WO2019196375A1 - Demand side response-based microgrid optimal unit and time-of-use electricity price optimization method - Google Patents

Demand side response-based microgrid optimal unit and time-of-use electricity price optimization method Download PDF

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WO2019196375A1
WO2019196375A1 PCT/CN2018/111211 CN2018111211W WO2019196375A1 WO 2019196375 A1 WO2019196375 A1 WO 2019196375A1 CN 2018111211 W CN2018111211 W CN 2018111211W WO 2019196375 A1 WO2019196375 A1 WO 2019196375A1
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microgrid
load
optimization
time
demand
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PCT/CN2018/111211
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French (fr)
Chinese (zh)
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季天瑶
叶秀珍
李梦诗
吴青华
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华南理工大学
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    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J3/381—Dispersed generators
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • H02J7/35—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering with light sensitive cells
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/10—The dispersed energy generation being of fossil origin, e.g. diesel generators
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • 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
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P80/00—Climate change mitigation technologies for sector-wide applications
    • Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
    • Y02P80/14—District level solutions, i.e. local energy networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/10—Energy trading, including energy flowing from end-user application to grid

Definitions

  • the invention relates to the technical field of optimal unit combination and time-sharing electricity price optimization of the micro-grid, in particular to an optimal method for the optimal unit and the time-sharing electricity price of the micro-grid based on the demand side response.
  • Microgrid As of the end of 2017, with the promotion of China's power market reform and the introduction of the incremental distribution network, the micro-grid as a practical operation mode of incremental distribution network has received great attention and attention from all walks of life. Microgrid is a reliable form to solve the problem of new energy in situ consumption and improve the consumption rate of new energy. Its high efficiency and safe operation are the key content of microgrid construction.
  • the supply of electric energy is partly derived from the renewable energy of the region where the microgrid is located, such as the conversion of renewable energy such as wind energy and solar energy.
  • renewable energy such as wind energy and solar energy.
  • both wind and solar energy have problems of excessive randomness of output, and there is no guarantee that the load demand will be met at any time. Therefore, there are often problems such as abandoning wind and abandoning light. Therefore, in order to improve the consumption rate of renewable energy and fully guarantee the green operation of the microgrid, it is necessary to properly configure the wind turbine and photovoltaic generator set.
  • the power consumption plan is appropriately adjusted to respond to changes in supply and demand, and correspondingly In the stage of power consumption, increase the input of adjustable load to meet its own demand for power consumption.
  • the user's response is motivated by the change in electricity price, so the rational optimization of the time-of-use pricing strategy helps to stimulate the power users to adjust the power plan at the right time to simultaneously ensure the user-side load and microgrid Minimize operating costs.
  • the object of the present invention is to overcome the shortcomings and shortcomings of the prior art, to break through the problem that the operating cost of the traditional micro-grid is not ideal and the shortage of the micro-grid user side is too high, and propose an optimal unit of the micro-grid based on the demand-side response.
  • the minimization of quantity optimization obtains the optimal unit combination and the time-sharing price strategy for the most favorable operation of the micro-grid, so as to minimize the operating cost of the micro-grid under the premise of meeting the demand-side electricity demand.
  • the technical solution provided by the present invention is: a method for optimizing an optimal unit of a microgrid and a time-of-use electricity price based on a demand side response, the micro grid being a typical micro grid containing renewable energy, by wind power
  • the generator set, the photovoltaic generator set, the diesel generator set and the large-scale energy storage system are powered, and the minimum operating cost of the micro-grid is the first optimization goal, and the micro-grid lack of load is the second optimization target, based on the micro-grid history.
  • the historical data of distributed energy such as load data, wind energy and solar energy are used to obtain the historical data of supply and demand of microgrid.
  • the particle swarm optimization algorithm and interior point method are used to decentralize the optimization objectives.
  • the objective function of the total operating cost of the microgrid is considered.
  • the scheduling cost of various units, the operating cost and the fuel consumption cost of the diesel unit, the conversion efficiency of the large-scale energy storage battery, and at the same time take into account the cost of electricity purchase caused by the exchange of electricity between the micro-grid and the main network; Electricity price to encourage users to participate in the demand side response to reduce the total amount of power shortage in the microgrid throughout the year, ensuring the microgrid Energizing satisfied;
  • the optimization method comprises the steps of:
  • microgrid supply and demand including microgrid internal load data, regional distributed renewable energy data: wind speed, light intensity, various generator sets: wind turbines, photovoltaic generators, diesel generators and large energy storage systems Unit parameters;
  • PSO Particle Swarm Optimization
  • IPM Interior Point Method
  • step 1) the microgrid supply and demand historical data refers to the load data inside the microgrid obtained by the dispatching department, the wind speed and the light intensity of the region obtained by the meteorological department; and the basics of various types of units within the microgrid are also obtained. parameter.
  • step 2) the supply and demand situation is divided into two aspects: 'supply' and 'need': in terms of energy supply, since the microgrid is a typical microgrid with distributed renewable energy, the energy source includes : Wind turbines, photovoltaic generator sets, diesel generator sets and large energy storage systems consisting of energy storage batteries inside the microgrid.
  • the wind turbine and photovoltaic generator set models are as follows:
  • Wind turbines are the main way to use wind energy. Generally, different types of wind turbines have different cut-in wind speeds, rated wind speeds and cut-out wind speeds. The output power of wind turbines is represented by wind speed, namely:
  • V(t) is the total output power of the wind turbine at time t
  • V(t) is the wind speed corresponding to the moment
  • V in , V R , V out are the cut-in, cut-out and rated of the wind turbine respectively.
  • Wind speed, N WT is the number of wind turbines in a wind power plant
  • P 0 is the rated output power of a single wind turbine; in order to accurately describe the output of wind turbines at different locations, the wind turbines absorbed by different heights
  • the wind speed is converted as follows:
  • V and V ref are the wind speeds at heights h and h ref , respectively, and f is the coefficient of friction, usually taking 1/7 during the day and 1/2 at night;
  • Photovoltaic solar panels absorb solar energy into DC electrical energy. The conversion process is affected by solar radiation intensity and environmental and temperature conditions. The output power of typical photovoltaic solar panels is expressed as:
  • P PV (t) is the total output power of the photovoltaic generator set at time t
  • N PV is the number of photovoltaic solar panels
  • P PV0 is the rated power of a photovoltaic solar panel
  • T(t) and G(t) are respectively t Time temperature (25 ° C) and light intensity (1 kW / m 2 )
  • T 0 and G 0 are the temperature (25 ° C) and light intensity (1 kW / m 2 ) under standard test conditions, respectively
  • k PV is the temperature coefficient of photovoltaic ;
  • the energy supply of each system is obtained according to the new energy power generation output model, and on this basis, the diesel generator set, the large energy storage system and the main network are adopted.
  • the power exchange between the power grids meets the supply and demand balance within the microgrid.
  • the load of the microgrid model includes a part of the load type that can perform the demand side response, that is, the type of load can be based on the change of the electricity price.
  • the demand curve is adjusted in real time, and the user response peak-to-valley time-of-use electricity price is used to quantify the load fluctuation.
  • the response is based on the response elasticity matrix M to explain the user's response to the electricity price.
  • P TOU is the load after the peak-valley electricity price
  • P L0 is the original load
  • P f0 , P p0 , P g0 and x 0 are respectively corresponding to the peak load, the flat section and the low valley before the peak-valley electricity price is adopted.
  • the load and the electricity price; x f , x p and x g respectively represent the electricity price corresponding to the peak section and the trough section after the peak-to-valley electricity price.
  • step 3 in order to meet the power demand of the internal users of the microgrid and reduce the operating cost, the minimum load on the user side and the minimum operating cost of the micro grid are respectively set as two objective functions of the distributed optimization, as described below. :
  • the microgrid operation should try to ensure a low load shedding/cutting load to meet the user's power demand. Therefore, the minimum load on the user side is the first optimization goal of decentralized optimization:
  • ⁇ im is the micro-grid demand side lack of load
  • P im is the total amount of electricity purchased by the micro-grid to the main network
  • x t is the real-time electricity price within one day
  • P L is the total load of the micro-grid, due to the dispersion optimization
  • the part of the load on the user side belongs to the load that can participate in the demand side response. Therefore, the total load of the microgrid at time t is related to the electricity price of the time period.
  • P di (t) is the output of the diesel generator set at t
  • P wt (t) , P pv (t) and P ba (t) are the output of wind turbines, photovoltaic generator sets and large energy storage systems at time t, respectively, because wind turbines, photovoltaic generator sets and large energy storage systems are on the DC side, so The conversion efficiency from the DC side to the AC side needs to be considered, expressed as ⁇ inv ;
  • the micro-grid operating cost should be reduced on the premise of satisfying the user-side power demand. Therefore, the micro-grid operating cost is the second optimization goal of decentralized optimization, which is described as follows:
  • ⁇ cos is the total cost of microgrid operation, including the scheduling cost of various units, the operating cost of various units, the fuel consumption cost of diesel generators, the battery loss of large energy storage systems, the cost of electricity purchase, and the income from electricity sales;
  • X represents the unit type;
  • a i (t) takes a value of 0 or 1, which means that unit i is called at time t;
  • ⁇ i (t) represents the scheduling cost of unit i at time t;
  • C op (i) represents unit i Operating cost;
  • P fuel (t) represents the output of the diesel engine at time t, and V fuel (t) represents the price of diesel;
  • P ex (t), P im (t) respectively represent the purchase/sales power of the micro grid at time t, P imp (t) and P exp (t) respectively represent the real-time purchase and sale price at time
  • step 4 the particle swarm optimization algorithm Particle Swarm Optimization (PSO) and the interior point method (IPM) are used to perform the distributed optimization solution; wherein the particle swarm search algorithm is a global optimization algorithm based on the whole,
  • the algorithm regards the habitat in the process of bird movement as a possible solution to the target problem, and each individual transmits information to each other, thus guiding the whole group to possible It is the direction of the optimal solution, and the possibility of finding a better solution is continuously improved during the movement; each bird is regarded as a "particle", and its position and speed are updated as follows:
  • v ij (t) wv ij (t-1)+c 1 r 1 [pbest ij (t-1)-x ij (t-1)]+c 2 r 2 [gbest ij (t-1)-x Ij (t-1)]
  • ij is the motion trajectory of the particle
  • t is the number of iterations
  • v ij (t) and x ij (t) are the velocity and position of the particle at the t-th iteration, respectively
  • c 1 and c 2 are respectively adjusting themselves.
