CN109510224B - Capacity allocation and operation optimization method combining photovoltaic energy storage and distributed energy - Google Patents
Capacity allocation and operation optimization method combining photovoltaic energy storage and distributed energy Download PDFInfo
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Abstract
The invention relates to a capacity allocation and operation optimization method combining photovoltaic energy storage and distributed energy, which comprises the following steps: (1) acquiring basic data of a distributed energy system and a thermal power and power load prediction data curve accurate to the hour of a year; (2) configuring the capacity of energy conversion equipment in the distributed energy system by taking the minimum annual total investment operation cost of the distributed energy system as an optimization target; (3) determining an optimized output curve of the distributed energy system; (4) determining a power net load curve; (5) collecting basic data of a photovoltaic energy storage system; (6) and optimally configuring the battery energy storage rated power, the battery energy storage rated capacity and the charge and discharge strategy in the photovoltaic energy storage system by taking the minimum annual total investment operation cost of the photovoltaic energy storage system as an optimization target according to the basic data and the electric net load curve of the photovoltaic energy storage system. Compared with the prior art, the method is rapid and comprehensive, and the result is accurate and reliable.
Description
Technical Field
The invention relates to a capacity configuration and operation optimization method for a comprehensive energy network, in particular to a capacity configuration and operation optimization method combining photovoltaic energy storage and distributed energy.
Background
The comprehensive energy system considers the synergistic effect of various energy sources such as cold/heat/electricity/gas and the like, meets the energy requirements of social energy terminal users on power supply, heat supply, cold supply and the like by integrating energy conversion and storage among the various energy network systems, plans and operates the various energy network systems as a whole, and improves the operation efficiency of the system so as to achieve the purposes of energy conservation and emission reduction. Under the framework of a comprehensive energy system, the cooperative optimization of a plurality of energy systems of electric power, heat and gas is realized by further introducing high-capacity energy storage, the large space-time range optimal configuration capacity of the energy system is improved, and the problems of renewable energy consumption, peak regulation and the like can be effectively solved.
In the aspect of distributed energy conversion equipment, as different geographical climate resource conditions have certain complexity, unified planning for integrating and accommodating various conditions is lacked, and various energy storage and conversion equipment are systematically integrated. The heat pump heating is widely used in Japan, northern Europe and Germany use high proportion of regional heating, and great amount of gas boilers are used for direct heating in the UK by means of developed natural gas pipe network infrastructures. Study doctor academic paper of zhou ji of qinghua university: in the modeling and optimization of the distributed energy system with multi-energy coordination, a set of energy system equipment optimization design model based on superstructure linearization modeling is provided for the distributed energy system with multi-energy coordination, but energy flow calculation (power flow, thermal flow and gas flow) of a multi-energy network is not considered. A double-layer optimization planning design method of a combined cooling heating and power micro-grid system is disclosed in the patent application No.: CN201310661953.8 discloses a two-stage modeling and planning method for coupling characteristics of planning design and operation optimization of a micro-grid, which can realize interactive optimization of an outer-layer device type, a capacity optimization module and an inner-layer operation strategy optimization module, but does not consider modeling of a thermal power grid and a gas grid, and the double-layer planning and solving speed is very slow, often reaching several hours or more.
In the aspect of configuration and operation of the photovoltaic-energy storage system, the capacity design and operation strategy of battery energy storage are key. Too small a capacity does not effectively take up photovoltaic power, and too large a capacity incurs too high an investment cost for the battery, so there is an optimum balance point between the capacity and price of the battery. The benefits of photovoltaic and battery energy storage coupling come from: 1. and 2, the difference between the grid electricity price when the redundant photovoltaic power is stored and the photovoltaic outgoing electricity price when the redundant photovoltaic power is stored and the grid electricity price when the part of power is released. The heavy use of energy conversion equipment such as heat pumps, CHP, air conditioners, electric vehicles and the like affects the power curve of the load, thereby affecting the configuration and operation of the photovoltaic energy storage system. At present, a great deal of research is carried out on photovoltaic-energy storage system configuration and operation scheduling at home and abroad, and the problem of maximum profit is discussed from an individual perspective by focusing on an individual user taking a house or a building as a unit. But research on the optimal configuration and operation of the energy conversion equipment and the photovoltaic energy storage system is not considered integrally.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a capacity configuration and operation optimization method combining photovoltaic energy storage and distributed energy sources.
The purpose of the invention can be realized by the following technical scheme:
a capacity configuration and operation optimization method combining photovoltaic energy storage and distributed energy sources comprises the following steps:
(1) acquiring basic data of a distributed energy system and a thermal power and power load prediction data curve accurate to the hour of a year;
(2) according to basic data of the distributed energy system and a thermal power and power load prediction data curve, the capacity of energy conversion equipment in the distributed energy system is configured by taking the minimum annual total investment and operation cost of the distributed energy system as an optimization target;
(3) determining an optimized output curve of the distributed energy system according to the capacity configuration result of the energy conversion equipment;
(4) determining an electric net load curve according to the electric load prediction data curve and the distributed energy system operation optimization output curve;
(5) collecting basic data of a photovoltaic energy storage system;
(6) and optimally configuring the battery energy storage rated power, the battery energy storage rated capacity and the charge and discharge strategy in the photovoltaic energy storage system by taking the minimum annual total investment operation cost of the photovoltaic energy storage system as an optimization target according to the basic data and the electric net load curve of the photovoltaic energy storage system.
