A multi-region low-carbon optimization method and system for a power distribution network based on shared energy storage

Abstract

The application discloses a power distribution network multi-region low-carbon optimization method and system based on shared energy storage, and belongs to the field of power systems. The method is as follows: firstly, an upper model is constructed with the minimum total operation cost of the power distribution network as an optimization target, and a first electricity selling price of each region is solved; then, according to the preset power generation power of each region and the first electricity selling price, a first electricity-carbon coupling electricity selling price is obtained by using an electricity-carbon coupling calculation method, and a first load power of each region is calculated; then, a lower model is constructed with the minimum total operation cost of the region as an optimization target, and the upper and lower models are jointly iterated and solved, and an optimal electricity-carbon coupling electricity selling price and an optimal shared energy storage carbon scheduling scheme are output; finally, the optimal result is used to determine the electricity-carbon coupling electricity selling price of each region and control the operation of each device. Therefore, by implementing the application, the problems of high operation cost and low carbonization level of the power distribution network in the prior art can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems, in particular to a multi-region low-carbon optimization method and system for distribution networks based on shared energy storage. BACKGROUND

[0002] In modern power systems, distribution networks, as the key hub of energy terminal consumption, have the ability to efficiently aggregate distributed photovoltaic, wind power, energy storage and other multi-energy sources through advanced communication and control technologies, and have become the core carrier for realizing energy structure transformation and promoting low-carbon economic operation. Demand response and energy storage technology are two key means for improving the consumption capacity of new energy in distribution networks. The former adjusts the load flexibly through price guidance, and the latter uses energy time and space allocation to stabilize fluctuations. The two work together to support the economic and low-carbon operation of the power grid.

[0003] In the prior art, the demand side response mechanism based on traditional price optimization has significant limitations: the model is only based on load power data, focuses on shifting the power consumption period, and does not take into account the carbon emissions of power sources (such as coal-fired and gas-fired units) and energy storage devices, nor does it form a coupled mechanism between electricity prices and carbon costs. Due to the above technical bottlenecks, the traditional method has exposed multiple problems in practical application: on the one hand, demand response has limited guiding effect on user low-carbon power consumption due to the lack of integration of electricity-carbon comprehensive price factors, and cannot effectively reduce the overall carbon emission intensity of the power grid from the demand side; on the other hand, the lack of sharing mechanism and low-carbon research of energy storage systems makes it difficult to fully release the economic value and environmental benefits of energy storage resources. SUMMARY

[0004] The present application provides a multi-region low-carbon optimization method and system for distribution networks based on shared energy storage, which can solve the problem of high cost and low carbonization level in the prior art during the operation of the distribution network.

[0005] In a first aspect, the present application provides a multi-region low-carbon optimization method for distribution networks based on shared energy storage, comprising:

[0006] An upper model is constructed with the minimum total operation cost of the distribution network as the optimization objective, and by solving the upper model, a first electricity selling price of each region is obtained;

[0007] An electricity-carbon coupling calculation method is used to obtain a first electricity-carbon coupling electricity selling price of each region according to the preset generation power of each region and the first electricity selling price of each region, and a first load power of each region is calculated based on the first electricity-carbon coupling electricity selling price of each region;

[0008] constructing a lower layer model with a minimum total operation cost of the regional power grid as an optimization target, and solving the lower layer model in combination with the first load power of each region to obtain the first wind power generation power of each region, the first photovoltaic power generation power of each region, the first gas output power of each region, and the first power purchase power of each region;

[0009] solving the upper layer model and the lower layer model in combination according to the first wind power generation power of each region, the first photovoltaic power generation power of each region, the first gas output power of each region, and the first power purchase power of each region, and outputting an optimal electricity-carbon coupling electricity selling price and an optimal shared energy storage carbon scheduling scheme;

[0010] controlling the electricity-carbon coupling electricity selling price of each region of the power grid and the operation of each power supply device according to the optimal electricity-carbon coupling electricity selling price and the optimal shared energy storage carbon scheduling scheme.

[0011] The embodiment of the present application firstly constructs an upper layer model with a minimum total operation cost of the power grid as an optimization target and solves the upper layer model, which can optimize the economic operation of the power grid from the overall level, reasonably determine the electricity selling price of each region, and provide economic guidance for subsequent electricity-carbon coupling calculation and load adjustment. Then, the present application performs electricity-carbon coupling calculation based on the preset power generation power and the obtained electricity selling price, obtains an electricity-carbon coupling electricity selling price, and calculates the load power according to the electricity selling price. The electricity selling price and carbon emission are closely combined to form an effective economic guidance mechanism, which promotes the adjustment of the load power of each region towards a low-carbon and economic direction, guides users to reasonably use electricity, and reduces the electricity demand in a high-carbon period. Next, the present application constructs a lower layer model with a minimum total operation cost of the regional power grid as an optimization target, and solves the power generation power in combination with the load power, which can realize the optimal scheduling of the energy output in each region, give full play to the role of coal-fired units, gas units, shared energy storage and other elements, improve the energy utilization efficiency of the region, and reduce the regional operation cost. Finally, the present application solves the upper layer model and the lower layer model in combination, realizes the benign interaction and data feedback between the upper layer and the lower layer, can comprehensively consider the economic and low-carbon targets of the overall power grid and each region, finally outputs an optimal electricity-carbon coupling electricity selling price and an optimal shared energy storage carbon scheduling scheme, and controls the electricity-carbon coupling electricity selling price of each region of the power grid and the operation of each power supply device according to the optimal electricity-carbon coupling electricity selling price and the optimal shared energy storage carbon scheduling scheme, which can effectively guide the multi-region system of the power grid to balance between economic operation and low-carbon emission, and finally reduce the cost of the operation of the power grid and improve the level of low carbonization.

[0012] As a preferred example of the first aspect, the constructing of the upper layer model with a minimum total operation cost of the power grid as an optimization target comprises:

[0013] According to a power purchase cost of the distribution network to an upper-level power grid, a power purchase cost of the distribution network to a region, and a power sale benefit of the distribution network to the region, a total operation cost of the distribution network is obtained;

[0014] The upper-level model is constructed based on the total operation cost of the distribution network and a first constraint condition. The first constraint condition includes a time-of-use electricity price constraint, a power consumption constraint, and a unit power consumption cost constraint.

[0015] In the preferred example, by including the power purchase cost of the distribution network to the upper-level power grid, the power purchase cost to the region, and the power sale benefit in the total operation cost calculation, the energy transaction income and expenditure of the distribution network with the external power grid and the internal region are comprehensively covered, the global economic optimization is ensured as the target of the upper-level model, and the overall cost rise caused by local optimization is avoided; secondly, the upper-level model is constructed based on the first constraint condition including the time-of-use electricity price constraint, the power consumption constraint, and the unit power consumption cost constraint, the differentiated price guidance in peak, valley, and flat periods is realized through the time-of-use electricity price constraint, the load side peak shifting is promoted to balance the power grid supply and demand, the reasonable range of electricity price is limited through the power consumption constraint and the unit power consumption cost constraint, the controllability of user power consumption cost and the stability of power grid benefit are ensured, the electricity price scheme output by the upper-level model has economic rationality and feasibility, a scientific price signal basis is provided for the lower-level regional dispatch, and then the multi-region system of the distribution network is promoted to form a benign interaction between economic operation and load regulation.

[0016] As a preferred example of the first aspect, expressions of the time-of-use electricity price constraint, the power consumption constraint, and the power consumption cost constraint include:

[0017] The expression of the time-of-use electricity price constraint is as follows:

[0018]

[0019]

[0020] wherein, is a power sale price of the distribution network to an ith region at time t, and the ith region is a region to which the distribution network sells power; , , are respectively a peak electricity price, a flat electricity price, and a valley electricity price of the power sale price of the distribution network to the ith region, are respectively a peak period, a flat period, and a valley period of the ith region; is a maximum peak-valley electricity price of the ith region;

[0021] The expression of the power consumption constraint is as follows:

[0022]

[0023] wherein, Let φ be the maximum average electricity price sold from the distribution network to the i-th region, and φ be the daily electricity consumption change rate of the park's load. The optimized load power for the i-th region of the distribution network at time t. The load power of the i-th region before time t;

[0024] The expression for the unit electricity cost constraint is as follows:

[0025]

[0026] in, The electricity price for the distribution network to the i-th region before optimization at time t.

