CN105279709A - Power grid day-ahead optimization scheduling method based on thermal inertia of hot water network - Google Patents

Abstract

The invention discloses a power grid day-ahead optimization scheduling method based on thermal inertia of a hot water network. The power grid day-ahead optimization scheduling method based on thermal inertia of a hot water network utilizes a traditional power grid day-ahead optimization scheduling model which does not consider the thermal inertia of a hot water network to simulate scheduling and search the serious period that a wind driven generator stops working, and then utilizes the thermal inertia of a hot water network to reasonably adjust the quantity of heat production for scheduling simulation under the premise that the indoor temperature of a heat consumer is satisfied, and can reduce the heat supply power of a cogeneration unit during the serious period that a wind driven generator stops working, and can optimize and adjust the reduced heat supply power to the no-serious period that a wind driven generator stops working, so the adjustable scope of the power supply power of the cogeneration unit during the serious period that a wind driven generator stops working can be increased so that more wind power can access the network; and according to the adjusted quantity of heat production, power grid day-ahead optimization scheduling is performed again. Compared with the prior art, the power grid day-ahead optimization scheduling method based on thermal inertia of a hot water network can improve the utilization rate of wind energy resources and reduce the generating cost under the premise that the indoor temperature requirement of the heat consumer is satisfied.

Description

A kind of electrical network based on heat supply network thermal inertia Optimization Scheduling a few days ago

Technical field

The present invention relates to a kind of electrical network Optimization Scheduling a few days ago, particularly relate to a kind of electrical network based on heat supply network thermal inertia Optimization Scheduling a few days ago, belong to electric power system dispatching technical field.

Background technology

Wind energy is the regenerative resource of most large-scale commercial potentiality to be exploited on our times.Large-scale develop and utilize wind power generation, become countries in the world and solved energy problem and environmental problem, improved energy structure, ensured the effective measures of national economy sustainable development.THE WIND ENERGY RESOURCES IN CHINA is enriched, and developable wind energy potential is huge, and the land total wind energy development amount adding sea about has 1000 ~ 1500GW, and wind-powered electricity generation has the resource base becoming important component part in China's future source of energy structure.Chinese Government is using greatly developing new forms of energy as reply climate change and the Important Action carrying out energy-saving and emission-reduction.Reach 15% for being accomplished to the proportion of the non-fossil energy of the year two thousand twenty China in primary energy consumption, and the year two thousand twenty GDP unit carbon intensity is from the target of decline 40% ~ 45% basis in 2005, following China greatly develops regenerative resource by continuing.

The regional wind energy aboundresources of China " three Norths ", but energy structure is unreasonable, install based on thermoelectricity, electrical network lacks effective peaking power source, and thermoelectricity unit proportion is huge in fired power generating unit, the impact being subject to heat supply winter seriously constrains the peak modulation capacity of this type of unit.Because hot water network has huge thermal inertia, the comfort level not affecting heat user is not mated in supply and demand in short-term.This makes to become possibility by reducing to abandon air quantity night to quantity of heat production adjustment.So not only utilize heat supply network thermal inertia to have dissolved wind-powered electricity generation, played heat supply network advantage capacious, have also been changed the original scheduling scheme of thermoelectricity unit, thus give full play to the peak modulation capacity of thermoelectricity unit, improve electrical network and to dissolve the ability of wind-powered electricity generation.

Summary of the invention

Technical matters to be solved by this invention is to overcome prior art deficiency, a kind of electrical network based on heat supply network thermal inertia Optimization Scheduling and device is a few days ago provided, can ensure, under the prerequisite that heat user indoor temperature meets the demands, to improve wind energy resources utilization factor, reduce cost of electricity-generating.

