization methods increase the flexibility of scheduling but reduce the reliability of operation. At the same time, stochastic optimization methods generally have a greater computational burden. Summary of the Invention

[0007] The embodiments of the present invention provide a robust and stochastic multi-granularity collaborative control method for service area microgrids to optimize the operation of server microgrids.

[0008] To achieve the above objectives, the present invention adopts the following technical solution.

[0009] A robust and stochastic multi-granularity collaborative control method for service area microgrids includes:

[0010] Establish a fine-grained model of the service area microgrid;

[0011] Establish a coarse-grained model of the service area microgrid;

[0012] Robust optimization scheduling design of the service area microgrid is carried out using the fine-grained model of the service area microgrid.

[0013] The service area microgrid coarse-grained model is used to design the stochastic optimization scheduling of the service area microgrid;

[0014] Robust optimization scheduling design and stochastic optimization scheduling design of service area micro-energy grids are combined under the rolling optimization framework. The robust optimization part is used for the near period of rolling optimization, and the stochastic optimization part is used for the longer period of rolling optimization, so as to obtain a multi-granularity source-load-storage collaborative scheduling scheme that combines robust optimization and stochastic optimization.

[0015] Preferably, the establishment of a fine-grained model of a service area microgrid includes:

[0016] The fine-grained model of the service area micro-energy grid includes: an integrated charging, swapping and storage unit, a heat pump unit, a hydrogen energy unit, a thermal energy storage unit, an absorption refrigeration unit, and system energy balance constraints;

[0017] 1) Integrated charging, swapping, and storage unit

[0018] The integrated charging, swapping, and storage unit includes a fast charging section and a battery swapping section. The status of the fast charging pile is indicated as follows:

[0019]

[0020] in, This indicates the number of fast charging stations that are currently in use. This indicates the number of fast charging stations that are currently idle.

[0021] The state transition equation for a fast charging station is expressed as:

[0022]

[0023] Among them, A occ B represents the fast charging system matrix. occ This represents the input matrix of the fast charging system. Represents the fast charging input variables; the system matrix A for the fast charging section. occ Represented as:

[0024]

[0025] Input matrix B of the fast charging section occ Represented as:

[0026] B occ =[b1,b2,...b m ,b f ] T

[0027] Where b1 = 1, b f =-1, the rest are zero;

[0028] The input variables for the fast charging section satisfy the following constraints:

[0029]

[0030] The charging energy corresponding to the fast charging part and average charging power Represented as:

[0031]

[0032]

[0033] The battery swapping section consists of a battery pack, which is divided into two parts: a fully charged section and a flexible charging and discharging section.

[0034] The energy equation for the battery swapping section is expressed as follows:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046] in, To collect the full amount of electricity, To flexibly collect power, For the amount of electricity used to provide battery swapping services, κ represents the proportion of the flexible set that is transferred to the fully charged set. and Indicates charging and discharging power, sw t This indicates the charging / discharging state; 1 indicates charging, and 0 indicates discharging. and Indicates charge / discharge efficiency. This indicates the battery demand that is currently in a standby state. Indicates charging / battery swapping needs;

[0047] 2) Heat pump unit

[0048] The operation model of the heat pump unit is constructed based on the first-order equivalent thermal parameter model;

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] in, Indicates indoor temperature, C r,i and R r,i Indicates indoor heat capacity and thermal resistance parameters. Indicates indoor thermal interference. Indicates the heat pump's thermal power. This indicates the total heat load demand;

[0055] 3) Hydrogen Energy Unit

[0056] Electrogenization section:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] The fuel cell section is modeled using a piecewise linearization approach:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] in, Indicates the rate of hydrogen production by electrolysis. Indicates the power of hydrogen production by electricity. Indicates the power of the hydrogen compressor. This indicates the mass of hydrogen gas in the storage tank. and Indicates the rate of hydrogen entering and exiting. Indicates the hydrogen consumption rate of the fuel cell. Indicates the power output of the fuel cell. Indicates the heat output power of the fuel cell, Table Show the piecewise variables of the fuel cell performance curve. and This indicates the per-unit values ​​of relative hydrogen consumption, per-unit electricity production, and per-unit heat production for fuel cells. and T represents the rated values ​​of hydrogen consumption, electricity production, and heat production of a fuel cell. t Indicates the total optimization cycle;

[0077] 4) Thermal energy storage unit

[0078] Thermal energy storage is used to store and release thermal energy, and to absorb renewable energy and provide thermal support.

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] in, Indicates thermal energy storage. and This indicates the heat release power and heat storage power of thermal energy storage;

[0086] 5) Absorption refrigeration unit

[0087] Absorption refrigeration units convert thermal energy into cold energy, and are modeled based on a piecewise linearization method, as follows:

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094] in, and This indicates the output cooling power and input heating power of an absorption chiller. and This indicates the per-unit value of the output cooling power and the per-unit value of the input heat power of an absorption chiller. Represents a piecewise variable;

[0095] 6) System energy balance constraints

[0096] Power balance:

[0097]

[0098] Thermal equilibrium:

[0099]

[0100] Hydrogen balance:

[0101]

[0102]

[0103] in, and This indicates the output of photovoltaic and wind turbines. Indicates the power purchased. Indicates grid feed management, and This indicates the electrical and thermal power of the heat pump. COP represents the coefficient of performance of a heat pump, P l t This indicates the electrical load required for basic operation and maintenance of the service area. This indicates the amount of electricity that has been abandoned by renewable energy sources.

