Virtual power plant optimization regulation and control method and system for electricity market and peak regulation market

Abstract

The invention discloses a virtual power plant optimization regulation and control method and system oriented to a power market and a peak regulation market. The method comprises the following steps: setting a process and a rule for a virtual power plant to participate in market transaction; the markets comprise a peak regulation market and an electricity market; establishing a benefit model of the virtual power plant participating in the market transaction; establishing an overall objective function of the virtual power plant participating in the market transaction; establishing operation constraint conditions of the virtual power plant participating in the market transaction; solving according to the established objective function and constraint conditions to obtain a scheduling instruction, and completing scheduling; according to the invention, the cost of virtual power plant optimization regulation and control for the electricity market and the peak regulation market can be reduced.

Description

Technical Field

[0001] The present invention relates to a virtual power plant optimization control method and system, and in particular to a virtual power plant optimization control method and system for the power market and the peak load regulation market, belonging to the field of virtual power plant control technology. Background Art

[0002] With the development of society and the advancement of science and technology, the energy consumption required for human development is increasing day by day. Traditional fossil energy has been gradually replaced by renewable energy because it is non-renewable and highly polluting. Increasing the penetration rate and power share of renewable energy has become a broad consensus in the world. Since renewable energy generation does not require the consumption of fossil fuels, more renewable energy will make electricity prices lower in the long run, which benefits many industries that rely on electricity production.

[0003] However, due to the inherent intermittency and volatility of renewable energy, a high proportion of renewable energy inevitably poses a challenge to the real-time balance of the power system. Since the power system requires real-time balance on the generation side and the load side, a high proportion of renewable energy will increase the pressure on the real-time balance of the power system.

[0004] Power dispatching operators in many countries and regions are looking for ways to deal with the deterioration of system stability caused by fluctuations in renewable energy. Adjustable loads can respond to demand by reducing output, and this part of electricity can be regarded as positive generation. In addition, by adjusting the output curve, energy storage can also be used as a load or power source. At present, the power grids in many countries have abundant flexible resources such as flexible loads, energy storage, and gas turbine units. But these large and decentralized flexible resources are difficult to be directly monitored by the dispatch control center.

[0005] In actual operation, virtual power plants need to respond quickly to market signals and changes in electricity demand. However, existing control systems and optimization algorithms may have response time lags, which will affect their regulation effects in rapidly changing environments. Optimizing the resource scheduling of virtual power plants involves multiple energy resources and market participants, and the complexity of scheduling strategies will increase with the expansion of the system scale. Existing technologies may face inefficiencies when dealing with scheduling problems of large-scale and multi-type resources. At the same time, achieving optimal regulation of virtual power plants requires a large amount of capital for system construction, maintenance, and technology updates. Current technologies may not perform well in terms of cost-effectiveness, especially in terms of initial investment and operating costs. Summary of the invention

[0006] Purpose of the invention: The purpose of the present invention is to provide a virtual power plant optimization control method and system for the electricity market and peak-shaving market that can reduce the control cost.

[0007] Technical solution: The virtual power plant optimization and control method for the power market and the peak load regulation market described in the present invention comprises:

[0008] S1: Setting up the process and rules for virtual power plants to participate in market transactions; the markets include peak load regulation market and electricity market;

[0009] S2: Establish a benefit model for virtual power plants to participate in market transactions;

[0010] S3: Establish the overall objective function of virtual power plants participating in market transactions;

[0011] S4: Establish operating constraints for virtual power plants to participate in market transactions;

[0012] S5: Solve according to the established objective function and constraints, obtain scheduling instructions, and complete scheduling.

[0013] Furthermore, the step S1 is specifically that the power trading center publishes the peak and valley period information and electricity price information of the next day's load and the peak-shaving demand information, peak and valley period information, and compensation price information of the peak-shaving market every day;

[0014] When participating in the electricity market, the virtual power plant is used as a price taker to conduct day-ahead power purchase and sales transactions; when participating in the peak-shaving market, the virtual power plant wins the most profit through fine bidding;

[0015] In the day-ahead market stage, the virtual power plant is required to complete the bidding submission in the power market before the preset time, and the power trading center is required to complete the market settlement at the preset time;

[0016] The power trading center settles with the virtual power plant based on the clearing price of the day-ahead market to obtain the bidding rights and market clearing price of the virtual power plant in this market transaction. The virtual power plant publishes the production plan for the next day based on the transaction results.

[0017] In the real-time market stage, the power trading center completes the difference transaction based on the virtual power plant's day-ahead settlement power and the actual delivery power, settles according to the preset time period, and conducts real-time settlement after the market is cleared.