[0027] In this preferred example, time-of-use pricing constraints are used to set the range of electricity prices for peak, flat, and valley periods. This provides a reasonable price signal to guide users to adjust their electricity consumption behavior at different times, encouraging load shifts to valley periods and reducing peak periods, thereby balancing the load pressure on the distribution network and improving energy efficiency. This application limits the maximum average electricity price and the daily load variation rate through electricity consumption constraints, ensuring that price adjustments remain within the user's affordability range and preventing drastic changes in electricity demand due to excessive price fluctuations, thus maintaining the stability and predictability of the distribution network load. This application also uses unit electricity cost constraints to ensure that the optimized unit electricity cost does not exceed the pre-optimization level, guaranteeing controllable electricity costs for users. In summary, these three factors work together to construct an electricity price control constraint that balances user interests, load stability, and low-carbon goals.

[0028] As a preferred example of the first aspect, the construction of the lower-level model with the optimization objective of minimizing the total regional operating cost includes:

[0029] The total operating cost of the region is obtained based on the region's electricity purchase cost from the distribution network, the power sharing cost between regions, the daily operating cost of wind turbines within the region, the daily operating cost of photovoltaic power units within the region, the daily operating cost of hydropower units within the region, the daily operating cost of shared energy storage, the daily operating cost of gas turbine units within the region, and the revenue from electricity sales from the region to the distribution network.

[0030] The lower-level model is constructed based on the total operating cost of the distribution network and the second constraint; wherein the second constraint includes power balance constraint, power transmission limit constraint, power interaction constraint and shared energy storage operation constraint.

[0031] In this preferred example, by incorporating the operating costs of diverse energy devices within the region, the operating costs of shared energy storage, the costs of inter-regional power exchange, and the revenue and expenditure from electricity purchase and sale into the calculation of the total regional operating cost, the economic elements of regional energy dispatch are comprehensively covered. This ensures that the lower-level model aims at regional economic optimization, achieving synergistic optimization of coal / gas-fired units, shared energy storage, and renewable energy output, thereby improving the economy and rationality of regional energy utilization. The lower-level model is constructed based on the second constraint condition, which includes power balance, power transmission limits, regional power interaction, and shared energy storage operation constraints. This ensures real-time matching of regional power supply and demand through power balance, ensures safe grid operation through power transmission limit constraints, promotes inter-regional energy exchange through regional power interaction constraints to revitalize existing resources, and combines shared energy storage operation constraints to control the charging and discharging status and state of charge of energy storage devices. This allows the lower-level model to optimize regional operating costs while also considering the safety and reliability of grid operation and the efficient utilization of shared energy storage.

[0032] As a preferred example of the first aspect, the expressions for the power balance constraint, the power transmission limit constraint, the power interaction constraint, and the shared energy storage operation constraint include:

[0033] The expression for the power balance constraint is as follows:

[0034]

[0035] in, Let be the wind power generation capacity of the i-th region at time t. Let be the photovoltaic power generation of the i-th region at time t. Let be the output power of the internal gas turbine in the i-th region at time t. Let be the power purchased from the distribution network by the i-th region at time t. To share the discharge power of the energy storage at time t, Let be the load power of the i-th region of the distribution network at time t. The charging power of the shared energy storage at time t;

[0036] The expression for the power transmission limit constraint is as follows:

[0037]

[0038] in, Let be the power purchased by the distribution network from the i-th region at time t. Let be the state variable of the power purchase from the i-th region at time t. Let be the maximum power purchased by the distribution network from the i-th region at time t. Let be the state variable for the i-th region purchasing electricity from the distribution network at time t. This represents the maximum power purchased from the distribution network by the i-th region at time t.

[0039] The expression for the power interaction constraint is as follows:

[0040]

[0041] in, The maximum allowable interaction power between subregions i and j. Let be the power transfer exchange rate between region i and region j at time t. Let be the interaction power between region i and region j at time t. Let be the interaction power between region j and region i at time t;

[0042] The expression for the shared energy storage operation constraints is as follows:

[0043]

[0044] in, The minimum state of charge for shared energy storage in the distribution network. The state of charge of the shared energy storage in the distribution network at time t. The maximum state of charge for shared energy storage in the distribution network. The charging state of the shared energy storage in the distribution network at time t. This represents the discharge state of the shared energy storage in the distribution network at time t.

[0045] In this preferred embodiment, power balance constraints are used to enforce power balance among power generation, power purchase, energy storage discharge, load consumption, and energy storage charging within each region, ensuring real-time matching of supply and demand in the distribution network at all times and avoiding system instability caused by power surplus or shortage. This application limits the upper limit of power purchase and state variables between the distribution network and regions through power transmission limit constraints, preventing power transmission from exceeding line capacity or equipment safety limits. This application allows power exchange between sub-regions within safe capacity limits through power interaction constraints, promoting resource complementarity between regions and improving overall energy utilization efficiency. By limiting the maximum interaction power and transmission exchange rate, network losses or stability problems caused by excessive power flow between regions are avoided, enhancing the coordinated scheduling capability of multi-regional systems. This application limits the upper and lower limits of the energy storage state of charge and the charging and discharging state through shared energy storage operation constraints, ensuring that shared energy storage operates within a safe range, avoiding overcharging and over-discharging that could damage battery life, and improving the economy and reliability of energy storage devices. State of charge (SOC) constraints provide a feasible range for energy storage dispatch. Combined with electricity price guidance through electricity-carbon coupling, energy storage charging and discharging strategies can be optimized to smooth peak-valley load differences, reduce peak-shaving pressure on traditional generating units, and reduce carbon emissions. In summary, the above constraints together construct the physical and logical dual-layer boundaries of multi-regional distribution networks, ensuring grid security and achieving real-time power balance. Under the premise of safety and reliability, the system can maximize the integration of new energy output, shared energy storage regulation, and regional load response.

[0046] As a preferred example of the first aspect, the joint iterative solution of the upper-level model and the lower-level model based on the first wind power generation capacity, the first photovoltaic power generation capacity, the first gas output capacity, and the first electricity purchase capacity of each region, to output the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme electricity-carbon coupled electricity sales price, includes:

[0047] Based on the first wind power generation, the first photovoltaic power generation, the first gas output, the first electricity purchase, the upper-level model, and the lower-level model of each region, the second electricity-carbon coupled electricity sales price, residual, and carbon dispatch scheme of each region are iteratively updated using the alternating direction multiplier algorithm until the residual of the current iteration is less than a preset threshold. Then, the second electricity-carbon coupled electricity sales price and carbon dispatch scheme of each region in the current iteration are output.

[0048] Based on the output of the second electricity-carbon coupled electricity sales price and carbon dispatch scheme for each region, determine the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme.

[0049] In this preferred example, by dynamically updating the electricity price, residual, and carbon dispatch scheme during the iteration process, combined with the residual threshold convergence criterion, the algorithm ensures that it approaches the global optimal solution while meeting the accuracy requirements. This enables the electricity price to accurately reflect the real-time carbon emission level and power supply and demand relationship in each region, and the carbon dispatch scheme to achieve refined control of the output of gas turbine units, shared energy storage, and renewable energy. The algorithm mechanism supports the formation of a virtuous cycle between the upper and lower level models during iteration—the upper-level electricity price guides the low-carbon response on the load side, and the lower-level dispatch results back-drive the optimization of the electricity price. The final optimal solution output is both economical and low-carbon, ensuring both the overall operating cost of the distribution network and the economic efficiency of power mutual assistance between regions, while reducing system carbon emissions through the coordinated dispatch of shared energy storage.