[0104] Preferably, the establishment of a coarse-grained model of a service area microgrid includes:

[0105] The coarse-grained model of the service area micro-energy grid includes an integrated charging, swapping and storage unit, a heat pump unit, a hydrogen energy unit, a thermal energy storage unit, an absorption refrigeration unit, and system energy balance constraints.

[0106] 1) Integrated charging, swapping, and storage unit

[0107] The coarse-grained model ignores the occupancy status of fast charging stations and merges the fully charged and flexible sets of battery swapping energy storage for unified processing. The simplified model is as follows:

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] Where, Nfc This indicates the total number of fast charging stations. Indicates the energy state of the battery;

[0114] The rest remains consistent with the fine-grained model;

[0115] 2) Heat pump unit

[0116] A coarse-grained model of heat pump clusters is established from the perspective of equivalent thermal energy storage;

[0117]

[0118]

[0119]

[0120] in, C represents the equivalent thermal energy storage of the service area's heat pump operating heat zone. r,a and R r,a This represents the average of the equivalent heat capacity and equivalent thermal resistance of the heat pump's operating hot zone. N represents the total thermal interference power in the hot region. hp η represents the total number of thermal regions. u and η d Indicates the upper and lower boundary coefficients of the equivalent thermal energy;

[0121] 3) Hydrogen Energy Unit

[0122] Ignoring the intermediate processes of hydrogen production by electricity and fuel cells, the hydrogen energy unit is regarded as an energy storage unit;

[0123]

[0124]

[0125]

[0126]

[0127] in, This represents the energy of an equivalent hydrogen energy unit. Indicates the calorific value of hydrogen. ∈ represents the energy release power of hydrogen energy, and ∈ represents the electrical power ratio coefficient of the energy release power of hydrogen energy;

[0128] 4) Thermal energy storage unit

[0129] The coarse-grained model and the fine-grained model of the thermal energy storage unit are consistent;

[0130] 5) Absorption refrigeration unit

[0131] The operating equations for absorption are expressed using conversion factors;

[0132]

[0133] 6) System energy balance constraints

[0134] In the coarse-grained model, the intermediate processes of hydrogen production by electricity and fuel cells in the hydrogen energy system are ignored. The power consumption of the compressor in the hydrogen balance and power balance is implicit in the formulas. The new energy balance is:

[0135]

[0136] The thermal balance remains consistent with that in the fine-grained model.

[0137] Preferably, the robust optimization scheduling design of the service area microgrid is carried out using the fine-grained model of the service area microgrid, including:

[0138] The rolling scheduling time domain is divided into two parts: an immediate period with a short scheduling cycle and a more distant period with a longer scheduling cycle. Robust optimization is used for scheduling in the immediate period, while stochastic optimization based on chance constraints is used for scheduling in the more distant period. A fine-grained model of service area microgrid is adopted in the robust optimization part.

[0139] a. Optimize goal setting

[0140] To maximize the self-sufficiency of the microgrid in the service area and reduce dependence on external power grid energy, an objective function is established, which is expressed as follows:

[0141]

[0142] b. Robust optimization-based near-period scheduling

[0143] In the rolling scheduling time domain, robust optimization is used for scheduling adjacent time periods, with a scheduling period T. r =4, scheduling time scale is set to Δt r =0.5h;

[0144] The uncertainty of solar power output, wind turbine output, and vehicle charging demand is represented using the form of a budget uncertainty set. These three factors are expressed as:

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151] in, and This represents the projected demand for solar power, wind power, and charging. and This represents the maximum possible deviation from the predicted demand for solar power, wind power, and charging, obtained from historical deviation data of the prediction system. and Representing uncertain variables in demand for solar power, wind turbines, and charging. and This represents the corresponding uncertain adjustment parameter, t. c Indicates the current scheduling cycle;

[0152] By introducing auxiliary variables absolute value Perform linearization, let This transforms the model;

[0153] Based on the established uncertainty set, the scheduling of adjacent time periods in the rolling scheduling time domain is designed into a robust optimization form:

[0154]

[0155] Constraints: Fine-grained model, t∈[t c +1,t c +T r ];

[0156] Where x represents the decision variables included in the fine-grained model of the formula, D represents its corresponding feasible region, and z * The decision variables represent the uncertain set of the formula. This represents the corresponding feasible region.

[0157] Preferably, the stochastic optimization scheduling design of the service area microgrid is carried out using the coarse-grained model of the service area microgrid, including:

[0158] Based on the coarse-grained model of the service area microgrid, the scheduling of longer time periods in the rolling scheduling time domain is designed as a stochastic optimization form based on chance constraints:

[0159] The design opportunity constraint is the charging and discharging power constraint of the battery swapping. The power imbalance caused by the opportunity constraint is reflected in the charging and discharging power of the swapping battery.

