diff --git a/docs/source/examples/dispatch_tutorial.ipynb b/docs/source/examples/dispatch_tutorial.ipynb
index dca9c8e..0b7f360 100644
--- a/docs/source/examples/dispatch_tutorial.ipynb
+++ b/docs/source/examples/dispatch_tutorial.ipynb
@@ -25,16 +25,25 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Solver set: cbc\n"
+     ]
+    }
+   ],
    "source": [
     "# basic imports\n",
     "import pandas as pd \n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from unyt import kW, minute, hour, day, MW\n",
+    "import sys\n",
     "\n",
     "# osier imports\n",
-    "from osier import DispatchModel\n",
+    "from osier import DispatchModel, LogicDispatchModel\n",
     "import osier.tech_library as lib\n",
     "\n",
     "\n",
@@ -210,7 +219,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -251,7 +260,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -313,8 +322,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0041415950000000005\n",
-      "Model ran in 0.987957 seconds.\n"
+      "0.0041415950022387\n",
+      "Model ran in 0.843126 seconds.\n"
      ]
     }
    ],
@@ -350,7 +359,7 @@
     {
      "data": {
       "text/plain": [
-       "True"
+       "np.True_"
       ]
      },
      "execution_count": 7,
@@ -415,7 +424,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>3.487597</td>\n",
+       "      <td>3.487598</td>\n",
        "      <td>-0.0</td>\n",
        "      <td>0.000078</td>\n",
        "    </tr>\n",
@@ -449,7 +458,7 @@
       ],
       "text/plain": [
        "   NaturalGas_Conv  Curtailment      Cost\n",
-       "0         3.487597         -0.0  0.000078\n",
+       "0         3.487598         -0.0  0.000078\n",
        "1         3.669428         -0.0  0.000082\n",
        "2         3.199753         -0.0  0.000072\n",
        "3         3.351018         -0.0  0.000075\n",
@@ -473,7 +482,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
@@ -508,6 +517,140 @@
    "source": [
     "We see that the natural gas plant perfectly fulfilled the demand at each time step."
    ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hierarchical Dispatch\n",
+    "\n",
+    "`osier` offers a second, faster, dispatch algorithm called\n",
+    "`osier.LogicDispatchModel`. Although this model is faster, it is myopic.\n",
+    "Therefore, optimality is not guaranteed. Especially for systems with a lot of\n",
+    "renewable energy and battery storage.\n",
+    "\n",
+    "`LogicDispatchModel` can be used as a drop-in replacement for the original `DispatchModel`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0041415950000000005\n",
+      "Model ran in 0.101489 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "start = time.perf_counter()\n",
+    "model = LogicDispatchModel(technology_list=technology_mix,\n",
+    "                      net_demand=demand\n",
+    "                     )\n",
+    "model.solve()  # add your preferred solver here!\n",
+    "end = time.perf_counter()\n",
+    "print(model.objective)\n",
+    "print(f\"Model ran in {(end-start):3f} seconds.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with plt.style.context(\"dark_background\"):\n",
+    "    fig, ax = plt.subplots(figsize=(8,4))\n",
+    "    ax.grid(alpha=0.2)\n",
+    "    ax.minorticks_on()\n",
+    "    ax.fill_between(hours, \n",
+    "                    y1=0, \n",
+    "                    y2=model.results['NaturalGas_Conv'].values, \n",
+    "                    color='tab:orange', \n",
+    "                    label='Natural Gas')\n",
+    "    ax.plot(hours, model.net_demand, color='cyan', label='Net Demand')\n",
+    "    ax.set_xlim(0,48)\n",
+    "    ax.set_ylim(0,5.5)\n",
+    "    ax.legend()\n",
+    "    ax.set_ylabel(\"Demand [MW]\")\n",
+    "    ax.set_xlabel(\"Time [hr]\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time Benchmarking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "955 ms ± 118 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "model = DispatchModel(technology_list=technology_mix,\n",
+    "                      net_demand=demand\n",
+    "                     )\n",
+    "model.solve(solver=solver)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "264 ms ± 2.28 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "model = LogicDispatchModel(technology_list=technology_mix,\n",
+    "                      net_demand=demand\n",
+    "                     )\n",
+    "model.solve() "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe almost a 4x speed-up using the `LogicDispatchModel` over the\n",
+    "`DispatchModel`."
+   ]
   }
  ],
  "metadata": {
@@ -526,7 +669,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.6"
   },
   "orig_nbformat": 4
  },
diff --git a/osier/__init__.py b/osier/__init__.py
index beb8d40..ea11fff 100644
--- a/osier/__init__.py
+++ b/osier/__init__.py
@@ -10,7 +10,9 @@
 
 
 from .technology import *
+from .models.model import *
 from .models.dispatch import *
+from .models.logic_dispatch import *
 from .models.capacity_expansion import *
 from .models.deap_runner import *
 from .utils import *
diff --git a/osier/models/capacity_expansion.py b/osier/models/capacity_expansion.py
index f59f4ae..68f4aba 100644
--- a/osier/models/capacity_expansion.py
+++ b/osier/models/capacity_expansion.py
@@ -7,12 +7,17 @@
 import functools
 import time
 