[0018] Furthermore, the step S2 is specifically as follows: assuming that the benefit of the virtual power plant participating in market transactions is equal to the sum of the profits of the virtual power plant participating in the peak load regulation market and the electricity market;

[0019] The profits of the virtual power plant participating in the peak load market are as follows:

[0020] B f =λ vf (t)·P vf (t)+λ pf (t)·P pf (t) (1)

[0021] P vf (t) = P ES,vf (t)+P DR,vf (t) (2)

[0022] P pf (t) = P ES,pf (t)+P DR,pf (t) (3)

[0023] Among them, B f is the profit of the virtual power plant peak load market, λ vf (t) and λ pf (t) are the valley filling compensation price and peak load compensation price of the peak load market during period t; P vf (t) and P pf (t) are the bidding capacity of the virtual power plant for valley filling and peak load regulation in period t; P ES,vf (t) and P ES,pf (t) is the bid capacity of energy storage for valley filling and peak regulation in period t, P DR,vf (t) and P DR,pf (t) is the bid capacity of the adjustable load for valley filling and peak regulation in period t;

[0024] The profits of the virtual power plant participating in the electricity market are as follows:

[0025]

[0026] Among them, B grid is the profit of the virtual power plant participating in the electricity market, P buy (t) and P sell (t) is the amount of electricity purchased and sold by the virtual power plant during the period t when participating in the day-ahead power market, λ buy (t) and λ sell (t) represents the purchase price and sales price of the virtual power plant in the period t; Δt is the time interval; T is the cycle of the market transaction;

[0027] The benefit model of the virtual power plant participating in the market is:

[0028] B VPP =B f +B grid (5).

[0029] Furthermore, the step S3 is specifically to establish an objective function for the virtual power plant to participate in market transactions with the goal of minimizing the overall operating cost:

[0030]

[0031] Among them, C VPPrepresents the overall operating cost of the virtual power plant, C grid is the electricity purchase cost of the virtual power plant and the electricity market, C CDG , C W and C PV are respectively the gas turbine power generation cost, wind power curtailment cost and photovoltaic power curtailment cost, C DR is the compensation cost of the virtual power plant for the load that can be removed:

[0032] C grid =-B grid =λ buy (t)·P buy (t)-λ sell (t)·P sell (t) (7)

[0033]

[0034] C W =λ W (t) ΔP W (t) (9)

[0035] C PV =λ PV (t)·ΔP PV (t) (10)

[0036] Among them, a, b and c are cost-related coefficients; P CDG (t) is the power generation of the gas turbine, ΔP W (t) is the difference between the predicted value of wind power generation of the virtual power plant on the day before and the actual value on the day, ΔP PV (t) is the difference between the predicted value of photovoltaic power generation of the virtual power plant and the actual value during the day, λ W (t) is the penalty price for wind power abandonment during period t, λ PV (t) represents the penalty electricity price for abandoned solar power in period t;

[0037] The cost of dispatching adjustable load by a virtual power plant is:

[0038] C DR =λ DR (t)·P DR (t) (11)

[0039]

[0040] Where P DR (t) is the load regulation, λ DR (t) is the compensation price for load regulation, and a1, b1 and c1 are constant parameters.

[0041] Furthermore, the step S4 specifically includes establishing market constraints, establishing own operation constraints, establishing power generation resource constraints and establishing load resource constraints.

[0042] Furthermore, the establishment of market constraints is specifically to establish the following market constraints for virtual power plants to participate in the electricity market:

[0043]

[0044] in, and are the maximum and minimum power purchases of the virtual power plant, respectively. B(t) is a discrete variable of 0 or 1, indicating the state of the virtual power plant participating in the power market. When B(t) = 1, it means purchasing power from the market, and when B(t) = 0, it means selling power.

[0045] The minimum peak load capacity of a virtual power plant should reach the market threshold:

[0046]

[0047] in, is the minimum bidding capacity requirement of the peak-shaving market, P f (t) is the peak load capacity provided by the virtual power plant, u f (t) is a discrete variable of 0 or 1, indicating the state of the virtual power plant participating in the peak load regulation market, and M is an infinite positive number.

