A method for collaborative optimization of a virtual power plant participating in a balancing unit in a regional power grid

Abstract

The application discloses a kind of regional power grid in virtual power plant participates in the collaborative optimization method of balancing unit, it is related to power system optimization and dispatching technical field, including the following steps: based on the result of load prediction and demand estimation, construct virtual power plant and balancing unit;With the minimum of regional power system operation cost as target, consider generation cost, balancing cost and load management cost comprehensively, construct upper model;With the maximum of the income of virtual power plant as optimization target as the subject of balancing responsibility, cover deviation service income, market transaction income and deviation penalty cost, construct lower model;Based on upper model and lower model, model solution is carried out through intelligent optimization algorithm, according to the solution result, adjust the power generation output in virtual power plant and balancing unit, energy storage charging and discharging and load management strategy, realize the balance of supply and demand of power system.The application provides theoretical basis and practical reference for new market subject to participate in power market transaction, promote the process of power marketization.

Description

Technical Field

[0001] This invention relates to the field of power system optimization and dispatching technology, and more specifically, to a collaborative optimization method for virtual power plants participating in balancing units in a regional power grid. Background Technology

[0002] With the continued advancement of high-proportion renewable energy sources in new power systems, clean energy sources such as photovoltaics and wind power, after being integrated into the grid on a large scale, have posed new technical challenges to the stability of the power system, power quality assurance, and dispatch planning. Against the backdrop of energy transition, exploring a balanced operation model for the power system that adapts to the high penetration rate of renewable energy has become crucial for ensuring the safe and stable operation of the power grid and promoting the efficient consumption of renewable energy.

[0003] Currently, the power system is at a critical stage of transitioning from a traditional planned dispatch system to a market-based operation mechanism. However, problems such as a relative scarcity of flexibility resources, the need to improve system reliability, and insufficient generation capacity are becoming increasingly prominent. In particular, regarding regional balancing mechanisms, although demand response mechanisms and ancillary service markets exist, the market potential of flexibility adjustment resources has not been fully released, making it difficult to effectively cope with the randomness and volatility of new energy generation.

[0004] Against this backdrop, traditional power system balancing models are no longer sufficient to meet the development needs of new power systems. Therefore, there is an urgent need for an innovative balancing mechanism that integrates heterogeneous resources such as distributed generation devices, energy storage units, and flexible loads to achieve real-time balance between power supply and demand. Summary of the Invention

[0005] To address the aforementioned issues, the present invention aims to provide a collaborative optimization method for virtual power plants participating in balancing units within a regional power grid. This method aims to minimize the operating costs of the regional power system and maximize the revenue of virtual power plants by constructing a two-layer optimization model, thus providing a theoretical basis and practical reference for the innovation of new power system balancing mechanisms.

[0006] To achieve the above technical objectives, this application provides a collaborative optimization method for virtual power plants participating in balancing units in a regional power grid, comprising the following steps: Based on the results of load forecasting and demand calculation, a virtual power plant and balancing unit are constructed. With the goal of minimizing the operating costs of the regional power system, an upper-level model is constructed by comprehensively considering generation costs, balancing costs, and load management costs. With the optimization objective of maximizing the revenue of virtual power plants as the balancing responsibility entities, a lower-level model is constructed, encompassing deviation service revenue, market transaction revenue, and deviation penalty costs. Based on the upper-level and lower-level models, the model is solved using intelligent optimization algorithms. According to the solution results, the power generation output, energy storage charging and discharging, and load management strategies in the virtual power plant and balancing unit are adjusted to achieve the supply and demand balance of the power system.

[0007] Preferably, when performing load forecasting and demand calculation, the changes in social electricity load over a future period are predicted in real time based on historical data; and the load adjustment demand for different time periods is determined according to the load changes, including long-term trend adjustment demand, short-term deviation adjustment demand and ultra-short-term fluctuation adjustment demand.

[0008] Preferably, when constructing virtual power plants and balancing units, the feasibility of virtual power plants participating in the balancing unit mechanism is considered, and a mechanism is established for virtual power plants to participate in the operation of balancing units as balancing responsible entities. In this mechanism, virtual power plants are treated as independent balancing responsible entities, integrating heterogeneous resources such as distributed generation devices, energy storage units, and flexible loads. Regional balancing units including virtual power plants are constructed, and the responsibilities and obligations of each balancing unit are clearly defined.

[0009] Preferably, when constructing the upper-level model, the upper-level model is represented as follows: In the formula, The power generation cost of the entire system, To balance system costs, For the internal load management cost of the system, The unit cost price of resources consumed by the power generation entity during time period t. Let Y be the power generation of the y-th generating unit in the power generation entity within the time period t, where T is one operating cycle and Y is the total number of generating units in the region. Adjusting costs for deviations for power transmission system operators. To reduce costs for power transmission system operators due to deviations. The cost of capacity purchased for transmission system operators. , Adjusting / lowering service prices for the deviation of the balancing service provider purchased by the transmission system operator for time period t; , The deviation reported by balancing unit A within time period t represents the upward / downward adjustment of demand power. , The deviation reported by balancing unit B within time period t represents the upward / downward adjustment of demand power. Let x be the unit cost price for purchasing type x capacity at time t. Let x be the capacity purchased at time t, where x is the total number of capacity purchase types. The unit cost price for load shedding during time period t; Let z be the load shedding amount of the main power generation entity within the time period t, and Z be the number of loads that can be shedding.

