CN117374942A - Energy storage economy optimization scheduling method based on production plan and load prediction - Google Patents
Energy storage economy optimization scheduling method based on production plan and load prediction Download PDFInfo
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Abstract
The invention relates to an energy storage economy optimization scheduling method based on production planning and load prediction, which comprises the following steps: step 1: data collection and processing; step 2: load prediction; step 3: a production plan; step 4: analyzing electricity price; step 5: modeling an energy storage system; step 6: optimizing a model; step 7: optimizing and solving; step 8: real-time control; the invention has the advantages of improving the energy utilization rate, optimizing the operation of the energy storage system, controlling the energy storage system in real time, reducing the energy waste and realizing the maximum energy saving benefit.
Description
Technical Field
The invention belongs to the technical field of energy storage economy optimization scheduling, and particularly relates to an energy storage economy optimization scheduling method based on production planning and load prediction.
Background
With the continuous update and improvement of the electric power system in China, the distributed power supply DG is continuously increased, the micro-grid MG is rapidly developed as a DG grid-connected carrier, but with the increase of the access scale of the micro-grid and the wide application of renewable energy sources and distributed energy sources, the number is increased to make the power distribution network more complicated to schedule, and the management and optimization of the electric power system become more complicated; in order to reduce the cost of electric energy, improve the utilization rate of energy and support sustainable development, research on an energy storage economic optimization scheduling strategy under the time-of-use electricity price based on production planning and load prediction is needed; therefore, it is very necessary to provide an energy storage economic optimization scheduling method based on production planning and load prediction, which improves the energy utilization rate, optimizes the operation of the energy storage system, controls the energy storage system in real time, reduces energy waste and achieves the maximum energy saving benefit.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an energy storage economic optimization scheduling method based on production planning and load prediction, which improves the energy utilization rate, optimizes the operation of an energy storage system, controls the energy storage system in real time, reduces energy waste and realizes the maximum energy saving benefit.
The purpose of the invention is realized in the following way: an energy storage economy optimization scheduling method based on production planning and load prediction, comprising the following steps:
step 1: data collection and processing: collecting historical power load data, electricity price data, renewable energy power generation data and factory production plans, wherein the data are used for load prediction and electricity price analysis, and the accuracy and usability of the data are ensured through data cleaning and processing;
step 2: load prediction: generating a load prediction curve over a future time period using a load prediction model based on historical load data, production plans, and weather forecast information, the prediction data being used for decision optimization;
step 3: production planning: production planning includes detailed planning of production and manufacturing activities, including production tasks, production quantities, production schedules, the production planning being closely related to power demand, and the need to coordinate with load forecasting to ensure that power supply matches production demand;
step 4: electricity price analysis: analyzing time-of-use electricity rate information, including electricity rates during peak hours, valley hours, and stationary hours, the electricity rate data being consolidated and modeled for cost-benefit analysis;
step 5: modeling an energy storage system: modeling an energy storage system, including energy storage capacity, charge-discharge efficiency, charge-discharge rate and technical characteristics, and determining operation limits and capabilities of the energy storage system;
step 6: optimizing a model: the established mathematical optimization model aims at minimizing the total electric energy cost, and the optimal charge and discharge strategy in each time period is determined by considering load prediction, electricity price information, production plans and energy storage system characteristics;
step 7: and (3) optimizing and solving: an ADN economic optimization scheduling model and an improved wolf group optimization algorithm are used for ensuring that the algorithm has instantaneity and high efficiency;
step 8: and (3) real-time control: and according to the optimization result, the charging and discharging behaviors of the energy storage system are controlled in real time, and the system is ensured to realize the maximum energy-saving benefit.
The ADN economic optimization scheduling model in the step 7 specifically comprises the following steps:
step 7.1: an objective function;
step 7.2: constraint conditions.
The objective function in the step 7.1 specifically includes: taking 24h a day as one scheduling period, the total ADN running cost in one scheduling period can be expressed as: minp=f 1 +F 2 +F 3 +F 4 (1) Wherein P is the total running cost of ADN in one scheduling period; f (F) 1 The method is the power generation cost of a thermal power unit in a power distribution network system; f (F) 2 The energy exchange cost with the upper power grid is realized; f (F) 3 Punishment cost for wind and light discarding; f (F) 4 Is the energy storage cost.