-from osier import DispatchModel
+from osier import DispatchModel, LogicDispatchModel
 
 from pymoo.core.problem import ElementwiseProblem
 
 LARGE_NUMBER = 1e20
 
+dispatch_models = {'lp': DispatchModel,
+                   'hierarchical': LogicDispatchModel,
+                   'logical': LogicDispatchModel,
+                   'rule_based': LogicDispatchModel}
+
 
 class CapacityExpansion(ElementwiseProblem):
     """
@@ -98,6 +103,7 @@ def __init__(self,
                  allow_blackout=False,
                  verbosity=50,
                  solver='cbc',
+                 model_engine='lp',
                  **kwargs):
         self.technology_list = deepcopy(technology_list)
         self.demand = demand
@@ -109,6 +115,7 @@ def __init__(self,
         self.curtailment = curtailment
         self.allow_blackout = allow_blackout
         self.verbosity = verbosity
+        self.model_engine = model_engine.lower()
         self.solver = solver
 
         if isinstance(demand, unyt_array):
@@ -186,13 +193,14 @@ def _evaluate(self, x, out, *args, **kwargs):
             - wind_gen \
             - solar_gen
 
-        model = DispatchModel(technology_list=self.dispatchable_techs,
-                              net_demand=net_demand,
-                              power_units=self.power_units,
-                              curtailment=self.curtailment,
-                              allow_blackout=self.allow_blackout,
-                              solver=self.solver,
-                              verbosity=self.verbosity)
+        dispatch_model = dispatch_models[self.model_engine]
+        model = dispatch_model(technology_list=self.dispatchable_techs,
+                               net_demand=net_demand,
+                               power_units=self.power_units,
+                               curtailment=self.curtailment,
+                               allow_blackout=self.allow_blackout,
+                               solver=self.solver,
+                               verbosity=self.verbosity)
         model.solve()
 
         if model.results is not None:
diff --git a/osier/models/dispatch.py b/osier/models/dispatch.py
index 392b3a9..9cfda74 100644
--- a/osier/models/dispatch.py
+++ b/osier/models/dispatch.py
@@ -108,7 +108,7 @@ class DispatchModel():
         Can be overridden by specifying a unit with the value.
     solver : str
         Indicates which solver to use. May require separate installation.
-        Accepts: ['cplex', 'cbc']. Other solvers will be added in the future.
+        Accepts: ['cplex', 'cbc', 'appsi_highs']. Other solvers will be added in the future.
     lower_bound : float
         The minimum amount of energy each technology can produce per time
         period. Default is 0.0.
@@ -598,10 +598,10 @@ def solve(self, solver=None):
         else:
             optimizer = po.SolverFactory(self.solver)
 