[0010] Preferably, when constructing the lower-level model, the lower-level model is represented as follows: In equation (8), To balance the total revenue of the responsible entities within the unit, The revenue obtained by the responsible party for providing deviation services to entities within the unit. To balance the revenue gained by the responsible party in the electricity market through the participation of adjustable resources in the electricity market transaction. The cost of deviation penalties incurred by power transmission system operators To balance the construction costs of resources by the responsible parties in each unit, To adjust the cost of resource usage and losses.

[0011] Preferably, when obtaining the deviation service revenue, the deviation service revenue is expressed as: In the formula, To balance the service fees charged by the responsible party for resource adjustment in balancing unit A, To balance the service fees charged by the responsible party for resource adjustment in balancing unit B, , Adjust the service price for deviations by setting a deviation adjustment for the responsible party of the balancing unit during time period t; , For time period t, the responsible party for the balancing unit A is responsible for the first time period t. Each adjustment resource provides the power to adjust deviations up or down; , For time period t, the responsible party for the balancing unit B is responsible for the first time period t. The power of each regulating resource to provide deviation upward / downward adjustment services; N is the total number of regulating resources owned by the balancing unit responsible party.

[0012] Preferably, when obtaining the market transaction gains of the balancing unit responsible entity, the market transaction gains are expressed as follows: In the formula, , The electricity price for sale / purchase in the market during time period t. , Let t be the power that the nth regulating resource of the balancing unit responsible for time period t can purchase / sell to the market.

[0013] Preferably, when obtaining the penalty cost for deviation of the balancing responsible party, the penalty cost for deviation of the balancing responsible party is expressed as: In the formula, Let be the deviation penalty coefficient for time period t. , The penalty for the balancing entity responsible for the balancing process during time period t, which is unable to meet the upward / downward adjustment requirements of balancing unit A due to deviation; , This refers to the penalty for the responsible party in time period t who is unable to meet the upward / downward adjustment requirements of balancing unit B due to deviation; the specific calculation formula for the penalty is as follows: In the formula, max(a,b) represents taking the maximum value among variables a and b. , The deviation of the power demand reported by the m-th power generation and consumption entity within balancing unit A during time period t to the responsible party of the balancing unit is the upward / downward adjustment of the power demand. , The deviation of the power demand reported by the m-th power generation and consumption entity within balancing unit B during time period t to the responsible party of the balancing unit is the upward / downward adjustment of the power demand. , The power provided by the i-th adjustment resource of balancing unit A to balancing unit B for deviation upward / downward adjustment service during time period t; , Let I be the power provided by the i-th regulating resource of balancing unit B to balancing unit A for deviation adjustment during time period t; I is the total number of adjustable resources in the balancing unit; and M is the total number of all power generation and consumption entities within the balancing unit.

[0014] Preferably, when obtaining the construction cost and usage loss cost of the balancing party's construction of the regulating resources, the construction cost of the balancing party's construction of the regulating resources is expressed as: In the formula, The average power cost coefficient for all regulating resources. To adjust the maximum adjustment power of the resource; The average capacity cost coefficient for all regulating resources. To adjust the maximum adjustment capacity of resources, To balance the average lifespan of the regulatory resources built by the responsible parties; The cost of adjusting resource usage losses is expressed as: In the formula, μ is the coefficient for adjusting the unit loss cost of resources.

[0015] Based on the same inventive concept, this invention also discloses a collaborative optimization system for virtual power plants participating in balancing units in a regional power grid, comprising: The module is used to build virtual power plants and balancing units based on the results of load forecasting and demand calculation; The upper-level model building module is used to construct an upper-level model with the goal of minimizing the regional power system operating costs, taking into account generation costs, balancing costs, and load management costs. The lower-level model construction module is used to construct the lower-level model with the optimization objective of maximizing the revenue of the virtual power plant as the balancing responsibility entity, covering deviation service revenue, market transaction revenue, and deviation penalty cost. The model solving and optimization adjustment module is used to solve the model based on the upper-level model and the lower-level model through intelligent optimization algorithms. Based on the solution results, it adjusts the power generation output, energy storage charging and discharging, and load management strategies in the virtual power plant and balance unit to achieve the supply and demand balance of the power system.

[0016] Based on the same concept, this application also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a collaborative optimization method for virtual power plants participating in balancing units in a regional power grid as described above.

[0017] Based on the same concept, this application also proposes a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to execute a collaborative optimization method for virtual power plants participating in balancing units in a regional power grid, as described above.

[0018] Based on the same concept, this application also proposes a computer program product, which includes a computer program executed by one or more processors to cause the processors to perform a collaborative optimization method for virtual power plants participating in balancing units in a regional power grid as described above.

