A day-ahead energy optimization method for carbon-reducing microgrid based on source-grid-load-storage interaction

Abstract

The present invention discloses a method for optimizing the day-ahead energy of a carbon-reducing microgrid based on the interaction of source, grid, load and storage, including the following steps: collecting data of a smart microgrid with interaction of source, grid, load and storage; setting the power demand of the load in the microgrid to meet the conditions of the distributed power generation part, energy storage part and external large power grid in the system; constructing a microgrid economic and carbon emission calculation model; calculating the net electricity cost of the microgrid; calculating the carbon emissions caused by the microgrid; setting the optimization target and its constraints; and using the optimization algorithm and its calculation model to obtain the optimal results of energy storage and its adjustable load. The above technical scheme constructs a microgrid economic and carbon emission calculation model, calculates the economic benefits and carbon emissions in the microgrid, realizes the optimized scheduling of the smart microgrid with interaction of source, grid, load and storage composed of renewable energy power generation, adjustable load, basic load, energy storage and large power grid, realizes carbon reduction through interaction of source, grid, load and storage, reduces carbon emissions in the microgrid, and improves the collaborative cooperation in the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrids, and in particular to a day-ahead energy optimization method for a carbon-reducing microgrid based on source-grid-load-storage interaction. Background Art

[0002] A microgrid is a small power generation and distribution system that organically integrates distributed power sources, loads, energy storage devices, converters, and monitoring and protection devices. Since traditional microgrids have a common bus and a hierarchical control structure, the structure and capacity expansion of the microgrid are very complex and expensive. A modular microgrid consists of a three-port converter, a battery, a load, a wind and solar power generation unit, and a backup diesel generator. Modular microgrids are easy to expand, and through operation control and energy management, they can realize independent or interconnected operation of modules, reduce the adverse effects of intermittent distributed power sources on the distribution network, maximize the use of renewable energy power output, improve power supply reliability and power quality, and realize the transformation from traditional diesel power generation systems to clean energy power generation systems.

[0003] According to data, for the low-carbon operation of microgrids, the existing technology has been studying the optimization control method of microgrid operation for many years, but the research direction is mainly concentrated in economic fields such as minimum investment and lowest comprehensive cost. In recent years, as people pay more and more attention to environmental protection, the low-carbon effect has attracted more attention. It is necessary to adopt certain low-carbon operation optimization control for microgrids to meet environmental protection needs. The few existing studies on low-carbon operation of microgrids do not take low carbon as the main optimization goal, but only take it as an additional optimization goal, and do not pay enough attention to it. At the same time, the microgrid carbon emission formula given by the study is too rough, and cannot accurately reflect the impact of energy efficiency and distribution location on carbon emissions, which will produce large errors. Current research has not given a detailed optimization control strategy for the goal of low carbon.

[0004] Existing microgrid optimization control methods mostly optimize economic indicators without considering low-carbon benefits; in the rare schemes that optimize low-carbon factors, low-carbon factors only account for a small part and are only a small branch under the economic goal. At the same time, there is a lot of room for optimization in the calculation formula of carbon emissions of distributed power sources, and there is no set of specific optimization measures for low-carbon operation.

[0005] Chinese patent document CN108306338B discloses a "modular microgrid and its method for day-ahead energy optimization and scheduling". It adopts the following methods: using the energy conservation relationship between the components in each module of the modular microgrid and the power exchange energy conservation relationship between the modules as constraints to establish an operating cost objective function; using the diesel generator output power time series in each module and the exchange power time series between the module and the microgrid as variables for day-ahead energy optimization, using a particle swarm optimization algorithm to solve the operating cost objective function and obtain a variable matrix with the minimum operating cost; configuring the day-ahead energy of the modules in the modular microgrid according to the obtained variable matrix. The above technical solution is too expensive, has excessive carbon emissions, and does not consider low-carbon benefits. Summary of the invention

