diff --git a/src/parameters/update_parameters.jl b/src/parameters/update_parameters.jl
index 671c1b59d7..ac021131bb 100644
--- a/src/parameters/update_parameters.jl
+++ b/src/parameters/update_parameters.jl
@@ -428,88 +428,6 @@ function update_parameter_values!(
     return
 end
 
-function update_parameter_values!(
-    model::OperationModel,
-    key::ParameterKey{T, U},
-    input::DatasetContainer{InMemoryDataset},
-) where {T <: EnergyLimitParameter, U <: PSY.Generator}
-    # Enable again for detailed debugging
-    # TimerOutputs.@timeit RUN_SIMULATION_TIMER "$T $U Parameter Update" begin
-    optimization_container = get_optimization_container(model)
-    # Note: Do not instantite a new key here because it might not match the param keys in the container
-    # if the keys have strings in the meta fields
-    parameter_array = get_parameter_array(optimization_container, key)
-    parameter_attributes = get_parameter_attributes(optimization_container, key)
-    internal = get_internal(model)
-    execution_count = internal.execution_count
-    current_time = get_current_time(model)
-    state_values = get_dataset_values(input, get_attribute_key(parameter_attributes))
-    component_names, time = axes(parameter_array)
-    resolution = get_resolution(model)
-    interval_time_steps = Int(get_interval(model.internal.store_parameters) / resolution)
-    state_data = get_dataset(input, get_attribute_key(parameter_attributes))
-    state_timestamps = state_data.timestamps
-    max_state_index = get_num_rows(state_data)
-
-    state_data_index = find_timestamp_index(state_timestamps, current_time)
-    sim_timestamps = range(current_time; step = resolution, length = time[end])
-    old_parameter_values = jump_value.(parameter_array)
-    # The current method uses older parameter values because when passing the energy output from one stage
-    # to the next, the aux variable values gets over-written by the lower level model after its solve.
-    # This approach is a temporary hack and will be replaced in future versions.
-    for t in time
-        timestamp_ix = min(max_state_index, state_data_index + 1)
-        @debug "parameter horizon is over the step" max_state_index > state_data_index + 1
-        if state_timestamps[timestamp_ix] <= sim_timestamps[t]
-            state_data_index = timestamp_ix
-        end
-        for name in component_names
-            # the if statement checks if its the first solve of the model and uses the values stored in the state
-            # and for subsequent solves uses the state data to update the parameter values for the last set of time periods
-            # that are equal to the length of the interval i.e. the time periods that dont overlap between each solves.
-            if execution_count == 0 || t > time[end] - interval_time_steps
-                # Pass indices in this way since JuMP DenseAxisArray don't support view()
-                state_value = state_values[name, state_data_index]
-                if !isfinite(state_value)
-                    error(
-                        "The value for the system state used in $(encode_key_as_string(key)) is not a finite value $(state_value) \
-                         This is commonly caused by referencing a state value at a time when such decision hasn't been made. \
-                         Consider reviewing your models' horizon and interval definitions",
-                    )
-                end
-                _set_param_value!(parameter_array, state_value, name, t)
-            else
-                # Currently the update method relies on using older parameter values of the EnergyLimitParameter
-                # to update the parameter for overlapping periods between solves i.e. we ingoring the parameter values
-                # in the model interval time periods.
-                state_value = state_values[name, state_data_index]
-                if !isfinite(state_value)
-                    error(
-                        "The value for the system state used in $(encode_key_as_string(key)) is not a finite value $(state_value) \
-                         This is commonly caused by referencing a state value at a time when such decision hasn't been made. \
-                         Consider reviewing your models' horizon and interval definitions",
-                    )
-                end
-                _set_param_value!(
-                    parameter_array,
-                    old_parameter_values[name, t + interval_time_steps],
-                    name,
-                    t,
-                )
-            end
-        end
-    end
-
-    IS.@record :execution ParameterUpdateEvent(
-        T,
-        U,
-        parameter_attributes,
-        get_current_timestamp(model),
-        get_name(model),
-    )
-    return
-end
-
 """
 Update parameter function an OperationModel
 """