[0050] Secondly, this application also provides a multi-region low-carbon optimization system for distribution networks based on shared energy storage, including: an upper-level module, an intermediate module, a lower-level module, an iterative solution module, and a control module;

[0051] The upper-level module is used to construct an upper-level model with the optimization objective of minimizing the total operating cost of the distribution network, and to obtain the first electricity sales price for each region by solving the upper-level model;

[0052] The intermediate module is used to calculate the first electricity-carbon coupled electricity price of each region by using the preset power generation of each region and the first electricity price of each region, and to calculate the first load power of each region based on the first electricity-carbon coupled electricity price of each region.

[0053] The lower-level module is used to construct a lower-level model with the optimization objective of minimizing the total regional operating cost. The lower-level model is solved by combining the first load power of each region to obtain the first wind power generation power, the first photovoltaic power generation power, the first gas output power, and the first electricity purchase power of each region.

[0054] The iterative solution module is used to perform joint iterative solution on the upper-level model and the lower-level model based on the first wind power generation power, the first photovoltaic power generation power, the first gas output power, and the first electricity purchase power of each region, and output the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme.

[0055] The control module is used to control the electricity price of each region of the distribution network and the operation of each power supply device according to the optimal electricity price of carbon coupling and the optimal shared energy storage carbon dispatch scheme.

[0056] As a preferred example of the second aspect, the upper-level module includes an upper-level cost calculation unit and an upper-level model building unit;

[0057] The upper-level cost calculation unit is used to obtain the total operating cost of the distribution network based on the power purchase cost of the distribution network from the upper-level power grid, the power purchase cost of the distribution network from the region, and the power sales revenue of the distribution network to the region.

[0058] The upper-level model construction unit is used to construct the upper-level model based on the total operating cost of the distribution network and the first constraint condition; wherein the first constraint condition includes time-of-use electricity price constraint, electricity consumption constraint and unit electricity cost constraint.

[0059] As a preferred example of the second aspect, the expressions for the time-of-use pricing constraint, the electricity consumption constraint, and the electricity cost constraint include:

[0060] The expression for the time-of-use pricing constraint is as follows:

[0061]

[0062]

[0063] in, Let be the electricity price sold by the distribution network to the i-th region at time t. , , These represent the peak, flat, and valley electricity prices for electricity sold from the distribution network to the i-th region. These represent the peak, flat, and valley periods of the i-th region, respectively. The maximum peak-valley electricity price for the i-th region;

[0064] The expression for the power consumption constraint is as follows:

[0065]

[0066] in, Let φ be the maximum average electricity price sold from the distribution network to the i-th region, and φ be the daily electricity consumption change rate of the park's load. The optimized load power for the i-th region of the distribution network at time t. The load power of the i-th region before time t;

[0067] The expression for the unit electricity cost constraint is as follows:

[0068]

[0069] in, The electricity price for the distribution network to the i-th region before optimization at time t.

[0070] As a preferred example of the second aspect, the lower-level module includes a lower-level cost calculation unit and a lower-level model building unit;

[0071] The lower-level cost calculation unit is used to obtain the total operating cost of the region based on the region's electricity purchase cost from the distribution network, the power sharing cost between regions, the daily operating cost of wind turbines in the region, the daily operating cost of photovoltaic power in the region, the daily operating cost of hydropower units in the region, the daily operating cost of shared energy storage, the daily operating cost of gas turbine units in the region, and the revenue from electricity sales from the region to the distribution network.

[0072] The lower-level model building unit is used to build the lower-level model based on the total operating cost of the distribution network and the second constraint condition; wherein the second constraint condition includes power balance constraint, power transmission limit constraint, power interaction constraint and shared energy storage operation constraint.

[0073] As a preferred example of the second aspect, the expressions for the power balance constraint, the power transmission limit constraint, the power interaction constraint, and the shared energy storage operation constraint include:

[0074] The expression for the power balance constraint is as follows:

[0075]

[0076] in, Let be the wind power generation capacity of the i-th region at time t. Let be the photovoltaic power generation of the i-th region at time t. Let be the output power of the internal gas turbine in the i-th region at time t. Let be the power purchased from the distribution network by the i-th region at time t. To share the discharge power of the energy storage at time t, Let be the load power of the i-th region of the distribution network at time t. The charging power of the shared energy storage at time t;

[0077] The expression for the power transmission limit constraint is as follows:

[0078]

[0079] in, Let be the power purchased by the distribution network from the i-th region at time t. Let be the state variable of the power purchase from the i-th region at time t. Let be the maximum power purchased by the distribution network from the i-th region at time t. Let be the state variable for the i-th region purchasing electricity from the distribution network at time t. This represents the maximum power purchased from the distribution network by the i-th region at time t.

[0080] The expression for the power interaction constraint is as follows:

[0081]

[0082] in, The maximum allowable interaction power between subregions i and j. Let be the power transfer exchange rate between region i and region j at time t. Let be the interaction power between region i and region j at time t. Let be the interaction power between region j and region i at time t;

[0083] The expression for the shared energy storage operation constraints is as follows:

[0084]

[0085] in, The minimum state of charge for shared energy storage in the distribution network. The state of charge of the shared energy storage in the distribution network at time t. The maximum state of charge for shared energy storage in the distribution network. The charging state of the shared energy storage in the distribution network at time t. This represents the discharge state of the shared energy storage in the distribution network at time t.

[0086] As a preferred example of the second aspect, the iterative solution module includes an iterative solution unit and an output unit;

[0087] The iterative solution unit is used to iteratively update the second electricity-carbon coupling electricity price, residual, and carbon dispatch scheme of each region based on the first wind power generation, the first photovoltaic power generation, the first gas output power, the first electricity purchase power, the upper-level model, and the lower-level model of each region, using the alternating direction multiplier algorithm, until the residual of the current iteration is less than a preset threshold, and output the second electricity-carbon coupling electricity price and carbon dispatch scheme of each region in the current iteration;

[0088] The output unit is used to determine the optimal electricity-carbon coupled electricity price and the optimal shared energy storage carbon dispatch scheme based on the second electricity-carbon coupled electricity price and carbon dispatch scheme of each region.

[0089] In summary, this application first constructs and solves an upper-level model with the goal of minimizing the total operating cost of the distribution network. This optimizes the economic operation of the distribution network from an overall perspective, rationally determines the electricity sales price in each region, and provides economic guidance for subsequent electricity-carbon coupling calculations and load adjustments. Then, based on the preset generation capacity and the obtained electricity sales price, this application performs electricity-carbon coupling calculations to obtain the electricity-carbon coupled sales price and calculates the load power accordingly. This closely links electricity prices with carbon emissions, forming an effective economic guidance mechanism that encourages load power adjustments in each region towards a low-carbon and economical direction, guides users to use electricity rationally, and reduces electricity demand during high-carbon periods. Finally, this application constructs a lower-level model with the goal of minimizing the total regional operating cost, and solves for the generation capacity in conjunction with the load power, enabling the management of energy output in each region. By optimizing the scheduling of power sources and fully leveraging the roles of coal-fired power units, gas-fired power units, and shared energy storage, this application improves regional energy utilization efficiency and reduces regional operating costs. Finally, through joint iterative solving of the upper and lower layer models, this application achieves positive interaction and data feedback between the upper and lower layers. It comprehensively considers the economic and low-carbon goals of the entire distribution network and each region, ultimately outputting the optimal electricity-carbon coupled sales price and the optimal shared energy storage carbon scheduling scheme. Based on these optimal electricity-carbon coupled sales price and the optimal shared energy storage carbon scheduling scheme, the application controls the electricity-carbon coupled sales price and the operation of each power supply device in each region of the distribution network. This effectively guides the multi-regional distribution network system to achieve a balance between economic operation and low-carbon emissions, ultimately reducing the operating costs of the distribution network and improving its low-carbon level. Attached Figure Description

[0090] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0091] Figure 1 This is a flowchart illustrating an embodiment of a multi-regional low-carbon optimization method for distribution networks based on shared energy storage provided by the present invention.

