WO2024109105A1 - 一种分布式可再生能源集群调度方法和装置 - Google Patents

一种分布式可再生能源集群调度方法和装置 Download PDF

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WO2024109105A1
WO2024109105A1 PCT/CN2023/106706 CN2023106706W WO2024109105A1 WO 2024109105 A1 WO2024109105 A1 WO 2024109105A1 CN 2023106706 W CN2023106706 W CN 2023106706W WO 2024109105 A1 WO2024109105 A1 WO 2024109105A1
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cluster
period
peak
day
scheduling
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PCT/CN2023/106706
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English (en)
French (fr)
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欧阳卫年
黎皓彬
赵瑞锋
廖峰
刘秀甫
区伟潮
聂家荣
朱延廷
张文骏
向琼
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广东电网有限责任公司佛山供电局
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Publication of WO2024109105A1 publication Critical patent/WO2024109105A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component

Definitions

  • the present invention relates to the technical field of power systems, and in particular to a distributed renewable energy cluster scheduling method and device.
  • the energy storage system that organically integrates multiple energy storage to achieve multi-energy complementarity will be the main form of energy storage system application in the future power grid.
  • Distributed energy is an energy supply method built on the user side, which can operate independently or in grid connection.
  • Distributed renewable energy refers to the full use of idle space resources to develop small-scale and distributed renewable energy, mainly distributed photovoltaic and distributed wind power.
  • the existing technology mainly uses battery energy storage technology to track planned power generation, smooth wind power output, and improve the ability of wind power generation to access the grid.
  • the present invention provides a distributed renewable energy cluster scheduling method and device, which solves the technical problem that in the existing technology, power generation is tracked by battery energy storage technology, which makes it difficult for centralized control to meet the accuracy and speed requirements of distribution network voltage regulation when large-scale, high-proportion, decentralized renewable energy units are connected, thereby reducing the access capacity of distributed renewable energy in the power grid.
  • a first aspect of the present invention provides a distributed renewable energy cluster scheduling method, comprising:
  • the optimization strategy function is solved in the peak-shaving period and the non-peak-shaving period to output an optimal dispatch curve.
  • the optimization strategy function includes an optimization function, a peak-shaving function and a non-peak-shaving function:
  • the peak shaving function is specifically:
  • Oj1 is the economic target
  • peak is the peak load period
  • valley is the valley load period
  • p up is the peak electricity price
  • p dn is the valley electricity price
  • c k is the power generation cost of local dispatch k
  • Oj2 is the local balance target
  • Pl k,t is the local forecast load of local dispatch k in period t
  • N is the number of ground regulation
  • T is the time dimension
  • the non-peak shaving function is specifically:
  • f is the optimization target
  • P k,t is the optimal power of the city-level dispatch at time t in city k
  • the optimization function is specifically:
  • H is the time dimension, which is 24 hours.
  • P clu,t is the external output power of cluster clu in period t.
  • the dispatching cost of cluster clu at time t is the dispatching cost of cluster clu at time t
  • c n,t is the electricity transaction price between the cluster and aggregator n during period t on the second day, It represents the external output power of cluster clu at time t.
  • the prediction data includes a cluster distributed renewable energy output prediction curve
  • the step of constructing a next-day target scheduling model according to the prediction data to obtain a next-day cluster scheduling instruction curve includes:
  • next day's predicted value, the next day's adjustable upper limit value, and the next day's adjustable lower limit value are used to generate a next day's cluster scheduling instruction curve.
  • the forecast data includes a total load curve
  • the step of determining the peak load period and the non-peak load period of the optimization strategy function according to the forecast data and each of the next day's cluster dispatch instruction curves includes:
  • the time corresponding to the first difference is determined as Peak load period
  • the time corresponding to the first difference is determined as the non-peak shaving time.
  • the step of solving the optimization strategy function in the peak-shaving period and the non-peak-shaving period based on the forecast data and the next-day cluster command curve to output the optimal dispatching curve includes:
  • the target period is a peak load period
  • the target data and the forecast data are input into the peak load function and the optimization function, and the scheduling data is output;
  • the target period is not a peak-shaving period
  • the target data and the predicted data are input into the non-peak-shaving function and the optimization function, and the scheduling data is output;
  • the scheduling data corresponding to each of the target time periods is used to generate an optimal scheduling curve.
  • a second aspect of the present invention provides a distributed renewable energy cluster scheduling device, comprising:
  • the optimization strategy function building module is used to respond to cluster scheduling requests and build the optimization strategy function corresponding to the cluster with the goal of maximizing the total benefit of the cluster;
  • a data acquisition module used to obtain prediction data of each of the clusters
  • An instruction curve construction module is used to construct a next-day target scheduling model according to the forecast data to obtain a next-day cluster scheduling instruction curve;
  • a data analysis module used to determine the peak-shaving period and the non-peak-shaving period of the optimization strategy function according to the prediction data and each of the next-day cluster scheduling instruction curves;
  • the scheduling curve calculation module is used to solve the optimization strategy function in the peak-shaving period and the non-peak-shaving period based on the predicted data and the next-day cluster instruction curve, and output the optimal scheduling curve.
  • the optimization strategy function includes an optimization function, a peak-shaving function and a non-peak-shaving function:
  • the peak shaving function is specifically:
  • Oj1 is the economic target
  • peak is the peak load period
  • valley is the valley load period
  • p up is the peak electricity price
  • p dn is the valley electricity price
  • c k is the power generation cost of local dispatch k
  • Oj2 is the local balance target
  • Pl k,t is the local forecast load of local dispatch k in period t
  • N is the number of ground regulation
  • T is the time dimension
  • the non-peak shaving function is specifically:
  • f is the optimization target
  • P k,t is the optimal power of the city-level dispatch at time t in city k
  • the optimization function is specifically:
  • H is the time dimension, which is 24 hours.
  • P clu,t is the external output power of cluster clu in period t.
  • the dispatching cost of cluster clu at time t is the dispatching cost of cluster clu at time t
  • c n,t is the electricity transaction price between the cluster and aggregator n during period t on the second day, It represents the external output power of cluster clu at time t.
  • command curve construction module includes:
  • a first value selection submodule is used to select the predicted values, the adjustable upper limit values and the adjustable lower limit values corresponding to the same time in each of the cluster distributed renewable energy output prediction curves;
  • a first superposition submodule is used to superimpose the predicted values to obtain a predicted value for the next day;
  • a second superposition submodule is used to superimpose the adjustable upper limit values to obtain an adjustable upper limit value for the next day;
  • a third superposition submodule is used to superimpose the adjustable lower limit values to obtain the adjustable lower limit value of the next day;
  • the command curve generation submodule is used to generate the next day's cluster scheduling command curve by using the next day's predicted value, the next day's adjustable upper limit value and the next day's adjustable lower limit value.
  • the forecast data includes a total load curve
  • the data analysis module includes:
  • a fourth superposition submodule used for inputting each of the next-day cluster scheduling command curves into the next-day target scheduling model, and superposing to obtain the next-day provincial scheduling command curve;
  • the second value selection submodule is used to obtain the next day's forecast values and the next day's adjustable upper limit values corresponding to multiple same time points in the next day's provincial dispatching instruction curve;
  • a third value selection submodule is used to select total load values corresponding to multiple same moments in the total load curve
  • a first difference calculation submodule used for calculating a first difference between the total load value and the next day's predicted value
  • a second difference calculation submodule used for calculating a second difference between the first difference and the next day's adjustable upper limit value
  • a first judgment submodule used to judge whether the first difference is greater than the second difference
  • the time corresponding to the first difference is determined as the non-peak shaving time.
  • the scheduling curve calculation module includes:
  • a fourth value selection submodule is used to select a plurality of target time periods in the next day's cluster scheduling instruction curve and target data corresponding to the target time periods;
  • a second judgment submodule is used to judge whether the target period is a peak load period
  • the target period is a peak load period
  • the target data and the forecast data are input into the peak load function and the optimization function, and the scheduling data is output;
  • the target data and the forecast data are input into input into the non-peak shaving function and the optimization function, and output scheduling data;
  • the optimal dispatch curve generation submodule is used to generate an optimal dispatch curve using the dispatch data corresponding to each of the target time periods.
  • the optimization strategy function corresponding to the cluster is constructed with the highest total cluster benefit as the goal, the forecast data of each cluster is obtained, the next day's target dispatch model is constructed based on the forecast data, and the next day's cluster dispatch instruction curve is obtained.
  • the peak-shaving period and non-peak-shaving period of the optimization strategy function are determined.
  • the forecast data and the next day's cluster instruction curve are input into the optimization strategy function to solve the optimal dispatch curve.
  • battery energy storage technology is used to track power generation.
  • FIG1 is a flowchart of a distributed renewable energy cluster scheduling method provided by Embodiment 1 of the present invention.