[0048] Furthermore, the establishment of self-operation constraints is specifically to ensure the internal power balance of the virtual power plant:

[0049] P buy (t)-P sell (t)+P W (t)+P PV (t)+P CDG (t) = P load (t)+P ES (t) (16)

[0050] P load (t) = P load0 -P cut (t)-P DR (t) (17)

[0051] Where P W (t) and P PV (t) The actual output value of wind power and photovoltaic power in the period t, P load0 and P load (t) are the values ​​before and after the flexible load adjustment, P cut (t) is the interruptible load reduction, P DR(t) is the dispatch value of the transferable load, P ES (t) is the charging and discharging power of the energy storage;

[0052] Let the gas turbine and adjustable load reserve capacity satisfy:

[0053]

[0054] in, and are the positive and negative reserves provided by the gas turbine in time period t, and are the positive and negative reserve of the adjustable load in time period t respectively.

[0055] Furthermore, the method of establishing power generation resource constraints is specifically as follows:

[0056] Let the output of wind power and photovoltaic power in the virtual power plant be less than or equal to the predicted value:

[0057]

[0058] Among them, P W (t) and P PV (t) are the actual wind power output and solar power output of the virtual power plant on the previous day, and They are the day-ahead wind power output forecast and solar power output forecast of the virtual power plant;

[0059] The reserve and upper and lower limits of the gas turbine output are set as follows:

[0060]

[0061] in, and are the lower and upper limits of the gas turbine output during period t respectively.

[0062] Furthermore, the establishment of load resource constraints is specifically as follows:

[0063] Assume that the capacity constraints of energy storage resources are as follows:

[0064] P ES (t) = P ES,e (t)-P ES,pf (t)+P ES,vf (t) (24)

[0065]

[0066] E ES,t0 =E ES,T (26)

[0067]

[0068] E ES (t) = E ES (t-1)+P ES,cha (t)·η c -P ES,dis (t) / η d +P ES,vf (t)·η c -P ES,pf (t) / η d (28)

[0070] P ES,e (t) = P ES,cha (t)-P ES,dis (t) (29)

[0071] Among them, E ES,t0 is the initial capacity of energy storage, E ES,T is the final capacity, E ES (t) is the capacity in period t, and are the minimum and maximum capacity of the energy storage device, P ES,cha (t) and P ES,dis (t) are the energy storage charging power and discharging power, η c and η d are the energy storage charging efficiency and discharging efficiency respectively;

[0072] Let the adjustable load operation scheduling of the adjustable load satisfy the adjustable maximum value constraint:

[0073]

[0074] in, is the maximum adjustable amount of the adjustable load, u c (t) is a discrete variable of 0 or 1 representing the adjustable load state.

[0075] The transferable load of the adjustable load satisfies the following constraints:

[0076] P DR (t) = P DR,e (t)+P DR,pf (t)-P DR,vf (t) (31)

[0077]

[0078] Where P DR,e (t) is the power of transferable load participating in the electricity market through price difference arbitrage, PDR,vf (t) and P DR,pf (t) is the power involved in valley filling and peak load regulation;

[0079] At the same time, the total load of the transferable load should remain unchanged within a dispatch day:

[0080]

[0081] Based on the same inventive concept, the present invention also provides a virtual power plant optimization and control system for the power market and the peak load regulation market, comprising:

[0082] A process rule setting module is used to set the process and rules for the virtual power plant to participate in market transactions; the market includes the peak load regulation market and the electricity market;

[0083] The benefit model building module is used to build a benefit model for virtual power plants to participate in market transactions;

[0084] The objective function establishment module is used to establish the overall objective function of the virtual power plant participating in market transactions;

[0085] The constraint establishment module is used to establish the operating constraints for the virtual power plant to participate in market transactions;

[0086] The scheduling module is used to solve the established objective function and constraints, obtain scheduling instructions, and complete scheduling.

[0087] Beneficial effects: Compared with the existing technology, the present invention can effectively aggregate and dispatch flexible resources, become a carrier of power load interaction and achieve power balance; the virtual power plant optimization and control strategy for the power market and peak-shaving market proposed in the present invention can guide the virtual power plant to participate in the power market and peak-shaving market, while minimizing the overall operating cost of the virtual power plant and maximizing the operating income of the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0089] Figure 2 A design diagram of a virtual power plant participating in a market transaction process according to an embodiment of the present invention;

[0090] Figure 3 This is an architecture diagram of a virtual power plant participating in the electricity market and peak load regulation market according to an embodiment of the present invention;

[0091] Figure 4 A graph showing wind power, photovoltaic power generation and load curves in a virtual power plant according to an embodiment of the present invention;

[0092] Figure 5 A schematic diagram of output power of each resource of a virtual power plant according to an embodiment of the present invention;

[0093] Figure 6 This is a schematic diagram of the results of the virtual power plant participating in the electricity market and the peak-shaving market according to an embodiment of the present invention. DETAILED DESCRIPTION

