Methods and equipment for virtual power plants to participate in the trading of electricity and reserve markets

Abstract

A method and equipment for virtual power plants participating in electricity and reserve market trading decisions are disclosed. The method includes: constructing an uncertainty model and generating multiple uncertainty scenarios; constructing day-ahead and real-time market decision optimization models for participating in the electricity and reserve markets based on the internal resources of the virtual power plant; constructing a distributed resource peer-to-peer trading model to provide peer-to-peer trading constraints for decision-making; introducing peer-to-peer trading constraints into the day-ahead market decision optimization model and solving for the day-ahead bidding strategy; randomly selecting an uncertainty scenario as the day-ahead market and calculating a simulated day-ahead bidding strategy for that scenario; based on the simulated day-ahead bidding strategy, introducing peer-to-peer trading constraints into the real-time market decision optimization model and solving for the real-time bidding strategy; and making trading decisions accordingly. This method solves the problem that virtual power plants cannot effectively aggregate surplus resources to participate in the market, thereby improving the resilience and reliability of the new power system.

Description

Technical Field

[0001] This invention relates to the technical field of virtual power plants, and in particular to a method and equipment for virtual power plants to participate in trading decisions in the electricity and reserve markets. Background Technology

[0002] With the continuous modernization of urban power systems and the diversification of loads, peak electricity loads are constantly increasing. Coupled with the impact of extreme weather, this places a significant burden on the power supply and demand balance in relevant regions. During peak load periods, the power grid faces risks and challenges such as excessively high regional grid load rates, peak-shifting power rationing, and seasonal local power shortages. The power grid exhibits significant "double-high" and "double-peak" characteristics, with insufficient reserve capacity and flexible adjustment capabilities. Massive amounts of diverse adjustable loads, distributed generation (DG), and energy storage are increasingly available on the distribution and consumption side. Virtual power plants (VPPs) combine these various types of resources. Constructing virtual power plants based on abundant resources plays a crucial role in improving system regulation capabilities and alleviating power shortages, and their full exploration and utilization are essential.

[0003] Currently, both domestic and international entities are actively exploring and applying virtual power plant (VPG) technology. In my country, VPG technology is generally in its initial exploratory and pilot demonstration stage, with many areas needing improvement and refinement in related business models and key technologies. Simultaneously, due to the introduction of massive and diverse distributed resources, traditional energy consumers are gradually transforming into energy producer-consumers (prosumers). Allowing peer-to-peer (P2P) transactions between prosumers is a crucial way to improve energy utilization, incentivize participation, and increase resource economics. However, domestic P2P and even distributed transaction mechanisms are not yet mature, failing to support the orderly integration of VPGs and distributed transactions. Furthermore, due to insufficient existing modeling technology, even with P2P transactions existing within internal resources, VPGs cannot aggregate surplus resources to participate in the market. Summary of the Invention

[0004] The purpose of this invention is to solve the problem that virtual power plants cannot effectively aggregate surplus resources to participate in the market, and to provide a method and equipment for virtual power plants to participate in the trading decision-making of the electricity and reserve market.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for making trading decisions for a virtual power plant participating in the electricity and reserve markets includes the following steps: S1: Constructing an uncertainty model and generating multiple uncertainty scenarios; S2: Based on the internal resource situation of the virtual power plant, constructing a day-ahead market decision optimization model and a real-time market decision optimization model for participating in the electricity and reserve markets; S3: Constructing a distributed resource peer-to-peer trading model to provide peer-to-peer trading constraints when making decisions; S4: Introducing the peer-to-peer trading constraints into the day-ahead market decision optimization model and solving the day-ahead market decision optimization model to obtain a day-ahead bidding strategy; S5: Randomly selecting one of the uncertainty scenarios as the day-ahead market and calculating a simulated day-ahead bidding strategy under that scenario. Based on the simulated day-ahead bidding strategy, introducing the peer-to-peer trading constraints into the real-time market decision optimization model and solving the real-time market decision optimization model to obtain a real-time bidding strategy; to make trading decisions.

