CN107134810B - Independent micro-energy-grid energy storage system optimal configuration solving method - Google Patents
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Abstract
The invention discloses an optimal configuration solving method for an independent micro energy network energy storage system, which comprises the following steps: establishing a self-sufficient probability demand of the micro energy network; establishing an energy storage system optimal configuration model of the micro energy network; aiming at an optimal configuration model of the energy storage system, estimating the service life of the storage battery by adopting a rain flow counting method to obtain an investment coefficient; and (3) adopting a proper operation strategy in the simulation operation process by predicting annual load data and wind power data, and selecting a traversal algorithm or a particle swarm algorithm according to an optimization range to seek the optimal energy storage system configuration. The invention provides self-sufficient probability requirements and pursues system stability while meeting the economy; the system stability is maintained while the economic optimization is achieved, and a balance point is found among the investment cost, the operation cost and the pollution control cost.
Description
Technical Field
The invention relates to the technical field of planning of independent micro energy network energy storage systems, in particular to an optimal configuration solving method for two aspects of independent micro energy network electricity energy storage and heat energy storage, and particularly relates to an optimal configuration solving method for an independent micro energy network energy storage system.
Background
With the gradual depletion of traditional fossil energy, environmental problems and the increasing severity of global warming problems, low-carbon new energy represented by wind and light is vigorously developed, and the improvement of the permeability of renewable energy of the existing power grid becomes one of important ways for solving the problems. Thus, the energy internet concept proposed by jirimm-rifugin has attracted widespread attention. The micro-energy network can be applied to micro-energy networks in areas such as factories, large buildings, urban and rural concentrated living areas, isolated islands and the like, and is one of the trends of future energy system development as an important composition form of an energy internet. The concept of the micro energy network is developed on the concept of a micro power grid, generally comprises 4 energy forms of cold, heat, electricity and gas, and all energy supply equipment in a region are uniformly integrated and scheduled by using the internet of things technology and the information technology so as to optimize energy supply for the cold, heat and electricity loads in the region and improve the energy utilization efficiency.
However, the renewable energy represented by wind and light has strong intermittent and random fluctuation, and phenomena such as wind and light abandonment are often caused. Especially during the heat supply period, the peak regulation capacity of the whole micro-energy grid electric energy can be reduced by the 'fixing the power by heat' operation mode of the cogeneration unit, and even a large amount of 'abandoned wind' is caused. To take up renewable energy and enhance the flexibility of micro energy networks, we introduce energy storage systems in micro energy networks that can be used for multi-energy storage. The invention relates to an independent micro-energy-grid energy storage system which comprises two forms of electricity storage and heat storage.
In summary, in the existing micro energy grid framework, it is necessary to invent an optimal configuration method for the energy storage system, so as to achieve the purposes of achieving optimal economy and reducing loss of the whole system under a certain stability.
Disclosure of Invention
The invention aims to provide an optimal configuration solving method for an independent micro energy network energy storage system, so that the independent micro energy network energy storage system can achieve economic optimization in the independent micro energy network, and meanwhile, the stability of the system is maintained.
Energy storage systems are divided into electrical energy storage systems (batteries) and thermal energy storage systems, and the configuration of the energy storage system includes a power configuration and a capacity configuration.
The storage battery energy storage system comprises a storage battery, a converter and other equipment, so that investment cost is settled in two forms of power and capacity respectively. The heat storage system comprises a heat storage tank, a heat conduction material and the like, so the investment cost is also settled in two forms of power and capacity.
In order to solve the technical problems, the following technical scheme is proposed to realize:
in the independent micro-energy-grid energy storage system, the configuration of the energy storage system influences the investment cost of the energy storage system, the operation cost of the whole micro-grid and the pollution control cost; the lower energy storage system can not achieve the expected economical efficiency and stability of the system, the operation cost can not be effectively reduced, and the discharged CO can not be effectively reduced2And harmful gasesThe volume content is higher; the higher energy storage system is configured, so that the investment cost is higher, and the overall maintenance cost is also relatively higher; therefore, in the method for solving the optimal configuration of the independent micro energy network energy storage system, the configuration selection of the optimal energy storage system can reach the balance among the investment cost, the operation cost and the pollution control cost, and the sum of the investment cost, the operation cost and the pollution control cost is found at a balance point, namely the configuration of the energy storage system with the minimum total cost.
