def create_rsv_acopf_model(model_data, include_feasibility_slack=False, pw_cost_model='delta'): model, md = _create_base_power_ac_model( model_data, include_feasibility_slack=include_feasibility_slack, pw_cost_model=pw_cost_model) bus_attrs = md.attributes(element_type='bus') branch_attrs = md.attributes(element_type='branch') bus_pairs = zip_items(branch_attrs['from_bus'], branch_attrs['to_bus']) unique_bus_pairs = list( OrderedDict((val, None) for idx, val in bus_pairs.items()).keys()) # declare the rectangular voltages neg_v_max = map_items(op.neg, bus_attrs['v_max']) vr_init = { k: bus_attrs['vm'][k] * pe.cos(radians(bus_attrs['va'][k])) for k in bus_attrs['vm'] } libbus.declare_var_vr(model, bus_attrs['names'], initialize=vr_init, bounds=zip_items(neg_v_max, bus_attrs['v_max'])) vj_init = { k: bus_attrs['vm'][k] * pe.sin(radians(bus_attrs['va'][k])) for k in bus_attrs['vm'] } libbus.declare_var_vj(model, bus_attrs['names'], initialize=vj_init, bounds=zip_items(neg_v_max, bus_attrs['v_max'])) # fix the reference bus ref_bus = md.data['system']['reference_bus'] ref_angle = md.data['system']['reference_bus_angle'] if ref_angle != 0.0: libbus.declare_eq_ref_bus_nonzero(model, ref_angle, ref_bus) else: model.vj[ref_bus].fix(0.0) model.vr[ref_bus].setlb(bus_attrs['v_min'][ref_bus]) # relate c, s, and vmsq to vm and va libbus.declare_eq_vmsq(model=model, index_set=bus_attrs['names'], coordinate_type=CoordinateType.RECTANGULAR) libbranch.declare_eq_c(model=model, index_set=unique_bus_pairs, coordinate_type=CoordinateType.RECTANGULAR) libbranch.declare_eq_s(model=model, index_set=unique_bus_pairs, coordinate_type=CoordinateType.RECTANGULAR) return model, md
def create_riv_acopf_model(model_data, include_feasibility_slack=False): md = model_data.clone_in_service() tx_utils.scale_ModelData_to_pu(md, inplace=True) gens = dict(md.elements(element_type='generator')) buses = dict(md.elements(element_type='bus')) branches = dict(md.elements(element_type='branch')) loads = dict(md.elements(element_type='load')) shunts = dict(md.elements(element_type='shunt')) gen_attrs = md.attributes(element_type='generator') bus_attrs = md.attributes(element_type='bus') branch_attrs = md.attributes(element_type='branch') load_attrs = md.attributes(element_type='load') shunt_attrs = md.attributes(element_type='shunt') inlet_branches_by_bus, outlet_branches_by_bus = \ tx_utils.inlet_outlet_branches_by_bus(branches, buses) gens_by_bus = tx_utils.gens_by_bus(buses, gens) model = pe.ConcreteModel() ### declare (and fix) the loads at the buses bus_p_loads, bus_q_loads = tx_utils.dict_of_bus_loads(buses, loads) libbus.declare_var_pl(model, bus_attrs['names'], initialize=bus_p_loads) libbus.declare_var_ql(model, bus_attrs['names'], initialize=bus_q_loads) model.pl.fix() model.ql.fix() ### declare the fixed shunts at the buses bus_bs_fixed_shunts, bus_gs_fixed_shunts = tx_utils.dict_of_bus_fixed_shunts( buses, shunts) ### declare the rectangular voltages neg_v_max = map_items(op.neg, bus_attrs['v_max']) vr_init = { k: bus_attrs['vm'][k] * pe.cos(bus_attrs['va'][k]) for k in bus_attrs['vm'] } libbus.declare_var_vr(model, bus_attrs['names'], initialize=vr_init, bounds=zip_items(neg_v_max, bus_attrs['v_max'])) vj_init = { k: bus_attrs['vm'][k] * pe.sin(bus_attrs['va'][k]) for