  • FIG2 is a flowchart of a distributed renewable energy cluster scheduling method provided by Embodiment 2 of the present invention.
  • FIG3 is a structural block diagram of a distributed renewable energy cluster scheduling device provided in Embodiment 3 of the present invention.
  • the embodiments of the present invention provide a distributed renewable energy cluster scheduling method and device, which are used to solve the technical problem that in the existing technology, power generation is tracked by battery energy storage technology, which makes it difficult for centralized control to meet the accuracy and speed requirements of distribution network voltage regulation when large-scale, high-proportion, decentralized renewable energy units are connected, thereby reducing the access capacity of distributed renewable energy in the power grid.
  • FIG. 1 is a flow chart of steps of a distributed renewable energy cluster scheduling method provided in Embodiment 1 of the present invention.
  • the present invention provides a distributed renewable energy cluster scheduling method, comprising:
  • Step 101 respond to a cluster scheduling request and construct an optimization strategy function corresponding to the cluster with the goal of maximizing the total benefit of the cluster.
  • a cluster dispatch request refers to a request instruction that sends the forecast curve of the cluster dispatch for the next day to the provincial power grid agency through the distributed renewable energy cluster.
  • the optimization strategy function means that the provincial power grid organizations, while retaining the frequency regulation margin, give priority to economic dispatch based on the optimal dispatch curves of local power grid organizations.
  • the local power grid organizations then conduct dispatch based on the optimal dispatch curves of each cluster in combination with the dispatch curves of the provincial power grid organizations.
  • an optimization strategy function corresponding to each cluster is constructed with the goal of maximizing the overall benefit of each cluster.
  • Step 102 Obtain prediction data for each cluster.
  • Forecast data refers to the data obtained by analyzing the output of each cluster for the next day. For example, peak electricity price, off-peak electricity price, power generation cost of local dispatch k, local balancing target, local forecast load of cluster k in period t, upper limit of output adjustment of cluster k in period t, number of local dispatch, number of clusters quantity, the optimal power of cluster k at time t, the adjustable margin reserved by the local dispatching agency k in time period t, the frequency regulation service constant, the dispatching cost of cluster clu at time t, the electricity transaction price between the cluster and the aggregator n in time period t on the second day, the external output power of cluster clu at time t, the external output power of cluster clu at time t-1, the usage of flexibility margin at time t, the upper limit value of flexibility margin of cluster clu at time t, the upper limit value of flexibility margin of cluster clu at time t, the predicted load of cluster clu in time period t, the cluster prediction curve and adjustable range, etc.
  • the prediction data of each cluster is obtained.
  • the local predicted load of local regulation k in period t can all be obtained by superimposing the predicted data of each cluster included in the local regulation.
  • Step 103 construct a next-day target scheduling model based on the forecast data to obtain a next-day cluster scheduling instruction curve.
  • the next-day target scheduling model refers to a functional model that integrates multiple cluster prediction curves and adjustable ranges.
  • the cluster prediction curve and adjustable range in the prediction data are selected, and according to the prefectural-level power grid organization and provincial-level power grid organization to which each cluster belongs, the prediction curve and adjustable range of the cluster belonging to the prefectural-level power grid organization are selected for superposition, or the prediction curve and adjustable range of the cluster belonging to the provincial-level power grid organization are selected for superposition.
  • Step 104 Determine the peak-shaving period and non-peak-shaving period of the optimization strategy function according to the forecast data and the cluster scheduling instruction curve of each next day.
  • Peak load period refers to the time when the power grid needs to put into use power generating units other than normal operation to meet demand.
  • non-peak time is that the power grid can meet demand without investing in generators other than those in normal operation during this time.
  • the total load curve of the whole province and the adjustable upper limit value of the cluster in the forecast data are selected, and the net load curve is calculated according to the total load curve of the whole province and the cluster dispatch instruction curve of the next day, and then the adjustable upper limit value of the cluster corresponding to the prefecture-level power grid organization is selected, and the adjustable upper limit values are superimposed to obtain the adjustable upper limit value of the prefecture-level power grid organization.
  • the adjustable lower limit value of the cluster corresponding to the local power grid organization can be obtained as the initial valley filling power, and the sum of the minimum net load value and the initial valley filling power can be calculated.
  • Step 105 Based on the forecast data and the cluster command curve for the next day, the optimization strategy function is solved in the peak-shaving period and the non-peak-shaving period, and the optimal dispatch curve is output.
  • the predicted data and the cluster instruction curve of the next day are combined and input into the optimization strategy function together.
  • the optimization strategy function aims to maximize the total benefit of the cluster and is solved under the condition of minimizing the dispatch deviation with the provincial power grid organization to obtain the optimal dispatch curve of the cluster on the next day.
  • an optimization strategy function is constructed with the goal of maximizing the total benefit of the cluster, and then the forecast data of each cluster is obtained.
  • the next day target scheduling model is constructed according to the forecast data, and the next day cluster scheduling curve is generated.
  • the forecast data and the next day cluster scheduling instruction curve the next day period is divided into peak-shaving period and non-peak-shaving period, and the forecast data and the next day cluster instruction curve are combined and input into the optimization strategy function to solve the optimal scheduling curve. It can adapt to the regulation of large-scale, high-proportion, decentralized clusters, reduce the waste of energy storage resources, and improve the access capacity of distributed renewable energy in the power grid.
  • FIG. 2 is a flow chart of the steps of a distributed renewable energy cluster scheduling method provided in Embodiment 2 of the present invention.
  • the present invention provides a distributed renewable energy cluster scheduling method, comprising:
  • Step 201 respond to a cluster scheduling request and construct an optimization strategy function corresponding to the cluster with the goal of maximizing the total benefit of the cluster.
  • the optimization strategy function includes optimization function, peak-shaving function and non-peak-shaving function:
  • the peak shaving function is as follows:
  • Oj1 is the economic target
  • peak is the peak load period
  • valley is the valley load period
  • p up is the peak electricity price
  • p dn is the valley electricity price
  • c k is the power generation cost of local dispatch k
  • Oj2 is the local balance target
  • Pl k,t is the local forecast load of local dispatch k in period t
  • N is the number of ground regulation
  • T is the time dimension
  • the non-peak shaving function is specifically:
  • f is the optimization target
  • P k,t is the optimal power of the city-level dispatch at time t in city k
  • the optimization function is as follows:
  • H is the time dimension, which is 24 hours.
  • P clu,t is the external output power of cluster clu in period t.
  • c nt is the electricity transaction price between the cluster and aggregator n during period t on the second day, It represents the external output power of cluster clu at time t. It represents the external output power of cluster clu at time t-1.
  • the cluster scheduling request when a cluster scheduling request is received from a staff member, the cluster scheduling request is parsed, and the goal of the optimization strategy function is obtained to maximize the total benefit of cluster operation, and the optimization strategy function is constructed according to the goal.
  • Step 202 Obtain prediction data for each cluster.
  • the The cluster's forecast data for the next day after the optimization strategy function is constructed, the The cluster's forecast data for the next day.
  • Step 203 Select the forecast values, adjustable upper limit values and adjustable lower limit values corresponding to multiple same time points in the distributed renewable energy output forecast curves of each cluster.
  • the prediction value, the adjustable upper limit value and the adjustable lower limit value at the same time in the distributed renewable energy output prediction curve of each cluster are selected.
  • forecast data includes the cluster distributed renewable energy output forecast curve.
  • Step 204 Superimpose the various predicted values to obtain the predicted value for the next day.
  • the predicted value for the next day is obtained by superimposing the selected predicted values.
  • Step 205 Superimpose the various adjustable upper limit values to obtain the adjustable upper limit value for the next day.
  • the adjustable upper limit reported by the prefecture-level power grid organization i to the provincial power grid organization at time t is the adjustable upper limit value of the jth cluster in the prefecture-level power grid organization
  • ⁇ is the margin coefficient of the upper adjustable range, which is between 0 and 1.
  • the adjustable upper limit value for the next day is obtained by superimposing the selected adjustable upper limit values.
  • Step 206 Superimpose the various adjustable lower limit values to obtain the adjustable lower limit value for the next day.
  • the adjustable lower limit value for the next day is obtained by superimposing the selected adjustable lower limit values.
  • Step 207 Generate a cluster scheduling instruction curve for the next day using the next day's predicted value, the next day's adjustable upper limit value, and the next day's adjustable lower limit value.
  • next day's predicted value, the next day's adjustable upper limit value and the next day's adjustable lower limit value are superimposed, and the next day's adjustable upper limit value and the next day's adjustable lower limit value are used as the adjustable upper and lower limits of the next day's cluster scheduling instruction curve.
  • the next day's cluster scheduling instruction curve is generated with each moment t as the x-axis and the next day's predicted value as the y-axis.
  • Step 208 Determine the peak-shaving period and non-peak-shaving period of the optimization strategy function according to the forecast data and the cluster scheduling instruction curve of each next day.