[0092] Figure 2 A schematic diagram of a multi-regional distribution network system model, representing an embodiment of a multi-regional low-carbon optimization method for distribution networks based on shared energy storage provided by the present invention.

[0093] Figure 3 The iterative solution logic flowchart of an embodiment of a multi-region low-carbon optimization method for distribution networks based on shared energy storage provided by the present invention;

[0094] Figure 4This is a module structure diagram of an embodiment of a multi-regional low-carbon optimization system for distribution networks based on shared energy storage provided by the present invention. Detailed Implementation

[0095] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0096] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0097] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0098] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0099] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0100] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0101] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0102] In this application, shared energy storage refers to an energy storage system (such as battery energy storage) used jointly by multiple areas in a distribution network, achieving time-situated energy distribution through cross-regional collaborative scheduling. Its carbon emission characteristics need to consider the equivalent carbon emissions when powered by traditional energy sources (such as coal-fired and gas-fired power plants), while carbon emissions are zero when powered by renewable energy sources (photovoltaics and wind power). Shared energy storage smooths load fluctuations through charge and discharge regulation, enhances the absorption capacity of new energy sources, and reduces regional operating costs.

[0103] The electricity price coupled with carbon emissions in this application refers to a comprehensive electricity pricing mechanism that combines electricity prices with carbon emission costs. By coupling dynamic carbon emission factors (reflecting the low-carbon level of the regional energy structure) with time-of-use pricing (differentiated pricing for peak / off-peak / valley periods), an economic guidance signal is formed to encourage users to adjust their electricity consumption behavior, reduce electricity demand during high-carbon periods, and promote the shift of load to low-carbon periods.

[0104] Example 1

[0105] See Figure 1 To address the issues of high operating costs and low carbon reduction levels in existing power distribution networks, an embodiment of this invention provides a multi-regional low-carbon optimization method for power distribution networks based on shared energy storage, comprising:

[0106] S1. Construct an upper-level model with the goal of minimizing the total operating cost of the distribution network, and obtain the first electricity price for each region by solving the upper-level model;

[0107] Furthermore, in some embodiments of this application, the construction of the upper-level model with the optimization objective of minimizing the total operating cost of the distribution network includes:

[0108] The total operating cost of the distribution network is obtained by calculating the electricity purchase cost from the upstream power grid, the electricity purchase cost from the region, and the electricity sales revenue from the region.

[0109] The upper-level model is constructed based on the total operating cost of the distribution network and the first constraint condition; wherein the first constraint condition includes time-of-use electricity price constraint, electricity consumption constraint and unit electricity cost constraint.

[0110] Specifically, minimizing the total operating cost of the distribution network as the optimization objective can be achieved through the following schemes:

[0111]

[0112] in, The total operating cost of the distribution network, The cost of electricity purchased by the distribution network from the upstream power grid. The cost of purchasing electricity from the distribution network to the region, Revenue from electricity sales from the distribution network to the region;

[0113] Specifically, the electricity purchase cost of the distribution network from the upstream power grid, the electricity purchase cost of the distribution network from the region, and the electricity sales revenue of the distribution network from the region can be obtained through the following calculation formulas:

[0114]

[0115]

[0116]

[0117] in, The electricity price that the distribution network purchases from the upstream power grid at time t. Let t represent the power purchased by the distribution network from the main grid at time t. Let t be the electricity purchase price of the distribution network to the i-th region at time t. Let be the power purchased by the distribution network from the i-th region at time t. Let t be the electricity price sold from the distribution network to the i-th region at time t. Let be the power sold by the distribution network to the i-th region at time t.

[0118] By incorporating the power purchase costs from the distribution network to the upper-level grid, the power purchase costs from the region, and the revenue from power sales into the total operating cost calculation, this approach comprehensively covers the energy trading revenue and expenditure between the distribution network and the external grid and internal regions. This ensures that the upper-level model aims for global economic optimization and avoids overall cost increases caused by local optimization. Secondly, the upper-level model is constructed based on the first constraint condition, which includes time-of-use pricing constraints, electricity consumption constraints, and unit electricity cost constraints. This allows for differentiated pricing guidance during peak, valley, and normal periods through time-of-use pricing constraints, promoting load-side peak-shifting to balance grid supply and demand. Furthermore, electricity consumption constraints and unit electricity cost constraints limit the reasonable range of electricity prices, ensuring controllable user electricity costs and stable grid revenue. This makes the electricity pricing scheme output by the upper-level model economical, reasonable, and feasible, providing a scientific price signal basis for lower-level regional dispatching. Ultimately, this promotes a positive interaction between economic operation and load regulation in the multi-regional distribution network system.

[0119] Furthermore, in some embodiments of this application, the expressions for the time-of-use pricing constraint, the electricity consumption constraint, and the electricity cost constraint include:

[0120] The expression for the time-of-use pricing constraint is as follows:

[0121]

[0122]

[0123] in, Let be the electricity price sold by the distribution network to the i-th region at time t. , , These represent the peak, flat, and valley electricity prices for electricity sold from the distribution network to the i-th region. These represent the peak, flat, and valley periods of the i-th region, respectively. The maximum peak-valley electricity price for the i-th region;

[0124] The expression for the power consumption constraint is as follows:

[0125]

[0126] in, Let φ be the maximum average electricity price sold from the distribution network to the i-th region, and φ be the daily electricity consumption change rate of the park's load. The optimized load power for the i-th region of the distribution network at time t. The load power of the i-th region before time t;

[0127] The expression for the unit electricity cost constraint is as follows:

[0128]

[0129] in, The electricity price for the distribution network to the i-th region before optimization at time t.

[0130] This approach, by setting time-of-use pricing constraints to define peak, off-peak, and valley electricity price ranges, guides users to adjust their electricity consumption behavior at different times through reasonable price signals. This encourages load shifting to valley periods and reducing peak periods, thereby balancing the load pressure on the distribution network and improving energy efficiency. This application also limits the maximum average electricity price and the daily load variation rate through electricity consumption constraints, ensuring that price adjustments remain within the user's affordability range and preventing drastic changes in electricity demand due to excessive price fluctuations, thus maintaining the stability and predictability of the distribution network load. Furthermore, this application uses unit electricity cost constraints to ensure that the optimized unit electricity cost does not exceed the pre-optimization level, guaranteeing controllable electricity costs for users. In summary, these three factors work together to construct an electricity price control constraint that balances user interests, load stability, and low-carbon goals.

[0131] S2. Based on the preset power generation capacity of each region and the first electricity sales price of each region, the first electricity carbon coupling calculation method is used to obtain the first electricity carbon coupling electricity sales price of each region, and the first load power of each region is calculated based on the first electricity carbon coupling electricity sales price of each region.

[0132] Specifically, the calculation method for the first electricity price coupled with carbon dioxide can be implemented through the following scheme:

[0133]

[0134] in, Let be the electricity price for the carbon-coupled electricity sales in the i-th region of the distribution network at time t. The optimized time-of-use electricity price for the i-th region of the distribution network at time t. Let be the reward / penalty coefficient of the dynamic carbon emission factor of the i-th regional system at time t after standardization. Let be the dynamic carbon emission factor of the i-th regional system at time t.

[0135] Specifically, It can be obtained through the following calculation formula:

[0136]

[0137] in, Let be the reward / penalty coefficient of the dynamic carbon emission factor of the i-th regional system at time t after standardization. Let be the minimum value of the dynamic carbon emission factor at time t in the i-th region. The maximum value of the dynamic carbon emission factor at time t in the i-th region.

[0138] Specifically, It can be obtained through the following calculation formula:

[0139]

[0140] in, Let be the dynamic carbon emission factor of the i-th regional system at time t; The percentage of non-green electricity in a regional distribution network composed of multiple areas is taken as 0.8. Let be the photovoltaic power generation at time t within the i-th regional system; Let be the power generation capacity of the wind turbines within the i-th regional system at time t; The power purchased from the distribution network for the i-th region.

