A method and system for evaluating the dispatch flexibility of a combined electric and thermal system

Abstract

The present invention provides a method for evaluating the dispatch flexibility of a combined electric and thermal system, comprising: determining uncertain scenarios of wind power and load the day before based on historical wind power data; bringing the forecast data of wind power, electric load and thermal load the day before under the uncertain scenarios into a pre-built optimization dispatch model to determine a unit combination plan; evaluating the dispatch flexibility of the combined electric and thermal system based on the unit combination plan using a pre-built safety verification model; wherein the safety verification model comprises: taking the minimization of wind abandonment and load shedding under uncertain scenarios as an objective function, introducing variables of the number of reserve shortage scenarios and the total reserve shortage into the objective function, and obtaining a pre-set dispatch flexibility evaluation index value according to the variables; the optimization dispatch model comprises: taking the minimization of the cost of thermal power units and cogeneration units as an objective; the index evaluation system takes into account the dispatch flexibility of conventional units and cogeneration units, and can reflect the adequacy of the system's upward and downward reserve.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching automation, and in particular relates to a dispatching flexibility evaluation method and system for an electric-heat combined system. Background Art

[0002] With the vigorous development of energy conversion equipment such as combined heat and power units, energy network coupling is becoming increasingly close. There are uncertain variables in the electric-heat coordinated optimization scheduling model. The sources of uncertainty include the prediction error of wind power output and the prediction error of electric and thermal load power. When the predicted value of wind power output is higher than the actual value or the predicted value of electric and thermal load is lower than the actual value, it is necessary to increase the output of controllable energy supply equipment or cut off part of the load to ensure the balance of electric power; conversely, when the predicted value of wind power output is lower than the actual value or the predicted value of electric and thermal load is higher than the actual value, it is necessary to reduce the output of controllable energy supply equipment or waste part of renewable energy, which will bring the risk of increased scheduling costs to the electric-heat combined system. The traditional "heat determines electricity" operation mode leads to a mandatory relationship between heat and electricity; while the mode in which thermal power units participate in peak regulation according to actual conditions lacks the flexibility of optimized overall planning, which limits the effect of thermal power units participating in peak regulation. Summary of the invention

[0003] In view of the existing traditional "heat determines electricity" operation mode, which leads to a mandatory relationship between heat and electricity; and the mode in which thermal power units participate in peak regulation according to actual conditions lacks the flexibility of optimized overall planning, which limits the effect of thermal power units participating in peak regulation. The present invention provides a scheduling flexibility evaluation method for a combined electric and thermal system, which specifically includes:

[0004] Determine the uncertain scenarios of wind power and load on the day ahead based on historical wind power data;

[0005] The forecast data of wind power, electric load and thermal load in the uncertain scenario are brought into the pre-built optimization scheduling model to determine the unit combination plan;

[0006] Based on the unit combination plan, the dispatch flexibility of the combined power and heat system is evaluated using a pre-built safety verification model;

[0007] The safety verification model includes: taking the minimum amount of wind curtailment and load shedding under uncertain scenarios as the objective function, introducing variables of the number of reserve shortage scenarios and the total amount of reserve shortage into the objective function, and obtaining a preset dispatch flexibility evaluation index value according to the variables;

[0008] The optimization scheduling model includes: taking the minimum cost of thermal power units and cogeneration units as the goal.

[0009] Preferably, the construction of the optimization scheduling model includes:

[0010] Determine the unit combination on the day before according to the forecast data of wind power, electric load and heat load on the day before under the uncertain scenario, and construct an objective function with the goal of minimizing the start-up and shutdown costs and operating costs of thermal power units, and the start-up and shutdown costs and operating costs of cogeneration units;

[0011] The constraints are CON unit power balance constraint, CON unit output upper and lower limit constraints, CON unit climbing constraint, CON unit transmission line capacity limit constraint, CHP unit power output constraint, CHP unit thermal output constraint, thermal system heating station constraint, thermal system heating network constraint, thermal system heat exchange station and thermal load constraint.

[0012] Preferably, the construction of the safety verification model includes:

[0013] The objective function is set to minimize the amount of wind curtailment and load shedding in the power system under uncertain scenarios;

[0014] At the same time, the constraints are CON unit power balance constraint, CON unit output upper and lower limit constraint, CON unit climbing constraint, CON unit transmission line capacity limit constraint, CHP unit power output constraint, CHP unit thermal output constraint, thermal system heating station constraint, thermal system heat network constraint, thermal system heat exchange station and thermal load constraint;

[0015] Introducing iteration variables of the number of reserve shortage scenarios and the total amount of reserve shortage and the number of iterations for the objective function;

[0016] Based on the iteration variables of the number of reserve shortage scenarios and the total reserve shortage amount and the number of iterations, respectively, when the system's reserve is insufficient for upward adjustment and when the system's reserve is insufficient for downward adjustment, the iterative total reserve shortage amount, the number of reserve shortage scenarios for upward adjustment, the total reserve shortage amount for downward adjustment and the number of occurrences of reserve shortage scenarios for downward adjustment are calculated;

[0017] Based on the pre-set scheduling flexibility evaluation indicators and the iterative total amount of increased reserve shortage, the number of increased reserve shortage scenarios, the total amount of reduced reserve shortage and the number of reduced reserve shortage scenarios and the number of iterations, the indicator value corresponding to each scheduling flexibility evaluation indicator is obtained.

[0018] Preferably, the scheduling flexibility evaluation index includes: an upward flexibility deficiency probability index, an upward flexibility deficiency expectation index, a downward flexibility deficiency probability index and a downward flexibility deficiency expectation index.

