public int[,] UCPSearch(
MGInput m,
float[] p_target,
Action <float> progressCallback)
{
// Exhaustive Search is very expensive so we use a coarser step.
// But we'll need to translate from the coarse step to the
// orginal step so we'll store the translation coefficient
// in stepSize.
int stepCount = options.stepCount;
int stepSize = m.tCount / stepCount;
int taskCount = options.taskCount;
// Get the total number of possible combinations.
long nCombinations = (long)1 << (m.genCount * stepCount);
// Out fitness variable.
float lowestCost = Mathf.Infinity;
// Pre-calculate fuel costs at max power because we'll use them
// later to calculate a presumptive total cost.
float[] cost_thr_at_max = ThermalGenerator.GetCostAtMaxPower(
m.genCount, m.p_thr_max, m.thr_c_a, m.thr_c_b, m.thr_c_c, 60.0f);
// We only need to verify min uptime/downtime constraints if our
// step size is smaller than the the biggest min u/dtime. Otherwise
// our step is large enough that the algorithm will never be in a
// situation where it's trying to switch a generator that can't be
// switched due to min downtime/uptime constraints.
bool[] isValidationNeeded = IsMinTimeVerificationNeeded(
m.genCount, stepSize, m.thr_min_dtime, m.thr_min_utime);
// t1 and t2 define the interval of time this step occupies.
var stepInterval = new Tuple <int, int> [stepCount];
for (int iStep = 0; iStep < stepCount; iStep++)
{
stepInterval[iStep] = Tuple.Create(
iStep * stepSize,
(iStep + 1) * stepSize - 1);
}
// It's expensive to compute the cost of the amount of power the
// microgrid needs to purchase inside the main loop. There are only
// 2^n * stepCount combinations possible which end up being
// redundantly recalculated a lot if done in the main loop.
// So we compute all of them ahead of time and store them in a
// dictionary.
Dictionary <StateStep, float> purchaseCostDict
= ConstructCostDictionary(m, stepCount, stepSize, 400, p_target);
// Running the search in threads makes pruning high cost
// combinations happen earlier and more often because a low cost
// variant is discovered earlier on.
long bestComb = -1;
var tasks = new Task[taskCount];
long combSegmentSize = nCombinations / taskCount;
var taskParams = new TaskParams(
p_target,
stepCount,
stepSize,
stepInterval,
isValidationNeeded,
cost_thr_at_max,
purchaseCostDict);
var taskProgress = new float[taskCount];
void progressCallbackInner(int id, float completion)
{
taskProgress[id] = completion;
float totalProgress = 0;
for (int i = 0; i < taskProgress.Length; i++)
{
totalProgress += taskProgress[i];
}
progressCallback(totalProgress / taskCount);
}
for (int iTask = 0; iTask < taskCount; iTask++)
{
taskProgress[iTask] = 0.0f;
long searchStart = iTask * combSegmentSize;
long searchEnd = (iTask + 1) * combSegmentSize;
int taskID = iTask;
var task = Task.Run(() => SearchTask(
m,
searchStart,
searchEnd,
ref bestComb,
ref lowestCost,
taskParams,
progressCallbackInner,
taskID));
tasks[iTask] = task;
}
var waiter = Task.WhenAll(tasks.ToArray());
waiter.Wait();
// Write out the step based state array
int[,] u_thr_best_step = new int[m.genCount, m.tCount];
for (int iStep = 0; iStep < stepCount; iStep++)
{
for (int iGen = m.genCount - 1; iGen >= 0; iGen--)
{
u_thr_best_step[iGen, iStep] = (int)(bestComb % 2);
bestComb /= 2;
}
}
// Translate step-based state array to original time based
// state array.
int[,] u_thr_best = new int[m.genCount, m.tCount];
for (int t = 0; t < m.tCount; t++)
{
int currStep = t / stepSize;
for (int iGen = 0; iGen < m.genCount; iGen++)
{
u_thr_best[iGen, t] = u_thr_best_step[iGen, currStep];
}
}
return(u_thr_best);
}