[0141] Specifically, the calculation of the first load power of each region based on the first electricity-carbon coupled electricity sales price of each region can be implemented through the following scheme:

[0142]

[0143] in, Let be the electricity price for the carbon-coupled electricity sales in the i-th region of the distribution network at time t. The optimized time-of-use electricity price for the i-th region of the distribution network at time t corresponds to the peak period of the i-th region. Normal period Hegu period Electricity price, Let be the load power of the i-th region of the distribution network after demand response at time t. Let be the load power of the i-th region of the distribution network before the demand response at time t. It is the self-elasticity matrix; This is the cross-elasticity matrix.

[0144] S3. Construct a lower-level model with the goal of minimizing the total regional operating cost. Solve the lower-level model by combining the first load power of each region to obtain the first wind power generation power, the first photovoltaic power generation power, the first gas output power, and the first electricity purchase power of each region.

[0145] Furthermore, in some embodiments of this application, the step of constructing the lower-level model with the goal of minimizing the total regional operating cost includes:

[0146] The total operating cost of the region is obtained based on the region's electricity purchase cost from the distribution network, the power sharing cost between regions, the daily operating cost of wind turbines within the region, the daily operating cost of photovoltaic power units within the region, the daily operating cost of hydropower units within the region, the daily operating cost of shared energy storage, the daily operating cost of gas turbine units within the region, and the revenue from electricity sales from the region to the distribution network.

[0147] The lower-level model is constructed based on the total operating cost of the distribution network and the second constraint; wherein the second constraint includes power balance constraint, power transmission limit constraint, power interaction constraint and shared energy storage operation constraint.

[0148] Specifically, the optimization objective of minimizing the total regional operating cost can be implemented through the following schemes:

[0149]

[0150] in, For the total operating cost of the region, The cost of purchasing electricity from the regional distribution network, For the cost of power exchange between regions, The daily operating cost of wind turbines in the region, The daily operating cost of photovoltaic power in the region, The daily operating cost of hydropower units in the region, The daily operating cost of shared energy storage on the distribution network side, The daily operating cost of gas turbine units within the region, Revenue from selling electricity to the regional distribution network.

[0151] Specifically, the power exchange cost between regions It can be obtained through the following calculation formula:

[0152]

[0153] in, The power interaction cost coefficient between regions. Let be the interaction power between region i and region j at time t.

[0154] Specifically, the daily operating cost of wind turbines in the region. It can be obtained through the following calculation formula:

[0155]

[0156] in, This represents the unit power operating cost coefficient for wind turbines within the region. Let t be the power generation capacity of the wind turbine in the i-th region at time t.

[0157] Specifically, the daily operating cost of photovoltaic power in the region. It can be obtained through the following calculation formula:

[0158]

[0159] in, This represents the unit power operating cost coefficient for photovoltaic power within the region. Let be the photovoltaic power generation of the i-th region at time t.

[0160] Specifically, the daily operating cost of shared energy storage on the distribution network side It can be obtained through the following calculation formula:

[0161]

[0162] in, To share the operating cost per unit power of energy storage, To share the discharge power of the energy storage at time t, The charging power of the shared energy storage at time t. and These represent the charging and discharging efficiencies of the shared energy storage, respectively.

[0163] Specifically, the daily operating cost of gas turbine units within the region. It can be obtained through the following calculation formula:

[0164]

[0165] in, , Let be the fuel cost coefficient for the gas turbine in the i-th region; Let be the output power of the gas turbine in the i-th region during time period t.

[0166] By incorporating the operating costs of diverse energy devices, shared energy storage, inter-regional power exchange costs, and electricity purchase and sale revenue into the calculation of total regional operating costs, this approach comprehensively covers the economic elements of regional energy dispatch. It ensures that the lower-level model aims for regional economic optimization, achieving synergistic optimization of coal / gas-fired power units, shared energy storage, and renewable energy output, thereby improving the economy and rationality of regional energy utilization. The lower-level model is constructed based on a second set of constraints, including power balance, power transmission limits, regional power interaction, and shared energy storage operation constraints. This ensures real-time matching of regional power supply and demand through power balance, guarantees grid safety through power transmission limit constraints, promotes inter-regional energy exchange through regional power interaction constraints to revitalize existing resources, and manages the charging, discharging, and state-of-charge status of energy storage devices through shared energy storage operation constraints. This allows the lower-level model to optimize regional operating costs while simultaneously ensuring grid safety, reliability, and efficient utilization of shared energy storage.

[0167] Furthermore, in some embodiments of this application, the expressions for the power balance constraint, the power transmission limit constraint, the power interaction constraint, and the shared energy storage operation constraint include:

[0168] The expression for the power balance constraint is as follows:

[0169]

[0170] in, Let be the wind power generation capacity of the i-th region at time t. Let be the photovoltaic power generation of the i-th region at time t. Let be the output power of the internal gas turbine in the i-th region at time t. Let be the power purchased from the distribution network by the i-th region at time t. To share the discharge power of the energy storage at time t, Let be the load power of the i-th region of the distribution network at time t. The charging power of the shared energy storage at time t;

[0171] The expression for the power transmission limit constraint is as follows:

[0172]

[0173] in, Let be the power purchased by the distribution network from the i-th region at time t. Let be the state variable of the power purchase from the i-th region at time t. Let be the maximum power purchased by the distribution network from the i-th region at time t. Let be the state variable for the i-th region purchasing electricity from the distribution network at time t. This represents the maximum power purchased from the distribution network by the i-th region at time t.

[0174] The expression for the power interaction constraint is as follows:

[0175]

[0176] in, The maximum allowable interaction power between subregions i and j. Let be the power transfer exchange rate between region i and region j at time t. Let be the interaction power between region i and region j at time t. Let be the interaction power between region j and region i at time t;

[0177] The expression for the shared energy storage operation constraints is as follows:

[0178]

[0179] in, The minimum state of charge for shared energy storage in the distribution network. The state of charge of the shared energy storage in the distribution network at time t. The maximum state of charge for shared energy storage in the distribution network. The charging state of the shared energy storage in the distribution network at time t. This represents the discharge state of the shared energy storage in the distribution network at time t.

[0180] This approach enforces power balance constraints to ensure real-time matching of supply and demand in the distribution network across different regions, preventing system instability caused by power surplus or shortage. Power transmission limit constraints restrict the upper limit of power purchase and state variables between the distribution network and regions, preventing power transmission from exceeding line capacity or equipment safety limits. Power interaction constraints allow for power exchange between sub-regions within safe capacity limits, promoting resource complementarity and improving overall energy efficiency. By limiting maximum interaction power and transmission exchange rate, excessive power flow between regions prevents network losses or stability issues, enhancing the coordinated dispatch capability of multi-regional systems. Shared energy storage operation constraints limit the upper and lower limits of the energy storage's state of charge and charging / discharging states, ensuring safe operation of shared energy storage, preventing overcharging and over-discharging from damaging battery life, and improving the economy and reliability of energy storage devices. State of charge constraints provide a feasible range for energy storage dispatch; combined with electricity-carbon coupling and electricity sales pricing guidance, this can optimize energy storage charging and discharging strategies, smooth peak-valley load differences, reduce peak-shaving pressure on traditional units, and reduce carbon emissions. In summary, the above constraints together construct the physical and logical dual-layer boundary of the multi-regional distribution network, ensuring grid security and achieving real-time power balance. Under the premise of safety and reliability, the system can maximize the integration of new energy output, shared energy storage regulation, and regional load response.

[0181] S4. Based on the first wind power generation, the first photovoltaic power generation, the first gas output power, and the first electricity purchase power of each region, the upper-level model and the lower-level model are jointly iteratively solved to output the optimal electricity-carbon coupling electricity sales price and the optimal shared energy storage carbon dispatch scheme.