[0007] In some embodiments of the present invention, in step S1, the uncertainty in the uncertainty model includes at least one of photovoltaic power generation, load demand level, electricity market price, and standby market price.

[0008] In some embodiments of the present invention, the day-ahead market decision optimization model is used to maximize day-ahead market participation profits, and the expression of the day-ahead market decision optimization model is as follows:

[0009]

[0010] in, This indicates the recent market gains reaped by virtual power plants. This indicates a scenario of uncertainty. The set representing the total number of uncertain scenarios. This represents the total number of distributed thermal power units. The set representing the total number of energy storage systems. This represents the set representing the total number of adjustable loads. This represents the probability of each scenario. Represents the total time. These respectively represent the electricity market and the reserve market in In the scene The market price at any given moment These represent virtual power plants in In the scene Bidding volume in the current energy market and the standby market. For the virtual power plant internal nodes The generator in In the scene Contribute effort at all times The cost of electricity generation, For the virtual power plant internal nodes Energy storage In the scene Charge and discharge power at any time The cost, For the virtual power plant internal nodes Adjustable load in In the scene Load adjustment amount at any time The subsidy amount.

[0011] In some embodiments of the present invention, the real-time market decision optimization model is used to maximize real-time market participation profits, and the expression of the real-time market decision optimization model is as follows:

[0012]

[0013] in, This indicates the benefits that virtual power plants reap in the real-time market. This corresponds to the real-time market price. This represents the adjustment amount for virtual power plants' bids in the real-time electricity market. This represents the change in the cost of generating electricity from a virtual power plant. For nodes, A set representing the total number of nodes / producers and consumers.

[0014] In some embodiments of the present invention, in step S3, the peer-to-peer transaction constraint includes the first peer-to-peer transaction constraint and the second peer-to-peer transaction constraint; the type of peer-to-peer transaction includes power matching transaction; the day-ahead market decision optimization model includes power supply balance constraint, and the first peer-to-peer transaction constraint is to introduce node transaction volume into the power supply balance constraint, wherein the node transaction volume is a part of the net energy output / input of the producer and consumer, and the expression of the first peer-to-peer transaction constraint is as follows:

[0015]

[0016] in, In order to be in Nodes in the scene In time Trading power in the middle, Indicates in Nodes in the scene Prosumers at one node sell power to consumers at other nodes, and conversely, they purchase power. For thermal power units in In the scene Constant effort For new energy units In the scene Constant effort In order to be in The energy storage charging and discharging efficiency of the node. For energy storage systems in In the scene Constant charging and discharging power, For nodes exist In the scene The load of time, In order to be in In the scene The cost of subsidies for each hour, For nodes exist In the scene The amount of electricity that is constantly contributed to the market bidding volume of the virtual power plant.

[0017] In some embodiments of the present invention, the real-time market decision optimization model includes a real-time power balance constraint, and the peer-to-peer transaction second constraint incorporates the node transaction volume into the real-time power balance constraint; the expression of the peer-to-peer transaction second constraint is as follows:

[0018]

[0019] in, For nodes In time Trading volume in For thermal power units Constant effort This represents the change in the output of thermal power units. For new energy units Constant effort The change in power output of new energy units For the charging and discharging power of the energy storage system, For nodes exist The load of time, To subsidize expenses, For nodes exist Real-time electricity market bidding volume contributed to the virtual power plant. This represents the real-time changes in electricity market bids.

[0020] In some embodiments of the present invention, the type of peer-to-peer transaction includes energy matching transactions; the peer-to-peer transaction constraints also include additional constraints, which are the energy requirements determined by the producer and consumer for each peer-to-peer transaction at different times.

[0021] In some embodiments of the present invention, the additional constraint is introduced in both the day-ahead market decision optimization model and the real-time market decision optimization model, and the expression of the additional constraint is:

[0022]

[0023] in, Indicates energy demand. Indicates different time periods, , , , Each time period is composed of independent hours. These represent the power requirements determined by each peer-to-peer transaction at different times.

[0024] The present invention also proposes a transaction decision-making device for virtual power plants participating in the electricity and reserve market, comprising 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 transaction decision-making method for virtual power plants participating in the electricity and reserve market as described above.