The power generation cost F of the thermal power generating unit 1 The method comprises the following steps:in (1) the->The power generation of the thermal power generating unit at the t hour is realized; a. b and c are respectively the power generation cost coefficients of the thermal power generating unit;
the electric energy exchange cost F 2 The method comprises the following steps:in (1) the->The electricity purchasing unit price and the electricity selling unit price at the t hour are respectively; />The electricity purchasing quantity and the electricity selling quantity are respectively the electricity purchasing quantity and the electricity selling quantity in the t hour;
the wind discarding and light discarding punishment cost F 3 The method comprises the following steps:in (1) the->The cost coefficients of the wind discarding and the light discarding punishment at the t hour are respectively; />Output power predicted values of wind power and photovoltaic respectively; />The actual output power values of wind power and photovoltaic are respectively;
the energy storage cost F 4 The method comprises the following steps:in (1) the->Is an energy storage cost coefficient;the charging power and the discharging power of the energy storage device at the t hour are respectively.
The constraint conditions in the step 7.2 comprise branch flow constraint, wind power constraint, photovoltaic output constraint, static reactive compensation device constraint, node voltage constraint and energy storage constraint.
The branch tidal current constraint is as follows:wherein P is j,t 、q j,t Active power and reactive power injected into node j respectively; r is (r) ij 、x ij The resistance and reactance of branch ij; p (P) jk,t 、Q jk,t Active power and reactive power of the head end of the branch jk are respectively; p (P) ij,t 、Q ij,t Active power and reactive power of the head end of the branch ij are respectively; i ij,t Current for branch ij; j-k is a set with the node as a father node of j; i.j is a set taking an i node as a father node;
the wind power and photovoltaic output constraint is as follows: the power constraint of the thermal power generating unit is as follows:in the method, in the process of the invention,maximum output power of the wind turbine generator and the photovoltaic at the t hour are respectively;
the static reactive compensation device is constrained as follows:in which Q i,t The compensation quantity of the reactive compensation device at the node i at the t-th hour; />The upper limit and the lower limit of the compensation quantity of the reactive compensation device at the node i are respectively;
the node voltage constraint is as follows: u (U) min ≤U i ≤U max (9) In the formula, U i Is the voltage of node i; u (U) min 、U max Respectively a node voltage minimum value and a node voltage maximum value;
the energy storage constraint is as follows:wherein E is t Capacity of the energy storage device at the first hour; e (E) min 、E max The upper limit and the lower limit of the capacity of the energy storage equipment are respectively; />Maximum charging power and maximum discharging power respectively; e (E) 0 、E T The energy storage capacity at the beginning and at the end of the schedule, respectively.
The wolf group optimization algorithm in the step 7, namely the WPA algorithm, is specifically: the wolf group comprises a head wolf, a probe wolf and a branchlet, the roles of which are respectively command, search and attack, and the WPA is defined as follows: the wolf group capacity is set as N, the space dimension is set as D, and then the wolf group can be expressedThe method comprises the following steps: x is X i ＝(x i1 ,x i2 ,...,x iD ) The method specifically comprises the following steps:
step a: generating a first wolf: finding the optimal solution Y in the initial solution lead With the iteration of the algorithm, the head wolves can be replaced, if the position of one wolf is better than that of the head wolves, the head wolves are replaced, otherwise, the head wolves are unchanged;
step b: search wolf: let the number of detection wolves be M, M depends on [ N/(alpha+1), N/alpha]Wherein alpha is a wolf scale factor; the number of the walk directions of the wolves is h, and the walk step length is step a Initial fitness is Y i ，x id The position of the i-th artificial wolf in the D (d=1, 2,., D) -th dimensional variable space to be optimized;indicating the position of the rear sounding wolf in the d-th dimensional space, proceeding in the p (p=1, 2,., h) direction, is: />At this time, a new fitness value Y is calculated ip If the new fitness ratio detects the current fitness value Y of wolf i Better, it is replaced and the wolf position X is updated i Then the current fitness value of the wolf is detected and the fitness value Y of the wolf is detected lead Comparing, if the current fitness value of the detecting wolf is better, replacing the head wolf with the detecting wolf, and summoning the strong wolf to move to the current position, otherwise, continuing to search until the maximum iteration number T max ；
Step c: the wolf moves: the number of the rag wolves is N-M, the rag wolves can immediately move towards the head wolves after receiving the calling information, and the step length of the rag wolves moving is step b The position of the wolf at the k+1st iteration is:in (1) the->Representing the position of the wolf at the kth+1st iteration of wolf i;representing the position of the slam wolf at the kth iteration of slam wolf i; />Is the position of the top wolf at the kth iteration; calculating the fitness value Y of the moving wolves i If Y i Ratio Y lead More preferably, the slam wolf is changed into the head wolf, and calls are sent to other slam wolves, otherwise, the rag continues to move towards the direction of the head wolf until the position away from the head wolf is smaller than d near Then attack is initiated on the prey, d near The calculation formula of (2) is as follows: />Wherein ω is a distance determination factor; max (max) d 、min d The maximum value and the minimum value of the d variable to be optimized are taken;
step d: attack prey: the wolf reaches a predetermined position, i.e. attacks the prey with the detecting wolf, the position of the wolf is considered because the wolf is very close to the preyI.e. the prey position, the attack step length is step c Lambda is [ -1,1]The random numbers distributed evenly among the wolf clusters are updated according to the following formula: />When a prey is attacked, if the position fitness value of the wolf group is better than the current fitness value, replacing, otherwise, keeping unchanged; step a 、step b Step c The relation of (2) is: />Wherein S is a step factor;
step e: wolf group updating: in the process of searching hunting objects by the wolf group, eliminating the R-wolf with the worst adaptability, and randomly supplementing the R-wolf to the wolf group, wherein the R-value interval [ N/(2 x beta) and N/(beta) ], wherein beta is an updated scale factor.