  • the learning factor of the optimal pbest and the global optimal gbest, r 1 and r 2 are random numbers between 0 and 1
  • w is the inertia weight of the particle motion
  • the interior point method is a method for solving constrained optimization propositions. Whether it is a linear programming proposition or a constrained quadratic programming problem, the interior point method shows excellent performance; the interior point method belongs to the constrained optimization algorithm.
  • the basic idea is to transform the constrained optimization problem into an unconstrained problem by introducing a utility function, and then use the optimization iterative process to continuously update the utility function to make the algorithm converge;
  • the particle swarm search algorithm is used to search for the optimal unit combination of each energy supply system based on historical data, and the time-point method is used to find the time-sharing that minimizes the micro-grid load loss.
  • the electricity price strategy greatly reduces the amount of calculation and promotes optimization efficiency.
  • the present invention has the following advantages and beneficial effects:
  • the present invention realizes the organic combination of the micro-grid operating cost and the minimization of the user's lack of load for the first time, breaking through the relative independence between the traditional 'source' and 'charge'.
  • the present invention realizes for the first time the analysis of supply and demand based on all historical data of the microgrid, including the supply of various renewable energy sources, and deeply analyzes the long-term historical supply and demand of the microgrid.
  • the invention uses intelligent algorithm, ie particle swarm search algorithm and interior point method to solve the optimal unit combination and time-sharing price optimization of the microgrid operation, which is reasonable and effective.
  • the combination of the ground greatly improves the optimization speed.
  • the present invention directly reduces the power purchase cost of the microgrid by using the time-sharing electricity price to stimulate the user to adjust the self-power plan in advance to meet the load curve of the supply and demand situation of the micro-grid.
  • the method of the invention has wide use space under the realistic conditions of further promotion of the power market and the emerging incremental distribution network.
  • the optimization method consumes time, high efficiency and adaptability, and reduces the operating cost of the micro grid. There is a broad prospect for reducing the amount of load on the user side and increasing the rate of new energy consumption.
  • FIG. 1 is a schematic diagram of power supply and typical load of a microgrid built by the present invention.
  • FIG. 2 is a schematic diagram of a logic flow of the present invention.
  • FIG. 3 is a microgrid supply and demand data collected by the present invention, including wind speed and light intensity data collected by the regional meteorological bureau and historical load data.
  • FIG. 4 is a comparison diagram of the purchase and sale electric power curves of the micro grid obtained before and after the dispersion optimization according to the present invention.
  • the optimization method of the optimal unit and the time-sharing price of the micro-grid based on the demand side response provided by the embodiment provides a typical micro-grid model including the participants on both sides of the supply and demand shown in FIG. 1 and optimizes it.
  • the process is illustrated by the logic flow diagram of Figure 2 and includes the following steps:
  • the SOC is the energy storage battery parameter that constitutes a large energy storage system, and the SOC indicates the state of the battery.
  • Wind turbines are the main way to use wind energy. Generally, different types of wind turbines have different cut-in wind speeds, rated wind speeds and cut-out wind speeds. The output power of wind turbines can be expressed by wind speed, namely:
  • N WT is the number of wind turbines in wind power plants
  • P 0 is the rated output power of a single wind turbine
  • V and V ref are the wind speeds at heights h and h ref , respectively, and f is the coefficient of friction. Generally, 1/7 is taken during the day and 1/2 at night.
  • Photovoltaic solar panels absorb solar energy into DC power, and the conversion process is affected by solar radiation intensity and environmental conditions, such as temperature and temperature.
  • the output power of typical photovoltaic solar panels can be expressed as:
  • P PV (t) is the total output power of the photovoltaic generator set at time t
  • N PV is the number of photovoltaic solar panels
  • P PV0 is the rated power of a photovoltaic solar panel
  • T(t) and G(t) are respectively t times Temperature (25 ° C) and light intensity (1 kW / m 2 )
  • T 0 and G 0 are the temperature (25 ° C) and light intensity (1 kW / m 2 ) under standard test conditions, respectively
  • k PV is the temperature coefficient of photovoltaic.
  • the energy supply of each system is obtained according to the new energy generation output model as above, and on this basis, the diesel generator set, the large energy storage system and the main
  • the power exchange between the networks meets the supply and demand balance within the microgrid.
  • the load of the microgrid model includes a part of the load type that can perform the demand side response, that is, the type of load can be based on
  • the price curve changes the demand curve in real time, and the user responds to the peak-to-valley time-of-use electricity price to quantify the load fluctuation.
  • the response is based on the response elastic matrix M to describe the user's response to the electricity price.
  • P TOU is the load after peak-to-valley electricity price
  • P L0 is the original load
  • P f0 , P p0 , P g0 and x 0 are respectively corresponding to the peak load section, the flat section and the trough section before the peak-valley electricity price is not used.
  • the load and the electricity price, x f , and x g respectively represent the electricity price corresponding to the peak section and the trough section after the peak-to-valley electricity price.
  • the minimum operating cost of the microgrid and the minimum load on the demand side are the two objectives of the decentralized optimization, and the objective function is set.
  • the decentralized optimization process shown in Fig. 2 performs the decentralized optimization based on historical supply and demand data to satisfy the micro
  • the electricity demand of the internal users of the power grid reduces the operating cost
  • the minimum load on the user side and the minimum operating cost of the micro grid are respectively set as two objective functions of the distributed optimization.
  • the microgrid operation should try to ensure a low load shedding/cutting load to meet the user's power demand. Therefore, the minimum load on the user side is the first optimization goal of decentralized optimization:
  • ⁇ im is the amount of power shortage on the demand side of the microgrid
  • P im is the total amount of electricity purchased by the micro grid to the main network
  • x t is the real-time electricity price within one day
  • P L is the total load of the micro grid, due to the dispersion optimization
  • the part of the load on the user side belongs to the load that can participate in the demand side response. Therefore, the total load of the microgrid at time t is related to the price of electricity during that period.
  • P di (t) is the output of the diesel power generation system
  • P wt (t) , P pv (t) and P ba (t) are the output of wind turbines, photovoltaic generator sets and large energy storage systems at time t, respectively, because wind turbines, photovoltaic generator sets and large energy storage systems are on the DC side, so The conversion efficiency from the DC side to the AC side needs to be considered, expressed as ⁇ inv .
  • the micro-grid operating cost should be reduced on the premise of satisfying the user-side power demand. Therefore, the micro-grid operating cost is the second optimization goal of decentralized optimization, which is described as follows:
  • ⁇ cos is the total cost of microgrid operation, including the scheduling cost of each type of unit (first item), where X represents the unit type, and A i (t) takes a value of 0 or 1, indicating whether unit i is called at time t , ⁇ i (t) represents the scheduling cost of unit i at time t; the operating cost of each unit (second item), where C op (i) represents the operating cost of unit i; the fuel consumption cost of diesel generator ( Three), where P fuel (t) represents the output of the diesel engine at time t, V fuel (t) represents the price of diesel, and battery loss of the large energy storage system (fourth), of which Represents the discharge/charge power of a large energy storage system at time t, ⁇ represents the energy conversion efficiency of the large energy storage system; the cost of electricity purchase (fifth item), the revenue from electricity sales (sixth item), where P ex (t) , P im (t) respectively represents the purchase/sales power of the microgrid at time t, P
  • PSO particle Swarm Optimization
  • IPM Interior Point Method
  • the particle swarm optimization algorithm is a global optimization algorithm based on the whole. It is a simulation of migration and clustering behavior in the foraging process of birds.
  • the algorithm regards the habitat in the process of bird movement as a possible solution to the target problem. Each individual transfers information to each other, thereby guiding the entire group to move in the direction that may be the optimal solution, and continuously improving the possibility of finding a better solution in the process of moving.
  • Each bird is seen as a "particle" whose position and speed are updated as follows:
  • v ij (t) wv ij (t-1)+c 1 r 1 [pbest ij (t-1)-x ij (t-1)]+c 2 r 2 [gbest ij (t-1)-x Ij (t-1)]
  • ij is the motion trajectory of the particle
  • t is the number of iterations
  • v ij (t) and x ij (t) are the velocity and position of the particle at the t-th iteration, respectively
  • c 1 and c 2 are the most The learning factor of excellent pbest and global optimal gbest
  • r 1 and r 2 are random numbers between 0 and 1
  • w is the inertia weight of particle motion.
  • the interior point method is a method for solving constrained optimization propositions. Whether it is a linear programming proposition or a constrained quadratic programming problem, the interior point method shows quite excellent performance.
  • the interior point method belongs to the constraint optimization algorithm. The basic idea is to transform the constraint optimization problem into an unconstrained problem by introducing the utility function method, and then use the optimization iterative process to continuously update the utility function, so that the algorithm converges, and the optimal unit combination is obtained. After the time-of-use electricity price strategy is applied to the real-time operation of the microgrid, the purchase and sale power curve between the microgrid and the main network is obtained, as shown in Fig. 4. When the purchase and sale power curve is greater than zero, the microgrid purchases electricity from the main network.
  • the main network When less than zero, the main network is sold.
  • the purchase and sale power curve is distributed above the zero-scale line, that is, the purchase demand is greater than the power-selling capability, and after the proposed dispersion optimization is used, The sales curve is more evenly distributed on the zero mark line, that is, the power purchase demand is lower than that before the optimization, and the power sales capability is improved.
  • the present invention provides a new method for economical and rational operation of the microgrid, and uses historical supply and demand data as a powerful basis for optimizing the optimal unit combination and time-sharing electricity price, thereby effectively alleviating the operation of the microgrid.
  • the problem of excessive cost and excessive internal load shortage ensures that the user's power demand is well met. It provides a good reference for effectively promoting the construction and development of China's microgrid operation mode, and has practical promotion value, which is worth promoting.

Abstract

A demand side response-based microgrid optimal unit and time-of-use electricity price optimization method. The method comprises the following steps: 1) acquiring supply and demand historical data of a microgrid, wherein the data comprises internal load data of the microgrid, a supply situation of distributed renewable energy and basic information of various types of energy supply units; 2) using the historical data to analyze outputs of various energy supply systems so as to analyze a supply and demand situation inside the microgrid; 3) setting an optimized target function subjected to distributed optimization; and 4) using a particle swarm optimization algorithm and an interior-point method to carry out optimized resolution. By means of the method, the supply situation of renewable energy of a microgrid and a demand side response under the excitation of time-of-use electricity price are organically combined, and an optimal unit combination of the microgrid and a time-of-use electricity price strategy are subjected to distributed optimization by means of a particle swarm algorithm and an interior-point method, so that the problems of excessive operating costs of the microgrid and excessive lack of internal loads are effectively relieved, ensuring that the electricity usage demands of a user are well met.