[0025] The present invention also proposes a storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to execute the virtual power plant's participation in the electricity and reserve market trading decision-making method as described above.

[0026] The present invention has the following beneficial effects:

[0027] The proposed method for virtual power plants participating in the electricity and reserve market trading decisions constructs an uncertainty model and generates multiple uncertainty scenarios, fully considering various situations and avoiding risks arising from inaccurate future forecasts. Based on the internal resource situation of the virtual power plant, it constructs day-ahead market decision optimization models and real-time market decision optimization models, fully considering various heterogeneous distributed resources and multi-temporal coupling relationships, making it more suitable for virtual power plants with multiple distributed resource accesses. A point-to-point trading model is constructed to provide point-to-point trading constraints when formulating bidding strategies. Point-to-point trading constraints are introduced into the day-ahead market decision optimization model, and the day-ahead... The market decision optimization model yields the day-ahead bidding strategy. An uncertain scenario is randomly selected as the day-ahead market, and a simulated day-ahead bidding strategy is calculated for this scenario. Based on the simulated day-ahead bidding strategy, peer-to-peer trading constraints are introduced into the real-time market decision optimization model, and the real-time bidding strategy is obtained by solving the real-time model. A market bidding strategy considering peer-to-peer trading by virtual power plants is also considered. This effectively solves the problems of easy confusion in the connection between virtual power plants and distributed trading, and the inability to effectively aggregate surplus resources to participate in the market. This fully leverages the potential of large-scale, flexible resources, promotes the diversified development of virtual power plants, and enhances the resilience and reliability of new power systems incorporating virtual power plants.

[0028] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0029] Figure 1 This is a flowchart of the steps in an embodiment of the present invention;

[0030] Figure 2 This is a diagram of the existing UK 95-node power distribution system topology.

[0031] Figure 3 This is a chart analyzing the day-ahead and real-time market bidding results of the virtual power plant in Example 1. Detailed Implementation

[0032] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0033] It should be noted that the directional terms such as left, right, up, down, top, and bottom used in this embodiment are only relative concepts or are based on the normal use of the product, and should not be considered as restrictive.

[0034] To address the aforementioned problems, the following embodiments of the present invention propose a method for virtual power plants to participate in electricity and reserve market trading decisions. (See attached document.) Figure 1The process includes the following steps: S1: Construct an uncertainty model and generate multiple uncertainty scenarios; S2: Based on the internal resource situation of the virtual power plant, construct a day-ahead market decision optimization model and a real-time market decision optimization model for participating in the electricity market and the reserve market; S3: Construct a distributed resource peer-to-peer trading model to provide peer-to-peer trading constraints when making decisions; S4: Introduce the peer-to-peer trading constraints into the day-ahead market decision optimization model and solve the day-ahead market decision optimization model to obtain a day-ahead bidding strategy; S5: Randomly select the uncertainty scenario as the day-ahead market and calculate the simulated day-ahead bidding strategy under the scenario. Based on the simulated day-ahead bidding strategy, introduce the peer-to-peer trading constraints into the real-time market decision optimization model and solve the real-time market decision optimization model to obtain a real-time bidding strategy; to make trading decisions so that the virtual power plant collects surplus energy to participate in the market after completing peer-to-peer trading between internal nodes, with each node having only one producer-consumer.

[0035] The proposed method for virtual power plants (VPS) participating in the electricity and reserve markets analyzes the capacity allocation and bidding strategy optimization problems in the electricity and reserve markets after VPS engages in peer-to-peer resource trading within the VPS. It studies the bidding strategies of VPS participation in the market from both day-ahead and real-time time scales, effectively solving the bottleneck caused by insufficient existing modeling techniques that prevent VPS from aggregating remaining resources to participate in the market when peer-to-peer resource trading exists. The constructed uncertainty model characterizes uncertain scenarios, fully considering multiple situations by generating a sufficient number of future scenarios, avoiding risks arising from inaccurate future predictions, and effectively improving the accuracy of VPS bidding in the market. By constructing a day-ahead market decision optimization model for VPS jointly participating in the electricity and reserve markets, compared with existing technologies, it fully considers multiple heterogeneous distributed resources and multi-temporal coupling relationships, making it more suitable for VPS with future access to multiple distributed resources. Based on considering more distributed resources, a real-time market decision optimization model is also constructed. By introducing peer-to-peer transaction-related constraints into the first two optimal decision-making models, the market bidding strategy for virtual power plants participating in peer-to-peer transactions is considered; this effectively solves the problem that virtual power plants are prone to confusion in their connection with distributed transactions and cannot effectively aggregate surplus resources to participate in the market.