[0160]

[0161]

[0162] in, and Indicates charging and discharging power, sw t ξ represents the charging / discharging state, where 1 indicates charging and 0 indicates discharging, and ξ represents the confidence level that the chance constraint has been violated.

[0163] Using scenario constraints to represent opportunity constraints deterministically:

[0164]

[0165]

[0166]

[0167]

[0168] Where s is a superscript indicating the scenario, and N s T represents the total number of scenes. s This represents the total period of the stochastic optimization. and This indicates the maximum charging power and maximum discharging power that can be achieved when all modules of the battery are replaced.

[0169] The scheduling of longer time periods in the rolling scheduling time domain is designed as a stochastic optimization form, expressed as:

[0170]

[0171] Constraints: Coarse-grained model of microgrid in service area, t∈[t c +T r +1,t c +T r +T s ]

[0172] Where E{} represents N s The mean values ​​for each scenario are all superscripted with 's' in the constraints of the stochastic optimization.

[0173] Preferably, the robust optimization scheduling design and stochastic optimization scheduling design for the service area microgrid are combined within a rolling optimization framework. The robust optimization component is used for the near-term rolling optimization, and the stochastic optimization component is used for the longer-term rolling optimization, resulting in a multi-granularity source-load-storage collaborative scheduling scheme that combines robust optimization and stochastic optimization. This includes:

[0174] By combining robust optimization and stochastic optimization in the rolling time domain, the two optimization strategies are used to achieve coordinated optimization of the service area microgrid operation at two time scales and two model granularities.

[0175] The objective function for multi-granularity collaborative scheduling, which combines robust optimization and stochastic optimization, is expressed as:

[0176]

[0177] Constraints at coupling points:

[0178] Heat pump coupling:

[0179]

[0180] Thermal energy storage coupling:

[0181]

[0182] Hydrogen energy system coupling:

[0183]

[0184] Integrated charging, swapping, and storage coupling:

[0185]

[0186]

[0187]

[0188] Under the framework of rolling optimization, the service area microgrid optimization scheduling is designed. The robust optimization scheduling based on the service area microgrid fine-grained model is used for the near-term of rolling optimization, and the stochastic optimization scheduling based on the service area microgrid coarse-grained model is used for the longer-term of rolling optimization. Under the common constraints of the fine-grained optimization model constraints, the coarse-grained optimization model constraints, and the multi-granularity model coupling point constraints, the multi-granularity source-load-storage collaborative scheduling scheme is obtained by solving the objective function of the multi-granularity collaborative scheduling that combines the robust optimization and stochastic optimization.

[0189] As can be seen from the technical solutions provided by the embodiments of the present invention above, the present invention constructs a fine-grained model—a refined model—of the distributed resources of each link of the source, load, and storage of the service area micro energy network, and establishes a coarse-grained model—a rough model—of each distributed resource based on the fine-grained model from the perspective of equivalent energy storage. The two granularity models of the distributed resources of each link of the source, load, and storage of the service area micro energy network are combined according to the needs of optimization calculation for the operation optimization of the micro energy network, thereby forming multi-granularity modeling and application, which can meet various needs of optimization operation calculation.

[0190] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of the invention. Attached Figure Description

[0191] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0192] Figure 1 A schematic diagram of an object provided for an embodiment of the present invention;

[0193] Figure 2 A flowchart illustrating a robust and stochastic multi-granularity collaborative control method for service area microgrids, provided in an embodiment of the present invention;

[0194] Figure 3 This is a schematic diagram illustrating a rolling framework combination of an optimized service area microgrid fine-grained model and a service area microgrid coarse-grained model, as provided in an embodiment of the present invention. Detailed Implementation

[0195] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0196] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or couplings. The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0197] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0198] To facilitate understanding of the embodiments of the present invention, the following will provide further explanation and description with reference to the accompanying drawings and several specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0199] An embodiment of the present invention provides a schematic diagram of an object as shown below. Figure 1 As shown, it includes: power grid, wind turbine, photovoltaic, heat pump, hydrogen energy system, integrated charging, swapping and storage system, cooling load, heating load and cold energy storage, etc.

[0200] Figure 2 The processing flowchart of a robust and stochastic multi-granularity collaborative control method for service area microgrids provided in this embodiment of the invention is as follows: Figure 2 As shown, the processing steps include the following:

[0201] Step S10: Establish a fine-grained model of the service area micro-energy network.

[0202] Step S20: Establish a coarse-grained model of the service area micro-energy network.

[0203] Step S30: Robustly optimize the fine-grained model of the service area micro-energy network.

[0204] Step S40: Perform stochastic optimization on the coarse-grained model of the service area micro-energy grid.

[0205] Step S50: Combine the optimized service area micro-energy grid fine-grained model and the service area micro-energy grid coarse-grained model with a rolling framework to obtain a multi-granularity source-load-storage collaborative scheduling scheme that combines optimization and stochastic optimization.