  • step 208 may include the following sub-steps:
  • each next-day cluster scheduling instruction curve is input into the next-day target scheduling model, and superimposed to generate the next-day provincial scheduling instruction curve.
  • next-day forecast value and the next-day adjustable upper limit value corresponding to the next-day provincial adjustment command curve at time t are selected.
  • the total load value corresponding to the time t in the total load curve is selected.
  • a first difference between the total load value and the next day's forecast value is calculated.
  • a second difference between the first difference and the next-day adjustable upper limit value corresponding to time t is calculated.
  • the adjustable upper limit value of the next day is used as the initial peak-shaving power
  • the second difference between the first difference and the initial peak-shaving power is used as the peak-shaving triggering power.
  • the time corresponding to the first difference is determined as the peak shaving period.
  • the time corresponding to the first difference is determined as a non-peak shaving time.
  • the time corresponding to the first difference is determined as the non-peak-shaving period.
  • the adjustable lower limit value of the next day can also be selected as the initial valley filling power, and the sum of the first difference and the initial valley filling power is calculated as the valley filling trigger power to determine whether the first difference is less than the valley filling trigger power. If the first difference is less than the valley filling trigger power, the time corresponding to the first difference is determined as the valley filling period; if the first difference is greater than or equal to the valley filling trigger power, the time corresponding to the first difference is determined as the valley de-roughing period.
  • Step 209 Based on the forecast data and the cluster command curve for the next day, the optimization strategy function is solved in the peak-shaving period and the non-peak-shaving period, and the optimal dispatch curve is output.
  • step 209 may include the following sub-steps:
  • multiple target time periods in the target cluster curve and target data corresponding to the target time periods are selected.
  • the target time period is selected, it is determined whether the target time period is a peak load period.
  • the target period is a peak-shaving period
  • the target data and the predicted data are input into the peak-shaving function and the optimization function, and the scheduling data is output.
  • the peak-shaving function and the optimization function of the optimization strategy function are called to calculate the target data and the predicted data.
  • the target period is not a peak-shaving period
  • the target data and the predicted data are input into the non-peak-shaving function and the optimization function, and the scheduling data is output.
  • the optimization strategy function when the target period is a non-peak period, the optimization strategy function is called The non-peak-shaving function and optimization function operate on the target data and the predicted data.
  • the scheduling data is calculated according to each target time period to generate an optimal scheduling curve.
  • an optimization strategy function corresponding to the cluster is constructed with the goal of maximizing the total benefit of the cluster, and then the prediction data of each cluster is obtained, and the prediction values, adjustable upper limits and adjustable lower limits corresponding to multiple same moments in the prediction curve of the distributed renewable energy output of each cluster in the prediction data are selected, and each prediction value, adjustable upper limit and adjustable lower limit are superimposed respectively to generate the cluster scheduling instruction curve for the next day.
  • the time period of the next day is divided into the peak-shaving time period and the non-peak-shaving time period.
  • the prediction data and the cluster scheduling instruction curve for the next day are combined to generate the optimal scheduling curve. It is possible to quickly and accurately control large-scale, high-proportion, and decentralized clusters, reduce the waste of energy storage resources, and improve the access capacity of distributed renewable energy in the power grid.
  • FIG. 3 is a structural block diagram of a distributed renewable energy cluster scheduling device provided in Embodiment 3 of the present invention.
  • An embodiment of the present invention provides a distributed renewable energy cluster scheduling device, including:
  • the optimization strategy function construction module 301 is used to respond to the cluster scheduling request and construct the optimization strategy function corresponding to the cluster with the goal of maximizing the total benefit of the cluster;
  • a data collection module 302 is used to obtain prediction data of each cluster
  • the command curve construction module 303 is used to construct a next-day target scheduling model according to the forecast data to obtain a next-day cluster scheduling command curve;
  • the data analysis module 304 is used to determine the peak-shaving period and the non-peak-shaving period of the optimization strategy function according to the forecast data and the cluster scheduling instruction curve of each next day;
  • the dispatch curve calculation module 305 is used to solve the optimization strategy function in the peak-shaving period and the non-peak-shaving period based on the predicted data and the cluster instruction curve of the next day, and output the optimal dispatch curve.
  • optimization strategy function includes an optimization function, a peak-shaving function, and a non-peak-shaving function:
  • the peak shaving function is as follows:
  • Oj1 is the economic target
  • peak is the peak load period
  • valley is the valley load period
  • p up is the peak electricity price
  • p dn is the valley electricity price
  • c k is the power generation cost of local dispatch k
  • Oj2 is the local balance target
  • Pl k,t is the local forecast load of local dispatch k in period t
  • N is the number of ground regulation
  • T is the time dimension
  • the non-peak shaving function is specifically:
  • f is the optimization target
  • P k,t is the optimal power of the city-level dispatch at time t in city k
  • the optimization function is as follows:
  • H is the time dimension, which is 24 hours.
  • P clu,t is the external output power of cluster clu in period t.
  • the dispatching cost of cluster clu at time t is the dispatching cost of cluster clu at time t
  • c n,t is the electricity transaction price between the cluster and aggregator n during period t on the second day, It represents the external output power of cluster clu at time t.
  • the instruction curve construction module 303 includes:
  • the first value selection submodule is used to select the predicted values, the adjustable upper limit values and the adjustable lower limit values corresponding to multiple same time points in the output prediction curve of distributed renewable energy of each cluster;
  • the first superposition submodule is used to superimpose the various predicted values to obtain the predicted value for the next day;
  • the second superposition submodule is used to superimpose the adjustable upper limit values to obtain the adjustable upper limit value of the next day;
  • the third superposition submodule is used to superimpose the various adjustable lower limit values to obtain the adjustable lower limit value of the next day;
  • the command curve generation submodule is used to generate the next day's cluster scheduling command curve by using the next day's predicted value, the next day's adjustable upper limit value and the next day's adjustable lower limit value.
  • the forecast data includes a total load curve
  • the data analysis module 304 includes:
  • the fourth superposition submodule is used to input each next-day cluster scheduling command curve into the next-day target scheduling model, and superimpose to obtain the next-day provincial scheduling command curve;
  • the second value selection submodule is used to obtain the next day's forecast values and the next day's adjustable upper limit values corresponding to multiple same time points in the next day's provincial dispatching instruction curve;
  • the third value selection submodule is used to select the total load values corresponding to multiple same moments in the total load curve
  • a first difference calculation submodule used for calculating a first difference between the total load value and the next day's predicted value
  • a second difference calculation submodule used for calculating a second difference between the first difference and the next day's adjustable upper limit value
  • a first judgment submodule used to judge whether the first difference is greater than the second difference
  • the time corresponding to the first difference is determined as the peak load period
  • the time corresponding to the first difference is determined as the non-peak shaving time.
  • the dispatch curve calculation module 305 includes:
  • the fourth value selection submodule is used to select multiple target time periods and target data corresponding to the target time periods in the cluster scheduling instruction curve of the next day;
  • the second judgment submodule is used to judge whether the target period is a peak load period
  • the target period is a peak load period
  • the target data and the forecast data are input into the peak load function and the optimization function, and the scheduling data is output;
  • the target data and forecast data are input into the non-peak-shaving function.
  • the scheduling data is output;
  • the optimal dispatch curve generation submodule is used to generate the optimal dispatch curve using the dispatch data corresponding to each target period.
  • the optimization strategy function construction module when the optimization strategy function construction module receives the cluster scheduling request issued by the technician, the optimization strategy function corresponding to the cluster is constructed with the goal of maximizing the total benefit of the cluster, and then the prediction data of each cluster is obtained through the data acquisition module.
  • the instruction curve construction module constructs the next day's target scheduling model according to the obtained prediction data, and produces the next day's cluster scheduling instruction curve.
  • the data analysis module analyzes the obtained prediction data and the next day's cluster scheduling instruction curve, and divides the next day's time period into a peak-shaving period and a non-peak-shaving period.
  • the scheduling curve calculation module solves the optimization strategy function in the peak-shaving period and the non-peak-shaving period through the prediction data and the next day's cluster instructions, and outputs the optimal scheduling curve.
  • the technical problem that the distribution network voltage cannot be accurately and quickly regulated in large-scale, decentralized cluster scheduling in the existing technical solution is solved, and the access capacity of distributed renewable energy in the power grid is improved.
  • the disclosed devices and methods can be implemented in other ways.
  • the device embodiments described above are only schematic.
  • the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.
  • Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