[0036] The steps of this embodiment of the invention are as follows:

[0037] S1: Construct an uncertainty model and generate multiple uncertainty scenarios.

[0038] The uncertainty model proposed in this invention effectively manages various uncertainties, such as the power generation of photovoltaics. Load demand level and current electricity backup market prices This is incorporated into the proposed trading decision-making method. The aforementioned uncertainty is simulated by a normal distribution:

[0039]

[0040] Where RES represents the new energy unit, and in this embodiment of the invention, only photovoltaic output is considered; s represents the uncertainty scenario currently under consideration, and there are a total of In this uncertain scenario, i represents the node owning the resource, t is the current time, T is the total time, d is the load within the virtual power plant, DA represents the day-ahead market, E represents the electricity market, R represents the reserve market, and f is the forecast value. Let represent the market price of electrical energy at time t in scenario s. This represents the market price of the standby market at time t in scenario s. These are the predicted values ​​for the aforementioned uncertainties, and the prediction errors are composed of their respective standard deviations. This embodiment of the invention uses the Monte Carlo method to generate... The scenario involves several uncertainties. In the day-ahead phase, the VPP computation considers a stochastic optimization problem for all possible scenarios, resulting in a day-ahead bidding strategy that comprehensively estimates real-time uncertainty. Subsequently, in the real-time phase, a random uncertainty scenario is selected from the previously randomly generated scenarios to simulate the real-world situation.

[0041] S2: Based on the internal resources of the virtual power plant, construct day-ahead market decision optimization models and real-time market decision optimization models for participating in the electricity market and reserve market. The expression for the day-ahead market decision optimization model is as follows:

[0042] (1) Current Market Decision Optimization Model

[0043] Virtual power plants participate in the day-ahead electricity market and the reserve market. The expression of the day-ahead market decision optimization model is the objective function formula (5), which is to maximize the market participation profit, i.e., the revenue from participating in multiple markets minus the power generation cost of the virtual power plant's internal power generation equipment, the operating cost of the energy storage system, and the adjustable load subsidy. The objective function formula (5) consists of three parts: 1) the revenue of the virtual power plant participating in the electricity market and the inter-provincial reserve market; 2) the power generation cost (Gen) of the virtual power plant's internal power generation equipment and the operating cost (Sto) of the energy storage system; 3) the adjustable load subsidy (Dsm) of the virtual power plant.

[0044]