[0006] The present invention mainly solves the technical problems that the original technical scheme does not consider low-carbon benefits when optimizing and controlling microgrids, and the prices are too high and the carbon emissions are too much. It provides a carbon-reducing microgrid day-ahead energy optimization method based on source-grid-load-storage interaction. By constructing a microgrid economic and carbon emission calculation model, the economic benefits and carbon emissions in the microgrid are calculated, and the optimized scheduling of the source-grid-load-storage interactive intelligent microgrid composed of renewable energy power generation, adjustable load, basic load, energy storage and large power grid is achieved, carbon reduction through source-grid-load-storage interaction is achieved, carbon emissions in the microgrid are reduced, and the collaborative cooperation within the power system is improved.

[0007] The above technical problem of the present invention is mainly solved by the following technical solution: The present invention comprises the following steps:

[0008] S1 collects data of smart microgrids with interactive source, grid, load and storage;

[0009] S2 sets the power demand of the load in the microgrid to meet the conditions of the distributed generation part, energy storage part and external large power grid in the system;

[0010] S3 builds a microgrid economy and its carbon emission calculation model;

[0011] S4 calculates the net electricity cost of the microgrid;

[0012] S5 calculates the carbon emissions caused by the microgrid;

[0013] S6 sets the optimization goal and its constraints;

[0014] S7 uses optimization algorithms and its calculation models to obtain the optimal results for energy storage and its adjustable load.

[0015] Preferably, the source-grid-load-storage interactive smart microgrid data in step S1 includes renewable energy generation, adjustable load, base load, energy storage and large power grid.

[0016] Preferably, the power demand of the load in the microgrid in step S2 needs to be met by the distributed generation part, the energy storage part and the external large power grid in the system. In order to ensure the power quality of the load, the power balance constraint in the system should be met at every moment, that is:

[0017]

[0018]

[0019] in, is the load CP power, PG power for the power generation part, is the energy storage system ES power, is the power of the external large power grid GP, the power of the power generation part PG and the load CP are unidirectional; it is positive when the energy storage system ES generates electricity to the microgrid, otherwise it is negative; it is positive when the external large power grid GP generates electricity to the microgrid, otherwise it is negative.

[0020] Preferably, the microgrid economy and its carbon emission calculation model in step S3 is:

[0021]

[0022] The first row represents the power demand of the load in the microgrid, which needs to be met by the distributed generation part, energy storage part and external large power grid in the system; the second row represents the net electricity cost of the microgrid, which consists of two parts, namely, revenue and cost; the third row represents the carbon emissions caused by the microgrid.

[0023] is the real-time net electricity cost of the microgrid, K C_PG The cost of generating electricity for PG, K C_ES is the electricity cost of ES charging and discharging, K C_EX is the electricity cost of EX, To consume carbon emissions in real time, K CD_PG is the PG cost of power generation, K CD-ES is the energy storage system ES cost, K CD-GP Cost of external large power grid GP

[0024] Preferably, the net electricity cost of the microgrid in step S4 includes a revenue part and a cost part, and the revenue part is ultimately realized by selling electricity to the power grid, that is, the revenue from interactive electricity sales between the microgrid and the outside.

[0025] As a preference, the cost part includes the power generation part PG power generation cost Energy storage system ES storage and power generation costs The benefits and costs of microgrid interaction with external power grid The power generation cost of the power generation part PG includes fuel cost, operating cost, and equipment depreciation cost; the storage and power generation cost of the energy storage system ES includes operation and maintenance, equipment depreciation, and energy loss cost. The net electricity cost generated in real time by the microgrid is composed of the power generation cost of the power generation part PG Energy storage system ES storage and power generation costs The interaction cost between microgrid and large power grid composition:

[0026]

[0027] As a preferred embodiment, the power generation cost of the power generation part PG It is composed of fuel consumption cost, operating cost and equipment depreciation cost, namely:

[0028]

[0029] Among them, K C_PG The cost of generating electricity for PG, K PG-f The fuel cost for PG to generate electricity, K PG-ll The depreciation cost of the PG equipment is K PG-opm The electricity operation and maintenance cost for PG is generated. Δt is the scheduling duration, T is 1h.