Description

基于需求侧响应的微电网最优机组及分时电价的优化方法Optimization method of optimal unit and time-sharing electricity price of microgrid based on demand side response 技术领域Technical field
本发明涉及微电网最优机组组合及分时电价优化的技术领域，尤其是指一种基于需求侧响应的微电网最优机组及分时电价的优化方法。The invention relates to the technical field of optimal unit combination and time-sharing electricity price optimization of the micro-grid, in particular to an optimal method for the optimal unit and the time-sharing electricity price of the micro-grid based on the demand side response.
背景技术Background technique
截至2017年底，随着我国电力市场改革的推进和伴之而出的增量配网的推出，微电网作为增量配网的一种实际运行模式，受到了各界的高度关注和重视。微电网做为一种解决新能源就地消纳，提高新能源消纳率的一种可靠形式，其高效，安全运营是微电网建设需要关心的重点内容。As of the end of 2017, with the promotion of China's power market reform and the introduction of the incremental distribution network, the micro-grid as a practical operation mode of incremental distribution network has received great attention and attention from all walks of life. Microgrid is a reliable form to solve the problem of new energy in situ consumption and improve the consumption rate of new energy. Its high efficiency and safe operation are the key content of microgrid construction.
在微电网的运营过程当中，首先针对供能侧来说，电能的供给有一部分源于微电网所在地区的可再生能源，如风能，太阳能等可再生能源的转化。对于这一部分能源出力，风能和太阳能均存在出力随机性过高的问题，而没法保证在任何时候都满足负荷的需求，故时常存在弃风，弃光等问题。因此，为提高可再生能源的消纳率，充分保证微电网的绿色运营，有必要对风力发电机组，光伏发电机组进行合理的配置。与此同时，针对需求侧，越来越多的大型电力用户参与需求侧响应，即在用电高峰，电能供应不足的情况下适当调整自身的用电计划来响应供需变化，而与此相对应，在用电低谷阶段，增加可调节负荷的投入，以满足自身对电能消耗的需求。用户的响应在是在电价变化的刺激下进行的，因此对分时电价策略的合理优化有助于刺激电力用户在适当的时机调整用电计划，以同时保证用户侧的缺负荷量和微电网运营成本的最小化。In the operation process of the microgrid, first of all, for the energy supply side, the supply of electric energy is partly derived from the renewable energy of the region where the microgrid is located, such as the conversion of renewable energy such as wind energy and solar energy. For this part of the energy output, both wind and solar energy have problems of excessive randomness of output, and there is no guarantee that the load demand will be met at any time. Therefore, there are often problems such as abandoning wind and abandoning light. Therefore, in order to improve the consumption rate of renewable energy and fully guarantee the green operation of the microgrid, it is necessary to properly configure the wind turbine and photovoltaic generator set. At the same time, on the demand side, more and more large-scale power users are participating in the demand-side response, that is, in the case of peak electricity consumption and insufficient power supply, the power consumption plan is appropriately adjusted to respond to changes in supply and demand, and correspondingly In the stage of power consumption, increase the input of adjustable load to meet its own demand for power consumption. The user's response is motivated by the change in electricity price, so the rational optimization of the time-of-use pricing strategy helps to stimulate the power users to adjust the power plan at the right time to simultaneously ensure the user-side load and microgrid Minimize operating costs.
发明内容Summary of the invention
本发明的目的在于克服现有技术的缺点与不足，突破传统微电网运营成本不理想以及微电网用户侧的缺负荷量过高的问题，提出了一种基于需求侧响应的微电网最优机组及分时电价的优化方法，将微电网供能侧和需求侧有机结合 在一起，通过基于粒子群搜索算法和内点法分散优化来获得来针对供能侧的运营成本和需求侧的缺负荷量的最小化优化获得针对微电网最有利运营的最优机组组合以及分时电价策略，从而在满足需求侧用电需求的前提下最大程度地降低微电网的运营成本。The object of the present invention is to overcome the shortcomings and shortcomings of the prior art, to break through the problem that the operating cost of the traditional micro-grid is not ideal and the shortage of the micro-grid user side is too high, and propose an optimal unit of the micro-grid based on the demand-side response. And the method of optimizing the time-of-use electricity price, combining the energy supply side and the demand side of the micro grid, and obtaining the operation cost on the energy supply side and the load side on the demand side by using the particle swarm search algorithm and the interior point method distributed optimization. The minimization of quantity optimization obtains the optimal unit combination and the time-sharing price strategy for the most favorable operation of the micro-grid, so as to minimize the operating cost of the micro-grid under the premise of meeting the demand-side electricity demand.
为实现上述目的，本发明所提供的技术方案为：一种基于需求侧响应的微电网最优机组及分时电价的优化方法，所述微电网为含有可再生能源的典型微电网，由风力发电机组、光伏发电机组、柴油发电机组、大型储能系统供能，以所述微电网的总运行成本最低为第一优化目标，微电网缺负荷量最小为第二优化目标，基于微电网历史负荷数据、风能、太阳能这些分布式能源的历史数据，以获得微电网供需历史数据，通过粒子群搜索算法和内点法对两个优化目标进行分散优化；针对微电网总运行成本的目标函数考虑各类机组的调度成本，运营成本以及柴油机组的油耗成本，大型储能电池的转换效率，与此同时还将微电网与主网之间电能交换所引起的的购电成本考虑在内；运用电价来激励用户参与需求侧响应来降低微电网全年缺负荷总量，确保微电网内部供能得到满足；所述优化方法包括以下步骤：In order to achieve the above object, the technical solution provided by the present invention is: a method for optimizing an optimal unit of a microgrid and a time-of-use electricity price based on a demand side response, the micro grid being a typical micro grid containing renewable energy, by wind power The generator set, the photovoltaic generator set, the diesel generator set and the large-scale energy storage system are powered, and the minimum operating cost of the micro-grid is the first optimization goal, and the micro-grid lack of load is the second optimization target, based on the micro-grid history. The historical data of distributed energy such as load data, wind energy and solar energy are used to obtain the historical data of supply and demand of microgrid. The particle swarm optimization algorithm and interior point method are used to decentralize the optimization objectives. The objective function of the total operating cost of the microgrid is considered. The scheduling cost of various units, the operating cost and the fuel consumption cost of the diesel unit, the conversion efficiency of the large-scale energy storage battery, and at the same time take into account the cost of electricity purchase caused by the exchange of electricity between the micro-grid and the main network; Electricity price to encourage users to participate in the demand side response to reduce the total amount of power shortage in the microgrid throughout the year, ensuring the microgrid Energizing satisfied; The optimization method comprises the steps of:
1)获取微电网供需历史数据，包括微电网内部负荷数据，地区分布式可再生能源数据：风速、光强，各类发电机组：风力发电机、光伏发电机、柴油发电机和大型储能系统的机组参数；1) Obtain historical data of microgrid supply and demand, including microgrid internal load data, regional distributed renewable energy data: wind speed, light intensity, various generator sets: wind turbines, photovoltaic generators, diesel generators and large energy storage systems Unit parameters;
2)利用获取的数据和各机组出力模型计算出各供能系统的出力大小，进行微电网内部的电能供给和需求情况分析；2) Calculate the output of each energy supply system by using the acquired data and each unit output model, and analyze the power supply and demand situation inside the micro grid;
3)以微电网运营成本最低和需求侧的缺负荷量最小为两个分散优化的目标，设定目标函数；3) Set the objective function with the minimum operating cost of the microgrid and the minimum load on the demand side as the minimum of two decentralized optimization goals;
4)利用粒子群搜索算法Particle Swarm Optimization(PSO)和内点法Interior  Point Method(IPM)进行优化，求解出微电网的各供能系统最优机组组合和分时电价策略。4) Using Particle Swarm Optimization (PSO) and Interior Point Method (IPM) to optimize the optimal unit combination and time-of-use pricing strategy for each energy supply system in the microgrid.
在步骤1)中，所述微电网供需历史数据是指调度部门获取的微电网内部的负荷数据，气象部门获取的该地区的风速以及光强；同时还获取该微电网内部各类机组的基本参数。In step 1), the microgrid supply and demand historical data refers to the load data inside the microgrid obtained by the dispatching department, the wind speed and the light intensity of the region obtained by the meteorological department; and the basics of various types of units within the microgrid are also obtained. parameter.