[0045] In formula (5) The probability for each scenario, where T is the total time. This represents the total number of distributed thermal power units. The set representing the total number of energy storage systems. This represents the set representing the total number of adjustable loads. The set of the total number of nodes / prosumers. This indicates the gains made by virtual power plants in the day-ahead (DA) market. Let represent the market prices of the electricity market and the reserve market at time t in scenario s, respectively. These represent the bidding volumes in the electricity market and the reserve market for the virtual power plant at time t in scenario s, respectively. For the generator at node i in the virtual power plant, the output at time t in scenario s The cost of electricity generation, The charging and discharging power of energy storage at node i within the virtual power plant at time t in scenario s. The cost, The load adjustment amount at time t for the adjustable load at node i within the virtual power plant in scenario s. The subsidy costs. Formulas (6-8) are the cost expressions mentioned above, respectively. Let be the secondary and primary cost coefficients of the thermal power unit at node i, and let be the secondary and primary cost coefficients of energy storage. and The adjustable load secondary and primary cost coefficients are: Equations (9) and (10) represent the power output constraints for thermal power units and new energy units. This represents the lower limit of the power generation output of thermal power units. This is the upper limit of the power generation output of thermal power units. For the output of new energy generating units at time t in scenario s. This represents the upper limit of the output of the new energy unit at time t in scenario s. Equation (11) describes the power supply balance constraint. To determine the energy storage charging and discharging efficiency at node i, Let represent the load of node i at time t in scenario s. Let $\mathbf{i}$ be the amount of electricity market bids contributed by node $i$ to VPP at time $t$ in scenario $s$, where positive represents selling and negative represents purchasing. The sum of these amounts represents the total amount of electricity market bids submitted by VPP. As shown in equation (16). Equations (12-15) describe the ramp-up constraints of the generator set. The ramp-up capacity of the unit at time t in scenario s should meet the unit's upper output limit. , , These refer to the unit's upward and downward climbing capabilities, respectively. Let t-1 represent the output of the thermal power unit and its capacity for climbing uphill, respectively. This refers to the total bidding volume of VPP in the standby market. Equation (17) describes that the adjustable load adjustment amount cannot exceed its upper limit. Equations (18-20) characterize the relevant constraints of the energy storage system. These represent the upper limits of the charging and discharging power of energy storage. and To store the energy at times t and t-1, upper and lower limits must be met. Equations (21) and (22) describe the limitations of the switching power at the system root node. This means that the power transmitted by a VPP participating in market bidding cannot exceed this limit.

[0046] (2) Real-time market decision optimization model for virtual power plants

[0047] Virtual power plants participate in the real-time energy market and the real-time reserve market in the real-time market. Virtual power plants submit and respond to day-ahead reserve trading capacity in the real-time market according to real-time system instructions. Because the uncertainties are known when virtual power plants participate in the real-time market, there is no need to consider uncertainty scenario s in the real-time market decision optimization model. In the real-time market decision optimization model, an uncertainty scenario s in the day-ahead market is randomly selected, and the results of each variable in this scenario are the day-ahead market decision values ​​considered in the subsequent real-time market decision model. The expression of the real-time market decision optimization model is the objective function formula (23), which is to maximize profit, i.e., the revenue from participating in multiple markets minus the internal power generation equipment and the adjusted power generation cost.

[0048]

[0049] In formula (23) This represents the benefits that virtual power plants gain in the real-time market (RT). This represents the adjustment amount for virtual power plants' bids in the real-time electricity market. This represents the change in the cost of generating electricity from a virtual power plant. The corresponding market price, The backup call signal released to the market is a 0 or 1 variable. This represents the set of the total number of nodes / producers and consumers. Formula (24) describes the output adjustment of thermal power units. Corresponding cost changes Formulas (25) and (26) indicate that the adjusted output of thermal power units and new energy units still needs to meet the upper and lower limits of output. Formulas (27-29) further clarify the upper and lower limits of output adjustment. and Furthermore, it is related to whether the reserve quantity of the previous day's bid has been called up, which also indicates the coupling relationship between the bid quantities of various markets. Formula (30) is the real-time power balance. Let be the load of node i at time t. Formula (31) represents the virtual power plant bidding adjustment, which is the energy market bidding adjustment contributed by node i to the VPP at time t. The formula (32) is a summary of the power limit of the system root node switching.

[0050] S3: Construct a distributed resource peer-to-peer transaction model to provide peer-to-peer transaction constraints when making decisions.

[0051] According to the market transaction sequence considered in this embodiment of the invention, P2P energy transactions between producers / consumers / nodes occur before the VPP operator accumulates excess energy or a demand deficit. Therefore, when the VPP considers a market bidding strategy, the transaction price and quantity of internal P2P transactions are known and are treated as parameters in the aforementioned day-ahead market decision optimization model and real-time market decision optimization model. Since the VPP is the sum of all producers and consumers, including buyers and sellers of P2P transactions, the P2P purchase cost and sales profit determined by the product of the price and quantity of each P2P transaction in the VPP operator's objective function offset each other. Therefore, the VPP's bidding strategy only needs to consider the power of each P2P transaction.

[0052] There are two different types of P2P transactions: power matching transactions and energy matching transactions.