The step (2) is specifically as follows:
(21) establishing an optimization objective function of the distributed energy system:
Min Ctotal-DER＝LFn·CCapex-DER+COpex-DER，
wherein, Ctotal-DERFor the annual total investment and operation cost of the distributed energy system, CCapex-DERInitial total investment cost of energy conversion equipment in distributed energy system，COpex-DERAnnual energy conversion equipment operating charge, LF, for a distributed energy systemnIn order to be a coefficient of capital recovery,
d is the discount rate, and n is the service life of the energy conversion equipment;
(22) establishing a constraint function of the distributed energy system, wherein the constraint function comprises an equality constraint function and an inequality constraint function, and the equality constraint function is as follows:
in the formula, PiIs the active power of the power of node i, ViIs the voltage of node i, VjIs the voltage of node j, NeIs the number of nodes, G, of the power systemijIs the conductance of line ij, BijIs the susceptance, θ, of line ijijIs the voltage angle difference of nodes i and j, QiElectric reactive power, C, for node ipIs the specific heat capacity of water, AhIs a heat power network association matrix and is characterized in that,
for mass flow, TsFor temperature of the water supply, ToFor the outlet water temperature, phi is the thermal power vector consumed or provided by each thermal node, BhIs a heat power network loop incidence matrix, KhIs the coefficient of resistance of the heat distribution network pipe, CsAs a water supply network coefficient matrix, bsIs a constant vector, CrAs a coefficient matrix of the backwater network, TrTo the return water temperature, brIs a constant vector, AgIs a gas network incidence matrix, vgIs node air pressure, vqTo node the gas flow rate, BgIs a gas network loop incidence matrix, KgIs the resistance coefficient of the gas network pipeline, and k is an exponential constant;
the inequality constraint function includes:
(a) and (3) limiting the upper limit and the lower limit of the capacity of the energy conversion equipment:
in the formula (I), the compound is shown in the specification,
for the value of the power active power provided by the conversion device at peak load,
the lower limit of the active power of the electricity supplied to the conversion equipment,
the upper limit of the electrical active power provided to the conversion equipment,
for converting the thermal power supplied by the device at load peaks,
the lower limit of the thermal power is,
is the upper limit of thermodynamic power;
(b) the power grid inequality constrains:
Vimin≤Vi≤Vimax,i＝1,…,Ne，
in the formula, ViIs the voltage of node i, ViminLower voltage limit, V, of node iimaxIs the upper voltage limit of node i, NeIs the number of nodes, PgeniFor the active power output of the ith generator,
the lower active power output limit of the ith generator,
upper limit of active power output, Q, of the ith generatorgeniIs the reactive power output of the ith generator,
the lower limit of reactive power output of the ith generator,
is the upper limit of reactive power output of the ith generator, NgeNumber of generators, SkIs the electrical power of the kth branch,
the lower limit of the electric power of the kth branch,
is the upper electric power limit of the kth branch, NleThe number of branches;
(c) the thermal power grid inequality constrains:
Ts_min≤Ts≤Ts_max，
Tr_min≤Tr≤Tr_max，
in the formula (I), the compound is shown in the specification,
the flow rate of each pipeline of the heating power network,
the lower limit of the flow of each pipeline of the heating power network,
is the upper limit of the flow of each pipeline of the heating power network, TsSupply of water temperature, T, to each node of the heating networks_minLower limit of water supply temperature, T, for each node of heating power networks_maxUpper limit of water supply temperature, T, for each node of heating power networkrFor the return water temperature, T, of each node of the heating power networkr_minThe lower limit of the return water temperature of each node of the heating power network, Tr_maxThe upper limit of the return water temperature of each node of the heating power network is set;
(d) the inequality constraint of the gas network is as follows:
pgmin≤pg≤pgmax，
vg_min≤vg≤vg_max，
in the formula, pgFor the gas pressure, p, at each node of the gas networkgminIs the lower limit of gas pressure of each node of the gas network, pgmaxIs the upper limit of gas pressure of each node of the gas network, vgIs the gas flow of each pipeline of the gas network, vg_minIs the lower limit of the gas flow of each pipeline of the gas network, vg_maxThe upper limit of the gas flow of each pipeline of the gas network;
(23) and optimizing and solving to obtain the capacity of the energy conversion equipment in the distributed energy system.
Initial total investment cost C of energy conversion equipment in distributed energy systemCapex-DERThe method specifically comprises the following steps:
wherein the content of the first and second substances,
in order to convert the initial investment cost of the equipment i,
the electrical power provided by the inverter device i at peak load,
the V-shaped index represents the operator "OR" for the thermal power supplied by the switching device i at load peaks.
Annual energy conversion equipment operating charge C in distributed energy systemOpex-DERThe method specifically comprises the following steps:
wherein, CeFor purchase of electricity price, Pimport(t) buying electric power to the upper grid for the t hour of the year, CgAs gas price, vgtotal(t) Total gas consumption Rate in the t hour of the year, CcarbonIs the carbon price, ξeIs the carbon emission intensity of the powergIs the carbon emission intensity of natural gas, CO&MFor operating maintenance costs, T is the number of hours of a year.
The step (6) is specifically as follows:
(61) establishing an optimization objective function of the photovoltaic energy storage system:
Min Ctotal-PVB＝CCapex-PVB+COpex-PVB，
wherein, Ctotal-PVBThe total annual investment and operation cost of the photovoltaic energy storage system, CCapex-PVBFor the annual investment costs of the photovoltaic energy storage system, COpex-PVBThe annual running cost of the photovoltaic energy storage system is saved;
(62) establishing a constraint function of the photovoltaic energy storage system, comprising:
(a) charge and discharge power constraint:
in the formula (I), the compound is shown in the specification,
charging power for the t hour on day d,
the discharge power at the t hour on day d,
d is more than or equal to 1 and less than or equal to 365, and t is more than or equal to 1 and less than or equal to 24;
(b) energy balance constraint:
in the formula, EBESS(d,t)EBESS(t) is the amount of energy stored in the battery at the time of day d and t, EBESS(d,t-1)EBESS(t-1) is the electric quantity of the battery stored energy at the t-1 moment on the day d, etacEfficiency of charging the battery, ηdFor the battery discharge efficiency, Δ t is the time step;
(c) battery storage capacity constraint:
in the formula, EBESS(d, t) is the electric quantity stored by the battery at the time t on the day d,
storing energy rating, SOC, for a batteryminAllowed by ratio of residual capacity to capacity of batteryMinimum value, SOCmaxThe minimum value allowed by the ratio of the energy storage residual capacity to the capacity of the battery is obtained;
(63) and optimizing and solving to obtain the battery energy storage rated power, the battery energy storage rated capacity and the charging and discharging power at each moment in the photovoltaic energy storage system.
