CN111784540B - Cloud energy storage optimization clearing method based on multi-target particle swarm optimization - Google Patents
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Abstract
The invention discloses a cloud energy storage optimization clearing method based on a multi-target particle swarm algorithm, which comprises the following steps of: (1) an operation mechanism participated by a benefit subject of three parties, namely a client, a distributed power generation operator and a cloud energy storage operator, is formulated; (2) analyzing a user side income source, preliminarily analyzing and defining the opportunity cost of the user for lending and storing energy, and establishing an opportunity cost model and an income model; (3) analyzing the income source of a distributed generation operator, and establishing an income model; (4) and (4) analyzing the income source of the cloud energy storage operator and establishing an optimized scheduling model, so that the problem of clearing the cloud energy storage pricing is solved at one time. According to the cloud energy storage optimization scheduling method, the profit sources of the cloud energy storage participants can be analyzed, the energy storage resources are optimally scheduled, and the cloud energy storage optimization scheduling problem is solved at one time.
Description
Technical Field
The invention relates to the technical field of cloud energy storage and power markets, in particular to a cloud energy storage optimization clearing method based on a multi-target particle swarm algorithm.
Background
One of the features of future power systems with high renewable energy penetration is flexible resource scarcity. The stored energy is a potential flexible resource, can provide various auxiliary services for the power generation side and the demand side across a plurality of time scales, and is favorable for improving the reliability and stability of power supply. The randomness and intermittency of wind speed and light intensity can cause obvious change of real-time electricity price and can also directly cause output power fluctuation of a distributed power generation system. These fluctuations also need to be eliminated by large-scale energy storage systems. This brings economic opportunities for energy storage operators, especially through energy arbitrage. But the wide application of user-side energy storage or distributed energy storage is limited by the high cost of energy storage at present.
The cloud energy storage concept is introduced by a learner, namely, a centralized energy storage facility is used for providing distributed storage service for residential and commercial users, reasonable scheduling and configuration of energy storage are enhanced while the load requirements of the users are met, and the information advantages and the scale economy of a platform are utilized to promote the popularization and the application of energy storage at the user side. In the original concept, it is emphasized that the cloud energy storage operator has not the physical energy storage but the virtual energy storage capacity pool. This capacity may refer to energy capacity or power capacity for different users. The potential benefits of energy storage in the electricity market are also divided into capacity and power, where the energy storage capacity can be arbitraged through renewable energy time shifting in addition to through electricity price differences, arbitraged through demand charges and charge reduction per use period, arbitraged through transmission and distribution upgrade delay and improvement of grid operation elasticity. However, existing cloud energy storage operation mechanisms only involve users, cloud energy storage operators, energy storage facilities and power grids, and omit a large number of distributed power generation operators. Most of the established models are in the basic analysis stage, and the number of optimized models is small. The potential revenue and opportunity cost of energy storage is not considered in the existing user-side model. Therefore, the audience scale of cloud energy storage needs to be expanded to the whole power utilization side, a new operation mechanism is established, the component parts of a participant income model are gradually refined, and the opportunity cost or potential profit behind the energy storage scheduling strategies of buyers and sellers and the trading behaviors thereof are analyzed to reflect the real value of the cloud energy storage platform.
Disclosure of Invention
The invention aims to solve the technical problem of providing a cloud energy storage optimization clearing method based on a multi-target particle swarm algorithm, which can analyze the profit sources of cloud energy storage participants and perform optimization scheduling on energy storage resources, and can solve the cloud energy storage optimization clearing problem at one time.
In order to solve the technical problems, the invention provides a cloud energy storage optimization clearing method based on a multi-target particle swarm algorithm, which comprises the following steps:
(1) an operation mechanism participated by a benefit main body of a client, a distributed power generation operator and a cloud energy storage operator is formulated;
(2) analyzing a user side income source, preliminarily analyzing and defining the opportunity cost of the user for lending and storing energy, and establishing an opportunity cost model and an income model;
(3) analyzing the profit sources of the distributed power generation operators and establishing a profit model;
(4) and (4) analyzing the income source of the cloud energy storage operator and establishing an optimized scheduling model, so that the problem of clearing the cloud energy storage pricing is solved at one time.
Preferably, in step (1), the operation mechanism is: the cloud energy storage participants are respectively a cloud energy storage operator, a user group holding energy storage and a distributed power generation operator; the users comprise energy storage facilities, common industrial and commercial users and residential users; in the day-ahead operation, a user provides information such as self load demand, energy storage facility states and borrowable energy storage capacity to a cloud energy storage operator, a distributed power generation operator provides a renewable energy output curve and borrowable energy storage capacity to the cloud energy storage operator, the cloud energy storage operator needs to acquire information of real-time electricity prices and energy clearing price prediction information of an auxiliary service market from the energy market, and after the information is acquired, the cloud energy storage operator needs to determine an energy storage charging and discharging strategy at first and optimize and decide optimal transaction capacity and unit price according to respective energy storage scheduling strategies of a user side and a distributed power generation side.