The method comprises the following steps:
step 1, establishing self-sufficiency probability requirements of a micro energy network;
step 2, establishing an energy storage system optimal configuration model of the micro energy network;
step 3, aiming at the optimal configuration model of the energy storage system, estimating the service life of the storage battery by adopting a rain flow counting method to obtain an investment coefficient;
and 4, adopting a proper operation strategy in the simulation operation process by predicting the annual load data and the wind power data, and selecting a traversal algorithm or a particle swarm algorithm according to the optimization range to seek the optimal energy storage system configuration.
Further, in step 1, the self-sufficient probability requirement is modeled as:
wherein, PSSeAnd PSShThe self-sufficient probabilities of the electric load and the heat load in the micro energy network are respectively, and the delta w, the delta d and the delta h respectively meet the wind output prediction error of normal distribution.
Further, in step 2, the specific process of establishing the energy storage system optimization configuration model of the micro energy network includes the following steps:
the comprehensive total cost of investment, operation and pollution control environment and fuel cost needs to be considered in the model, and the model is established by taking the minimum total cost as an objective function:
Min(IC+OC+PC) (3)
wherein, IC is the investment cost of the energy storage system, OC is the operation cost of the micro energy network, PC is the pollution control cost of the micro energy network, alpha is the unit power investment coefficient of the storage battery, beta is the unit capacity investment coefficient of the storage battery, chi is the unit power investment coefficient of the heat storage system, delta is the unit capacity investment coefficient of the heat storage system,
is the maximum power of the storage battery,
is the maximum capacity of the storage battery,
is the maximum power of the heat storage system,
is the maximum capacity of the heat storage system, NT is the total number of days, NH is the total number of hours, NG is the total number of conventional thermal power units, NL is the total number of cogeneration units, PithIs the power, F, generated by a conventional unit during a certain period of timeeIs a function of the power and cost, IithIs the state index of whether the distributed power supply works, the work is 1, the non-work is 0, PlthIs produced by a certain cogeneration unit in a certain period of timePower of generation, FhIs a function of the power and cost, LlthThe index is the state index of whether the distributed power supply works, the work is 1, and the non-work is 0; SUth，SDthStarting or stopping costs, alpha, of the generator set, respectivelyKRepresenting the cost coefficient of treatment, beta, of different pollutantsKExpressing the emission coefficients of different pollutants, and NK expresses the total amount of the pollutants;
wherein, the unit capacity investment coefficient of the storage battery is as follows:
in the formula, CEIs the total electrical energy storage unit capacity investment cost, CmIs the sum of the repair, maintenance and equipment disposal costs per unit volume of equipment; the expenses are evenly distributed in the period life of the energy storage system, so that a unit investment coefficient in a planning period is obtained;
the coal consumption cost can be expressed as a quadratic function form of the generated power; the power cost functions of the conventional thermal power generating unit and the cogeneration unit are respectively as follows:
the constraint conditions of the energy storage system optimization configuration model of the micro energy network are as follows:
electric power balance constraint, heat supply balance constraint, wind power output constraint, unit constraint, power storage system constraint and heat storage system constraint; the unit constraints comprise unit output upper and lower limit constraints, extraction type unit heat output upper and lower limit constraints, extraction type unit net power generation output upper and lower limit constraints, total power climbing constraints of the unit and heat climbing constraints of the extraction type unit; the specific content of the constraint condition is as follows:
(1) electric power balance constraint:
where NR is the amount of new energy, PrthIs the power generated by the new energy source, PESSThe charging or discharging power of the energy storage system is provided, the charging is a negative value, and the discharging is a positive value; pload,thThe power required by the load for the period of time;
(2) and (3) heat supply balance constraint:
in the formula, hlthThe thermal power of the thermoelectric power unit l in the time interval; h ishsThe storage and heat release power of the heat storage tank in the time period; heat release is positive, heat storage is negative, hload,thThe thermal load of the system for the period of time; NL is the number of all thermoelectric units;
(3) wind power output restraint:
in the formula (I), the compound is shown in the specification,
is the rated power, v, of the wind turbineCI，vRAnd vCORespectively representing the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan, vhtIs the wind speed for a certain period of time;
(4) unit restraint:
1) and (3) restraining the upper and lower limits of the unit output:
pi,min≤pi,t≤pi,max (13)
in the formula, pi,min、pi,maxRespectively the minimum output and the maximum output of the unit under the pure condensation working condition;
2) the upper limit and the lower limit of the thermal output of the thermoelectric unit are restricted:
0≤hi,t≤hi,max (14)
in the formula, hi,maxThe maximum limit value of the thermal output of the unit i is determined, and the value is mainly determined by the capacity of the heat exchanger;
3) and (3) total power climbing constraint of the unit:
Pith-Pit(h-1)≤URi(1-yith)+Pi minyith (15)
Pith(h-1)-Pith≤DRi(1-zith)+Pi minzith (16)
in the formula, URiIs a ramp-up limit, yithIs whether the unit is started or not, Pi minIs the minimum power generation amount, DR, of the generator setiIs a ramp down limit, zithIs the amount of whether the unit is in a shutdown state or not;
4) thermal ramp restraint of the thermoelectric unit:
hi,t-hi,t-1≤Δhu,i (17)
hi,t-1-hi,t≤Δhd,i (18)
in the formula,. DELTA.hu,i、Δhd,iRespectively the maximum variation of the thermal power of the extraction type unit in unit time;
(5) and (3) electric storage system constraint:
the charging process is
The discharge process is
SOC (t) is the residual electric quantity of the energy storage system at the end of the tth period; SOC (t-1) is the residual electric quantity of the energy storage system at the end of the t-1 th time period; delta is the self-discharge rate of the energy storage system; pc、PdRespectively charging and discharging power of the energy storage system; etac、ηdRespectively the charging and discharging efficiency of the electricity storage system;
rated capacity of the power storage system;
(6) and (3) heat storage system constraint:
in the formula, HHS(t) thermal energy storage capacity for time period t; mu is heat storage and heat dissipation loss rate; qHS_ch(t)、QHS_dis(t) and ηhch、ηhdisRespectively the heat absorption and discharge power and efficiency in the time period t.
Further, in step 3, the life of the storage battery is estimated by using a rain flow counting method, which specifically comprises the following steps:
the method is mainly used in engineering, and is particularly widely applied to fatigue life calculation. The strain-time history data recording is rotated by 90 degrees, the time coordinate axis is vertical downwards, the data recording is like a series of roofs, and rainwater flows downwards along the roofs, so the method is called a rainwater flow counting method.
The service life of the energy storage battery is influenced by factors including the discharge depth, the rate performance, the charge-discharge cut-off voltage and the ambient temperature of the battery; the influence of the multiplying power performance of the battery on the service life of the battery is not considered, and the rated value is taken as the maximum power of the energy storage battery; the influence of the charge-discharge cut-off voltage of the battery on the service life of the battery is not considered, and the capacity value range of the energy storage battery is set; the ambient temperature is taken as room temperature, regardless of the effect of temperature on battery life; through simplification, the life of the energy storage battery is estimated by using the following formula:
T＝ceil(1/365·dloss) (23)
wherein d islossThe damage rate of the service life of the electric energy storage system is one day, theta is a period coefficient, the full period is 1, and the half period is 0.5; cyciThe maximum cycle number corresponding to the ith cycle period; t is the life cycle, ceil is the ceiling function; the service life of the energy storage system can be estimated by constructing a charging and discharging curve of the energy storage system in one day.
Further, in step 4, the energy storage system configuration that is optimized according to the optimization range is selected through a traversal algorithm or a particle swarm optimization, and the specific process is as follows:
determining an adopted method according to the configuration range of the energy storage system, and if the range is smaller and the configuration types are less, adopting a traversal algorithm; if the range is large, the particle swarm algorithm is adopted to seek the optimal energy storage system configuration to respectively obtain the capacity and the power of the energy storage system and the capacity and the power of the heat storage system; when the optimizing range is smaller, selecting a configuration calculation cost from the configuration range of the energy storage system meeting the self-sufficient probability condition, judging whether the configuration used in the given range is completed or not, completing the calculation process if the traversal of all the configurations in the given range is completed, and continuing the calculation of the next configuration if the traversal is not completed; the method is comprehensive and is suitable for the optimization problem with a small range;
when the optimizing range is large, a particle swarm optimization algorithm is adopted, wherein the algorithm is an iterative-based optimization tool; the algorithm starts from a group of random solutions and searches an optimal solution through continuous iteration; in each iteration, updating the population by tracking individual extrema and global extrema; the algorithm has the advantages of simplicity in implementation, high speed in convergence, high precision and the like, and is widely applied to engineering practice.
