diff --git a/src/rtctools_heat_network/asset_sizing_mixin.py b/src/rtctools_heat_network/asset_sizing_mixin.py index 2176cc30..5c716dcc 100644 --- a/src/rtctools_heat_network/asset_sizing_mixin.py +++ b/src/rtctools_heat_network/asset_sizing_mixin.py @@ -1918,6 +1918,30 @@ def __max_size_constraints(self, ensemble_member): return constraints + def __add_optional_asset_path_constraints( + self, constraints, asset_name, nominal_value, nominal_var, single_power, state_var + ): + aggregation_count = self.__asset_aggregation_count_var[ + self._asset_aggregation_count_var_map[asset_name] + ] + constraint_nominal = (nominal_value * nominal_var) ** 0.5 + + constraints.append( + ( + (single_power * aggregation_count - state_var) / constraint_nominal, + 0.0, + np.inf, + ) + ) + constraints.append( + ( + (-single_power * aggregation_count - state_var) / constraint_nominal, + -np.inf, + 0.0, + ) + ) + return constraints + def __optional_asset_path_constraints(self, ensemble_member): """ This function adds constraints that set the _aggregation_count variable. This variable is @@ -1943,6 +1967,17 @@ def __optional_asset_path_constraints(self, ensemble_member): *self.energy_system_components.get("geothermal", []), *self.energy_system_components.get("ates", []), ]: + # changing flow bounds as a result of different aggregation count, additional + # step for geothermal and ates as they have subsurface flow limits. + state_var = self.state(f"{asset_name}.Q") + single_flow = ( + bounds[f"{asset_name}.Q"][1] / parameters[f"{asset_name}.nr_of_doublets"] + ) + nominal_value = 2.0 * bounds[f"{asset_name}.Q"][1] + nominal_var = self.variable_nominal(f"{asset_name}.Q") + constraints = self.__add_optional_asset_path_constraints( + constraints, asset_name, nominal_value, nominal_var, single_flow, state_var + ) state_var = self.state(f"{asset_name}.Heat_flow") single_power = parameters[f"{asset_name}.single_doublet_power"] nominal_value = 2.0 * bounds[f"{asset_name}.Heat_flow"][1] @@ -1965,25 +2000,11 @@ def __optional_asset_path_constraints(self, ensemble_member): single_power = bounds[f"{asset_name}.Heat_flow"][1] nominal_value = single_power nominal_var = self.variable_nominal(f"{asset_name}.Heat_flow") - aggregation_count = self.__asset_aggregation_count_var[ - self._asset_aggregation_count_var_map[asset_name] - ] - constraint_nominal = (nominal_value * nominal_var) ** 0.5 - constraints.append( - ( - (single_power * aggregation_count - state_var) / constraint_nominal, - 0.0, - np.inf, - ) - ) - constraints.append( - ( - (-single_power * aggregation_count - state_var) / constraint_nominal, - -np.inf, - 0.0, - ) + constraints = self.__add_optional_asset_path_constraints( + constraints, asset_name, nominal_value, nominal_var, single_power, state_var ) + elif parameters[f"{asset_name}.state"] == 1: aggregation_count = self.__asset_aggregation_count_var[ self._asset_aggregation_count_var_map[asset_name] diff --git a/src/rtctools_heat_network/esdl/esdl_heat_model.py b/src/rtctools_heat_network/esdl/esdl_heat_model.py index cc17f9b7..28b9c920 100644 --- a/src/rtctools_heat_network/esdl/esdl_heat_model.py +++ b/src/rtctools_heat_network/esdl/esdl_heat_model.py @@ -611,13 +611,19 @@ def convert_heat_exchanger(self, asset: Asset) -> Tuple[Type[HeatExchanger], MOD / 2.0 ) + max_heat_transport = ( + params_t["Primary"]["T_supply"] + * max_power + / (params_t["Primary"]["T_supply"] - params_t["Primary"]["T_return"]) + ) + prim_heat = dict( HeatIn=dict( - Heat=dict(min=-max_power, max=max_power, nominal=max_power / 2.0), + Heat=dict(min=-max_heat_transport, max=max_heat_transport, nominal=max_power / 2.0), Hydraulic_power=dict(nominal=params_q["Primary"]["Q_nominal"] * 16.0e5), ), HeatOut=dict( - Heat=dict(min=-max_power, max=max_power, nominal=max_power / 2.0), + Heat=dict(min=-max_heat_transport, max=max_heat_transport, nominal=max_power / 2.0), Hydraulic_power=dict(nominal=params_q["Primary"]["Q_nominal"] * 16.0e5), ), Q_nominal=max_power @@ -630,11 +636,11 @@ def convert_heat_exchanger(self, asset: Asset) -> Tuple[Type[HeatExchanger], MOD ) sec_heat = dict( HeatIn=dict( - Heat=dict(min=-max_power, max=max_power, nominal=max_power / 2.0), + Heat=dict(min=-max_heat_transport, max=max_heat_transport, nominal=max_power / 2.0), Hydraulic_power=dict(nominal=params_q["Secondary"]["Q_nominal"] * 16.0e5), ), HeatOut=dict( - Heat=dict(min=-max_power, max=max_power, nominal=max_power / 2.0), + Heat=dict(min=-max_heat_transport, max=max_heat_transport, nominal=max_power / 2.0), Hydraulic_power=dict(nominal=params_q["Secondary"]["Q_nominal"] * 16.0e5), ), Q_nominal=max_power @@ -895,7 +901,16 @@ def convert_ates(self, asset: Asset) -> Tuple[Type[ATES], MODIFIERS]: if not efficiency: efficiency = 0.7 - q_nominal = self._get_connected_q_nominal(asset) + # TODO: temporary value for standard dT on which capacity is based, Q in m3/s + temperatures = self._supply_return_temperature_modifiers(asset) + dt = temperatures["T_supply"] - temperatures["T_return"] + rho = self.rho + cp = self.cp + q_max_ates = hfr_discharge_max / (cp * rho * dt) + + q_nominal = min( + self._get_connected_q_nominal(asset), q_max_ates * asset.attributes["aggregationCount"] + ) modifiers = dict( technical_life=self.get_asset_attribute_value( @@ -909,6 +924,17 @@ def convert_ates(self, asset: Asset) -> Tuple[Type[ATES], MODIFIERS]: asset, "discountRate", default_value=0.0, min_value=0.0, max_value=100.0 ), Q_nominal=q_nominal, + Q=dict( + min=-q_max_ates * asset.attributes["aggregationCount"], + max=q_max_ates * asset.attributes["aggregationCount"], + nominal=q_nominal, + ), + T_amb=asset.attributes["aquiferMidTemperature"], + Temperature_ates=dict( + min=temperatures["T_return"], # or potentially 0 + max=temperatures["T_supply"], + nominal=temperatures["T_return"], + ), single_doublet_power=single_doublet_power, heat_loss_coeff=(1.0 - efficiency ** (1.0 / 100.0)) / (3600.0 * 24.0), state=self.get_state(asset), diff --git a/src/rtctools_heat_network/heat_physics_mixin.py b/src/rtctools_heat_network/heat_physics_mixin.py index db7729f7..0825df5d 100644 --- a/src/rtctools_heat_network/heat_physics_mixin.py +++ b/src/rtctools_heat_network/heat_physics_mixin.py @@ -170,11 +170,32 @@ def __init__(self, *args, **kwargs): self.__temperature_regime_var = {} self.__temperature_regime_var_bounds = {} + # Variable of selected ATES discrete temperature + self.__ates_temperature_disc_var = {} + self.__ates_temperature_disc_var_bounds = {} + + # Integer variable whether discrete temperature option for ates has been selected + self.__ates_temperature_selected_var = {} + self.__ates_temperature_selected_var_bounds = {} + + self.__ates_temperature_ordering_var = {} + self.__ates_temperature_ordering_var_bounds = {} + + self.__ates_temperature_disc_ordering_var = {} + self.__ates_temperature_disc_ordering_var_bounds = {} + + self.__ates_max_stored_heat_var = {} + self.__ates_max_stored_heat_bounds = {} + self.__ates_max_stored_heat_nominals = {} + # Integer variable whether discrete temperature option has been selected self.__carrier_selected_var = {} self.__carrier_selected_var_bounds = {} - # Dict to write the milp loss in the parameters + self.__carrier_temperature_disc_ordering_var = {} + self.__carrier_temperature_disc_ordering_var_bounds = {} + + # Dict to write the heat loss in the parameters self.__pipe_heat_loss_parameters = [] # Part of the physics constraints are inherently linked to the sizing optimization. Since @@ -338,6 +359,69 @@ def _get_min_bound(bound): self.__control_valve_direction_var[flow_dir_var] = ca.MX.sym(flow_dir_var) self.__control_valve_direction_var_bounds[flow_dir_var] = (0.0, 1.0) + for ates, ( + (hot_pipe, _hot_pipe_orientation), + (_cold_pipe, _cold_pipe_orientation), + ) in self.energy_system_topology.ates.items(): + + ates_temp_disc_var_name = f"{ates}__temperature_ates_disc" + self.__ates_temperature_disc_var[ates_temp_disc_var_name] = ca.MX.sym( + ates_temp_disc_var_name + ) + carrier_id = parameters[f"{hot_pipe}.carrier_id"] + temperatures = self.temperature_regimes(carrier_id) + if len(temperatures) == 0: + temperature = parameters[f"{hot_pipe}.temperature"] + self.__ates_temperature_disc_var_bounds[ates_temp_disc_var_name] = ( + temperature, + temperature, + ) + elif len(temperatures) == 1: + temperature = temperatures[0] + self.__ates_temperature_disc_var_bounds[ates_temp_disc_var_name] = ( + temperature, + temperature, + ) + else: + self.__ates_temperature_disc_var_bounds[ates_temp_disc_var_name] = ( + min(temperatures), + max(temperatures), + ) + for temperature in temperatures: + ates_temperature_selected_var = f"{ates}__temperature_disc_{temperature}" + self.__ates_temperature_selected_var[ates_temperature_selected_var] = ca.MX.sym( + ates_temperature_selected_var + ) + self.__ates_temperature_selected_var_bounds[ates_temperature_selected_var] = ( + 0.0, + 1.0, + ) + + ates_temperature_ordering_var_name = f"{ates}__{temperature}_ordering" + self.