k in bus_attrs['vm'] } libbus.declare_var_vj(model, bus_attrs['names'], initialize=vj_init, bounds=zip_items(neg_v_max, bus_attrs['v_max'])) ### include the feasibility slack for the bus balances p_rhs_kwargs = {} q_rhs_kwargs = {} if include_feasibility_slack: p_marginal_slack_penalty, q_marginal_slack_penalty = _validate_and_extract_slack_penalties( md) p_rhs_kwargs, q_rhs_kwargs, penalty_expr = _include_feasibility_slack( model, bus_attrs, gen_attrs, bus_p_loads, bus_q_loads, p_marginal_slack_penalty, q_marginal_slack_penalty) ### fix the reference bus ref_bus = md.data['system']['reference_bus'] ref_angle = md.data['system']['reference_bus_angle'] if ref_angle != 0.0: libbus.declare_eq_ref_bus_nonzero(model, ref_angle, ref_bus) else: model.vj[ref_bus].fix(0.0) model.vr[ref_bus].setlb(0.0) ### declare the generator real and reactive power pg_init = { k: (gen_attrs['p_min'][k] + gen_attrs['p_max'][k]) / 2.0 for k in gen_attrs['pg'] } libgen.declare_var_pg(model, gen_attrs['names'], initialize=pg_init, bounds=zip_items(gen_attrs['p_min'], gen_attrs['p_max'])) qg_init = { k: (gen_attrs['q_min'][k] + gen_attrs['q_max'][k]) / 2.0 for k in gen_attrs['qg'] } libgen.declare_var_qg(model, gen_attrs['names'], initialize=qg_init, bounds=zip_items(gen_attrs['q_min'], gen_attrs['q_max'])) ### declare the current flows in the branches branch_currents = tx_utils.dict_of_branch_currents(branches, buses) s_max = {k: branches[k]['rating_long_term'] for k in branches.keys()} if_bounds = dict() it_bounds = dict() ifr_init = dict() ifj_init = dict() itr_init = dict() itj_init = dict() for branch_name, branch in branches.items(): from_bus = branch['from_bus'] to_bus = branch['to_bus'] y_matrix = tx_calc.calculate_y_matrix_from_branch(branch) ifr_init[branch_name] = tx_calc.calculate_ifr(vr_init[from_bus], vj_init[from_bus], vr_init[to_bus], vj_init[to_bus], y_matrix) ifj_init[branch_name] = tx_calc.calculate_ifj(vr_init[from_bus], vj_init[from_bus], vr_init[to_bus], vj_init[to_bus], y_matrix) itr_init[branch_name] = tx_calc.calculate_itr(vr_init[from_bus], vj_init[from_bus], vr_init[to_bus], vj_init[to_bus], y_matrix) itj_init[branch_name] = tx_calc.calculate_itj(vr_init[from_bus], vj_init[from_bus], vr_init[to_bus], vj_init[to_bus], y_matrix) if s_max[branch_name] is None: if_bounds[branch_name] = (None, None) it_bounds[branch_name] = (None, None) else: if_max = s_max[branch_name] / buses[branches[branch_name] ['from_bus']]['v_min'] it_max = s_max[branch_name] / buses[branches[branch_name] ['to_bus']]['v_min'] if_bounds[branch_name] = (-if_max, if_max) it_bounds[branch_name] = (-it_max, it_max) libbranch.declare_var_ifr(model=model, index_set=branch_attrs['names'], initialize=ifr_init, bounds=if_bounds) libbranch.declare_var_ifj(model=model, index_set=branch_attrs['names'], initialize=ifj_init, bounds=if_bounds) libbranch.declare_var_itr(model=model, index_set=branch_attrs['names'], initialize=itr_init, bounds=it_bounds) libbranch.declare_var_itj(model=model, index_set=branch_attrs['names'], initialize=itj_init, bounds=it_bounds) ir_init = dict() ij_init = dict() for bus_name, bus in buses.items(): ir_expr = sum([ ifr_init[branch_name] for branch_name in outlet_branches_by_bus[bus_name] ]) ir_expr += sum([ itr_init[branch_name] for