The particle swarm algorithm comprises the following basic steps:
1) randomly initializing the position and speed of each particle in the population;
2) evaluating the fitness of each particle, storing the current position and the adaptive value of each particle in pbest of each particle, and storing the position and the adaptive value of an individual with the optimal adaptive value in all pbest in gbest;
3) by the formula (17) hi,t-hi,t-1≤Δhu,iUpdate the velocity and displacement of the particles:
4) for each particle, comparing its fitness value with its experienced best position, and if so, taking it as the current best position;
5) comparing the values of all pbest and gbest at present, and updating the gbest;
6) if the preset stopping condition of the operation precision or the iteration times is met, stopping the search and outputting the result, otherwise, returning to the step 3 and continuing the search.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention considers the factors comprehensively and provides a method for configuring the energy local area network multi-energy storage system under the background of the energy Internet; applying a background update taking into account the lifetime of the electrical energy storage system;
2. the invention provides self-sufficient probability requirements and pursues system stability while meeting the economy; the system stability is maintained while the economic optimization is achieved, and a balance point is found among the investment cost, the operation cost and the pollution control cost;
3. in the aspect of algorithm selection, one algorithm is specifically selected according to the optimizing range, and the calculation result is accurate;
4. the invention better accords with the characteristics of the independent micro energy network, has strong practicability, achieves the economic optimization in the independent micro energy network, and simultaneously maintains the stability of the system; the purposes of realizing optimal economy and reducing loss of the whole system under certain stability are achieved.
Drawings
FIG. 1 is a basic schematic of the method of the present invention;
FIG. 2 is a basic flow diagram of the present invention;
fig. 3 is an explanatory view of the rain flow counting method.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The invention discloses an optimal configuration solving method for an independent micro-energy-grid energy storage system, which is characterized in that a basic schematic diagram is shown in figure 1, and the configuration of the energy storage system influences three aspects of investment cost of the energy storage system, operation cost of the whole micro-grid and pollution control cost; the lower energy storage system can not achieve the expected economical efficiency and stability of the system, the operation cost can not be effectively reduced, and the discharged CO can not be effectively reduced2And some harmful gases are higher in content; the higher energy storage system is configured, so that the investment cost is higher, and the overall maintenance cost is also relatively higher; therefore, the configuration selection of the optimal energy storage system can reach balance among investment cost, operation cost and pollution control cost, and the sum of the investment cost, the operation cost and the pollution control cost is found at a balance point, namely the configuration of the energy storage system with the minimum total cost is reached;
the basic flow chart of the method of the invention is shown in fig. 2, and the steps of the method are as follows:
step 1, establishing self-sufficient probability requirements of a micro energy network, and establishing a self-sufficient probability requirement model; the self-sufficient probability demand model is as follows:
wherein, PSSeAnd PSShThe self-sufficient probabilities of the electric load and the heat load in the micro energy network are respectively, and the delta w, the delta d and the delta h respectively meet the wind output prediction error of normal distribution.
Step 2, establishing an energy storage system optimal configuration model of the micro energy network;
the method for establishing the energy storage system optimization configuration model of the micro energy network comprises the following specific processes:
the comprehensive total cost of investment, operation and pollution control environment and fuel cost needs to be considered in the model, the minimum total cost is taken as an objective function, and the model is established as follows:
Min(IC+OC+PC) (3)
in the formula, IC is the investment cost of the energy storage system, OC is the operation cost of the micro-grid, PC is the pollution control cost of the micro-grid, alpha is the unit power investment coefficient of the storage battery, beta is the unit capacity investment coefficient of the storage battery, chi is the unit power investment coefficient of the heat storage system, delta is the unit capacity investment coefficient of the heat storage system,
is the maximum power of the storage battery,
is the maximum capacity of the storage battery,
is the maximum power of the heat storage system,
is the maximum capacity of the heat storage system, NT is the total number of days, NH is the total number of hours, NG is the total number of conventional thermal power units, NL is the total heat and powerNumber of co-production units, PithIs the power, F, generated by a conventional unit during a certain period of timeeIs a function of the power and cost, IithIs the state index of whether the distributed power supply works, the work is 1, the non-work is 0, PlthIs the power, F, generated by a cogeneration unit during a certain period of timehIs a function of the power and cost, LlthThe index is the state index of whether the distributed power supply works, the work is 1, and the non-work is 0; SUth， SDthStarting or stopping costs, alpha, of the generator set, respectivelyKRepresenting the cost coefficient of treatment, beta, of different pollutantsKExpressing the emission coefficients of different pollutants, and NK expresses the total amount of the pollutants;
wherein the investment coefficient per unit capacity for the storage battery is:
in the formula, CEIs the total electrical energy storage unit capacity investment cost, CmIs the sum of the repair, maintenance and equipment disposal costs per unit volume of equipment; the expenses are evenly distributed in the period life of the energy storage system, so that a unit investment coefficient in a planning period is obtained;
the coal consumption cost can be expressed as a quadratic function form of the generated power; the power cost functions of the conventional thermal power generating unit and the cogeneration unit are respectively as follows:
the constraint conditions of the energy storage system optimization configuration model of the micro energy network are as follows:
electric power balance constraint, heat supply balance constraint, wind power output constraint, unit constraint, power storage system constraint and heat storage system constraint; the unit constraints comprise unit output upper and lower limit constraints, extraction type unit heat output upper and lower limit constraints, extraction type unit net power generation output upper and lower limit constraints, total power climbing constraints of the unit and heat climbing constraints of the extraction type unit; the specific content of the constraint condition is as follows:
(1) electric power balance constraint:
where NR is the amount of new energy, PrthIs the power generated by the new energy source, PESSThe charging or discharging power of the energy storage system is provided, the charging is a negative value, and the discharging is a positive value; pload,thThe power required by the load for the period of time;
(2) and (3) heat supply balance constraint:
in the formula, hlthThe thermal power of the thermoelectric power unit l in the time interval; h ishsThe storage and heat release power of the heat storage tank in the time period; heat release is positive, heat storage is negative, hload,thThe thermal load of the system for the period of time; NL is the number of all thermoelectric units;
(3) wind power output restraint:
in the formula (I), the compound is shown in the specification,
is the rated power, v, of the wind turbineCI，vRAnd vCORespectively representing the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan, vhtIs the wind speed for a certain period of time;
(4) unit restraint:
1) and (3) restraining the upper and lower limits of the unit output:
pi,min≤pi,t≤pi,max (13)
in the formula, pi,min、pi,maxRespectively the minimum output and the maximum output of the unit under the pure condensation working condition;
2) the upper limit and the lower limit of the thermal output of the thermoelectric unit are restricted:
0≤hi,t≤hi,max (14)
in the formula, hi,maxThe maximum limit value of the thermal output of the unit i is determined, and the value is mainly determined by the capacity of the heat exchanger;
3) and (3) total power climbing constraint of the unit:
Pith-Pit(h-1)≤URi(1-yith)+Pi minyith (15)
Pith(h-1)-Pith≤DRi(1-zith)+Pi minzith (16)
in the formula, URiIs a ramp-up limit, yithIs whether the unit is started or not, Pi minIs the minimum power generation amount, DR, of the generator setiIs a ramp down limit, zithIs the amount of whether the unit is in a shutdown state or not;
4) thermal ramp restraint of the thermoelectric unit:
hi,t-hi,t-1≤Δhu,i (17)
hi,t-1-hi,t≤Δhd,i (18)
in the formula,. DELTA.hu,i、Δhd,iRespectively the maximum variation of the thermal power of the extraction type unit in unit time;
(5) and (3) electric storage system constraint:
the charging process is
The discharge process is
SOC (t) is the residual electric quantity of the energy storage system at the end of the tth period; SOC (t-1) is the residual electric quantity of the energy storage system at the end of the t-1 th time period; delta is the self-discharge rate of the energy storage system; pc、PdRespectively charging and discharging power of the energy storage system; etac、ηdRespectively the charging and discharging efficiency of the electricity storage system;
rated capacity of the power storage system;
(6) and (3) heat storage system constraint:
in the formula, HHS(t) thermal energy storage capacity for time period t; mu is heat storage and heat dissipation loss rate; qHS_ch(t)、QHS_dis(t) and ηhch、ηhdisRespectively the heat absorption and discharge power and efficiency in the time period t.
Step 3, aiming at the optimal configuration model of the energy storage system, estimating the service life of the storage battery by adopting a rain flow counting method to obtain an investment coefficient;
the method for estimating the service life of the storage battery by adopting the rain flow counting method comprises the following specific steps:
the method is mainly used in engineering, and is particularly widely applied to fatigue life calculation. The strain-time history data recording is rotated by 90 degrees, the time coordinate axis is vertical downwards, the data recording is like a series of roofs, and rainwater flows downwards along the roofs, so the method is called a rainwater flow counting method.
The service life of the energy storage battery is influenced by factors including the discharge depth, the rate performance, the charge-discharge cut-off voltage and the ambient temperature of the battery; the influence of the multiplying power performance of the battery on the service life of the battery is not considered, and the rated value is taken as the maximum power of the energy storage battery; the influence of the charge-discharge cut-off voltage of the battery on the service life of the battery is not considered, and the capacity value range of the energy storage battery is set; the ambient temperature is taken as room temperature, regardless of the effect of temperature on battery life;
1) the SOC-time curve is rotated clockwise, the rain flow starts at the beginning of the recording and consequently at the inner edge of each peak;
2) rain drops vertically down at the peak (i.e., eave), one to the opposite with a maximum more positive than the initial maximum, or one to the opposite with a minimum more negative than the initial minimum;
3) when the rain flow meets the rain flowing from the roof above, the flow is stopped, and a cycle is formed;
4) drawing each cycle according to the starting point and the end point of the raindrop flow, taking out all the cycles one by one, and recording the peak-valley value of the cycles;
5) the horizontal length of each rain stream is taken as the depth of discharge for that cycle.