[0182] Furthermore, in some embodiments of this application, the step of jointly iteratively solving the upper-level model and the lower-level model based on the first wind power generation capacity, the first photovoltaic power generation capacity, the first gas output capacity, and the first electricity purchase capacity of each region, and outputting the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme electricity-carbon coupled electricity sales price, includes:

[0183] Based on the first wind power generation, the first photovoltaic power generation, the first gas output, the first electricity purchase, the upper-level model, and the lower-level model of each region, the second electricity-carbon coupled electricity sales price, residual, and carbon dispatch scheme of each region are iteratively updated using the alternating direction multiplier algorithm until the residual of the current iteration is less than a preset threshold. Then, the second electricity-carbon coupled electricity sales price and carbon dispatch scheme of each region in the current iteration are output.

[0184] Based on the output of the second electricity-carbon coupled electricity sales price and carbon dispatch scheme for each region, determine the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme.

[0185] Specifically, to fully explain the above steps, the following scheme will be used as an example:

[0186] like Figure 2 The diagram shown is a schematic of the improved IEEE 33-node distribution network multi-area system model described in this application. The distribution network includes 33 nodes, with 2 photovoltaic (PV) systems, 1 wind turbine, 3 gas turbines, and 1 shared energy storage system connected. PV systems are connected at nodes 15 and 20, each with an installed capacity of 3000 kVA. Gas turbines are connected at nodes 4, 11, and 28, each with an installed capacity of 1000 kVA. Wind turbines are connected at node 31, with an installed capacity of 4000 kVA. Shared energy storage is connected at node 6, with a power and capacity of 1800 kW and 2500 kWh, respectively.

[0187] like Figure 3 The diagram shown is a flowchart of the joint iterative solution. The specific steps are as follows:

[0188] ① Initialize the network parameters of the multi-regional distribution network system and the parameters of photovoltaic, wind turbine, gas turbine, shared energy storage and load power;

[0189] ②Initialize the maximum and minimum values ​​of peak, flat, and valley electricity prices;

[0190] ③ The computer uses the MATLAB platform and calls the Gurobi solver to solve the optimal electricity price model;

[0191] ④ Conduct demand-side response and calculate the corresponding load power data;

[0192] ⑤ Initialize the Alternating Direction Multiplier Method (ADMM) algorithm parameters, step size, and convergence threshold;

[0193] ⑥ Solve for low-carbon operation schemes in each of the lower-level areas;

[0194] ⑦ The ADMM algorithm calculates the residuals and Lagrange multipliers and determines whether they are less than the threshold.

[0195] ⑧ Repeat the above iterative process to obtain the optimal low-carbon scheduling scheme for each region of the lower-level model;

[0196] ⑨ Output the optimal electricity price for electricity sales coupled with carbon emissions and the optimal low-carbon dispatch scheme for each region of the distribution network.

[0197] The active power distribution system operation optimization method of this application was verified by simulation analysis. The simulation results are shown in Table 1.

[0198] Table 1: Optimization Results

[0199]

[0200] By dynamically updating the electricity price, residual, and carbon dispatch scheme during the iteration process, combined with the residual threshold convergence criterion, the algorithm ensures that it approaches the global optimal solution while meeting accuracy requirements. This enables the electricity price to accurately reflect the real-time carbon emission level and power supply and demand relationship in each region, and the carbon dispatch scheme to achieve refined control of gas turbine units, shared energy storage, and renewable energy output. The algorithm mechanism supports the formation of a virtuous cycle between the upper and lower level models during iteration—the upper-level electricity price guides the low-carbon response on the load side, and the lower-level dispatch results back-drive the optimization of the electricity price. The final optimal solution output is both economical and low-carbon, ensuring both the overall operating cost of the distribution network and the economic efficiency of power mutual assistance between regions, while reducing system carbon emissions through the coordinated dispatch of shared energy storage.

[0201] S5. Based on the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme, determine the electricity-carbon coupled electricity sales price for each area of ​​the distribution network, and control the operation of each power supply device and each shared energy storage device.

[0202] In summary, this application first constructs and solves an upper-level model with the goal of minimizing the total operating cost of the distribution network. This optimizes the economic operation of the distribution network from an overall perspective, rationally determines the electricity sales price in each region, and provides economic guidance for subsequent electricity-carbon coupling calculations and load adjustments. Then, based on the preset generation capacity and the obtained electricity sales price, this application performs electricity-carbon coupling calculations to obtain the electricity-carbon coupled sales price and calculates the load power accordingly. This closely links electricity prices with carbon emissions, forming an effective economic guidance mechanism that encourages load power adjustments in each region towards a low-carbon and economical direction, guides users to use electricity rationally, and reduces electricity demand during high-carbon periods. Finally, this application constructs a lower-level model with the goal of minimizing the total regional operating cost, and solves for the generation capacity in conjunction with the load power, enabling the management of energy output in each region. By optimizing the scheduling of power sources and fully leveraging the roles of coal-fired power units, gas-fired power units, and shared energy storage, this application improves regional energy utilization efficiency and reduces regional operating costs. Finally, through joint iterative solving of the upper and lower layer models, this application achieves positive interaction and data feedback between the upper and lower layers. It comprehensively considers the economic and low-carbon goals of the entire distribution network and each region, ultimately outputting the optimal electricity-carbon coupled sales price and the optimal shared energy storage carbon scheduling scheme. Based on these optimal electricity-carbon coupled sales price and the optimal shared energy storage carbon scheduling scheme, the application controls the electricity-carbon coupled sales price and the operation of each power supply device in each region of the distribution network. This effectively guides the multi-regional distribution network system to achieve a balance between economic operation and low-carbon emissions, ultimately reducing the operating costs of the distribution network and improving its low-carbon level.

[0203] Example 2

[0204] like Figure 4 As shown, based on the above method embodiments, corresponding system embodiments are provided;

[0205] One embodiment of the present invention provides a new energy configuration system to support the power supply of the power grid, including: an upper-layer module 41, an intermediate module 42, a lower-layer module 43, an iterative solution module 44, and a control module 45;

[0206] Further, in some embodiments of this application, the upper-layer module 41 is used to construct an upper-layer model with the optimization objective of minimizing the total operating cost of the distribution network, and obtain the first electricity sales price of each region by solving the upper-layer model; the middle-layer module 42 is used to obtain the first electricity-carbon coupled electricity sales price of each region by using the preset power generation of each region and the first electricity sales price of each region through the electricity-carbon coupling calculation method, and calculate the first load power of each region based on the first electricity-carbon coupled electricity sales price of each region; the lower-layer module 43 is used to construct a lower-layer model with the optimization objective of minimizing the total operating cost of the region, and solve the lower-layer model in combination with the first load power of each region to obtain the first electricity sales price of each region. The upper and lower layer models are jointly iteratively solved based on the first wind power generation, the first photovoltaic power generation, the first gas output power, and the first electricity purchase power of each region. The iterative solution module 44 is used to solve the upper and lower layer models together based on the first wind power generation, the first photovoltaic power generation, the first gas output power, and the first electricity purchase power of each region, and output the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme. The control module 45 is used to control the electricity-carbon coupled electricity sales price and the operation of each power supply device in each region of the distribution network based on the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme.

[0207] Furthermore, in some embodiments of this application, the upper-layer module 41 includes an upper-layer cost calculation unit and an upper-layer model construction unit; the upper-layer cost calculation unit is used to obtain the total operating cost of the distribution network based on the power purchase cost of the distribution network from the upper-level power grid, the power purchase cost of the distribution network from the region, and the power sales revenue of the distribution network from the region; the upper-layer model construction unit is used to construct the upper-layer model based on the total operating cost of the distribution network and a first constraint condition; wherein the first constraint condition includes time-of-use electricity price constraint, electricity consumption constraint, and unit electricity cost constraint.