Annual investment cost C of photovoltaic energy storage systemCapex-PVBThe method specifically comprises the following steps:
in the formula (I), the compound is shown in the specification,
the rated capacity of the energy storage of the battery,
the rated power is stored for the battery,
in order to obtain the rated output power of the photovoltaic,
the capacity investment cost per kWh for battery energy storage,
the power investment cost per kW for battery energy storage,
is the unit power investment cost of the photovoltaic.
Annual operating charge C of photovoltaic energy storage systemOpex-PVBThe method specifically comprises the following steps:
in the formula, PPV→load(d, t) is the power of the photovoltaic supply load at the tth day, PPV→bess(d, t) isPower of photovoltaic charging of battery at t hour on day d, PPV→grid(d, t) is the power of photovoltaic externally transmitted to a superior power grid in the tth hour on the day d, PPV(d, t) is the generated power of the photovoltaic,
charging power for the t hour on day d,
discharge power at day d, t hour, Ce(t) is the power grid electricity price at the moment t,
is the electricity price of the photovoltaic power generation outgoing power grid,
d is more than or equal to 1 and less than or equal to 365, and t is more than or equal to 1 and less than or equal to 24 for the subsidy price per kW of photovoltaic power generation.
Compared with the prior art, the invention has the following advantages:
the invention provides a rapid, comprehensive and extensible capacity configuration and operation optimization method for photovoltaic energy storage and distributed energy combination, aiming at the mutual relation of energy storage and conversion equipment influence load curves, a whole system design and operation model of a comprehensive energy network is established, the whole optimization is realized, the influence of the deployment of a distributed energy system (DER) on net load change is calculated according to the configuration result of the conversion equipment in the DER, on the basis, the design and operation of a photovoltaic energy storage system (PVB) are researched, and economic incentive is provided for owners of a photovoltaic cell energy storage system.
Drawings
FIG. 1 is a block flow diagram of a method for capacity allocation and operation optimization of photovoltaic energy storage in conjunction with distributed energy resources in accordance with the present invention;
FIG. 2 is a block diagram of capacity allocation and operation optimization of the distributed energy system of the integrated energy grid according to the present invention;
FIG. 3 is a block diagram illustrating capacity allocation and operation optimization of the energy storage and conversion device according to the present invention;
FIG. 4 is a peak daily curve of power and heating load in the example;
FIG. 5 is an exploded view of the cost of the energy conversion device planning result in three scenarios of the embodiment;
FIG. 6 is a daily net load curve every half hour for the example;
FIG. 7 is a diagram showing the relationship between the design of the capacity of the battery for storing energy and the price under three scenarios in the embodiment;
fig. 8 is a power balance diagram every half hour for the three scenarios in the example.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
As shown in fig. 1, a capacity allocation and operation optimization method for photovoltaic energy storage and distributed energy resource combination includes the following steps:
(1) acquiring basic data of a distributed energy system and a thermal power and power load prediction data curve accurate to the hour of a year;
(2) according to basic data of the distributed energy system and a thermal power and power load prediction data curve, the capacity of energy conversion equipment in the distributed energy system is configured by taking the minimum annual total investment and operation cost of the distributed energy system as an optimization target;
(3) determining an optimized output curve of the distributed energy system according to the capacity configuration result of the energy conversion equipment;
(4) determining an electric net load curve according to the electric load prediction data curve and the distributed energy system operation optimization output curve;
(5) collecting basic data of a photovoltaic energy storage system;
(6) and optimally configuring the battery energy storage rated power, the battery energy storage rated capacity and the charge and discharge strategy in the photovoltaic energy storage system by taking the minimum annual total investment operation cost of the photovoltaic energy storage system as an optimization target according to the basic data and the electric net load curve of the photovoltaic energy storage system.
Specifically, the basic data of the distributed energy system (DER) in step (1) include investment cost, service life, efficiency, fuel price, carbon strength, carbon price, grid electricity price of CHP power generation, load factor and the like of DER equipment (such as CHP, heat pump and boiler). The time scale of the collected thermal and power load peak daily curve is hours or half-hour or 15 minutes or 5 minutes.
And (2) realizing capacity allocation and operation optimization of the distributed energy system, wherein a block diagram of the capacity allocation and operation optimization of the distributed energy system of the comprehensive energy network is shown in fig. 2, the distribution situation of cold/heat/electricity/gas energy and the load prediction situation in the selected area are comprehensively considered, the minimum total annual investment and operation cost in a planning period is taken as a target, and an optimization framework is shown in fig. 2. The investment cost is determined by the reduction of the total equipment investment to the annual investment cost within its operating period. The model obtains the optimal configuration and operation of the energy conversion equipment with multi-energy coordination by minimizing the annual cost. The input of the optimization problem is the current state parameters of a cold/heat/electricity/gas network, the future new energy, the load output and the like in the planned area, and the output is the results of the capacity, the investment, the operation cost and the like of the energy conversion equipment. The optimization model is used for determining the installation type, the capacity and the operation strategy of the energy conversion equipment, and the multi-energy flow optimization problem is solved by constructing multi-energy flow analysis into equality constraint of the multi-energy flow optimization model and calling an interior point method. The large use of energy conversion equipment such as heat pumps, CHP, air conditioners, electric vehicles and the like affects the power load curve, thereby affecting the configuration and operation of the photovoltaic energy storage system.