The wolf-swarm optimization algorithm in the step 7, namely the WPA algorithm, solves the ADN economic optimization scheduling model, and specifically comprises the following steps:
step S1: setting ADN related parameters, setting WPA related parameters, setting wolf number as N and initial position X i Maximum number of walks T max And the number of iterations k max Step factor S, distance judging factor omega, wolf scale factor alpha and updating scale factor beta;
step S2: the ADN total running cost is taken as a fitness function, and the wandering position of the wolf is updated according to the formula (11) until the wolf fitness Y is detected i ＞Y lead The first wolf is replaced by the detected wolf, and the next step is carried out, otherwise, the walking is continued until the maximum number of times T is reached max Then carrying out the next step;
step S3: updating the position of the bristletail according to the formula (12) until the bristletail detection fitness Y i ＞Y lead Replacing the head wolf with the slam wolf, otherwise continuing to attack until the distance from the head wolf is less than d near Then the next step is carried out;
step S4: the prey is enclosed according to the formula (14), and the wolf is updated;
step S5: updating the whole wolf group;
step S6: and (3) judging that the maximum iteration times or the allowable errors are reached, if so, outputting an optimizing result, otherwise, returning to the step (S2).
The invention has the beneficial effects that: the invention relates to an energy storage economic optimization scheduling method based on production plan and load prediction, in use, the invention establishes an ADN economic optimization scheduling model by considering an energy storage economic optimization scheduling strategy under the time-of-use electricity price of production plan and load prediction, and solves by using an improved wolf group optimization algorithm; according to the optimization result, the energy storage system is controlled in real time, so that the system is ensured to realize the maximum energy-saving benefit; the invention can improve the energy utilization rate, optimize the operation of the energy storage system, improve the energy utilization efficiency and reduce the energy waste; the built energy storage system improves the stability of the power system and can be used for generating power by using renewable energy with smooth fluctuation; optimizing the production plan according to the production plan and a scheduling strategy under the load forecast time-of-use electricity price, and ensuring that the power supply is matched with the production demand; the invention provides an advanced power system management method which is suitable for renewable energy integration and the intellectualization of a power system and has wide application prospect in the power industry and the sustainable energy field; the invention has the advantages of improving the energy utilization rate, optimizing the operation of the energy storage system, controlling the energy storage system in real time, reducing the energy waste and realizing the maximum energy saving benefit.
Drawings
FIG. 1 is a flow chart of an improved WPA of an energy storage economy optimization scheduling method based on production planning and load prediction of the present invention.
FIG. 2 is a flow chart of an implementation of an energy storage economy optimization scheduling method based on production planning and load prediction.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Example 1
As shown in fig. 1-2, an energy storage economy optimization scheduling method based on production planning and load prediction, the method comprises the following steps:
step 1: data collection and processing: collecting historical power load data, electricity price data, renewable energy power generation data and factory production plans, wherein the data are used for load prediction and electricity price analysis, and the accuracy and usability of the data are ensured through data cleaning and processing;
step 2: load prediction: generating a load prediction curve over a future time period using a load prediction model based on historical load data, production plans, and weather forecast information, the prediction data being used for decision optimization;
step 3: production planning: production planning includes detailed planning of production and manufacturing activities, including production tasks, production quantities, production schedules, the production planning being closely related to power demand, and the need to coordinate with load forecasting to ensure that power supply matches production demand;
step 4: electricity price analysis: analyzing time-of-use electricity rate information, including electricity rates during peak hours, valley hours, and stationary hours, the electricity rate data being consolidated and modeled for cost-benefit analysis;
step 5: modeling an energy storage system: modeling an energy storage system, including energy storage capacity, charge-discharge efficiency, charge-discharge rate and technical characteristics, and determining operation limits and capabilities of the energy storage system;
step 6: optimizing a model: the established mathematical optimization model aims at minimizing the total electric energy cost, and the optimal charge and discharge strategy in each time period is determined by considering load prediction, electricity price information, production plans and energy storage system characteristics;
step 7: and (3) optimizing and solving: an ADN economic optimization scheduling model and an improved wolf group optimization algorithm are used for ensuring that the algorithm has instantaneity and high efficiency;
step 8: and (3) real-time control: and according to the optimization result, the charging and discharging behaviors of the energy storage system are controlled in real time, and the system is ensured to realize the maximum energy-saving benefit.