-        results = optimizer.solve(self.model, tee=self.verbose)
         try:
+            optimizer.solve(self.model, tee=self.verbose)
             self.objective = self.model.objective()
-        except ValueError:
+        except (ValueError, RuntimeError):
             if self.verbosity <= 30:
                 warnings.warn(
                     f"Infeasible or no solution. Objective set to {LARGE_NUMBER}")
diff --git a/osier/models/logic_dispatch.py b/osier/models/logic_dispatch.py
new file mode 100644
index 0000000..24b5cbe
--- /dev/null
+++ b/osier/models/logic_dispatch.py
@@ -0,0 +1,100 @@
+from osier import OsierModel
+from osier.utils import get_tech_names
+from copy import deepcopy
+from unyt import unyt_array, MW
+import pandas as pd
+import numpy as np
+import warnings
+
+LARGE_NUMBER = 1e20
+
+
+class LogicDispatchModel(OsierModel):
+
+    def __init__(self,
+                 technology_list,
+                 net_demand,
+                 allow_blackout=False,
+                 curtailment=True,
+                 verbosity=50,
+                 *args, **kwargs):
+        super().__init__(technology_list=technology_list,
+                         net_demand=net_demand,
+                         *args, **kwargs)
+        self.technology_list = technology_list
+        self.technology_list.sort()
+        self.original_order = get_tech_names(self.technology_list)
+        self.cost_history = np.zeros(len(net_demand))
+        self.covered_demand = None
+        self.objective = None
+        self.results = None
+        self.verbosity = verbosity
+        self.allow_blackout = allow_blackout
+        self.curtailment = curtailment
+
+    def _reset_all(self):
+        for t in self.technology_list:
+            t.reset_history()
+        return
+
+    def _format_results(self):
+        data = {}
+        for t in self.technology_list:
+            data[f"{t.technology_name}"] = unyt_array(
+                t.power_history).to_ndarray()
+            if t.technology_type == 'storage':
+                data[f"{t.technology_name}_level"] = unyt_array(
+                    t.storage_history).to_ndarray()
+                data[f"{t.technology_name}_charge"] = unyt_array(
+                    t.charge_history).to_ndarray()
+        data["Curtailment"] = np.array(
+            [v if v <= 0 else 0 for v in self.covered_demand])
+        data["Shortfall"] = np.array(
+            [v if v > 0 else 0 for v in self.covered_demand])
+        self.results = pd.DataFrame(data)
+        return
+
+    def _calculate_objective(self):
+        self.objective = sum(np.array(t.power_history).sum()
+                             * t.variable_cost.to_value()
+                             for t in self.technology_list)
+        return
+
+    def solve(self):
+        """
+        This function executes the model solve with a rule-based approach.
+        Net demand is copied, then the technology histories are reset.
+        """
+        self.covered_demand = self.net_demand.copy()
+        self._reset_all()
+        try:
+            for i, v in enumerate(self.covered_demand):
+                for t in self.technology_list:
+                    power_out = t.power_output(v, time_delta=self.time_delta)
+                    v -= power_out
+
+                self.covered_demand[i] = v
+                if not self.allow_blackout and (v > 0):
+                    if self.verbosity <= 20:
+                        print('solve failed -- unmet demand')
+                    raise ValueError
+
+                if not self.curtailment and (v < 0):
+                    if self.verbosity <= 20:
+                        print(
+                            ('solve failed -- '
+                            'too much overproduction '
+                            '(no curtailment allowed)'))
+                    raise ValueError
+
+            self._format_results()
+            self._calculate_objective()
+        except ValueError:
+            if self.verbosity <= 30:
+                warnings.warn(
+                    (f"Infeasible or no solution." 
+                     f"Objective set to {LARGE_NUMBER}")
+                    )
+            self.objective = LARGE_NUMBER
+
+        return
diff --git a/osier/models/model.py b/osier/models/model.py
new file mode 100644
index 0000000..675fe91
--- /dev/null
+++ b/osier/models/model.py
@@ -0,0 +1,80 @@
+import pandas as pd
+import numpy as np
+from unyt import unyt_array, hr, MW, kW, GW
+import itertools as it
+from osier import Technology
+from osier.technology import _validate_quantity, _validate_unit
+from osier.utils import synchronize_units
+import warnings
+import logging
+
+_freq_opts = {'D': 'day',
+              'H': 'hour',
+              'S': 'second',
+              'T': 'minute'}
+
+
+class OsierModel():
+    """
+    A class for instantiating energy models in Osier.
+    """
+
+    def __init__(self,
+                 technology_list,
+                 net_demand,
+                 time_delta=None,
+                 power_units=MW,
+                 **kwargs):
+        self.net_demand = net_demand
+        self.time_delta = time_delta
+        self.results = None
+        self.objective = None
+
+        if isinstance(net_demand, unyt_array):
+            self.power_units = net_demand.units
+        elif isinstance(net_demand, (np.ndarray, list)):
+            self.power_units = power_units
+            self.net_demand = np.array(self.net_demand) * self.power_units
+        elif isinstance(net_demand, pd.core.series.Series):
+            self.power_units = power_units
+            self.net_demand = np.array(self.net_demand) * self.power_units
+        else:
+            self.power_units = power_units
+
+        self.technology_list = synchronize_units(
+            technology_list,
+            unit_power=self.power_units,
+            unit_time=self.time_delta.units)
+
+    @property
+    def time_delta(self):
+        return self._time_delta
+
+    @time_delta.setter
+    def time_delta(self, value):
+        if value:
+            valid_quantity = _validate_quantity(value, dimension='time')
+            self._time_delta = valid_quantity
+        else:
+            if isinstance(self.net_demand, pd.DataFrame):
+                try:
+                    freq_list = list(self.net_demand.index.inferred_freq)
+                    freq_key = freq_list[-1]
+                    try:
+                        value = float(freq_list[0])
+                    except ValueError:
+                        warnings.warn((f"Could not convert value "
+                                       f"{freq_list[0]} to float. "
+                                       "Setting to 1.0."),
+                                      UserWarning)
+                        value = 1.0
+                    self._time_delta = _validate_quantity(
+                        f"{value} {_freq_opts[freq_key]}", dimension='time')
+                except KeyError:
+                    warnings.warn(
+                        (f"Could not infer time delta with freq {freq_key} "
+                         "from pandas dataframe. Setting delta to 1 hour."),
+                        UserWarning)
+                    self._time_delta = 1 * hr
+            else:
+                self._time_delta = 1 * hr
diff --git a/osier/tech_library.py b/osier/tech_library.py
index 0501aad..2f0ccb2 100644
--- a/osier/tech_library.py
+++ b/osier/tech_library.py
@@ -96,7 +96,7 @@
                             fuel_cost=0*to_MDOLLARS,
                             storage_duration=4,
                             efficiency=0.85,
-                            initial_storage=0,
+                            initial_storage=0.0*MW*hr,
                             capacity_credit=0.5,
                             lifecycle_co2_rate=3.3e-5*co2_eq_units,
                             land_intensity=0.0,
diff --git a/osier/technology.py b/osier/technology.py
index 16b165a..91de10d 100644
--- a/osier/technology.py
+++ b/osier/technology.py
@@ -1,5 +1,5 @@
 import unyt
-from unyt import MW, hr, kg, km, m, megatonnes
+from unyt import MW, hr, kg, km, m, megatonnes, MWh
 from unyt import unyt_quantity, unyt_array
 from unyt.exceptions import UnitParseError
 from collections import OrderedDict
@@ -10,7 +10,7 @@
 
 _dim_opts = {'time': hr,
              'power': MW,
-             'energy': MW * hr,
+             'energy': MWh,
              'mass': kg,
              'length': km,
              'area': km**2,
@@ -18,8 +18,8 @@
              'specific_time': hr**-1,
              'specific_mass': kg**-1,
              'specific_power': MW**-1,
-             'specific_energy': (MW * hr)**-1,
-             'mass_per_energy': megatonnes * (MW * hr)**-1,
+             'specific_energy': (MWh)**-1,
+             'mass_per_energy': megatonnes * (MWh)**-1,
              'area_per_power': km**2 * MW**-1}
 