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Abstract

一种分布式可再生能源集群调度方法和装置，涉及电力***技术领域，通过响应集群调度请求，以集群总效益最大化为目标构建集群对应优化策略函数，获取各个集群的预测数据，根据预测数据构建次日目标调度模型，得到次日集群调度指令曲线，根据预测数据和各次日集群调度指令曲线，确定优化策略函数的调峰时段和非调峰时段，结合预测数据和次日集群指令曲线求解优化策略函数，输出最优调度曲线。解决了现在技术中通过电池储能技术来跟踪发电，在大规模高比例、分散化的可再生能源机组接入时，会导致集中控制难以满足配网电压调控的精确性和快速性要求，降低了电网分布式可再生能源的接入能力的技术问题。

Description

一种分布式可再生能源集群调度方法和装置
本申请要求于2022年11月24日提交中国专利局、申请号为202211482506.1、发明名称为“一种分布式可再生能源集群调度方法和装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及电力***技术领域，尤其涉及一种分布式可再生能源集群调度方法和装置。
背景技术
未来高比例分布式能源接入，对电网的影响日趋显著，多种储能有机整合实现多能互补的储能体系将是未来储能***在电网中应用的主要形式。分布式能源是一种建在用户端的能源供应方式，可独立运行，也可并网运行，分布式可再生能源是指充分利用闲散空间资源，发展小型化、分布化的可再生能源，主要是分布式光伏和分布式风电。
现有技术中主要是通过利用电池储能技术来跟踪计划发电、平滑风电功率输出、提升风力发电接入电网能力。
但在上述现有技术中，通过利用电池储能技术来跟踪技术发电，仅限于局部、小规模范围，当大规模高比例、分散化的可再生能源机组的接入时，会导致集中控制难以满足配网电压调控的精确性和快速性要求，降低了电网分布式可再生能源的接入能力。
发明内容
本发明提供了一种分布式可再生能源集群调度方法和装置，解决了现在技术中通过电池储能技术来跟踪发电，在大规模高比例、分散化的可再生能源机组接入时，会导致集中控制难以满足配网电压调控的精确性和快速性要求，降低了电网分布式可再生能源的接入能力的技术问题。
本发明第一方面提供的一种分布式可再生能源集群调度方法，包括：
响应集群调度请求，以集群总效益最大化为目标构建集群对应的优化 策略函数；
获取各个所述集群的预测数据；
根据所述预测数据构建次日目标调度模型，得到次日集群调度指令曲线；
根据所述预测数据和各所述次日集群调度指令曲线，确定所述优化策略函数的调峰时段和非调峰时段；
基于所述预测数据和所述次日集群指令曲线，在所述调峰时段和非调峰时段下求解所述优化策略函数，输出最优调度曲线。
可选地，所述优化策略函数包括优化函数、调峰函数和非调峰函数：
所述调峰函数具体为：



其中，Oj1为经济性目标，peak为调峰时段，valley为填谷时段，pup为峰值电价，pdn谷时电价，ck为地调k的发电成本，Oj2为就地平衡目标，Plk,t为t时段地调k的当地预测负荷，为t时段地调k出力调整量的上限值，为t时段地调k出力调整量的下限值，N为地调数量，T为时间维度，为t时段省调对地调k的出力调整量；
所述非调峰函数具体为：

其中，f为优化目标，Pk,t为k市t时刻地级调度最优功率，为地级调度机构k在t时段所保留的可调裕度，为调频服务常数；
所述优化函数具体为：



H为时间维度，取24小时,Pclu,t为集群clu在t时段的对外输出功率,为省调对地调k在t时段的对外功率交互调度指令,为t时刻集群clu调度成本，cn,t为第二日t时段集群与聚合商n的电量交易价格，表示t时刻集群clu对外输出功率，表示t-1时刻集群clu对外输出功率，为集群clu在t时刻的灵活性裕度使用量，为集群clu在t时刻的灵活性裕度上限值，为集群clu在t时刻的灵活性裕度上限值,Lclu,t为集群clu在t时段的预测负荷。
可选地，所述预测数据包括集群分布式可再生能源出力预测曲线，所述根据所述预测数据构建次日目标调度模型，得到次日集群调度指令曲线的步骤，包括：
选取各个所述集群分布式可再生能源出力预测曲线中多个相同时刻对应的预测数值、可调上限值和可调下限值；
将各个所述预测数值进行叠加，得到次日预测数值；
将各个所述可调上限值进行叠加，得到次日可调上限值；
将各个所述可调下限值进行叠加，得到次日可调下限值；
采用所述次日预测数值、所述次日可调上限值和所述次日可调下限值生成次日集群调度指令曲线。
可选地，所述预测数据包括总负荷曲线，所述根据所述预测数据和各所述次日集群调度指令曲线，确定所述优化策略函数的调峰时段和非调峰时段的步骤，包括
将各所述次日集群调度指令曲线输入至所述次日目标调度模型中，叠加得到次日省调度指令曲线；
选取所述次日省调度指令曲线中多个相同时刻对应的所述次日预测数值和所述次日可调上限值；
选取所述总负荷曲线中多个相同时刻对应的总负荷值；
计算所述总负荷值与所述次日预测数值之间的第一差值；
计算所述第一差值与所述次日可调上限值的第二差值；
判断所述第一差值是否大于所述第二差值；
若所述第一差值大于第二差值，则将所述第一差值对应的时刻确定为 调峰时段；
若所述第一差值小于或等于第二差值，则将所述第一差值对应的时刻确定为非调峰时刻。
可选地，所述基于所述预测数据和所述次日集群指令曲线，在所述调峰时段和非调峰时段下求解所述优化策略函数，输出最优调度曲线的步骤，包括：
选取所述次日集群调度指令曲线中多个目标时段和所述目标时段对应的目标数据；
判断所述目标时段是否为调峰时段；
若所述目标时段是调峰时段，则将所述目标数据和所述预测数据输入到所述调峰函数和所述优化函数中，输出调度数据；
若所述目标时段不是调峰时段，则将所述目标数据和所述预测数据输入到所述非调峰函数和所述优化函数中，输出调度数据；
采用各个所述目标时段对应的所述调度数据生成最优调度曲线。
本发明第二方面提供的种分布式可再生能源集群调度装置,包括：
优化策略函数构建模块，用于响应集群调度请求，以集群总效益最大化为目标构建集群对应的优化策略函数；
数据采集模块，用于获取各个所述集群的预测数据；
指令曲线构建模块，用于根据所述预测数据构建次日目标调度模型，得到次日集群调度指令曲线；
数据分析模块，用于根据所述预测数据和各所述次日集群调度指令曲线，确定所述优化策略函数的调峰时段和非调峰时段；
调度曲线计算模块，用于基于所述预测数据和所述次日集群指令曲线，在所述调峰时段和非调峰时段下求解所述优化策略函数，输出最优调度曲线。
可选地，所述优化策略函数包括优化函数、调峰函数和非调峰函数：
所述调峰函数具体为：