branch_name in inlet_branches_by_bus[bus_name] ]) ij_expr = sum([ ifj_init[branch_name] for branch_name in outlet_branches_by_bus[bus_name] ]) ij_expr += sum([ itj_init[branch_name] for branch_name in inlet_branches_by_bus[bus_name] ]) if bus_gs_fixed_shunts[bus_name] != 0.0: ir_expr += bus_gs_fixed_shunts[bus_name] * vr_init[bus_name] ij_expr += bus_gs_fixed_shunts[bus_name] * vj_init[bus_name] if bus_bs_fixed_shunts[bus_name] != 0.0: ir_expr += bus_bs_fixed_shunts[bus_name] * vj_init[bus_name] ij_expr += bus_bs_fixed_shunts[bus_name] * vr_init[bus_name] ir_init[bus_name] = ir_expr ij_init[bus_name] = ij_expr # TODO: Implement better bounds (?) for these aggregated variables -- note, these are unbounded in old Egret libbus.declare_var_ir_aggregation_at_bus(model=model, index_set=bus_attrs['names'], initialize=ir_init, bounds=(None, None)) libbus.declare_var_ij_aggregation_at_bus(model=model, index_set=bus_attrs['names'], initialize=ij_init, bounds=(None, None)) ### declare the branch current flow constraints libbranch.declare_eq_branch_current(model=model, index_set=branch_attrs['names'], branches=branches) ### declare the ir/ij_aggregation constraints libbus.declare_eq_i_aggregation_at_bus( model=model, index_set=bus_attrs['names'], bus_bs_fixed_shunts=bus_bs_fixed_shunts, bus_gs_fixed_shunts=bus_gs_fixed_shunts, inlet_branches_by_bus=inlet_branches_by_bus, outlet_branches_by_bus=outlet_branches_by_bus) ### declare the pq balances libbus.declare_eq_p_balance_with_i_aggregation( model=model, index_set=bus_attrs['names'], bus_p_loads=bus_p_loads, gens_by_bus=gens_by_bus, **p_rhs_kwargs) libbus.declare_eq_q_balance_with_i_aggregation( model=model, index_set=bus_attrs['names'], bus_q_loads=bus_q_loads, gens_by_bus=gens_by_bus, **q_rhs_kwargs) ### declare the thermal limits libbranch.declare_ineq_s_branch_thermal_limit( model=model, index_set=branch_attrs['names'], branches=branches, s_thermal_limits=s_max, flow_type=FlowType.CURRENT) ### declare the voltage min and max inequalities libbus.declare_ineq_vm_bus_lbub(model=model, index_set=bus_attrs['names'], buses=buses, coordinate_type=CoordinateType.RECTANGULAR) ### declare angle difference limits on interconnected buses libbranch.declare_ineq_angle_diff_branch_lbub( model=model, index_set=branch_attrs['names'], branches=branches, coordinate_type=CoordinateType.RECTANGULAR) ### declare the generator cost objective libgen.declare_expression_pgqg_operating_cost(model=model, index_set=gen_attrs['names'], p_costs=gen_attrs['p_cost'], q_costs=gen_attrs.get( 'q_cost', None)) obj_expr = sum(model.pg_operating_cost[gen_name] for gen_name in model.pg_operating_cost) if include_feasibility_slack: obj_expr += penalty_expr if hasattr(model, 'qg_operating_cost'): obj_expr += sum(model.qg_operating_cost[gen_name] for gen_name in model.qg_operating_cost) model.obj = pe.Objective(expr=obj_expr) return model, md