 _constant_types = (int, float, unyt_quantity)
@@ -194,10 +194,10 @@ class Technology(object):
         usable all of the time.
     capacity_credit : Optional, float
         Specifies the fraction of a technology's capacity that counts
-        towards reliability requirements. Most frequently used for 
-        renewable technologies. For example, a solar farm might have 
-        a capacity credit of 0.2. This means that in order to meet a 
-        capacity requirement of 1 GW, 1.25 GW of solar would need to 
+        towards reliability requirements. Most frequently used for
+        renewable technologies. For example, a solar farm might have
+        a capacity credit of 0.2. This means that in order to meet a
+        capacity requirement of 1 GW, 1.25 GW of solar would need to
         be installed.
         Default is 1.0, i.e. all of the technology's capacity contributes
         to capacity requirements.
@@ -206,9 +206,9 @@ class Technology(object):
         Generally only applicable for fossil fueled plants.
         If float, the default units are megatonnes per MWh
     lifecycle_co2_rate : float or :class:`unyt.array.unyt_quantity`
-        Specifies the rate at which of CO2eq emissions over a typical lifetime. 
+        Specifies the rate at which of CO2eq emissions over a typical lifetime.
         Unless you are reading this in a future where the economy is fully
-        decarbonized, all technologies should have a non-zero value for this 
+        decarbonized, all technologies should have a non-zero value for this
         attribute.
         If float, the default units are megatonnes per MWh
     land_intensity : float or :class:`unyt.array.unyt_quantity`
@@ -306,17 +306,58 @@ def __init__(self,
         self.om_cost_fixed = om_cost_fixed
         self.om_cost_variable = om_cost_variable
         self.fuel_cost = fuel_cost
+        self.power_level = self.capacity
         self.co2_rate = co2_rate
         self.lifecycle_co2_rate = lifecycle_co2_rate
         self.land_intensity = land_intensity
 
+        self.power_history = []
+
     def __repr__(self) -> str:
         return (f"{self.technology_name}: {self.capacity}")
 
     def __eq__(self, tech) -> bool:
         """Test technology equality"""
         if ((self.technology_name == tech.technology_name)
-                and (self.capacity == tech.capacity)):
+                and (self.capacity == tech.capacity)
+                and (self.variable_cost == tech.variable_cost)):
+            return True
+        else:
+            return False
+
+    def __ge__(self, tech) -> bool:
+        """Tests greater or equal to."""
+        if (self.variable_cost == tech.variable_cost):
+            return self.efficiency >= tech.efficiency
+        elif self.variable_cost >= tech.variable_cost:
+            return True
+        else:
+            return False
+
+    def __le__(self, tech) -> bool:
+        """Tests greater or equal to."""
+        if (self.variable_cost == tech.variable_cost):
+            return self.efficiency <= tech.efficiency
+        elif self.variable_cost <= tech.variable_cost:
+            return True
+        else:
+            return False
+
+    def __lt__(self, tech) -> bool:
+        """Tests greater or equal to."""
+        if (self.variable_cost == tech.variable_cost):
+            return self.efficiency < tech.efficiency
+        elif self.variable_cost < tech.variable_cost:
+            return True
+        else:
+            return False
+
+    def __gt__(self, tech) -> bool:
+        """Tests greater or equal to."""
+
+        if (self.variable_cost == tech.variable_cost):
+            return self.efficiency > tech.efficiency
+        elif self.variable_cost > tech.variable_cost:
             return True
         else:
             return False
@@ -385,6 +426,7 @@ def capacity(self):
     def capacity(self, value):
         valid_quantity = _validate_quantity(value, dimension="power")
         self._capacity = valid_quantity.to(self._unit_power)
+        self.power_level = self._capacity
 
     @property
     def capital_cost(self):
@@ -445,11 +487,13 @@ def co2_rate(self, value):
 
     @property
     def lifecycle_co2_rate(self):
-        return self._lifecycle_co2_rate.to(self.unit_mass * self.unit_energy**-1)
+        return self._lifecycle_co2_rate.to(
+            self.unit_mass * self.unit_energy**-1)
 
     @lifecycle_co2_rate.setter
     def lifecycle_co2_rate(self, value):
-        self._lifecycle_co2_rate = _validate_quantity(value, dimension="mass_per_energy")
+        self._lifecycle_co2_rate = _validate_quantity(
+            value, dimension="mass_per_energy")
 