[0094] In order to enable those skilled in the art to better understand the present application scheme, the principles and features of the present invention will be described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention. The present invention is described in more detail by way of example with reference to the accompanying drawings in the following paragraphs. The advantages and features of the present invention will become clearer according to the following description and claims. It should be noted that the drawings are all in a very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0095] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0096] As attached Figure 1 As shown, the virtual power plant optimization control method for the power market and the peak load regulation market of this embodiment includes:

[0097] S1: Setting up the process and rules for virtual power plants to participate in market transactions; the markets include peak load regulation market and electricity market;

[0098] S2: Establish a benefit model for virtual power plants to participate in market transactions;

[0099] S3: Establish the overall objective function of virtual power plants participating in market transactions;

[0100] S4: Establish operating constraints for virtual power plants to participate in market transactions;

[0101] S5: Solve according to the established objective function and constraints, obtain scheduling instructions, and complete scheduling.

[0102] Specifically, step S1 includes the following steps to clarify the process and rules for virtual power plants to participate in market transactions:

[0103] As attached Figure 2 and attached Figure 3 The power trading center releases the peak and valley period information and electricity price information of the next day's load, as well as the peak-shaving demand, peak and valley period, compensation price and other information of the peak-shaving market. The virtual power plant operator analyzes its internal resource output forecast information and power market price information;

[0104] When participating in the electricity market, virtual power plants act as price takers to conduct day-ahead power purchase and sales transactions; when participating in the peak-shaving market, virtual power plants earn the most profits through fine-grained bidding;

[0105] In the day-ahead market stage, virtual power plants should complete bidding submissions in the power market before 16:00, and the power trading center should complete market settlement at 18:00;

[0106] The trading center settles with the virtual power plant based on the clearing price of the day-ahead market to obtain the bidding rights and market clearing price of the virtual power plant in this market transaction. The virtual power plant will release its own production plan for the next day based on the transaction results.

[0107] In the real-time market stage, the power trading center completes the difference transaction based on the virtual power plant's day-ahead settlement power and the actual delivery power, with settlements every 15 minutes. Real-time settlement is carried out after the market is cleared.

[0108] Furthermore, step S2 includes the following steps to establish a benefit model for the virtual power plant to participate in market transactions:

[0109] Virtual power plants participate in both the peak load market and the electricity market to gain benefits;

[0110] The profit calculation of virtual power plants participating in the peak load market is as follows:

[0111] B f =λ vf (t)·P vf (t)+λ pf (t)·P pf (t) (1)

[0112] P vf (t) = P ES,vf (t)+P DR,vf (t) (2)

[0113] P pf (t) = P ES,pf (t)+P DR,pf (t) (3)

[0114] Among them, B f is the profit of the virtual power plant peak load market, λ vf (t) and λ pf (t) is the peak-loading compensation price in the peak-loading market during period t; P vf (t) and P pf (t) are the bidding capacity of the virtual power plant for valley filling and peak load regulation in period t; P ES,vf (t) and P ES,pf (t) is the bid capacity of energy storage for valley filling and peak regulation in period t, P DR,vf (t) and PDR,pf (t) is the bid capacity of the adjustable load for valley filling and peak regulation in period t.

[0115] The profit of a virtual power plant participating in the electricity market is the electricity sales revenue minus the electricity purchase cost, calculated as follows:

[0116]

[0117] Among them, B grid is the profit of the virtual power plant participating in the electricity market, P buy (t) and P sell (t) is the amount of electricity purchased and sold by the virtual power plant during the period t when participating in the day-ahead power market, λ buy (t) and λ sell (t) represents the power purchase price and power sales price of the virtual power plant in time period t; Δt is the time interval; T is the market transaction cycle, and its value is 24 hours.

[0118] The total profit of virtual power plants participating in the market (B VPP ) is the sum of the peak load market profit and the electricity market profit:

[0119] B VPP =B f +B grid (5)

[0120] Further, step S3 includes the following steps to establish the overall objective function of the virtual power plant participating in market transactions:

[0121] The purpose of virtual power plants participating in market transactions is to minimize overall operating costs:

[0122]

[0123] Among them, C VPP represents the overall operating cost of the virtual power plant, C grid is the cost of purchasing / selling electricity between the virtual power plant and the electricity market, C CDG , C W and C PV are respectively the gas turbine power generation cost, wind power curtailment cost and photovoltaic power curtailment cost, C DR is the compensation cost of the virtual power plant for the load that can be removed. The detailed calculation method of the above cost is as follows:

[0124] C grid =-B grid =λ buy (t)·P buy (t)-λ sell (t)·P sell (t) (7)

[0125]

[0126] C W =λ W (t)·ΔP W (t) (9)

[0127] C PV =λ PV (t)·ΔP PV (t) (10)

[0128] Among them, the cost of purchasing electricity from the power market of the virtual power plant is the purchase expenditure minus the income from selling electricity, which is the opposite of the total profit from selling electricity; the relationship between the gas turbine and the power generation cost is a quadratic function, and a, b and c are the cost correlation coefficients; P CDG (t) is the power generation of the gas turbine; in equations (9) and (10), ΔP W (t) is the difference between the predicted value of wind power generation of the virtual power plant on the day before and the actual value on the day, ΔP PV (t) is the difference between the predicted value of photovoltaic power generation of the virtual power plant and the actual value during the day, λ W (t) is the penalty price for wind power abandonment during period t, θ PV (t) represents the penalty electricity price for curtailment of solar power during period t.

[0129] In addition, the adjustable load will cooperate with the virtual power plant in the incentive demand response mode, and the virtual power plant dispatching the adjustable load also requires a certain cost:

[0130] C DR =λ DR (t)·P DR (t) (11)

[0131]

[0132] Where P DR (t) is the load regulation, λ DR (t) is the compensation price for load regulation, P DR (t) and λ DR (t) is a quadratic function, and a1, b1, and c1 are constant parameters.

[0133] Furthermore, step S4 includes the following steps for establishing operating constraints for the virtual power plant to participate in market transactions:

[0134] The constraints that virtual power plants need to meet in participating in the electricity market and peak-shaving market include: market constraints, their own operation constraints, power generation resource constraints, and load resource constraints;

[0135] Furthermore, the following steps are included to establish market constraints for virtual power plants to participate in market transactions:

[0136] The market constraints for virtual power plants to participate in market transactions include power market constraints and peak load market constraints. The market constraints for virtual power plants to participate in the power market are as follows:

[0137]

[0138] in, and are the maximum and minimum power purchases of the virtual power plant respectively. B(t) is a 0-1 discrete variable, which indicates the state of the virtual power plant participating in the electricity market. When B(t) = 1, it means purchasing electricity from the market, otherwise it means selling electricity.

[0139] In order to participate in the peak load regulation market, virtual power plants need to meet the operating rules and peak load regulation requirements of the peak load regulation market, and the minimum peak load regulation capacity should reach the market threshold:

[0140]

[0141] in, is the minimum bidding capacity requirement of the peak-shaving market, P f (t) is the peak load capacity provided by the virtual power plant, u f (t) is a discrete variable of 0 or 1, indicating the state of the virtual power plant participating in the peak-shaving market, and M represents an infinite positive number.

[0142] Furthermore, the following steps are included to establish the operating constraints of the virtual power plant for participating in market transactions:

[0143] The virtual power plant includes gas turbines, wind power generation and photovoltaic power generation. The electrical equipment has loads, and energy storage can generate and consume electricity. At the same time, it relies on the electricity market to purchase and sell electricity.

[0144] In order to maintain stable operation of the virtual power plant, the virtual power plant needs to ensure internal power balance:

[0145] P buy (t)-P sell (t)+P W (t)+P PV (t)+P CDG (t) = P load (t)+P ES (t) (16)

[0146] P load (t) = P load0 -P cut (t)-P DR (t) (17)

[0147] Where P W (t) and P PV(t) The actual output value of wind power and photovoltaic power in the period t, P load0 and P load (t) are the values ​​before and after the flexible load adjustment, P cut (t) is the interruptible load reduction, P DR (t) is the dispatch value of the transferable load, P ES (t) is the charging and discharging power of the energy storage.

[0148] Since it is difficult to accurately predict the output level of wind power and photovoltaic power, it is necessary to leave spare capacity through gas turbines and flexible loads to cope with possible uncertainties, that is, to meet

[0149]

[0150] in, and are the positive and negative reserves provided by the gas turbine in time period t, and are the positive and negative reserve of the adjustable load in time period t respectively.

[0151] Furthermore, the following steps are included to establish power generation resource constraints for the virtual power plant to participate in market transactions:

[0152] The power generation resources in the virtual power plant include wind power, photovoltaic power and gas turbines. The output of wind power and photovoltaic power in the virtual power plant should not exceed their predicted values:

[0153]

[0154] Among them, P W (t) and P PV (t) are the actual wind power output and solar power output of the virtual power plant on the previous day, and They are the day-ahead wind power output forecast and solar power output forecast of the virtual power plant respectively.