Therefore, the step (2) is specifically:
(21) the model obtains the optimal planning configuration of the multifunctional cooperative comprehensive energy system conversion equipment by minimizing the annual total investment and operation cost, and realizes the lowest investment cost CAPEX and operation cost OPEX of the whole energy system. The model optimizes the production and conversion of electric power and heat of each network energy conversion device, so that an optimization objective function of the distributed energy system is established:
Min Ctotal-DER＝LFn·CCapex-DER+COpex-DER，
wherein, Ctotal-DERFor the annual total investment and operation cost of the distributed energy system, CCapex-DERFor the initial total investment cost, C, of energy conversion equipment in a distributed energy systemOpex-DERAnnual energy conversion equipment operating charge, LF, for a distributed energy systemnIn order to be a coefficient of capital recovery,
d is the discount rate, and n is the service life of the energy conversion equipment;
initial total investment cost C of energy conversion equipment in distributed energy systemCapex-DERThe method specifically comprises the following steps:
wherein the content of the first and second substances,
in order to convert the initial investment cost of the equipment i,
the electrical power provided by the inverter device i at peak load,
the V-shaped index represents the operator "OR" for the thermal power supplied by the switching device i at load peaks.
Annual energy conversion equipment operating charge C in distributed energy systemOpex-DERThe method specifically comprises the following steps:
wherein, CeFor purchase of electricity price, Pimport(t) buying electric power to the upper grid for the t hour of the year, CgAs gas price, vgtotal(t) is the tth hour of the yearTotal gas consumption rate, CcarbonIs the carbon price, ξeIs the carbon emission intensity of the powergIs the carbon emission intensity of natural gas, CO&MFor the operation and maintenance cost, T is the hour of one year, and T is 8760 h.
In the embodiment, the operation cost is optimized based on the load of each hour in the whole year, and then the operation cost in the whole year is obtained through accumulation, and the calculation time of the method is too slow, so that the peak load of the power and the heat in the use year and the introduced load rate are used. In this case, the calculated running cost uses only the peak load, not the 8760 hour load. This approach greatly reduces the complexity and computation time of the problem. If the number of running points is 8760 hours, the running time is shortened by 8760 times. The equation is therefore simplified as follows:
in the formula (I), the compound is shown in the specification,
for the operating costs of the system at peak load, etalfThe load factor is used.
(22) Establishing constraint functions of the distributed energy system, including equality constraint functions and inequality constraint functions,
the equality constraint function establishes an energy flow analysis model of independent networks such as a natural gas network and a thermal power network through analogy with power system analysis. The power flow calculation of the power system is used for analyzing the voltage and the power of each node of the power line, so that power supply capacity check, line loss analysis and the like are performed. The heat flow calculation is used for analyzing the temperature, flow, pressure and state change conditions of the heat transfer medium, so that the heat energy loss and the flow pressure loss are analyzed. And (3) calculating and analyzing the flow, pressure and state change conditions of the fuel gas in the transmission and distribution gas pipeline by using the gas flow, thereby analyzing the gas flow pressure loss. Performing combined modeling analysis on each network energy flow and energy conversion equipment of the comprehensive energy system, and considering coupling elements for connecting a power network, a heating power network and a gas network, such as a Combined Heat and Power (CHP), a heat pump, a gas boiler and the like, wherein the network couplingThe resultant part is characterized by a multi-vector conversion efficiency matrix and a permutation matrix. The conversion device model (P, phi, v) in the multi-energy flow joint equation is linked by the conversion efficiency matrixq) And realizing the coupling of the electric/heat/gas network. The multiple energy flow power equality constraint of the comprehensive energy system for balancing the electric power, the heat power, the fuel gas and the load is a combined equation equality of the comprehensive energy system.
Specifically, the equality constraint function is:
in the formula, PiIs the active power of the power of node i, ViIs the voltage of node i, VjIs the voltage of node j, NeIs the number of nodes, G, of the power systemijIs the conductance of line ij, BijIs the susceptance, θ, of line ijijIs the voltage angle difference of nodes i and j, QiElectric reactive power, C, for node ipIs the specific heat capacity of water, AhIs a heat power network association matrix and is characterized in that,
for mass flow, TsFor temperature of the water supply, ToFor the outlet water temperature, phi is the thermal power vector consumed or provided by each thermal node, BhIs a heat power network loop incidence matrix, KhIs the coefficient of resistance of the heat distribution network pipe, CsAs a water supply network coefficient matrix, bsIs a constant vector, CrAs a coefficient matrix of the backwater network, TrTo the return water temperature, brIs a constant vector, AgIs a gas network incidence matrix, vgIs node air pressure, vqTo node the gas flow rate, BgIs a gas network loop incidence matrix, KgIs the resistance coefficient of the gas network pipeline, and k is an exponential constant;
the inequality constraint function includes:
(a) and (3) limiting the upper limit and the lower limit of the capacity of the energy conversion equipment:
in the formula (I), the compound is shown in the specification,
for the value of the power active power provided by the conversion device at peak load,
the lower limit of the active power of the electricity supplied to the conversion equipment,
the upper limit of the electrical active power provided to the conversion equipment,
for converting the thermal power supplied by the device at load peaks,
the lower limit of the thermal power is,
is the upper limit of thermodynamic power;
(b) the power grid inequality constrains:
Vimin≤Vi≤Vimax,i＝1,…,Ne，
in the formula, ViIs the voltage of node i, ViminLower voltage limit, V, of node iimaxIs the upper voltage limit of node i, NeIs the number of nodes, PgeniFor the active power output of the ith generator,
the lower active power output limit of the ith generator,
upper limit of active power output, Q, of the ith generatorgeniIs the reactive power output of the ith generator,