     @property
     def land_intensity(self):
@@ -535,7 +579,7 @@ def variable_cost_ts(self, size):
                 raise AssertionError(
                     f"Variable cost data too short ({len(var_cost_ts)} < {size})")
             return var_cost_ts
-        
+
     def to_dataframe(self, cast_to_string=True):
         """
         Writes all technology attributes to a :class:`pandas.DataFrame` for export
@@ -555,7 +599,7 @@ def to_dataframe(self, cast_to_string=True):
                 continue
             elif value is None:
                 col = key.strip('_')
-                tech_data[col] = [str(value)] 
+                tech_data[col] = [str(value)]
             else:
                 if isinstance(value, unyt.unit_object.Unit):
                     continue
@@ -564,21 +608,44 @@ def to_dataframe(self, cast_to_string=True):
                     if cast_to_string:
                         tech_data[col] = ["{:.3g}".format(value.to_value())]
                     else:
-                        tech_data[col] = [np.round(value.to_value(),10)]
+                        tech_data[col] = [np.round(value.to_value(), 10)]
                 elif isinstance(value, (int, float)):
                     col = key.strip('_')
                     if cast_to_string:
                         tech_data[col] = ["{:.3g}".format(value)]
                     else:
-                        tech_data[col] = [np.round(value,10)]
+                        tech_data[col] = [np.round(value, 10)]
                 else:
                     continue
 
         tech_dataframe = pd.DataFrame(tech_data).set_index('technology_name')
 
         return tech_dataframe
-                
-            
+
+    def reset_history(self):
+        """
+        Resets the technology's power history for a new simulation.
+        """
+        self.power_history = []
+        self.power_level = self.capacity
+
+    def power_output(self,
+                     demand: unyt_quantity,
+                     **kwargs):
+        """
+        Raise or lower the power level to meet demand. Returns
+        current power level and appends to power history.
+
+        Parameters
+        ----------
+        demand : :class:`unyt.unyt_quantity`
+            The demand at a particular timestep. Must be a :class:`unyt.unyt_quantity`
+            to avoid ambiguity.
+        """
+        assert isinstance(demand, unyt_quantity)
+        self.power_level = max(0 * demand.units, min(demand, self.capacity))
+        self.power_history.append(self.power_level.copy())
+        return self.power_level
 
 
 class RampingTechnology(Technology):
@@ -645,6 +712,69 @@ def ramp_down(self):
             self.unit_time**-1
         )
 
+    def max_power(self, time_delta: unyt_quantity = 1 * hr):
+        """
+        Calculates the maximum achievable power for a technology
+        in the next timestep.
+
+        Parameters
+        ----------
+        time_delta : :class:`unyt.unyt_quantity`
+            The difference between two timesteps. Default is one hour.
+        """
+
+        output = self.power_level + self.ramp_up * time_delta
+        return min(self.capacity, output)
+
+    def min_power(self, time_delta: unyt_quantity = 1 * hr):
+        """
+        Calculates the minimum achievable power for a technology
+        in the next timestep.
+
+        Parameters
+        ----------
+        time_delta : :class:`unyt.unyt_quantity`
+            The difference between two timesteps. Default is one hour.
+        """
+
+        output = self.power_level - self.ramp_down * time_delta
+        return max(0 * self.unit_power, output)
+
+    def power_output(self,
+                     demand: unyt_quantity,
+                     time_delta: unyt_quantity = 1 * hr):
+        """
+        Raise or lower the power level to meet demand. Returns
+        current power level and appends to power history.
+        Checks if the power level can be achieved given the
+        technology's ramp rate.
+
+        Parameters
+        ----------
+        demand : :class:`unyt.unyt_quantity`
+            The demand at a particular timestep. Must be a :class:`unyt.unyt_quantity`
+            to avoid ambiguity.
+        time_delta : :class:`unyt.unyt_quantity`
+            The difference between two timesteps. Default is one hour.
+        """
+
+        assert isinstance(demand, unyt_quantity)
+        if self.power_level > demand:  # power must be lowered
+            self.power_level = max(
+                self.min_power(time_delta),
+                demand).to(
+                demand.units)
+        elif (self.power_level <= demand) and \
+             (self.capacity >= demand):  # power must be raised
+            self.power_level = (min(self.max_power(time_delta),
+                                    demand)).to(demand.units)
+        elif (self.power_level <= demand) and \
+             (self.capacity <= demand):
+            self.power_level = self.max_power(time_delta).to(demand.units)
+
+        self.power_history.append(self.power_level)
+        return self.power_level
+
 
 class ThermalTechnology(RampingTechnology):
     """
@@ -669,6 +799,7 @@ def __init__(
                          *args, **kwargs)
 
         self.heat_rate = heat_rate
+        self.power_level = self.capacity
 
 
 class StorageTechnology(Technology):
@@ -697,6 +828,9 @@ def __init__(
 
         self.storage_duration = storage_duration
         self.initial_storage = initial_storage
+        self.storage_level = self.initial_storage
+        self.storage_history = []
+        self.charge_history = []
 
     @property
     def storage_duration(self):
@@ -724,3 +858,58 @@ def initial_storage(self, value):
             raise AssertionError("Initial storage exceeds storage capacity.")
 