在步骤2)中，所述供给和需求情况分为‘供’和‘需’两个方面：在供能方面，由于微电网是一个含分布式可再生能源的典型微电网，供能来源包括：风力发电机组、光伏发电机组、柴油发电机组以及微电网内部的由储能电池构成的大型储能系统，其中风力发电机组和光伏发电机组模型如下：In step 2), the supply and demand situation is divided into two aspects: 'supply' and 'need': in terms of energy supply, since the microgrid is a typical microgrid with distributed renewable energy, the energy source includes : Wind turbines, photovoltaic generator sets, diesel generator sets and large energy storage systems consisting of energy storage batteries inside the microgrid. The wind turbine and photovoltaic generator set models are as follows:
2.1)风力发电机组模型2.1) Wind turbine model
风力发电机组是利用风能的主要方式，通常情况不同类型的风力发电机组有不同的切入风速、额定风速和切出风速，风力发电机组的输出功率由风速来表示，即：Wind turbines are the main way to use wind energy. Generally, different types of wind turbines have different cut-in wind speeds, rated wind speeds and cut-out wind speeds. The output power of wind turbines is represented by wind speed, namely:
Figure PCTCN2018111211-appb-000001
Figure PCTCN2018111211-appb-000001
其中，P WT(t)是风力发电机组在t时刻的总输出功率，V(t)是该时刻对应的风速，V in、V R、V out分别是风力发电机组的切入、切出和额定风速，N WT是风力发电厂的风力发电机组数，P 0是单台风力发电机组的额定输出功率；为了准确地描述不同位置风力发电机组的出力情况，对不同高度的风力发电机组所吸收的风速进行如下转换： Where P WT (t) is the total output power of the wind turbine at time t, V(t) is the wind speed corresponding to the moment, and V in , V R , V out are the cut-in, cut-out and rated of the wind turbine respectively. Wind speed, N WT is the number of wind turbines in a wind power plant, P 0 is the rated output power of a single wind turbine; in order to accurately describe the output of wind turbines at different locations, the wind turbines absorbed by different heights The wind speed is converted as follows:
Figure PCTCN2018111211-appb-000002
Figure PCTCN2018111211-appb-000002
其中，V和V ref分别为在高度h和h ref时的风速，f为摩擦系数，通常白天取1/7，夜晚取1/2； Where V and V ref are the wind speeds at heights h and h ref , respectively, and f is the coefficient of friction, usually taking 1/7 during the day and 1/2 at night;
2.2)光伏发电机组模型2.2) Photovoltaic generator set model
光伏太阳能板吸收太阳能转化为直流电能，其转化过程受到太阳辐射强度以及环境、温度这些外界条件的影响，典型光伏太阳能板的输出功率表示为：Photovoltaic solar panels absorb solar energy into DC electrical energy. The conversion process is affected by solar radiation intensity and environmental and temperature conditions. The output power of typical photovoltaic solar panels is expressed as:
Figure PCTCN2018111211-appb-000003
Figure PCTCN2018111211-appb-000003
其中，P PV(t)是t时刻光伏发电机组的总输出功率，N PV是光伏太阳能板的数量，P PV0是一个光伏太阳能板的额定功率，T(t)和G(t)分别是t时刻的温度(25℃)和光照强度(1kW/m 2)，T 0和G 0分别是标准测试条件下的温度(25℃)和光照强度(1kW/m 2)，k PV是光伏温度系数； Where P PV (t) is the total output power of the photovoltaic generator set at time t, N PV is the number of photovoltaic solar panels, P PV0 is the rated power of a photovoltaic solar panel, T(t) and G(t) are respectively t Time temperature (25 ° C) and light intensity (1 kW / m 2 ), T 0 and G 0 are the temperature (25 ° C) and light intensity (1 kW / m 2 ) under standard test conditions, respectively, k PV is the temperature coefficient of photovoltaic ;
在从气象部门获得该地区风速、光强这些自然气象数据之后，根据新能源发电出力模型获得各系统的供能情况，并在此基础上通过柴油发电机组，大型储能系统和与主网之间的电能交流来满足微电网内部的供需平衡，与此同时，在需求侧方面，由于微电网模型的负荷包括一部分可进行需求侧响应的负荷类型，即：该类型负荷能够根据电价的变动来实时调整需求曲线，采用用户响应峰谷分时电价来量化负荷变动情况，以响应前后总负荷不变为前提，基于响应弹性矩阵M来说明用户对电价的响应情况，描述如下：After obtaining the natural meteorological data of wind speed and light intensity in the region from the meteorological department, the energy supply of each system is obtained according to the new energy power generation output model, and on this basis, the diesel generator set, the large energy storage system and the main network are adopted. The power exchange between the power grids meets the supply and demand balance within the microgrid. At the same time, on the demand side, the load of the microgrid model includes a part of the load type that can perform the demand side response, that is, the type of load can be based on the change of the electricity price. The demand curve is adjusted in real time, and the user response peak-to-valley time-of-use electricity price is used to quantify the load fluctuation. The response is based on the response elasticity matrix M to explain the user's response to the electricity price. The description is as follows:
Figure PCTCN2018111211-appb-000004
Figure PCTCN2018111211-appb-000004
其中，P TOU是采用峰谷电价之后的负荷；P L0是原始负荷，P f0、P p0、P g0和x 0分别是未采用峰谷电价之前，在负荷高峰段、平段和低谷段对应的负荷以及电价；x f、x p和x g分别表示采用峰谷电价之后，在负荷高峰段，平段和低谷段对 应的电价。 Among them, P TOU is the load after the peak-valley electricity price; P L0 is the original load, P f0 , P p0 , P g0 and x 0 are respectively corresponding to the peak load, the flat section and the low valley before the peak-valley electricity price is adopted. The load and the electricity price; x f , x p and x g respectively represent the electricity price corresponding to the peak section and the trough section after the peak-to-valley electricity price.
在步骤3)中，为满足微电网内部用户的用电需求的同时降低运营成本，将用户侧的缺负荷量最小和微电网运营成本最低分别设为分散优化的两个目标函数，具体描述如下：In step 3), in order to meet the power demand of the internal users of the microgrid and reduce the operating cost, the minimum load on the user side and the minimum operating cost of the micro grid are respectively set as two objective functions of the distributed optimization, as described below. :
3.1)用户侧的缺负荷量3.1) The amount of load on the user side
从用户侧出发，微电网运营应尽量保证较低的甩负荷/切负荷量，以满足用户的用电需求，故用户侧的缺负荷量最小为分散优化的第一优化目标：Starting from the user side, the microgrid operation should try to ensure a low load shedding/cutting load to meet the user's power demand. Therefore, the minimum load on the user side is the first optimization goal of decentralized optimization:
Figure PCTCN2018111211-appb-000005
Figure PCTCN2018111211-appb-000005
其中，Ω im是微电网需求侧缺负荷量，P im是微电网向主网的购电总量，x t是一天之内的实时电价，P L是微电网的总负荷，由于该分散优化用户侧有部分负荷属于可参与需求侧响应的负荷，故：微电网在t时刻的负荷总量与该时段的电价相关，P di(t)是柴油发电机组在t出力；P wt(t)、P pv(t)和P ba(t)分别是风力发电机组、光伏发电机组和大型储能系统在t时刻的出力，由于风力发电机组、光伏发电机组和大型储能系统在直流侧，故需考虑直流侧到交流侧的转换效率，表示为θ inv； Among them, Ω im is the micro-grid demand side lack of load, P im is the total amount of electricity purchased by the micro-grid to the main network, x t is the real-time electricity price within one day, and P L is the total load of the micro-grid, due to the dispersion optimization The part of the load on the user side belongs to the load that can participate in the demand side response. Therefore, the total load of the microgrid at time t is related to the electricity price of the time period. P di (t) is the output of the diesel generator set at t; P wt (t) , P pv (t) and P ba (t) are the output of wind turbines, photovoltaic generator sets and large energy storage systems at time t, respectively, because wind turbines, photovoltaic generator sets and large energy storage systems are on the DC side, so The conversion efficiency from the DC side to the AC side needs to be considered, expressed as θ inv ;
3.2)微电网运营成本3.2) Microgrid operating costs
从微电网运营经济性的角度出发，在满足用户侧用电需求的前提下应降低微电网运营成本，故：微电网运营成本最低为分散优化的第二优化目标，描述如下：From the perspective of micro-grid operation economy, the micro-grid operating cost should be reduced on the premise of satisfying the user-side power demand. Therefore, the micro-grid operating cost is the second optimization goal of decentralized optimization, which is described as follows:
Figure PCTCN2018111211-appb-000006
Figure PCTCN2018111211-appb-000006
其中，Ψ cos是微电网运营总成本，包括各类机组的调度成本、各类机组的运行成本、柴油发电机的耗油成本、大型储能系统的电池损耗、购电成本、售电收益；X表示机组类型；A i(t)取值为0或1，代表机组i在t时刻是否被调用；λ i(t)表示机组i在t时刻的调度成本；C op(i)表示机组i的运行成本；P fuel(t)表示t时刻柴油机的出力，V fuel(t)表示柴油的价格；
Figure PCTCN2018111211-appb-000007
表示大型储能系统在t时刻的放/充电功率，σ表示该大型储能系统的能量转换效率；P ex(t)，P im(t)分别表示微电网在t时刻的购/售电量，P imp(t)和P exp(t)分别表示t时刻的实时购、售电价格；各供能系统满足各自的出力约束。 Among them, Ψ cos is the total cost of microgrid operation, including the scheduling cost of various units, the operating cost of various units, the fuel consumption cost of diesel generators, the battery loss of large energy storage systems, the cost of electricity purchase, and the income from electricity sales; X represents the unit type; A i (t) takes a value of 0 or 1, which means that unit i is called at time t; λ i (t) represents the scheduling cost of unit i at time t; C op (i) represents unit i Operating cost; P fuel (t) represents the output of the diesel engine at time t, and V fuel (t) represents the price of diesel;
Figure PCTCN2018111211-appb-000007
Indicates the discharge/charge power of the large energy storage system at time t, σ represents the energy conversion efficiency of the large energy storage system; P ex (t), P im (t) respectively represent the purchase/sales power of the micro grid at time t, P imp (t) and P exp (t) respectively represent the real-time purchase and sale price at time t; each energy supply system satisfies its respective output constraints.
在步骤4)中，分别利用粒子群搜索算法Particle Swarm Optimization(PSO)和内点法Interior Point Method(IPM)进行分散优化求解；其中，粒子群搜索算法是一种基于全体的全局优化算法，是对鸟群觅食过程中的迁徙和群聚行为的模拟，该算法将鸟群运动过程中的栖息地看作目标问题中可能的解，每个个体间互相传递信息，从而引导整个群体向可能是最优解的方向移动，并在移动的过程中不断提高发现更好解的可能性；每一只鸟被看做是一个“粒子”，其自身的位置及速度分别按下式进行更新：In step 4), the particle swarm optimization algorithm Particle Swarm Optimization (PSO) and the interior point method (IPM) are used to perform the distributed optimization solution; wherein the particle swarm search algorithm is a global optimization algorithm based on the whole, For the simulation of migratory and clustering behavior during the foraging of birds, the algorithm regards the habitat in the process of bird movement as a possible solution to the target problem, and each individual transmits information to each other, thus guiding the whole group to possible It is the direction of the optimal solution, and the possibility of finding a better solution is continuously improved during the movement; each bird is regarded as a "particle", and its position and speed are updated as follows:
v ij(t)＝wv ij(t-1)+c 1r 1[pbest ij(t-1)-x ij(t-1)]+c 2r 2[gbest ij(t-1)-x ij(t-1)] v ij (t)=wv ij (t-1)+c 1 r 1 [pbest ij (t-1)-x ij (t-1)]+c 2 r 2 [gbest ij (t-1)-x Ij (t-1)]
其中，ij为粒子的运动轨迹，t为迭代次数，v ij(t)和x ij(t)分别为第t次迭代时粒子的速度和所处的位置，c 1、c 2分别为调节自身最优pbest和全局最优gbest的学习因子，r 1、r 2是0至1之间的随机数，w为粒子运动的惯性权重； Where ij is the motion trajectory of the particle, t is the number of iterations, v ij (t) and x ij (t) are the velocity and position of the particle at the t-th iteration, respectively, and c 1 and c 2 are respectively adjusting themselves. The learning factor of the optimal pbest and the global optimal gbest, r 1 and r 2 are random numbers between 0 and 1, and w is the inertia weight of the particle motion;
内点法是用于求解带约束的优化命题的方法，无论是面对线性规划命题还是带约束的二次规划问题，内点法都显示出极好的性能；内点法属于约束优化算法，基本思想是通过引入效用函数的方法将约束优化问题转换成无约束问题，再利用优化迭代过程不断地更新效用函数，以使得算法收敛；The interior point method is a method for solving constrained optimization propositions. Whether it is a linear programming proposition or a constrained quadratic programming problem, the interior point method shows excellent performance; the interior point method belongs to the constrained optimization algorithm. The basic idea is to transform the constrained optimization problem into an unconstrained problem by introducing a utility function, and then use the optimization iterative process to continuously update the utility function to make the algorithm converge;
在各个供能系统的出力约束下，由粒子群搜索算法来搜索求解得基于历史数据的各供能系统的最优机组组合，同时由内点法求得使得微电网缺负荷量最小的分时电价策略，极大程度得减小计算量，促进优化效率。Under the output constraints of each energy supply system, the particle swarm search algorithm is used to search for the optimal unit combination of each energy supply system based on historical data, and the time-point method is used to find the time-sharing that minimizes the micro-grid load loss. The electricity price strategy greatly reduces the amount of calculation and promotes optimization efficiency.