As shown in FIG. 3, the gray solid line (A-B-C-D-E-F-G) represents the battery SOC variation curve. By using a rain flow counting method, a cycle counting period 1(B-C-B ', the depth of discharge is 0.075), a cycle 2(E-F-E', the depth of discharge is 0.05), a cycle counting half period 3(A-B-B '-D, the depth of discharge is 0.28) and a half period 4(D-E-E' -G, the depth of discharge is 0.2) can be obtained.
Through simplification, the life of the energy storage battery is estimated by using the following formula:
T＝ceil(1/365·dloss) (23)
wherein d islossThe damage rate of the service life of the electric energy storage system is one day, theta is a period coefficient, the full period is 1, and the half period is 0.5; cyciIs the ith cycleMaximum cycle number corresponding to the cycle of the loop; t is the life cycle, ceil is the ceiling function; the service life of the energy storage system can be estimated by constructing a charging and discharging curve of the energy storage system in one day.
And 4, adopting a proper operation strategy in the simulation operation process by predicting the annual load data and the wind power data, and selecting a traversal algorithm or a particle swarm algorithm according to the optimization range to seek the optimal energy storage system configuration.
The method comprises the following specific steps of selecting a traversal algorithm or a particle swarm algorithm according to an optimization range to seek the optimized energy storage system configuration, wherein the specific steps are as follows: determining an adopted method according to the configuration range of the energy storage system, and if the range is smaller and the configuration types are less, adopting a traversal algorithm; if the range is large, the particle swarm algorithm is adopted to seek the optimal energy storage system configuration to respectively obtain the capacity and the power of the energy storage system and the capacity and the power of the heat storage system; when the optimizing range is smaller, selecting one configuration from the configuration ranges of the energy storage system meeting the self-sufficient probability condition, calculating the cost and judging whether the configuration used in the given range is completed or not, completing the calculation process if the traversal of all the configurations in the given range is completed, and continuing the calculation of the next configuration if the traversal is not completed; the method is comprehensive and is suitable for the optimization problem with a small range;
when the optimizing range is large, a particle swarm optimization algorithm is adopted, wherein the algorithm is an iterative-based optimization tool; the algorithm starts from a group of random solutions and searches an optimal solution through continuous iteration; in each iteration, updating the population by tracking individual extrema and global extrema; the algorithm has the advantages of simplicity in implementation, high speed in convergence, high precision and the like, and is widely applied to engineering practice.
The particle swarm algorithm comprises the following basic steps:
1) randomly initializing the position and speed of each particle in the population;
2) evaluating the fitness of each particle, storing the current position and the adaptive value of each particle in pbest of each particle, and storing the position and the adaptive value of an individual with the optimal adaptive value in all pbest in gbest;
3) by the formula (17) hi,t-hi,t-1≤Δhu,iUpdate the velocity and displacement of the particles:
4) for each particle, comparing its fitness value with its experienced best position, and if so, taking it as the current best position;
5) comparing the values of all pbest and gbest at present, and updating the gbest;
6) if the preset stopping condition of the operation precision or the iteration times is met, stopping the search and outputting the result, otherwise, returning to the step 3 and continuing the search.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solution of the present invention by those skilled in the art should fall within the protection scope defined by the claims of the present invention without departing from the spirit of the present invention.