[0155] For gas turbines, their output is constrained by reserve and upper and lower limits:

[0156]

[0157] in, and are the lower and upper limits of the gas turbine output during period t respectively.

[0158] Furthermore, the following steps are included to establish load resource constraints for the virtual power plant to participate in market transactions:

[0159] The operation of energy storage resources is subject to capacity constraints:

[0160] P ES (t) = P ES,e (t)-P ES,pf (t)+P ES,vf (t) (24)

[0161]

[0162] E ES,t0 =E ES,T (27)

[0163]

[0164] E ES (t) = E ES (t-1)+P ES,cha (t)·η c -P ES,dis (t) / η d +P ES,vf (t)·η c -P ES,pf (t) / η d (28)

[0166] P ES,e (t) = P ES,cha (t)-P ES,dis (t) (29)

[0167] Among them, E ES,t0 is the initial capacity of energy storage, E ES,T is the final capacity, E ES (t) is the capacity in period t, and are the minimum and maximum capacity of the energy storage device, P ES,cha (t) and P ES,dis (t) are the energy storage charging power and discharging power, η c and η d They are the energy storage charging efficiency and discharging efficiency respectively.

[0168] Adjustable loads can be divided into interruptible loads and transferable loads. Adjustable load operation scheduling needs to meet the adjustable maximum value constraint.

[0169]

[0170] in, is the maximum adjustable amount of the adjustable load, u c (t) is a discrete variable of 0 or 1 representing the adjustable load state.

[0171] For transferable loads, in order to avoid market bidding power conflicts, the following constraints need to be met:

[0172] P DR (t) = P DR,e (t)+P DR,pf (t)-P DR,vf (t) (31)

[0173]

[0174] Where P DR,e (t) is the power of transferable load participating in the electricity market through price difference arbitrage, P DR,vf (t) and P DR,pf (t) is the power involved in valley filling and the power involved in peak regulation.

[0175] At the same time, the total load of the transferable load should remain unchanged within a dispatch day:

[0176]

[0177] In order to verify the effectiveness and rationality of the proposed bidding strategy, a simulation is built for case analysis. Generally speaking, the day-ahead forecast of wind power and photovoltaic power has a certain error, and it is believed that the error obeys a normal distribution with a mean of 0 to a certain extent. Figure 4 The forecast data of wind power and photovoltaic power as well as the load data within the virtual power plant are given, and the transferable load and interruptible load in each cycle of the virtual power plant are set to 25% and 10% of the total load capacity respectively.

[0178] The virtual power plant operator analyzes the basic information of the day-ahead power market and the peak-shaving market, and then optimizes the dispatch of aggregated resources such as wind power, photovoltaics and energy storage. Figure 5 The output of each resource in the virtual power plant is given.

[0179] from Figure 5 It can be seen that the market electricity price is the lowest during the period of 1-8 o'clock, and wind power and photovoltaic power generation are relatively low; virtual power plants participate in the electricity market as electricity buyers and increase consumption during low peak periods through transferable loads and energy storage, playing the role of valley filling;

[0180] The peak electricity consumption periods are from September to December and from November 18 to 21, when the market electricity price is the highest. Virtual power plants increase available power by disconnecting interruptible loads and discharging energy storage devices. In this way, virtual power plants increase electricity sales to the electricity market and play a role in peak load regulation.

[0181] The results of virtual power plants participating in these two markets are as follows Figure 6As shown in the figure, the virtual power plant purchases electricity when the electricity price is low and sells electricity when the electricity price is high. After participating in the peak-shaving market, the load curve becomes flatter.

[0182] Based on the same inventive concept, this embodiment also provides a virtual power plant optimization and control system for the power market and the peak load regulation market, including:

[0183] A process rule setting module is used to set the process and rules for the virtual power plant to participate in market transactions; the market includes the peak load regulation market and the electricity market;

[0184] The benefit model building module is used to build a benefit model for virtual power plants to participate in market transactions;

[0185] The objective function establishment module is used to establish the overall objective function of the virtual power plant participating in market transactions;

[0186] The constraint establishment module is used to establish the operating constraints for the virtual power plant to participate in market transactions;

[0187] The scheduling module is used to solve the established objective function and constraints, obtain scheduling instructions, and complete scheduling.