__ates_temperature_ordering_var[ates_temperature_ordering_var_name] = ( + ca.MX.sym(ates_temperature_ordering_var_name) + ) + self.__ates_temperature_ordering_var_bounds[ates_temperature_ordering_var_name] = ( + 0.0, + 1.0, + ) + + ates_temperature_disc_ordering_var_name = f"{ates}__{temperature}_ordering_disc" + self.__ates_temperature_disc_ordering_var[ + ates_temperature_disc_ordering_var_name + ] = ca.MX.sym(ates_temperature_disc_ordering_var_name) + self.__ates_temperature_disc_ordering_var_bounds[ + ates_temperature_disc_ordering_var_name + ] = (0.0, 1.0) + + max_heat = bounds[f"{ates}.Stored_heat"][1] + ates_max_stored_heat_var_name = f"{ates}__max_stored_heat" + self.__ates_max_stored_heat_var[ates_max_stored_heat_var_name] = ca.MX.sym( + ates_max_stored_heat_var_name + ) + self.__ates_max_stored_heat_bounds[ates_max_stored_heat_var_name] = (0, max_heat) + self.__ates_max_stored_heat_nominals[ates_max_stored_heat_var_name] = max_heat / 2 + for _carrier, temperatures in self.temperature_carriers().items(): carrier_id_number_mapping = str(temperatures["id_number_mapping"]) temp_var_name = carrier_id_number_mapping + "_temperature" @@ -360,6 +444,16 @@ def _get_min_bound(bound): self.__carrier_selected_var[carrier_selected_var] = ca.MX.sym(carrier_selected_var) self.__carrier_selected_var_bounds[carrier_selected_var] = (0.0, 1.0) + carrier_temperature_disc_ordering_var_name = ( + f"{carrier_id_number_mapping}__{temperature_regime}_ordering_disc" + ) + self.__carrier_temperature_disc_ordering_var[ + carrier_temperature_disc_ordering_var_name + ] = ca.MX.sym(carrier_temperature_disc_ordering_var_name) + self.__carrier_temperature_disc_ordering_var_bounds[ + carrier_temperature_disc_ordering_var_name + ] = (0.0, 1.0) + for _ in range(self.ensemble_size): self.__pipe_heat_loss_parameters.append({}) @@ -496,6 +590,7 @@ def energy_system_options(self): options["neglect_pipe_heat_losses"] = False options["heat_loss_disconnected_pipe"] = True options["include_demand_insulation_options"] = False + options["include_ates_temperature_options"] = False return options @@ -524,6 +619,7 @@ def extra_variables(self): """ variables = super().extra_variables.copy() variables.extend(self.__pipe_heat_loss_var.values()) + variables.extend(self.__ates_max_stored_heat_var.values()) return variables @property @@ -543,8 +639,13 @@ def path_variables(self): variables.extend(self.__pipe_linear_line_segment_var.values()) variables.extend(self.__temperature_regime_var.values()) variables.extend(self.__carrier_selected_var.values()) + variables.extend(self.__ates_temperature_disc_var.values()) + variables.extend(self.__ates_temperature_selected_var.values()) variables.extend(self.__disabled_hex_var.values()) variables.extend(self.__pipe_heat_loss_path_var.values()) + variables.extend(self.__ates_temperature_ordering_var.values()) + variables.extend(self.__ates_temperature_disc_ordering_var.values()) + variables.extend(self.__carrier_temperature_disc_ordering_var.values()) return variables def variable_is_discrete(self, variable): @@ -559,7 +660,11 @@ def variable_is_discrete(self, variable): or variable in self.__demand_insulation_class_var or variable in self.__pipe_linear_line_segment_var or variable in self.__carrier_selected_var + or variable in self.__ates_temperature_selected_var or variable in self.__disabled_hex_var + or variable in self.__ates_temperature_ordering_var + or variable in self.__ates_temperature_disc_ordering_var + or variable in self.__carrier_temperature_disc_ordering_var ): return True else: @@ -573,6 +678,8 @@ def variable_nominal(self, variable): return self._pipe_heat_loss_nominals[variable] elif variable in self.__pipe_head_loss_nominals: return self.__pipe_head_loss_nominals[variable] + elif variable in self.__ates_max_stored_heat_nominals: + return self.__ates_max_stored_heat_nominals[variable] else: return super().variable_nominal(variable) @@ -592,10 +699,16 @@ def bounds(self): bounds.update(self._pipe_heat_loss_var_bounds) bounds.update(self.__temperature_regime_var_bounds) bounds.update(self.__carrier_selected_var_bounds) + bounds.update(self.__ates_temperature_disc_var_bounds) + bounds.update(self.__ates_temperature_selected_var_bounds) bounds.update(self.__disabled_hex_var_bounds) bounds.update(self.__pipe_head_loss_bounds) bounds.update(self.__pipe_head_loss_zero_bounds) + bounds.update(self.__ates_temperature_ordering_var_bounds) + bounds.update(self.__ates_temperature_disc_ordering_var_bounds) + bounds.update(self.__carrier_temperature_disc_ordering_var_bounds) + bounds.update(self.__ates_max_stored_heat_bounds) for k, v in self.__pipe_head_bounds.items(): bounds[k] = self.merge_bounds(bounds[k], v) @@ -1527,20 +1640,50 @@ def __pipe_heat_to_discharge_path_constraints(self, ensemble_member): # in a rather hard yes/no constraint as far as feasibility on e.g. # a single source system is concerned. Use a factor of 2 to give # some slack. - # big_m = 2 * sum_heat_losses * heat_to_discharge_fac big_m = 2.0 * np.max( np.abs((*self.bounds()[f"{p}.HeatIn.Heat"], *self.bounds()[f"{p}.HeatOut.Heat"])) ) + carrier = parameters[f"{p}.carrier_id"] + temperatures = self.temperature_regimes(carrier) + for heat in [scaled_heat_in, scaled_heat_out]: if self.energy_system_options()["neglect_pipe_heat_losses"]: - constraints.append( - ( - (heat - pipe_q * (cp * rho * temp)) / heat_nominal, - 0.0, - 0.0, + if len(temperatures) == 0: + constraints.append( + ( + (heat - pipe_q * (cp * rho * temp)) / heat_nominal, + 0.0, + 0.0, + ) ) - ) + else: + for temperature in temperatures: + temperature_is_selected = self.state(f"{carrier}_{temperature}") + constraints.append( + ( + ( + heat + - pipe_q * (cp * rho * temperature) + + (1.0 - temperature_is_selected) * big_m + ) + / big_m, + 0.0, + np.inf, + ) + ) + constraints.append( + ( + ( + heat + - pipe_q * (cp * rho * temperature) + - (1.0 - temperature_is_selected) * big_m + ) + / big_m, + -np.inf, + 0.0, + ) + ) else: assert big_m > 0.0 @@ -1593,6 +1736,531 @@ def __pipe_heat_to_discharge_path_constraints(self, ensemble_member): ) return constraints + def __ates_temperature_path_constraints(self, ensemble_member): + """ + This function adds constraints to determine the temperature state of the ATES and the + requirements for connecting pipes. + + The temperature of the ATES is modelled as a continuous variable which is linked to a + discretized ates temperature for the linearisation of the equations. This discretized ates + temperature variable is a continuous variable for which only predefined values can be set. + + The same discretized temperatures for ATES are used as the ones available at the carrier and + the pipe connected to the inport to ensure compatibility. + + The relation between the continuous and discrete ates temperature is: + ates_temperature (continuous) >= ates_temperature_disc + The following reasons are key for this choice: + - This relation might cause an underestimation of the discrete ates temperature which in + turn would result in an underestimation of the temperature and heat loss. However, + assuming that the allowable temperature steps are small enough and the stored heat is an + important factor, this should not be too big of a difference. + - Furthermore, due to the setup of the linearisation of the temperature and heat loss, + there will always be over-estimations at those specific temperatures. + - Simultaneously there might be an under estimation of the heatloss in the pipe connecting + the heat pump and the ATES, however due to the small temperature steps available and the + short pipe length between these two, this underestimation should be small. + - Since the ATES for the temperature configuration will always be connected to a heat + pump, the ates discrete temperature will also affect COP of the heat pump. An + underestimation of the temperature will result in a larger temperature lift the heatpump + has to provide, thus using more electricity and requiring a larger heatpump capacity. + Which will thus result in an over-estimation of the electricity used, an overestimation + in the required heat pump capacity (in case of sizing) or even a lower limit on the heat + that the heat pump can produce from the ATES. + - Finally a lower temperature also results in a lower heat transport capacity of the + pipes and a smaller heat extraction at those time steps due to the limit on the volumetric + flow in the ATES. This will result in a longer time for the extraction of heat from the + ATES to empty it, which will again result in more heat losses. + + During discharging of the ATES, the temperature of the pipe should be the same as the + discretized temperature of the ATES. During charging the temperature of the carrier/pipe + should be larger or equal to the discretized temperature of the ates, since one does not + want to reduce the temperature during charging. + """ + + constraints = [] + parameters = self.parameters(ensemble_member) + options = self.energy_system_options() + + for ates_asset, ( + (hot_pipe, _hot_pipe_orientation), + (_cold_pipe, _cold_pipe_orientation), + ) in {**self.energy_system_topology.ates}.items(): + + flow_dir_var = self._pipe_to_flow_direct_map[hot_pipe] + is_buffer_charging = self.state(flow_dir_var) * _hot_pipe_orientation + + sup_carrier = parameters[f"{ates_asset}.T_supply_id"] + supply_temperatures = self.temperature_regimes(sup_carrier) + ates_temperature = self.state(f"{ates_asset}.Temperature_ates") + ates_temperature_disc = self.state(f"{ates_asset}__temperature_ates_disc") + + # discretized tempeature should alwyas be smaller or equal to ATES temperature + constraints.append((ates_temperature - ates_temperature_disc, 0.0, np.inf)) + + if options["include_ates_temperature_options"] and len(supply_temperatures) != 0: + # ensures it selects the closest temperature + # supplytemperature needs to be reducing + # TODO: this could use ordering strategy + big_m = max(supply_temperatures) + for temperature in supply_temperatures[1:]: + temp_selected = self.state(f"{ates_asset}__temperature_disc_{temperature}") + prev_temp = supply_temperatures[supply_temperatures.index(temperature) - 1] + constraints.append( + ( + ates_temperature + - ates_temperature_disc + - temp_selected * (prev_temp - temperature) + - (1 - temp_selected) * big_m, + -np.inf, + 0.0, + ) + ) + + """ + This function adds constraints to ensure that only one temperature level is active + for the ates temperature. Furthermore, it sets the + temperature variable to the temperature associated with the temperature of the + integer variable. Potentially these lines could be a separate function. + """ + variable_sum = 0.0 + for temperature in supply_temperatures: + temp_selected = self.state(f"{ates_asset}__temperature_disc_{temperature}") + variable_sum += temp_selected + big_m = 2.0 * max(supply_temperatures) + constraints.append( + ( + (temperature - ates_temperature_disc + (1.0 - temp_selected) * big_m), + 0.0, + np.inf, + ) + ) + constraints.append( + ( + (temperature - ates_temperature_disc - (1.0 - temp_selected) * big_m), + -np.inf, + 0.0, + ) + ) + if len(supply_temperatures) > 0: + constraints.append((variable_sum, 1.0, 1.0)) + + # equality constraint during charging to ensure charging at highest temperature + big_m = 2.0 * max(supply_temperatures) + sup_temperature_disc = self.state(f"{sup_carrier}_temperature") + + constraints.append( + ( + ( + max(supply_temperatures) + - sup_temperature_disc + + big_m * (1.0 - is_buffer_charging) + ), + 0.0, + np.inf, + ) + ) + constraints.append( + ( + ( + max(supply_temperatures) + - sup_temperature_disc + - big_m * (1.0 - is_buffer_charging) + ), + -np.inf, + 0.0, + ) + ) + + # Equality constraint if discharging using big_m; + # discr_temp_carrier == discr_temp_ates + constraints.append( + ( + ates_temperature_disc - sup_temperature_disc + is_buffer_charging * big_m, + 0.0, + np.inf, + ) + ) + constraints.append( + ( + ates_temperature_disc - sup_temperature_disc - is_buffer_charging * big_m, + -np.inf, + 0.0, + ) + ) + # inequality constraint when charging, carrier temperature>= ates temperature + constraints.append( + ( + sup_temperature_disc - ates_temperature + (1 - is_buffer_charging) * big_m, + 0.0, + np.inf, + ) + ) + else: + constraints.append( + (parameters[f"{ates_asset}.T_supply"] - ates_temperature_disc, 0.0, 0.0) + ) + + return constraints + + def __get_linear_temperature_loss_vs_storedheat( + self, max_stored_heat, temperature_ates, temperature_ambient=17, n_lines=5 + ): + """ + Function to linearise the temperature loss based on: + - the current discrete temperature of the ATES well + - the current ambient temperature + - the current stored heat + The current stored heat is split in segments of equal size to linearise into n_lines + numbers of lines + Tloss/dt = a*stored_heat/heat_max + b + """ + + heat_points = np.linspace(1, max_stored_heat, n_lines + 1) / max_stored_heat # cannot be 0 + + def algebraic_temperature_loss_ates( + heat_factor, max_stored_heat, temperature_ates, temperature_ambient + ): + # TODO: function needs to be updated with realistic function + # coefficient currently based on: + # 30°C temperature drop over 3 months = 30/(3600*24*30*3)=3.86e-6 + # dTloss/dt = c ((Tates-Tamb)/(Tatesmin-Tamb)-1)*e^(-stored_heat_normalised) # Tatesmin + # currently hardcoded as 40 + # assuming temperature ates of 70°C and 17°C ambient throughout + # assuming 50% of max stored heat throughout 7.3e-7 + dtemperature_dt = ( + 6.13e-6 + * ((temperature_ates - temperature_ambient) / (40 - temperature_ambient) - 1) + * np.exp(-heat_factor) + ) + return dtemperature_dt + + temperature_loss_dt_points = np.array( + [ + algebraic_temperature_loss_ates( + h, max_stored_heat, temperature_ates, temperature_ambient + ) + for h in heat_points + ] + ) + + a = np.diff(temperature_loss_dt_points) / np.diff(heat_points) + b = temperature_loss_dt_points[1:] - a * heat_points[1:] + + return a, b + + def __get_linear_temperature_charging_vs_heatates( + self, max_heat_ates, temperature_ates, temperature_supply, n_lines=1 + ): + """ + Function to linearise the temperature loss based on: + - the current discrete temperature of the ATES well (not yet included) + - the current temperature of the carrier which will charge the well (not yet included) + - the current stored heat (not yet included) + - the added heat during charging + The current stored heat is split in segments of equal size to linearise into n_lines + numbers of lines + Tloss/dt = a*heat_ates/heat_max + b + """ + + heat_points = np.linspace(0, max_heat_ates, n_lines + 1) / max_heat_ates + + def algebraic_temperature_charge_ates( + heat_ates, max_heat_ates, temperature_ates, temperature_supply + ): + # TODO: function needs to be updated with realistic function + # This coefficient currently results in about a maximum temperature increase of 25°C + # over a month during maximum charging + # 1e-5*(temperature_supply-temperature_ates)*heat_ates + dtemperature_dt = 1e-5 * heat_ates + return dtemperature_dt + + temperature_loss_dt_points = np.array( + [ + algebraic_temperature_charge_ates( + h, max_heat_ates, temperature_ates, temperature_supply + ) + for h in heat_points + ] + ) + + a = np.diff(temperature_loss_dt_points) / np.diff(heat_points) + b = temperature_loss_dt_points[1:] - a * heat_points[1:] + + return a, b + + def __get_linear_heatloss_vs_storedheat( + self, heat_stored_max, temperature_ates, temperature_ambient=17, n_lines=1 + ): + """ + Function to linearise the temperature loss based on: + - the current discrete temperature of the ATES well + - the current ambient temperature + - the current stored heat + The current stored heat is split in segments of equal size to linearise into n_lines + numbers of lines + heatloss/dt = a*stored_heat + b + """ + # TODO: ensure heatloss and temperature loss linearisation rely on the same equations + heat_points = np.linspace(1.0, heat_stored_max, n_lines + 1) # cannot be 0 + + def algebraic_heatloss_ates( + heat_points, heat_stored_max, temperature_ates, temperature_ambient + ): + # TODO: function needs to be updated with realistic function, woudl normally non convex + # coefficient currently based on: + # 30°C temperature drop: 125 MJ / m3 + # assume over 3 months: 16.2 W/m3 -> xx W/J + # max_stored_volume = max_stored_heat/(40 (dT assumed) *ro*cp)= 334e3 m3 + # nominal_volume = max_stored_volume/2 + # heatloss = 16.2*max_stored_volume W + # heatloss = 16.2 / (70-17) * stored_volume * (T_ates - T_amb) + # stored_volume = stored_heat / (40 (dT assumed) *rho*cp) + # multiplied coefficient with 1e-1 + heatloss = 1.5e-10 * heat_points * (temperature_ates - temperature_ambient) + # heatloss = 1e4 * (temperature_ates - temperature_ambient) * ( + # heat_points / heat_stored_max)**2 + return heatloss + + heatloss_dt_points = np.array( + [ + algebraic_heatloss_ates(h, heat_stored_max, temperature_ates, temperature_ambient) + for h in heat_points + ] + ) + + a = np.diff(heatloss_dt_points) / np.diff(heat_points) + b = heatloss_dt_points[1:] - a * (heat_points[1:]) + + return a, b + + def __ates_temperature_changing_path_constraints(self, ensemble_member): + """ + Contains constraints for the temperature losses and gains in the ates: + - If there are different temperatures available for the ates; + the heat loss which is a function of the current temperature of the ATES, the ambient + ground temperature and the stored heat, is transformed in a piecewise linear function of + the stored heat and the equations are selected based on the ates temperature. + - If there are no other temperatures available for the ates; + the heat loss is defined as a linear equality constraint based on a given efficiency and a + function of the stored heat. + """ + constraints = [] + parameters = self.parameters(ensemble_member) + bounds = self.bounds() + options = self.energy_system_options() + + for