[0030] As a preferred embodiment, the energy storage and power generation costs of the energy storage system ES are It is composed of its operation and maintenance, equipment depreciation and energy loss costs, namely:

[0031]

[0032] Among them, K C_ES is the electricity cost of ES charging and discharging, K Bat-opm is the operation and maintenance cost of ES charging and discharging, K Bat-ll is the equipment depreciation cost of ES charging and discharging, K Bat-el is the energy loss cost of ES charging and discharging, Δt is the scheduling duration, T is 1h.

[0033] As a preferred embodiment, the income and cost generated by the interaction between the microgrid and the external power grid

[0034]

[0035] in, Δt is the scheduling duration, T is 1h, K C_EX is the electricity cost of EX. When the microgrid purchases electricity from the large power grid, it uses the electricity purchase price C b When the microgrid sells electricity to the large grid, the electricity price C is adopted.b When the microgrid purchases electricity from the large grid, the purchase price f s ;

[0036]

[0037] Among them, f s is the microgrid electricity price, c b The electricity price for microgrids,

[0038] Microgrid at 0-t n The total net electricity cost accumulated during the period is the sum of each dispatch period:

[0039]

[0040] Preferably, the carbon emissions caused by the microgrid in step S5 are:

[0041] Real-time consumption of carbon emissions, carbon emissions caused by the power generation part PG Carbon emissions caused by energy storage systems ES Carbon emissions caused by the external large power grid GP Composition, namely:

[0042]

[0043] Carbon emissions caused by PG for power generation, Carbon emissions caused by the energy storage system ES, Carbon emissions caused by the external large power grid GP;

[0044] In the wind-solar hybrid microgrid:

[0045]

[0046]

[0047]

[0048] Among them, K CD_PV The cost of electricity generated by photovoltaic generators is is the power of the photovoltaic generator PV, K CD_WT The cost of generating electricity WT for wind turbines is, is the power generated by the wind turbine WT, Δt is the scheduling duration, T is 1h,

[0049] At 0-t n The total carbon emissions accumulated during the period are the sum of each scheduling period:

[0050]

[0051] The beneficial effects of the present invention are: by constructing a microgrid economic and carbon emission calculation model, calculating the economic benefits and carbon emissions within the microgrid, achieving optimal scheduling of a source-grid-load-storage interactive intelligent microgrid consisting of renewable energy power generation, adjustable loads, basic loads, energy storage and a large power grid, achieving source-grid-load-storage interactive carbon reduction, reducing carbon emissions within the microgrid, and improving the collaborative cooperation within the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of the present invention.

[0053] Figure 2 It is a flow chart for implementing the day-ahead optimization scheduling of carbon reduction targets of the present invention.

[0054] Figure 3 It is a schematic diagram of a photovoltaic, wind turbine and basic load output forecasting method according to the present invention.

[0055] Figure 4 It is a schematic diagram of the output of an electric bus before and after optimization according to the present invention.

[0056] Figure 5 It is a microgrid supply and demand diagram before and after optimization of the present invention. DETAILED DESCRIPTION

[0057] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0058] Embodiment: A method for optimizing the day-ahead energy of a carbon-reducing microgrid based on the interaction of source, grid, load and storage is provided in this embodiment. Figure 1 As shown, the following steps are included

[0059] S1 collects data of the source-grid-load-storage interactive smart microgrid, including renewable energy generation, adjustable load, base load, energy storage and the large power grid.

[0060] S2 sets the power demand of the load in the microgrid to meet the conditions of the distributed generation part, energy storage part and external large power grid in the system. In order to ensure the power quality of the load, the power balance constraint in the system should be met at every moment, that is,

[0061]

[0062]

[0063] Among them, the power of the power generation part PG and the load CP are both unidirectional; the power of the energy storage system ES is positive when it generates power into the microgrid, otherwise it is negative; the power of the external large power grid GP is positive when it generates power into the microgrid, otherwise it is negative.