[0019] The present invention discloses the following technical effects: This invention effectively reduces the operating costs of regional power systems and improves economic efficiency by optimizing dispatch strategies; it uses virtual power plants as independent balancing entities, enabling rapid response to load changes and improving the flexibility and reliability of the power system; it enhances the absorption capacity of renewable energy by integrating distributed generation devices and energy storage units, promoting the optimization and upgrading of the energy structure; and it provides theoretical basis and practical reference for new market entities to participate in electricity market transactions, thus promoting the marketization process of the electricity market. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the method described in this invention; Figure 2 This is a schematic diagram of the mechanism by which the virtual power plant, as described in this invention, participates in the balancing unit as a balancing responsible party; Figure 3 This is a flowchart of the model solving process described in this invention; Figure 4 These are the 24-hour output and load curves of all power generation and consumption entities described in this invention; Figure 5 This is the time-of-use electricity pricing described in this invention; Figure 6 This refers to the pre-adjustment deviation situation described in this invention; Figure 7 This refers to the deviation situation after adjustment as described in this invention; Figure 8 This describes the benefits and costs described in this invention; Figure 9This is a comparison of the regional operating costs of the various solutions described in this invention; Figure 10 This is a schematic diagram of a specific electronic device hardware structure according to an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0023] like Figures 1-10 As shown, this invention provides a collaborative optimization method for virtual power plants participating in balancing units in a regional power grid, specifically including the following steps: Step 1, Load Forecasting and Demand Calculation: Based on historical data, forecast the changes in social electricity load over a future period in real time; determine the load adjustment demand for different time periods based on the load changes, including long-term trend adjustment demand, short-term deviation adjustment demand, and ultra-short-term fluctuation adjustment demand. Step 2, Construction of Virtual Power Plant and Balancing Unit: The virtual power plant is established as an independent balancing entity, integrating heterogeneous resources such as distributed generation devices, energy storage units, and flexible loads; regional balancing units including the virtual power plant are constructed, and the responsibilities and obligations of each balancing unit are clarified. Step 3: Construction of a two-layer optimization model: The upper-layer model takes the minimization of the regional power system operating cost as the objective function, considering factors such as generation cost, balancing cost, and load management cost; the lower-layer model takes the maximization of the revenue of the virtual power plant as the balancing responsibility entity as the objective function, considering factors such as deviation service revenue, market transaction revenue, and deviation penalty cost. Step 4: Model Solving: Intelligent optimization algorithms such as particle swarm optimization are used to solve the two-layer optimization model to obtain the optimal scheduling strategy. Based on the solution results, the power generation output, energy storage charging and discharging, and load management strategies in the virtual power plant and balancing unit are adjusted to achieve power system supply and demand balance.

[0024] Step 1 includes the following specific steps: (1) Based on historical data, predict the changes in social electricity load in real time over a period of time; determine the load adjustment needs for different time periods according to the load changes, including long-term trend adjustment needs, short-term deviation adjustment needs and ultra-short-term fluctuation adjustment needs. In this embodiment, photovoltaic power stations, wind farms, and user loads in a certain region are taken as the research objects. Based on historical output and load curves, the total output and load demand for 24 hours are predicted. Figure 4 (As shown). The time-of-use electricity prices in the market are as follows: Figure 5 As shown.

[0025] Table 1. Internal Adjustment Resource Parameter Settings for Independent Balancing Service Providers Table 2 Relevant Price Parameter Settings Step 2: Further consider the feasibility of virtual power plants participating in the balancing unit mechanism, and establish a mechanism for virtual power plants to participate in the operation of the balancing unit as the balancing responsibility entity; Step 2 includes the following specific steps: Virtual power plants are treated as independent balancing entities, integrating heterogeneous resources such as distributed generation devices, energy storage units, and flexible loads; regional balancing units including virtual power plants are constructed, and the responsibilities and obligations of each balancing unit are clearly defined. A balancing unit consists of multiple market participants, such as power generators, power retailers, and users. Members must achieve internal balance between power production and consumption; if insufficient, adjustments must be made through market transactions. The balancing unit must forecast power supply and demand, adjust its buying and selling plans based on the forecasts, and report to the transmission network company to achieve overall power balance. In a given region, there are four major transmission system operators, with approximately 100 to 200 balancing units in each dispatching area. Balancing units are composed of market participants within the same transmission control area; members must belong to the same dispatching area and cross-regional formation is not permitted. In this model, market participants voluntarily assume balancing responsibility, accepting deviation settlements when imbalances occur, thus incentivizing balancing units to ensure self-balance between power generation and consumption through control strategies and market transactions.