其中，Oj1为经济性目标，peak为调峰时段，valley为填谷时段，pup为峰值电价，pdn谷时电价，ck为地调k的发电成本，Oj2为就地平衡目标，Plk,t为t时段地调k的当地预测负荷，为t时段地调k出力调整量的上限值，为t时段地调k出力调整量的下限值，N为地调数量，T为时间维度，为t时段省调对地调k的出力调整量；
所述非调峰函数具体为：

其中，f为优化目标，Pk,t为k市t时刻地级调度最优功率，为地级调度机构k在t时段所保留的可调裕度，为调频服务常数；
所述优化函数具体为：



H为时间维度，取24小时,Pclu,t为集群clu在t时段的对外输出功率,为省调对地调k在t时段的对外功率交互调度指令,为t时刻集群clu调度成本，cn,t为第二日t时段集群与聚合商n的电量交易价格，表示t时刻集群clu对外输出功率，表示t-1时刻集群clu对外输出功率，为集群clu在t时刻的灵活性裕度使用量，为集群clu在t时刻的灵活性裕度上限值，为集群clu在t时刻的灵活性裕度上限值,Lclu,t为集群clu在t时段的预测负荷。
可选地，所述指令曲线构建模块包括：
第一数值选取子模块，用于选取各个所述集群分布式可再生能源出力预测曲线中多个相同时刻对应的预测数值、可调上限值和可调下限值；
第一叠加子模块，用于将各个所述预测数值进行叠加，得到次日预测数值；
第二叠加子模块，用于将各个所述可调上限值进行叠加，得到次日可调上限值；
第三叠加子模块，用于将各个所述可调下限值进行叠加，得到次日可调下限值；
指令曲线生成子模块，用于采用所述次日预测数值、所述次日可调上限值和所述次日可调下限值生成次日集群调度指令曲线。
可选地，所述预测数据包括总负荷曲线，所述数据分析模块包括：
第四叠加子模块，用于将各所述次日集群调度指令曲线输入至所述次日目标调度模型中，叠加得到次日省调度指令曲线；
第二数值选取子模块，用于取所述次日省调度指令曲线中多个相同时刻对应的所述次日预测数值和所述次日可调上限值；
第三数值选取子模块，用于选取所述总负荷曲线中多个相同时刻对应的总负荷值；
第一差值计算子模块，用于计算所述总负荷值与所述次日预测数值之间的第一差值；
第二差值计算子模块，用于计算所述第一差值与所述次日可调上限值的第二差值；
第一判断子模块，用于判断所述第一差值是否大于所述第二差值；
若所述第一差值大于第二差值，则将所述第一差值对应的时刻确定为调峰时段；
若所述第一差值小于或等于第二差值，则将所述第一差值对应的时刻确定为非调峰时刻。
可选地，所述调度曲线计算模块包括：
第四数值选取子模块，用于选取所述次日集群调度指令曲线中多个目标时段和所述目标时段对应的目标数据；
第二判断子模块，用于判断所述目标时段是否为调峰时段；
若所述目标时段是调峰时段，则将所述目标数据和所述预测数据输入到所述调峰函数和所述优化函数中，输出调度数据；
若所述目标时段不是调峰时段，则将所述目标数据和所述预测数据输 入到所述非调峰函数和所述优化函数中，输出调度数据；
最优调度曲线生成子模块，用于采用各个所述目标时段对应的所述调度数据生成最优调度曲线。
从以上技术方案可以看出，本发明具有以下优点：
当接收到集群调度请求时，以集群总效益最高为目标构建集群对应的优化策略函数，获取各个集群的预测数据，根据预测数据构建次日目标调度模型，得到次日集群调度指令曲线，根据预测数据和各次日集群调度指令曲线，确定优化策略函数的调峰时段和非调峰时段，根据分配好的调峰时段和非调峰时段，将预测数据和次日集群指令曲线输入到优化策略函数中，求解最优调度曲线。现在技术中通过电池储能技术来跟踪发电，在大规模高比例、分散化的可再生能源机组接入时，会导致集中控制难以满足配网电压调控的精确性和快速性要求，降低了电网分布式可再生能源的接入能力的技术问题，提高了电网分布式可再生能源的接入能力，保证了电网的稳定运行。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其它的附图。
图1为本发明实施例一提供的一种分布式可再生能源集群调度方法的步骤流程图；
图2为本发明实施例二提供的一种分布式可再生能源集群调度方法的步骤流程图；
图3为本发明实施例三提供的一种分布式可再生能源集群调度装置的结构框图。
具体实施方式
本发明实施例提供了一种分布式可再生能源集群调度方法和装置，用于解决现有现在技术中通过电池储能技术来跟踪发电，在大规模高比例、分散化的可再生能源机组接入时，会导致集中控制难以满足配网电压调控的精确性和快速性要求，降低了电网分布式可再生能源的接入能力的技术问题。
为使得本发明的发明目的、特征、优点能够更加的明显和易懂，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，下面所描述的实施例仅仅是本发明一部分实施例，而非全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。
请参阅图1，图1为本发明实施例一提供的一种分布式可再生能源集群调度方法的步骤流程图。
本发明提供的一种分布式可再生能源集群调度方法，包括：
步骤101、响应集群调度请求，以集群总效益最大化为目标构建集群对应的优化策略函数。
集群调度请求指的是，通过分布式可再生能源集群将次日集群调度的预测曲线发送至省级电网机构的请求指令。
优化策略函数指的是，省级电网机构在保留调频裕度的前提下，以各地级电网机构调度最优曲线为基础，优先进行经济性调度，地级电网机构再结合省级电网机构的调度曲线，以各集群调度最优曲线为基础，进行调度。
在本发明实施例中，当接收到技术人员发出的分布式再生能源集群调度请求时，以各个集群整体的总效益最高为目标构建各个集群对应的优化策略函数。
步骤102、获取各个集群的预测数据。
预测数据指的是各个集群对次日出力情况预测分析得到的数据。例如，峰值电价、谷时电价、地调k的发电成本、就地平衡目标、t时段集群k的当地预测负荷、为t时段集群k出力调整量的上限值、地调数量、集群数 量、k集群t时刻集群调度最优功率、地级调度机构k在t时段所保留的可调裕度、调频服务常数、t时刻集群clu调度成本、第二日t时段集群与聚合商n的电量交易价格、t时刻集群clu对外输出功率、t-1时刻集群clu对外输出功率、t时刻的灵活性裕度使用量、集群clu在t时刻的灵活性裕度上限值、集群clu在t时刻的灵活性裕度上限值、集群clu在t时段的预测负荷、集群预测曲线和可调范围等。
在本发明实施例中，在构建完成优化策略函数后，获取每一个集群的预测数据。
需要说明的是，t时段地调k的当地预测负荷、t时段地调k出力调整量的上限值、k市t时刻地调调度最优功率等，均可通过地调所包含的各个集群的预测数据叠加得到。
步骤103、根据预测数据构建次日目标调度模型，得到次日集群调度指令曲线。
次日目标调度模型指的是，将多集群预测曲线和可调范围整合在一起的函数模型。
在本发明实施例中，当获取到集群的预测数据后，选取预测数据中的集群预测曲线和可调范围，根据各个集群所属的地级电网机构和省级电网机构，选取地级电网机构所属集群的预测曲线和可调范围进行叠加，或选取省级电网机构所属集群的预测曲线和可调范围进行叠加。
步骤104、根据预测数据和各次日集群调度指令曲线，确定优化策略函数的调峰时段和非调峰时段。
调峰时段指的是，电网在该时间内需要投入在正常运行以外的发电机组才能满足需求。
非调峰时间值的是，电网在该时间内不需要投入在正常运行以外的发电机组也能满足需求。
在本发明实施例中，在获取到各次日集群调度指令曲线后，选取预测数据中的全省总负荷曲线和集群的可调上限值，根据全省总负荷曲线和次日集群调度指令曲线，计算出净负荷曲线，再选取地级电网机构对应集群的可调上限值，将可调上限值进行叠加，得到地级电网机构的可调上限值， 计算地级电网机构与净负荷曲线在该时刻的最大净负荷值的差值，当t时刻净负荷值大于差值，则判定t时刻为调峰时刻，当t时刻净负荷值小于或等于差值，则判定t时刻为非调峰时刻。
需要说明的是，可获取t手段地级电网机构对应集群的可调下限值作为初始填谷功率，计算最小净负荷值与初始填谷功率的和值。
步骤105、基于预测数据和次日集群指令曲线，在调峰时段和非调峰时段下求解优化策略函数，输出最优调度曲线。
在本发明实施例中，在得到次日的调峰时段和非调峰时段后，结合预测数据和次日集群指令曲线，共同输入到优化策略函数中，优化策略函数以集群总效益最大化为目标，在满足与省级电网机构的调度偏差最小的条件下进行求解，得到集群在次日的最优调度曲线。
在本发明实施例中，通过响应集群调度请求，以集群总效益最大化为目标构建优化策略函数，再获取各个集群的预测数据，而为了实现集群调度准确性，根据预测数据构建次日目标调度模型，生成次日集群调度曲线，根据预测数据和次日集群调度指令曲线，将次日时段划分为调峰时段和非调峰时段，并结合预测数据和次日集群指令曲线，共同输入到优化策略函数中，求解最优调度曲线。可以适应对大规模高比例、分散化的集群进行调控，减少储能资源的浪费，提高了电网分布式可再生能源的接入能力。
请参阅图2，图2为本发明实施例二提供的一种分布式可再生能源集群调度方法的步骤流程图。
本发明提供的一种分布式可再生能源集群调度方法,包括：
步骤201、响应集群调度请求，以集群总效益最大化为目标构建集群对应的优化策略函数。
优化策略函数包括优化函数、调峰函数和非调峰函数：
调峰函数具体为：