The invention relates to an energy storage economic optimization scheduling method based on production plan and load prediction, in use, the invention establishes an ADN economic optimization scheduling model by considering an energy storage economic optimization scheduling strategy under the time-of-use electricity price of production plan and load prediction, and solves by using an improved wolf group optimization algorithm; according to the optimization result, the energy storage system is controlled in real time, so that the system is ensured to realize the maximum energy-saving benefit; the invention can improve the energy utilization rate, optimize the operation of the energy storage system, improve the energy utilization efficiency and reduce the energy waste; the built energy storage system improves the stability of the power system and can be used for generating power by using renewable energy with smooth fluctuation; optimizing the production plan according to the production plan and a scheduling strategy under the load forecast time-of-use electricity price, and ensuring that the power supply is matched with the production demand; the invention provides an advanced power system management method which is suitable for renewable energy integration and the intellectualization of a power system and has wide application prospect in the power industry and the sustainable energy field; the invention has the advantages of improving the energy utilization rate, optimizing the operation of the energy storage system, controlling the energy storage system in real time, reducing the energy waste and realizing the maximum energy saving benefit.
Example 2
As shown in fig. 1-2, an energy storage economy optimization scheduling method based on production planning and load prediction, the method comprises the following steps:
step 1: data collection and processing: collecting historical power load data, electricity price data, renewable energy power generation data and factory production plans, wherein the data are used for load prediction and electricity price analysis, and the accuracy and usability of the data are ensured through data cleaning and processing;
step 2: load prediction: generating a load prediction curve over a future time period using a load prediction model based on historical load data, production plans, and weather forecast information, the prediction data being used for decision optimization;
step 3: production planning: production planning includes detailed planning of production and manufacturing activities, including production tasks, production quantities, production schedules, the production planning being closely related to power demand, and the need to coordinate with load forecasting to ensure that power supply matches production demand;
step 4: electricity price analysis: analyzing time-of-use electricity rate information, including electricity rates during peak hours, valley hours, and stationary hours, the electricity rate data being consolidated and modeled for cost-benefit analysis;
step 5: modeling an energy storage system: modeling an energy storage system, including energy storage capacity, charge-discharge efficiency, charge-discharge rate and technical characteristics, and determining operation limits and capabilities of the energy storage system;
step 6: optimizing a model: the established mathematical optimization model aims at minimizing the total electric energy cost, and the optimal charge and discharge strategy in each time period is determined by considering load prediction, electricity price information, production plans and energy storage system characteristics;
step 7: and (3) optimizing and solving: an ADN economic optimization scheduling model and an improved wolf group optimization algorithm are used for ensuring that the algorithm has instantaneity and high efficiency;
step 8: and (3) real-time control: and according to the optimization result, the charging and discharging behaviors of the energy storage system are controlled in real time, and the system is ensured to realize the maximum energy-saving benefit.
The ADN economic optimization scheduling model in the step 7 specifically comprises the following steps:
step 7.1: an objective function;
step 7.2: constraint conditions.
The objective function in the step 7.1 specifically includes: taking 24h a day as one scheduling period, the total ADN running cost in one scheduling period can be expressed as: minp=f 1 +F 2 +F 3 +F 4 (1) Wherein P is the total running cost of ADN in one scheduling period; f (F) 1 The method is the power generation cost of a thermal power unit in a power distribution network system; f (F) 2 The energy exchange cost with the upper power grid is realized; f (F) 3 Punishment cost for wind and light discarding; f (F) 4 Is the energy storage cost.