         self._initial_storage = valid_quantity
+        self.storage_level = valid_quantity
+
+    @property
+    def max_rate(self):
+        return self.capacity * self.unit_time
+
+    def reset_history(self):
+        """
+        Resets the technology's power history for a new simulation.
+        """
+        self.storage_history = []
+        self.storage_level = self._initial_storage
+        self.power_history = []
+        self.power_level = self.capacity
+        self.charge_history = []
+
+    def discharge(self, demand: unyt_quantity, time_delta=1 * hr):
+
+        # check that the battery has power to discharge fully.
+        power_out = max(0 * demand.units, min(demand, self.capacity))
+
+        # check that the battery has enough energy to meet demand.
+        energy_out = min(power_out * time_delta, self.storage_level)
+
+        out = self.storage_level - energy_out
+        self.storage_level = out
+        self.storage_history.append(out)
+        self.power_level = energy_out / time_delta
+        self.power_history.append(self.power_level)
+        self.charge_history.append(0 * demand.units)
+        return self.power_level.to(demand.units)
+
+    def charge(self, surplus, time_delta=1 * hr):
+
+        # check that the battery has enough power to consume surplus.
+        power_in = min(np.abs(min(0 * surplus.units, surplus)), self.capacity)
+
+        # check that the battery has enough space to store surplus.
+        energy_in = min((self.storage_capacity - self.storage_level),
+                        power_in * time_delta)
+
+        out = self.storage_level + energy_in
+        self.storage_level = out
+        self.storage_history.append(out)
+        self.power_level = -energy_in / time_delta
+        self.charge_history.append(self.power_level)
+        self.power_history.append(0 * surplus.units)
+        return self.power_level.to(surplus.units)
+
+    def power_output(self, v, time_delta=1 * hr):
+        if v >= 0:
+            output = self.discharge(demand=v, time_delta=time_delta)
+        else:
+            output = self.charge(surplus=v, time_delta=time_delta)
+        return output
diff --git a/osier/utils.py b/osier/utils.py
index 52f45fd..f8acf3e 100644
--- a/osier/utils.py
+++ b/osier/utils.py
@@ -51,7 +51,6 @@ def synchronize_units(tech_list: Iterable[Technology],
     return synced_list
 
 
-
 def get_tech_names(technology_list):
     """
     Returns the a list of :class:`osier.Technology` name strings.
@@ -74,7 +73,7 @@ def get_tech_names(technology_list):
 
 def get_dispatchable_techs(technology_list):
     """
-    Returns a list of :class:`osier.Technology` objects 
+    Returns a list of :class:`osier.Technology` objects
     where :attr:`dispatchable` is `True`.
 
     Parameters
@@ -84,7 +83,7 @@ def get_dispatchable_techs(technology_list):
 
     Returns
     -------
-    tech_names : list of :class:`osier.Technology`
+    dispatchable_techs : list of :class:`osier.Technology`
         The list of dispatchable technologies.
     """
 
@@ -95,7 +94,7 @@ def get_dispatchable_techs(technology_list):
 
 def get_nondispatchable_techs(technology_list):
     """
-    Returns a list of :class:`osier.Technology` objects 
+    Returns a list of :class:`osier.Technology` objects
     where :attr:`dispatchable` is `False`.
 
     Parameters
@@ -114,6 +113,50 @@ def get_nondispatchable_techs(technology_list):
     return non_dispatchable_techs
 
 
+def get_storage_techs(technology_list):
+    """
+    Returns a list of :class:`osier.Technology` objects
+    that have the attribute :attr:`storage_level`.
+
+    Parameters
+    ----------
+    technology_list : list of :class:`osier.Technology` objects
+        The list of technologies.
+
+    Returns
+    -------
+    storage_techs : list of :class:`osier.Technology`
+        The list of storage technologies.
+    """
+
+    storage_techs = [t for t in technology_list
+                     if hasattr(t, 'storage_level')]
+
+    return storage_techs
+
+
+def get_nonstorage_techs(technology_list):
+    """
+    Returns a list of :class:`osier.Technology` objects
+    that do not have the attribute :attr:`storage_level`.
+
+    Parameters
+    ----------
+    technology_list : list of :class:`osier.Technology` objects
+        The list of technologies.
+
+    Returns
+    -------
+    storage_techs : list of :class:`osier.Technology`
+        The list of non-storage technologies.
+    """
+
+    nonstorage_techs = [t for t in technology_list
+                        if not hasattr(t, 'storage_level')]
+
+    return nonstorage_techs
+
+
 def get_dispatchable_names(technology_list):
     """
     Returns a list of :class:`osier.Technology` name strings
@@ -160,5 +203,3 @@ def technology_dataframe(technology_list, cast_to_string=True):
     technology_dataframe = pd.concat(frames, axis=0)
 
     return technology_dataframe
-    
-
diff --git a/tests/test_models.py b/tests/test_models.py
index 2370a51..ed918f4 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -1,4 +1,4 @@
-from osier import DispatchModel
+from osier import DispatchModel, LogicDispatchModel
 from osier import Technology, ThermalTechnology, StorageTechnology, RampingTechnology
 from unyt import unyt_array
 import unyt
@@ -271,6 +271,7 @@ def test_dispatch_model_solve_case4(technology_set_4, net_demand):
     assert (total_gen - net_demand.sum()) == pytest.approx(0, abs=TOL)
     assert binary_charging == pytest.approx(0, abs=TOL)
 