ates, ( + (hot_pipe, _hot_pipe_orientation), + (_cold_pipe, _cold_pipe_orientation), + ) in {**self.energy_system_topology.ates}.items(): + + ates_dt_charging = self.state(f"{ates}.Temperature_change_charging") + ates_dt_loss = self.state(f"{ates}.Temperature_loss") + + sup_carrier = parameters[f"{ates}.T_supply_id"] + supply_temperatures = self.temperature_regimes(sup_carrier) + + if options["include_ates_temperature_options"] and len(supply_temperatures) != 0: + soil_temperature = parameters[f"{ates}.T_amb"] + ates_temperature_loss_nominal = self.variable_nominal(f"{ates}.Temperature_loss") + ates_dt_charging_nominal = self.variable_nominal( + f"{ates}.Temperature_change_charging" + ) + + flow_dir_var = self._pipe_to_flow_direct_map[hot_pipe] + is_buffer_charging = self.state(flow_dir_var) * _hot_pipe_orientation + heat_stored_max = bounds[f"{ates}.Stored_heat"][1] + heat_ates_max = bounds[f"{ates}.Heat_ates"][1] + heat_ates = self.state(f"{ates}.Heat_ates") + stored_heat = self.state(f"{ates}.Stored_heat") + + big_m = 2.0 * max(bounds[f"{ates}.Temperature_change_charging"][1], 1e-5) + + # ensures no ates temperature change because of charging when discharging + constraints.append( + ( + (ates_dt_charging - is_buffer_charging * big_m) / ates_dt_charging_nominal, + -np.inf, + 0.0, + ) + ) + + # TODO temporary: still add relation to bound ates_dt_charging, should also be + # piecewise linear as a function of the stored heat, added heat and temperature + # difference + # TODO: later placed inside ates_temperature loop, in which a nested loop for the + # supply temperature of the carrier should be located, only needs to create + # constraint for carrier_temperatures>ates_temperature. + a, b = self.__get_linear_temperature_charging_vs_heatates(heat_ates_max, 50, 70) + # constraint provides upper bound of the temperature change that can occur due to + # charging + constraints.append( + ( + ( + ates_dt_charging + - (a * heat_ates / heat_ates_max + b) + - (1 - is_buffer_charging) * big_m + ) + / ates_dt_charging_nominal, + -np.inf, + 0.0, + ) + ) + + big_m = 2.0 * bounds[f"{ates}.Temperature_loss"][1] + for ates_temperature in supply_temperatures: + ates_temperature_is_selected = self.state( + f"{ates}__temperature_disc_{ates_temperature}" + ) + # setting temperature losses to zero when lowest discrete temperature is + # selected, to ensure temperature does not further drop and requires + # ates_dt_charging to cover the difference + if ates_temperature == min(supply_temperatures): + constraints.append( + ( + (ates_dt_loss + big_m * (1 - ates_temperature_is_selected)) + / ates_temperature_loss_nominal, + 0.0, + np.inf, + ) + ) + constraints.append( + ( + (ates_dt_loss - big_m * (1 - ates_temperature_is_selected)) + / ates_temperature_loss_nominal, + -np.inf, + 0.0, + ) + ) + else: + # if is selected, then specific temperature loss constraint should be + # applicable, which will be a function of the stored heat + a, b = self.__get_linear_temperature_loss_vs_storedheat( + heat_stored_max, ates_temperature, temperature_ambient=soil_temperature + ) + stored_heat_vec = ca.repmat(stored_heat, len(a)) + is_buffer_charging_vec = ca.repmat(is_buffer_charging, len(a)) + ates_dt_loss_vec = ca.repmat(ates_dt_loss, len(a)) + ates_temperature_is_selected_vec = ca.repmat( + ates_temperature_is_selected, len(a) + ) + + # under discharge + constraints.append( + ( + ( + ates_dt_loss_vec + - (a * stored_heat_vec / heat_stored_max + b) + + big_m * (1.0 - ates_temperature_is_selected_vec) + + big_m * is_buffer_charging_vec + ) + / ates_temperature_loss_nominal, + 0.0, + np.inf, + ) + ) + + # #under charge dt_loss=0 + # constraints.append( + # ( + # ( + # ates_dt_loss_vec + # + big_m * (1.0*np.ones(len(a))-is_buffer_charging_vec) + # ) + # / ates_temperature_loss_nominal, + # 0.0, + # np.inf, + # ) + # ) + constraints.append( + ( + (ates_dt_loss - big_m * (1.0 - is_buffer_charging)) + / ates_temperature_loss_nominal, + -np.inf, + 0.0, + ) + ) + else: + constraints.append((ates_dt_charging, 0.0, 0.0)) + constraints.append((ates_dt_loss, 0.0, 0.0)) + return constraints + + def __ates_heat_losses_path_constraints(self, ensemble_member): + """ + Constraints for the heat losses in the ates, either one of the two options is selected: + - If there are different temperatures available for the ates; + the heat loss which is a function of the current temperature of the ATES, the ambient + ground temperature and the stored heat, is transformed in a piecewise linear function of + the stored heat and the equations are selected based on the ates temperature. + - If there are no other temperatures available for the ates; + the heat loss is defined as a linear equality constraint based on a given efficiency and a + function of the stored heat. + """ + constraints = [] + parameters = self.parameters(ensemble_member) + bounds = self.bounds() + options = self.energy_system_options() + + for ates in self.energy_system_components.get("ates", []): + heat_loss_nominal = self.variable_nominal(f"{ates}.Heat_loss") + soil_temperature = parameters[f"{ates}.T_amb"] + heat_stored_max = bounds[f"{ates}.Stored_heat"][1] + + stored_heat = self.state(f"{ates}.Stored_heat") + heat_loss = self.state(f"{ates}.Heat_loss") + + sup_carrier = parameters[f"{ates}.T_supply_id"] + supply_temperatures = self.temperature_regimes(sup_carrier) + + if options["include_ates_temperature_options"] and len(supply_temperatures) != 0: + big_m_heatloss = 2 * heat_loss_nominal + for ates_temperature in supply_temperatures: + ates_temperature_is_selected = self.state( + f"{ates}__temperature_disc_{ates_temperature}" + ) + # linearisation of heatloss + a, b = self.__get_linear_heatloss_vs_storedheat( + heat_stored_max, ates_temperature, temperature_ambient=soil_temperature + ) + stored_heat_vec = ca.repmat(stored_heat, len(a)) + ates_heat_loss_vec = ca.repmat(heat_loss, len(a)) + ates_temperature_is_selected_vec = ca.repmat( + ates_temperature_is_selected, len(a) + ) + constraints.append( + ( + ( + ates_heat_loss_vec + - (a * stored_heat_vec + b) + + big_m_heatloss * (1 - ates_temperature_is_selected_vec) + ) + / heat_loss_nominal, + 0.0, + np.inf, + ) + ) + # TODO: check if needed: add the constraint below incase we want piecewise + # linear equality for the ates heat loss + # constraints.append( + # ((ates_heat_loss_vec - (a * stored_heat_vec + b) - big_m_heatloss * ( + # 1 - ates_temperature_is_selected_vec)) / heat_loss_nominal, + # -np.inf, + # 0.0) + # ) + else: + # no temperature states available + coeff_efficiency_ates = parameters[f"{ates}.heat_loss_coeff"] + constraints.append( + ( + (heat_loss - stored_heat * coeff_efficiency_ates) / heat_loss_nominal, + 0.0, + 0.0, + ) + ) + + return constraints + def __storage_heat_to_discharge_path_constraints(self, ensemble_member): """ This function adds constraints linking the flow to the thermal power at the pipe assets. @@ -1672,6 +2340,26 @@ def __storage_heat_to_discharge_path_constraints(self, ensemble_member): ) ) else: + bounds = self.bounds() + max_discharge = bounds[f"{b}.Q"][1] + constraint_nominal = ( + heat_nominal * cp * rho * max(supply_temperatures) * q_nominal + ) ** 0.5 + temperature_var = self.state(f"{sup_carrier}_temperature") + constraints.append( + ( + (heat_in - max_discharge * cp * rho * temperature_var) / constraint_nominal, + -np.inf, + 0.0, + ) + ) + constraints.append( + ( + (heat_in + max_discharge * cp * rho * temperature_var) / constraint_nominal, + 0.0, + np.inf, + ) + ) for supply_temperature in supply_temperatures: sup_temperature_is_selected = self.state(f"{sup_carrier}_{supply_temperature}") constraint_nominal = ( @@ -2529,7 +3217,23 @@ class and the selected temperature in the network. return constraints - def __heat_pump_cop_constraints(self, ensemble_member): + def __ates_max_stored_heat_constriants(self, ensemble_member): + constraints = [] + + for ates in self.energy_system_components.get("ates", []): + max_var_name = f"{ates}__max_stored_heat" + max_var = self.extra_variable(max_var_name, ensemble_member) + stored_heat = self.