[0019] Preferably, the calculation of the occurrence times of the insufficient reserve scenario and the insufficient reserve amount is as follows:

[0020]

[0021] In the formula, δ down To reduce the number of occurrences of insufficient reserve scenarios, η down In order to reduce the total amount of insufficient reserves, is the wind abandonment at the sth iteration, T is the total time period, t is the time period, N w is the total number of wind farms, w is the number of wind farms;

[0022] The calculation of the number of reserve shortage scenarios and the total amount of reserve shortage is shown in the following formula:

[0023]

[0024] In the formula, δ up To increase the number of backup shortage scenarios, η up To increase the total reserve deficit, is the load shedding amount at the sth iteration, N d is the total number of load nodes, d is the load node;

[0025] The upward adjustment of the probability index of insufficient flexibility, the upward adjustment of the expected index of insufficient flexibility, the downward adjustment of the probability index of insufficient flexibility and the downward adjustment of the expected index of insufficient flexibility are calculated as follows:

[0026]

[0027] Where P UFNS，t To increase the probability index of insufficient flexibility, E UFNS，t To adjust the flexibility deficiency expectation upward, P DFNS，t To reduce the probability of insufficient flexibility, E DFNS，t The flexibility for downward adjustment is insufficient as expected.

[0028] Preferably, the method of determining the uncertain scenario of wind power and load on the previous day based on historical wind power data includes:

[0029] Divide the wind power output of the power system into intervals;

[0030] Acquire historical wind power data of each interval, and obtain historical prediction error distribution of wind power output based on the historical wind power data of each interval;

[0031] Based on the historical prediction error distribution, determine the covariance matrix of the day-ahead dynamic scenario, and generate multiple uncertain scenarios using MATLAB based on the covariance matrix;

[0032] The historical wind power data include: forecast data and actual data of wind power and load.

[0033] Preferably, the covariance matrix of the day-ahead dynamic scenario is determined based on the historical prediction error distribution, and multiple uncertain scenarios are generated using MATLAB based on the covariance matrix, including:

[0034] Based on the historical forecast error distribution, the covariance of any two multivariate normal random vectors in different time periods is calculated using an exponential function method, and a covariance matrix is ​​constructed from the covariance to determine the day-ahead dynamic scenario;

[0035] Based on the day-ahead dynamic scenario determined by the covariance matrix, a sample of a multivariate normal random vector is obtained by using a mathematical algorithm;

[0036] Based on the historical prediction error distribution, a relative prediction error is obtained by fitting the pre-acquired wind power prediction value using a cumulative empirical probability distribution function;

[0037] Obtaining an error scenario based on the multivariate normal random vector sample and the relative prediction error calculation;

[0038] Based on the multivariate normal random vector samples and the error scenario, the uncertain scenario is calculated using the cumulative probability distribution function of the standard normal distribution.

[0039] Preferably, the calculation of the cumulative empirical probability distribution function is as shown below:

[0040]

[0041]

[0042] In the formula, F l is the cumulative empirical probability distribution function of the prediction error, θ is the wind power random variable e and the sample δ k The relationship function is: K is the number of historical wind power forecast data in each interval, δ k It is the historical wind power forecast data of the interval;

[0043] The calculation of the uncertain scenario is as follows:

[0044] Φ(Z t )=F l (Δw t )

[0045]

[0046] Where Φ(·) is the uncertain scenario obtained by cumulative calculation of the cumulative probability distribution function of the standard normal distribution, Z t is a multivariate normal random vector sample, Δw t For error scenarios.

[0047] Based on the same concept, the present invention provides a dispatch flexibility evaluation system for a combined electric and thermal system, comprising: a scenario module, a unit combination module and an evaluation module;

[0048] The scenario module is used to determine the uncertain scenarios of wind power and load on the previous day based on historical wind power data;

[0049] The unit combination module is used to bring the forecast data of wind power, electric load and thermal load in the uncertain scenario into the pre-built optimization scheduling model to determine the unit combination plan;

[0050] The evaluation module is used to evaluate the dispatch flexibility of the combined power and heat system based on the unit combination plan using a pre-built safety verification model;

[0051] The safety verification model includes: taking the minimum amount of wind curtailment and load shedding under uncertain scenarios as the objective function, introducing variables of the number of reserve shortage scenarios and the total amount of reserve shortage into the objective function, and obtaining a preset dispatch flexibility evaluation index value according to the variables;

[0052] The optimization scheduling model includes: taking the minimum cost of thermal power units and cogeneration units as the goal.

[0053] Preferably, the unit combination module includes: an optimization scheduling model target submodule and an optimization scheduling model constraint submodule;

[0054] The target submodule of the optimization scheduling model is used to determine the unit combination on the day before according to the forecast data of the wind power, electric load and thermal load on the day before under the uncertain scenario, and to construct an objective function with the goal of minimizing the start-up and shutdown costs and operating costs of the thermal power units, and the start-up and shutdown costs and operating costs of the cogeneration units;

[0055] The optimization scheduling model constraint submodule is used to take CON unit power balance constraints, CON unit output upper and lower limit constraints, CON unit climbing constraints, CON unit transmission line capacity limit constraints, CHP unit power output constraints, CHP unit thermal output constraints, thermal system heating station constraints, thermal system heat network constraints, thermal system heat exchange station and thermal load constraints as constraint conditions.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. The present invention provides a method for evaluating the dispatch flexibility of a combined electric and thermal system, comprising: determining uncertain scenarios of wind power and load the day before based on historical wind power data; bringing the forecast data of wind power, electric load and thermal load the day before under the uncertain scenarios into a pre-built optimization dispatch model to determine a unit combination plan; evaluating the dispatch flexibility of the combined electric and thermal system based on the unit combination plan using a pre-built safety verification model; wherein the safety verification model comprises: taking the minimization of wind abandonment and load shedding under uncertain scenarios as an objective function, introducing variables of the number of reserve shortage scenarios and the total reserve shortage into the objective function, and obtaining a pre-set dispatch flexibility evaluation index value according to the variables; the optimization dispatch model comprises: taking the minimization of the cost of thermal power units and cogeneration units as a goal; the index evaluation system takes into account the dispatch flexibility of conventional units and cogeneration units, and can respectively reflect the adequacy of the system's upward and downward reserve. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flow chart of the method provided by the present invention;