[0026] In a regional electricity market, the balancing entity is a key entity ensuring the balance between electricity supply and demand. This includes large power plants, large industrial users, and new market participants such as aggregators. The balancing entity manages a certain amount of generation and load resources, encompassing both renewable energy and traditional units. Through two core processes—"reporting" and "adjustment"—the balancing entity is responsible for developing and updating the power output curve in real time to maintain the power balance in its assigned region. The balancing entity must report the projected output curve to the transmission system operator and adjust it based on actual operating data to reduce forecast deviations. The transmission system operator records the output deviations of the balancing entity and settles accounts through the reserve market, with the balancing entity bearing the imbalance costs. To incentivize the balancing entity to effectively manage the power balance, the reserve market price is higher than the spot market price, creating an imbalance price difference and providing economic incentives. The specific functions of the balancing entity are shown in the table below: Table 3 Specific Functions of the Balancing Responsibility Party As the core operating institution of the power system, the transmission system operator undertakes the important function of regional balance dispatch. Its specific responsibilities include managing the real-time dispatch of the balancing responsibility parties' declared power curves and reserve resources; optimizing the intraday trading closing time and time granularity of trading products in the spot market, improving the adjustment capability of the balancing responsibility parties' declared power curves, and thus enhancing the real-time operating efficiency of the system. The transmission system operator is responsible for the operation of the transmission network, coordinating day-ahead and real-time power transactions, resolving network congestion issues, and ensuring system power balance by participating in the spot market and reserve market to procure various types of reserve products. Simultaneously, the transmission system operator is also responsible for the settlement of imbalance funds and the monitoring of power supply security, ensuring the stable operation of the power system under extreme conditions.

[0027] The participation of balancing service providers is crucial for the stable operation of the power system, especially with the increasing proportion of renewable energy, making their role even more important. Balancing service providers are market participants with standby supply units or groups, capable of providing balancing services to transmission system operators, including balancing energy and balancing capacity. Their role in the electricity market is to respond to dispatch instructions from transmission system operators by rapidly increasing or decreasing power output to balance unexpected fluctuations in the grid. Balancing service providers receive capacity payments for their availability and energy payments when transmission system operators activate the balancing market. Details are shown in the table below: Table 4 Specific Functions of Balance Service Providers Against the backdrop of the development of new power systems, this paper focuses on the complex operating environment caused by the high proportion of renewable energy grid integration, diversified loads, and the access of emerging market players. It systematically analyzes core issues such as the increased risk of grid imbalance, rising system balancing costs, and the inefficiency of traditional balancing models. Drawing on the design concept of German balancing units, this invention proposes a balancing unit model and its operating mechanism adapted to the characteristics of my country's new power system. By integrating virtual power plants, an emerging market player, into the balancing unit operating framework, a two-layer optimization model is constructed, with the upper-level objective of minimizing regional power system operating costs and the lower-level objective of maximizing the revenue of virtual power plants as the balancing responsibility entity. Figure 2 The diagram shown is the main flowchart of the virtual power plant participating in the balancing unit mechanism.

[0028] In this embodiment, when the established virtual power plant participates in the balancing unit as the balancing responsible party, the virtual power plant includes two regulating units, A and B: Unit A is configured with 4 regulating resources, and Unit B is configured with 2 regulating resources, to coordinate the supply and demand deviation of the power generation and consumption entities. The construction cost of the regulating resources adopts an amortization model, with an average power cost of 100,000 yuan / MW and an average capacity cost of 50,000 yuan / MWh.

[0029] Step 3: Establish a two-layer optimization model in which virtual power plants within the region participate as balancing entities in the balancing unit; Step 3 includes the following specific steps: (1) The upper layer of the constructed two-layer optimization model is a system containing two balancing units, and its optimization objective is to minimize the system operating cost. The power generation entity generates electricity based on output forecast and load demand, resulting in power generation costs; the transmission system operator pre-purchases capacity and purchases upward and downward adjustment services based on deviation adjustment prices, resulting in balancing costs; the system implements load management under the demand response mechanism, resulting in compensation costs. Based on the above, a regional system internal operating cost minimization model is constructed, as shown in formula (1).

[0030] (1) In equation (1), The total power generation cost of the entire system, To balance system costs, This refers to the cost of internal load management within the system.

[0031] The internal power generation cost refers to all costs involved in generating electricity by a power plant, including fuel costs, operating costs, etc. Power generation resources will be lost in each power generation call. Since the load level is related to time t and the power generation cost will vary depending on the energy type and regional differences, this invention considers the power generation to be a thermal power unit in a certain region, as shown in the formula.

[0032] (2) In equation (2), The unit cost price of resources consumed by the power generation entity during time period t; Let Y be the power generation of the y-th generator unit within the power generation entity in the region during time period t. T is one operating cycle, and Y is the total number of generator units in the region.

[0033] To balance costs, Adjusting costs for deviations for power transmission system operators. To reduce costs for power transmission system operators due to deviations. The cost of the capacity purchased for the power transmission system operator is expressed as in equation (3).

[0034] (3) (4) (5) (6) In equations (4)-(6), , Adjusting / lowering service prices for the deviation of the balancing service provider purchased by the transmission system operator for time period t; , The deviation reported by balancing unit A within time period t represents the upward / downward adjustment of demand power. , The deviation reported by balancing unit B within time period t represents the upward / downward adjustment of demand power. The unit cost price for purchasing type x capacity at time t. Let x be the capacity purchased at time t for type x. X is the total number of capacity purchase types.

[0035] The internal load management cost of the system is the cost that users are notified in advance under the demand response framework, including the cost of load shedding, as shown in the formula.

[0036] (7) In equation (7), The unit cost price for load shedding during time period t; Let z be the load shedding amount of the main power generation entity within the time period t. Z is the number of loads that can be shedding.