+
 def test_dispatch_model_solve_case5(technology_set_4, net_demand):
     """
     Tests that the curtailment technology behaves as expected.
@@ -302,3 +303,25 @@ def test_dispatch_model_solve_case6(technology_set_4, net_demand):
                                'Curtailment',
                                'LoadLoss']].sum().sum()
     assert (total_gen - net_demand.sum()) == pytest.approx(0, abs=TOL)
+
+
+def test_hierarchical_model_solve_case1(technology_set_1, net_demand):
+    """
+    Tests that the dispatch model produces expected results. Where all
+    the technologies are simply :class:`Technology` objects. The model
+    should always choose the cheapest technology, as long as it has
+    enough capacity to meet the demand.
+    """
+    model = LogicDispatchModel(technology_list=technology_set_1,
+                               net_demand=net_demand,
+                               curtailment=False,
+                               allow_blackout=False)
+    model.solve()
+    cheapest_tech = unyt_array(
+        [t.variable_cost for t in technology_set_1]).min()
+    expected_result = cheapest_tech * net_demand.sum()
+
+    assert model.objective == pytest.approx(expected_result, rel=TOL)
+    assert model.results['Nuclear'].sum(
+    ) == pytest.approx(net_demand.sum(), TOL)
+    assert model.results['NaturalGas'].sum() == pytest.approx(0.0, rel=TOL)
diff --git a/tests/test_technology.py b/tests/test_technology.py
index 889678d..cc646f2 100644
--- a/tests/test_technology.py
+++ b/tests/test_technology.py
@@ -3,10 +3,11 @@
 import numpy as np
 import pandas as pd
 import osier
-from unyt import kW, MW, hr, BTU, Horsepower, day, kg, GW, megatonnes
+from unyt import kW, MW, hr, BTU, Horsepower, day, kg, GW, megatonnes, MWh
 from osier import Technology
 from osier.technology import _validate_unit, _validate_quantity
 from unyt.exceptions import UnitParseError
+from osier.tech_library import *
 
 TECH_NAME = "PlanetExpress"
 energy_unyt = 10.0 * MW * hr
@@ -34,9 +35,6 @@
 time_series_unyt = np.array(time_series_list) * time_unyt
 
 
-
-
-
 @pytest.fixture
 def advanced_tech():
     PLANET_EXPRESS = Technology(TECH_NAME)
@@ -47,7 +45,10 @@ def test_validate_unit():
     assert _validate_unit("MW", 'power').same_dimensions_as(Horsepower)
     assert _validate_unit("BTU", 'energy').same_dimensions_as(MW * hr)
     assert _validate_unit("day", 'time').same_dimensions_as(hr)
-    assert _validate_unit(mass_energy_str, 'mass_per_energy').same_dimensions_as(kg*BTU**-1)
+    assert _validate_unit(
+        mass_energy_str,
+        'mass_per_energy').same_dimensions_as(
+        kg * BTU**-1)
     assert _validate_unit(
         "Horsepower**-1",
         'specific_power').same_dimensions_as(
@@ -63,6 +64,7 @@ def test_validate_unit():
     with pytest.raises(KeyError) as e:
         _validate_unit("darkmatter", "fuel")
 
+
 def test_validate_quantity():
     assert _validate_quantity(power_unyt, 'power') == 10 * (MW)
     assert _validate_quantity(energy_unyt, 'energy') == 10 * (MW * hr)
@@ -80,11 +82,24 @@ def test_validate_quantity():
     with pytest.raises(KeyError) as e:
         _validate_quantity("10 darkmatter", "fuel")
 
+
 def test_validate_quantity_time_series():
-    assert (_validate_quantity(time_series_list, 'time') == time_series_unyt).all()
-    assert (_validate_quantity(time_series_np, 'time') == time_series_unyt).all()
-    assert (_validate_quantity(time_series_pd, 'time') == time_series_unyt).all()
-    assert (_validate_quantity(time_series_unyt, 'time') == time_series_unyt).all()
+    assert (
+        _validate_quantity(
+            time_series_list,
+            'time') == time_series_unyt).all()
+    assert (
+        _validate_quantity(
+            time_series_np,
+            'time') == time_series_unyt).all()
+    assert (
+        _validate_quantity(
+            time_series_pd,
+            'time') == time_series_unyt).all()
+    assert (
+        _validate_quantity(
+            time_series_unyt,
+            'time') == time_series_unyt).all()
 
 
 def test_initialize(advanced_tech):
@@ -239,7 +254,7 @@ def test_om_cost_variable(advanced_tech):
     with pytest.raises(ValueError) as e:
         advanced_tech.om_cost_variable = spec_energy_str
     assert advanced_tech.om_cost_variable.value == 0.0
-    assert advanced_tech.om_cost_variable.units == (MW*hr)**-1
+    assert advanced_tech.om_cost_variable.units == (MW * hr)**-1
 
     advanced_tech.om_cost_variable = spec_energy_unyt
     assert advanced_tech.om_cost_variable.value == 10.0
@@ -296,7 +311,7 @@ def test_fuel_cost(advanced_tech):
 
 
 def test_co2_rate(advanced_tech):
-    expected_unit = megatonnes*(MW*hr)**-1
+    expected_unit = megatonnes * (MW * hr)**-1
     with pytest.raises(ValueError) as e:
         advanced_tech.co2_rate = dict_type
     with pytest.raises(UnitParseError) as e:
@@ -308,7 +323,7 @@ def test_co2_rate(advanced_tech):
     assert advanced_tech.co2_rate.value == 0.0
     assert advanced_tech.co2_rate.units == expected_unit
 
-    advanced_tech.co2_rate = megatonnes*spec_energy_unyt
+    advanced_tech.co2_rate = megatonnes * spec_energy_unyt
     assert advanced_tech.co2_rate.value == 10.0
     assert advanced_tech.co2_rate.units == expected_unit
 