[0059] Figure 2 An evaluation flow chart of a method for evaluating the scheduling flexibility of a combined electric and thermal system provided in an embodiment of the present invention;

[0060] Figure 3 A flow chart of system flexibility evaluation based on dynamic scenario generation provided in an embodiment of the present invention;

[0061] Figure 4 IEEE-24 node system diagram provided for an embodiment of the present invention;

[0062] Figure 5 A 16-node thermal system diagram provided for an embodiment of the present invention;

[0063] Figure 6 A forecast data diagram of wind power, load, and outdoor temperature provided by an embodiment of the present invention;

[0064] Figure 7 An uncertain scene diagram of wind power provided by an embodiment of the present invention;

[0065] Figure 8 An uncertain scenario graph of a load provided by an embodiment of the present invention;

[0066] Fig. 9 A system structure diagram provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The embodiments of the present invention are further described in conjunction with the accompanying drawings.

[0068] Embodiment 1:

[0069] The present invention provides a dispatch flexibility evaluation method for a combined heat and power system. First, considering the uncertainty of wind power and its coordination with conventional units and cogeneration units, a system operation flexibility index system is defined. The index system takes into account the upward flexibility and downward flexibility of conventional units and cogeneration units, and can reflect the adequacy of the system's upward and downward reserve reserves. Then, based on a dynamic scenario generation algorithm, a dispatch flexibility evaluation method for a combined heat and power system is proposed. The system flexibility is evaluated according to the system dispatch results, and combined with the system operation flexibility index system, the system operation flexibility index system is defined. Figure 1 The method flow chart is introduced, which specifically includes:

[0070] Step 1: Determine the uncertain scenarios of wind power and load on the day before based on historical wind power data;

[0071] Step 2: Bring the forecast data of wind power, electric load and thermal load of the day before under the uncertain scenario into the pre-built optimization scheduling model to determine the unit combination plan;

[0072] Step 3: Based on the unit combination plan, the dispatch flexibility of the combined power and heat system is evaluated using a pre-built safety verification model;

[0073] Among them, step 1: determine the uncertain scenarios of wind power and load on the day before based on historical wind power data, specifically including:

[0074] Define a flexibility index system for the coordination capability between conventional units and wind farm output during the operation phase;

[0075] The parameter PUFNS, probability of up flexibility not supplied, refers to the probability that the unit's up flexibility cannot meet demand during the operating day.

[0076]

[0077] Where: P UFNS，t To increase the probability of insufficient flexibility, RU t is the available increase capacity of the system at time t; P net，t+1 and P net，t are the net load at time t and t+1 respectively; and P i，t are the upper limit of the output of unit i and the actual output at time t; UR i is the ramp rate of unit i; Δt is the scheduling interval; Pr{·} represents the probability; N G is the total number of generators;

[0078] The parameter EUFNS, expected up flexibility not supplied, refers to the expected value of the difference between the up reserve that the unit can provide and the actual demand during the operating day.

[0079]

[0080] In the formula, E UFNS，t To adjust the flexibility to less than expected, ΔRU t The difference between the reserve capacity provided by the unit and the actual demand;

[0081] The parameter PDFNS, probability of down flexibility not supplied, refers to the probability that the unit's down flexibility cannot meet demand during the operating day.

[0082]

[0083] Where: P DFNS，t To reduce the probability of insufficient flexibility, RD t is the down-regulation capacity available to the system at time t; DR i For its lower climbing rate;

[0084] The parameter EDFNS, expected down flexibility not supplied, refers to the expected value of the difference between the down flexibility that the unit can provide and the actual demand during the operating day.

[0085]

[0086] In the formula, E DFNS，t The flexibility for downward adjustment is insufficient as expected.

[0087] A dynamic scenario generation method is proposed, which generates scenarios taking into account the correlation of random variables based on a large amount of historical data. Figure 3 The system flexibility evaluation flow chart based on dynamic scenario generation is introduced:

[0088] Generate wind power "forecast boxes" for each output range. Divide the wind power output range according to historical wind power forecast data (historical wind power and load forecast data), put the historical forecast data and actual output obtained by statistics into each "forecast box" according to the size of the forecast value, and calculate the distribution of the relative forecast error of each forecast box.

[0089] Determine the covariance matrix ∑ of the day-ahead dynamic scenario. The covariance matrix ∑ can be expressed as:

[0090]

[0091] In the formula, σ m，n is the covariance of the random variables in period m and period n, that is, the correlation between the two random variables.

[0092] An exponential function method is used to calculate the covariance:

[0093]

[0094] In the formula, ε is used to determine the correlation of random variables in time periods m and n.

[0095] After determining the covariance matrix, the MATLAB mathematical algorithm is used to generate S sample random vectors Z of multivariate normal random vectors that obey N(0,∑).

[0096] Fit the cumulative empirical probability distribution function of the relative prediction error of the data in each prediction box. For the first prediction box, there are K data in the prediction box, and the data (wind power prediction value) are arranged from small to large as δ1, δ2, ..., δ K Then the cumulative empirical probability distribution function of wind power prediction error e is:

[0097]

[0098]

[0099] Perform an inverse transformation on the multivariate normal random vector sample. Using the equal probability inverse transformation formula shown in equations (5)-(6), the S random vectors Z are transformed into S correlated error scenarios Δw t , and then generate S wind power scenarios based on the day-ahead wind power forecast.