[0037] (2) The lower-level model takes the maximization of revenue for the virtual power plant as the balancing responsibility entity as the optimization objective. To meet the unit balancing needs and reduce the assessment risk of the balancing responsibility entity, it is assumed that each balancing unit is composed of power generation and power consumption entities and is managed by a single balancing responsibility entity. Each entity within the unit predicts the power generation and submits a plan to the balancing responsibility entity. The balancing responsibility entity can participate in spot market transactions based on real-time market prices and conduct power buying and selling. The balancing responsibility entity provides deviation adjustment services by reasonably scheduling its own adjustment resources within the unit and charges service fees to the entities within the unit; the costs include the deviation assessment fees borne by the balancing responsibility entity, the loss costs of its own adjustment resources, and the construction costs of the adjustment resources. Based on this, a revenue maximization model for the balancing responsibility entity is constructed, and its revenue allocation problem is shown in Equation (8).

[0038] (8) In equation (8), To balance the total revenue of the responsible parties in the unit, The revenue obtained by the responsible party for providing deviation services to entities within the unit. To balance the revenue gained by the responsible party in the electricity market through the participation of adjustable resources in the electricity market transaction. The cost of deviation penalties incurred by power transmission system operators To balance the construction costs of resources by the responsible parties in each unit, To adjust the cost of resource usage and losses.

[0039] The economic benefits obtained by balancing the deviation service revenue, balancing the responsibility of the responsible entity to dynamically optimize the deviation service pricing strategy, scheduling and adjusting resources to provide deviation adjustment services, and charging the corresponding service fees to the power generation and consumption entities are shown in equation (9).

[0040] (9) (10) (11) In equations (9)-(11), To balance the service fees charged by the responsible party for resource adjustment in balancing unit A, To balance the service fees charged by the responsible party for resource adjustment in balancing unit B, , Adjust the service price for deviations by setting a deviation adjustment for the responsible party of the balancing unit during time period t; , For time period t, the responsible party for the balancing unit A is responsible for the first time period t. Each adjustment resource provides the power to adjust deviations up or down; , For time period t, the responsible party for the balancing unit B is responsible for the first time period t. The power of each regulating resource to provide deviation upward / downward adjustment services; N is the total number of regulating resources owned by the balancing unit responsible party.

[0041] To balance the revenue of the responsible entity participating in market transactions, this invention considers the arbitrage revenue earned by purchasing electricity during periods of low electricity prices and selling it during periods of high electricity prices, as shown in equation (12).

[0042] (12) In equation (12), , The electricity price for sale / purchase in the market during time period t. , Let t be the power that the nth regulating resource of the balancing unit responsible for time period t can purchase / sell to the market.

[0043] To balance the deviation penalty cost of the responsible party, when there is still a deviation between the power generation and consumption entities within the balancing unit and all the adjustment resources of the responsible party of the balancing unit have no adjustment capacity and the unit cannot be balanced through transactions, the responsible party of the balancing unit shall bear the deviation penalty cost for the power generation and consumption entities. The calculation method of this cost is shown in Equation (13).

[0044] (13) (14) (15) In equations (13)-(15), Let be the deviation penalty coefficient for time period t. , This refers to the penalty for upward / downward adjustment that the responsible entity for balancing during time period t must bear because it cannot meet the upward / downward adjustment requirements of balancing unit A. , This refers to the penalty for upward / downward adjustment that the responsible party for balancing during time period t must bear due to its inability to meet the upward / downward adjustment requirements of balancing unit B. The specific calculation formula for this penalty is as follows: (16) (17) (18) (19) In equations (16)-(19), max(a,b) represents taking the maximum value among variables a and b. , The deviation of the power demand reported by the m-th power generation and consumption entity within balancing unit A during time period t to the responsible party of the balancing unit is the upward / downward adjustment of the power demand. , The deviation of the power demand reported by the m-th power generation and consumption entity within balancing unit B during time period t to the responsible party of the balancing unit is the upward / downward adjustment of the power demand. , The power provided by the i-th adjustment resource of balancing unit A to balancing unit B for deviation upward / downward adjustment service during time period t; , Let I be the power provided by the i-th regulating resource of balancing unit B to balancing unit A for deviation adjustment during time period t; I is the total number of adjustable resources in the balancing unit; and M is the total number of all power generation and consumption entities within the balancing unit.

[0045] To balance the construction costs of the regulatory resources by the responsible parties, this invention treats all regulatory resources as a whole and considers their average construction costs from both power and capacity perspectives, as shown in Equation (20).

[0046] (20) In equation (20), The average power cost coefficient for all regulating resources. To adjust the maximum adjustment power of the resource; The average capacity cost coefficient for all regulating resources. To adjust the maximum adjustment capacity of resources, To balance the average lifespan of the regulatory resources built by the responsible parties.

[0047] To adjust the cost of resource usage loss, as shown in equation (21).

[0048] (twenty one) (twenty two) (twenty three) In equations (21)-(23), μ is the coefficient for adjusting the unit loss cost of resources.