[0206] a. Fine-grained model of service area microgrid

[0207] 1) Integrated charging, swapping, and storage unit

[0208] The integrated charging, swapping, and storage unit includes a fast charging section and a battery swapping section. It is assumed that fast charging can fully charge the battery in one hour. The minimum scheduling time scale is set to 0.5 hours, and the status of the fast charging station can be represented as follows:

[0209]

[0210] in, This indicates the number of fast charging stations that are currently in use. This indicates the number of fast charging stations that are currently idle.

[0211] The state transition equation for a fast charging station can be expressed as:

[0212]

[0213] Among them, Aocc B represents the fast charging system matrix. occ This represents the input matrix of the fast charging system. This represents the input variable for fast charging.

[0214] System matrix A for fast charging occ It can be represented as:

[0215]

[0216] Input matrix B of the fast charging section occ It can be represented as:

[0217] B occ =[b1,b2,...b m ,b f ] T

[0218] Where b1 = 1, b f =-1, the rest are zero.

[0219] The input variables for the fast charging section satisfy the following constraints:

[0220]

[0221] The charging energy corresponding to the fast charging part and average charging power It can be represented as:

[0222]

[0223]

[0224] The battery swapping section consists of battery packs, which can be divided into two parts: a fully charged section and a flexible charge / discharge section. The fully charged section provides battery swapping services, while the flexible charge / discharge section refers to the charging units, which can discharge when the system needs power, providing power support to the system. In actual operation, the division between the fully charged section and the flexible charge / discharge section is reasonable.

[0225] The energy equation for the battery swapping section can be expressed as:

[0226]

[0227]

[0228]

[0229]

[0230]

[0231]

[0232]

[0233]

[0234]

[0235]

[0236]

[0237] in, To collect the full amount of electricity, To flexibly collect power, For the amount of electricity used to provide battery swapping services, κ represents the proportion of the flexible set that is transferred to the fully charged set. and Indicates charging and discharging power, sw t This indicates the charging / discharging state; 1 indicates charging, and 0 indicates discharging. and Indicates charge / discharge efficiency. This indicates the battery demand that is currently in a standby state. This indicates charging / battery swapping needs.

[0238] 2) Heat pump unit

[0239] The operation model of the heat pump unit is constructed based on the first-order equivalent thermal parameter model.

[0240]

[0241]

[0242]

[0243]

[0244]

[0245] in, Indicates indoor temperature, C r,i and R r,i Indicates indoor heat capacity and thermal resistance parameters. Indicates indoor thermal interference. Indicates the heat pump's thermal power. This indicates the total heat load demand.

[0246] 3) Hydrogen Energy Unit

[0247] Electrogenization section:

[0248]

[0249]

[0250]

[0251]

[0252]

[0253]

[0254]

[0255]

[0256]

[0257] For the fuel cell section, due to the nonlinear characteristics of fuel cell power generation, a piecewise linearization approach is used for modeling:

[0258]

[0259]

[0260]

[0261]

[0262]

[0263]

[0264]

[0265]

[0266]

[0267] in, Indicates the rate of hydrogen production by electrolysis. Indicates the power of hydrogen production by electricity. Indicates the power of the hydrogen compressor. This indicates the mass of hydrogen gas in the storage tank. and Indicates the rate of hydrogen entering and exiting. Indicates the hydrogen consumption rate of the fuel cell. Indicates the power output of the fuel cell. Indicates the heat output power of the fuel cell, Table Show the piecewise variables of the fuel cell performance curve. and This indicates the per-unit values ​​of relative hydrogen consumption, per-unit electricity production, and per-unit heat production for fuel cells. and T represents the rated values ​​of hydrogen consumption, electricity production, and heat production of a fuel cell. t This indicates the total cycle time for optimization.

[0268] 4) Thermal energy storage unit

[0269] Thermal energy storage is used to store and release thermal energy, and to absorb renewable energy and provide thermal support.

[0270]

[0271]

[0272]

[0273]

[0274]

[0275]

[0276] in, Indicates thermal energy storage. and This represents the heat release power and heat storage power of thermal energy storage.

[0277] 5) Absorption refrigeration unit

[0278] Absorption refrigeration units convert thermal energy into cold energy to meet the service's thermal energy requirements. Their equations are nonlinear, and are modeled using a piecewise linearization method, as follows:

[0279]

[0280]

[0281]

[0282]

[0283]

[0284]

[0285] in, and This indicates the output cooling power and input heating power of an absorption chiller. and This indicates the per-unit value of the output cooling power and the per-unit value of the input heat power of an absorption chiller. This represents a piecewise variable.

[0286] 6) System energy balance constraints

[0287] Power balance:

[0288]

[0289] Thermal equilibrium:

[0290]

[0291] Hydrogen balance:

[0292]

[0293]

[0294] in, and This indicates the output of photovoltaic and wind turbines. Indicates the power purchased. Indicates grid feed management, and This indicates the electrical and thermal power of the heat pump. COP represents the coefficient of performance of a heat pump, P l t represents the electrical load required for basic operation and maintenance of the service area. This indicates the amount of electricity that has been abandoned by renewable energy sources.