Preferably, in step (2), the opportunity cost for lending stored energy on the user side is defined as:
the opportunity cost of the user for borrowing and storing the energy refers to the difference between the income obtained after the user borrows and the income obtained after the user does not borrow the part of the stored energy;
now suppose that the user uses this portion of stored energy to meet his own load demand or to participate in an auxiliary service market;
the opportunity cost model for the user side to borrow stored energy is as follows:
M(P ESS )＝P ESS ·Δt·C con
π(X ESS -d)＝π rc (X ESS -d)+π g (X ESS -d)
π rc (X ESS -d)＝(X ESS -d)·p rc
π g (X ESS -d)＝c％·(X ESS -d)·p g
g con is the opportunity cost, P, of the user lending the energy storage capacity ESS Is the output power of the energy storage facility, if P ESS If positive, the energy storage facility is in a discharge state, otherwise, the energy storage facility is in a charge state, M represents the network charge saved by the user if the user does not borrow the capacity, C con Is the unit price of electric energy, and pi represents the income gained by the energy storage capacity to the auxiliary service market, X ESS Is the energy storage capacity the user is willing to loan, d is the load demand on the user side, pi rc Refers to the gain, p, of reserve capacity rc Is the unit price of reserve capacity in the auxiliary service market, pi g The yield obtained by the generation capacity in the reserve capacity, p g Is the unit price of the generating capacity, and c% is the proportion of the generating capacity in the whole spare capacity;
the user side profit model is as follows:
B con ＝f con -g con -h con
f con ＝X ESS ·C X
h con ＝α·(E 0 -E f )·MCP
B con representing user-side revenue, f con Is a charge received from a cloud energy storage operator, C X Is a unit price of lending energy storage capacity, h con Is the reward given by the user when the buyer finds an increase in energy in the device after returning this portion of stored energy, alpha is a reward factor, E 0 Is the initial energy of the energy storage device, E f The MCP is the energy of the energy storage device during returning, and the MCP is the unit price for clearing the energy market.
Preferably, in step (3), the revenue model of the distributed power generation side is:
B DG ＝X ESS ·C DG -X ESS ·C Y -h DG
h DG ＝β·(E 0 -E f )·MCP
B DG is a profit of the distributed generation operator, C DG Is the net charge of unit electric energy, C Y Price per sale of energy capacity, h, by cloud energy storage operators DG Is the reimbursement paid to the user for the energy lost before and after return, and β is a penalty factor.
Preferably, in the step (4), the cloud energy storage optimization scheduling model is as follows:
(a) objective function
The method comprises the following steps of taking the maximum income of a cloud energy storage operator in one day as an optimization target, wherein the income of the cloud energy storage operator comes from a user side and a distributed power generation operator side;
(b) constraining
SOC t Is the state of charge, η, of the energy storage device at time t C And η D Is the charge-discharge efficiency, P max Is the maximum charge-discharge power, X max Is the maximum energy capacity that the user is willing to loan;
the invention has the beneficial effects that: the cloud energy storage optimization method has the advantages that audiences of the cloud energy storage are expanded to the whole electricity utilization side, a new operation mechanism is established, the components of a participant profit model are analyzed in a detailed mode, energy storage scheduling strategies of buyers and sellers and opportunity cost or potential profit behind trading behaviors of the energy storage scheduling strategies are analyzed, the cloud energy storage optimization problem is solved once, and the real value of a cloud energy storage platform is reflected.
Drawings
FIG. 1 is a schematic view of the present invention showing the day-ahead load demand information of all customers.
FIG. 2 is a schematic diagram of a capacity-price curve for the auxiliary service market of the present invention.
FIG. 3 is a schematic diagram of a wind power output curve of the distributed power generation side of the present invention.
Fig. 4 is a schematic diagram of a photovoltaic output curve of the distributed power generation side of the present invention.
FIG. 5 is a schematic diagram of energy price curves corresponding to the inverse peak, the positive peak and the flat peak of the present invention.
FIG. 6 is a non-inferior illustration of the user loan capacity when the wind output curve is an inverse peak according to the present invention.
FIG. 7 is a non-inferior illustration of the user's loan capacity when the wind output curve is positive according to the present invention.