The power generation cost F of the thermal power generating unit 1 The method comprises the following steps:in (1) the->The power generation of the thermal power generating unit at the t hour is realized; a. b and c are respectively the power generation cost coefficients of the thermal power generating unit;
the electric energy exchange cost F 2 The method comprises the following steps:in (1) the->The electricity purchasing unit price and the electricity selling unit price at the t hour are respectively; />The electricity purchasing quantity and the electricity selling quantity are respectively the electricity purchasing quantity and the electricity selling quantity in the t hour;
the wind discarding and light discarding punishment cost F 3 The method comprises the following steps:in (1) the->The cost coefficients of the wind discarding and the light discarding punishment at the t hour are respectively; />Output power predicted values of wind power and photovoltaic respectively; />The actual output power values of wind power and photovoltaic are respectively;
the energy storage cost F 4 The method comprises the following steps:in (1) the->Is an energy storage cost coefficient;the charging power and the discharging power of the energy storage device at the t hour are respectively.
The constraint conditions in the step 7.2 comprise branch flow constraint, wind power constraint, photovoltaic output constraint, static reactive compensation device constraint, node voltage constraint and energy storage constraint.
The branch tidal current constraint is as follows:wherein P is j,t 、q j,t Active power and reactive power injected into node j respectively; r is (r) ij 、x ij The resistance and reactance of branch ij; p (P) jk,t 、Q jk,t Active power and reactive power of the head end of the branch jk are respectively; p (P) ij,t 、Q ij,t Active power and reactive power of the head end of the branch ij are respectively; i ij,t Current for branch ij; j-k is a set with the node as a father node of j; i: i →j is a set taking the i node as a father node;
the wind power and photovoltaic output constraint is as follows: the power constraint of the thermal power generating unit is as follows:in the method, in the process of the invention,maximum output power of the wind turbine generator and the photovoltaic at the t hour are respectively;
the static reactive compensation device is constrained as follows:in which Q i,t The compensation quantity of the reactive compensation device at the node i at the t-th hour; />The upper limit and the lower limit of the compensation quantity of the reactive compensation device at the node i are respectively;
the node voltage constraint is as follows: u (U) min ≤U i ≤U max (9) In the formula, U i Is the voltage of node i; u (U) min 、U max Respectively a node voltage minimum value and a node voltage maximum value;
the energy storage constraint is as follows:wherein E is t Capacity of the energy storage device at the first hour; e (E) min 、E max The upper limit and the lower limit of the capacity of the energy storage equipment are respectively; />Maximum charging power and maximum discharging power respectively; e (E) 0 、E T The energy storage capacity at the beginning and at the end of the schedule, respectively.
The wolf group optimization algorithm in the step 7, namely the WPA algorithm, is specifically: the wolf group comprises head wolves, exploring wolves and slash wolves, the roles of which are respectively command, search and attack, and the WPA is defined asThe following steps: setting the wolf group capacity as N and the spatial dimension as D, the wolf group can be expressed as: x is X i ＝(x i1 ,x i2 ,...,x iD ) The method specifically comprises the following steps:
step a: generating a first wolf: finding the optimal solution Y in the initial solution lead With the iteration of the algorithm, the head wolves can be replaced, if the position of one wolf is better than that of the head wolves, the head wolves are replaced, otherwise, the head wolves are unchanged;
step b: search wolf: let the number of detection wolves be M, M depends on [ N/(alpha+1), N/alpha]Wherein alpha is a wolf scale factor; the number of the walk directions of the wolves is h, and the walk step length is step a Initial fitness is Y i ，x id The position of the i-th artificial wolf in the D (d=1, 2,., D) -th dimensional variable space to be optimized;indicating the position of the rear sounding wolf in the d-th dimensional space, proceeding in the p (p=1, 2,., h) direction, is: />At this time, a new fitness value Y is calculated ip If the new fitness ratio detects the current fitness value Y of wolf i Better, it is replaced and the wolf position X is updated i Then the current fitness value of the wolf is detected and the fitness value Y of the wolf is detected lead Comparing, if the current fitness value of the detecting wolf is better, replacing the head wolf with the detecting wolf, and summoning the strong wolf to move to the current position, otherwise, continuing to search until the maximum iteration number T max ；
Step c: the wolf moves: the number of the rag wolves is N-M, the rag wolves can immediately move towards the head wolves after receiving the calling information, and the step length of the rag wolves moving is step b The position of the wolf at the k+1st iteration is:in (1) the->Represents the k+th of wolf iThe position of the wolf at 1 iteration;representing the position of the slam wolf at the kth iteration of slam wolf i; />Is the position of the top wolf at the kth iteration; calculating the fitness value Y of the moving wolves i If Y i Ratio Y lead More preferably, the slam wolf is changed into the head wolf, and calls are sent to other slam wolves, otherwise, the rag continues to move towards the direction of the head wolf until the position away from the head wolf is smaller than d near Then attack is initiated on the prey, d near The calculation formula of (2) is as follows: />Wherein ω is a distance determination factor; max (max) d 、min d The maximum value and the minimum value of the d variable to be optimized are taken;
step d: attack prey: the wolf reaches a predetermined position, i.e. attacks the prey with the detecting wolf, the position of the wolf is considered because the wolf is very close to the preyI.e. the prey position, the attack step length is step c Lambda is [ -1,1]The random numbers distributed evenly among the wolf clusters are updated according to the following formula: />When a prey is attacked, if the position fitness value of the wolf group is better than the current fitness value, replacing, otherwise, keeping unchanged; step a 、step b Step c The relation of (2) is: />Wherein S is a step factor;
step e: wolf group updating: in the process of searching hunting objects by the wolf group, eliminating the R-wolf with the worst adaptability, and randomly supplementing the R-wolf to the wolf group, wherein the R-value interval [ N/(2 x beta) and N/(beta) ], wherein beta is an updated scale factor.