[0295] b. Coarse-grained model of service area microgrid

[0296] From the perspective of equivalent energy storage, a corresponding coarse-grained model is established based on the fine-grained model of the energy unit of the micro energy network in the service area.

[0297] 1) Integrated charging, swapping, and storage unit

[0298] In the coarse-grained model, the occupancy status of fast charging stations can be ignored. Furthermore, the fully charged and flexible sets of battery swapping storage can be merged and processed uniformly, resulting in a simplified model:

[0299]

[0300]

[0301]

[0302]

[0303]

[0304] Where, N fc This indicates the total number of fast charging stations. This indicates the energy state of the battery.

[0305] The rest remains consistent with the fine-grained model.

[0306] 2) Heat pump unit

[0307] To simplify the complexity of modeling large-scale thermal regions, a coarse-grained model of heat pump clusters is established from the perspective of equivalent thermal energy storage.

[0308]

[0309]

[0310]

[0311] in, C represents the equivalent thermal energy storage of the service area's heat pump operating heat zone. r,a and R r,a This represents the average of the equivalent heat capacity and equivalent thermal resistance of the heat pump's operating hot zone. N represents the total thermal interference power in the hot region. hp η represents the total number of thermal regions. u and η d It represents the upper and lower boundary coefficients of the equivalent thermal energy.

[0312] 3) Hydrogen Energy Unit

[0313] Ignoring the intermediate processes of hydrogen production by electricity and fuel cells, the hydrogen energy unit is considered as an energy storage unit.

[0314]

[0315]

[0316]

[0317]

[0318] in, This represents the energy of an equivalent hydrogen energy unit. Indicates the calorific value of hydrogen. denoted by , where ∈ represents the electrical power ratio of the hydrogen energy release.

[0319] 4) Thermal energy storage unit

[0320] The coarse-grained model of the thermal energy storage unit is consistent with the fine-grained model.

[0321] 5) Absorption refrigeration unit

[0322] The operating equations for absorption are expressed using conversion coefficients.

[0323]

[0324] 6) System energy balance constraints

[0325] In the coarse-grained model, since the intermediate processes of hydrogen production by electricity and fuel cells in the hydrogen energy system are ignored, the power consumption of the compressor in the hydrogen balance and power balance is implicit in the formulas. Therefore, the new energy balance is:

[0326]

[0327] The thermal balance remains consistent with that in the fine-grained model.

[0328] (2) Optimization algorithm construction

[0329] Figure 3 This invention provides a schematic diagram of a rolling framework combining an optimized fine-grained model and a coarse-grained model of a service area microgrid. Considering that the prediction error of online optimization increases with the time scale, the accuracy of prediction data in the near future is generally higher, while the accuracy of prediction data in the distant future is lower. This invention divides the scheduling time domain into two parts: a near-term period with a short scheduling cycle and a distant period with a long scheduling cycle. Robust optimization is used for scheduling in the near-term period to fully utilize the high accuracy of the prediction data and improve the reliability of system operation. Stochastic optimization based on chance constraints is used for scheduling in the distant future to reduce the conservatism of robust optimization and address the uncertainty of the prediction data. Then, robust optimization and stochastic optimization are integrated under the rolling optimization framework to formulate the service area microgrid schedule. Simultaneously, considering that the prediction deviation is large and the uncertainty is high in the scheduling formulation of the distant future period, using a fine-grained model would reduce the benefits and increase the computational burden. A fine-grained model is used in the robust optimization part, and a coarse-grained model is used in the stochastic optimization part to achieve a balance between optimization effect and computational burden.

[0330] a. Optimize goal setting

[0331] To maximize the self-sufficiency of the microgrid in the service area and reduce dependence on external power grid energy, an objective function is established, which can be expressed as follows:

[0332]

[0333] b. Robust optimization-based near-period scheduling

[0334] In the rolling scheduling time domain, robust optimization is used for scheduling adjacent time periods, with a scheduling period T. r=4, scheduling time scale is set to Δt r =0.5h.

[0335] Based on the form of a budget uncertainty set to represent the uncertainty of photovoltaic (PV) and wind turbine output and vehicle charging demand, these three factors can be expressed as:

[0336]

[0337]

[0338]

[0339]

[0340]

[0341]

[0342] in, and This represents the projected demand for solar power, wind power, and charging. and This indicates the maximum possible deviation from the predicted photovoltaic, wind turbine, and charging demand, which can be obtained from historical deviation data of the prediction system. and Representing uncertain variables in demand for solar power, wind turbines, and charging. and This represents the corresponding uncertain adjustment parameter, t. c This indicates the current scheduling cycle.

[0343] By introducing auxiliary variables absolute value Perform linearization, let This allows the model to be transformed.

[0344] Based on the established uncertainty set, the scheduling of adjacent time periods in the scheduling time domain is designed into a robust optimization form:

[0345]

[0346] Constraints: Fine-grained model, t∈[t c +1,t c +T r ].

[0347] Where x represents the decision variables included in the fine-grained model of the formula, and D represents its corresponding feasible region. * The decision variables represent the uncertain set of the formula. This represents the corresponding feasible region.