FIG. 8 is a non-inferior illustration of the loan capacity of a user when the wind output curve of the present invention is flat.
FIG. 9 is a schematic flow chart of the method of the present invention.
Detailed Description
As shown in fig. 9, a cloud energy storage optimization clearing method based on a multi-objective particle swarm algorithm includes the following steps:
1. and establishing an operation mechanism participated by a three-party interest main body of a client, a distributed power generation operator and a cloud energy storage operator.
The cloud energy storage participants are respectively a cloud energy storage operator, a user group holding energy storage and a distributed power generation operator. The users include energy storage facilities, general industrial and commercial users and residential users. In the day-ahead operation, the user provides the cloud energy storage operator with information such as the load demand, the energy storage facility state and the available borrowed energy storage capacity. And the distributed power generation operator provides a renewable energy output curve and information such as the energy storage capacity required to be borrowed to the cloud energy storage operator. The cloud energy storage operator needs to obtain information of real-time electricity prices from the energy market and energy clearing price prediction information of the auxiliary service market. After the information is obtained, the cloud energy storage operator needs to determine an energy storage charging and discharging strategy at first, and then optimize and decide the optimal transaction capacity and unit price according to respective energy storage scheduling strategies of the user side and the distributed power generation side.
2. And analyzing the user side profit source, preliminarily analyzing and defining the opportunity cost of the user for lending the stored energy, and establishing an opportunity cost model and a user side profit model of the user for lending the stored energy.
Opportunity cost definition of user-side loan energy storage: the difference between the profit obtained by the user when the user borrows the stored energy and the profit obtained when the user does not borrow the stored energy. This requires exploration of how the user can use this part of the capacity if he does not lend it. Assuming that the user uses the part of the stored energy to satisfy his own load demand or participate in the auxiliary service market, his opportunity cost is discussed in two cases, namely whether the part of the stored energy capacity lent exceeds his own load demand. When the lent part of the stored energy does not exceed the load demand of the user, the discharge opportunity cost in a certain period is equal to the net charge spent by the user for obtaining the electric quantity from the power grid by using the discharge of the part of the stored energy in the period. When the lent part of the stored energy exceeds the load demand of the user, the discharge opportunity cost in a certain period not only comprises the net charge saved when the part of the stored energy is owned, but also comprises the profit earned by using the residual stored energy capacity exceeding the load demand part for participating in the auxiliary service market.
The user-side loan energy-storage opportunity cost model:
M(P ESS )＝P ESS ·Δt·C con
π(X ESS -d)＝π rc (X ESS -d)+π g (X ESS -d)
π rc (X ESS -d)＝(X ESS -d)·p rc
π g (X ESS -d)＝c％·(X ESS -d)·p g
g con is the opportunity cost to the user to borrow energy storage capacity. P is ESS Is the output power of the energy storage facility. If P ESS If the voltage is positive, the energy storage facility is in a discharging state, otherwise, the energy storage facility is in a charging state. MIndicating the net charge that the user can save if the user does not lend the capacity, C con Is the unit price of electric energy. π Representing the gain, X, of energy storage capacity into the ancillary service market ESS Is the energy storage capacity the user is willing to loan and d is the load demand on the user side. Pi rc Is the gain, p, of spare capacity rc Is the unit price of spare capacity in the auxiliary service market. Pi g Yield, p, of generation capacity in the reserve capacity g Is the unit price of the power generation capacity. c% is the proportion of the generating capacity to the total reserve capacity.
User-side revenue model:
B con ＝f con -g con -h con
f con ＝X ESS ·C X
h con ＝α·(E 0 -E f )·MCP
B con representing user-side revenue. f. of con Is a fee collected from the cloud energy storage operator. C X Is the unit price of lending energy storage capacity. h is con Is the reward given by the user when the buyer finds an increase in energy in the device after returning this portion of stored energy. α Is a reward factor. E 0 Is the initial energy of the energy storage device. E f Is the energy of the energy storage device at return. MCP is the clearing unit price in the energy market.
3. And establishing a revenue model of the distributed power generation operator side.
Revenue model of distributed generation side:
B DG ＝X ESS ·C DG -X ESS ·C Y -h DG
h DG ＝β·(E 0 -E f )·MCP
B DG is a revenue for the distributed generation operator. C DG Is the net charge per unit of electricity. C Y Cloud energy storage operators sell energy capacity at a price. h is DG Is the reimbursement paid to the user for the energy loss before and after return, and β is a penalty factor.
4. And (4) analyzing the income source of the cloud energy storage operator and establishing an optimized scheduling model, so that the problem of clearing the cloud energy storage pricing is solved at one time.