其中，Oj1为经济性目标，peak为调峰时段，valley为填谷时段，pup为峰值电价，pdn谷时电价，ck为地调k的发电成本，Oj2为就地平衡目标，Plk,t为t时段地调k的当地预测负荷，为t时段地调k出力调整量的上限值，为t时段地调k出力调整量的下限值，N为地调数量，T为时间维度，为t时段省调对地调k的出力调整量；
非调峰函数具体为：

其中，f为优化目标，Pk,t为k市t时刻地级调度最优功率，为地级调度机构k在t时段所保留的可调裕度，为调频服务常数；
优化函数具体为：



H为时间维度，取24小时,Pclu,t为集群clu在t时段的对外输出功率,为省调对地调k在t时段的对外功率交互调度指令,为t时刻集群clu调度成本，cnt为第二日t时段集群与聚合商n的电量交易价格，表示t时刻集群clu对外输出功率，表示t-1时刻集群clu对外输出功率，为集群clu在t时刻的灵活性裕度使用量，为集群clu在t时刻的灵活性裕度上限值，为集群clu在t时刻的灵活性裕度上限值,Lclu,t为集群clu在t时段的预测负荷。
在本发明实施例中，当接收到工作人员发出的集群调度请求时，对集群调度请求进行解析，获取到优化策略函数的目标为集群运行总效益最大，根据目标构建优化策略函数。
步骤202、获取各个集群的预测数据。
在本发明实施例中，在优化策略函数构建完成后，获取在电网中各个 集群对次日的预测数据。
步骤203、选取各个集群分布式可再生能源出力预测曲线中多个相同时刻对应的预测数值、可调上限值和可调下限值。
在本发明实施例中，在获取到集群的预测数据后，选取每一个集群的分布式可再生能源出力预测曲线中相同时刻的预测数值、可调上限值和可调下限值。
需要说明的是，预测数据中包括了集群分布式可再生能源出力预测曲线。
步骤204、将各个预测数值进行叠加，得到次日预测数值。
在本发明实施例中，通过将选取到预测数值进行叠加，得到次日预测数值。
步骤205、将各个可调上限值进行叠加，得到次日可调上限值。
其中，为地级电网机构i在t时刻上报给省级电网机构的可调上限值，为地级电网机构内第j个集群的可调上限值，λ为上可调范围的裕度系数，取0到1之间。
在本发明实施例中，通过将选取到可调上限值进行叠加，得到次日可调上限值。
步骤206、将各个可调下限值进行叠加，得到次日可调下限值。
其中，为地级电网机构i在t时刻上报给地级电网机构的可调下限值，为地级电网机构内第j个集群的可调下限值，为下可调范围的裕度系数，取0到1之间
在本发明实施例中，通过将选取到可调下限值进行叠加，得到次日可调下限值。
步骤207、采用次日预测数值、次日可调上限值和次日可调下限值生成次日集群调度指令曲线。
其中，为地级电网机构i在t时刻上报给省级电网机构的分布式可再生能源出力预测曲线，为地级电网机构区域内第j个分布式可再生 能源出力预测曲线。
在本发明实施例中，将叠加得到的次日预测数值、次日可调上限值和次日可调下限值，以次日可调上限值和次日可调下限值作为次日集群调度指令曲线的可调上下限值，以次日每一时刻t作为x轴，次日预测数值作为y轴生成次日集群调度指令曲线。
步骤208、根据预测数据和各次日集群调度指令曲线，确定优化策略函数的调峰时段和非调峰时段。
进一步地，预测数据包括总负荷曲线，步骤208可以包括以下子步骤：
S11、将各次日集群调度指令曲线输入至次日目标调度模型中，叠加得到次日省调度指令曲线。
在本发明实施例中，将各个次日集群调度指令曲线输入到次日目标调度模型中，进行叠加生成次日省调度指令曲线。
S12、选取次日省调度指令曲线中多个相同时刻对应的次日预测数值和次日可调上限值。
在本发明实施例中，选取次日省调指令曲线在t时刻对应的次日预测数值和次日可调上限值。
S13、选取总负荷曲线中多个相同时刻对应的总负荷值。
在本发明实施例中，选取总负荷曲线中在t时刻对应的总负荷值。
S14、计算总负荷值与次日预测数值之间的第一差值。
在本发明实施例中，在选取出t时刻的总负荷值和次日预测数值后，计算总负荷值与次日预测数值的第一差值。
S15、计算第一差值与次日可调上限值的第二差值。
在本发明实施例中，在计算出第一差值后，计算第一差值与t时刻对应的次日可调上限值的第二差值。
需要说明的是，将次日可调上限值作为初始削峰功率，将第一差值与初始削峰功率的第二差值作为调峰触发功率。
S16、判断第一差值是否大于第二差值。
在本发明实施例中，判断第一差值是否大于调峰触发功率。
S17、若第一差值大于第二差值，则将第一差值对应的时刻确定为调峰 时段。
在本发明实施例中，若第一差值大于调峰触发功率，则将第一差值对应的时刻确定为调峰时段。
S18、若第一差值小于或等于第二差值，则将第一差值对应的时刻确定为非调峰时刻。
在本发明实施例中，若第一差值小于或等于调峰触发功率，则将第一差值对应的时刻确定为非调峰时段。
需要说明的是，还可选取次日可调下限值作为初始填谷功率，通过计算第一差值与初始填谷功率的和值作为填谷触发功率，判断第一差值是否小于填谷触发功率，若第一差值小于填谷触发功率，则将第一差值对应的时间确定为填谷时段，若第一差值大于或等于填谷触发功率，则将第一差值对应的时间确定为退谷时段。
步骤209、基于预测数据和次日集群指令曲线，在调峰时段和非调峰时段下求解优化策略函数，输出最优调度曲线。
进一步地，步骤209可以包括以下子步骤：
S21、选取次日集群调度指令曲线中多个目标时段和目标时段对应的目标数据。
在本发明实施例中，在获取到次日集群调度指令曲线后，选取目标集群曲线中的多个目标时段和目标时段对应的目标数据。
S22、判断目标时段是否为调峰时段。
在本发明实施例中，在选取出目标时段后，判断目标时段是否为调峰时段。
S23、若目标时段是调峰时段，则将目标数据和预测数据输入到调峰函数和优化函数中，输出调度数据。
在本发明实施例中，当目标时段为调峰时段时，调用优化策略函数的调峰函数和优化函数对目标数据和预测数据进行运算。
S24、若目标时段不是调峰时段，则将目标数据和预测数据输入到非调峰函数和优化函数中，输出调度数据。
在本发明实施例中，当目标时段为非调峰时段时，调用优化策略函数 的非调峰函数和优化函数对目标数据和预测数据进行运算。
S25、采用各个目标时段对应的调度数据生成最优调度曲线。
在本发明实施例中，根据各个目标时段计算得到调度数据，生成最优调度曲线。
在本发明实施例中，通过响应集群调度请求，以集群总效益最大化为目标构建集群对应的优化策略函数，再获取各个集群的预测数据，选取预测数据中各个集群分布式可再生能源出力预测曲线中多个相同时刻对应的预测数值、可调上限值和可调下限值，分别对各个预测数值、可调上限值和可调下限值进行叠加，生成次日集群调度指令曲线，根据预测数据和次日集群调度指令曲线，将次日时段划分为调峰时段和非调峰时段，根据调峰时段和非调峰时段调用的运算函数不同，结合预测数据和次日集群调度指令曲线，生成最优化调度曲线。可以快速的对大规模高比例、分散化的集群进行精准调控，减少了储能资源的浪费，提高了电网分布式可再生能源的接入能力。
请参阅图3，图3为本发明实施例三提供的一种分布式可再生能源集群调度装置的结构框图。
本发明实施例提供了一种分布式可再生能源集群调度装置，包括：
优化策略函数构建模块301，用于响应集群调度请求，以集群总效益最大化为目标构建集群对应的优化策略函数；
数据采集模块302，用于获取各个集群的预测数据；
指令曲线构建模块303，用于根据预测数据构建次日目标调度模型，得到次日集群调度指令曲线；
数据分析模块304，用于根据预测数据和各次日集群调度指令曲线，确定优化策略函数的调峰时段和非调峰时段；
调度曲线计算模块305，用于基于预测数据和次日集群指令曲线，在调峰时段和非调峰时段下求解优化策略函数，输出最优调度曲线。
进一步地，优化策略函数包括优化函数、调峰函数和非调峰函数：
调峰函数具体为：