[0064] S3 builds a microgrid economy and its carbon emission calculation model,

[0065]

[0066] The first row represents the power demand of the load in the microgrid, which needs to be met by the distributed generation part, energy storage part and external large power grid within the system; the second row represents the net electricity cost of the microgrid, which consists of two parts, namely, revenue and cost; the third row represents the carbon emissions caused by the microgrid.

[0067] S4 calculates the net electricity cost of the microgrid, which consists of two parts, namely, revenue and cost.

[0068] Income section:

[0069] The profit of the whole system should be based on the premise of energy conservation. Whether it is the peak-valley arbitrage of energy storage or the profit generated by the generator, it is ultimately achieved by selling electricity to the power grid. That is, the profit of the microgrid’s interactive electricity sales with the outside world.

[0070] Cost section:

[0071] Including the power generation part PG power generation cost Energy storage system ES storage and power generation costs The benefits and costs of microgrid interaction with external power grid The power generation cost of the power generation part PG includes fuel cost, operating cost, and equipment depreciation cost; the storage and power generation cost of the energy storage system ES includes operation and maintenance, equipment depreciation, and energy loss cost. The net electricity cost generated in real time by the microgrid is:

[0072]

[0073] Power generation part PG power generation cost

[0074]

[0075] Among them, K PG The cost of generating electricity for PG, K PG-f The fuel cost for PG to generate electricity, K PG-ll The depreciation cost of the PG equipment is K PG-opm The electricity operation and maintenance cost for PG is generated. Δt is the scheduling duration, T is 1h.

[0076] Energy storage system ES storage and power generation costs

[0077]

[0078] Among them, K C_ES is the electricity cost of ES charging and discharging, K Bat-opm is the operation and maintenance cost of ES charging and discharging, K Bat-ll is the equipment depreciation cost of ES charging and discharging, K Bat-el The energy loss cost of charging and discharging ES.

[0079] The benefits and costs of microgrid interaction with external power grid

[0080]

[0081] in, Δt is the scheduling duration, T is 1h, K C_EX is the electricity cost of EX,

[0082]

[0083] Among them, f s is the microgrid electricity price, c b The electricity price for microgrids,

[0084] Microgrid at 0-t n The cumulative net electricity cost during the period is:

[0085]

[0086] S5 calculates the carbon emissions caused by the microgrid and consumes carbon emissions in real time:

[0087]

[0088] Carbon emissions caused by PG for power generation, Carbon emissions caused by the energy storage system ES, Carbon emissions caused by the external large power grid GP;

[0089] In the wind-solar hybrid microgrid:

[0090]

[0091]

[0092]

[0093] Among them, K CD_PV The cost of electricity generated by photovoltaic generators is is the power of the photovoltaic generator PV, K CD_WT The cost of generating electricity WT for wind turbines is, is the power generated by the wind turbine WT, Δt is the scheduling duration, T is 1h,

[0094] At 0-t n Cumulative carbon emissions during the period:

[0095]

[0096] S6 sets the optimization goal and its constraints;

[0097] A day-ahead optimal dispatching method based on source-grid-load-storage interaction with the goal of carbon reduction and its case study

[0098] (1) The optimization goal is to minimize the carbon emissions of the microgrid within a daily dispatch cycle, that is:

[0099] minF(t)={E CD}

[0100] Constraints:

[0101] 1) Power balance constraints of supply and demand in microgrid:

[0102]

[0103] 2) Output power limit of each generator set:

[0104] Wind turbine power generation limit:

[0105]

[0106] Photovoltaic generator power limit:

[0107]

[0108] Power limit of grid tie line:

[0109]

[0110] 3) When the electric bus battery leaves the station, it must be able to complete the next dispatch task. When the electric bus leaves the station, the upper and lower limits of the battery SOC state are:

[0111]

[0112] When an electric bus leaves the station, the upper limit of the battery SOC state is:

[0113]

[0114] Charging power limit for electric buses:

[0115]

[0116] Specific implementation process and methods are as follows Figure 2 shown.