[0188] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the drawings and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A virtual power plant optimization and control method for the power market and peak load regulation market, characterized in that: include: S1: Setting up the process and rules for virtual power plants to participate in market transactions; the markets include peak load regulation market and electricity market; S2: Establish a benefit model for virtual power plants to participate in market transactions; S3: Establish the overall objective function of virtual power plants participating in market transactions; S4: Establish operating constraints for virtual power plants to participate in market transactions; S5: Solve according to the established objective function and constraints, obtain scheduling instructions, and complete scheduling.

2. The virtual power plant optimization and control method for the power market and peak load market according to claim 1 is characterized in that: Specifically, the step S1 includes that the power trading center releases the peak and valley period information and electricity price information of the next day's load, as well as the peak-shaving demand information, peak and valley period information, and compensation price information of the peak-shaving market every day; When participating in the electricity market, the virtual power plant is used as a price taker to conduct day-ahead power purchase and sales transactions; when participating in the peak-shaving market, the virtual power plant wins the most profit through fine bidding; In the day-ahead market stage, the virtual power plant is required to complete the bidding submission in the power market before the preset time, and the power trading center is required to complete the market settlement at the preset time; The power trading center settles with the virtual power plant based on the clearing price of the day-ahead market to obtain the bidding rights and market clearing price of the virtual power plant in this market transaction. The virtual power plant publishes the production plan for the next day based on the transaction results. In the real-time market stage, the power trading center completes the difference transaction based on the virtual power plant's day-ahead settlement power and the actual delivery power, settles according to the preset time period, and conducts real-time settlement after the market is cleared.

3. The virtual power plant optimization and control method for the power market and peak load regulation market according to claim 1 is characterized in that: Specifically, step S2 is to assume that the benefit of the virtual power plant participating in market transactions is equal to the sum of the profits of the virtual power plant participating in the peak load regulation market and the electricity market; The profits of the virtual power plant participating in the peak load market are as follows: B f =λ vf (t)·P vf (t)+λ pf (t)·P pf (t) (1) P vf (t)=P ES,vf (t)+P DR,vf (t) (2) P pf (t)=P ES,pf (t)+P DR,pf (t) (3) Among them, B f is the profit of the virtual power plant peak load market, λ vf (t) and λ pf (t) are the valley filling compensation price and peak load compensation price of the peak load market during period t; P vf (t) and P pf (t) are the bidding capacity of the virtual power plant for valley filling and peak load regulation in period t; P ES,vf (t) and P ES,pf (t) is the bid capacity of energy storage for valley filling and peak regulation in period t, P DR,vf (t) and P DR,pf (t) is the bid capacity of the adjustable load for valley filling and peak regulation in period t; The profits of the virtual power plant participating in the electricity market are as follows: Among them, B grid is the profit of the virtual power plant participating in the electricity market, P buy (t) and P sell (t) is the amount of electricity purchased and sold by the virtual power plant during the period t in the day-ahead power market, λ buy (t) and λ sell (t) represents the purchase price and sales price of the virtual power plant in the period t; Δt is the time interval; T is the cycle of the market transaction; The benefit model of the virtual power plant participating in the market is: B VPP =B f +B grid (5)。 4. The virtual power plant optimization and control method for the power market and peak load market according to claim 3 is characterized in that: Specifically, step S3 is to establish an objective function for virtual power plants to participate in market transactions with the goal of minimizing overall operating costs: Among them, C VPP represents the overall operating cost of the virtual power plant, C grid is the electricity purchase cost of the virtual power plant and the electricity market, C CDG , C W and C PV are respectively the gas turbine power generation cost, wind power curtailment cost and photovoltaic power curtailment cost, C DR is the compensation cost of the virtual power plant for the load that can be removed: C grid =-B grid =λ buy (t)·P buy (t)-λ sell (t)·P sell (t) (7) C W =λ W (t)|ΔP W (t)| (9) C PV =λ PV (t)·|ΔP PV (t)| (10) Among them, a, b and c are cost-related coefficients; P CDG (t) is the power generation of the gas turbine, ΔP W (t) is the difference between the predicted value of wind power generation of the virtual power plant on the day before and the actual value on the day, ΔP PV (t) is the difference between the predicted value of photovoltaic power generation of the virtual power plant and the actual value during the day, λ W (t) is the penalty price for wind power abandonment during period t, λ PV (t) represents the penalty electricity price for abandoned solar power in period t; The cost of dispatching adjustable load by a virtual power plant is: C DR =λ DR (t)·P DR (t) (11) Where P DR (t) is the load regulation, λ DR (t) is the compensation price for load regulation, and a1, b1 and c1 are constant parameters.