[0100] Φ(Z t )=F l (Δw t ) (5)

[0101]

[0102] Where Φ(.) is the cumulative probability distribution function of the standard normal distribution.

[0103] Based on the generated uncertain scenarios, flexibility indicators are calculated.

[0104] Step 2: Bring the forecast data of wind power, electric load and thermal load in the uncertain scenario into the pre-built optimization scheduling model to determine the unit combination plan, including:

[0105] The unit combination of the day is determined according to the forecast data of wind power, electric load and heat load in the previous day, and the start and stop status of the conventional unit and the cogeneration unit at time t is obtained as U i，t (0-1 variable), set the number of simulations s = 0, and set the total number of iterations to S. The model for establishing the day-ahead unit commitment is as follows.

[0106] The model of day-ahead unit combination is established with optimal economy (lowest cost) as the objective function, and an optimal dispatching model that coordinates the power system and the district heating network is constructed.

[0107] The objective function includes the start-up and shutdown costs and operating costs of thermal power units, the start-up and shutdown costs and operating costs of cogeneration units, and the system's wind abandonment and load shedding penalties; thermal power units and CHP units, as well as wind abandonment and load shedding penalties are the probability weightings of the costs in each scenario. The mathematical form of the objective function is shown in the following formula:

[0108]

[0109] In the formula, It represents the startup cost of pure condensation (CON) unit; It represents the downtime cost of CON unit; It represents the startup cost of CHP unit; represents the downtime cost of the CHP unit; It represents the operating cost of the CON unit; represents the operating cost of the CHP unit; σ D is the load shedding penalty cost coefficient; They represent the CON unit output and the CHP unit output respectively.

[0110] The basic constraints of the power system include power balance constraint (8), upper and lower limit constraints of unit output (9), wind farm output constraint (10), minimum start-up and shutdown time constraint of unit (11)-(12), start-up and shutdown cost constraint of unit (13)-(14), unit ramp constraint (15)-(16), unit flexible adjustment constraint (17), and transmission line capacity constraint (18)-(19).

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] PL l，t =(θ n，t -θ o，t ) / x l ,θ ref，t =0 (18)

[0122]

[0123] Where PL l，t is the transmission power of the transmission line; PD d，t Indicates the load amount of the load node; Indicates wind power forecast value; PW w，t represents wind power output; G(n) represents the set of CON units located at node n, C(n) represents the set of CHP units located at node n, W(n) represents the set of wind farms located at node n, L(n) represents the set of transmission lines connected to node n, and D(n) represents the set of load users located at node n; Indicates the start and stop status of the CON unit; It is the lower and upper limits of the unit output; Indicates the time the unit has been started and stopped continuously; T on，i , T off，i Indicates the startup and shutdown time constraints of the unit; su i and sd i Indicates the unit start-up and shutdown cost of the unit. UR i and DR i is the unit climbing up and down constraint; θ n，t and θ o，t is the phase angle of the node connected to line l, θ ref，t is the phase angle of the equilibrium node, x l is the reactance of line l; is the maximum transmission power capacity of the line.

[0124] Among the above constraints, only the operating constraints of the CON unit are given. The electrical output and thermal output of the CHP unit are shown in equation (20). The operating cost of the CHP unit is shown in equation (21). The other constraints of the CHP unit are the same as those of the CON unit, and no detailed formulas are given.

[0125]

[0126]

[0127] In the formula, are the electrical output power and thermal output power of the jth CHP unit at time t, respectively; They represent the electrical output power and thermal output power of the j-th cogeneration unit corresponding to the k-th extreme point in the feasible domain; NK represents the output coefficient of the kth extreme point of the jth CHP unit at time t; j is the number of extreme points in the feasible region of the jth cogeneration unit; C chp (·) is the operating cost of the cogeneration unit; Represents the operating cost of each extreme point.

[0128] The constraints of the thermal system include heating station constraints (22)-(23), heating network constraints (24)-(28), heat exchange station and heat load constraints (29)-(31).

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139] In the formula, c w Represents the specific heat capacity of the fluid in the pipe; mc g，t represents the mass flow rate of the heat exchange station; Indicates the heating temperature of the heat exchange station; Indicates the heat recovery temperature of the heat exchange station; S g Indicates the serial number of the heat exchange station. and They respectively represent the lower limit and upper limit of the heating temperature of the heating station. represents the outlet temperature of the water supply pipe p at time t; represents the inlet temperature of the water supply pipe p at time t; Indicates outdoor temperature; μ p , L p , R p They represent the heat loss coefficient, length, and radius of the pipe respectively; Represents the mass flow rate of the fluid in the water supply pipe; ρ w represents the density of the fluid; Δt represents the scheduling time interval. Indicates the outlet temperature of the return pipe; Indicates the mass flow rate of the fluid in the return pipe; and Represents the temperature at node m of the water supply network and return network; Ω pipe- and Ω pipe+ They represent pipelines with node m as the end and start respectively. Indicates heat load; mh h，t , Indicates the hot water flow, supply water temperature, and return water temperature at the inlet of the heat exchange station. They are the lower and upper limits of the return water temperature respectively. Indicates the indoor temperature of a building; Indicates outdoor temperature; h，t is the heat transfer coefficient per unit temperature difference.

[0140] Step 3: Based on the unit combination plan, the dispatch flexibility of the combined power and heat system is evaluated using a pre-built safety verification model, including:

[0141] According to the historical forecast error distribution of wind power and electric load, the dynamic scenario generation method is used to generate the wind power and load time series curves, and based on the unit start and stop plan, the wind abandonment and load shedding variables are set to solve the following safety verification model:

[0142] The goal is to minimize the penalty for wind curtailment and load shedding under uncertain scenarios. The objective function is:

[0143]

[0144] Where: and are the wind curtailment and load shedding under scenario s, c W and c D They are wind curtailment and load shedding penalties respectively.