[0208] Furthermore, in some embodiments of this application, the expressions for the time-of-use pricing constraint, the electricity consumption constraint, and the electricity cost constraint include:

[0209] The expression for the time-of-use pricing constraint is as follows:

[0210]

[0211]

[0212] in, Let be the electricity price sold by the distribution network to the i-th region at time t. , , These represent the peak, flat, and valley electricity prices for electricity sold from the distribution network to the i-th region. These represent the peak, flat, and valley periods of the i-th region, respectively. The maximum peak-valley electricity price for the i-th region;

[0213] The expression for the power consumption constraint is as follows:

[0214]

[0215] in, Let φ be the maximum average electricity price sold from the distribution network to the i-th region, and φ be the daily electricity consumption change rate of the park's load. The optimized load power for the i-th region of the distribution network at time t. The load power of the i-th region before time t;

[0216] The expression for the unit electricity cost constraint is as follows:

[0217]

[0218] in, The electricity price for the distribution network to the i-th region before optimization at time t.

[0219] Furthermore, in some embodiments of this application, the lower-level module 43 includes a lower-level cost calculation unit and a lower-level model construction unit; the lower-level cost calculation unit is used to obtain the total operating cost of the region based on the region's electricity purchase cost from the distribution network, the power mutual assistance cost between regions, the daily operating cost of wind turbines in the region, the daily operating cost of photovoltaics in the region, the daily operating cost of hydropower units in the region, the daily operating cost of shared energy storage, the daily operating cost of gas turbine units in the region, and the revenue from electricity sales from the region to the distribution network; the lower-level model construction unit is used to construct the lower-level model based on the total operating cost of the distribution network and a second constraint condition; wherein the second constraint condition includes power balance constraint, power transmission limit constraint, power interaction constraint, and shared energy storage operation constraint.

[0220] Furthermore, in some embodiments of this application, the expressions for the power balance constraint, the power transmission limit constraint, the power interaction constraint, and the shared energy storage operation constraint include:

[0221] The expression for the power balance constraint is as follows:

[0222]

[0223] in, Let be the wind power generation capacity of the i-th region at time t. Let be the photovoltaic power generation of the i-th region at time t. Let be the output power of the internal gas turbine in the i-th region at time t. Let be the power purchased from the distribution network by the i-th region at time t. To share the discharge power of the energy storage at time t, Let be the load power of the i-th region of the distribution network at time t. The charging power of the shared energy storage at time t;

[0224] The expression for the power transmission limit constraint is as follows:

[0225]

[0226] in, Let be the power purchased by the distribution network from the i-th region at time t. Let be the state variable of the power purchase from the i-th region at time t. Let be the maximum power purchased by the distribution network from the i-th region at time t. Let be the state variable for the i-th region purchasing electricity from the distribution network at time t. This represents the maximum power purchased from the distribution network by the i-th region at time t.

[0227] The expression for the power interaction constraint is as follows:

[0228]

[0229] in, The maximum allowable interaction power between subregions i and j. Let be the power transfer exchange rate between region i and region j at time t. Let be the interaction power between region i and region j at time t. Let be the interaction power between region j and region i at time t;

[0230] The expression for the shared energy storage operation constraints is as follows:

[0231]

[0232] in, The minimum state of charge for shared energy storage in the distribution network. The state of charge of the shared energy storage in the distribution network at time t. The maximum state of charge for shared energy storage in the distribution network. The charging state of the shared energy storage in the distribution network at time t. This represents the discharge state of the shared energy storage in the distribution network at time t.

[0233] Further, in some embodiments of this application, the iterative solution module 44 includes an iterative solution unit and an output unit; the iterative solution unit is used to iteratively update the second electricity-carbon coupling electricity price, residual, and carbon dispatch scheme of each region using an alternating direction multiplier algorithm based on the first wind power generation power, the first photovoltaic power generation power, the first gas output power, the first electricity purchase power, the upper-level model, and the lower-level model of each region, until the residual of the current iteration is less than a preset threshold, and output the second electricity-carbon coupling electricity price and carbon dispatch scheme of each region in the current iteration; the output unit is used to determine the optimal electricity-carbon coupling electricity price and the optimal shared energy storage carbon dispatch scheme based on the output second electricity-carbon coupling electricity price and carbon dispatch scheme of each region.

[0234] For more detailed steps and working principles of this embodiment, please refer to the relevant description in Embodiment 2.

[0235] In summary, this application first constructs and solves an upper-level model with the goal of minimizing the total operating cost of the distribution network. This optimizes the economic operation of the distribution network from an overall perspective, rationally determines the electricity sales price in each region, and provides economic guidance for subsequent electricity-carbon coupling calculations and load adjustments. Then, based on the preset generation capacity and the obtained electricity sales price, this application performs electricity-carbon coupling calculations to obtain the electricity-carbon coupled sales price and calculates the load power accordingly. This closely links electricity prices with carbon emissions, forming an effective economic guidance mechanism that encourages load power adjustments in each region towards a low-carbon and economical direction, guides users to use electricity rationally, and reduces electricity demand during high-carbon periods. Finally, this application constructs a lower-level model with the goal of minimizing the total regional operating cost, and solves for the generation capacity in conjunction with the load power, enabling the management of energy output in each region. By optimizing the scheduling of power sources and fully leveraging the roles of coal-fired power units, gas-fired power units, and shared energy storage, this application improves regional energy utilization efficiency and reduces regional operating costs. Finally, through joint iterative solving of the upper and lower layer models, this application achieves positive interaction and data feedback between the upper and lower layers. It comprehensively considers the economic and low-carbon goals of the entire distribution network and each region, ultimately outputting the optimal electricity-carbon coupled sales price and the optimal shared energy storage carbon scheduling scheme. Based on these optimal electricity-carbon coupled sales price and the optimal shared energy storage carbon scheduling scheme, the application controls the electricity-carbon coupled sales price and the operation of each power supply device in each region of the distribution network. This effectively guides the multi-regional distribution network system to achieve a balance between economic operation and low-carbon emissions, ultimately reducing the operating costs of the distribution network and improving its low-carbon level.

[0236] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can realize the multi-region low-carbon optimization method for distribution networks based on shared energy storage provided by any of the above-described method embodiments of the present invention.

[0237] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0238] Example 3

[0239] Based on the above embodiments of the multi-regional low-carbon optimization method for distribution networks based on shared energy storage, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the multi-regional low-carbon optimization method for distribution networks based on shared energy storage according to any embodiment of the present invention.

[0240] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0241] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0242] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0243] Example 4

[0244] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the multi-region low-carbon optimization method for distribution networks based on shared energy storage as described in any of the above-described method embodiments of the present invention.

[0245] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0246] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A multi-regional low-carbon optimization method for distribution networks based on shared energy storage, characterized in that, include: An upper-level model is constructed with the goal of minimizing the total operating cost of the distribution network, and the first electricity price for each region is obtained by solving the upper-level model. Based on the preset power generation capacity of each region and the first electricity sales price of each region, the first electricity carbon coupling calculation method is used to obtain the first electricity carbon coupling electricity sales price of each region, and the first load power of each region is calculated based on the first electricity carbon coupling electricity sales price of each region. A lower-level model is constructed with the goal of minimizing the total regional operating cost. The lower-level model is then solved by combining the first load power of each region to obtain the first wind power generation power, the first photovoltaic power generation power, the first gas output power, and the first electricity purchase power of each region. Based on the first wind power generation, the first photovoltaic power generation, the first gas output power, and the first electricity purchase power of each region, the upper-level model and the lower-level model are jointly iteratively solved to output the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme. Based on the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme, the electricity-carbon coupled electricity sales price for each area of ​​the distribution network is determined, and the operation of each power supply device and each shared energy storage device is controlled. The upper-level model, constructed with the goal of minimizing the total operating cost of the distribution network, includes: The total operating cost of the distribution network is obtained by calculating the electricity purchase cost from the upstream power grid, the electricity purchase cost from the region, and the electricity sales revenue from the region. The upper-level model is constructed based on the total operating cost of the distribution network and the first constraint condition; wherein the first constraint condition includes time-of-use electricity price constraint, electricity consumption constraint and unit electricity cost constraint. The lower-level model, constructed with the goal of minimizing the total regional operating cost, includes: The total operating cost of the region is obtained based on the region's electricity purchase cost from the distribution network, the power sharing cost between regions, the daily operating cost of wind turbines within the region, the daily operating cost of photovoltaic power units within the region, the daily operating cost of hydropower units within the region, the daily operating cost of shared energy storage, the daily operating cost of gas turbine units within the region, and the revenue from electricity sales from the region to the distribution network. The lower-level model is constructed based on the total operating cost of the distribution network and the second constraint; wherein the second constraint includes power balance constraint, power transmission limit constraint, power interaction constraint and shared energy storage operation constraint; The method involves jointly iteratively solving the upper-level model and the lower-level model based on the first wind power generation, the first photovoltaic power generation, the first gas output power, and the first electricity purchase power of each region, to output the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme, including: Based on the first wind power generation, the first photovoltaic power generation, the first gas output, the first electricity purchase, the upper-level model, and the lower-level model of each region, the second electricity-carbon coupled electricity sales price, residual, and carbon dispatch scheme of each region are iteratively updated using the alternating direction multiplier algorithm until the residual of the current iteration is less than a preset threshold. Then, the second electricity-carbon coupled electricity sales price and carbon dispatch scheme of each region in the current iteration are output. Based on the output of the second electricity-carbon coupled electricity sales price and carbon dispatch scheme for each region, determine the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme.