the lower limit of reactive power output of the ith generator,
is the upper limit of reactive power output of the ith generator, NgeNumber of generators, SkIs the electrical power of the kth branch,
the lower limit of the electric power of the kth branch,
is the upper electric power limit of the kth branch, NleThe number of branches;
(c) the thermal power grid inequality constrains:
Ts_min≤Ts≤Ts_max，
Tr_min≤Tr≤Tr_max，
in the formula (I), the compound is shown in the specification,
the flow rate of each pipeline of the heating power network,
the lower limit of the flow of each pipeline of the heating power network,
is the upper limit of the flow of each pipeline of the heating power network, TsSupply of water temperature, T, to each node of the heating networks_minLower limit of water supply temperature, T, for each node of heating power networks_maxUpper limit of water supply temperature, T, for each node of heating power networkrFor the return water temperature, T, of each node of the heating power networkr_minThe lower limit of the return water temperature of each node of the heating power network, Tr_maxThe upper limit of the return water temperature of each node of the heating power network is set;
(d) the inequality constraint of the gas network is as follows:
pgmin≤pg≤pgmax，
vg_min≤vg≤vg_max，
in the formula, pgFor the gas pressure, p, at each node of the gas networkgminIs the lower limit of gas pressure of each node of the gas network, pgmaxIs the upper limit of gas pressure of each node of the gas network, vgIs the gas flow of each pipeline of the gas network, vg_minIs the lower limit of the gas flow of each pipeline of the gas network, vg_maxThe upper limit of the gas flow of each pipeline of the gas network;
(23) and optimizing and solving to obtain the capacity of the energy conversion equipment in the distributed energy system.
And (3) determining an optimized output curve of the distributed energy system according to the capacity configuration result of the energy conversion equipment on the basis.
And (4) subtracting the output of the distributed energy from the electric load by the electric net load in the electric net load curve.
And (5) the basic data of the photovoltaic energy storage system comprise a typical daily curve of photovoltaic power generation, PVB investment cost, service life, efficiency, power grid time-sharing electricity price, photovoltaic on-grid electricity price, photovoltaic peak value and annual utilization hours.
After the capacity configuration and operation optimization of the distributed energy system is completed, the capacity configuration and operation optimization of the photovoltaic energy storage system is performed, and fig. 3 is a block diagram of the capacity configuration and operation optimization of the energy storage and conversion equipment.
Specifically, the capacity configuration and operation optimization of the photovoltaic energy storage system are realized in the step (6), which mainly comprises the establishment of an optimization objective function of the photovoltaic energy storage system and the establishment of a constraint function of the photovoltaic energy storage system, and before that, the power flow modeling of the photovoltaic energy storage system is firstly carried out.
Specifically, the power flow of the photovoltaic energy storage system is modeled as:
photovoltaic power PPVIs directly absorbed by load PPV→loadOr storing P by batteryPV→bessOr delivered to higher-level grid PPV→gridSatisfying the equation:
PPV→load(d,t)+PPV→bess(d,t)+PPV→grid(d,t)＝PPV(d,t)，
PPV→load(d, t) is the power of the photovoltaic supply load at the tth day, PPV→bess(d, t) is the power of the photovoltaic cell to charge the cell at the tth day on day d, PPV→grid(d, t) is the power of photovoltaic externally transmitted to a superior power grid in the tth hour on the day d, PPVAnd (d, t) is the photovoltaic power generation power, d is more than or equal to 1 and less than or equal to 365, and t is more than or equal to 1 and less than or equal to 24.
The power balance equation of the load expresses that the load power is equal to the sum of the photovoltaic power generation, the energy storage charge and discharge and the power of the superior power grid:
thus, it is possible to obtain:
the photovoltaic power flow is dependent on the photovoltaic versus load power as shown in table 1.
(a) If the photovoltaic power is less than the load, the photovoltaic is fully supplied to the load.
(b) If the photovoltaic power is larger than the load and the redundant part does not exceed the energy storage charging limit, the charging power P of the photovoltaic to the batteryPV→bessEqual to this remaining part PPV-PloadElse PPV→bessEqual to the maximum charging power of the battery
(c) If the photovoltaic cell has P remaining after charging the battery at maximum powerPV-Pload＞PPV→bessAnd then the power P of the photovoltaic outgoing superior power gridPV→gridIf the difference is not between the two, otherwise PPV→grid＝0。
TABLE 1 photovoltaic Power output flow Direction vs. load relationship
Thus, the step (6) includes the steps of:
(61) establishing an optimization objective function of the photovoltaic energy storage system:
Min Ctotal-PVB＝CCapex-PVB+COpex-PVB，
wherein, Ctotal-PVBThe total annual investment and operation cost of the photovoltaic energy storage system, CCapex-PVBFor the annual investment costs of the photovoltaic energy storage system, COpex-PVBThe annual running cost of the photovoltaic energy storage system is saved;
(62) establishing a constraint function of the photovoltaic energy storage system, comprising:
(a) charge and discharge power constraint:
in the formula (I), the compound is shown in the specification,
charging power for the t hour on day d,
the discharge power at the t hour on day d,
d is more than or equal to 1 and less than or equal to 365, and t is more than or equal to 1 and less than or equal to 24;
(b) energy balance constraint:
in the formula, EBESS(d,t)EBESS(t) is the amount of energy stored in the battery at the time of day d and t, EBESS(d,t-1)EBESS(t-1) is the electric quantity of the battery stored energy at the t-1 moment on the day d, etacEfficiency of charging the battery, ηdFor the battery discharge efficiency, Δ t is the time step;
(c) battery storage capacity constraint:
in the formula, EBESS(d, t) is the electric quantity stored by the battery at the time t on the day d,
storing energy rating, SOC, for a batteryminFor storing the minimum value allowed by the ratio of the residual capacity to the capacity of the battery, SOCmaxThe minimum value allowed by the ratio of the energy storage residual capacity to the capacity of the battery is obtained;
(63) and optimizing and solving to obtain the battery energy storage rated power, the battery energy storage rated capacity and the charging and discharging power at each moment in the photovoltaic energy storage system.