[0145] The constraints include: the basic constraints of the power system include power balance constraints (33), upper and lower limits of unit output constraints (34), unit ramp constraints (35)-(36), and transmission line capacity constraints (37)-(38). The meanings of the variables in the following constraints are the same as those in equations (8)-(19) and will not be explained again.

[0146]

[0147]

[0148]

[0149]

[0150] PL l，t，s =(θ n，t，s -θ o，t，s ) / x l ,θ ref，t =0 (37)

[0151]

[0152] In the formula, To solve the start and stop states of the CON units in the above dispatch model The start-stop plan obtained; represents the output of conventional units under scenario s; PL l，t，s is the transmission power of the transmission line under the uncertain scenario s; represents the load under uncertain scenario s; Represents the wind power output under scenario s.

[0153] The above constraints only give the operating constraints of the CON unit (pure condensing thermal power unit). The electrical output and thermal output of the CHP unit are shown in equation (39). The other constraints of the CHP unit (combined heat and power unit) are the same as those of the CON unit, and no detailed formulas are given.

[0154]

[0155] In the formula, is the start and stop plan of CHP units in the dispatch model obtained by solving; are the electrical output power and thermal output power of the jth CHP unit at time t under scenario s, respectively; are the electrical output power and thermal output power corresponding to the kth extreme point in the feasible domain of the jth cogeneration unit under scenario s, respectively; represents the output coefficient of the kth extreme point of the jth CHP unit at time t under scenario s; NK jis the number of extreme points in the feasible region of the jth cogeneration unit.

[0156] The constraints of the thermal system include heating station constraints (40)-(41), heating network constraints (42)-(46), heat exchange station and heat load constraints (47)-(49).

[0157]

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167] In the formula, the subscript s represents the uncertain scenario; Indicates the heating temperature of the heat exchange station; Indicates the heat recovery temperature of the heat exchange station; represents the outlet temperature of the water supply pipe p at time t; represents the inlet temperature of the water supply pipe p at time t; Indicates the outlet temperature of the return pipe; and Represents the temperature at node m of the water supply network and return network; Indicates the supply water temperature and return water temperature of the heat exchange station.

[0168] Set 4 variables δ up , δ down , η up , η down (The initial value is 0), used to record the simulation results. According to the calculation results, if the variable ΔW w，t，s When it is not all 0, it means that there is wind abandonment in the sth iteration, indicating that the system's down-regulation reserve is insufficient, and it is calculated according to the following formula:

[0169]

[0170] If the variable ΔD d，t，sWhen they are not all 0, it means that there is load shedding in the sth iteration, indicating that the system's upward reserve is insufficient, and the calculation is performed according to the following formula:

[0171]

[0172] Set s as the number of iterations, and stop iterating until the number of iterations reaches the set total number S. If it is satisfied, the simulation process ends and the system flexibility index is output. The flexibility index is shown in the following formula.

[0173]

[0174] Where: up Increase the number of backup shortage scenarios, δ down To reduce the number of occurrences of insufficient reserve scenarios, η up To increase the total reserve deficit, η down To reduce the total amount of insufficient reserve; N w is the total number of wind farms; N d is the total number of load nodes; T is the total time period.

[0175] In summary, the overall process of a dispatch flexibility evaluation method for a combined power and heat system is as follows: Figure 2 shown.

[0176] In order to enable those skilled in the art to better understand the present invention and appreciate the advantages of the present invention over the prior art, the applicant further explains the present invention in conjunction with specific embodiments.

[0177] The effectiveness of the proposed electric-thermal coordinated stochastic optimization scheduling model is verified based on the improved IEEE-24-bus power system and 16-bus thermal system. Figure 4 and Figure 5 The improved IEEE-24 node system includes 10 generators. The improvement is that 4 CON units are replaced by CHP units. In addition, it includes 6 CON units and 1 wind farm. The thermal system includes 16 nodes and 14 heat pipes. The forecast data of wind power, electric load and outdoor temperature are shown in Figure 6 As shown in the figure, the wind curtailment cost is set to 100$ / MWh and the load loss cost is set to 600$ / MWh. The proposed electric-heat coordinated stochastic optimization scheduling model is implemented by calling YALMIP and Gurobi-8.0.1 on MATLAB 2017b.

[0178] The flexibility assessment model proposed in this section first needs to use the dynamic scenario generation method to generate random wind power and load scenarios. The dynamic scenario generation method needs to establish a cumulative empirical probability distribution function through historical data. The historical data of wind power and load are derived from the data from September 2017 to August 2018 provided by Elia, a Belgian transmission operator, and the historical wind power and load data are adjusted proportionally.

[0179] The dynamic scenario generation method is used to randomly generate 1000 wind power and load scenarios, such as Figure 7 and Figure 8 shown.

[0180] This section evaluates the flexibility of the combined power and heat system considering system heating. In the IEEE-24 node system, units 1-6 are conventional thermal power units, and units 7-10 are cogeneration units. First, the unit combination decision and the unit's electrical output and thermal output under the prediction scenario are obtained.

[0181] After obtaining the unit combination plan under the expected scenario, the four indicators for evaluating system flexibility defined in this section are evaluated according to the generated dynamic scenario: upward flexibility deficiency rate, downward flexibility deficiency rate, upward flexibility deficiency expectation, and downward flexibility deficiency expectation.

[0182] Table 1 gives the system operation cost and system flexibility evaluation index values.