其中，Oj1为经济性目标，peak为调峰时段，valley为填谷时段，pup为峰值电价，pdn谷时电价，ck为地调k的发电成本，Oj2为就地平衡目标，Plk,t为t时段地调k的当地预测负荷，为t时段地调k出力调整量的上限值，为t时段地调k出力调整量的下限值，N为地调数量，T为时间维度，为t时段省调对地调k的出力调整量；
非调峰函数具体为：

其中，f为优化目标，Pk,t为k市t时刻地级调度最优功率，为地级调度机构k在t时段所保留的可调裕度，为调频服务常数；
优化函数具体为：



H为时间维度，取24小时,Pclu,t为集群clu在t时段的对外输出功率,为省调对地调k在t时段的对外功率交互调度指令,为t时刻集群clu调度成本，cn,t为第二日t时段集群与聚合商n的电量交易价格，表示t时刻集群clu对外输出功率，表示t-1时刻集群clu对外输出功率，为集群clu在t时刻的灵活性裕度使用量，为集群clu在t时刻的灵活性裕度上限值，为集群clu在t时刻的灵活性裕度上限值,Lclu,t为集群clu在t时段的预测负荷。
进一步地，指令曲线构建模块303包括：
第一数值选取子模块，用于选取各个集群分布式可再生能源出力预测曲线中多个相同时刻对应的预测数值、可调上限值和可调下限值；
第一叠加子模块，用于将各个预测数值进行叠加，得到次日预测数值；
第二叠加子模块，用于将各个可调上限值进行叠加，得到次日可调上限值；
第三叠加子模块，用于将各个可调下限值进行叠加，得到次日可调下限值；
指令曲线生成子模块，用于采用次日预测数值、次日可调上限值和次日可调下限值生成次日集群调度指令曲线。
进一步地，预测数据包括总负荷曲线，数据分析模块304包括：
第四叠加子模块，用于将各次日集群调度指令曲线输入至次日目标调度模型中，叠加得到次日省调度指令曲线；
第二数值选取子模块，用于取次日省调度指令曲线中多个相同时刻对应的次日预测数值和次日可调上限值；
第三数值选取子模块，用于选取总负荷曲线中多个相同时刻对应的总负荷值；
第一差值计算子模块，用于计算总负荷值与次日预测数值之间的第一差值；
第二差值计算子模块，用于计算第一差值与次日可调上限值的第二差值；
第一判断子模块，用于判断第一差值是否大于第二差值；
若第一差值大于第二差值，则将第一差值对应的时刻确定为调峰时段；
若第一差值小于或等于第二差值，则将第一差值对应的时刻确定为非调峰时刻。
进一步地，调度曲线计算模块305包括：
第四数值选取子模块，用于选取次日集群调度指令曲线中多个目标时段和目标时段对应的目标数据；
第二判断子模块，用于判断目标时段是否为调峰时段；
若目标时段是调峰时段，则将目标数据和预测数据输入到调峰函数和优化函数中，输出调度数据；
若目标时段不是调峰时段，则将目标数据和预测数据输入到非调峰函 数和优化函数中，输出调度数据；
最优调度曲线生成子模块，用于采用各个目标时段对应的调度数据生成最优调度曲线。
在本发明实施例中，当优化策略函数构建模块接收到技术人员发出的集群调度请求，以集群总效益最大化为目标构建集群对应的优化策略函数，再通过数据采集模块获取各个集群的预测数据，指令曲线构建模块根据获取到的预测数据构建次日目标调度模型，生产次日集群调度指令曲线，数据分析模块根据得到的预测数据和次日集群调度指令曲线进行分析，将次日时段划分为调峰时段和非调峰时段，最后由调度曲线计算模块，通过预测数据和次日集群指令，在调峰时段和非调峰时段求解优化策略函数，输出最优调度曲线。解决了现有技术方案中在大规模、分散化的集群调度时无法精准快速的对配电网电压进行调控的技术问题，提高了电网分布式可再生能源的接入能力。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的***，装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个装置，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
以上所述，以上实施例仅用以说明本发明的技术方案，而非对其限制； 尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。

Claims (10)

  1. 一种分布式可再生能源集群调度方法，其特征在于,包括：
    响应集群调度请求，以集群总效益最大化为目标构建集群对应的优化策略函数；
    获取各个所述集群的预测数据；
    根据所述预测数据构建次日目标调度模型，得到次日集群调度指令曲线；
    根据所述预测数据和各所述次日集群调度指令曲线，确定所述优化策略函数的调峰时段和非调峰时段；
    基于所述预测数据和所述次日集群指令曲线，在所述调峰时段和非调峰时段下求解所述优化策略函数，输出最优调度曲线。
  2. 根据权利要求1所述的分布式可再生能源集群调度方法,其特征在于，所述优化策略函数包括优化函数、调峰函数和非调峰函数：
    所述调峰函数具体为：