Annual investment cost C of photovoltaic energy storage systemCapex-PVBThe method specifically comprises the following steps:
in the formula (I), the compound is shown in the specification,
the rated capacity of the energy storage of the battery,
the rated power is stored for the battery,
in order to obtain the rated output power of the photovoltaic,
the capacity investment cost per kWh for battery energy storage,
the power investment cost per kW for battery energy storage,
is the unit power investment cost of the photovoltaic.
Annual operating charge C of photovoltaic energy storage systemOpex-PVBThe method specifically comprises the following steps:
in the formula, PPV→load(d, t) is the power of the photovoltaic supply load at the tth day, PPV→bess(d, t) is the power of the photovoltaic cell to charge the cell at the tth day on day d, PPV→grid(d, t) is the power of photovoltaic externally transmitted to a superior power grid in the tth hour on the day d, PPV(d, t) is the generated power of the photovoltaic,
charging power for the t hour on day d,
discharge power at day d, t hour, Ce(t) is the power grid electricity price at the moment t,
is the electricity price of the photovoltaic power generation outgoing power grid,
d is more than or equal to 1 and less than or equal to 365, and t is more than or equal to 1 and less than or equal to 24 for the subsidy price per kW of photovoltaic power generation.
The capacity configuration and operation optimization of the photovoltaic energy storage system are completed, the internal yield rate IRR, the photovoltaic absorption rate SCR and the self-load self-supporting rate SSR of the PVB system can be obtained, and the economic benefit of the photovoltaic system depends on the local absorption of the photovoltaic and is not the income delivered to a superior power grid. The battery energy storage increases matching between the photovoltaic and the load, and improves the self-consistency ratio (SCR) and the self-sufficiency ratio (SSR) of the photovoltaic.
The photovoltaic consumption rate SCR is defined as the amount of electricity E consumed locally by the photovoltaicPV,usedTotal electric quantity E generated by photovoltaicPV,genThe ratio of:
the self-sufficiency SSR of the load is defined as the amount E of electricity consumed locally by the photovoltaicPV,usedTotal amount of electricity consumed by load EloadThe ratio of:
in the embodiment, campus data of the university of manchester in the united kingdom are adopted, the campus data comprise a 6.6kV power distribution network, a heat power network and a gas network, the gas network is adopted in an east area A of the Oxford road, and the steam heat power network is adopted in a west area B of the Oxford road. The peak daily curves of thermal and electrical load are shown in figure 4. The energy conversion device configuration for the three energy supply scenarios is shown in table 2:
table 2 energy conversion device configuration for three energy supply scenarios
The energy price, carbon strength and carbon emission price are shown in table 3:
TABLE 3 energy price, carbon strength and carbon emission price
The parameters and prices of the energy conversion devices are shown in table 4:
TABLE 4 parameters and prices of energy conversion devices
Factors influencing the economical efficiency of the photovoltaic energy storage system are shown in table 5, the higher the grid peak-valley difference electricity price is, the lower the photovoltaic on-grid electricity price is, the better the energy storage benefit is
TABLE 5 factors affecting photovoltaic energy storage system economics
The solution of the optimization problem is accelerated by introducing the load rate into the objective function and parallel computing, and the computing time is less than 1 minute. The planned capacity and cost decomposition of the energy conversion device in the three scenarios is shown in fig. 5, fig. 5(a) is an exploded view of the planned capacity and cost of the energy conversion device in 2016, and fig. 5(b) is a decomposition of the planned capacity and cost of the energy conversion device in 2030. The planning results quantify the impact of energy and carbon prices on energy conversion equipment planning. CHP scenario 2 has advantages over other options without the objective function taking into account carbon prices. Scenario 2 still has advantages if the objective function considers carbon prices using 2016 financial data. However, as the grid carbon strength decreased significantly and the carbon price increased in 2030, the option of heat pump scenario 3 dominates. The results show that when the intensity of the carbon emission of the power grid is reduced to be below 130gCO2/kW, the total cost of the heat pump is lower than that of CHP.
The load peak day operation simulation was performed based on the planned capacities of the boiler, CHP and heat pump, and the net load curve obtained is shown in fig. 6. Where net load refers to the original load plus the power consumed by the heat pump or minus the power generated by the CHP.
The relationship between the optimal configuration capacity of the battery energy storage and the price in the scenario 3 is shown in fig. 7, and fig. 7(a), 7(b), and 7(c) correspond to scenario 1, scenario 2, and scenario 3 in this order.
When the battery energy storage price is reduced to be lower than 150$/kWh, the battery energy storage installation capacity is greatly increased, and the benefit of the photovoltaic energy storage system is greatly improved.
In the CHP scenario, the photovoltaic absorption rate SCR is very low at 0.34, and the self-supporting rate SSR is very high; in the heat pump scenario, the opposite is true, SCR 0.99 ~ 1.
The higher the photovoltaic absorption rate, the higher the Internal Return Rate (IRR) of the photovoltaic energy storage system. In the CHP scenario, most of the photovoltaic is delivered to the upper grid, and therefore the IRR of the photovoltaic energy storage system is low.