[0117] Specific implementation cases and policy results

[0118] like Figure 3 The figure shows the day-ahead forecast data of wind turbines, photovoltaics, and basic loads obtained in this case. The marked part in the figure is the part where the power supplied by renewable energy is greater than the load consumption. At this time, the renewable energy is sent to the distribution network and enters the large power grid to be consumed by distant loads; when renewable energy is insufficient, it is necessary to purchase electricity from the large power grid. According to the previous article, the carbon emissions of purchasing electricity from the large power grid are much higher than those of renewable energy.

[0119] For microgrid systems, the carbon emissions caused by using renewable energy to generate electricity are much smaller than those of the power grid. Figure 4 , Figure 5 As shown, through the optimization method proposed in the invention, the load of electric buses can be transferred to areas with abundant renewable energy, and renewable energy can be utilized as much as possible on a daily scale, thereby achieving the goal of carbon reduction.

[0120]

[0121]

[0122] The invention proposes a smart microgrid based on source-grid-load-storage interaction and its intraday optimization method, which can reduce carbon emissions in the microgrid compared with ordinary microgrids.

[0123] The specific embodiments described herein are merely examples of the spirit of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in similar ways, but they will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

[0124] Although the terms such as power generation part, energy storage part and external large power grid are used more frequently in this article, the possibility of using other terms is not excluded. These terms are used only to more conveniently describe and explain the essence of the present invention; interpreting them as any additional restrictions is contrary to the spirit of the present invention.

Claims

1. A method for day-ahead energy optimization of a carbon-reducing microgrid based on source-grid-load-storage interaction, characterized in that: The following steps are involved: S1 collects data of smart microgrids with interactive source, grid, load and storage; S2 sets the power demand of the load in the microgrid to meet the conditions of the distributed generation part, energy storage part and external large power grid in the system; is the load CP power, PG power for the power generation part, is the energy storage system ES power, is the external large power grid GP power; S3 builds a microgrid economy and its carbon emission calculation model, is the real-time net electricity cost of the microgrid, K C_PG The cost of generating electricity for PG, K C_ES is the electricity cost of ES charging and discharging, K C_EX is the electricity cost of EX, To consume carbon emissions in real time, K CD_PG is the PG cost of power generation, K CD-ES is the energy storage system ES cost, K CD-GP is the external large power grid GP cost, Δt is the scheduling duration, T is 1h; S4 calculates the net electricity cost of the microgrid; S5 calculates the carbon emissions caused by the microgrid; S6 sets the optimization goal and its constraints; S7 uses optimization algorithms and its calculation models to obtain the optimal results for energy storage and its adjustable load.

2. The method for day-ahead energy optimization of a carbon-reducing microgrid based on source-grid-load-storage interaction according to claim 1 is characterized in that: The source-grid-load-storage interactive smart microgrid data in step S1 includes renewable energy generation, adjustable load, base load, energy storage and large power grid.

3. The method for day-ahead energy optimization of a carbon-reducing microgrid based on source-grid-load-storage interaction according to claim 1 is characterized in that: The power demand of the load in the microgrid in step S2 needs to be met by the distributed generation part, energy storage part and external large power grid in the system. In order to ensure the power quality of the load, the power balance constraint in the system should be met at every moment, that is: The value is positive when the energy storage system ES generates electricity for the microgrid, otherwise it is negative; the value is positive when the external large power grid GP generates electricity for the microgrid, otherwise it is negative.

4. The method for day-ahead energy optimization of a carbon-reducing microgrid based on source-grid-load-storage interaction according to claim 3 is characterized in that: The microgrid economy and its carbon emission calculation model in step S3 is: The first row represents the power demand of the load in the microgrid, which needs to be met by the distributed generation part, energy storage part and external large power grid within the system; the second row represents the net electricity cost of the microgrid, which consists of two parts, namely, revenue and cost; the third row represents the carbon emissions caused by the microgrid.