[0348] c. Long-term scheduling based on stochastic optimization

[0349] Based on the established coarse-grained model, the scheduling of longer time periods in the rolling scheduling time domain is designed as a stochastic optimization form based on chance constraints:

[0350] Considering that the uncertainty of photovoltaic, wind turbine, and charging demand can cause the original constraints to be violated, this embodiment of the invention designs the opportunity constraint as the charging and discharging power constraint of battery swapping. In this way, the power imbalance caused by the opportunity constraint is reflected by the charging and discharging power of battery swapping.

[0351]

[0352]

[0353] Where ξ represents the confidence level that the opportunity constraint is violated.

[0354] To transform chance constraints into deterministic constraints for easier computation, the above constraints are transformed as follows:

[0355] Considering historical bias data based on the prediction system, the maximum range of bias in the prediction data within a nearby time period can be obtained, such as... Figure 3 As shown, based on this range, we obtained and This has already been used to formulate the robust optimization part of the rolling scheduler. Simultaneously, we can formulate error scenarios based on a large number of historical deviations of the prediction system, and design the stochastic optimization part of the rolling scheduler based on these possible error scenarios.

[0356] Using scenario constraints to represent opportunity constraints deterministically:

[0357]

[0358]

[0359]

[0360]

[0361] Where s is a superscript indicating the scenario, and N s T represents the total number of scenes. s This represents the total period of the stochastic optimization. and This indicates the maximum charging and discharging power that all modules of the battery swapping device can achieve, which is limited by the actual charging and discharging devices of the swapping unit.

[0362] Therefore, designing the scheduling of longer time periods in the rolling scheduling time domain as a stochastic optimization can be expressed as:

[0363]

[0364] Constraints: Coarse-grained model, t∈[t c +T r +1,t c +T r +T s ].

[0365] Where E{} represents N s The mean values ​​for each scenario are all superscripted with 's' in the constraints of stochastic optimization. In particular, because the coarse-grained model corresponding to stochastic optimization has low complexity, it is computationally friendly for multiple scenarios and will not cause a large computational burden.

[0366] d. Multi-granularity cooperative scheduling combining robust optimization and stochastic optimization

[0367] like Figure 3 As shown, by constructing a rolling time domain that combines robust optimization and stochastic optimization, the two optimization strategies can be used to synergistically optimize the operation of the service area microgrid at two time scales and two model granularities, thereby improving the reliability of the service area microgrid operation while reducing its conservatism.

[0368] The objective function for multi-granularity collaborative scheduling, which combines robust optimization and stochastic optimization, can be expressed as:

[0369]

[0370] Constraints at coupling points:

[0371] Heat pump coupling:

[0372]

[0373] Thermal energy storage coupling:

[0374]

[0375] Hydrogen energy system coupling:

[0376]

[0377] Integrated charging, swapping, and storage coupling:

[0378]

[0379]

[0380]

[0381] Under the framework of rolling optimization, the service area microgrid optimization scheduling is designed. The robust optimization scheduling based on the service area microgrid fine-grained model is used for the near-term of rolling optimization, and the stochastic optimization scheduling based on the service area microgrid coarse-grained model is used for the longer-term of rolling optimization. Under the common constraints of the fine-grained optimization model constraints, the coarse-grained optimization model constraints, and the multi-granularity model coupling point constraints, the multi-granularity source-load-storage collaborative scheduling scheme is obtained by solving the objective function of the multi-granularity collaborative scheduling that combines the robust optimization and stochastic optimization.

[0382] In summary, this invention constructs a fine-grained model—the refined model—of the distributed resources in each link of the source, load, and storage of a service area micro-energy network. Based on the equivalent energy storage perspective, a coarse-grained model—the rough model—is also established for each distributed resource. These two granular models of distributed resources in each link of the service area micro-energy network are combined according to the needs of optimization calculations for the operation optimization of the micro-energy network, thus forming multi-granularity modeling and application that can meet various needs of optimization operation calculations.

[0383] This invention proposes a multi-granularity collaborative optimization scheduling strategy for source-load-storage in high-speed service area micro-energy networks, combining robust optimization and stochastic optimization based on the distribution characteristics of online optimization prediction errors. This invention combines the advantages of robust optimization and stochastic optimization in online optimization, ensuring reliability while reducing conservatism. The embodiments of this invention establish a "source-load-storage" collaborative operation mode considering the coordination of multiple energy units in the energy system of high-speed service areas, which can supplement existing shortcomings.