[0049] Step 4: Based on Step 3, use an optimization algorithm to iteratively optimize the two-layer model and find the optimal solution.

[0050] Step 4 includes the following specific steps: Intelligent optimization algorithms such as particle swarm optimization are used to solve the two-level optimization model to obtain the optimal scheduling strategy. Based on the solution results, the power generation output, energy storage charging and discharging, and load management strategies in the virtual power plant and balancing unit are adjusted to achieve the supply and demand balance of the power system.

[0051] (1) Parameter initialization and variable setting: Define the parameters required for the model and algorithm, and use random functions to initialize the decision variables (including power, price and deviation requirements submitted by each subject) and their constraints of the upper and lower level models.

[0052] (2) Fitness assessment and particle update: Calculate the fitness value of each particle and update the velocity and position information of the particles based on this to explore a better solution space. Perform boundary detection and correction on the particle velocity to ensure that the updated velocity and position meet the preset constraints.

[0053] (3) Optimal solution update and policy storage: Update the local optimal solution of individual particles and the global optimal solution of the particle swarm, and store and update the decision variable policy and its corresponding objective function value related to the model.

[0054] (4) Iteration termination judgment and result output: Determine whether the current iteration number has reached the maximum number of iterations preset by the algorithm. If not, return to step (2) to continue iterative calculation; if it has reached, terminate the solution and output the optimal value.

[0055] In this embodiment, MATLAB software is used to model and solve the problem, and the number of particles is set to 10 for 5000 algorithm iterations. The deviation service price is shown in the appendix of Table 5.

[0056] Table 5: Optimal Service Price Adjustments for Different Time Periods Each power generation and consumption entity adjusts the service price upwards and downwards based on the time-of-use deviations stipulated by the balancing responsibility party. According to their respective positive and negative deviations, they determine their optimal purchasing strategy for deviation adjustment services, and formulate their adjustments according to the specific circumstances of different power generation and consumption entities. A comparison of the deviations before and after the adjustments is provided below. Figure 6 and Figure 7As shown in the figure, there are positive and negative deviations of varying magnitudes throughout the day, most of which can be successfully adjusted by the balancing party. Negative deviations are mostly caused by predicted load values ​​exceeding actual values, while positive deviations are due to wind and solar power output exceeding predictions. At times 1:00, 2:00, 7:00, 20:00, and 23:00, there are unadjusted positive and negative deviations, with larger positive deviations at 2:00 and 23:00, and a larger negative deviation at 7:00. At 23:00, wind power output is very high, resulting in a large positive deviation. The balancing party incentivizes renewable energy providers to purchase deviation-reduction services by setting lower negative electricity prices, but this incurs a high deviation penalty. The reduced electricity allocated to power generators during this period is insufficient, and market electricity prices are at peak levels. The balancing party then adjusts priority power sales resources to obtain price difference revenue.

[0057] The virtual power plant, acting as the balancing party, considers the various benefits and costs at different times, such as... Figure 8 As shown, when internal adjustment resources successfully adjust positive and negative deviations, significant service and transaction revenues can be generated. Conversely, if positive and negative deviations remain unaddressed, substantial penalty costs will be incurred. During certain periods, such as 7:00 AM and 2:00 PM, negative service revenues are generated due to the negative service price for adjusting deviations. Market electricity prices are mostly flat or peak, and deviations from demand are generally within the adjustable capacity range. The balancing party can profit from selling electricity in market transactions while simultaneously obtaining higher service revenues. Overall, the benefits gained by the balancing party significantly outweigh the costs incurred.

[0058] Finally, from the perspectives of regional system operating costs and virtual power plants as the balancing party, the new model is compared with various existing mechanisms to comprehensively evaluate its economic benefits and costs. Regarding the analysis of regional power operating costs, the market entity application scenarios and key parameters set above remain unchanged; the comparison results under different schemes are detailed in Table 6.

[0059] Table 6. Deviation Adjustment Costs for Different Schemes like Figure 9As shown, Scheme 1, serving as the benchmark, adopts a passive operation mode. Its operational results directly reflect the supply-demand imbalance caused by the lack of flexible adjustment methods in traditional power systems. The high cost under this operating state essentially reflects the efficiency loss caused by insufficient system adjustment capacity. Scheme 2 improves the operating mechanism by introducing a deviation assessment mechanism for transmission system operators. Power generation companies need to reserve corresponding adjustment capacity based on installed capacity or maximum load level, energy storage operators need to bear the full life-cycle construction cost of supporting energy storage facilities, and transmission system operators need to conduct deviation assessments. Although it achieves approximately 29.3% cost optimization compared to Scheme 1, it still has significant drawbacks: 1) Market participants face high initial investment thresholds, especially energy storage operators who need to bear heavy fixed asset investment pressure; 2) The fixed price mechanism leads to a lack of revenue elasticity, making it difficult to reflect the true value of resources. Scheme 3 is the innovative scheme of this invention, the core of which lies in constructing a dual adjustment system of "professional services from balancing service providers + deviation management by virtual power plants as balancing responsible parties." By introducing a specialized market for balancing service providers, high-quality real-time balancing services are offered to the system. New market players, such as virtual power plants, are incorporated into the balancing responsibility management framework, with a deviation assessment mechanism incentivizing them to improve the accuracy of generation / load forecasting. Simultaneously, dynamic market price signals are used to accurately reflect the spatiotemporal value of system regulation resources. This synergistic operation mechanism of "market incentives and professional regulation" not only significantly reduces the total system cost but also promotes the efficient consumption of clean energy and deep participation of demand-side resources, providing a reference market-oriented operation paradigm for building a new type of power system.