The wolf-swarm optimization algorithm in the step 7, namely the WPA algorithm, solves the ADN economic optimization scheduling model, and specifically comprises the following steps:
step S1: setting ADN related parameters, setting WPA related parameters, setting wolf number as N and initial position X i Maximum number of walks T max And the number of iterations k max Step factor S, distance judging factor omega, wolf scale factor alpha and updating scale factor beta;
step S2: the ADN total running cost is taken as a fitness function, and the wandering position of the wolf is updated according to the formula (11) until the wolf fitness Y is detected i ＞Y lead The first wolf is replaced by the detected wolf, and the next step is carried out, otherwise, the walking is continued until the maximum number of times T is reached max Then carrying out the next step;
step S3: updating the position of the bristletail according to the formula (12) until the bristletail detection fitness Y i ＞Y lead Replacing the head wolf with the slam wolf, otherwise continuing to attack until the distance from the head wolf is less than d near Then the next step is carried out;
step S4: the prey is enclosed according to the formula (14), and the wolf is updated;
step S5: updating the whole wolf group;
step S6: and (3) judging that the maximum iteration times or the allowable errors are reached, if so, outputting an optimizing result, otherwise, returning to the step (S2).
The invention relates to an energy storage economic optimization scheduling method based on production plan and load prediction, in use, the invention establishes an ADN economic optimization scheduling model by considering an energy storage economic optimization scheduling strategy under the time-of-use electricity price of production plan and load prediction, and solves by using an improved wolf group optimization algorithm; according to the optimization result, the energy storage system is controlled in real time, so that the system is ensured to realize the maximum energy-saving benefit; the invention can improve the energy utilization rate, optimize the operation of the energy storage system, improve the energy utilization efficiency and reduce the energy waste; the built energy storage system improves the stability of the power system and can be used for generating power by using renewable energy with smooth fluctuation; optimizing the production plan according to the production plan and a scheduling strategy under the load forecast time-of-use electricity price, and ensuring that the power supply is matched with the production demand; the invention provides an advanced power system management method which is suitable for renewable energy integration and the intellectualization of a power system and has wide application prospect in the power industry and the sustainable energy field; the invention has the advantages of improving the energy utilization rate, optimizing the operation of the energy storage system, controlling the energy storage system in real time, reducing the energy waste and realizing the maximum energy saving benefit.
Claims (8)
1. An energy storage economy optimization scheduling method based on production planning and load prediction is characterized by comprising the following steps of: the method comprises the following steps:
step 1: data collection and processing: collecting historical power load data, electricity price data, renewable energy power generation data and factory production plans, wherein the data are used for load prediction and electricity price analysis, and the accuracy and usability of the data are ensured through data cleaning and processing;
step 2: load prediction: generating a load prediction curve over a future time period using a load prediction model based on historical load data, production plans, and weather forecast information, the prediction data being used for decision optimization;
step 3: production planning: production planning includes detailed planning of production and manufacturing activities, including production tasks, production quantities, production schedules, the production planning being closely related to power demand, and the need to coordinate with load forecasting to ensure that power supply matches production demand;
step 4: electricity price analysis: analyzing time-of-use electricity rate information, including electricity rates during peak hours, valley hours, and stationary hours, the electricity rate data being consolidated and modeled for cost-benefit analysis;
step 5: modeling an energy storage system: modeling an energy storage system, including energy storage capacity, charge-discharge efficiency, charge-discharge rate and technical characteristics, and determining operation limits and capabilities of the energy storage system;
step 6: optimizing a model: the established mathematical optimization model aims at minimizing the total electric energy cost, and the optimal charge and discharge strategy in each time period is determined by considering load prediction, electricity price information, production plans and energy storage system characteristics;
step 7: and (3) optimizing and solving: an ADN economic optimization scheduling model and an improved wolf group optimization algorithm are used for ensuring that the algorithm has instantaneity and high efficiency;
step 8: and (3) real-time control: and according to the optimization result, the charging and discharging behaviors of the energy storage system are controlled in real time, and the system is ensured to realize the maximum energy-saving benefit.