@@ -327,12 +342,12 @@ def test_co2_rate(advanced_tech):
 
     advanced_tech.unit_power = "kW"
     advanced_tech.unit_time = "day"
-    assert advanced_tech.co2_rate.units == megatonnes*(kW * day)**-1
+    assert advanced_tech.co2_rate.units == megatonnes * (kW * day)**-1
 
     advanced_tech.unit_power = "GW"
     advanced_tech.unit_time = "hr"
     advanced_tech.unit_mass = "ton"
-    advanced_tech.co2_rate = 1e-2 * megatonnes*(GW*hr)**-1
+    advanced_tech.co2_rate = 1e-2 * megatonnes * (GW * hr)**-1
     assert advanced_tech.co2_rate == pytest.approx(11023.113, 0.1)
 
 
@@ -381,3 +396,117 @@ def test_unit_energy(advanced_tech):
     assert advanced_tech.unit_energy == MW * hr
     advanced_tech.unit_energy = "Horsepower*day"
     assert advanced_tech.unit_energy == MW * hr
+
+
+def test_comparison_operators(advanced_tech):
+    NIMBUS = Technology(technology_name="The Nimbus",
+                        om_cost_variable=1.0)
+
+    assert advanced_tech < NIMBUS
+    assert advanced_tech <= NIMBUS
+    assert NIMBUS >= advanced_tech
+
+    ships = [NIMBUS, advanced_tech]
+    ships.sort()
+
+    assert ships == [advanced_tech, NIMBUS]
+
+
+def test_single_power_output():
+    capacity = 18 * GW
+    natural_gas.capacity = capacity
+    assert natural_gas.capacity == capacity
+
+    demand = 10 * GW
+    output = natural_gas.power_output(demand)
+    print(output)
+    assert output == demand
+    assert natural_gas.power_level == demand
+    assert len(natural_gas.power_history) == 1
+
+    demand = 17 * GW
+    output = natural_gas.power_output(demand)
+    assert output == demand
+    assert natural_gas.power_level == demand
+    assert len(natural_gas.power_history) == 2
+
+    demand = 20 * GW
+    output = natural_gas.power_output(demand)
+    assert output == capacity
+    assert natural_gas.power_level == capacity
+    assert len(natural_gas.power_history) == 3
+
+
+def test_reset_history():
+    natural_gas.reset_history()
+    assert len(natural_gas.power_history) == 0
+
+
+def test_multiple_power_output():
+    capacity = 18 * GW
+    natural_gas.capacity = capacity
+    assert natural_gas.capacity == capacity
+
+    demand = np.array([10, 17, 20]) * GW
+    output = unyt_array(np.zeros(len(demand))) * demand.units
+    expected = unyt_array([demand[0], demand[1], capacity])
+    for i, d in enumerate(demand):
+        out = natural_gas.power_output(d)
+        output[i] = out
+    assert np.all(output == expected)
+    assert len(natural_gas.power_history) == 3
+
+
+def test_thermal_power_output():
+    capacity = 18 * GW
+    ramp_rate = 0.25 * hr**-1
+    nuclear_adv.capacity = capacity
+    nuclear_adv.ramp_up_rate = ramp_rate
+    nuclear_adv.ramp_down_rate = ramp_rate
+    assert nuclear_adv.capacity == capacity
+    assert nuclear_adv.ramp_up_rate == ramp_rate
+    assert nuclear_adv.ramp_down_rate == ramp_rate
+
+    demand = np.array([5, 17, 20]) * GW
+    output = unyt_array(np.zeros(len(demand))) * demand.units
+    expected = unyt_array([13.5, 17, 18]) * GW
+    for i, d in enumerate(demand):
+        out = nuclear_adv.power_output(d)
+        output[i] = out
+    assert np.all(output == expected)
+    assert np.all(unyt_array(nuclear_adv.power_history) == expected)
+
+
+def test_storage_charge():
+    capacity = 1e3 * MW
+    initial_storage = 100 * MW * hour
+    storage_duration = 4 * hour
+    battery.capacity = capacity
+    battery.initial_storage = initial_storage
+    battery.storage_duration = storage_duration
+    assert battery.capacity == capacity
+    assert battery.storage_capacity == storage_duration * capacity
+
+    # charging
+    demand = np.array([-400, -500, -1000, -3500, -1000, -5]) * MW
+    for i, d in enumerate(demand):
+        battery.charge(d)
+    expected_storage = np.array([500, 1000, 2000, 3000, 4000, 4000]) * MWh
+    expected_charge = -np.array([400, 500, 1000, 1000, 1000, 0]) * MW
+    expected_power = np.zeros(len(demand)) * MW
+    assert np.all(battery.storage_history == expected_storage)
+    assert np.all(battery.power_history == expected_power)
+    assert np.all(battery.charge_history == expected_charge)
+
+
+def test_storage_power_out():
+    # discharging
+    demand = np.array([1e3, 1e3, 1e3, 500, 1e3]) * MW
+    for i, d in enumerate(demand):
+        battery.power_output(d)
+    expected_storage = np.array([3000, 2000, 1000, 500, 0.0]) * MWh
+    expected_power = np.array([1e3, 1e3, 1e3, 500, 500]) * MW
+    print(battery.storage_history[6:])
+    print(battery.power_history[6:])
+    assert np.all(battery.storage_history[6:] == expected_storage)
+    assert np.all(battery.power_history[6:] == expected_power)