[0060] Finally, by setting various parameters that are the same as or similar to those of the balancing party, as shown in Table 7, the aggregation operation mode of the existing virtual power plant of the load aggregator is compared with that of the balancing party.

[0061] Table 7 Revenue from Different Operating Models Compared to the model where virtual power plants and load aggregators generate revenue by providing peak-shaving ancillary services and demand response, virtual power plants, as balancing entities, obtain higher returns through more flexible and market-oriented proactive resource allocation services, including deviation adjustment services and price arbitrage.

[0062] This invention achieves the minimization of regional power system operating costs and the maximization of virtual power plant revenues by constructing upper-level and lower-level models and employing intelligent optimization algorithms such as particle swarm optimization. The upper-level model aims to minimize regional power system operating costs, comprehensively considering generation costs, balancing costs, and load management costs. The lower-level model aims to maximize virtual power plant revenues, encompassing factors such as deviation service revenues, market transaction revenues, and deviation penalty costs. Through real-time load forecasting and demand calculation, heterogeneous resources such as distributed generation devices, energy storage units, and flexible loads are integrated to construct a regional balancing unit including virtual power plants, ultimately obtaining the optimal dispatch strategy to ensure the supply and demand balance of the power system.

[0063] Figure 10 This diagram illustrates a more specific hardware structure of an electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0064] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0065] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0066] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0067] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and power equipment. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0068] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0069] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0070] The electronic devices described above are used to implement a collaborative optimization method for virtual power plants participating in balancing units in a regional power grid, as described in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0071] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, which are used to cause the computer to execute a collaborative optimization method for virtual power plants participating in balancing units in a regional power grid as described in any of the above embodiments.

[0072] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0073] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute a collaborative optimization method for virtual power plants participating in balancing units in a regional power grid as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0074] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a computer program product, which includes a computer program. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to execute the collaborative optimization method for virtual power plants participating in balancing units in a regional power grid as described in the above embodiments. Corresponding to the execution entity for each step in each embodiment of the collaborative optimization method for virtual power plants participating in balancing units in a regional power grid, the processor executing the corresponding step can belong to the corresponding execution entity.

[0075] To simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0076] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0077] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of this application as described above, which are not provided in the details for the sake of brevity.

[0078] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A collaborative optimization method for virtual power plants participating in balancing units in a regional power grid, characterized in that, Includes the following steps: Based on the results of load forecasting and demand calculation, a virtual power plant and balancing unit are constructed. With the goal of minimizing the operating costs of the regional power system, an upper-level model is constructed by comprehensively considering generation costs, balancing costs, and load management costs. With the optimization objective of maximizing the revenue of virtual power plants as the balancing responsibility entities, a lower-level model is constructed, encompassing deviation service revenue, market transaction revenue, and deviation penalty costs. Based on the upper-level model and the lower-level model, the model is solved using an intelligent optimization algorithm. Based on the solution results, the power generation output, energy storage charging and discharging, and load management strategies within the virtual power plant and balancing unit are adjusted.

2. The collaborative optimization method for virtual power plants participating in balancing units in a regional power grid according to claim 1, characterized in that: When conducting load forecasting and demand calculation, based on historical data, the changes in social electricity load over a future period are predicted in real time. According to the load changes, the load adjustment demand for different time periods is determined, including long-term trend adjustment demand, short-term deviation adjustment demand, and ultra-short-term fluctuation adjustment demand.

3. The collaborative optimization method for virtual power plants participating in balancing units in a regional power grid according to claim 2, characterized in that: When constructing virtual power plants and balancing units, the feasibility of virtual power plants participating in the balancing unit mechanism should be considered. A mechanism should be established for virtual power plants to participate in the operation of balancing units as balancing responsible entities. In this mechanism, virtual power plants should be treated as independent balancing responsible entities, integrating heterogeneous resources such as distributed generation devices, energy storage units, and flexible loads. Regional balancing units including virtual power plants should be constructed, and the responsibilities and obligations of each balancing unit should be clearly defined.

4. The collaborative optimization method for virtual power plants participating in balancing units in a regional power grid according to claim 3, characterized in that: When constructing the upper-level model, the upper-level model is represented as follows: In the formula, The total power generation cost of the entire system, To balance system costs, For the internal load management cost of the system, The unit cost price of resources consumed by the power generation entity during time period t. Let Y be the power generation of the y-th generating unit in the power generation entity within the time period t, where T is one operating cycle and Y is the total number of generating units in the region. Adjusting costs for deviations for power transmission system operators. To reduce costs for power transmission system operators due to deviations. The cost of capacity purchased for transmission system operators. , Adjusting / lowering service prices for the deviation of the balancing service provider purchased by the transmission system operator for time period t; , The deviation reported by balancing unit A within time period t represents the upward / downward adjustment of demand power. , The deviation reported by balancing unit B within time period t represents the upward / downward adjustment of demand power. Let x be the unit cost price for purchasing type x capacity at time t. Let x be the capacity purchased at time t, where x is the total number of capacity purchase types. The unit cost price for load shedding during time period t; Let z be the load shedding amount of the main power generation entity within the time period t, and Z be the number of loads that can be shedding.