[0183] Table 1 System operation cost and flexibility evaluation indicators

[0184]

[0185] As can be seen from the table, in the flexibility assessment of the electric-thermal system, the system's downward flexibility deficiency rate is relatively high. That is, when the actual wind power output is higher than the predicted output or the actual load demand is lower than the predicted load, the unit cannot reduce its processing, resulting in wind abandonment. Unlike the power system analyzed in the previous section, the downward flexibility of the electric-thermal combined system is poor. Through analysis, it can be seen that since the cogeneration unit needs to provide heat in winter, the power output range of the cogeneration unit is constrained by the operating domain of the cogeneration unit. When the wind power output is higher than the predicted output, the cogeneration unit cannot reduce its power output, resulting in wind abandonment.

[0186] According to the method proposed in this section, the flexibility of the units in the system is evaluated next. The sum of the up / down flexible adjustment power provided by each unit during the entire dispatch period is given, as shown in Table 2.

[0187] Table 2 Flexible power provided by each unit during the entire dispatch period (MW)

[0188] unit Adjust flexible power Adjust flexible power downward 1 0 0 2 0 0 3 196.1716 7.989474 4 23.0625 1708.556 5 40.94228 1858.622 6 263.4084 207.6948 7 1570.19 18.77087 8 1626.938 78.06511 9 623.3696 99.31938 10 1402.116 49.42499

[0189] Table 2 shows the sum of the up / down flexible adjustment power of all units during the entire dispatch period. As can be seen from Table 2, units 4 and 5 mainly bear the system's down-regulation reserve. The upward flexibility of the cogeneration units is relatively high, while the downward flexibility is insufficient. As can be seen from the table, the average downward adjustment power of unit 7 is the lowest, and it can be considered that unit 7 is the key unit affecting the system peak regulation.

[0190] Embodiment 2:

[0191] Based on the same concept, the present invention provides a dispatch flexibility evaluation system for a combined electric and thermal system. Fig. 9 The system structure diagram is introduced, which specifically includes: scenario module, unit combination module and evaluation module;

[0192] The scenario module is used to determine the uncertain scenarios of wind power and load on the previous day based on historical wind power data;

[0193] The unit combination module is used to bring the forecast data of wind power, electric load and thermal load in the uncertain scenario into the pre-built optimization scheduling model to determine the unit combination plan;

[0194] The evaluation module is used to evaluate the dispatch flexibility of the combined power and heat system based on the unit combination plan using a pre-built safety verification model;

[0195] The safety verification model includes: taking the minimum amount of wind curtailment and load shedding under uncertain scenarios as the objective function, introducing variables of the number of reserve shortage scenarios and the total amount of reserve shortage into the objective function, and obtaining a preset dispatch flexibility evaluation index value according to the variables;

[0196] The optimization scheduling model includes: taking the minimum cost of thermal power units and cogeneration units as the goal.

[0197] The unit combination module includes: an optimization scheduling model target submodule and an optimization scheduling model constraint submodule;

[0198] The target submodule of the optimization scheduling model is used to determine the unit combination on the day before according to the forecast data of the wind power, electric load and thermal load on the day before under the uncertain scenario, and to construct an objective function with the goal of minimizing the start-up and shutdown costs and operating costs of the thermal power units, and the start-up and shutdown costs and operating costs of the cogeneration units;

[0199] The optimization scheduling model constraint submodule is used to take CON unit power balance constraints, CON unit output upper and lower limit constraints, CON unit climbing constraints, CON unit transmission line capacity limit constraints, CHP unit power output constraints, CHP unit thermal output constraints, thermal system heating station constraints, thermal system heat network constraints, thermal system heat exchange station and thermal load constraints as constraint conditions.

[0200] The evaluation module includes: a safety verification model target submodule, a safety verification model constraint submodule, a safety verification model variable submodule, an iteration variable submodule and an indicator value submodule;

[0201] The safety verification model target submodule is used to set the target function with the goal of minimizing the amount of wind abandonment and load shedding of the power system under uncertain scenarios;

[0202] The safety verification model constraint submodule is used to simultaneously use CON unit power balance constraints, CON unit output upper and lower limit constraints, CON unit climbing constraints, CON unit transmission line capacity limit constraints, CHP unit power output constraints, CHP unit thermal output constraints, thermal system heating station constraints, thermal system heat network constraints, thermal system heat exchange station and thermal load constraints as constraint conditions;

[0203] The safety verification model variable submodule is used to introduce iteration variables and iteration times of the number of reserve shortage scenarios and the total amount of reserve shortage into the objective function;

[0204] The iterative variable submodule is used to calculate the iterative total amount of insufficient reserve, the number of insufficient reserve scenarios, the total amount of insufficient reserve and the number of occurrences of insufficient reserve scenarios after iteration respectively when the system is insufficient to increase reserve and when the system is insufficient to decrease reserve based on the iterative variables of the insufficient reserve scenario number and the insufficient reserve total amount and the number of iterations, respectively.

[0205] The indicator value submodule is used to obtain the indicator value corresponding to each scheduling flexibility evaluation indicator based on the pre-set scheduling flexibility evaluation indicator and the iterative total amount of increased reserve shortage, the number of increased reserve shortage scenarios, the number of decreased reserve shortage scenarios and the number of iterations.

[0206] The scene module includes: a division submodule, an error distribution submodule and a generation submodule;

[0207] The division submodule is used to divide the wind power output of the power system into intervals;

[0208] The error distribution submodule is used to obtain historical wind power data of each interval, and obtain historical prediction error distribution of wind power output based on the historical wind power data of each interval;

[0209] The generating submodule is used to determine the covariance matrix of the day-ahead dynamic scenario based on the historical prediction error distribution, and generate multiple uncertain scenarios using MATLAB based on the covariance matrix;

[0210] The historical wind power data include: forecast data and actual data of wind power and load.