本发明与现有技术相比，具有如下优点与有益效果：Compared with the prior art, the present invention has the following advantages and beneficial effects:
1、本发明首次实现了微电网运营成本和用户缺负荷量最小化的有机结合，突破了传统‘源’和‘荷’之间的相对独立。1. The present invention realizes the organic combination of the micro-grid operating cost and the minimization of the user's lack of load for the first time, breaking through the relative independence between the traditional 'source' and 'charge'.
2、本发明首次实现了基于微电网所有历史数据，包括各类可再生能源的供给情况在内的供需情况分析，深入剖析了该微电网长期以来的历史供需情况。2. The present invention realizes for the first time the analysis of supply and demand based on all historical data of the microgrid, including the supply of various renewable energy sources, and deeply analyzes the long-term historical supply and demand of the microgrid.
3、本发明首次基于微电网长期的供需情况，利用智能算法，即粒子群搜索算法和内点法来求解该微电网运营最优机组组合和分时电价的分散优化，将二者合理，有效地结合在一起，极大地提高了优化速度。3. For the first time, based on the long-term supply and demand situation of microgrid, the invention uses intelligent algorithm, ie particle swarm search algorithm and interior point method to solve the optimal unit combination and time-sharing price optimization of the microgrid operation, which is reasonable and effective. The combination of the ground greatly improves the optimization speed.
4、本发明通过利用分时电价刺激用户提前调整自身用电计划来达到迎合该微电网内部供需情况的负荷曲线的目的，直接降低了微电网的购电成本。4. The present invention directly reduces the power purchase cost of the microgrid by using the time-sharing electricity price to stimulate the user to adjust the self-power plan in advance to meet the load curve of the supply and demand situation of the micro-grid.
5、本发明方法在电力市场进一步推广以及伴之而生的增量配网初露头角的现实条件下具有广泛的使用空间，该优化方法耗时段、效率高、适应性强，在降低微电网运营成本、降低用户侧的缺负荷量以及提高新能源消纳率上有广阔的前景。5. The method of the invention has wide use space under the realistic conditions of further promotion of the power market and the emerging incremental distribution network. The optimization method consumes time, high efficiency and adaptability, and reduces the operating cost of the micro grid. There is a broad prospect for reducing the amount of load on the user side and increasing the rate of new energy consumption.
附图说明DRAWINGS
图1为本发明搭建的微电网供能和典型负荷示意图。FIG. 1 is a schematic diagram of power supply and typical load of a microgrid built by the present invention.
图2为本发明逻辑流程示意图。2 is a schematic diagram of a logic flow of the present invention.
图3为本发明采集的微电网供需数据，包括该地区气象局采集的风速和光照强度数据以及历史负荷数据。FIG. 3 is a microgrid supply and demand data collected by the present invention, including wind speed and light intensity data collected by the regional meteorological bureau and historical load data.
图4为本发明通过分散优化前后得到的微电网的购售电曲线对比图。FIG. 4 is a comparison diagram of the purchase and sale electric power curves of the micro grid obtained before and after the dispersion optimization according to the present invention.
具体实施方式detailed description
下面结合具体实施例对本发明作进一步说明。The invention will now be further described in conjunction with specific embodiments.
如图1所示，本实施例所提供的基于需求侧响应的微电网最优机组及分时电价的优化方法，搭建了包含图1所示供需两侧参与者的典型微电网模型，其优化过程由图2逻辑流程示意图所示，包括以下步骤：As shown in FIG. 1 , the optimization method of the optimal unit and the time-sharing price of the micro-grid based on the demand side response provided by the embodiment provides a typical micro-grid model including the participants on both sides of the supply and demand shown in FIG. 1 and optimizes it. The process is illustrated by the logic flow diagram of Figure 2 and includes the following steps:
1)获取基础数据，包括微电网负荷历史数据、风速、光强等分布式可再生能源数据，其中，所述微电网负荷历史数据是指通过调度部门获取的该微电网地区长期以来的负荷数据，包括时间戳、负荷量等；所述风速、光强等信息是指气象部门获取的该地区风电场，光伏发电站所在地的风速和光强，为了能更清楚地表示该数据，图3历史数据均只画出了1000个采样点。1) Acquiring basic data, including historical data of microgrid load history, wind speed, light intensity, etc., wherein the microgrid load history data refers to long-term load data of the microgrid area obtained by the dispatching department. , including time stamp, load, etc.; the wind speed, light intensity and other information refers to the wind farm in the region obtained by the meteorological department, the wind speed and light intensity of the location of the photovoltaic power station, in order to more clearly represent the data, the history of Figure 3 The data only draws 1000 sampling points.
获取的各类型机组参数如表1所示：The parameters of each type of unit obtained are shown in Table 1:
表1各类型机组参数Table 1 various types of unit parameters
Figure PCTCN2018111211-appb-000008
Figure PCTCN2018111211-appb-000008
其中SOC是构成大型储能系统的储能电池参数，SOC表示电池的状态。The SOC is the energy storage battery parameter that constitutes a large energy storage system, and the SOC indicates the state of the battery.
2)获取风速、光强等数据后进行风力发电机组和光伏发电机组的出力计算，包括以下步骤：2) Calculate the output of wind turbines and PV generator sets after obtaining data such as wind speed and light intensity, including the following steps:
2.1)风力发电机组模型2.1) Wind turbine model
风力发电机组是利用风能的主要方式，通常情况不同类型的风力发电机组有不同的切入风速，额定风速和切出风速，风力发电机组的输出功率可由风速 来表示，即：Wind turbines are the main way to use wind energy. Generally, different types of wind turbines have different cut-in wind speeds, rated wind speeds and cut-out wind speeds. The output power of wind turbines can be expressed by wind speed, namely:
Figure PCTCN2018111211-appb-000009
Figure PCTCN2018111211-appb-000009
其中P WT(t)是风力发电机组在t时刻的总输出功率，V(t)是该时刻对应的风速，V in、V R、V out分别是风力发电机组的切入、切出和额定风速，N WT是风力发电厂的风力发电机组数，P 0是单台风力发电机组的额定输出功率；为了准确地描述不同位置风力发电机组的出力情况，对不同高度的风力发电机组所吸收的风速进行如下转换： Where P WT (t) is the total output power of the wind turbine at time t, V(t) is the wind speed corresponding to the moment, and V in , V R , V out are the cut-in, cut-out and rated wind speed of the wind turbine respectively. , N WT is the number of wind turbines in wind power plants, P 0 is the rated output power of a single wind turbine; in order to accurately describe the output of wind turbines at different locations, the wind speed absorbed by wind turbines of different heights Make the following conversion:
Figure PCTCN2018111211-appb-000010
Figure PCTCN2018111211-appb-000010
其中V和V ref分别为在高度h和h ref时的风速，f为摩擦系数，一般而言，白天取1/7，夜晚取1/2。 Where V and V ref are the wind speeds at heights h and h ref , respectively, and f is the coefficient of friction. Generally, 1/7 is taken during the day and 1/2 at night.
2.2)光伏发电机组模型2.2) Photovoltaic generator set model
光伏太阳能板吸收太阳能转化为直流电能，其转化过程受到太阳辐射强度以及环境，温度等外界条件的影响，典型光伏太阳能板的输出功率可表示为：Photovoltaic solar panels absorb solar energy into DC power, and the conversion process is affected by solar radiation intensity and environmental conditions, such as temperature and temperature. The output power of typical photovoltaic solar panels can be expressed as:
Figure PCTCN2018111211-appb-000011
Figure PCTCN2018111211-appb-000011
其中P PV(t)是t时刻光伏发电机组的总输出功率，N PV是光伏太阳能板的数量，P PV0是一个光伏太阳能板的额定功率，T(t)和G(t)分别是t时刻的温度(25℃)和光照强度(1kW/m 2)，T 0和G 0分别是标准测试条件下的温度(25℃)和光照强度(1kW/m 2)，k PV是光伏温度系数。 Where P PV (t) is the total output power of the photovoltaic generator set at time t, N PV is the number of photovoltaic solar panels, P PV0 is the rated power of a photovoltaic solar panel, and T(t) and G(t) are respectively t times Temperature (25 ° C) and light intensity (1 kW / m 2 ), T 0 and G 0 are the temperature (25 ° C) and light intensity (1 kW / m 2 ) under standard test conditions, respectively, and k PV is the temperature coefficient of photovoltaic.