    其中，Oj1为经济性目标，peak为调峰时段，valley为填谷时段，pup为峰值电价，pdn谷时电价，ck为地调k的发电成本，Oj2为就地平衡目标，Plk,t为t时段地调k的当地预测负荷，为t时段地调k出力调整量的上限值，为t时段地调k出力调整量的下限值，N为地调数量，T为时间维度，为t时段省调对地调k的出力调整量；
    所述非调峰函数具体为：

    其中，f为优化目标，Pk,t为k市t时刻地级调度最优功率，为地级调度机构k在t时段所保留的可调裕度，为调频服务常数；
    所述优化函数具体为：



    H为时间维度，取24小时,Pclu,t为集群clu在t时段的对外输出功率,为省调对地调k在t时段的对外功率交互调度指令,为t时刻集群clu调度成本，cn,t为第二日t时段集群与聚合商n的电量交易价格，表示t时刻集群clu对外输出功率，表示t-1时刻集群clu对外输出功率，为集群clu在t时刻的灵活性裕度使用量，为集群clu在t时刻的灵活性裕度上限值，为集群clu在t时刻的灵活性裕度上限值,Lclu,t为集群clu在t时段的预测负荷。
  3. 根据权利要求1所述的分布式可再生能源集群调度方法,其特征在于，所述预测数据包括集群分布式可再生能源出力预测曲线，所述根据所述预测数据构建次日目标调度模型，得到次日集群调度指令曲线的步骤，包括：
    选取各个所述集群分布式可再生能源出力预测曲线中多个相同时刻对应的预测数值、可调上限值和可调下限值；
    将各个所述预测数值进行叠加，得到次日预测数值；
    将各个所述可调上限值进行叠加，得到次日可调上限值；
    将各个所述可调下限值进行叠加，得到次日可调下限值；
    采用所述次日预测数值、所述次日可调上限值和所述次日可调下限值生成次日集群调度指令曲线。
  4. 根据权利要求3所述的分布式可再生能源集群调度方法,其特征在于，所述预测数据包括总负荷曲线，所述根据所述预测数据和各所述次日集群调度指令曲线，确定所述优化策略函数的调峰时段和非调峰时段的步骤，包括
    将各所述次日集群调度指令曲线输入至所述次日目标调度模型中，叠加得到次日省调度指令曲线；
    选取所述次日省调度指令曲线中多个相同时刻对应的所述次日预测数值和所述次日可调上限值；
    选取所述总负荷曲线中多个相同时刻对应的总负荷值；
    计算所述总负荷值与所述次日预测数值之间的第一差值；
    计算所述第一差值与所述次日可调上限值的第二差值；
    判断所述第一差值是否大于所述第二差值；
    若所述第一差值大于第二差值，则将所述第一差值对应的时刻确定为调峰时段；
    若所述第一差值小于或等于第二差值，则将所述第一差值对应的时刻确定为非调峰时刻。
  5. 根据权利要求2所述的分布式可再生能源集群调度方法,其特征在于，所述基于所述预测数据和所述次日集群指令曲线，在所述调峰时段和非调峰时段下求解所述优化策略函数，输出最优调度曲线的步骤，包括：
    选取所述次日集群调度指令曲线中多个目标时段和所述目标时段对应的目标数据；
    判断所述目标时段是否为调峰时段；
    若所述目标时段是调峰时段，则将所述目标数据和所述预测数据输入到所述调峰函数和所述优化函数中，输出调度数据；
    若所述目标时段不是调峰时段，则将所述目标数据和所述预测数据输入到所述非调峰函数和所述优化函数中，输出调度数据；
    采用各个所述目标时段对应的所述调度数据生成最优调度曲线。
  6. 一种分布式可再生能源集群调度装置，其特征在于,包括：
    优化策略函数构建模块，用于响应集群调度请求，以集群总效益最大化为目标构建集群对应的优化策略函数；
    数据采集模块，用于获取各个所述集群的预测数据；
    指令曲线构建模块，用于根据所述预测数据构建次日目标调度模型，得到次日集群调度指令曲线；
    数据分析模块，用于根据所述预测数据和各所述次日集群调度指令曲线，确定所述优化策略函数的调峰时段和非调峰时段；
    调度曲线计算模块，用于基于所述预测数据和所述次日集群指令曲线，在所述调峰时段和非调峰时段下求解所述优化策略函数，输出最优调度曲 线。
  7. 根据权利要求6所述的分布式可再生能源集群调度装置,其特征在于，所述优化策略函数包括优化函数、调峰函数和非调峰函数：
    所述调峰函数具体为：



    其中，Oj1为经济性目标，peak为调峰时段，valley为填谷时段，pup为峰值电价，pdn谷时电价，ck为地调k的发电成本，Oj2为就地平衡目标，Plk,t为t时段地调k的当地预测负荷，为t时段地调k出力调整量的上限值，为t时段地调k出力调整量的下限值，N为地调数量，T为时间维度，为t时段省调对地调k的出力调整量；
    所述非调峰函数具体为：

    其中，f为优化目标，Pk,t为k市t时刻地级调度最优功率，为地级调度机构k在t时段所保留的可调裕度，为调频服务常数；
    所述优化函数具体为：



    H为时间维度，取24小时,Pclu,t为集群clu在t时段的对外输出功率,为省调对地调k在t时段的对外功率交互调度指令,为t时刻集群clu调度成本，cn,t为第二日t时段集群与聚合商n的电量交易价格，表示t时刻集群clu对外输出功率，表示t-1时刻集群clu对外输出功率，为集群clu在t时刻的灵活性裕度使用量，为集群clu在t 时刻的灵活性裕度上限值，为集群clu在t时刻的灵活性裕度上限值,Lclu,t为集群clu在t时段的预测负荷。
  8. 根据权利要求6所述的分布式可再生能源集群调度装置,其特征在于，所述指令曲线构建模块包括：
    第一数值选取子模块，用于选取各个所述集群分布式可再生能源出力预测曲线中多个相同时刻对应的预测数值、可调上限值和可调下限值；
    第一叠加子模块，用于将各个所述预测数值进行叠加，得到次日预测数值；
    第二叠加子模块，用于将各个所述可调上限值进行叠加，得到次日可调上限值；
    第三叠加子模块，用于将各个所述可调下限值进行叠加，得到次日可调下限值；
    指令曲线生成子模块，用于采用所述次日预测数值、所述次日可调上限值和所述次日可调下限值生成次日集群调度指令曲线。
  9. 根据权利要求8所述的分布式可再生能源集群调度装置,其特征在于，所述预测数据包括总负荷曲线，所述数据分析模块包括：
    第四叠加子模块，用于将各所述次日集群调度指令曲线输入至所述次日目标调度模型中，叠加得到次日省调度指令曲线；
    第二数值选取子模块，用于取所述次日省调度指令曲线中多个相同时刻对应的所述次日预测数值和所述次日可调上限值；
    第三数值选取子模块，用于选取所述总负荷曲线中多个相同时刻对应的总负荷值；
    第一差值计算子模块，用于计算所述总负荷值与所述次日预测数值之间的第一差值；
    第二差值计算子模块，用于计算所述第一差值与所述次日可调上限值的第二差值；
    第一判断子模块，用于判断所述第一差值是否大于所述第二差值；
    若所述第一差值大于第二差值，则将所述第一差值对应的时刻确定为调峰时段；
    若所述第一差值小于或等于第二差值，则将所述第一差值对应的时刻确定为非调峰时刻。
  10. 根据权利要求7所述的分布式可再生能源集群调度装置,其特征在于，所述调度曲线计算模块包括：
    第四数值选取子模块，用于选取所述次日集群调度指令曲线中多个目标时段和所述目标时段对应的目标数据；
    第二判断子模块，用于判断所述目标时段是否为调峰时段；
    若所述目标时段是调峰时段，则将所述目标数据和所述预测数据输入到所述调峰函数和所述优化函数中，输出调度数据；
    若所述目标时段不是调峰时段，则将所述目标数据和所述预测数据输入到所述非调峰函数和所述优化函数中，输出调度数据；
    最优调度曲线生成子模块，用于采用各个所述目标时段对应的所述调度数据生成最优调度曲线。
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