5. The virtual power plant optimization and control method for the power market and peak load market according to claim 4 is characterized in that: The step S4 specifically includes establishing market constraints, establishing own operation constraints, establishing power generation resource constraints and establishing load resource constraints.

6. The virtual power plant optimization and control method for the power market and peak load regulation market according to claim 5 is characterized in that: The establishment of market constraints is specifically to establish the following market constraints for virtual power plants to participate in the electricity market: in, and are the maximum and minimum power purchases of the virtual power plant, respectively. B(t) is a discrete variable of 0 or 1, indicating the state of the virtual power plant participating in the power market. When B(t) = 1, it means purchasing power from the market, and when B(t) = 0, it means selling power. The minimum peak load capacity of a virtual power plant should reach the market threshold: in, is the minimum bidding capacity requirement of the peak-shaving market, P f (t) is the peak load capacity provided by the virtual power plant, u f (t) is a discrete variable of 0 or 1, indicating the state of the virtual power plant participating in the peak-shaving market, and M is an infinite positive number.

7. The virtual power plant optimization and control method for the power market and peak load regulation market according to claim 6 is characterized in that: The establishment of self-operation constraints is specifically to ensure the internal power balance of the virtual power plant: P buy (t)-P sell (t)+P W (t)+P PV (t)+P CDG (t)=P load (t)+P ES (t) (16) P load (t)=P load0 -P cut (t)-P DR (t) (17) Where P W (t) and P PV (t) The actual output value of wind power and photovoltaic power in the period t, P load0 and P load (t) are the values ​​before and after the flexible load adjustment, P cut (t) is the interruptible load reduction, P DR (t) is the dispatch value of the transferable load, P ES (t) is the charging and discharging power of the energy storage; Let the gas turbine and adjustable load reserve capacity satisfy: in, and are the positive and negative reserves provided by the gas turbine in time period t, and are the positive and negative reserve of the adjustable load in time period t respectively.

8. The virtual power plant optimization and control method for the power market and peak load regulation market according to claim 7 is characterized in that: The method for establishing power generation resource constraints is specifically as follows: Let the output of wind power and photovoltaic power in the virtual power plant be less than or equal to the predicted value: Among them, P W (t) and P PV (t) are the actual wind power output and solar power output of the virtual power plant on the previous day, and They are the day-ahead wind power output forecast and solar power output forecast of the virtual power plant; The reserve and upper and lower limits of the gas turbine output are set as follows: in, and are the lower and upper limits of the gas turbine output during period t respectively.

9. The virtual power plant optimization and control method for the power market and peak load market according to claim 8 is characterized in that: The establishment of load resource constraints is specifically as follows: Assume that the capacity constraints of energy storage resources are as follows: P ES (t)=P ES,e (t)-P ES,pf (t)+P ES,vf (t) (24) AND ES,t0 =And ES,T (26) E ES (t)=E ES (t-1)+P ES,cha (t)·h c -P ES,dis (t) / h d +P ES,vf (t)·h c -P ES,pf (t) / h d (28) P ES,e (t)=P ES,cha (t)-P ES,dis (t) (29) Among them, E ES,t0 is the initial capacity of energy storage, E ES,T is the final capacity, E ES (t) is the capacity in period t, and are the minimum and maximum capacity of the energy storage device, P ES,cha (t) and P ES,dis (t) are the energy storage charging power and discharging power, η c and η d are the energy storage charging efficiency and discharging efficiency respectively; Let the adjustable load operation scheduling of the adjustable load satisfy the adjustable maximum value constraint: in, is the maximum adjustable amount of the adjustable load, u c (t) is a discrete variable of 0 or 1 representing the adjustable load state. The transferable load of the adjustable load satisfies the following constraints: P DR (t)=P DR,e (t)+P DR,pf (t)-P DR,vf (t) (31) Where P DR,e (t) is the power of the transferable load participating in the electricity market through price difference arbitrage, P DR,vf (t) and P DR,pf (t) are the power involved in valley filling and peak load regulation respectively; At the same time, the total load of the transferable load should remain unchanged within a dispatch day:

10. A virtual power plant optimization and control system for the power market and peak load regulation market, characterized in that: include: A process rule setting module is used to set the process and rules for the virtual power plant to participate in market transactions; the market includes the peak load regulation market and the electricity market; The benefit model building module is used to build a benefit model for virtual power plants to participate in market transactions; The objective function establishment module is used to establish the overall objective function of the virtual power plant participating in market transactions; The constraint establishment module is used to establish the operating constraints for the virtual power plant to participate in market transactions; The scheduling module is used to solve the established objective function and constraints, obtain scheduling instructions, and complete scheduling.

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