[0211] The generation submodule includes: a day-ahead dynamic scene unit, a sample unit, a relative prediction error unit, an error scene unit, and an uncertain scene unit;

[0212] The day-ahead dynamic scene unit is used to calculate the covariance of any two multivariate normal random vectors in different time periods based on the historical prediction error distribution by using an exponential function method, and to construct a covariance matrix from the covariance to determine the day-ahead dynamic scene;

[0213] The sample unit is used to obtain a sample of a multivariate normal random vector using a mathematical algorithm based on the day-ahead dynamic scenario determined by the covariance matrix;

[0214] The relative prediction error unit is used to obtain a relative prediction error by fitting based on the historical prediction error distribution and the pre-acquired wind power prediction value using a cumulative empirical probability distribution function;

[0215] The error scenario unit is used to calculate the error scenario based on the multivariate normal random vector sample and the relative prediction error;

[0216] The uncertain scenario unit is used to calculate the uncertain scenario based on the multivariate normal random vector sample and the error scenario using the cumulative probability distribution function of the standard normal distribution.

[0217] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0218] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0219] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0220] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0221] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for evaluating the dispatch flexibility of a combined power and heat system, characterized in that: include: Determine the uncertain scenarios of wind power and load on the day ahead based on historical wind power data; The forecast data of wind power, electric load and thermal load in the uncertain scenario are brought into the pre-built optimization scheduling model to determine the unit combination plan; Based on the unit combination plan, the dispatch flexibility of the combined power and heat system is evaluated using a pre-built safety verification model; The safety verification model includes: taking the minimum amount of wind curtailment and load shedding under uncertain scenarios as the objective function, introducing variables of the number of reserve shortage scenarios and the total amount of reserve shortage into the objective function, and obtaining a preset dispatch flexibility evaluation index value according to the variables; The optimization scheduling model includes: taking the minimum cost of thermal power units and cogeneration units as the goal; The construction of the safety verification model includes: The objective function is set to minimize the amount of wind curtailment and load shedding in the power system under uncertain scenarios; At the same time, the constraints are CON unit power balance constraint, CON unit output upper and lower limit constraint, CON unit climbing constraint, CON unit transmission line capacity limit constraint, CHP unit power output constraint, CHP unit thermal output constraint, thermal system heating station constraint, thermal system heat network constraint, thermal system heat exchange station and thermal load constraint; Introducing iteration variables of the number of reserve shortage scenarios and the total amount of reserve shortage and the number of iterations for the objective function; Based on the iteration variables of the number of reserve shortage scenarios and the total reserve shortage amount and the number of iterations, respectively, when the system's reserve is insufficient for upward adjustment and when the system's reserve is insufficient for downward adjustment, the iterative total reserve shortage amount, the number of reserve shortage scenarios for upward adjustment, the total reserve shortage amount for downward adjustment and the number of occurrences of reserve shortage scenarios for downward adjustment are calculated; Based on the preset scheduling flexibility evaluation index and the iterative total amount of insufficient reserve, the number of insufficient reserve scenarios, the total amount of insufficient reserve and the number of occurrences of insufficient reserve scenarios, and the number of iterations, the index value corresponding to each scheduling flexibility evaluation index is obtained; The scheduling flexibility evaluation index includes: an upward adjustment of the probability index of insufficient flexibility, an upward adjustment of the expected index of insufficient flexibility, a downward adjustment of the probability index of insufficient flexibility, and a downward adjustment of the expected index of insufficient flexibility; The calculation of the number of occurrences of the insufficient reserve scenario and the insufficient reserve amount is shown in the following formula: In the formula, δ down To reduce the number of occurrences of insufficient reserve scenarios, η down In order to reduce the total amount of insufficient reserves, is the wind abandonment at the sth iteration, T is the total time period, t is the time period, N w is the total number of wind farms, w is the number of wind farms; The calculation of the number of reserve shortage scenarios and the total amount of reserve shortage is shown in the following formula: In the formula, δ up To increase the number of backup shortage scenarios, η up To increase the total reserve deficit, is the load shedding amount at the sth iteration, N d is the total number of load nodes, d is the load node; The upward adjustment of the probability index of insufficient flexibility, the upward adjustment of the expected index of insufficient flexibility, the downward adjustment of the probability index of insufficient flexibility and the downward adjustment of the expected index of insufficient flexibility are calculated as follows: Where P UFNS,t To increase the probability index of insufficient flexibility, E UFNS,t To adjust the flexibility deficiency expectation upward, P DFNS,t To reduce the probability of insufficient flexibility, E DFNS,t The flexibility for downward adjustment is insufficient as expected.

2. The method according to claim 1, characterized in that The construction of the optimization scheduling model includes: Determine the unit combination on the day before according to the forecast data of wind power, electric load and heat load on the day before under the uncertain scenario, and construct an objective function with the goal of minimizing the start-up and shutdown costs and operating costs of thermal power units, and the start-up and shutdown costs and operating costs of cogeneration units; The constraints are CON unit power balance constraint, CON unit output upper and lower limit constraints, CON unit climbing constraint, CON unit transmission line capacity limit constraint, CHP unit power output constraint, CHP unit thermal output constraint, thermal system heating station constraint, thermal system heating network constraint, thermal system heat exchange station and thermal load constraint.

3. The method according to claim 1, characterized in that The uncertain scenarios of determining the day-ahead wind power and load based on historical wind power data include: Divide the wind power output of the power system into intervals; Acquire historical wind power data of each interval, and obtain historical prediction error distribution of wind power output based on the historical wind power data of each interval; Determine the covariance matrix of the day-ahead dynamic scenario based on the historical forecast error distribution, and generate multiple uncertain scenarios using MATLAB based on the covariance matrix; The historical wind power data include: forecast data and actual data of wind power and load.