The present invention specifically solves the problems of the technologies described above by the following technical solutions:

Based on an electrical network Optimization Scheduling a few days ago for heat supply network thermal inertia, described electrical network comprises wind-powered electricity generation and cogeneration units; Described Optimization Scheduling a few days ago comprises:

Step 1, when not considering heat supply network thermal inertia, to abandon, air quantity is less and/or cost of electricity-generating is lower as optimization aim, carries out Optimized Simulated scheduling a few days ago to electrical network;

Step 2, find out from the result of the scheduling of Optimized Simulated a few days ago and abandon the wind serious period;

Step 3, calculate the heating power respectively abandoned the serious period planted agent of wind and reduce, and abandon the total amount Δ Q that heating power in the wind serious period should reduce; And using non-abandon the wind serious period abandon air quantity and/or cost of electricity-generating lower as optimization distribute target, minimum room temperature standard is not less than for constraint condition with the indoor temperature of abandoning wind serious period heat user, Δ Q is optimized be dispensed to and respectively non-ly abandon the wind serious period, obtain respectively non-ly abandoning the heating power that the wind serious period should increase; According to respectively abandoning of the calculating heating power adjustment amount correction that wind serious period and Ge Fei abandon the wind serious period day heating power curve that uses of Optimized Simulated dispatching office a few days ago, obtain revising rear day thermal load curve; Wherein, the heating power Δ Q that the serious period t planted agent of wind reduces is abandoned _tinitial value determine according to the following formula:

ΔQ _t＝k _t(P _wf,t-P _wp,t)+b _t

In formula, P _{wf, t}, P _{wp, t}the wind-powered electricity generation predicted power of t period, wind-powered electricity generation real power respectively; k _t, b _tbe and the pyroelecthc properties of cogeneration units and abandon the relevant parameter of air quantity;

Step 4, according to correction after day thermal load curve, to abandon, air quantity is less and/or cost of electricity-generating is lower as optimization aim, carries out Optimized Operation a few days ago to electrical network.

Preferably, k _tvalue be the mean value of cogeneration units Seebeck coefficient inverse, b _tfor the t period abandons 50% ~ 70% of air quantity.

Preferably, the indoor temperature of heat user is associated by following heat supply network model with between heating power adjustment amount:

$T_{g,t}=T_{n,t}+\frac{1}{2}(T_g^{\′}+T_h^{\′}-2T_n^{\′}){\left(\frac{T_{n,t}-T_{w,t}}{T_n^{\′}-T_w^{\′}}\right)}^{\frac{1}{1+B}}+\frac{1}{2{\stackrel{\‾}{G}}_t}(T_g^{\′}-T_h^{\′})\left(\frac{T_{n,t}-T_{w,t}}{T_n^{\′}-T_w^{\′}}\right)=\frac{Q_{load,t}-{\mathrm{\ΔQ}}_t}{C_{water}\×G_t}$

Wherein, T _g,t, T _g' represent supply water temperature and the water supply design temperature of t respectively; T _n,t, T _n' represent t indoor temperature and indoor design temperature respectively; T _w,t, T _w' represent t outdoor temperature and designed outside temperature respectively; T _h' represent backwater design temperature; B represents the heat radiation index of heating radiator; represent the relative discharge of t, i.e. the ratio of actual motion flow and design discharge; Q _{load, t}represent the prediction thermal load of t; Δ Q _trepresent the heating power adjustment amount of t; C _waterrepresent that specific heat of water holds, G _trepresent the total flow of water in t hot pipe network.

Compared to existing technology, technical solution of the present invention has following beneficial effect:

The present invention utilizes heat supply network thermal inertia, ensureing, under the prerequisite that heat user indoor temperature meets the demands, the quantity of heat production of cogeneration plant is optimized distribution between each time period, can to dissolve more fully on the one hand wind-powered electricity generation, improve wind power resources utilization factor, effectively can reduce consumption and the cost of electricity-generating of non-renewable resources on the other hand.

Accompanying drawing explanation

Fig. 1 is the schematic flow sheet of the present invention's Optimization Scheduling a few days ago;

Fig. 2 is the day electric load and wind-powered electricity generation prediction curve of input;

Fig. 3 is the day thermal load curve of input;

Fig. 4 be a few days ago Optimized Simulated dispatching office obtain abandon generator volume curve;

Fig. 5 is correction rear day thermal load curve;

Fig. 6 is that the wind electricity digestion situation of dispatching method of the present invention and conventional scheduling method contrasts.