[0053] 1) Power Matching Trading

[0054] Power matching transactions are the most common type of P2P transactions. The peer-to-peer transaction constraints include the first peer-to-peer transaction constraint and the second peer-to-peer transaction constraint. When the VPP formulates its day-ahead market bidding strategy, the first peer-to-peer transaction constraint is introduced into the power matching transaction, that is, the node is introduced into formula (11) in the power supply balance constraint. Trading power at time t Because it represents a node As part of the net energy output / input at the point, the expression for the first constraint of the peer-to-peer transaction is as described in formula (33):

[0055]

[0056] In the formula Indicate node A producer at one node sells this fixed amount of power to producers at other nodes, or conversely, purchases it.

[0057] When VPP formulates a real-time optimal market bidding strategy, a second constraint on peer-to-peer transactions is introduced, namely, the node transaction volume is introduced into the real-time power balance constraint formula (30). Furthermore, given that the uncertainty is known in the real-time market, there is no need to consider uncertainty scenario s. The expression for the second constraint of peer-to-peer transactions is as described in (34):

[0058]

[0059] 2) Energy Matching Trading: This involves producers and consumers setting their total trading energy for different time periods. Buyers and sellers can change their trading power at any time within this period, but must satisfy energy constraints. Therefore, the adjustments to the day-ahead and real-time energy balance constraints remain unchanged in the previous model. Furthermore, an additional constraint is introduced: producers and consumers are for different time periods... The energy demand determined for each P2P transaction is composed of independent hours, such as , , and This energy demand is expressed as a set of parameters. Both the day-to-day phase (with a scenario) and the real-time phase must include the following additional constraints:

[0060]

[0061] It is important to note that the roles of producers and consumers in P2P transactions may vary over time, so it is necessary to establish designated time slots in which producers and consumers can maintain a consistent identity.

[0062] S4: Introduce the peer-to-peer transaction constraint into the day-ahead market decision optimization model, and solve the day-ahead market decision optimization model to obtain the day-ahead bidding strategy;

[0063] S5: Randomly select the uncertain scenario as the day-ahead market and calculate the simulated day-ahead bidding strategy under the scenario. Based on the simulated day-ahead bidding strategy, introduce the peer-to-peer transaction constraint into the real-time market decision optimization model and solve the real-time market decision optimization model to obtain the real-time bidding strategy; so that the virtual power plant can collect the remaining energy and participate in the market after completing peer-to-peer transactions between internal producers / consumers / nodes.

[0064] This invention uses the UK 95-node power distribution system (UKGDS95) to verify the effectiveness of the proposed model. Figure 2As shown. The power system is supplied by a 33 / 11 kV substation transformer. The voltage level is 11 kV, and the basic power is 10 MVA. The VPP consists of 18 prosumers. This embodiment of the invention considers two types of distributed generation (DG): DG outputs at nodes 28, 61, and 83 range from (0.3-2) MW with a ramping capacity of 1 MW per hour; the remaining DG outputs range from (0.15-3) MW with a ramping capacity of 1.5 MW per hour. Prosumers located at nodes 7, 9, 16, 26, 31, 77, and 84 have different types of photovoltaic (PV) panels with maximum outputs ranging from (1.5-6.7) MW. Furthermore, the average daily projected demand for prosumers located at nodes 2, 3, and 7 is between 0.75-1.25 MW, while the projected demand for other prosumers is between 0.5-1.0 MW. It should be noted that the load of the aforementioned prosumers (maximum 0.45 MW) may decrease during hours 10-12 and 14-18. Each prosumer has its own storage system with a maximum charging / discharging power of 0.5 kW, for a total capacity of 1.25 MW. The exchange power of substation nodes is limited to 55 MW. The price of the energy and reserve market is referenced to the typical daily price of the energy and reserve market from the PJM organization in the United States. The example is modeled using the YALMIP toolbox in Matlab R2019B, and solved using the commercial solver Gurobi 9.0.0. In the figure, the Transformer represents the root node (substation) transformer of the distribution system.