2. The method for multi-regional low-carbon optimization of distribution networks based on shared energy storage as described in claim 1, characterized in that, The expressions for the time-of-use pricing constraint, the electricity consumption constraint, and the electricity cost constraint include: The expression for the time-of-use pricing constraint is as follows: in, Let be the electricity price sold by the distribution network to the i-th region at time t. , , These represent the peak, flat, and valley electricity prices for electricity sold from the distribution network to the i-th region. These represent the peak, flat, and valley periods of the i-th region, respectively. The maximum peak-valley electricity price for the i-th region; The expression for the power consumption constraint is as follows: in, Let φ be the maximum average electricity price sold from the distribution network to the i-th region, and φ be the daily electricity consumption change rate of the park's load. The optimized load power for the i-th region of the distribution network at time t. The load power of the i-th region before time t; The expression for the unit electricity cost constraint is as follows: in, The electricity price for the distribution network to the i-th region before optimization at time t.

3. The method for multi-regional low-carbon optimization of distribution networks based on shared energy storage as described in claim 1, characterized in that, The expressions for the power balance constraint, the power transmission limit constraint, the power interaction constraint, and the shared energy storage operation constraint include: The expression for the power balance constraint is as follows: in, Let be the wind power generation capacity of the i-th region at time t. Let be the photovoltaic power generation of the i-th region at time t. Let be the output power of the internal gas turbine in the i-th region at time t. Let be the power purchased from the distribution network by the i-th region at time t. To share the discharge power of the energy storage at time t, Let be the load power of the i-th region of the distribution network at time t. The charging power of the shared energy storage at time t; The expression for the power transmission limit constraint is as follows: in, Let be the power purchased by the distribution network from the i-th region at time t. Let be the state variable of the power purchase from the i-th region at time t. Let be the maximum power purchased by the distribution network from the i-th region at time t. Let be the state variable for the i-th region purchasing electricity from the distribution network at time t. This represents the maximum power purchased from the distribution network by the i-th region at time t. The expression for the power interaction constraint is as follows: in, The maximum allowable interaction power between subregions i and j. Let be the power transfer exchange rate between region i and region j at time t. Let be the interaction power between region i and region j at time t. Let be the interaction power between region j and region i at time t; The expression for the shared energy storage operation constraints is as follows: in, The minimum state of charge for shared energy storage in the distribution network. The state of charge of the shared energy storage in the distribution network at time t. The maximum state of charge for shared energy storage in the distribution network. The charging state of the shared energy storage in the distribution network at time t. This represents the discharge state of the shared energy storage in the distribution network at time t.

4. A multi-regional low-carbon optimization system for distribution networks based on shared energy storage, characterized in that, include: Upper-level module, middle-level module, lower-level module, iterative solution module, and control module; The upper-level module is used to construct an upper-level model with the optimization objective of minimizing the total operating cost of the distribution network, and to obtain the first electricity sales price for each region by solving the upper-level model; The intermediate module is used to calculate the first electricity-carbon coupled electricity price of each region by using the preset power generation of each region and the first electricity price of each region, and to calculate the first load power of each region based on the first electricity-carbon coupled electricity price of each region. The lower-level module is used to construct a lower-level model with the optimization objective of minimizing the total regional operating cost. The lower-level model is solved by combining the first load power of each region to obtain the first wind power generation power, the first photovoltaic power generation power, the first gas output power, and the first electricity purchase power of each region. The iterative solution module is used to perform joint iterative solution on the upper-level model and the lower-level model based on the first wind power generation power, the first photovoltaic power generation power, the first gas output power, and the first electricity purchase power of each region, and output the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme. The control module is used to control the electricity price of each region of the distribution network and the operation of each power supply device according to the optimal electricity price of carbon coupling and the optimal shared energy storage carbon dispatch scheme. The upper-level model, constructed with the goal of minimizing the total operating cost of the distribution network, includes: The total operating cost of the distribution network is obtained by calculating the electricity purchase cost from the upstream power grid, the electricity purchase cost from the region, and the electricity sales revenue from the region. The upper-level model is constructed based on the total operating cost of the distribution network and the first constraint condition; wherein the first constraint condition includes time-of-use electricity price constraint, electricity consumption constraint and unit electricity cost constraint. The lower-level model, constructed with the goal of minimizing the total regional operating cost, includes: The total operating cost of the region is obtained based on the region's electricity purchase cost from the distribution network, the power sharing cost between regions, the daily operating cost of wind turbines within the region, the daily operating cost of photovoltaic power units within the region, the daily operating cost of hydropower units within the region, the daily operating cost of shared energy storage, the daily operating cost of gas turbine units within the region, and the revenue from electricity sales from the region to the distribution network. The lower-level model is constructed based on the total operating cost of the distribution network and the second constraint; wherein the second constraint includes power balance constraint, power transmission limit constraint, power interaction constraint and shared energy storage operation constraint; The method involves jointly iteratively solving the upper-level model and the lower-level model based on the first wind power generation, the first photovoltaic power generation, the first gas output power, and the first electricity purchase power of each region, to output the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme, including: Based on the first wind power generation, the first photovoltaic power generation, the first gas output, the first electricity purchase, the upper-level model, and the lower-level model of each region, the second electricity-carbon coupled electricity sales price, residual, and carbon dispatch scheme of each region are iteratively updated using the alternating direction multiplier algorithm until the residual of the current iteration is less than a preset threshold. Then, the second electricity-carbon coupled electricity sales price and carbon dispatch scheme of each region in the current iteration are output. Based on the output of the second electricity-carbon coupled electricity sales price and carbon dispatch scheme for each region, determine the optimal electricity-carbon coupled electricity sales price and the optimal shared energy storage carbon dispatch scheme.

5. The multi-regional low-carbon optimization system for distribution networks based on shared energy storage as described in claim 4, characterized in that, The expressions for the time-of-use pricing constraint, the electricity consumption constraint, and the electricity cost constraint include: The expression for the time-of-use pricing constraint is as follows: in, Let be the electricity price sold by the distribution network to the i-th region at time t. , , These represent the peak, flat, and valley electricity prices for electricity sold from the distribution network to the i-th region. These represent the peak, flat, and valley periods of the i-th region, respectively. The maximum peak-valley electricity price for the i-th region; The expression for the power consumption constraint is as follows: in, Let φ be the maximum average electricity price sold from the distribution network to the i-th region, and φ be the daily electricity consumption change rate of the park's load. The optimized load power for the i-th region of the distribution network at time t. The load power of the i-th region before time t; The expression for the unit electricity cost constraint is as follows: in, The electricity price for the distribution network to the i-th region before optimization at time t.

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