Embodiment

Below in conjunction with accompanying drawing, technical scheme of the present invention is described in detail:

In order to overcome the deficiencies in the prior art, thinking of the present invention be first utilize traditional electrical network not considering heat supply network thermal inertia a few days ago Optimal Operation Model carry out operation simulation, find abandon the wind serious period; Then heat supply network thermal inertia is utilized, meeting the quantity of heat production of Reasonable adjustment operation simulation under the prerequisite that heat user indoor temperature meets the demands, abandoning the heating power of the serious period reduction cogeneration units of wind, and reduced heating power is optimized and revised abandon the wind serious period to non-, thus increase the adjustable extent of abandoning the output power of the serious period cogeneration units of wind, to receive more wind-powered electricity generation to network; Finally according to the quantity of heat production after adjustment, again carry out electrical network Optimized Operation a few days ago.

For the ease of public understanding, below by a specific embodiment, technical solution of the present invention is described in detail.Electrical network in the present embodiment a few days ago Optimization Scheduling as shown in Figure 1, specifically comprises the following steps:

Step one, input underlying parameter and electricity, thermal load data.Described underlying parameter comprises a day wind-powered electricity generation predicted data, the machine set type in this region and correlation parameter, the data such as hot water network flow and water temperature.Input in the present embodiment day electric load and wind-powered electricity generation prediction curve as shown in Figure 2, input day thermal load curve as shown in Figure 3.

Step 2, when not considering heat supply network thermal inertia, to abandon, air quantity is less and/or cost of electricity-generating is lower as objective function, carries out Optimized Simulated scheduling a few days ago to electrical network.

Various existing Optimal Operation Model can be adopted in this step, such as to abandon the less Optimal Operation Model for optimization aim of air quantity, with the lower Optimal Operation Model for optimization aim of cost of electricity-generating, or to abandon the less and lower Optimal Operation Model as optimization aim of cost of electricity-generating of air quantity.These existing Optimal Operation Models all do not consider the impact of heat supply network thermal inertia.Fig. 4 show to abandon the less and cost of electricity-generating of air quantity lower obtain for objective optimization abandon wind result, the difference wherein between day part wind-powered electricity generation predicted power and wind-powered electricity generation real power is abandons air quantity.

Step 3, find out from the result of the scheduling of Optimized Simulated a few days ago and abandon the wind serious period, specific as follows

If R is the set of abandoning the wind serious period:

R＝{t|P _wf,t-P _wp,t≥P _bound}(1)

Wherein P _{wf, t}the wind-powered electricity generation predicted power of t, P _{wp, t}the wind-powered electricity generation real power of t, P _boundfor abandoning the maximum magnitude of wind.

Step 4, thermal load curve to be revised.

Serious owing to abandoning wind in the moment belonging to R set, the reduction heating power that cogeneration units should be suitable.In the t ∈ R moment, the summation of all unit heating powers variable quantity is Δ Q _t.Δ Q _tinitial value can be determined by following formula:

ΔQ _t＝k _t(P _wf,t-P _wp,t)+b _t(2)

K in formula _t, b _twith the pyroelecthc properties of thermoelectricity unit with to abandon air quantity relevant, k _tgenerally get the mean value of cogeneration units Seebeck coefficient inverse.B _tbe generally the 50%-70% abandoning air quantity.

Then abandoning the total amount that in the wind serious period, heating power should reduce is d represents scheduling time inter.In order to ensure that in one day, cogeneration units gross heat input is constant, optimization distributes the total quantity of heat production Δ Q abandoning the reduction of wind serious period and abandons the wind serious period (supposing total τ) to non-.Optimize and distribute and should be not less than minimum room temperature standard (being generally 16 DEG C) for constraint condition with the indoor temperature of abandoning wind serious period heat user, using non-abandon the wind serious period abandon air quantity and/or cost of electricity-generating is lower distributes target as optimization, thus obtain respectively non-ly abandoning the heating power that the wind serious period should increase.Such as, following optimization object function can be adopted to be optimized distribution:

$\min \mathrm{\α}\underset{t\∈\mathrm{\τ}}{\Σ}(P_{wf,t}-P_{wp,t})+\mathrm{\β}\left(\underset{t\∈\mathrm{\τ}}{\Σ}\underset{i=1}{\overset{N_G}{\Σ}}{f}_{NG}\right(P_{i,t})+\underset{t\∈\mathrm{\τ}}{\Σ}\underset{i=1}{\overset{N_c}{\Σ}}{f}_{CHP}(P_{ci,t},Q_{i,t}\left)\right)---\left(3\right)$

N _g, N _cbe respectively pure condensate formula unit number and steam-extracting type unit number; α and β represents weight factor; f _nG(P _i,t), f _cHP(P _{ci, t}, Q _i,t) being respectively the cost function of pure condensate formula unit and steam-extracting type unit, conventional quadratic function represents:

$f_{NG}\left(P_{i,t}\right)=a_i{P}_{i,t}^2+b_i{P}_{i,t}+c_i---\left(4\right)$

$f_{CHP}(P_{ci,t},Q_{i,t})=d_{0,i}+d_{1,i}{P}_{ci,t}+d_{2,i}{Q}_{i,t}+d_{3,i}{P}_{ci,t}^2+d_{4,i}{P}_{ci,t}{Q}_{i,t}+d_{5,i}{Q}_{i,t}^2---\left(5\right)$

P _i,trepresent the output power of i-th pure condensing turbine in t, P _{ci, t}, Q _i,trepresent that i-th cogeneration units is at the output power of t and heating power respectively.

The indoor temperature of heat user is associated by various existing heat supply network model with between heating power adjustment amount, adopts following heat supply network model in the present embodiment:

$T_{g,t}=T_{n,t}+\frac{1}{2}(T_g^{\′}+T_h^{\′}-2T_n^{\′}){\left(\frac{T_{n,t}-T_{w,t}}{T_n^{\′}-T_w^{\′}}\right)}^{\frac{1}{1+B}}+\frac{1}{2{\stackrel{\‾}{G}}_t}(T_g^{\′}-T_h^{\′})\left(\frac{T_{n,t}-T_{w,t}}{T_n^{\′}-T_w^{\′}}\right)---\left(6\right)$

T in formula (6) _g,t, T _g' represent supply water temperature and the water supply design temperature of t respectively; T _n,t, T _n' represent t indoor temperature and indoor design temperature respectively; T _w,t, T _w' represent t outdoor temperature and designed outside temperature respectively; T _h' represent backwater design temperature; B represents the heat radiation index (experiment draws) of heating radiator, and its value is usually between 0.14-0.37; represent the relative discharge of t, i.e. the ratio of actual motion flow and design discharge.

The wherein hot water temperature T in t of a pipe network _g,tcan be obtained by following formula:

Wherein, Q _{load, t}represent the prediction thermal load of t; Δ Q _trepresent the heating power adjustment amount of t; C _waterrepresent that specific heat of water holds, G _trepresent the total flow of water in t hot pipe network.

In order to ensure the comfort level of heat user, indoor temperature can be controlled between 16 DEG C to 20 DEG C further, and be no more than 1 DEG C in the temperature variation of adjacent scheduling instance, namely the indoor temperature constraint condition of heat user is as follows:

$\{\begin{array}{c}16\≤T_{n,t}\≤20\\ -1\≤T_{n,t+1}-T_{n,t}\≤1\end{array}---\left(8\right)$

In formula, T _n,t, T _{n, t+1}represent t, the indoor temperature in t+1 moment respectively.

The heating power adjustment amount Δ Q of day part in a day (comprise and abandon the wind serious period and non-ly abandon the wind serious period) can be obtained by above process _t, utilize it to revise the day heating power curve that Optimized Simulated dispatching office a few days ago uses, obtain revising rear day thermal load curve, as shown in Figure 5.

Step 5, according to correction after day thermal load curve, to abandon, air quantity is less and/or cost of electricity-generating is lower as optimization aim, carries out Optimized Operation a few days ago to electrical network.