[0065] Based on the proposed model and the constructed real system, the bidding results of the virtual power plant participating in the day-ahead real-time market can be obtained as follows: Figure 3 As shown, the horizontal axis represents time (hours), and the vertical axis represents market bidding volume (MW). The P2P transaction volume settings between VPP producers and consumers are as follows: Figure 2As shown, S represents the P2P seller, B represents the P2P buyer, and the arrow indicates each P2P transaction, with the transaction capacity being half of the P2P buyer's load. The role of VPP operators in the electricity market depends primarily on market prices. When the electricity market price is lower than their operating costs (such as the generation cost of DG), VPPs tend to purchase energy from the market to supply their internal load deficit. Conversely, at other times, VPPs prefer to sell surplus energy from prosumers to the electricity market for higher profits. Because the price in the reserve market is significantly lower than in the energy market, VPP operators submit limited reserve capacity. Since VPP operators must compensate for internal peak demand to avoid any potential power shortages, the number of bids in both the electricity market and the reserve market decreases during peak load periods (around 12:00). Furthermore, VPP operators can dynamically adjust the number of bids in the real-time energy market based on revealed market prices, load demand, and uncertainties in renewable energy units. Therefore, the proposed strategy enables VPPs to fully utilize the surplus energy from prosumers comprised of various distributed resources located at different nodes after peer-to-peer transactions, thereby maximizing economic returns.

[0066] This paper analyzes the capacity allocation and bidding strategy optimization problem of virtual power plants (VPS) after internal peer-to-peer resource trading, where they aggregate surplus resources from various producers and consumers to participate in the electricity market and reserve market. The bidding strategies of VPS in the market are studied from both day-ahead and real-time time scales. Numerical examples demonstrate the effectiveness of the proposed model in increasing the economic benefits of VPS and mobilizing distributed resources. By fully leveraging the potential of scalable and flexible resources and considering various peer-to-peer transactions, the paper promotes the diversified development of VPS and enhances the resilience and reliability of the new power system.

[0067] This invention proposes a transaction decision-making method for virtual power plants participating in a joint energy and reserve market, considering distributed trading. Under various distributed resource peer-to-peer trading scenarios, it guides the transaction management of virtual power plants aggregating resource markets. This method is applicable to transaction decisions involving virtual power plants with massive distributed resources participating in a joint energy reserve market. This invention can provide a reference for decision-making by power companies, virtual power plant operators, and third-party distributed resource aggregation institutions, and has significant implications for accelerating the construction of new power systems.

[0068] This invention also proposes a virtual power plant participation in the electricity and reserve market transaction decision-making device, including 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 virtual power plant participation in the electricity and reserve market transaction decision-making method as described above.

[0069] This invention also proposes a storage medium comprising a stored computer program, wherein, when the computer program is running, it controls the device containing the storage medium to execute the virtual power plant's participation in the electricity and reserve market trading decision-making method and device as described above.

[0070] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, several equivalent substitutions or obvious modifications can be made without departing from the concept of the present invention, and all such modifications, achieving the same performance or purpose, should be considered within the scope of protection of the present invention.

