A bidding method of distributed energy resource in micro-grid based on artificial immunity comprising the following steps:
- Processing the collected information by a bidding unit agent to form artificial immune quotation environmental antigen;
- Performing solving based on artificial immune algorithm to obtain antibody meeting the interest of a distributed energy resource;
- and
- Decoding the antibody to obtain a bidding scheme of the distributed energy resource;
- wherein the steps of performing solving based on artificial immune algorithm to obtain antibody meeting interest of a distributed energy resource and decoding the antibody to obtain a bidding scheme of the distributed energy resource are divided into the following steps:
- (1) Establish an antigen knowledge base based on typical characteristics of a bidding device and typical external environment;
- (2) Initiate a gene base based on history bidding strategy of the bidding process;
- (3) Identify the antigen of the bidding environment and compare it with a typical antigen stored in the antigen knowledge base, if a matched antigen is found, then perform step (4), otherwise perform step (5);
- (4) Perform a secondary artificial immunity response, take an antibody corresponding to the antigen in the antigen knowledge base as an antibody representative, and perform step (6);
- (5) Perform a primary response to a mismatched antigen to achieve an antibody meeting affinity as an antibody representative, and then perform step (6);
- and
- (6) Decode the obtained antibody and submit the bidding result to a bidding management agent or other agent;
- wherein the step of performing a primary response to a mismatched antigen to achieve an antibody meeting affinity as an antibody representative is divided into the following steps:
- (1) Select an antibody gene from a gene base randomly to construct an initialized antibody collection, t=0;
- (2) Calculate affinity of each antibody in the antibody collection upon the antigen of the bidding unit;
- (3) Evolve allele in artificial immune gene base based on the antibody affinity;
- (4) Determine whether a termination condition is met, if yes, perform step (5), otherwise perform step (7);
- (5) Perform immune evolution to the antibody collection to achieve a new antibody collection, and then perform step (6);
- (6) Set t=t+1 and perform step (2);
- (7) Take an antibody with largest affinity in the antibody collection as an antibody representative, and perform step (8);
- and
- (8) Update the antigen knowledge base, take the antigen and obtained antibody representative as a scene and store them into the antigen knowledge base;
- wherein the termination condition comprises:
- (a) The obtained affinity is greater than an object functionAffinityset;
- or
- (b) The variation of affinity between two adjacent calculations is smaller than a threshold value ε;
- or
- (c) Operation of the algorithm exceeds certain generation;
- Affinity
-
- (
- Ab
- )
- =
- {
- π
- i
-
- (
- b
- i
- ,
- c
- i
- )
- +
- C
- min
- ,
- if
-
-
- π
- i
-
- (
- b
- i
- ,
- c
- i
- )
- +
- C
- min
- >
- 0
- 0
- ,
- if
-
-
- π
- i
-
- (
- b
- i
- ,
- c
- i
- )
- +
- C
- min
- ≤
- 0
- wherein the calculation formula of the affinity is expressed as:
- max πi(bi,ci)=Qi·Pcl−b0iQi+1/2c0iQi2−UiCstart
- Wherein, Affinity(Ab) is the affinity of the antibody Ab;
- Cmin is an opposite value of fixed production cost for unit capacity of power generation of the distributed energy resource;
- and πi(bi, ci) is interest of the bidding unit based on artificial immune antibody, and the two gene element bi, ci are obtained according to the following formula:
- W herein, Pcl is electric energy clearance price between the micro-grid distributed energy resource and load;
- b0i and c0i are variable cost coefficient of a unit set;
- Qi is output of the generator;
- Cstart is the start price of the unit set;
- and for photovoltaic or wind power generation, Cstart=0;
- and Ui is a Boolean variable.