The gains of photovoltaic → stored energy (P _ (PV → bess)) come from: 1 storing the difference between the grid electricity price when the redundant photovoltaic power is stored and the photovoltaic outgoing electricity price, and 2 storing the difference between the grid electricity price when the redundant photovoltaic power is stored and the grid electricity price when the part of power is released. In the two-stage time-of-use electricity price, because the electricity transferred to the photovoltaic through the stored energy has no electricity price difference, and the profit of photovoltaic → stored energy comes from the difference between the electricity price of the power grid and the electricity price of photovoltaic delivery, the photovoltaic consumption rate SCR is in direct proportion to the system profit. The use of a heat pump increases the electrical load and therefore increases the photovoltaic rate of absorption SCR and thus the yield of the photovoltaic energy storage system. In the three-part time-of-use electricity price, the photovoltaic power profit absorbed by the heat pump may be smaller than the profit stored in the part of the photovoltaic power and released at the evening peak price, so the use of the heat pump does not necessarily increase the profit of the photovoltaic energy storage system.
Using the predicted 2025 year battery price 96$/kWh, the power balance every half hour for the 3 scenarios is shown in fig. 8, where fig. 8(a), 8(b), and 8(c) correspond in sequence to scenario 1, scenario 2, and scenario 3. The histogram shows the photovoltaic, battery charge and discharge, upper grid, CHP and heat pump power balance. The operation strategy of the battery energy storage is to charge the power grid at the valley price and with redundant photovoltaic power, and discharge the power grid at the peak price and at the evening without photovoltaic power.
In the reference scenario 1, the redundant photovoltaic power is used for storing and charging energy for the battery in the daytime, and the photovoltaic power is released in the evening.
In CHP scenario 2, most of the excess photovoltaic power during the day is sent out to the upper grid, since the power for the load is already supplied by the CHP during the evening.
In heat pump scenario 3, most of the redundant photovoltaic power is consumed by the heat pump during the day, and the battery energy storage is mainly charged by using the midnight valley price.
The energy storage arbitrage of scenario 1 mainly comes from the difference between the grid electricity price and the photovoltaic delivery electricity price. The arbitrage of scenario 3 mainly comes from the peak-to-valley difference of the grid electricity prices.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.
Claims (5)
1. A capacity allocation and operation optimization method combining photovoltaic energy storage and distributed energy sources is characterized by comprising the following steps:
(1) acquiring basic data of a distributed energy system and a thermal power and power load prediction data curve accurate to the hour of a year;
(2) according to basic data of the distributed energy system and a thermal power and power load prediction data curve, the capacity of energy conversion equipment in the distributed energy system is configured by taking the minimum annual total investment and operation cost of the distributed energy system as an optimization target;
(3) determining an optimized output curve of the distributed energy system according to the capacity configuration result of the energy conversion equipment;
(4) determining an electric net load curve according to the electric load prediction data curve and the distributed energy system operation optimization output curve;
(5) collecting basic data of a photovoltaic energy storage system;
(6) according to basic data and an electric net load curve of the photovoltaic energy storage system, optimally configuring battery energy storage rated power, battery energy storage rated capacity and a charging and discharging strategy in the photovoltaic energy storage system by taking the minimum annual total investment operation cost of the photovoltaic energy storage system as an optimization target;
the step (2) is specifically as follows:
(21) establishing an optimization objective function of the distributed energy system:
Min Ctotal-DER＝LFn·CCapex-DER+COpex-DER，
wherein, Ctotal-DERFor the annual total investment and operation cost of the distributed energy system, CCapex-DERFor the initial total investment cost, C, of energy conversion equipment in a distributed energy systemOpex-DERAnnual energy conversion equipment operating charge, LF, for a distributed energy systemnIn order to be a coefficient of capital recovery,
d is the discount rate, and n is the service life of the energy conversion equipment;
(22) establishing constraint functions of the distributed energy system, including equality constraint functions and inequality constraint functions,
wherein the equality constraint function is:
in the formula, PiIs the active power of the power of node i, ViIs the voltage of node i, VjIs the voltage of node j, NeIs the number of nodes, G, of the power systemijIs the conductance of line ij, BijIs the susceptance, θ, of line ijijIs the voltage angle difference of nodes i and j, QiElectric reactive power, C, for node ipIs the specific heat capacity of water, AhIs a heat power network association matrix and is characterized in that,
for mass flow, TsFor temperature of the water supply, ToFor the outlet water temperature, phi is the thermal power vector consumed or provided by each thermal node, BhIs a heat power network loop incidence matrix, KhIs the coefficient of resistance of the heat distribution network pipe, CsAs a water supply network coefficient matrix, bsIs a constant vector, CrAs a coefficient matrix of the backwater network, TrTo the return water temperature, brIs a constant vector, AgIs a gas network incidence matrix, vgIs node air pressure, vqTo node the gas flow rate, BgIs a gas network loop incidence matrix, KgIs the resistance coefficient of the gas network pipeline, and k is an exponential constant;
the inequality constraint function includes:
(a) and (3) limiting the upper limit and the lower limit of the capacity of the energy conversion equipment:
in the formula (I), the compound is shown in the specification,
for the value of the power active power provided by the conversion device at peak load,
the lower limit of the active power of the electricity supplied to the conversion equipment,
the upper limit of the electrical active power provided to the conversion equipment,
for converting the thermal power supplied by the device at load peaks,
the lower limit of the thermal power is,
is the upper limit of thermodynamic power;
(b) the power grid inequality constrains:
Vimin≤Vi≤Vimax,i＝1,…,Ne，
