Patent ID: 11862973
Assignee: XIANGTAN UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G  H | IPC H

Claim 7:
8. The optimization method for capacity of a heat pump and power of various sets of energy source equipment in an energy hub according to claim 7, wherein the step (5) specifically comprises:
(5-1) obtaining heat pump capacity, lmid, mid, and rmid, from the upper-layer model; optimizing the capacity of each heat pump by operating the lower-layer model, and transmitting optimized results to the upper-layer model, wherein t=1 needs to be set every time;
(5-2) setting algorithm parameters corresponding to time t, and performing gene coding on decision variables, wherein the used real number encoding manners respectively comprise: Pe,CH, Pe,HP, Pe,EH, Pf;
(5-3) initializing a population P;
(5-4) calculating objective function values, operation cost Cop,t and exhaust emission C02,t at time t;
(5-5) calculating crowding distance between individuals in the population P;
(5-6) performing selection operation by using a binary tournament selection method,
wherein, in the binary tournament selection method, a tournament selection strategy is that two individuals are taken out from a population (put back for sampling), and then a better one is selected to enter an offspring population; the operation is repeated until a new population scale reaches the original population scale;
(5-7) performing crossover operation by using a simulated binary crossover operator (SBX); step (5-7) specifically includes:, x
          
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wherein, uj ∈ U(0,1), η=1 is a distribution index;
(5-8) performing mutation operation by using a polynomial mutation operator PM; step (5-8) specifically comprises:, x
          
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wherein, uj ∈ U(0,1), η=1 is a distribution index;
(5-9) merging a parent population P and an offspring population Q into a population R; sorting the population R according to a constraint violation degree and a non-dominant relationship; selecting the former N individuals of the ranked population to enter the next generation for evolution, wherein N is the size of the population; step (5-9) specifically includes:

CV=g1+g2+g3   (43)

the constraints (22), (23), (24), (25), (26), (28), and (29) are ensured to be strictly followed during initialization and evolution; loss functions, g1, g2, and g3, are calculated according to the formulae (18), (19), and (30), and the constraint violation degree is obtained through a formula (43); the individuals with the constraint violation degree of 0 are selected to enter the population P; if the population P is full, then crossover and mutation are started to be selected; otherwise, if the population P is not full, the individuals with low constraint violation degrees are selected; front individuals in a non-dominated sorting layer are selected when the constraint violation degrees are the same, and the individuals with large crowing distance are selected when non-dominated sorting layers are the same;
(5-10) determining whether an algorithm ending condition is satisfied, which specifically includes:
storing the solutions at the time t when continuous N generations of objective function values are converged to a small interval or reaches the maximum number of evolutionary iterations, including the objective function values and decision variables:
Cop(t), CO2(t), Pe,EH (t); Pe,HP(t); Pe,CH(t); Pf(t); executing step (5-11);
otherwise, re-executing step (5-4);
(5-11) determining whether the solutions at k typical times have been solved or not, which specifically includes that:
there are 8760 times in a year, and it can be known from a load function curve that the annual load demands are symmetrical, so the solutions of 4380 times need to be solved; in order to improve the optimization efficiency, the present disclosure only optimizes the solutions of k typical times by using the idea of typical times; if the solutions at k typical times have been solved, step (5-12) is executed, and otherwise, step (5-2) is re-executed;
(5-12) approaching the solutions of non-typical times by using a linear fitting method; step (5-12) specifically comprises that:
there are many linear fitting methods, wherein a Lagrange interpolation method and a cubic spline curve are commonly used; the present disclosure approaches the solutions of non-typical times by using the Lagrange interpolation method; Lagrange interpolation is performed between every two typical times to obtain the solutions of all non-typical times; finally, the solutions of the typical times and the non-typical times are merged;
(5-13) calculating optimal total annual operation cost Cop and optimal total annual exhaust emission CO2,tot, wherein

Cop=k*ΣCop,t=k*Σ(φfPf(t)+φe,energyPe(t))   (44)

is obtained according to the formulae (5), (6), (7), and (8);

CO2,tot=k*Σ(λePe(t)+λfPf(t))   (45)

Pe(t)=(Pe,EH(t)+Pe,HP(t)+Pe,CH(t)+Le)   (46)

are obtained according to the formulae (9), (10), and (11); and
(5-14) obtaining annual operation plans, optimal total annual operation cost, lmidCop, midCop, rmidCop, and optimal total annual exhaust emission lmidCo2tot, midCo2tot, and rmidCo2tot of various sets of energy source equipment in the energy hub corresponding to lmid, mid, and rmid according to the statistics of the power information, and Cop and CO2,tot of various sets of energy source equipment of all times, and transmitting the information the upper-layer model as parameters for subsequent optimization.