[0384] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0385] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0386] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0387] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-granularity collaborative control method for service area microgrids combining robustness and stochasticity, characterized in that, include: Establish a fine-grained model of the service area microgrid; Establish a coarse-grained model of the service area microgrid; Robust optimization scheduling design of the service area microgrid is carried out using the fine-grained model of the service area microgrid. The service area microgrid coarse-grained model is used to design the stochastic optimization scheduling of the service area microgrid; Robust optimization scheduling design and stochastic optimization scheduling design of service area micro-energy grid are combined under the rolling optimization framework. The robust optimization part is used for the near period of rolling optimization, and the stochastic optimization part is used for the far period of rolling optimization, so as to obtain a multi-granularity source-load-storage collaborative scheduling scheme that combines robust optimization and stochastic optimization. The establishment of a fine-grained model for a service area microgrid includes: The fine-grained model of the service area micro-energy grid includes: an integrated charging, swapping and storage unit, a heat pump unit, a hydrogen energy unit, a thermal energy storage unit, an absorption refrigeration unit, and system energy balance constraints; 1) Integrated charging, swapping, and storage unit The integrated charging, swapping, and storage unit includes a fast charging section and a battery swapping section. The status of the fast charging pile is indicated as follows: in, This indicates the number of fast charging stations that are currently in use. This indicates the number of fast charging stations that are currently idle. The state transition equation for a fast charging station is expressed as: in, Represents the fast charging system matrix. This represents the input matrix of the fast charging system. Indicates the input variable for fast charging; The system matrix for fast charging Represented as: Input matrix of fast charging section Represented as: in, , The rest are zero; The input variables for the fast charging section satisfy the following constraints: The charging energy corresponding to the fast charging part and average charging power Represented as: The battery swapping section consists of a battery pack, which is divided into two parts: a fully charged section and a flexible charging and discharging section. The energy equation for the battery swapping section is expressed as follows: in, To collect the full amount of electricity, To flexibly collect power, To provide the electricity for battery swapping services, The proportion of flexible sets transferred to fully charged sets, and Indicates charging and discharging power. This indicates the charging / discharging state; 1 indicates charging, and 0 indicates discharging. and Indicates charge / discharge efficiency. This indicates the battery demand that is currently in a standby state. Indicates charging / battery swapping needs; 2) Heat pump unit The operation model of the heat pump unit is constructed based on the first-order equivalent thermal parameter model; in, Indicates indoor temperature. and Indicates indoor heat capacity and thermal resistance parameters. Indicates indoor thermal interference. Indicates the heat pump's thermal power. This indicates the total heat load demand; 3) Hydrogen Energy Unit Electrogenization section: The fuel cell section is modeled using a piecewise linearization approach: in, Indicates the rate of hydrogen production by electrolysis. Indicates the power of hydrogen production by electricity. Indicates the power of the hydrogen compressor. This indicates the mass of hydrogen in the storage tank. and Indicates the rate of hydrogen entering and exiting. Indicates the hydrogen consumption rate of the fuel cell. Indicates the power output of the fuel cell. Indicates the heat output power of the fuel cell, Table Show the piecewise variables of the fuel cell performance curve. , and This indicates the per-unit values ​​of relative hydrogen consumption, per-unit electricity production, and per-unit heat production for fuel cells. , and This indicates the rated values ​​for hydrogen consumption, electricity production, and heat production of a fuel cell. Indicates the total optimization cycle; 4) Thermal energy storage unit Thermal energy storage is used to store and release thermal energy, and to absorb renewable energy and provide thermal support. in, Indicates thermal energy storage. and This indicates the heat release power and heat storage power of thermal energy storage; 5) Absorption refrigeration unit Absorption refrigeration units convert thermal energy into cold energy, and are modeled based on a piecewise linearization method, as follows: in, and This indicates the output cooling power and input heating power of an absorption chiller. and This indicates the per-unit value of the output cooling power and the per-unit value of the input heat power of an absorption chiller. Represents a piecewise variable; 6) System energy balance constraints Power balance: Thermal equilibrium: Hydrogen balance: in, and This indicates the output of photovoltaic and wind turbines. Indicates the power purchased. Indicates grid feed management, and This indicates the electrical and thermal power of the heat pump. , Indicates the coefficient of performance (COP) of a heat pump. This indicates the electrical load required for basic operation and maintenance of the service area. This indicates the amount of renewable energy that has been abandoned. The establishment of a coarse-grained model for a service area microgrid includes: The coarse-grained model of the service area micro-energy grid includes an integrated charging, swapping and storage unit, a heat pump unit, a hydrogen energy unit, a thermal energy storage unit, an absorption refrigeration unit, and system energy balance constraints. 1) Integrated charging, swapping, and storage unit The coarse-grained model ignores the occupancy status of fast charging stations and merges the fully charged and flexible sets of battery swapping energy storage for unified processing. The simplified model is as follows: in, This indicates the total number of fast charging stations. Indicates the energy state of the battery; The rest remains consistent with the fine-grained model; 2) Heat pump unit A coarse-grained model of heat pump clusters is established from the perspective of equivalent thermal energy storage; in, This represents the equivalent thermal energy storage in the heat pump operating area of ​​the service area. and This represents the average of the equivalent heat capacity and equivalent thermal resistance of the heat pump's operating hot zone. This represents the total thermal interference power in the hot region. Indicates the total number of hot areas. and Indicates the upper and lower boundary coefficients of the equivalent thermal energy; 3) Hydrogen Energy Unit Ignoring the intermediate processes of hydrogen production by electricity and fuel cells, the hydrogen energy unit is regarded as an energy storage unit; in, This represents the energy of an equivalent hydrogen energy unit. Indicates the calorific value of hydrogen. This indicates the energy release power of hydrogen. The electrical power ratio coefficient representing the energy released by hydrogen energy; 4) Thermal energy storage unit The coarse-grained model and the fine-grained model of the thermal energy storage unit are consistent; 5) Absorption refrigeration unit The operating equations for absorption are expressed using conversion factors; 6) System energy balance constraints In the coarse-grained model, the intermediate processes of hydrogen production by electricity and fuel cells in the hydrogen energy system are ignored. The power consumption of the compressor in the hydrogen balance and power balance is implicit in the formulas. The new energy balance is: The thermal balance remains consistent with that in the fine-grained model.