Claims (3)
1. An independent micro energy network energy storage system optimal configuration solving method is characterized in that:
the configuration of the energy storage system influences the investment cost of the energy storage system, the operation cost of the whole microgrid and the pollution control cost; the lower energy storage system can not achieve the expected economical efficiency and stability of the system, the operation cost can not be effectively reduced, and the discharged CO can not be effectively reduced2And the content of harmful gases is higher; the higher energy storage system is configured, so that the investment cost is higher, and the overall maintenance cost is also relatively higher; therefore, the configuration of the optimal energy storage system is selected to be balanced among the investment cost, the operation cost and the pollution control cost, and the sum of the investment cost, the operation cost and the pollution control cost is found at a balance point, namely the configuration of the energy storage system with the minimum total cost is obtained;
the method comprises the following steps:
step 1, establishing self-sufficiency probability requirements of a micro energy network;
step 2, establishing an energy storage system optimal configuration model of the micro energy network;
step 3, aiming at the optimal configuration model of the energy storage system, estimating the service life of the storage battery by adopting a rain flow counting method to obtain an investment coefficient;
step 4, by predicting load data and wind power data of one year, adopting a proper operation strategy in the simulation operation process, and selecting a traversal algorithm or a particle swarm algorithm according to an optimization range to seek the optimal energy storage system configuration;
in step 1, the self-sufficient probability requirement is modeled as:
wherein, PSSeAnd PSShThe self-sufficient probabilities of the electric load and the heat load in the micro energy network are respectively, and the delta w, the delta d and the delta h respectively meet the wind output prediction error of normal distribution;
in step 2, the specific process of establishing the energy storage system optimization configuration model of the micro energy network includes the following steps:
the comprehensive total cost of investment, operation and pollution control environment and fuel cost needs to be considered in the model, and the model is established by taking the minimum total cost as an objective function:
Min(IC+OC+PC) (3)
wherein, IC is the investment cost of the energy storage system, OC is the operation cost of the micro energy network, PC is the pollution control cost of the micro energy network, alpha is the unit power investment coefficient of the storage battery, beta is the unit capacity investment coefficient of the storage battery, chi is the unit power investment coefficient of the heat storage system, delta is the unit capacity investment coefficient of the heat storage system,
is the maximum power of the storage battery,
is the maximum capacity of the storage battery,
is the maximum power of the heat storage system,
is the maximum capacity of the heat storage system, NT is the total number of days, NH is the total number of hours, NG is the total number of conventional thermal power units, NL is the total number of cogeneration units, PithIs the power, F, generated by a conventional unit during a certain period of timeeIs a function of the power and cost, IithIs the state index of whether the distributed power supply works, the work is 1, the non-work is 0, PlthIs the power, F, generated by a cogeneration unit during a certain period of timehIs a function of the power and cost, LlthThe index is the state index of whether the distributed power supply works, the work is 1, and the non-work is 0; SUth，SDthStarting or stopping costs, alpha, of the generator set, respectivelyKRepresenting the cost coefficient of treatment, beta, of different pollutantsKExpressing the emission coefficients of different pollutants, and NK expresses the total amount of the pollutants;
wherein, the unit capacity investment coefficient of the storage battery is as follows:
in the formula, CEIs the total electrical energy storage unit capacity investment cost, CmIs the sum of the repair, maintenance and equipment disposal costs per unit volume of equipment; the expenses are evenly distributed in the period life of the energy storage system, so that a unit investment coefficient in a planning period is obtained;
the coal consumption cost is expressed as a quadratic function form of the generated power; the power cost functions of the conventional thermal power generating unit and the cogeneration unit are respectively as follows:
the constraint conditions of the energy storage system optimization configuration model of the micro energy network are as follows:
electric power balance constraint, heat supply balance constraint, wind power output constraint, unit constraint, power storage system constraint and heat storage system constraint; the unit constraints comprise unit output upper and lower limit constraints, extraction type unit heat output upper and lower limit constraints, extraction type unit net power generation output upper and lower limit constraints, total power climbing constraints of the unit and heat climbing constraints of the extraction type unit; the specific content of the constraint condition is as follows:
(1) electric power balance constraint:
where NR is the amount of new energy, PrthIs the power generated by the new energy source, PESSThe charging or discharging power of the energy storage system is provided, the charging is a negative value, and the discharging is a positive value; pload,thThe power required by the load for the period of time;
(2) and (3) heat supply balance constraint:
in the formula, hlthThe thermal power of the thermoelectric power unit l in the time interval; h ishsThe storage and heat release power of the heat storage tank in the time period; heat release is positive, heat storage is negative, hload,thThe thermal load of the system for the period of time; NL is the number of all thermoelectric units;
(3) wind power output restraint:
in the formula (I), the compound is shown in the specification,
is the rated power, v, of the wind turbineCI，vRAnd vCORespectively representing the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan, vhtIs the wind speed for a certain period of time;