in the formula, ViIs the voltage of node i, ViminIs a nodeLower voltage limit of i, VimaxIs the upper voltage limit of node i, NeIs the number of nodes, PgeniFor the active power output of the ith generator,
the lower active power output limit of the ith generator,
upper limit of active power output, Q, of the ith generatorgeniIs the reactive power output of the ith generator,
the lower limit of reactive power output of the ith generator,
is the upper limit of reactive power output of the ith generator, NgeNumber of generators, SkIs the electrical power of the kth branch,
the lower limit of the electric power of the kth branch,
is the upper electric power limit of the kth branch, NleThe number of branches;
(c) the thermal power grid inequality constrains:
Ts_min≤Ts≤Ts_max，
Tr_min≤Tr≤Tr_max，
in the formula (I), the compound is shown in the specification,
the flow rate of each pipeline of the heating power network,
the lower limit of the flow of each pipeline of the heating power network,
is the upper limit of the flow of each pipeline of the heating power network, TsSupply of water temperature, T, to each node of the heating networks_minLower limit of water supply temperature, T, for each node of heating power networks_maxUpper limit of water supply temperature, T, for each node of heating power networkrFor the return water temperature, T, of each node of the heating power networkr_minThe lower limit of the return water temperature of each node of the heating power network, Tr_maxThe upper limit of the return water temperature of each node of the heating power network is set;
(d) the inequality constraint of the gas network is as follows:
pgmin≤pg≤pgmax，
vg_min≤vg≤vg_max，
in the formula, pgFor the gas pressure, p, at each node of the gas networkgminIs the lower limit of gas pressure of each node of the gas network, pgmaxIs the upper limit of gas pressure of each node of the gas network, vgIs the gas flow of each pipeline of the gas network, vg_minIs the lower limit of the gas flow of each pipeline of the gas network, vg_maxThe upper limit of the gas flow of each pipeline of the gas network;
(23) optimizing and solving to obtain the capacity of energy conversion equipment in the distributed energy system;
the step (6) is specifically as follows:
(61) establishing an optimization objective function of the photovoltaic energy storage system:
Min Ctotal-PVB＝CCapex-PVB+COpex-PVB，
wherein, Ctotal-PVBThe total annual investment and operation cost of the photovoltaic energy storage system, CCapex-PVBFor the annual investment costs of the photovoltaic energy storage system, COpex-PVBThe annual running cost of the photovoltaic energy storage system is saved;
(62) establishing a constraint function of the photovoltaic energy storage system, comprising:
(a) charge and discharge power constraint:
in the formula (I), the compound is shown in the specification,
charging power for the t hour on day d,
the discharge power at the t hour on day d,
d is more than or equal to 1 and less than or equal to 365, and t is more than or equal to 1 and less than or equal to 24;
(b) energy balance constraint:
in the formula, EBESS(d,t)EBESS(t) is the amount of energy stored in the battery at the time of day d and t, EBESS(d,t-1)EBESS(t-1) is the electric quantity of the battery stored energy at the t-1 moment on the day d, etacEfficiency of charging the battery, ηdFor the battery discharge efficiency, Δ t is the time step;
(c) battery storage capacity constraint:
in the formula, EBESS(d, t) is day d, time tThe amount of electricity that the battery stores,
storing energy rating, SOC, for a batteryminFor storing the minimum value allowed by the ratio of the residual capacity to the capacity of the battery, SOCmaxThe minimum value allowed by the ratio of the energy storage residual capacity to the capacity of the battery is obtained;
(63) and optimizing and solving to obtain the battery energy storage rated power, the battery energy storage rated capacity and the charging and discharging power at each moment in the photovoltaic energy storage system.
2. The method of claim 1, wherein the initial total investment cost C of the energy conversion equipment in the distributed energy system isCapex-DERThe method specifically comprises the following steps:
wherein the content of the first and second substances,
in order to convert the initial investment cost of the equipment i,
the electrical power provided by the inverter device i at peak load,
the V-shaped index represents the operator "OR" for the thermal power supplied by the switching device i at load peaks.
3. The method of claim 1, wherein the annual energy conversion equipment operating cost C in the distributed energy system isOpex-DERThe method specifically comprises the following steps:
wherein, CeFor purchase of electricity price, Pimport(t) buying electric power to the upper grid for the t hour of the year, CgIn order to be the price of the gas,
the total gas consumption rate in the t hour of the year, CcarbonIs the carbon price, ξeIs the carbon emission intensity of the powergIs the carbon emission intensity of natural gas, CO&MFor operating maintenance costs, T is the number of hours of a year.
4. The method for optimizing capacity allocation and operation of photovoltaic energy storage and distributed energy resource combination of claim 1, wherein the annual investment cost C of the photovoltaic energy storage systemCapex-PVBThe method specifically comprises the following steps:
in the formula (I), the compound is shown in the specification,
the rated capacity of the energy storage of the battery,
the rated power is stored for the battery,
in order to obtain the rated output power of the photovoltaic,
the capacity investment cost per kWh for battery energy storage,
the power investment cost per kW for battery energy storage,
is the unit power investment cost of the photovoltaic.
5. The method of claim 1, wherein the photovoltaic energy storage and distributed energy combined capacity allocation and operation optimization method is characterized in that the photovoltaic energy storage system is operated at an annual cost COpex-PVBThe method specifically comprises the following steps:
in the formula, PPV→load(d, t) is the power of the photovoltaic supply load at the tth day, PPV→bess(d, t) is the power of the photovoltaic cell to charge the cell at the tth day on day d, PPV→grid(d, t) is the power of photovoltaic externally transmitted to a superior power grid in the tth hour on the day d, PPV(d, t) is the generated power of the photovoltaic,
charging power for the t hour on day d,
discharge power at day d, t hour, Ce(t) is the power grid electricity price at the moment t,
is the electricity price of the photovoltaic power generation outgoing power grid,
d is more than or equal to 1 and less than or equal to 365, and t is more than or equal to 1 and less than or equal to 24 for the subsidy price per kW of photovoltaic power generation.
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