The cloud energy storage operator earning source is as follows: when a cloud energy storage operator purchases distributed energy for a relatively low price, the reduced energy waste is part of its profit, and its profit space may also contain opportunity costs for various energy storage capacity rentals. Therefore, the cloud energy storage operator can share the overall income of the user side and the overall income of the distributed power generation operator so as to ensure the operation income of the cloud energy storage operator.
The cloud energy storage optimization scheduling model comprises the following steps:
(a) objective function
The method takes the maximization of the income of the cloud energy storage operator in one day as an optimization target. In the invention, the cloud energy storage operator
The revenue of (b) comes from the customer side and the distributed generation operator side.
(b) Constraining
SOC t Is the state of charge of the energy storage device at time t. Eta C And η D Is the charge-discharge efficiency. P is max Is the maximum charge and discharge power. X max Is the maximum energy capacity that the user is willing to loan.
SOC min ≤SOC t ≤SOC max
E min ≤E t ≤E max
0＜X ESS ≤X max
X max ＝E max -E min
-P max ≤P ESS ≤P max
C con ＞C DG C X ＞C Y
B con ＞0B DG ＞0
1≤α≤1.1 0.9≤β≤1
In order to simplify the cloud energy storage optimization scheduling model based on the multi-target particle swarm algorithm, the multi-target particle swarm algorithm is described.
The multi-target particle swarm algorithm is mainly used for optimizing each target to simultaneously achieve a comprehensive optimal value. The optimized scheduling model provided by the invention has three optimization targets, namely, the profit of a customer is maximized, the profit of a distributed power generation operator is maximized, and the overall profit is maximized, namely the profit of the cloud energy storage operator. The most central thing in the multi-objective problem solving is a non-inferior solution. In the process of solving the actual problem, too many non-inferior solutions cannot be directly applied, and only one solution closest to the expected target can be selected as the final solution. Here, a multi-target problem is converted into a single target to be solved to select an optimal solution and lock the position of the optimal solution.
The multi-target particle swarm algorithm comprises the following specific steps:
(1) generating i sets of decision variables (X) ESS,i ,Y ESS,i ,C X,i ,C Y,i ) And setting the data fluctuation range.
(2) And reading the table data of the user side load demand, the energy market clearing price, the photovoltaic wind power output and the like within 24 hours.
(3) And defining an equation and an objective function.
(4) Calculating i groups corresponding to the i groups of decision variables
(5) For i groups of decision variables (X) ESS,i ,Y ESS,i ,C X,i ,C Y,i ) And adjusting to obtain a new i groups of decision variables.
(6) And (4) jumping to the step 4, and circulating m iterations until the optimal solution is obtained under the condition that the constraint condition is met.
(7) The algorithm ends.
The whole process can be realized by using MATLAB programming.
In order to verify the effectiveness of the algorithm, the scene with high popularization rate of renewable energy sources is selected for research. We randomly choose 100 users to participate in the cloud energy storage service.
Fig. 1 shows day-ahead load demand information for all customers. Fig. 2 shows a capacity-price curve for the auxiliary service market. Fig. 3 and 4 show wind power output curves and photovoltaic output curves of the distributed power generation side respectively. Fig. 5 shows energy price curves for the corresponding inverse peak, positive peak and flat peak conditions.
In fig. 3, 4 and 5, the curves with diamond marks represent the inverse peak state, the curves with square marks represent the positive peak state, and the curves with triangle marks represent the flat peak state.
FIG. 6 when the wind output curve is peak-inverted, all customers borrow 1458.68MWh of energy capacity in total a day. The cloud energy storage operators purchase the power generation system at the price of 16.66$/MWh, and then sell the power generation system to the distributed power generation operators at the price of 1.44 $/MWh. The total profit is now maximized at $ 4709754.
FIG. 7 when the wind output curve is positive, all customers borrow 1166.30MWh of energy capacity in total a day. The cloud energy storage operators purchase the power generation system at the price of 13.02/MWh, and then sell the power generation system to the distributed power generation operators at the price of 1.60/MWh. The total profit is now at a maximum of $ 4414106.
FIG. 8 when the wind output curve is flat, all customers borrow 1625.24MWh of energy capacity in total a day. The cloud energy storage operators purchase the power generation system at a price of 17.52$/MWh, and then sell the power generation system to the distributed power generation operators at a price of 2.45 $/MWh. The total profit is now maximized at $ 5686264.