4. The method according to claim 3, characterized in that The method of determining a covariance matrix of a day-ahead dynamic scenario based on the historical prediction error distribution and generating a plurality of uncertain scenarios using MATLAB based on the covariance matrix includes: Based on the historical forecast error distribution, the covariance of any two multivariate normal random vectors in different time periods is calculated using an exponential function method, and a covariance matrix is ​​constructed from the covariance to determine the day-ahead dynamic scenario; Based on the day-ahead dynamic scenario determined by the covariance matrix, a sample of a multivariate normal random vector is obtained by using a mathematical algorithm; Based on the historical prediction error distribution, a relative prediction error is obtained by fitting the pre-acquired wind power prediction value using a cumulative empirical probability distribution function; Obtaining an error scenario based on the multivariate normal random vector sample and the relative prediction error calculation; Based on the multivariate normal random vector samples and the error scenario, the uncertain scenario is calculated using the cumulative probability distribution function of the standard normal distribution.

5. The method according to claim 4, characterized in that The calculation of the cumulative empirical probability distribution function is shown in the following formula: In the formula, F l is the cumulative empirical probability distribution function of the prediction error, θ is the wind power random variable e and the sample δ k The relationship function is: K is the number of historical wind power forecast data in each interval, δ k It is the historical wind power forecast data of the interval; The calculation of the uncertain scenario is as follows: Φ(Z t )=F l (Δw t ) Δw t =F l -1 (Φ(Z t )) Where Φ(·) is the uncertain scenario obtained by cumulative calculation of the cumulative probability distribution function of the standard normal distribution, Z t is a multivariate normal random vector sample, Δw t For error scenarios.

6. A dispatch flexibility evaluation system for a combined electric and thermal system, characterized in that: include: Scenario module, crew combination module and evaluation module; The scenario module is used to determine the uncertain scenarios of wind power and load on the previous day based on historical wind power data; The unit combination module is used to bring the forecast data of wind power, electric load and thermal load in the uncertain scenario into the pre-built optimization scheduling model to determine the unit combination plan; The evaluation module is used to evaluate the dispatch flexibility of the combined power and heat system based on the unit combination plan using a pre-built safety verification model; The safety verification model includes: taking the minimum amount of wind curtailment and load shedding under uncertain scenarios as the objective function, introducing variables of the number of reserve shortage scenarios and the total amount of reserve shortage into the objective function, and obtaining a preset dispatch flexibility evaluation index value according to the variables; The optimization scheduling model includes: taking the minimum cost of thermal power units and cogeneration units as the goal; The unit combination module includes: an optimization scheduling model target submodule and an optimization scheduling model constraint submodule; The target submodule of the optimization scheduling model is used to determine the unit combination on the day before according to the forecast data of the wind power, electric load and thermal load on the day before under the uncertain scenario, and to construct an objective function with the goal of minimizing the start-up and shutdown costs and operating costs of the thermal power units, and the start-up and shutdown costs and operating costs of the cogeneration units; The optimization scheduling model constraint submodule is used to take the CON unit power balance constraint, CON unit output upper and lower limit constraints, CON unit climbing constraint, CON unit transmission line capacity limit constraint, CHP unit power output constraint, CHP unit thermal output constraint, thermal system heating station constraint, thermal system heat network constraint, thermal system heat exchange station and thermal load constraint as constraint conditions; Introducing iteration variables of the number of reserve shortage scenarios and the total amount of reserve shortage and the number of iterations for the objective function; Based on the iteration variables of the number of reserve shortage scenarios and the total reserve shortage amount and the number of iterations, respectively, when the system's reserve is insufficient for upward adjustment and when the system's reserve is insufficient for downward adjustment, the iterative total reserve shortage amount, the number of reserve shortage scenarios for upward adjustment, the total reserve shortage amount for downward adjustment and the number of occurrences of reserve shortage scenarios for downward adjustment are calculated; Based on the preset scheduling flexibility evaluation index and the iterative total amount of insufficient reserve, the number of insufficient reserve scenarios, the total amount of insufficient reserve and the number of occurrences of insufficient reserve scenarios, and the number of iterations, the index value corresponding to each scheduling flexibility evaluation index is obtained; The scheduling flexibility evaluation indicators in the evaluation module include: an upward adjustment of the probability index of insufficient flexibility, an upward adjustment of the expected index of insufficient flexibility, a downward adjustment of the probability index of insufficient flexibility, and a downward adjustment of the expected index of insufficient flexibility; The calculation of the number of occurrences of the insufficient reserve scenario and the insufficient reserve amount is shown in the following formula: In the formula, δ down To reduce the number of occurrences of insufficient reserve scenarios, η down To reduce the total amount of insufficient reserves, is the wind abandonment at the sth iteration, T is the total time period, t is the time period, N w is the total number of wind farms, w is the number of wind farms; The calculation of the number of reserve shortage scenarios and the total amount of reserve shortage is shown in the following formula: In the formula, δ up To increase the number of backup shortage scenarios, η up To increase the total reserve deficit, is the load shedding amount at the sth iteration, N d is the total number of load nodes, d is the load node; The upward adjustment of the probability index of insufficient flexibility, the upward adjustment of the expected index of insufficient flexibility, the downward adjustment of the probability index of insufficient flexibility and the downward adjustment of the expected index of insufficient flexibility are calculated as follows: Where P UFNS,t To increase the probability index of insufficient flexibility, E UFNS,t To adjust the flexibility deficiency expectation upward, P DFNS,t To reduce the probability of insufficient flexibility, E DFNS,t The flexibility for downward adjustment is insufficient as expected.

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