5. The collaborative optimization method for virtual power plants participating in balancing units in a regional power grid according to claim 4, characterized in that: When constructing the lower-level model, the lower-level model is represented as follows: In equation (8), To balance the total revenue of the responsible parties in the unit, The revenue obtained by the responsible party for providing deviation services to entities within the unit. To balance the revenue gained by the responsible party in the electricity market through the participation of adjustable resources in the electricity market transaction. The cost of deviation penalties incurred by power transmission system operators To balance the construction costs of resources by the responsible parties in each unit, To adjust the cost of resource usage and losses.

6. The collaborative optimization method for virtual power plants participating in balancing units in a regional power grid according to claim 5, characterized in that: When obtaining the revenue from deviation services, the revenue from deviation services is expressed as follows: In the formula, To balance the service fees charged by the responsible party for resource adjustment in balancing unit A, To balance the service fees charged by the responsible party for resource adjustment in balancing unit B, , Adjust the service price for deviations by setting a deviation adjustment for the responsible party of the balancing unit during time period t; , For time period t, the responsible party for the balancing unit A is responsible for the first time period t. Each adjustment resource provides the power to adjust deviations up or down; , For time period t, the responsible party for the balancing unit B is responsible for the first time period t. The power of each regulating resource to provide deviation upward / downward adjustment services; N is the total number of regulating resources possessed by the balancing unit responsible party; Specifically, when obtaining the market transaction gains of the balancing unit responsible entity, the market transaction gains are expressed as follows: In the formula, , The electricity price for sale / purchase in the market during time period t. , Let n be the power that the nth regulating resource of the balancing unit responsible for time period t can purchase / sell electricity to the market; When obtaining the penalty cost for deviation of the responsible party in balancing the liability, the penalty cost for deviation of the responsible party in balancing the liability is expressed as follows: In the formula, Let be the deviation penalty coefficient for time period t. , The penalty for the balancing entity responsible for the balancing process during time period t, which is unable to meet the upward / downward adjustment requirements of balancing unit A due to deviation; , This refers to the penalty for the responsible party in time period t who is unable to meet the upward / downward adjustment requirements of balancing unit B due to deviation; the specific calculation formula for the penalty is as follows: In the formula, max(a,b) represents taking the maximum value among variables a and b. , The deviation of the power demand reported by the m-th power generation and consumption entity within balancing unit A during time period t to the responsible party of the balancing unit is the upward / downward adjustment of the power demand. , The deviation of the power demand reported by the m-th power generation and consumption entity within balancing unit B during time period t to the responsible party of the balancing unit is the upward / downward adjustment of the power demand. , The power provided by the i-th adjustment resource of balancing unit A to balancing unit B for deviation upward / downward adjustment service during time period t; , Let I be the power provided by the i-th regulating resource of balancing unit B to balancing unit A for deviation adjustment during time period t; I is the total number of adjustable resources in the balancing unit; and M is the total number of all power generation and consumption entities within the balancing unit.

7. The collaborative optimization method for virtual power plants participating in balancing units in a regional power grid according to claim 6, characterized in that: When obtaining the construction cost and usage loss cost of the balancing party's construction and adjustment resources, the construction cost of the balancing party's construction and adjustment resources is expressed as follows: In the formula, The average power cost coefficient for all regulating resources. To adjust the maximum adjustment power of the resource; The average capacity cost coefficient for all regulating resources. To adjust the maximum adjustment capacity of resources, To balance the average lifespan of the regulatory resources built by the responsible parties; The cost of adjusting resource usage losses is expressed as: In the formula, μ is the coefficient for adjusting the unit loss cost of resources.

8. A collaborative optimization system for virtual power plants participating in balancing units in a regional power grid, used to execute the collaborative optimization method for virtual power plants participating in balancing units in a regional power grid as described in any one of claims 1-7, characterized in that, include: The module is used to build virtual power plants and balancing units based on the results of load forecasting and demand calculation; The upper-level model building module is used to construct an upper-level model with the goal of minimizing the regional power system operating costs, taking into account generation costs, balancing costs, and load management costs. The lower-level model construction module is used to construct the lower-level model with the optimization objective of maximizing the revenue of the virtual power plant as the balancing responsibility entity, covering deviation service revenue, market transaction revenue, and deviation penalty cost. The model solving and optimization adjustment module is used to solve the model based on the upper-level model and the lower-level model using an intelligent optimization algorithm, and adjust the power generation output, energy storage charging and discharging, and load management strategies in the virtual power plant and balancing unit according to the solution results.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to perform the method described in any one of claims 1 to 7.

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