2. The energy storage economy optimization scheduling method based on production planning and load prediction according to claim 1, wherein: the ADN economic optimization scheduling model in the step 7 specifically comprises the following steps:
step 7.1: an objective function;
step 7.2: constraint conditions.
3. The energy storage economy optimization scheduling method based on production planning and load prediction according to claim 2, wherein: the objective function in the step 7.1 specifically includes: taking 24h a day as one scheduling period, the total ADN running cost in one scheduling period can be expressed as: minp=f 1 +F 2 +F 3 +F 4 (1) Wherein P is the total running cost of ADN in one scheduling period; f (F) 1 The method is the power generation cost of a thermal power unit in a power distribution network system; f (F) 2 The energy exchange cost with the upper power grid is realized; f (F) 3 Punishment cost for wind and light discarding; f (F) 4 Is the energy storage cost.
4. A method of energy storage economy optimization scheduling based on production planning and load prediction as claimed in claim 3, wherein: the power generation cost F of the thermal power generating unit 1 The method comprises the following steps:wherein P is t Th The power generation of the thermal power generating unit at the t hour is realized; a. b and c are respectively the power generation cost coefficients of the thermal power generating unit;
the electric energy exchange cost F 2 The method comprises the following steps:in (1) the->The electricity purchasing unit price and the electricity selling unit price at the t hour are respectively; p (P) t buy 、P t sell The electricity purchasing quantity and the electricity selling quantity are respectively the electricity purchasing quantity and the electricity selling quantity in the t hour;
the wind discarding and light discarding punishment cost F 3 The method comprises the following steps:in (1) the->The cost coefficients of the wind discarding and the light discarding punishment at the t hour are respectively; p (P) t predict1 、P t predict2 Output power predicted values of wind power and photovoltaic respectively; p (P) t W 、P t PV The actual output power values of wind power and photovoltaic are respectively;
the energy storage cost F 4 The method comprises the following steps:in (1) the->Is an energy storage cost coefficient; p (P) t C 、P t D The charging power and the discharging power of the energy storage device at the t hour are respectively.
5. The energy storage economy optimization scheduling method based on production planning and load prediction according to claim 2, wherein: the constraint conditions in the step 7.2 comprise branch flow constraint, wind power constraint, photovoltaic output constraint, static reactive compensation device constraint, node voltage constraint and energy storage constraint.
6. The energy storage economy optimization scheduling method based on production planning and load prediction according to claim 5, wherein: the branch tidal current constraint is as follows:wherein P is j,t 、q j,t Active power and reactive power injected into node j respectively; r is (r) ij 、x ij The resistance and reactance of branch ij; p (P) jk,t 、Q jk,t Active power and reactive power of the head end of the branch jk are respectively; p (P) ij,t 、Q ij,t Active power and reactive power of the head end of the branch ij are respectively; i ij,t Current for branch ij; j-k is a set with the node as a father node of j; i.j is a set taking an i node as a father node;
the wind power and photovoltaic output constraint is as follows: the power constraint of the thermal power generating unit is as follows:in the method, in the process of the invention,maximum output power of the wind turbine generator and the photovoltaic at the t hour are respectively;
the static reactive compensation device is constrained as follows:in which Q i,t The compensation quantity of the reactive compensation device at the node i at the t-th hour; />The upper limit and the lower limit of the compensation quantity of the reactive compensation device at the node i are respectively;
the node voltage constraint is as follows: u (U) min ≤U i ≤U max (9) In the formula, U i Is the voltage of node i; u (U) min 、U max Respectively a node voltage minimum value and a node voltage maximum value;
the energy storage constraint is as follows:wherein E is t Capacity of the energy storage device at the first hour; e (E) min 、E max The upper limit and the lower limit of the capacity of the energy storage equipment are respectively; />Maximum charging power and maximum discharging power respectively; e (E) 0 、E T The energy storage capacity at the beginning and at the end of the schedule, respectively.