Claims (4)
1. A cloud energy storage optimization clearing method based on a multi-target particle swarm algorithm is characterized by comprising the following steps:
(1) an operation mechanism participated by a benefit subject of three parties, namely a client, a distributed power generation operator and a cloud energy storage operator, is formulated;
(2) analyzing a user side income source, preliminarily analyzing and defining the opportunity cost of user lending and storing energy, and establishing an opportunity cost model and an income model; the opportunity cost for the user to loan out stored energy is defined as:
the opportunity cost of the user for borrowing and storing the energy refers to the difference between the income obtained after the user borrows and the income obtained after the user does not borrow the part of the stored energy;
now suppose that the user uses this portion of stored energy to meet his own load demand or to participate in an auxiliary service market;
the opportunity cost model for the user side to borrow stored energy is as follows:
M(P ESS )＝P ESS ·Δt·C con
π(X ESS -d)＝π rc (X ESS -d)+π g (X ESS -d)
π rc (X ESS -d)＝(X ESS -d)·p rc
π g (X ESS -d)＝c％·(X ESS -d)·p g
g con is the opportunity cost, P, of the user lending energy storage capacity ESS Is the output power of the energy storage facility, if P ESS If positive, the energy storage facility is in a discharge state, otherwise, the energy storage facility is in a charge state, M represents the network charge saved by the user if the user does not borrow the capacity, C con Is the unit price of electric energy, and pi represents the income gained by the energy storage capacity to the auxiliary service market, X ESS Is the energy storage capacity the user is willing to loan out, d is the load demand on the user side, pi rc Is the gain, p, of spare capacity rc Is the unit price of reserve capacity in the auxiliary service market, pi g Yield, p, of generation capacity in the reserve capacity g Is the unit price of the generating capacity, and c% is the proportion of the generating capacity in the whole spare capacity;
the user side profit model is as follows:
B con ＝f con -g con -h con
f con ＝X ESS ·C X
h con ＝α·(E 0 -E f )·MCP
B con representing user-side revenue, f con Is a charge received from a cloud energy storage operator, C X Is a unit price of lending energy storage capacity, h con Is the reward given by the user when the buyer finds an increase in energy in the device after returning this portion of stored energy, alpha is a reward factor, E 0 Is the initial energy of the energy storage device, E f The MCP is the unit price of clearing energy in the energy market;
(3) analyzing the income source of a distributed generation operator, and establishing an income model;
(4) and (4) analyzing the income source of the cloud energy storage operator and establishing an optimized scheduling model, so that the problem of clearing the cloud energy storage pricing is solved at one time.
2. The cloud energy storage optimization clearing method based on the multi-objective particle swarm algorithm as claimed in claim 1, wherein in the step (1), the operation mechanism is: the cloud energy storage participants are respectively a cloud energy storage operator, a user group holding energy storage and a distributed power generation operator; the users comprise energy storage facilities, common industrial and commercial users and residential users; in the day-ahead operation, a user provides the load demand, the energy storage facility state and the borrowable energy storage capacity information to a cloud energy storage operator, the distributed power generation operator provides a renewable energy output curve and the borrowed energy storage capacity information to the cloud energy storage operator, the cloud energy storage operator needs to acquire the real-time electricity price information and the energy clearing price prediction information of an auxiliary service market from the energy market, after the information is obtained, the cloud energy storage operator needs to determine an energy storage charging and discharging strategy at first, and then the optimal transaction capacity and the optimal transaction price are optimized and decided according to respective energy storage scheduling strategies of the user side and the distributed power generation side.
3. The cloud energy storage optimization clearing method based on the multi-objective particle swarm optimization algorithm as claimed in claim 1, wherein in the step (3), the profit model of the distributed power generation side is as follows:
B DG ＝X ESS ·C DG -X ESS ·C Y -h DG
h DG ＝β·(E 0 -E f )·MCP
B DG is a profit of the distributed generation operator, C DG Is the net charge of unit electric energy, C Y Price per sale, h, of energy capacity by cloud energy storage operators DG Is the reimbursement paid to the user for the energy loss before and after return, and β is a penalty factor.
4. The cloud energy storage optimization clearing method based on the multi-objective particle swarm optimization algorithm according to claim 1, wherein in the step (4), the cloud energy storage optimization scheduling model is as follows:
(a) objective function
The method comprises the following steps of taking the maximum income of a cloud energy storage operator in one day as an optimization target, wherein the income of the cloud energy storage operator comes from a user side and a distributed power generation operator side;
(b) constraining
SOC t Is the state of charge, η, of the energy storage device at time t C And η D Efficiency of charge and discharge, P max Is the maximum charge-discharge power, X max Is the maximum energy capacity that the user is willing to loan;
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| CN109657946A (en) * | 2018-09-19 | 2019-04-19 | 清华大学 | The mathematical model and planing method of Regional Energy internet planning based on game theory |
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