在从气象部门获得该地区风速，光强等自然气象数据之后，根据如上的新 能源发电出力模型获得各系统的供能情况，并在此基础上通过柴油发电机组，大型储能系统和与主网之间的电能交流来满足微电网内部的供需平衡，与此同时，在需求侧方面，由于该微电网模型的负荷包括了一部分可进行需求侧响应的负荷类型，即：该类型负荷可以根据电价的变动来实时调整需求曲线，采用用户响应峰谷分时电价来量化负荷变动情况，以响应前后总负荷不变为前提，基于响应弹性矩阵M来说明用户对电价的响应情况，描述如下：After obtaining the natural meteorological data such as wind speed and light intensity from the meteorological department, the energy supply of each system is obtained according to the new energy generation output model as above, and on this basis, the diesel generator set, the large energy storage system and the main The power exchange between the networks meets the supply and demand balance within the microgrid. At the same time, on the demand side, since the load of the microgrid model includes a part of the load type that can perform the demand side response, that is, the type of load can be based on The price curve changes the demand curve in real time, and the user responds to the peak-to-valley time-of-use electricity price to quantify the load fluctuation. The response is based on the response elastic matrix M to describe the user's response to the electricity price. The description is as follows:
Figure PCTCN2018111211-appb-000012
Figure PCTCN2018111211-appb-000012
其中P TOU是采用峰谷电价之后的负荷，P L0是原始负荷，P f0，P p0，P g0和x 0分别是未采用峰谷电价之前，在负荷高峰段，平段和低谷段对应的负荷以及电价，x f，和x g分别表示采用峰谷电价之后，在负荷高峰段，平段和低谷段对应的电价。 Where P TOU is the load after peak-to-valley electricity price, P L0 is the original load, P f0 , P p0 , P g0 and x 0 are respectively corresponding to the peak load section, the flat section and the trough section before the peak-valley electricity price is not used. The load and the electricity price, x f , and x g respectively represent the electricity price corresponding to the peak section and the trough section after the peak-to-valley electricity price.
3)以微电网运营成本最低和需求侧的缺负荷量最小为两个分散优化的目标，设定目标函数，如图2所示的分散优化流程进行基于历史供需数据的分散优化，为满足微电网内部用户的用电需求的同时降低运营成本，将用户侧的缺负荷量最小和微电网运营成本最低分别设为分散优化的两个目标函数，具体描述如下：3) The minimum operating cost of the microgrid and the minimum load on the demand side are the two objectives of the decentralized optimization, and the objective function is set. The decentralized optimization process shown in Fig. 2 performs the decentralized optimization based on historical supply and demand data to satisfy the micro The electricity demand of the internal users of the power grid reduces the operating cost, and the minimum load on the user side and the minimum operating cost of the micro grid are respectively set as two objective functions of the distributed optimization. The specific description is as follows:
3.1)用户侧的缺负荷量3.1) The amount of load on the user side
从用户侧出发，微电网运营应尽量保证较低的甩负荷/切负荷量，以满足用户的用电需求，故用户侧的缺负荷量最小为分散优化的第一优化目标：Starting from the user side, the microgrid operation should try to ensure a low load shedding/cutting load to meet the user's power demand. Therefore, the minimum load on the user side is the first optimization goal of decentralized optimization:
Figure PCTCN2018111211-appb-000013
Figure PCTCN2018111211-appb-000013
其中Ω im是微电网需求侧的缺负荷量，P im是微电网向主网的购电总量，x t是 一天之内的实时电价，P L是微电网的总负荷，由于该分散优化用户侧有部分负荷属于可参与需求侧响应的负荷，故：微电网在t时刻的负荷总量与该时段的电价相关，P di(t)是柴油发电系统在t出力，P wt(t)、P pv(t)和P ba(t)分别是风力发电机组、光伏发电机组和大型储能系统在t时刻的出力，由于风力发电机组、光伏发电机组和大型储能系统在直流侧，故需考虑直流侧到交流侧的转换效率，表示为θ inv。 Where Ω im is the amount of power shortage on the demand side of the microgrid, P im is the total amount of electricity purchased by the micro grid to the main network, x t is the real-time electricity price within one day, and P L is the total load of the micro grid, due to the dispersion optimization The part of the load on the user side belongs to the load that can participate in the demand side response. Therefore, the total load of the microgrid at time t is related to the price of electricity during that period. P di (t) is the output of the diesel power generation system, P wt (t) , P pv (t) and P ba (t) are the output of wind turbines, photovoltaic generator sets and large energy storage systems at time t, respectively, because wind turbines, photovoltaic generator sets and large energy storage systems are on the DC side, so The conversion efficiency from the DC side to the AC side needs to be considered, expressed as θ inv .
3.2)微电网运营成本3.2) Microgrid operating costs
从微电网运营经济性的角度出发，在满足用户侧用电需求的前提下应降低微电网运营成本，故：微电网运营成本最低为分散优化的第二优化目标，描述如下：From the perspective of micro-grid operation economy, the micro-grid operating cost should be reduced on the premise of satisfying the user-side power demand. Therefore, the micro-grid operating cost is the second optimization goal of decentralized optimization, which is described as follows:
Figure PCTCN2018111211-appb-000014
Figure PCTCN2018111211-appb-000014
其中Ψ cos是微电网运营总成本，包括各类机组的调度成本(第一项)，其中X表示机组类型，A i(t)取值为0或1，代表机组i在t时刻是否被调用，λ i(t)表示机组i在t时刻的调度成本；各类机组的运行成本(第二项)，其中C op(i)表示机组i的运行成本；柴油发电机的耗油成本(第三项)，其中P fuel(t)表示t时刻柴油机的出力，V fuel(t)表示柴油的价格；大型储能系统的电池损耗(第四项)，其中
Figure PCTCN2018111211-appb-000015
表示大型储能系统在t时刻的放/充电功率，σ表示该大型储能系统的能量转换效率；购电成本(第五项)，售电收益(第六项)，其中P ex(t)，P im(t)分别表示微电网在t时刻的购/售电量，P imp(t)和P exp(t)分别表示t时刻的实时购、售电价格；各供能系统满足各自的出力约束。 Where Ψ cos is the total cost of microgrid operation, including the scheduling cost of each type of unit (first item), where X represents the unit type, and A i (t) takes a value of 0 or 1, indicating whether unit i is called at time t , λ i (t) represents the scheduling cost of unit i at time t; the operating cost of each unit (second item), where C op (i) represents the operating cost of unit i; the fuel consumption cost of diesel generator ( Three), where P fuel (t) represents the output of the diesel engine at time t, V fuel (t) represents the price of diesel, and battery loss of the large energy storage system (fourth), of which
Figure PCTCN2018111211-appb-000015
Represents the discharge/charge power of a large energy storage system at time t, σ represents the energy conversion efficiency of the large energy storage system; the cost of electricity purchase (fifth item), the revenue from electricity sales (sixth item), where P ex (t) , P im (t) respectively represents the purchase/sales power of the microgrid at time t, P imp (t) and P exp (t) respectively represent the real-time purchase and sale price at time t; each energy supply system satisfies its respective output constraint.
4)分别利用粒子群搜索算法Particle Swarm Optimization(PSO)和内点法 Interior Point Method(IPM)进行分散优化求解。4) The particle swarm optimization algorithm Particle Swarm Optimization (PSO) and interior point method Interior Point Method (IPM) are used to solve the distributed optimization.
粒子群搜索算法是一种基于全体的全局优化算法，是对鸟群觅食过程中的迁徙和群聚行为的模拟，该算法将鸟群运动过程中的栖息地看作目标问题中可能的解，每个个体间互相传递信息，从而引导整个群体向可能是最优解的方向移动，并在移动的过程中不断提高发现较好解的可能性。每一只鸟被看做是一个“粒子”，其自身的位置及速度分别按下式进行更新：The particle swarm optimization algorithm is a global optimization algorithm based on the whole. It is a simulation of migration and clustering behavior in the foraging process of birds. The algorithm regards the habitat in the process of bird movement as a possible solution to the target problem. Each individual transfers information to each other, thereby guiding the entire group to move in the direction that may be the optimal solution, and continuously improving the possibility of finding a better solution in the process of moving. Each bird is seen as a "particle" whose position and speed are updated as follows:
v ij(t)＝wv ij(t-1)+c 1r 1[pbest ij(t-1)-x ij(t-1)]+c 2r 2[gbest ij(t-1)-x ij(t-1)] v ij (t)=wv ij (t-1)+c 1 r 1 [pbest ij (t-1)-x ij (t-1)]+c 2 r 2 [gbest ij (t-1)-x Ij (t-1)]
其中ij为粒子的运动轨迹，t为迭代次数，v ij(t)和x ij(t)分别为第t次迭代时粒子的速度和所处的位置，c 1、c 2分别为调节自身最优pbest和全局最优gbest的学习因子，r 1、r 2是0至1之间的随机数，w为粒子运动的惯性权重。 Where ij is the motion trajectory of the particle, t is the number of iterations, v ij (t) and x ij (t) are the velocity and position of the particle at the t-th iteration, respectively, and c 1 and c 2 are the most The learning factor of excellent pbest and global optimal gbest, r 1 and r 2 are random numbers between 0 and 1, and w is the inertia weight of particle motion.
内点法是用于求解带约束的优化命题的方法，无论是面对线性规划命题还是带约束的二次规划问题，内点法都显示出了相当的极好的性能。内点法属于约束优化算法，基本思想是通过引入效用函数的方法将约束优化问题转换成无约束问题，再利用优化迭代过程不断地更新效用函数，以使得算法收敛，在获得最优机组组合和分时电价策略之后运用到微电网实时运行当中，得到微电网与主网之间的购售电曲线图，如图4所示，其中购售电曲线大于零时表示微电网向主网购电，小于零时向主网售电。在使用提出的分散优化之前，利用传统的基于经验的机组组合和电价方案，其购售电曲线分布在零刻度线以上，即购电需求大于售电能力，在使用提出的分散优化之后，购售电曲线较为均衡地分布在零刻度线上下，即购电需求比优化之前降低的同时售电能力得到了提升。The interior point method is a method for solving constrained optimization propositions. Whether it is a linear programming proposition or a constrained quadratic programming problem, the interior point method shows quite excellent performance. The interior point method belongs to the constraint optimization algorithm. The basic idea is to transform the constraint optimization problem into an unconstrained problem by introducing the utility function method, and then use the optimization iterative process to continuously update the utility function, so that the algorithm converges, and the optimal unit combination is obtained. After the time-of-use electricity price strategy is applied to the real-time operation of the microgrid, the purchase and sale power curve between the microgrid and the main network is obtained, as shown in Fig. 4. When the purchase and sale power curve is greater than zero, the microgrid purchases electricity from the main network. When less than zero, the main network is sold. Before using the proposed dispersion optimization, using the traditional experience-based unit combination and electricity price scheme, the purchase and sale power curve is distributed above the zero-scale line, that is, the purchase demand is greater than the power-selling capability, and after the proposed dispersion optimization is used, The sales curve is more evenly distributed on the zero mark line, that is, the power purchase demand is lower than that before the optimization, and the power sales capability is improved.