With the day thermal load curve of input in day thermal load curve alternative steps one after revising, other underlying parameter is constant, utilizes various existing Optimal Operation Model to be a few days ago optimized scheduling, can obtain the final scheme of Optimized Operation a few days ago.Use in the present embodiment and abandon the objective function that air quantity is less and cost of electricity-generating is lower, formula specific as follows:

$\min \mathrm{\α}\underset{t=1}{\overset{N}{\Σ}}(P_{wf,t}-P_{wp,t})+\mathrm{\β}\left(\underset{t=1}{\overset{N}{\Σ}}\underset{i=1}{\overset{N_G}{\Σ}}{f}_{NG}\right(P_{i,t})+\underset{t=1}{\overset{N}{\Σ}}\underset{i=1}{\overset{N_C}{\Σ}}{f}_{CHP}(P_{ci,t},Q_{i,t}\left)\right)---\left(9\right)$

In formula: N, N _g, N _cbe respectively time hop count, pure condensate formula unit number and the steam-extracting type unit number of one day; α and β represents weight factor; f _nG(P _i,t), f _cHP(P _{ci, t}, Q _i,t) be respectively the cost function of pure condensate formula unit and steam-extracting type unit.

Add the constraint of following Optimized Operation again:

Active balance retrains:

$\underset{i=1}{\overset{N_G}{\Σ}}{P}_{i,t}+\underset{i=1}{\overset{N_C}{\Σ}}{P}_{ci,t}+P_{wp,t}-P_{ex,t}=P_{load,t}---\left(10\right)$

P _{load, t}represent the burden with power of t; P _{ex, t}for system is at the outside transmission power of t, P _{ex, t}> 0 represents the outside transmission power of this region t, P _{ex, t}< 0 represents other regions in t to this region transmission power.

Grid power exchanges constraint:

P _ex,min≤P _ex,t≤P _ex,max(11)

Thermal equilibrium retrains:

$\underset{i=1}{\overset{N_c}{\&Sigma;}}{Q}_{i,t}=Q_{load,t}-{\mathrm{\&Delta;Q}}_t---\left(12\right)$

Wind power output retrains:

0≤P _wp,t≤P _wf,t(13)

Unit ramping rate constraints:

-DP _i≤P _i,t+1-P _i,t≤UP _i(14)

-DP _ci≤P _ci,t+1-P _ci,t≤UP _ci(15)

-DQ _i≤Q _i,t+1-Q _i,t≤UQ _i(16)

DP in above formula _i, UP _iand DP _ci, DP _cirepresent conventional power unit and cogeneration units output power respectively maximum downwards, upwards creep speed, DQ _ci, UQ _cirepresent cogeneration units heating power respectively maximum downwards, upwards creep speed.

According to the obtained scheme of Optimized Operation a few days ago, its wind electricity digestion situation as shown in Figure 6.In order to verify effect of the present invention, also show the wind electricity digestion situation adopting traditional Optimization Scheduling in figure, to compare.As can be seen from the figure, the present invention effectively can reduce and abandons wind, improves wind energy utilization.

In this example, abandon air quantity and reduce 54.1%, cost reduction 7.6%.This shows that utilizing heat supply network thermal inertia to reduce abandons wind, owing to having received more wind-powered electricity generation to make non-Wind turbines generating total amount decline, decreases the burning of fossil fuel.In addition, owing to utilizing heat supply network thermal inertia, and define indoor temperature between 16 DEG C-20 DEG C, so the quantity of heat production of cogeneration plant can more reasonably distribute, although intraday heat production total amount does not change, but the cogeneration units after heating load adjustment can at section operation more economically, and this just further reduces power supply heat cost.

In addition, for convenience of explanation, large-scale heat accumulation equipment is not considered in this embodiment.Unnecessary wind-powered electricity generation can be converted into heat energy by large centralised heat accumulation equipment, and the moment auxiliary heat pipe network higher in thermal load carries out heat supply.Utilize distributed heat pump also can as a kind of method of wind-powered electricity generation of dissolving.By this kind equipment and technical solution of the present invention with the use of, the digestion capability of electrical network to wind-powered electricity generation can be improved further, and reduce the use to non-renewable energy resources.