5. The method for day-ahead energy optimization of a carbon-reducing microgrid based on source-grid-load-storage interaction according to claim 1 is characterized in that: The net electricity cost of the microgrid in step S4 includes a revenue part and a cost part. The revenue part is ultimately realized by selling electricity to the power grid, that is, the revenue from interactive electricity sales between the microgrid and the outside world.

6. The method for day-ahead energy optimization of a carbon-reducing microgrid based on source-grid-load-storage interaction according to claim 5 is characterized in that: The cost part includes the power generation part PG power generation cost Energy storage system ES storage and power generation costs The benefits and costs of microgrid interaction with external power grid The power generation cost of the power generation part PG includes fuel cost, operating cost, and equipment depreciation cost; the storage and power generation cost of the energy storage system ES includes operation and maintenance, equipment depreciation, and energy loss cost. The net electricity cost generated in real time by the microgrid is composed of the power generation cost of the power generation part PG Energy storage system ES storage and power generation costs The interaction cost between microgrid and large power grid composition:

7. The method for day-ahead energy optimization of a carbon-reducing microgrid based on source-grid-load-storage interaction according to claim 6 is characterized in that: The power generation cost of PG It is composed of its fuel consumption cost, operating cost and equipment depreciation cost, namely: Among them, K C_PG The cost of generating electricity for PG, K PG-f The fuel cost for PG to generate electricity, K PG-ll The depreciation cost of the PG equipment is K PG-opm The electricity operation and maintenance cost for PG is generated. Δt is the scheduling duration, T is 1h, PG power for the power generation part.

8. The method for day-ahead energy optimization of a carbon-reducing microgrid based on source-grid-load-storage interaction according to claim 6 is characterized in that: The energy storage system ES energy storage and power generation costs It is composed of its operation and maintenance, equipment depreciation and energy loss costs, namely: Among them, K C_ES is the electricity cost of ES charging and discharging, K Bat-opm is the operation and maintenance cost of ES charging and discharging, K Bat-ll is the equipment depreciation cost of ES charging and discharging, K Bat-el is the energy loss cost of ES charging and discharging, Δt is the scheduling duration, T is 1h, is the energy storage system ES power.

9. The method for day-ahead energy optimization of a carbon-reducing microgrid based on source-grid-load-storage interaction according to claim 6 is characterized in that: The benefits and costs generated by the interaction between the microgrid and the external grid in, Δt is the scheduling duration, T is 1h, K C_EX is the electricity cost of EX. When the microgrid purchases electricity from the large grid, it uses the electricity purchase price C b When the microgrid sells electricity to the large grid, the electricity price C is adopted. b When the microgrid purchases electricity from the large grid, the purchase price f s , is the external large power grid GP power; Among them, f s is the microgrid electricity price, c b The electricity price for microgrids, Microgrid at 0-t n The total net electricity cost accumulated during the period is the sum of each dispatch period:

10. The method for day-ahead energy optimization of a carbon-reducing microgrid based on source-grid-load-storage interaction according to claim 1, characterized in that: The carbon emissions caused by the microgrid in step S5 are: Real-time consumption of carbon emissions, carbon emissions caused by the power generation part PG Carbon emissions caused by energy storage systems ES Carbon emissions caused by the external large power grid GP Composition, namely: Carbon emissions caused by PG for power generation, Carbon emissions caused by the energy storage system ES, Carbon emissions caused by the external large power grid GP; In the wind-solar hybrid microgrid: Among them, K CD_PV The cost of electricity generated by photovoltaic generators is is the power of photovoltaic generator PV, K CD_WT The cost of generating electricity WT for wind turbines is, is the power generated by the wind turbine WT, Δt is the scheduling duration, T is 1h, K CD-ES is the energy storage system ES cost, is the power of the energy storage system ES, K CD-GP is the external large power grid GP cost, is the external large power grid GP power; At 0-t n The total carbon emissions accumulated during the period are the sum of each scheduling period:

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