Source Code for Module pypower.makeApq

  1  # Copyright (C) 1996-2011 Power System Engineering Research Center (PSERC) 
  2  # Copyright (C) 2011 Richard Lincoln 
  3  # 
  4  # PYPOWER is free software: you can redistribute it and/or modify 
  5  # it under the terms of the GNU General Public License as published 
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  7  # or (at your option) any later version. 
  8  # 
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 10  # but WITHOUT ANY WARRANTY; without even the implied warranty of 
 11  # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 
 12  # GNU General Public License for more details. 
 13  # 
 14  # You should have received a copy of the GNU General Public License 
 15  # along with PYPOWER. If not, see <http://www.gnu.org/licenses/>. 
 16   
 17  """Construct linear constraints for generator capability curves. 
 18  """ 
 19   
 20  from numpy import array, linalg, zeros, arange, r_, c_ 
 21  from numpy import flatnonzero as find 
 22  from scipy.sparse import csr_matrix as sparse 
 23   
 24  from idx_gen import PC1, PC2, QC1MIN, QC1MAX, QC2MIN, QC2MAX 
 25   
 26  from hasPQcap import hasPQcap 
 27   
 28   
 29 -def makeApq(baseMVA, gen): 
 30      """Construct linear constraints for generator capability curves. 
 31   
 32      Constructs the parameters for the following linear constraints 
 33      implementing trapezoidal generator capability curves, where 
 34      C{Pg} and C{Qg} are the real and reactive generator injections:: 
 35   
 36          Apqh * [Pg, Qg] <= ubpqh 
 37          Apql * [Pg, Qg] <= ubpql 
 38   
 39      C{data} constains additional information as shown below. 
 40   
 41      Example:: 
 42          Apqh, ubpqh, Apql, ubpql, data = makeApq(baseMVA, gen) 
 43   
 44          data['h']      [Qc1max-Qc2max, Pc2-Pc1] 
 45          data['l']      [Qc2min-Qc1min, Pc1-Pc2] 
 46          data['ipqh']   indices of gens with general PQ cap curves (upper) 
 47          data['ipql']   indices of gens with general PQ cap curves (lower) 
 48   
 49      @author: Ray Zimmerman (PSERC Cornell) 
 50      @author: Carlos E. Murillo-Sanchez (PSERC Cornell & Universidad 
 51      Autonoma de Manizales) 
 52      @author: Richard Lincoln 
 53      """ 
 54      data = {} 
 55      ## data dimensions 
 56      ng = gen.shape[0]      ## number of dispatchable injections 
 57   
 58      ## which generators require additional linear constraints 
 59      ## (in addition to simple box constraints) on (Pg,Qg) to correctly 
 60      ## model their PQ capability curves 
 61      ipqh = find( hasPQcap(gen, 'U') ) 
 62      ipql = find( hasPQcap(gen, 'L') ) 
 63      npqh = ipqh.shape[0]   ## number of general PQ capability curves (upper) 
 64      npql = ipql.shape[0]   ## number of general PQ capability curves (lower) 
 65   
 66      ## make Apqh if there is a need to add general PQ capability curves 
 67      ## use normalized coefficient rows so multipliers have right scaling 
 68      ## in $$/pu 
 69      if npqh > 0: 
 70          data["h"] = c_[gen[ipqh, QC1MAX] - gen[ipqh, QC2MAX], 
 71                         gen[ipqh, PC2] - gen[ipqh, PC1]] 
 72          ubpqh = data["h"][:, 0] * gen[ipqh, PC1] + \ 
 73                  data["h"][:, 1] * gen[ipqh, QC1MAX] 
 74          for i in range(npqh): 
 75              tmp = linalg.norm(data["h"][i, :]) 
 76              data["h"][i, :] = data["h"][i, :] / tmp 
 77              ubpqh[i] = ubpqh[i] / tmp 
 78          Apqh = sparse((data["h"].flatten('F'), 
 79                         (r_[arange(npqh), arange(npqh)], r_[ipqh, ipqh+ng])), 
 80                        (npqh, 2*ng)) 
 81          ubpqh = ubpqh / baseMVA 
 82      else: 
 83          data["h"] = array([]) 
 84          Apqh  = zeros((0, 2*ng)) 
 85          ubpqh = array([]) 
 86   
 87      ## similarly Apql 
 88      if npql > 0: 
 89          data["l"] = c_[gen[ipql, QC2MIN] - gen[ipql, QC1MIN], 
 90                         gen[ipql, PC1] - gen[ipql, PC2]] 
 91          ubpql = data["l"][:, 0] * gen[ipql, PC1] + \ 
 92                  data["l"][:, 1] * gen[ipql, QC1MIN] 
 93          for i in range(npql): 
 94              tmp = linalg.norm(data["l"][i, :]) 
 95              data["l"][i, :] = data["l"][i, :] / tmp 
 96              ubpql[i] = ubpql[i] / tmp 
 97          Apql = sparse((data["l"].flatten('F'), 
 98                         (r_[arange(npql), arange(npql)], r_[ipql, ipql+ng])), 
 99                        (npql, 2*ng)) 
100          ubpql = ubpql / baseMVA 
101      else: 
102          data["l"] = array([]) 
103          Apql  = zeros((0, 2*ng)) 
104          ubpql = array([]) 
105   
106      data["ipql"] = ipql 
107      data["ipqh"] = ipqh 
108   
109      return Apqh, ubpqh, Apql, ubpql, data 
110