使用上述优化方法之后，日平均购售电量及微电网日平均运营成本如表2所示：After using the above optimization method, the average daily purchase and sale of electricity and the daily average operating cost of the microgrid are shown in Table 2:
表2各类型机组参数Table 2 Various types of unit parameters
Figure PCTCN2018111211-appb-000016
Figure PCTCN2018111211-appb-000016
本发明优化方法具有以下优点：The optimization method of the invention has the following advantages:
1、将最优机组组合和分时电价策略有机结合，分散处理；1. Organically combine the optimal unit combination and the time-sharing electricity price strategy, and disperse the processing;
2、克服传统的基于经验的机组组合和峰谷电价方案；2. Overcoming the traditional experience-based unit combination and peak-to-valley price plan;
3、针对不同的可再生能源供给情况获得不同的风力发电机组和光伏发电机组的出力情况之后再进行分时电价策略的优化，使得分时电价策略更优；3. Optimize the output of different wind turbines and photovoltaic generator sets for different renewable energy supply situations, and then optimize the time-of-use electricity price strategy to make the time-of-use electricity price strategy better;
4、用户进行需求侧响应的效果更加突出有效。4. The effect of the user's demand side response is more prominent and effective.
综上所述，在采用以上方案后，本发明为微电网的经济合理运营提供了新的方法，将历史供需数据作为优化最优机组组合和分时电价的有力根据，从而有效缓解微电网运营成本过高，内部缺负荷量过大的问题，确保了用户的用电需求得到了很好的满足。为有效推动我国微电网运营模式的建设和发展提供了良好的借鉴，具有实际推广价值，值得推广。In summary, after adopting the above scheme, the present invention provides a new method for economical and rational operation of the microgrid, and uses historical supply and demand data as a powerful basis for optimizing the optimal unit combination and time-sharing electricity price, thereby effectively alleviating the operation of the microgrid. The problem of excessive cost and excessive internal load shortage ensures that the user's power demand is well met. It provides a good reference for effectively promoting the construction and development of China's microgrid operation mode, and has practical promotion value, which is worth promoting.
以上所述实施例只为本发明之较佳实施例，并非以此限制本发明的实施范围，故凡依本发明之形状、原理所作的变化，均应涵盖在本发明的保护范围内。The above-mentioned embodiments are only the preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Therefore, variations in the shapes and principles of the present invention are intended to be included within the scope of the present invention.

Claims (5)

  1. 基于需求侧响应的微电网最优机组及分时电价的优化方法，其特征在于：所述微电网为含有可再生能源的典型微电网，由风力发电机组、光伏发电机组、柴油发电机组、大型储能系统供能，以所述微电网的总运行成本最低为第一优化目标，微电网缺负荷量最小为第二优化目标，基于微电网历史负荷数据、风能、太阳能这些分布式能源的历史数据，以获得微电网供需历史数据，通过粒子群搜索算法和内点法对两个优化目标进行分散优化；针对微电网总运行成本的目标函数考虑各类机组的调度成本，运营成本以及柴油机组的油耗成本，大型储能电池的转换效率，与此同时还将微电网与主网之间电能交换所引起的的购电成本考虑在内；运用电价来激励用户参与需求侧响应来降低微电网全年缺负荷总量，确保微电网内部供能得到满足；所述优化方法包括以下步骤：An optimal method for optimizing the optimal power generation unit and the time-sharing electricity price based on the demand side response, wherein the micro-grid is a typical micro-grid containing renewable energy, consisting of a wind turbine, a photovoltaic generator set, a diesel generator set, and a large-scale The energy storage system is energized, and the minimum operating cost of the microgrid is the first optimization goal, and the microgrid lack of load is the second optimization goal. The history of distributed energy based on historical load data, wind energy and solar energy of the microgrid Data to obtain historical data of supply and demand of microgrid, decentralized optimization of two optimization targets by particle swarm optimization algorithm and interior point method; considering the scheduling cost, operating cost and diesel unit of various units for the objective function of total operating cost of microgrid The fuel consumption cost, the conversion efficiency of large energy storage batteries, while taking into account the cost of electricity purchase caused by the exchange of electricity between the microgrid and the main network; using electricity prices to motivate users to participate in the demand side response to reduce the microgrid The total amount of load is lost throughout the year to ensure that the internal energy supply of the microgrid is satisfied; the optimization method includes Steps:
    1)获取微电网供需历史数据，包括微电网内部负荷数据，地区分布式可再生能源数据：风速、光强，各类发电机组：风力发电机、光伏发电机、柴油发电机和大型储能系统的机组参数；1) Obtain historical data of microgrid supply and demand, including microgrid internal load data, regional distributed renewable energy data: wind speed, light intensity, various generator sets: wind turbines, photovoltaic generators, diesel generators and large energy storage systems Unit parameters;
    2)利用获取的数据和各机组出力模型计算出各供能系统的出力大小，进行微电网内部的电能供给和需求情况分析；2) Calculate the output of each energy supply system by using the acquired data and each unit output model, and analyze the power supply and demand situation inside the micro grid;
    3)以微电网运营成本最低和需求侧的缺负荷量最小为两个分散优化的目标，设定目标函数；3) Set the objective function with the minimum operating cost of the microgrid and the minimum load on the demand side as the minimum of two decentralized optimization goals;
    4)利用粒子群搜索算法Particle Swarm Optimization即PSO和内点法Interior Point Method即IPM进行优化，求解出微电网的各供能系统最优机组组合和分时电价策略。4) Using Particle Swarm Optimization (Partial Swarm Optimization) PSO and Interior Point Method (IPM) to optimize the optimal unit combination and time-of-use pricing strategy for each energy supply system of the microgrid.
  2. 根据权利要求1所述的基于需求侧响应的微电网最优机组及分时电价的优化方法，其特征在于：在步骤1)中，所述微电网供需历史数据是指调度部门获取的微电网内部的负荷数据，气象部门获取的该地区的风速以及光强；同时 还获取该微电网内部各类机组的基本参数。The method for optimizing an optimal unit of a microgrid and a time-of-use electricity price based on demand side response according to claim 1, wherein in step 1), the historical data of supply and demand of the micro grid refers to a microgrid acquired by the dispatching department. The internal load data, the wind speed and light intensity of the area obtained by the meteorological department; and the basic parameters of various units within the micro-grid are also obtained.
  3. 根据权利要求1所述的基于需求侧响应的微电网最优机组及分时电价的优化方法，其特征在于：在步骤2)中，所述供给和需求情况分为‘供’和‘需’两个方面：在供能方面，由于微电网是一个含分布式可再生能源的典型微电网，供能来源包括：风力发电机组、光伏发电机组、柴油发电机组以及微电网内部的由储能电池构成的大型储能系统，其中风力发电机组和光伏发电机组模型如下：The method according to claim 1, wherein the supply and demand conditions are divided into 'supply' and 'need' in step 2). Two aspects: In terms of energy supply, since the microgrid is a typical microgrid with distributed renewable energy sources, the energy sources include: wind turbines, photovoltaic generator sets, diesel generator sets, and energy storage batteries inside the microgrid. A large-scale energy storage system is constructed, in which the wind turbine and photovoltaic generator set models are as follows:
    2.1)风力发电机组模型2.1) Wind turbine model
    风力发电机组是利用风能的主要方式，通常情况不同类型的风力发电机组有不同的切入风速、额定风速和切出风速，风力发电机组的输出功率由风速来表示，即：Wind turbines are the main way to use wind energy. Generally, different types of wind turbines have different cut-in wind speeds, rated wind speeds and cut-out wind speeds. The output power of wind turbines is represented by wind speed, namely:
    Figure PCTCN2018111211-appb-100001
    Figure PCTCN2018111211-appb-100001
    其中，P WT(t)是风力发电机组在t时刻的总输出功率，V(t)是该时刻对应的风速，V in、V R、V out分别是风力发电机组的切入、切出和额定风速，N WT是风力发电厂的风力发电机组数，P 0是单台风力发电机组的额定输出功率；为了准确地描述不同位置风力发电机组的出力情况，对不同高度的风力发电机组所吸收的风速进行如下转换： Where P WT (t) is the total output power of the wind turbine at time t, V(t) is the wind speed corresponding to the moment, and V in , V R , V out are the cut-in, cut-out and rated of the wind turbine respectively. Wind speed, N WT is the number of wind turbines in a wind power plant, P 0 is the rated output power of a single wind turbine; in order to accurately describe the output of wind turbines at different locations, the wind turbines absorbed by different heights The wind speed is converted as follows:
    Figure PCTCN2018111211-appb-100002
    Figure PCTCN2018111211-appb-100002
    其中，V和V ref分别为在高度h和h ref时的风速，f为摩擦系数，通常白天取1/7，夜晚取1/2； Where V and V ref are the wind speeds at heights h and h ref , respectively, and f is the coefficient of friction, usually taking 1/7 during the day and 1/2 at night;
    2.2)光伏发电机组模型2.2) Photovoltaic generator set model
    光伏太阳能板吸收太阳能转化为直流电能，其转化过程受到太阳辐射强度以及环境、温度这些外界条件的影响，典型光伏太阳能板的输出功率表示为：Photovoltaic solar panels absorb solar energy into DC electrical energy. The conversion process is affected by solar radiation intensity and environmental and temperature conditions. The output power of typical photovoltaic solar panels is expressed as:
    Figure PCTCN2018111211-appb-100003
    Figure PCTCN2018111211-appb-100003
    其中，P PV(t)是t时刻光伏发电机组的总输出功率，N PV是光伏太阳能板的数量，P PV0是一个光伏太阳能板的额定功率，T(t)和G(t)分别是t时刻的温度和光照强度，T 0和G 0分别是标准测试条件下的温度和光照强度，k PV是光伏温度系数； Where P PV (t) is the total output power of the photovoltaic generator set at time t, N PV is the number of photovoltaic solar panels, P PV0 is the rated power of a photovoltaic solar panel, T(t) and G(t) are respectively t Temperature and light intensity at the moment, T 0 and G 0 are the temperature and light intensity under standard test conditions, respectively, and k PV is the temperature coefficient of photovoltaic;
    在从气象部门获得该地区风速、光强这些自然气象数据之后，根据新能源发电出力模型获得各系统的供能情况，并在此基础上通过柴油发电机组，大型储能系统和与主网之间的电能交流来满足微电网内部的供需平衡，与此同时，在需求侧方面，由于微电网模型的负荷包括一部分可进行需求侧响应的负荷类型，即：该类型负荷能够根据电价的变动来实时调整需求曲线，采用用户响应峰谷分时电价来量化负荷变动情况，以响应前后总负荷不变为前提，基于响应弹性矩阵M来说明用户对电价的响应情况，描述如下：After obtaining the natural meteorological data of wind speed and light intensity in the region from the meteorological department, the energy supply of each system is obtained according to the new energy power generation output model, and on this basis, the diesel generator set, the large energy storage system and the main network are adopted. The power exchange between the power grids meets the supply and demand balance within the microgrid. At the same time, on the demand side, the load of the microgrid model includes a part of the load type that can perform the demand side response, that is, the type of load can be based on the change of the electricity price. The demand curve is adjusted in real time, and the user response peak-to-valley time-of-use electricity price is used to quantify the load fluctuation. The response is based on the response elasticity matrix M to explain the user's response to the electricity price. The description is as follows:
    Figure PCTCN2018111211-appb-100004
    Figure PCTCN2018111211-appb-100004
    其中，P TOU是采用峰谷电价之后的负荷；P L0是原始负荷，P f0、P p0、P g0和x 0分别是未采用峰谷电价之前，在负荷高峰段、平段和低谷段对应的负荷以及电价；x f、x p和x g分别表示采用峰谷电价之后，在负荷高峰段，平段和低谷段对应的电价。 Among them, P TOU is the load after the peak-valley electricity price; P L0 is the original load, P f0 , P p0 , P g0 and x 0 are respectively corresponding to the peak load, the flat section and the low valley before the peak-valley electricity price is adopted. The load and the electricity price; x f , x p and x g respectively represent the electricity price corresponding to the peak section and the trough section after the peak-to-valley electricity price.