__state_vector_scaled(f"{ates}.Stored_heat", ensemble_member) + nominal = self.variable_nominal(max_var_name) + + constraints.append( + ((stored_heat - np.ones(len(self.times())) * max_var) / nominal, -np.inf, 0.0) + ) + + return constraints + + def __heat_pump_cop_path_constraints(self, ensemble_member): + constraints = [] parameters = self.parameters(ensemble_member) @@ -2629,6 +3333,59 @@ def __heat_pump_cop_constraints(self, ensemble_member): 0.0, ) ) + return constraints + + def __ates_temperature_ordering_path_constraints(self, ensemble_member): + constraints = [] + + parameters = self.parameters(ensemble_member) + + for ates in self.energy_system_components.get("ates", []): + + sup_carrier = parameters[f"{ates}.T_supply_id"] + supply_temperatures = self.temperature_regimes(sup_carrier) + if len(supply_temperatures) > 1: + big_m = 2.0 * max(supply_temperatures) + min_dt = abs(min(np.diff(supply_temperatures))) + + for temperature in supply_temperatures: + # TODO: fix the ordering of the ates temperatures + + # ordering_disc = self.state(f"{ates}__{temperature}_ordering_disc") + # ordering = self.state(f"{ates}__{temperature}_ordering") + # ates_temp_disc = self.state(f"{ates}__temperature_ates_disc") + # ates_temp = self.state(f"{ates}.Temperature_ates") + + # ordering should be 1. if temperature is larger than temperature selected. + # constraints.append(((temperature - ates_temp_disc + big_m * ordering_disc), + # min_dt / 2., np.inf)) + # constraints.append(((temperature - ates_temp_disc - big_m * ( + # 1. - ordering_disc)), -np.inf, 0.)) + # + # constraints.append( + # ((temperature - ates_temp + big_m * ordering), 0., np.inf)) + # constraints.append( + # ((temperature - ates_temp - big_m * (1. - ordering)), -np.inf, 0.)) + + # TODO: these variable temperature ordering should be move to a general part for + # variable network temperatures + temperature_var = self.state(f"{sup_carrier}_temperature") + ordering_disc_carr = self.state(f"{sup_carrier}__{temperature}_ordering_disc") + + constraints.append( + ( + (temperature - temperature_var + big_m * ordering_disc_carr), + min_dt / 2.0, + np.inf, + ) + ) + constraints.append( + ( + (temperature - temperature_var - big_m * (1.0 - ordering_disc_carr)), + -np.inf, + 0.0, + ) + ) return constraints @@ -2747,7 +3504,11 @@ def path_constraints(self, ensemble_member): constraints.extend(self.__check_valve_head_discharge_path_constraints(ensemble_member)) constraints.extend(self.__control_valve_head_discharge_path_constraints(ensemble_member)) constraints.extend(self.__network_temperature_path_constraints(ensemble_member)) - constraints.extend(self.__heat_pump_cop_constraints(ensemble_member)) + constraints.extend(self.__ates_temperature_path_constraints(ensemble_member)) + constraints.extend(self.__ates_temperature_changing_path_constraints(ensemble_member)) + constraints.extend(self.__ates_heat_losses_path_constraints(ensemble_member)) + constraints.extend(self.__ates_temperature_ordering_path_constraints(ensemble_member)) + constraints.extend(self.__heat_pump_cop_path_constraints(ensemble_member)) constraints.extend(self.__storage_hydraulic_power_path_constraints(ensemble_member)) return constraints @@ -2770,6 +3531,7 @@ def constraints(self, ensemble_member): if self.energy_system_options()["include_demand_insulation_options"]: constraints.extend(self.__heat_matching_demand_insulation_constraints(ensemble_member)) + constraints.extend(self.__ates_max_stored_heat_constriants(ensemble_member)) return constraints def history(self, ensemble_member): @@ -2800,7 +3562,8 @@ def history(self, ensemble_member): ): history[f"{b}.Heat_buffer"] = Timeseries(initial_time, [0.0]) - # TODO: add ATES when component is available + # TODO: add ATES when component is available, also add initial temperature state ates + # for ates in self.energy_system_components.get("ates", []): return history diff --git a/src/rtctools_heat_network/pycml/component_library/milp/heat/ates.py b/src/rtctools_heat_network/pycml/component_library/milp/heat/ates.py index 4550d169..0930234a 100644 --- a/src/rtctools_heat_network/pycml/component_library/milp/heat/ates.py +++ b/src/rtctools_heat_network/pycml/component_library/milp/heat/ates.py @@ -29,6 +29,7 @@ def __init__(self, name, **modifiers): self.component_type = "ates" self.Q_nominal = 1.0 + self.T_amb = 10 self.T_supply = nan self.T_return = nan self.T_supply_id = -1 @@ -41,6 +42,20 @@ def __init__(self, name, **modifiers): self.minimum_pressure_drop = 1.0e5 # 1 bar of pressure drop self.pump_efficiency = 0.5 + max_temp_change = self.T_supply / (3600 * 24) # loses full temperature in a day + nom_temp_change = max_temp_change / 100 # loses full temperature in 100 days. + self.add_variable(Variable, "Temperature_ates", nominal=self.T_return) + self.add_variable( + Variable, "Temperature_loss", min=0, max=max_temp_change, nominal=nom_temp_change + ) + self.add_variable( + Variable, + "Temperature_change_charging", + min=0, + max=max_temp_change, + nominal=nom_temp_change, + ) + self.heat_loss_coeff = 0.005 / (24.0 * 3600.0) self.single_doublet_power = nan self.nr_of_doublets = 1.0 @@ -65,16 +80,20 @@ def __init__(self, name, **modifiers): min=0.0, nominal=self._nominal_stored_heat, ) + self.add_variable( + Variable, + "Stored_volume", + min=0.0, + nominal=self._typical_fill_time * self.Q_nominal, + ) self.add_variable(Variable, "Q", nominal=self.Q_nominal) self.add_variable( Variable, "Pump_power", min=0.0, nominal=self.Q_nominal * self.nominal_pressure ) - # For nicer constraint coefficient scaling, we shift a bit more error into - # the state vector entry of `Heat_loss`. In other words, with a factor of - # 10.0, we aim for a state vector entry of ~0.1 (instead of 1.0) - self._heat_loss_error_to_state_factor = 10.0 + + self._heat_loss_error_to_state_factor = 1 self._nominal_heat_loss = ( - self._nominal_stored_heat * self.heat_loss_coeff * self._heat_loss_error_to_state_factor + self.Stored_heat.nominal * self.heat_loss_coeff * self._heat_loss_error_to_state_factor ) self.add_variable(Variable, "Heat_loss", min=0.0, nominal=self._nominal_heat_loss) @@ -88,9 +107,19 @@ def __init__(self, name, **modifiers): (self.der(self.Stored_heat) - self.Heat_ates + self.Heat_loss) / self._heat_loss_eq_nominal_ates ) + self.add_equation((self.der(self.Stored_volume) - self.Q) / self.Q_nominal) + self.add_equation( - (self.Heat_loss - self.Stored_heat * self.heat_loss_coeff) / self._nominal_heat_loss + ( + ( + self.der(self.Temperature_ates) + - self.Temperature_change_charging + + self.Temperature_loss + ) + / nom_temp_change + ) ) + self.add_equation( (self.HeatIn.Heat - (self.HeatOut.Heat + self.Heat_ates)) / self.Heat_nominal ) diff --git a/src/rtctools_heat_network/workflows/grow_workflow.py b/src/rtctools_heat_network/workflows/grow_workflow.py index 41c07a92..8f5e04f8 100644 --- a/src/rtctools_heat_network/workflows/grow_workflow.py +++ b/src/rtctools_heat_network/workflows/grow_workflow.py @@ -244,6 +244,8 @@ def constraints(self, ensemble_member): for a in self.energy_system_components.get("ates", []): stored_heat = self.state_vector(f"{a}.Stored_heat") constraints.append(((stored_heat[-1] - stored_heat[0]), 0.0, np.inf)) + ates_temperature = self.state_vector(f"{a}.Temperature_ates") + constraints.append(((ates_temperature[-1] - ates_temperature[0]), 0.0, np.inf)) for b in self.energy_system_components.get("heat_buffer", {}): vars = self.state_vector(f"{b}.Heat_buffer") diff --git a/tests/models/ates_temperature/input/Warmte_test_3.csv b/tests/models/ates_temperature/input/Warmte_test_3.csv new file mode 100644 index 00000000..710b1c65 --- /dev/null +++ b/tests/models/ates_temperature/input/Warmte_test_3.csv @@ -0,0 +1,8761 @@ +,DateTime,HeatingDemand_1,HeatingDemand_2,HeatingDemand_3 +0,1-1-2019 00:00,885996.6024,226606.7,226606.7 +1,1-1-2019 01:00,885788.1031999999,226553.36,226553.36 +2,1-1-2019 02:00,929386.5671999999,237704.34,237704.34 +3,1-1-2019 03:00,1301350.8144,332839.66,332839.66 +4,1-1-2019 04:00,1625846.1855999997,415834.12,415834.12 +5,1-1-2019 05:00,1800563.5648,460520.68,460520.68 +6,1-1-2019 06:00,2051336.9967999998,524659.72,524659.72 +7,1-1-2019 07:00,6218210.2528,1590399.04,1590399.04 +8,1-1-2019 08:00,7439399.967999999,1902736.16,1902736.16 +9,1-1-2019 09:00,10482350.8608,2681015.84,2681015.84 +10,1-1-2019 10:00,8486389.184,2170519.04,2170519.04 +11,1-1-2019 11:00,3627207.0016,927711.68,927711.68 +12,1-1-2019 12:00,4111597.8176,1051601.84,1051601.84 +13,1-1-2019 13:00,3421734.8256,875159.2,875159.2 +14,1-1-2019 14:00,3174385.1776,811895.84,811895.84 +15,1-1-2019 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01:00,957722.64,222906.02,222906.02 +8618,26-12-2019 02:00,969055.76,225543.78,225543.78 +8619,26-12-2019 03:00,982803.52,228743.54,228743.54 +8620,26-12-2019 04:00,1415918.88,329549.32,329549.32 +8621,26-12-2019 05:00,1698105.6,395227.16,395227.16 +8622,26-12-2019 06:00,2188078.56,509266.32,509266.32 +8623,26-12-2019 07:00,5854755.2,1362670.24,1362670.24 +8624,26-12-2019 08:00,7354007.04,1711615.04,1711615.04 +8625,26-12-2019 09:00,11040307.2,2569586.72,2569586.72 +8626,26-12-2019 10:00,8351200.0,1943707.84,1943707.84 +8627,26-12-2019 11:00,3565585.6,829875.52,829875.52 +8628,26-12-2019 12:00,4151752.96,966303.44,966303.44 +8629,26-12-2019 13:00,3455457.92,804243.68,804243.68 +8630,26-12-2019 14:00,3253131.84,757153.12,757153.12 +8631,26-12-2019 15:00,5065251.52,1178916.48,1178916.48 +8632,26-12-2019 16:00,5876679.04,1367773.12,1367773.12 +8633,26-12-2019 17:00,4954529.92,1153146.64,1153146.64 +8634,26-12-2019 18:00,13358945.28,3109240.32,3109240.32 +8635,26-12-2019 19:00,9196334.72,2140409.6,2140409.6 +8636,26-12-2019 20:00,5981746.56,1392227.2,1392227.2 +8637,26-12-2019 21:00,5880702.72,1368709.76,1368709.76 +8638,26-12-2019 22:00,3187410.56,741856.88,741856.88 +8639,26-12-2019 23:00,1066235.84,248162.04,248162.04 +8640,27-12-2019 00:00,1042460.48,242628.42,242628.42 +8641,27-12-2019 01:00,1038763.68,241768.0,241768.0 +8642,27-12-2019 02:00,1029899.44,239704.9,239704.9 +8643,27-12-2019 03:00,1189656.16,276887.64,276887.64 +8644,27-12-2019 04:00,1634045.92,380317.48,380317.48 +8645,27-12-2019 05:00,2030585.28,472610.52,472610.52 +8646,27-12-2019 06:00,2450089.6,570248.36,570248.36 +8647,27-12-2019 07:00,6638784.0,1545149.92,1545149.92 +8648,27-12-2019 08:00,7934737.28,1846777.76,1846777.76 +8649,27-12-2019 09:00,11596166.4,2698960.32,2698960.32 +8650,27-12-2019 10:00,8219687.68,1913098.88,1913098.88 +8651,27-12-2019 11:00,2996256.0,697366.32,697366.32 +8652,27-12-2019 12:00,3820751.04,889264.24,889264.24 +8653,27-12-2019 13:00,2625017.6,610962.08,610962.08 +8654,27-12-2019 14:00,2293978.24,533914.0,533914.0 +8655,27-12-2019 15:00,5184870.08,1206757.36,1206757.36 +8656,27-12-2019 16:00,6278600.32,1461318.88,1461318.88 +8657,27-12-2019 17:00,5539160.96,1289217.2,1289217.2 +8658,27-12-2019 18:00,14032156.16,3265927.36,3265927.36 +8659,27-12-2019 19:00,10374373.12,2414592.96,2414592.96 +8660,27-12-2019 20:00,7113618.56,1655665.6,1655665.6 +8661,27-12-2019 21:00,6936925.44,1614540.96,1614540.96 +8662,27-12-2019 22:00,3802465.28,885008.32,885008.32 +8663,27-12-2019 23:00,1359920.8,316516.02,316516.02 +8664,28-12-2019 00:00,1292364.0,300792.44,300792.44 +8665,28-12-2019 01:00,1338678.32,311571.92,311571.92 +8666,28-12-2019 02:00,1943291.68,452293.2,452293.2 +8667,28-12-2019 03:00,2661985.6,619566.24,619566.24 +8668,28-12-2019 04:00,3001931.2,698687.12,698687.12 +8669,28-12-2019 05:00,3140827.84,731014.8,731014.8 +8670,28-12-2019 06:00,3499936.64,814595.92,814595.92 +8671,28-12-2019 07:00,5263982.4,1225170.32,1225170.32 +8672,28-12-2019 08:00,9048196.48,2105930.72,2105930.72 +8673,28-12-2019 09:00,16720121.6,3891540.16,3891540.16 +8674,28-12-2019 10:00,17143577.6,3990097.6,3990097.6 +8675,28-12-2019 11:00,9112981.76,2121008.96,2121008.96 +8676,28-12-2019 12:00,7816170.24,1819181.6,1819181.6 +8677,28-12-2019 13:00,5840204.8,1359283.84,1359283.84 +8678,28-12-2019 14:00,5367071.68,1249164.08,1249164.08 +8679,28-12-2019 15:00,6921948.16,1611055.2,1611055.2 +8680,28-12-2019 16:00,7343782.4,1709235.36,1709235.36 +8681,28-12-2019 17:00,7392339.84,1720537.12,1720537.12 +8682,28-12-2019 18:00,7523040.64,1750956.8,1750956.8 +8683,28-12-2019 19:00,7546680.96,1756459.36,1756459.36 +8684,28-12-2019 20:00,7554064.64,1758177.76,1758177.76 +8685,28-12-2019 21:00,5988067.84,1393698.56,1393698.56 +8686,28-12-2019 22:00,2774204.48,645684.76,645684.76 +8687,28-12-2019 23:00,1692399.68,393899.12,393899.12 +8688,29-12-2019 00:00,1687984.48,392871.48,392871.48 +8689,29-12-2019 01:00,2158182.56,502308.12,502308.12 +8690,29-12-2019 02:00,2597900.8,604650.88,604650.88 +8691,29-12-2019 03:00,3030120.32,705248.16,705248.16 +8692,29-12-2019 04:00,3248652.16,756110.56,756110.56 +8693,29-12-2019 05:00,3498210.24,814194.08,814194.08 +8694,29-12-2019 06:00,3760919.36,875338.72,875338.72 +8695,29-12-2019 07:00,5621308.8,1308336.8,1308336.8 +8696,29-12-2019 08:00,9194606.72,2140007.04,2140007.04 +8697,29-12-2019 09:00,16863183.36,3924836.8,3924836.8 +8698,29-12-2019 10:00,17473189.12,4066813.12,4066813.12 +8699,29-12-2019 11:00,9429368.32,2194647.2,2194647.2 +8700,29-12-2019 12:00,8176617.6,1903074.4,1903074.4 +8701,29-12-2019 13:00,6149621.12,1431299.36,1431299.36 +8702,29-12-2019 14:00,5591032.32,1301289.92,1301289.92 +8703,29-12-2019 15:00,7056051.84,1642267.36,1642267.36 +8704,29-12-2019 16:00,7382759.04,1718307.2,1718307.2 +8705,29-12-2019 17:00,7404699.52,1723413.76,1723413.76 +8706,29-12-2019 18:00,7526519.04,1751766.56,1751766.56 +8707,29-12-2019 19:00,7487637.12,1742717.12,1742717.12 +8708,29-12-2019 20:00,7377904.0,1717176.96,1717176.96 +8709,29-12-2019 21:00,5761018.88,1340853.76,1340853.76 +8710,29-12-2019 22:00,2680613.76,623901.92,623901.92 +8711,29-12-2019 23:00,1619037.28,376824.36,376824.36 +8712,30-12-2019 00:00,1641076.64,381953.92,381953.92 +8713,30-12-2019 01:00,1945385.6,452780.56,452780.56 +8714,30-12-2019 02:00,2391875.2,556699.2,556699.2 +8715,30-12-2019 03:00,2695198.72,627296.44,627296.44 +8716,30-12-2019 04:00,2849519.68,663214.12,663214.12 +8717,30-12-2019 05:00,3037381.12,706938.08,706938.08 +8718,30-12-2019 06:00,3360961.28,782250.16,782250.16 +8719,30-12-2019 07:00,8598188.16,2001193.28,2001193.28 +8720,30-12-2019 08:00,8759758.72,2038797.92,2038797.92 +8721,30-12-2019 09:00,12524510.72,2915028.48,2915028.48 +8722,30-12-2019 10:00,9874219.52,2298184.64,2298184.64 +8723,30-12-2019 11:00,3879474.88,902931.92,902931.92 +8724,30-12-2019 12:00,4469944.32,1040361.2,1040361.2 +8725,30-12-2019 13:00,3107525.44,723263.84,723263.84 +8726,30-12-2019 14:00,2698320.32,628023.0,628023.0 +8727,30-12-2019 15:00,5398840.32,1256558.08,1256558.08 +8728,30-12-2019 16:00,6666987.52,1551714.24,1551714.24 +8729,30-12-2019 17:00,5877036.8,1367856.64,1367856.64 +8730,30-12-2019 18:00,14022828.8,3263756.16,3263756.16 +8731,30-12-2019 19:00,10358757.12,2410958.08,2410958.08 +8732,30-12-2019 20:00,7092086.4,1650654.08,1650654.08 +8733,30-12-2019 21:00,6908589.44,1607945.92,1607945.92 +8734,30-12-2019 22:00,3749925.44,872779.84,872779.84 +8735,30-12-2019 23:00,1280132.72,297945.68,297945.68 +8736,31-12-2019 00:00,1265957.12,294646.36,294646.36 +8737,31-12-2019 01:00,1321720.56,307625.1,307625.1 +8738,31-12-2019 02:00,1716530.4,399515.44,399515.44 +8739,31-12-2019 03:00,2154276.32,501399.0,501399.0 +8740,31-12-2019 04:00,2432908.16,566249.44,566249.44 +8741,31-12-2019 05:00,2650838.08,616971.8,616971.8 +8742,31-12-2019 06:00,2960116.8,688955.04,688955.04 +8743,31-12-2019 07:00,8006268.8,1863426.56,1863426.56 +8744,31-12-2019 08:00,8764845.44,2039981.92,2039981.92 +8745,31-12-2019 09:00,12706632.96,2957416.96,2957416.96 +8746,31-12-2019 10:00,10326184.32,2403377.12,2403377.12 +8747,31-12-2019 11:00,4668014.08,1086461.2,1086461.2 +8748,31-12-2019 12:00,4802051.2,1117657.84,1117657.84 +8749,31-12-2019 13:00,3911379.52,910357.68,910357.68 +8750,31-12-2019 14:00,3664505.28,852898.72,852898.72 +8751,31-12-2019 15:00,5685004.8,1323161.76,1323161.76 +8752,31-12-2019 16:00,7136534.4,1660999.36,1660999.36 +8753,31-12-2019 17:00,6671386.24,1552738.08,1552738.08 +8754,31-12-2019 18:00,14448642.56,3362862.4,3362862.4 +8755,31-12-2019 19:00,10760880.64,2504551.04,2504551.04 +8756,31-12-2019 20:00,7609045.12,1770974.4,1770974.4 +8757,31-12-2019 21:00,6987389.44,1626286.56,1626286.56 +8758,31-12-2019 22:00,3805974.72,885825.12,885825.12 +8759,31-12-2019 23:00,3805974.72,885825.12,885825.12 diff --git a/tests/models/ates_temperature/model/HP_ATES with return network.esdl b/tests/models/ates_temperature/model/HP_ATES with return network.esdl new file mode 100644 index 00000000..e4ee3e04 --- /dev/null +++ b/tests/models/ates_temperature/model/HP_ATES with return network.esdl @@ -0,0 +1,406 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/models/ates_temperature/src/run_ates_temperature.py b/tests/models/ates_temperature/src/run_ates_temperature.py new file mode 100644 index 00000000..9d91963a --- /dev/null +++ b/tests/models/ates_temperature/src/run_ates_temperature.py @@ -0,0 +1,358 @@ +import numpy as np + +from rtctools.data.storage import DataStore +from rtctools.optimization.collocated_integrated_optimization_problem import ( + CollocatedIntegratedOptimizationProblem, +) +from rtctools.optimization.goal_programming_mixin import Goal +from rtctools.optimization.linearized_order_goal_programming_mixin import ( + LinearizedOrderGoalProgrammingMixin, +) +from rtctools.optimization.single_pass_goal_programming_mixin import ( + GoalProgrammingMixin, +) +from rtctools.util import run_optimization_problem + +from rtctools_heat_network.esdl.esdl_mixin import ESDLMixin +from rtctools_heat_network.esdl.esdl_parser import ESDLFileParser +from rtctools_heat_network.esdl.profile_parser import ProfileReaderFromFile +from rtctools_heat_network.head_loss_class import HeadLossOption +from rtctools_heat_network.techno_economic_mixin import TechnoEconomicMixin +from rtctools_heat_network.workflows.io.write_output import ScenarioOutput + +ns = {"fews": "http://www.wldelft.nl/fews", "pi": "http://www.wldelft.nl/fews/PI"} + +WATT_TO_MEGA_WATT = 1.0e6 +WATT_TO_KILO_WATT = 1.0e3 + + +class TargetDemandGoal(Goal): + priority = 1 + + order = 2 + + def __init__(self, state, target): + self.state = state + + self.target_min = target + self.target_max = target + self.function_range = (-1.0, 2.0 * max(target.values)) + self.function_nominal = np.median(target.values) + + def function(self, optimization_problem, ensemble_member): + return optimization_problem.state(self.state) + + +class MinimizeCostHeatGoal(Goal): + priority = 2 + + order = 1 + + def __init__(self, source): + self.target_max = 0.0 + self.function_range = (0.0, 1.0e4) + self.source = source + self.function_nominal = 1e0 + + def function(self, optimization_problem, ensemble_member): + try: + state = optimization_problem.state(f"{self.source}.Heat_source") + except KeyError: + state = optimization_problem.state( + f"{self.source}.Power_elec" + ) # heatpumps are not yet in the variable_operational_costs in financial_mixin + var_costs = optimization_problem.parameters(0)[ + f"{self.source}.variable_operational_cost_coefficient" + ] + return state * var_costs + + +class MinimizeATESStoredHeat(Goal): + priority = 3 + + order = 1 + + def __init__(self, ates): + self.target_max = 0.0 + self.function_range = (0.0, 1e2) + self.ates = ates + # self.function_nominal = 1e0 + + def function(self, optimization_problem, ensemble_member): + # return optimization_problem.state(f"{self.ates}.Stored_heat")/ + # optimization_problem.variable_nominal(f"{self.ates}.Stored_heat") + return optimization_problem.extra_variable( + f"{self.ates}__max_stored_heat" + ) / optimization_problem.variable_nominal(f"{self.ates}__max_stored_heat") + + +class _GoalsAndOptions: + def path_goals(self): + goals = super().path_goals().copy() + + for demand in self.energy_system_components.get("heat_demand"): + target = self.get_timeseries(f"{demand}.target_heat_demand") + state = f"{demand}.Heat_demand" + + goals.append(TargetDemandGoal(state, target)) + + for s in [ + *self.energy_system_components.get("heat_source"), + *self.energy_system_components.get("heat_pump"), + ]: + goals.append(MinimizeCostHeatGoal(s)) + + # for ates in self.energy_system_components.get("ates", []): + # goals.append(MinimizeATESStored_heat(ates)) + + return goals + + def goals(self): + goals = super().goals().copy() + + # for ates in self.energy_system_components.get("ates", []): + # goals.append(MinimizeATESStored_heat(ates)) + + return goals + + def solver_options(self): + options = super().solver_options() + options["solver"] = "highs" + highs_options = options["highs"] = {} + highs_options["mip_rel_gap"] = 0.01 + # options["solver"] = "gurobi" + # gurobi_options = options["gurobi"] = {} + # gurobi_options["MIPgap"] = 0.01 + # gurobi_options["OptimalityTol"] = 1.e-3 + + return options + + +class HeatProblem( + ScenarioOutput, + _GoalsAndOptions, + TechnoEconomicMixin, + LinearizedOrderGoalProgrammingMixin, + GoalProgrammingMixin, + ESDLMixin, + CollocatedIntegratedOptimizationProblem, +): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def path_goals(self): + goals = super().path_goals().copy() + + return goals + + def energy_system_options(self): + options = super().energy_system_options() + self.heat_network_settings["minimum_velocity"] = 0.0001 + options["heat_loss_disconnected_pipe"] = ( + False # required since we want to disconnect HP & HEX + ) + self.heat_network_settings["head_loss_option"] = HeadLossOption.NO_HEADLOSS + options["neglect_pipe_heat_losses"] = True + options["include_ates_temperature_options"] = True + return options + + def temperature_carriers(self): + return self.esdl_carriers + + def temperature_regimes(self, carrier): + temperatures = [] + if carrier == 41770304791669983859190: + # supply + # temperatures = np.linspace(50, 70, 9).tolist()[::-1] + # temperatures = np.linspace(52.5, 65, 6).tolist()[::-1] + # temperatures.extend(np.linspace(45, 50, 6).tolist()[::-1]) + + temperatures = np.linspace(40, 70, 7).tolist()[::-1] + + return temperatures + + def path_constraints(self, ensemble_member): + constraints = super().path_constraints(ensemble_member) + + # To prevent heat being consumer by hex to upgrade it (add heat) by heatpump to match + # demand without loading/unloading ates. + sum_disabled_vars = 0 + for asset in [ + *self.energy_system_components.get("heat_pump"), + *self.energy_system_components.get("heat_exchanger"), + ]: + disabled_var = self.state(f"{asset}__disabled") + sum_disabled_vars += disabled_var + + constraints.append((sum_disabled_vars, 1.0, 2.0)) + + # when using compound asset instead of separate assets, one could still use this constraint + # but potentially add the constraint that if hex is enabled, ates is loading and if hp is + # enabled ates is unloading (dis_hex-ates_charging, 0.0, 0.0) + + return constraints + + def constraints(self, ensemble_member): + constraints = super().constraints(ensemble_member) + + for a in self.energy_system_components.get("ates", []): + stored_heat = self.state_vector(f"{a}.Stored_heat") + heat_ates = self.state_vector(f"{a}.Heat_ates") + constraints.append((stored_heat[0] - stored_heat[-1], 0.0, 0.0)) + # stored_volume only to be used if temperature loss ates is also dependent on + # stored_volume instead of stored_heat + constraints.append((heat_ates[0], 0.0, 0.0)) + ates_temperature_disc = self.__state_vector_scaled( + f"{a}__temperature_ates_disc", ensemble_member + ) + constraints.append(((ates_temperature_disc[-1] - ates_temperature_disc[0]), 0.0, 0.0)) + + return constraints + + def __state_vector_scaled(self, variable, ensemble_member): + """ + This functions returns the casadi symbols scaled with their nominal for the entire time + horizon. + """ + canonical, sign = self.alias_relation.canonical_signed(variable) + return ( + self.state_vector(canonical, ensemble_member) * self.variable_nominal(canonical) * sign + ) + + def read(self): + """ + Reads the yearly profile with hourly time steps and adapt to a 5 day averaged profile. + """ + super().read() + + demands = self.energy_system_components.get("heat_demand", []) + new_datastore = DataStore(self) + new_datastore.reference_datetime = self.io.datetimes[0] + + for ensemble_member in range(self.ensemble_size): + total_demand = sum( + self.get_timeseries(f"{demand}.target_heat_demand", ensemble_member).values + for demand in demands + ) + + # TODO: check if ordering is possible for ates temperature over time + day_step = 28 + + nr_of_days = len(total_demand) // (24 * day_step) + new_date_times = list() + for day in range(0, nr_of_days): + new_date_times.append(self.io.datetimes[day * 24 * day_step]) + new_date_times = np.asarray(new_date_times) + + for demand in demands: + var_name = f"{demand}.target_heat_demand" + data = self.get_timeseries( + variable=var_name, ensemble_member=ensemble_member + ).values + new_data = list() + for day in range(0, nr_of_days): + data_for_day = data[day * 24 * day_step : (day + 1) * 24 * day_step] + new_data.append(np.mean(data_for_day)) + new_datastore.set_timeseries( + variable=var_name, + datetimes=new_date_times, + values=np.asarray(new_data) * 2.0, + ensemble_member=ensemble_member, + check_duplicates=True, + ) + + self.io = new_datastore + + # def _get_runinfo_path_root(self): + # runinfo_path = Path(self.esdl_run_info_path).resolve() + # tree = ET.parse(runinfo_path) + # return tree.getroot() + + # def post(self): + # root = self._get_runinfo_path_root() + # workdir = os.path.join(os.path.dirname(os.getcwd()), 'output') + # super().post() + # results = self.extract_results() + # parameters = self.parameters(0) + # bounds = self.bounds() + # parameters_dict = dict() + # + # parameter_path = os.path.join(workdir, "parameters.json") + # for key, value in parameters.items(): + # new_value = value # [x for x in value] + # parameters_dict[key] = new_value + # if parameter_path is None: + # workdir = root.findtext("pi:workDir", namespaces=ns) + # parameter_path = os.path.join(workdir, "parameters.json") + # if not Path(workdir).is_absolute(): + # parameter_path = Path(workdir).resolve().parent + # parameter_path = os.path.join(parameter_path.__str__() + "parameters.json") + # with open(parameter_path, "w") as file: + # json.dump(parameters_dict, fp=file) + # + # root = self._get_runinfo_path_root() + # bounds_dict = dict() + # bounds_path = root.findtext("pi:outputResultsFile", namespaces=ns) + # bounds_path = os.path.join(workdir, "bounds.json") + # for key, value in bounds.items(): + # if "Stored_heat" not in key: + # new_value = value # [x for x in value] + # # if len(new_value) == 1: + # # new_value = new_value[0] + # bounds_dict[key] = new_value + # if bounds_path is None: + # workdir = root.findtext("pi:workDir", namespaces=ns) + # bounds_path = os.path.join(workdir, "bounds.json") + # if not Path(workdir).is_absolute(): + # bounds_path = Path(workdir).resolve().parent + # bounds_path = os.path.join(bounds_path.__str__() + "bounds.json") + # with open(bounds_path, "w") as file: + # json.dump(bounds_dict, fp=file) + # + # root = self._get_runinfo_path_root() + # results_path = root.findtext("pi:outputResultsFile", namespaces=ns) + # results_dict = dict() + # + # for key, values in results.items(): + # new_value = values.tolist() + # if len(new_value) == 1: + # new_value = new_value[0] + # results_dict[key] = new_value + # + # results_path = os.path.join(workdir, "results.json") + # if results_path is None: + # workdir = root.findtext("pi:workDir", namespaces=ns) + # results_path = os.path.join(workdir, "results.json") + # if not Path(workdir).is_absolute(): + # results_path = Path(workdir).resolve().parent + # results_path = os.path.join(results_path.