def create_rsv_acopf_model(model_data, include_feasibility_slack=False): md = model_data.clone_in_service() tx_utils.scale_ModelData_to_pu(md, inplace=True) gens = dict(md.elements(element_type='generator')) buses = dict(md.elements(element_type='bus')) branches = dict(md.elements(element_type='branch')) loads = dict(md.elements(element_type='load')) shunts = dict(md.elements(element_type='shunt')) gen_attrs = md.attributes(element_type='generator') bus_attrs = md.attributes(element_type='bus') branch_attrs = md.attributes(element_type='branch') load_attrs = md.attributes(element_type='load') shunt_attrs = md.attributes(element_type='shunt') inlet_branches_by_bus, outlet_branches_by_bus = \ tx_utils.inlet_outlet_branches_by_bus(branches, buses) gens_by_bus = tx_utils.gens_by_bus(buses, gens) model = pe.ConcreteModel() ### declare (and fix) the loads at the buses bus_p_loads, bus_q_loads = tx_utils.dict_of_bus_loads(buses, loads) libbus.declare_var_pl(model, bus_attrs['names'], initialize=bus_p_loads) libbus.declare_var_ql(model, bus_attrs['names'], initialize=bus_q_loads) model.pl.fix() model.ql.fix() ### declare the fixed shunts at the buses bus_bs_fixed_shunts, bus_gs_fixed_shunts = tx_utils.dict_of_bus_fixed_shunts( buses, shunts) ### declare the rectangular voltages neg_v_max = map_items(op.neg, bus_attrs['v_max']) vr_init = { k: bus_attrs['vm'][k] * pe.cos(bus_attrs['va'][k]) for k in bus_attrs['vm'] } libbus.declare_var_vr(model, bus_attrs['names'], initialize=vr_init, bounds=zip_items(neg_v_max, bus_attrs['v_max'])) vj_init = { k: bus_attrs['vm'][k] * pe.sin(bus_attrs['va'][k]) for k in bus_attrs['vm'] } libbus.declare_var_vj(model, bus_attrs['names'], initialize=vj_init, bounds=zip_items(neg_v_max, bus_attrs['v_max'])) libbus.declare_expr_vmsq(model=model, index_set=bus_attrs['names'], coordinate_type=CoordinateType.RECTANGULAR) ### include the feasibility slack for the bus balances p_rhs_kwargs = {} q_rhs_kwargs = {} if include_feasibility_slack: p_rhs_kwargs, q_rhs_kwargs, penalty_expr = _include_feasibility_slack( model, bus_attrs, gen_attrs, bus_p_loads, bus_q_loads) ### fix the reference bus ref_bus = md.data['system']['reference_bus'] ref_angle = md.data['system']['reference_bus_angle'] if ref_angle != 0.0: libbus.declare_eq_ref_bus_nonzero(model, ref_angle, ref_bus) else: model.vj[ref_bus].fix(0.0) model.vr[ref_bus].setlb(0.0) ### declare the generator real and reactive power pg_init = { k: (gen_attrs['p_min'][k] + gen_attrs['p_max'][k]) / 2.0 for k in gen_attrs['pg'] } libgen.declare_var_pg(model, gen_attrs['names'], initialize=pg_init, bounds=zip_items(gen_attrs['p_min'], gen_attrs['p_max'])) qg_init = { k: (gen_attrs['q_min'][k] + gen_attrs['q_max'][k]) / 2.0 for k in gen_attrs['qg'] } libgen.declare_var_qg(model, gen_attrs['names'], initialize=qg_init, bounds=zip_items(gen_attrs['q_min'], gen_attrs['q_max'])) ### declare the current flows in the branches s_max = {k: branches[k]['rating_long_term'] for k in branches.keys()} s_lbub = dict() for k in branches.keys(): if s_max[k] is None: s_lbub[k] = (None, None) else: s_lbub[k] = (-s_max[k], s_max[k]) pf_bounds = s_lbub pt_bounds = s_lbub qf_bounds = s_lbub qt_bounds = s_lbub pf_init = dict() pt_init = dict() qf_init = dict() qt_init = dict() for branch_name, branch in branches.items(): from_bus = branch['from_bus'] to_bus = branch['to_bus'] y_matrix = tx_calc.calculate_y_matrix_from_branch(branch) ifr_init = tx_calc.calculate_ifr(vr_init[from_bus], vj_init[from_bus], vr_init[to_bus], vj_init[to_bus], y_matrix) ifj_init = tx_calc.calculate_ifj(vr_init[from_bus], vj_init[from_bus], vr_init[to_bus], vj_init[to_bus], y_matrix) itr_init = tx_calc.calculate_itr(vr_init[from_bus], vj_init[from_bus], vr_init[to_bus], vj_init[to_bus], y_matrix) itj_init = tx_calc.calculate_itj(vr_init[from_bus], vj_init[from_bus], vr_init[to_bus], vj_init[to_bus], y_matrix) pf_init[branch_name] = tx_calc.calculate_p(ifr_init, ifj_init, vr_init[from_bus], vj_init[from_bus]) pt_init[branch_name] = tx_calc.calculate_p(itr_init, itj_init, vr_init[to_bus], vj_init[to_bus]) qf_init[branch_name] = tx_calc.calculate_q(ifr_init, ifj_init, vr_init[from_bus], vj_init[from_bus]) qt_init[branch_name] = tx_calc.calculate_q(itr_init, itj_init, vr_init[to_bus], vj_init[to_bus]) libbranch.declare_var_pf(model=model, index_set=branch_attrs['names'], initialize=pf_init, bounds=pf_bounds) libbranch.declare_var_pt(model=model, index_set=branch_attrs['names'], initialize=pt_init, bounds=pt_bounds) libbranch.declare_var_qf(model=model, index_set=branch_attrs['names'], initialize=qf_init, bounds=qf_bounds) libbranch.declare_var_qt(model=model, index_set=branch_attrs['names'], initialize=qt_init, bounds=qt_bounds) ### declare the branch power flow constraints bus_pairs = zip_items(branch_attrs['from_bus'], branch_attrs['to_bus']) unique_bus_pairs = list( OrderedDict((val, None) for idx, val in bus_pairs.items()).keys()) libbranch.declare_expr_c(model=model, index_set=unique_bus_pairs, coordinate_type=CoordinateType.RECTANGULAR) libbranch.declare_expr_s(model=model, index_set=unique_bus_pairs, coordinate_type=CoordinateType.RECTANGULAR) libbranch.declare_eq_branch_power(model=model, index_set=branch_attrs['names'], branches=branches) ### declare the pq balances libbus.declare_eq_p_balance(model=model, index_set=bus_attrs['names'], bus_p_loads=bus_p_loads, gens_by_bus=gens_by_bus, bus_gs_fixed_shunts=bus_gs_fixed_shunts, inlet_branches_by_bus=inlet_branches_by_bus, outlet_branches_by_bus=outlet_branches_by_bus, **p_rhs_kwargs) libbus.declare_eq_q_balance(model=model, index_set=bus_attrs['names'], bus_q_loads=bus_q_loads, gens_by_bus=gens_by_bus, bus_bs_fixed_shunts=bus_bs_fixed_shunts, inlet_branches_by_bus=inlet_branches_by_bus, outlet_branches_by_bus=outlet_branches_by_bus, **q_rhs_kwargs) ### declare the thermal limits libbranch.declare_ineq_s_branch_thermal_limit( model=model, index_set=branch_attrs['names'], branches=branches, s_thermal_limits=s_max, flow_type=FlowType.POWER) ### declare the voltage min and max inequalities libbus.declare_ineq_vm_bus_lbub(model=model, index_set=bus_attrs['names'], buses=buses, coordinate_type=CoordinateType.RECTANGULAR) ### declare angle difference limits on interconnected buses libbranch.declare_ineq_angle_diff_branch_lbub( model=model, index_set=branch_attrs['names'], branches=branches, coordinate_type=CoordinateType.RECTANGULAR) ### declare the generator cost objective libgen.declare_expression_pgqg_operating_cost(model=model, index_set=gen_attrs['names'], p_costs=gen_attrs['p_cost'], q_costs=gen_attrs.get( 'q_cost', None)) obj_expr = sum(model.pg_operating_cost[gen_name] for gen_name in model.pg_operating_cost) if include_feasibility_slack: obj_expr += penalty_expr if hasattr(model, 'qg_operating_cost'): obj_expr += sum(model.qg_operating_cost[gen_name] for gen_name in model.qg_operating_cost) model.obj = pe.Objective(expr=obj_expr) return model, md