Claims

1. A method for virtual power plant participating in the transaction decision of electricity energy and reserve market, characterized in that, Includes the following steps: S1: Construct an uncertainty model and generate multiple uncertainty scenarios; S2: Based on the internal resources of the virtual power plant, construct a day-ahead market decision optimization model and a real-time market decision optimization model for participating in the electricity market and the reserve market; S3: Construct a distributed resource peer-to-peer transaction model to provide peer-to-peer transaction constraints when making decisions; S4: Introduce the peer-to-peer transaction constraint into the day-ahead market decision optimization model, and solve the day-ahead market decision optimization model to obtain the day-ahead bidding strategy; S5: Randomly select the uncertainty scenario as the day-ahead market and calculate the simulated day-ahead bidding strategy under the scenario. Based on the simulated day-ahead bidding strategy, introduce the point-to-point transaction constraint into the real-time market decision optimization model, and solve the real-time market decision optimization model to obtain the real-time bidding strategy for transaction decision-making. The day-ahead market decision optimization model is used to maximize day-ahead market participation profits, and the expression of the day-ahead market decision optimization model is as follows: in, This indicates the recent market gains reaped by virtual power plants. This indicates a scenario of uncertainty. The set representing the total number of uncertain scenarios. This represents the total number of distributed thermal power units. The set representing the total number of energy storage systems. This represents the set of total adjustable loads. This represents the probability of each scenario. Represents the total time. These respectively represent the electricity market and the reserve market in In the scene The market price at any given moment These represent virtual power plants in In the scene Bidding volume in the current energy market and the standby market. For the virtual power plant internal nodes The generator in In the scene Contribute effort at all times The cost of electricity generation, For the virtual power plant internal nodes Energy storage In the scene Charge and discharge power at any time The cost, For the virtual power plant internal nodes Adjustable load in In the scene Load adjustment amount at any time The subsidy amount; The real-time market decision optimization model is used to maximize the profit of real-time market participation, and the expression of the real-time market decision optimization model is as follows: in, This indicates the benefits that virtual power plants reap in the real-time market. This corresponds to the real-time market price. This represents the adjustment amount for virtual power plants' bids in the real-time electricity market. This represents the change in the cost of generating electricity from a virtual power plant. For nodes, A set representing the total number of nodes / prosumers; In step S3, the peer-to-peer transaction constraints include a first peer-to-peer transaction constraint and a second peer-to-peer transaction constraint; the type of peer-to-peer transaction includes power matching transactions; the day-ahead market decision optimization model includes power supply balance constraints, and the first peer-to-peer transaction constraint introduces node transaction volume into the power supply balance constraints. The node transaction volume is a portion of the net energy output / input of the producer and consumer. The expression of the first peer-to-peer transaction constraint is as follows: in, In order to be in Nodes in the scene In time Trading power in the middle, Indicates in Nodes in the scene Prosumers at one node sell power to consumers at other nodes, and conversely, they purchase power. For thermal power units in In the scene Constant effort For new energy units In the scene Constant effort In order to be in The energy storage charging and discharging efficiency of the node. For energy storage systems in In the scene Constant charging and discharging power, For nodes exist In the scene The load of time, In order to be in In the scene The cost of subsidies for each hour, For nodes exist In the scene The amount of electricity contributed to the market bidding volume of the virtual power plant at all times; The real-time market decision optimization model includes a real-time power balance constraint, and the peer-to-peer transaction second constraint incorporates the node transaction volume into the real-time power balance constraint; the expression of the peer-to-peer transaction second constraint is as follows: in, For nodes In time Trading volume in For thermal power units Constant effort This represents the change in the output of thermal power units. For new energy units Constant effort The change in power output of new energy units For the charging and discharging power of the energy storage system, For nodes exist The load of time, To subsidize expenses, For nodes exist Real-time electricity market bidding volume contributed to the virtual power plant. This represents the real-time changes in electricity market bids.

2. The method for virtual power plants participating in the electricity and reserve market trading decision-making process according to claim 1, characterized in that, In step S1, the uncertainties in the uncertainty model include at least one of photovoltaic power generation, load demand level, electricity market price, and standby market price.

3. The method for virtual power plants participating in the electricity and reserve market trading decision-making process according to claim 1, characterized in that, The types of peer-to-peer transactions also include energy matching transactions; the peer-to-peer transaction constraints also include additional constraints, which are the energy requirements determined by the producer and consumer for each peer-to-peer transaction at different times.

4. The method for virtual power plants participating in the electricity and reserve market trading decision-making process according to claim 3, characterized in that, The additional constraint is introduced in both the day-ahead market decision optimization model and the real-time market decision optimization model. The expression for the additional constraint is: in, Indicates energy demand. Indicates different time periods, , , , Each time period is composed of independent hours. , , , These represent the power requirements determined by each peer-to-peer transaction at different times.

5. A virtual power plant participating in the electricity and reserve market trading decision-making device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the virtual power plant participation in the electricity and reserve market trading decision method as described in any one of claims 1 to 4.

6. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to perform the virtual power plant participation in the electricity and reserve market trading decision method as described in any one of claims 1 to 4.

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