__str__() + "results.json") + # with open(results_path, "w") as file: + # json.dump(results_dict, fp=file) + + +class HeatProblemMaxFlow(HeatProblem): + + def read(self): + super().read() + + demand_timeseries = self.get_timeseries("HeatingDemand_1.target_heat_demand") + demand_timeseries.values[2] = demand_timeseries.values[2] * 2 + self.set_timeseries("HeatingDemand_1.target_heat_demand", demand_timeseries) + + +if __name__ == "__main__": + import time + + t0 = time.time() + + sol = run_optimization_problem( + HeatProblem, + esdl_file_name="HP_ATES with return network.esdl", + esdl_parser=ESDLFileParser, + profile_reader=ProfileReaderFromFile, + input_timeseries_file="Warmte_test_3.csv", + ) + # sol._write_updated_esdl() + results = sol.extract_results() + print(f"time: {time.time() - t0}") + a = 1 diff --git a/tests/test_temperature_ates_hp.py b/tests/test_temperature_ates_hp.py new file mode 100644 index 00000000..cfb3fa89 --- /dev/null +++ b/tests/test_temperature_ates_hp.py @@ -0,0 +1,205 @@ +from pathlib import Path +from unittest import TestCase + +import numpy as np + +from rtctools.util import run_optimization_problem + +from rtctools_heat_network.esdl.esdl_parser import ESDLFileParser +from rtctools_heat_network.esdl.profile_parser import ProfileReaderFromFile + +from utils_tests import demand_matching_test, energy_conservation_test, heat_to_discharge_test + + +class TestAtesTemperature(TestCase): + """ + Checks the constraints concerning the temperature changes in the ates as a result of heat + loss and charging + """ + + def test_ates_temperature(self): + """ + check if + - discrete temperature ates is equal to temperature of pipe at inport during discharging + - discrete temperature ates is equal or lower then temperature ates + - discrete temperature ates is equal to the set booleans of ates temperature + - temperature change ates continues is equal to the sum of temperature loss and + temperature change charging + + - only heatpump or heat exchanger is in operation, solely for charging/discharging ates + - if ates is charging (heat_ates>0), hex is enabled. if ates is discharging (heat<0), + hp is enabled + - Discharge heat and mass flow is corresponding to the temperature regime (flow rate + remains the same, but heat changes) + + TODO: still have to add checks: + - temperature loss>= relation of temperature loss + - temperature addition charging + - heat loss ates>= relation of heat loss + """ + import models.ates_temperature.src.run_ates_temperature as run_ates_temperature + from models.ates_temperature.src.run_ates_temperature import HeatProblem + + basefolder = Path(run_ates_temperature.__file__).resolve().parent.parent + + solution = run_optimization_problem( + HeatProblem, + base_folder=basefolder, + esdl_file_name="HP_ATES with return network.esdl", + esdl_parser=ESDLFileParser, + profile_reader=ProfileReaderFromFile, + input_timeseries_file="Warmte_test_3.csv", + ) + + results = solution.extract_results() + parameters = solution.parameters(0) + + times = solution.times() + + tol = 1e-6 + + demand_matching_test(solution, results) + energy_conservation_test(solution, results) + heat_to_discharge_test(solution, results) + + ates_charging = results["Pipe1__flow_direct_var"] # =1 if charging + ates_temperature = results["ATES_cb47.Temperature_ates"] + ates_temperature_disc = results["ATES_cb47__temperature_ates_disc"] + carrier_temperature = results["41770304791669983859190_temperature"] + temperature_regimes = solution.temperature_regimes(41770304791669983859190) + + ates_temperature_loss = results["ATES_cb47.Temperature_loss"] + ates_temperature_change_charging = results["ATES_cb47.Temperature_change_charging"] + + # heat_pump_sec = results["HeatPump_7f2c.Secondary_heat"] + heat_ates = results["ATES_cb47.Heat_ates"] + heat_loss_ates = results["ATES_cb47.Heat_loss"] + ates_stored_heat = results["ATES_cb47.Stored_heat"] + hex_disabled = results["HeatExchange_32ba__disabled"] + hp_disabled = results["HeatPump_7f2c__disabled"] + + # geo_source = results["GeothermalSource_4e5b.Heat_source"] + objective = solution.objective_value + + objective_calc = ( + sum( + parameters["GeothermalSource_4e5b.variable_operational_cost_coefficient"] + * results["GeothermalSource_4e5b.Heat_source"] + ) + + sum( + parameters["HeatPump_7f2c.variable_operational_cost_coefficient"] + * results["HeatPump_7f2c.Power_elec"] + ) + + sum( + parameters["GenericProducer_4dfe.variable_operational_cost_coefficient"] + * results["GenericProducer_4dfe.Heat_source"] + ) + ) + + feasibility = solution.solver_stats["return_status"] + + self.assertTrue((feasibility == "Optimal")) + + np.testing.assert_allclose(objective_calc / 1e4, objective) + + np.testing.assert_array_less(ates_temperature_disc - tol, ates_temperature) + np.testing.assert_array_less( + ates_temperature_disc - tol, + sum( + [ + results[f"ATES_cb47__temperature_disc_{temp}"] * temp + for temp in temperature_regimes + ] + ), + ) + np.testing.assert_allclose( + (1 - ates_charging) * ates_temperature_disc, + (1 - ates_charging) * carrier_temperature, + atol=tol, + ) + np.testing.assert_allclose( + ates_temperature[1:] - ates_temperature[:-1], + (ates_temperature_change_charging[1:] - ates_temperature_loss[1:]) + * (times[1:] - times[:-1]), + atol=tol, + ) + + np.testing.assert_allclose( + heat_ates[1:] - heat_loss_ates[1:], + (ates_stored_heat[1:] - ates_stored_heat[:-1]) / (times[1:] - times[:-1]), + atol=tol, + ) + # TODO: potentially update example such that the commented checks will also hold. + # np.testing.assert_array_less(heat_pump_sec, geo_source) + + charging = np.array([int(val > 0) for val in heat_ates]) + # array less then because ates charging boolean can be either 0 or 1 when there is no flow, + # or just flow to compensate the heatloss + np.testing.assert_array_less(np.ones(len(hex_disabled)) - tol, hex_disabled + hp_disabled) + np.testing.assert_array_less(charging - tol, hp_disabled) + np.testing.assert_array_less(charging[1:] - tol, 1 - hex_disabled[1:]) + + # np.alltrue( + # [ + # True if (g < 6e6 and hp <= 0) or g >= 6e6 - tol else False + # for (g, hp) in zip(geo_source, heat_pump_sec) + # ] + # ) + + def test_ates_max_flow(self): + """ + Checks if the maximum flow is limiting due to the decreased temperature and not the + maximum heat for the ates + + Highs acts slow in solving this problem, already in the first goal 'matching the demand' + because it cannot be matched. + """ + + import models.ates_temperature.src.run_ates_temperature as run_ates_temperature + from models.ates_temperature.src.run_ates_temperature import HeatProblemMaxFlow + + basefolder = Path(run_ates_temperature.__file__).resolve().parent.parent + + solution = run_optimization_problem( + HeatProblemMaxFlow, + base_folder=basefolder, + esdl_file_name="HP_ATES with return network.esdl", + esdl_parser=ESDLFileParser, + profile_reader=ProfileReaderFromFile, + input_timeseries_file="Warmte_test_3.csv", + ) + + results = solution.extract_results() + parameters = solution.parameters(0) + bounds = solution.bounds() + + # energy_conservation_test(solution, results) + # heat_to_discharge_test(solution, results) + + ates_flow = results["ATES_cb47.Q"] + ates_flow_bound = bounds["ATES_cb47.Q"][1] + + ates_heat = results["ATES_cb47.Heat_ates"] + ates_heat_bound = bounds["ATES_cb47.Heat_ates"][1] + + heat_demand = results["HeatingDemand_1.Heat_demand"] + target = solution.get_timeseries("HeatingDemand_1.target_heat_demand").values + + demand_not_matched = (heat_demand - target) < -1 + + ates_temperature = results["ATES_cb47.Temperature_ates"] + ates_temp_ret = parameters["ATES_cb47.T_return"] + cp = parameters["ATES_cb47.cp"] + rho = parameters["ATES_cb47.rho"] + + np.testing.assert_allclose(abs(ates_flow[demand_not_matched]), ates_flow_bound) + np.testing.assert_array_less(abs(ates_heat[demand_not_matched]), ates_heat_bound) + np.testing.assert_array_less( + abs(ates_heat[demand_not_matched]), + abs( + ates_flow[demand_not_matched] + * cp + * rho + * (ates_temperature[demand_not_matched] - ates_temp_ret) + ), + ) diff --git a/tests/utils_tests.py b/tests/utils_tests.py index 5d51c444..0dcf283c 100644 --- a/tests/utils_tests.py +++ b/tests/utils_tests.py @@ -220,12 +220,12 @@ def heat_to_discharge_test(solution, results): heat = results[f"{d}.{p}_heat"] if p == "Primary": - np.testing.assert_allclose(heat_out, discharge * rho * cp * return_t) + np.testing.assert_allclose(heat_out, discharge * rho * cp * return_t, atol=tol) test.assertTrue(expr=all(heat_in <= discharge * rho * cp * supply_t + tol)) test.assertTrue(expr=all(heat <= discharge * rho * cp * dt + tol)) elif p == "Secondary": test.assertTrue(expr=all(heat >= discharge * rho * cp * dt - tol)) - np.testing.assert_allclose(heat_out, discharge * rho * cp * supply_t) + np.testing.assert_allclose(heat_out, discharge * rho * cp * supply_t, atol=tol) test.assertTrue(expr=all(heat_in <= discharge * rho * cp * return_t + tol)) for p in solution.energy_system_components.get("heat_pipe", []):