(4) unit restraint:
1) and (3) restraining the upper and lower limits of the unit output:
pi,min≤pi,t≤pi,max (13)
in the formula, pi,min、pi,maxRespectively the minimum output and the maximum output of the unit under the pure condensation working condition;
2) the upper limit and the lower limit of the thermal output of the thermoelectric unit are restricted:
0≤hi,t≤hi,max (14)
in the formula, hi,maxThe maximum limit value of the heat output of the unit is determined, and the value is mainly determined by the capacity of the heat exchanger;
3) and (3) total power climbing constraint of the unit:
Pith-Pit(h-1)≤URi(1-yith)+Pi minyith (15)
Pit(h-1)-Pith≤DRi(1-zith)+Pi minzith (16)
in the formula, URiIs a ramp-up limit, yithIs whether the unit is started or not, Pi minIs the minimum power generation amount, DR, of the generator setiIs a ramp down limit, zithIs the amount of whether the unit is in a shutdown state or not;
4) thermal ramp restraint of the thermoelectric unit:
hi,t-hi,t-1≤Δhu,i (17)
hi,t-1-hi,t≤Δhd,i (18)
in the formula,. DELTA.hu,i、Δhd,iRespectively the maximum variation of the thermal power of the extraction type unit in unit time;
(5) and (3) electric storage system constraint:
the charging process is
The discharge process is
SOC (t) is the residual electric quantity of the energy storage system at the end of the tth period; SOC (t-1) is the residual electric quantity of the energy storage system at the end of the t-1 th time period; delta is the self-discharge rate of the energy storage system; pc、PdRespectively charging and discharging power of the energy storage system; etac、ηdRespectively the charging and discharging efficiency of the electricity storage system;
rated capacity of the power storage system;
(6) and (3) heat storage system constraint:
in the formula, HHS(t) thermal energy storage capacity for time period t; mu is heat storage and heat dissipation loss rate; qHS_ch(t)、QHS_dis(t) and ηhch、ηhdisRespectively the heat absorption and discharge power and efficiency in the time period t.
2. The method for solving the optimal configuration of the independent micro energy network energy storage system according to claim 1, wherein the method comprises the following steps: in step 3, estimating the service life of the storage battery by using a rain flow counting method, which comprises the following specific steps:
the service life of the energy storage battery is influenced by factors including the discharge depth, the rate performance, the charge-discharge cut-off voltage and the ambient temperature of the battery; the influence of the multiplying power performance of the battery on the service life of the battery is not considered, and the rated value is taken as the maximum power of the energy storage battery; the influence of the charge-discharge cut-off voltage of the battery on the service life of the battery is not considered, and the capacity value range of the energy storage battery is set; the ambient temperature is taken as room temperature, regardless of the effect of temperature on battery life; estimating the life of the energy storage battery by using the following formula:
T＝ceil(1/365·dloss) (23)
wherein d islossThe damage rate of the service life of the electric energy storage system is one day, theta is a period coefficient, the full period is 1, and the half period is 0.5; cyciThe maximum cycle number corresponding to the ith cycle period; t is the life cycle, ceil is the ceiling function; the service life of the energy storage system can be estimated by constructing a charging and discharging curve of the energy storage system in one day.
3. The method for solving the optimal configuration of the independent micro energy network energy storage system according to claim 1, wherein the method comprises the following steps: in step 4, the energy storage system configuration which is optimized according to the optimization range selection traversal algorithm or the particle swarm optimization algorithm is searched, and the specific process is as follows:
determining an adopted method according to the configuration range of the energy storage system, and if the range is smaller and the configuration types are less, adopting a traversal algorithm; if the range is large, the particle swarm algorithm is adopted to seek the optimal energy storage system configuration to respectively obtain the capacity and the power of the energy storage system and the capacity and the power of the heat storage system; when the optimizing range is smaller, selecting a configuration calculation cost from the configuration range of the energy storage system meeting the self-sufficient probability condition, judging whether the configuration used in the given range is completed or not, completing the calculation process if the traversal of all the configurations in the given range is completed, and continuing the calculation of the next configuration if the traversal is not completed; the method is comprehensive and is suitable for the optimization problem with a small range;
when the optimizing range is large, a particle swarm optimization algorithm is adopted, wherein the algorithm is an iterative-based optimization tool; the algorithm starts from a group of random solutions and searches an optimal solution through continuous iteration; in each iteration, updating the population by tracking individual extrema and global extrema; the particle swarm algorithm comprises the following basic steps:
1) randomly initializing the position and speed of each particle in the population;
2) evaluating the fitness of each particle, storing the current position and the adaptive value of each particle in pbest of each particle, and storing the position and the adaptive value of an individual with the optimal adaptive value in all pbest in gbest;
3) by the formula hl,t(h-1)-hl,th≤Δhd,lUpdate the velocity and displacement of the particles:
4) for each particle, comparing its fitness value with its experienced best position, and if so, taking it as the current best position;
5) comparing the values of all pbest and gbest at present, and updating the gbest;
6) if the preset stopping condition of the operation precision or the iteration times is met, stopping the search and outputting the result, otherwise, returning to the step 3 and continuing the search.
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