2. The method according to claim 1, characterized in that, Robust optimization scheduling design of the service area microgrid is carried out using the fine-grained model of the service area microgrid, including: The rolling scheduling time domain is divided into two parts: an immediate period with a short scheduling cycle and a more distant period with a longer scheduling cycle. Robust optimization is used for scheduling in the immediate period, while stochastic optimization based on chance constraints is used for scheduling in the more distant period. A fine-grained model of service area microgrid is adopted in the robust optimization part. a. Optimize goal setting To maximize the self-sufficiency of the microgrid in the service area and reduce dependence on external power grid energy, an objective function is established, which is expressed as follows: b. Near-term scheduling based on robust optimization In the rolling scheduling time domain, robust optimization is adopted for scheduling of adjacent time periods, with a scheduling cycle of The scheduling time scale is set to ; The uncertainty of solar power output, wind turbine output, and vehicle charging demand is represented using the form of a budget uncertainty set. These three factors are expressed as: in, , and This represents the projected demand for solar power, wind power, and charging. , and This represents the maximum possible deviation from the predicted demand for solar power, wind power, and charging, obtained from historical deviation data of the prediction system. , and Representing uncertain variables in demand for solar power, wind turbines, and charging. , and This indicates the corresponding uncertain adjustment parameter. Indicates the current scheduling cycle; By introducing auxiliary variables absolute value Perform linearization, let , This allows the model to be transformed; Based on the established uncertainty set, the scheduling of adjacent time periods in the rolling scheduling time domain is designed into a robust optimization form: Constraints: Fine-grained model ; in, This indicates the decision variables included in the fine-grained model of the formula. This represents the corresponding feasible region. The decision variables represent the uncertain set of the formula. This represents the corresponding feasible region.

3. The method according to claim 1, characterized in that, The stochastic optimization scheduling design of the service area microgrid is carried out using the coarse-grained model of the service area microgrid, including: Based on the coarse-grained model of the service area microgrid, the scheduling of longer time periods in the rolling scheduling time domain is designed as a stochastic optimization form based on chance constraints: The design opportunity constraint is the charging and discharging power constraint of the battery swapping. The power imbalance caused by the opportunity constraint is reflected in the charging and discharging power of the swapping battery. in, and Indicates charging and discharging power. This indicates the charging / discharging state; 1 indicates charging, and 0 indicates discharging. This indicates the confidence level that the opportunity constraint has been violated. Using scenario constraints to represent opportunity constraints deterministically: in, Superscript indicates a scenario. Indicates the total number of scenes. This represents the total period of the stochastic optimization. and This indicates the maximum charging power and maximum discharging power that can be achieved when all modules of the battery are replaced. The scheduling of longer time periods in the rolling scheduling time domain is designed as a stochastic optimization form, expressed as: Constraints: Coarse-grained model of microgrid in service area. in, express The mean values ​​for each scenario are all superscripted in the constraints of the stochastic optimization. .

4. The method according to claim 3, characterized in that, The robust optimization scheduling design and stochastic optimization scheduling design for the service area microgrid are combined within a rolling optimization framework. The robust optimization component is used for the near-term rolling optimization, while the stochastic optimization component is used for the longer-term rolling optimization, resulting in a multi-granularity source-load-storage collaborative scheduling scheme that combines robust optimization and stochastic optimization. This scheme includes: By combining robust optimization and stochastic optimization in the rolling time domain, the two optimization strategies are used to achieve coordinated optimization of the service area microgrid operation at two time scales and two model granularities. The objective function for multi-granularity collaborative scheduling, which combines robust optimization and stochastic optimization, is expressed as: Constraints at coupling points: Heat pump coupling: Thermal energy storage coupling: Hydrogen energy system coupling: Integrated charging, swapping, and storage coupling: Under the framework of rolling optimization, the service area microgrid optimization scheduling is designed. The robust optimization scheduling based on the service area microgrid fine-grained model is used for the near-term of rolling optimization, and the stochastic optimization scheduling based on the service area microgrid coarse-grained model is used for the longer-term of rolling optimization. Under the common constraints of the fine-grained optimization model constraints, the coarse-grained optimization model constraints, and the multi-granularity model coupling point constraints, the multi-granularity source-load-storage collaborative scheduling scheme is obtained by solving the objective function of the multi-granularity collaborative scheduling that combines the robust optimization and stochastic optimization.

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