Claims (4)

1., based on an electrical network Optimization Scheduling a few days ago for heat supply network thermal inertia, described electrical network comprises wind-powered electricity generation and cogeneration units; It is characterized in that, described Optimization Scheduling a few days ago comprises:

Step 1, when not considering heat supply network thermal inertia, to abandon, air quantity is less and/or cost of electricity-generating is lower as optimization aim, carries out Optimized Simulated scheduling a few days ago to electrical network;

Step 2, find out from the result of the scheduling of Optimized Simulated a few days ago and abandon the wind serious period;

Step 3, calculate the heating power respectively abandoned the serious period planted agent of wind and reduce, and abandon the total amount Δ Q that heating power in the wind serious period should reduce; And using non-abandon the wind serious period abandon air quantity and/or cost of electricity-generating lower as optimization distribute target, minimum room temperature standard is not less than for constraint condition with the indoor temperature of abandoning wind serious period heat user, Δ Q is optimized be dispensed to and respectively non-ly abandon the wind serious period, obtain respectively non-ly abandoning the heating power that the wind serious period should increase; According to respectively abandoning of the calculating heating power adjustment amount correction that wind serious period and Ge Fei abandon the wind serious period day heating power curve that uses of Optimized Simulated dispatching office a few days ago, obtain revising rear day thermal load curve; Wherein, the heating power Δ Q that the serious period t planted agent of wind reduces is abandoned _tinitial value determine according to the following formula:

ΔQ _t＝k _t(P _wf,t-P _wp,t)+b _t

In formula, P _{wf, t}, P _{wp, t}the wind-powered electricity generation predicted power of t period, wind-powered electricity generation real power respectively; k _t, b _tbe and the pyroelecthc properties of cogeneration units and abandon the relevant parameter of air quantity;

Step 4, according to correction after day thermal load curve, to abandon, air quantity is less and/or cost of electricity-generating is lower as optimization aim, carries out Optimized Operation a few days ago to electrical network.

2. electrical network Optimization Scheduling a few days ago as claimed in claim 1, is characterized in that, is optimized by Δ Q to be dispensed to each non-constraint condition when abandoning the wind serious period and also to comprise: the change of indoor temperature between adjacent scheduling instance of heat user is no more than 1 DEG C.

3. electrical network Optimization Scheduling a few days ago as claimed in claim 1, is characterized in that, k _tvalue be the mean value of cogeneration units Seebeck coefficient inverse, b _tfor the t period abandons 50% ~ 70% of air quantity.

4. electrical network Optimization Scheduling a few days ago as claimed in claim 1, it is characterized in that, the indoor temperature of heat user is associated by following heat supply network model with between heating power adjustment amount:

$T_{g,t}=T_{n,t}+\frac{1}{2}\left(T_g^{\&prime;}+T_h^{\&prime;}-2T_n^{\&prime;}\right){\left(\frac{T_{n,t}-T_{w,t}}{T_n^{\&prime;}-T_w^{\&prime;}}\right)}^{\frac{1}{1+B}}+\frac{1}{2{\stackrel{\&OverBar;}{G}}_t}\left(T_g^{\&prime;}-T_h^{\&prime;}\right)\left(\frac{T_{n,t}-T_{w,t}}{T_n^{\&prime;}-T_w^{\&prime;}}\right)=\frac{Q_{load,t}-{\mathrm{\&Delta;Q}}_t}{C_{water}\&times;G_t}$

Wherein, T _g,t, T ' _grepresent supply water temperature and the water supply design temperature of t respectively; T _n,t, T ' _nrepresent t indoor temperature and indoor design temperature respectively; T _w,t, T ' _wrepresent t outdoor temperature and designed outside temperature respectively; T ' _hrepresent backwater design temperature; B represents the heat radiation index of heating radiator; represent the relative discharge of t, i.e. the ratio of actual motion flow and design discharge; Q _{load, t}represent the prediction thermal load of t; Δ Q _trepresent the heating power adjustment amount of t; C _waterrepresent that specific heat of water holds, G _trepresent the total flow of water in t hot pipe network.