7. The energy storage economy optimization scheduling method based on production planning and load prediction according to claim 1, wherein: the wolf group optimization algorithm in the step 7, namely the WPA algorithm, is specifically: the wolf group comprises a head wolf, a probe wolf and a branchlet, the roles of which are respectively command, search and attack, and the WPA is defined as follows: setting the wolf group capacity as N and the spatial dimension as D, the wolf group can be expressed as: x is X i ＝(x i1 ,x i2 ,...,x iD ) The method specifically comprises the following steps:
step a: generating a first wolf: finding the optimal solution Y in the initial solution lead With the iteration of the algorithm, the head wolves can be replaced, if the position of one wolf is better than that of the head wolves, the head wolves are replaced, otherwise, the head wolves are unchanged;
step b: search wolf: let the number of detection wolves be M, M depends on [ N/(alpha+1), N/alpha]Wherein alpha is a wolf scale factor; the number of the walk directions of the wolves is h, and the walk step length is step a Initial fitness is Y i ，x id The position of the i-th artificial wolf in the D (d=1, 2,., D) -th dimensional variable space to be optimized;indicating the position of the rear sounding wolf in the d-th dimensional space, proceeding in the p (p=1, 2,., h) direction, is: />At this time, a new fitness value Y is calculated ip If the new fitness ratio detects the current fitness value Y of wolf i Better, it is replaced and the wolf position X is updated i Then the current fitness value of the wolf is detected and the fitness value Y of the wolf is detected lead Comparing, if the current fitness value of the detecting wolf is better, replacing the head wolf with the detecting wolf, and summoning the strong wolf to move to the current position, otherwise, continuing to search until the maximum iteration number T max ；
Step c: the wolf moves: the number of the rag wolves is N-M, the rag wolves can immediately move towards the head wolves after receiving the calling information, and the step length of the rag wolves moving is step b The position of the wolf at the k+1st iteration is:in (1) the->Representing the position of the wolf at the kth+1st iteration of wolf i; />Representing the position of the slam wolf at the kth iteration of slam wolf i; />Is the position of the top wolf at the kth iteration; calculating the fitness value Y of the moving wolves i If Y i Ratio Y lead More preferably, the slam wolf is changed into the head wolf, and calls are sent to other slam wolves, otherwise, the rag continues to move towards the direction of the head wolf until the position away from the head wolf is smaller than d near Then attack is initiated on the prey, d near The calculation formula of (2) is as follows: />Wherein ω is a distance determination factor; max (max) d 、min d The maximum value and the minimum value of the d variable to be optimized are taken;
step d: attack prey: the wolf reaches a predetermined position, i.e. attacks the prey with the detecting wolf, the position of the wolf is considered because the wolf is very close to the preyI.e. the prey position, the attack step length is step c Lambda is [ -1,1]The random numbers distributed evenly among the wolf clusters are updated according to the following formula: />When a prey is attacked, if the position fitness value of the wolf group is better than the current fitness value, replacing, otherwise, keeping unchanged; step a 、step b Step c The relation of (2) is: />Wherein S is a step factor;
step e: wolf group updating: in the process of searching hunting objects by the wolf group, eliminating the R-wolf with the worst adaptability, and randomly supplementing the R-wolf to the wolf group, wherein the R-value interval [ N/(2 x beta) and N/(beta) ], wherein beta is an updated scale factor.
8. The energy storage economy optimization scheduling method based on production planning and load prediction according to claim 7, wherein: the wolf-swarm optimization algorithm in the step 7, namely the WPA algorithm, solves the ADN economic optimization scheduling model, and specifically comprises the following steps:
step S1: setting ADN related parameters, setting WPA related parameters, setting wolf number as N and initial position X i Maximum number of walks T max And the number of iterations k max Step length factor S, distance judgmentA constant factor omega, a wolf scale factor alpha and an updated scale factor beta;
step S2: the ADN total running cost is taken as a fitness function, and the wandering position of the wolf is updated according to the formula (11) until the wolf fitness Y is detected i ＞Y lead The first wolf is replaced by the detected wolf, and the next step is carried out, otherwise, the walking is continued until the maximum number of times T is reached max Then carrying out the next step;
step S3: updating the position of the bristletail according to the formula (12) until the bristletail detection fitness Y i ＞Y lead Replacing the head wolf with the slam wolf, otherwise continuing to attack until the distance from the head wolf is less than d near Then the next step is carried out;
step S4: the prey is enclosed according to the formula (14), and the wolf is updated;
step S5: updating the whole wolf group;
step S6: and (3) judging that the maximum iteration times or the allowable errors are reached, if so, outputting an optimizing result, otherwise, returning to the step (S2).
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