  4. 根据权利要求1所述的基于需求侧响应的微电网最优机组及分时电价的优化方法，其特征在于：在步骤3)中，为满足微电网内部用户的用电需求的同 时降低运营成本，将用户侧的缺负荷量最小和微电网运营成本最低分别设为分散优化的两个目标函数，具体描述如下：The method for optimizing an optimal unit of a microgrid and a time-of-use electricity price based on demand side response according to claim 1, wherein in step 3), the operating cost is reduced while satisfying the power demand of the internal users of the micro grid. The minimum load on the user side and the minimum operating cost of the microgrid are respectively set as two objective functions of the decentralized optimization, as described below:
    3.1)用户侧的缺负荷量3.1) The amount of load on the user side
    从用户侧出发，微电网运营应尽量保证较低的甩负荷/切负荷量，以满足用户的用电需求，故用户侧的缺负荷量最小为分散优化的第一优化目标：Starting from the user side, the microgrid operation should try to ensure a low load shedding/cutting load to meet the user's power demand. Therefore, the minimum load on the user side is the first optimization goal of decentralized optimization:
    Figure PCTCN2018111211-appb-100005
    Figure PCTCN2018111211-appb-100005
    其中，Ω im是微电网需求侧缺负荷量，P im是微电网向主网的购电总量，x t是一天之内的实时电价，P L是微电网的总负荷，由于该分散优化用户侧有部分负荷属于可参与需求侧响应的负荷，故：微电网在t时刻的负荷总量与该时段的电价相关，P di(t)是柴油发电机组在t出力；P wt(t)、P pv(t)和P ba(t)分别是风力发电机组、光伏发电机组和大型储能系统在t时刻的出力，由于风力发电机组、光伏发电机组和大型储能系统在直流侧，故需考虑直流侧到交流侧的转换效率，表示为θ inv； Among them, Ω im is the micro-grid demand side lack of load, P im is the total amount of electricity purchased by the micro-grid to the main network, x t is the real-time electricity price within one day, and P L is the total load of the micro-grid, due to the dispersion optimization The part of the load on the user side belongs to the load that can participate in the demand side response. Therefore, the total load of the microgrid at time t is related to the electricity price of the time period. P di (t) is the output of the diesel generator set at t; P wt (t) , P pv (t) and P ba (t) are the output of wind turbines, photovoltaic generator sets and large energy storage systems at time t, respectively, because wind turbines, photovoltaic generator sets and large energy storage systems are on the DC side, so The conversion efficiency from the DC side to the AC side needs to be considered, expressed as θ inv ;
    3.2)微电网运营成本3.2) Microgrid operating costs
    从微电网运营经济性的角度出发，在满足用户侧用电需求的前提下应降低微电网运营成本，故：微电网运营成本最低为分散优化的第二优化目标，描述如下：From the perspective of micro-grid operation economy, the micro-grid operating cost should be reduced on the premise of satisfying the user-side power demand. Therefore, the micro-grid operating cost is the second optimization goal of decentralized optimization, which is described as follows:
    Figure PCTCN2018111211-appb-100006
    Figure PCTCN2018111211-appb-100006
    其中，Ψ cos是微电网运营总成本，包括各类机组的调度成本、各类机组的运行成本、柴油发电机的耗油成本、大型储能系统的电池损耗、购电成本、售电 收益；X表示机组类型；A i(t)取值为0或1，代表机组i在t时刻是否被调用；λ i(t)表示机组i在t时刻的调度成本；C op(i)表示机组i的运行成本；P fuel(t)表示t时刻柴油机的出力，V fuel(t)表示柴油的价格；
    Figure PCTCN2018111211-appb-100007
    表示大型储能系统在t时刻的放/充电功率，σ表示该大型储能系统的能量转换效率；P ex(t)，P im(t)分别表示微电网在t时刻的购/售电量，P imp(t)和P exp(t)分别表示t时刻的实时购、售电价格；各供能系统满足各自的出力约束。     Among them, Ψ cos is the total cost of microgrid operation, including the scheduling cost of various units, the operating cost of various units, the fuel consumption cost of diesel generators, the battery loss of large energy storage systems, the cost of electricity purchase, and the income from electricity sales; X represents the unit type; A i (t) takes a value of 0 or 1, which means that unit i is called at time t; λ i (t) represents the scheduling cost of unit i at time t; C op (i) represents unit i Operating cost; P fuel (t) represents the output of the diesel engine at time t, and V fuel (t) represents the price of diesel;
    Figure PCTCN2018111211-appb-100007
    Indicates the discharge/charge power of the large energy storage system at time t, σ represents the energy conversion efficiency of the large energy storage system; P ex (t), P im (t) respectively represent the purchase/sales power of the micro grid at time t, P imp (t) and P exp (t) respectively represent the real-time purchase and sale price at time t; each energy supply system satisfies its respective output constraints.
  5. 根据权利要求1所述的基于需求侧响应的微电网最优机组及分时电价的优化方法，其特征在于：在步骤4)中，分别利用粒子群搜索算法Particle Swarm Optimization即PSO和内点法Interior Point Method即IPM进行分散优化求解；其中，粒子群搜索算法是一种基于全体的全局优化算法，是对鸟群觅食过程中的迁徙和群聚行为的模拟，该算法将鸟群运动过程中的栖息地看作目标问题中可能的解，每个个体间互相传递信息，从而引导整个群体向可能是最优解的方向移动，并在移动的过程中不断提高发现更好解的可能性；每一只鸟被看做是一个“粒子”，其自身的位置及速度分别按下式进行更新：The method for optimizing an optimal unit of a microgrid and a time-of-use electricity price based on demand side response according to claim 1, wherein in step 4), a particle swarm optimization algorithm, Particle Swarm Optimization, PSO and interior point method are respectively used. Interior Point Method is IPM for distributed optimization. Among them, particle swarm search algorithm is a global optimization algorithm based on the whole, which is a simulation of migration and clustering behavior in the foraging process of birds. The habitat in the middle is regarded as a possible solution in the target problem. Each individual transmits information to each other, which guides the whole group to move in the direction of the possible optimal solution, and continuously improves the possibility of finding a better solution in the process of moving. Each bird is seen as a "particle" whose position and speed are updated as follows:
    v ij(t)＝wv ij(t-1)+c 1r 1[pbest ij(t-1)-x ij(t-1)]+c 2r 2[gbest ij(t-1)-x ij(t-1)] v ij (t)=wv ij (t-1)+c 1 r 1 [pbest ij (t-1)-x ij (t-1)]+c 2 r 2 [gbest ij (t-1)-x Ij (t-1)]
    其中，ij为粒子的运动轨迹，t为迭代次数，v ij(t)和x ij(t)分别为第t次迭代时粒子的速度和所处的位置，c 1、c 2分别为调节自身最优pbest和全局最优gbest的学习因子，r 1、r 2是0至1之间的随机数，w为粒子运动的惯性权重； Where ij is the motion trajectory of the particle, t is the number of iterations, v ij (t) and x ij (t) are the velocity and position of the particle at the t-th iteration, respectively, and c 1 and c 2 are respectively adjusting themselves. The learning factor of the optimal pbest and the global optimal gbest, r 1 and r 2 are random numbers between 0 and 1, and w is the inertia weight of the particle motion;
    内点法是用于求解带约束的优化命题的方法，无论是面对线性规划命题还是带约束的二次规划问题，内点法都显示出极好的性能；内点法属于约束优化算法，基本思想是通过引入效用函数的方法将约束优化问题转换成无约束问题，再利用优化迭代过程不断地更新效用函数，以使得算法收敛；The interior point method is a method for solving constrained optimization propositions. Whether it is a linear programming proposition or a constrained quadratic programming problem, the interior point method shows excellent performance; the interior point method belongs to the constrained optimization algorithm. The basic idea is to transform the constrained optimization problem into an unconstrained problem by introducing a utility function, and then use the optimization iterative process to continuously update the utility function to make the algorithm converge;
    在各个供能系统的出力约束下，由粒子群搜索算法来搜索求解得基于历史 数据的各供能系统的最优机组组合，同时由内点法求得使得微电网缺负荷量最小的分时电价策略，极大程度得减小计算量，促进优化效率。Under the output constraints of each energy supply system, the particle swarm search algorithm is used to search for the optimal unit combination of each energy supply system based on historical data, and the time-point method is used to find the time-sharing that minimizes the micro-grid load loss. The electricity price strategy greatly reduces the amount of calculation and promotes optimization efficiency.
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