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"benchmark": {
"system": "case33bw",
"published": {
"base_loss_kw": 202.67,
"optimal_loss_kw": 139.55,
"optimal_reduction_pct": 31.15,
"optimal_open_switches": "7, 9, 14, 32, 37",
"source": "Baran & Wu 1989, widely reproduced (PSO, GA, MILP, Branch Exchange)"
},
"baseline_loss_kw": 202.68,
"methods": {
"classical": {
"loss_kw": 139.55,
"reduction_pct": 31.15,
"time_sec": 12.177,
"open_lines": [
36,
31,
6,
13,
8
]
},
"quantum_sa": {
"loss_kw": 139.55,
"reduction_pct": 31.15,
"time_sec": 19.652,
"open_lines": [
6,
8,
13,
31,
36
]
},
"hybrid": {
"loss_kw": 139.55,
"reduction_pct": 31.15,
"time_sec": 20.233,
"open_lines": [
6,
8,
13,
31,
36
]
}
}
},
"multi_load": {
"load_scenarios": [
{
"load_multiplier": 0.7,
"baseline_loss_kw": 94.91,
"optimized_loss_kw": 66.99,
"reduction_pct": 29.42,
"min_voltage_before": 0.9407,
"min_voltage_after": 0.9596,
"open_lines": [
6,
9,
13,
27,
31
]
},
{
"load_multiplier": 0.85,
"baseline_loss_kw": 143.09,
"optimized_loss_kw": 102.11,
"reduction_pct": 28.64,
"min_voltage_before": 0.9271,
"min_voltage_after": 0.9476,
"open_lines": [
6,
9,
31,
33,
36
]
},
{
"load_multiplier": 1.0,
"baseline_loss_kw": 202.68,
"optimized_loss_kw": 141.92,
"reduction_pct": 29.98,
"min_voltage_before": 0.9131,
"min_voltage_after": 0.9378,
"open_lines": [
6,
8,
13,
27,
35
]
},
{
"load_multiplier": 1.15,
"baseline_loss_kw": 274.58,
"optimized_loss_kw": 187.9,
"reduction_pct": 31.57,
"min_voltage_before": 0.8987,
"min_voltage_after": 0.9319,
"open_lines": [
6,
8,
13,
27,
31
]
},
{
"load_multiplier": 1.3,
"baseline_loss_kw": 359.82,
"optimized_loss_kw": 243.8,
"reduction_pct": 32.24,
"min_voltage_before": 0.8839,
"min_voltage_after": 0.9224,
"open_lines": [
6,
8,
13,
27,
31
]
}
]
},
"footprint": {
"computation_time_sec": 12.177,
"server_tdp_watts": 350.0,
"solution_energy_kwh": 0.001184,
"solution_co2_kg": 0.000562,
"emission_factor_used": 0.475
},
"net_benefit": {
"baseline_waste_kwh_year": 1775477.0,
"optimized_waste_kwh_year": 1222458.0,
"waste_eliminated_kwh_year": 553020.0,
"waste_eliminated_pct": 31.15,
"solution_energy_kwh_year": 41.49,
"solution_overhead_pct_of_savings": 0.0075,
"runs_per_year": 35040,
"co2_eliminated_kg_year": 262680.0,
"solution_co2_kg_year": 19.6925,
"trustworthiness": "Energy savings are computed from pandapower's Newton-Raphson AC power flow \u2014 an industry-standard, physics-validated solver used by grid operators worldwide. The loss values are derived from Kirchhoff's laws and validated line impedances, not approximations. Annualisation assumes constant load; real-world savings are ~60-80% of this figure due to load variation. Solution computational overhead is 0.0075% of savings (effectively zero)."
},
"egypt_impact": {
"loss_reduction_pct_applied": 31.15,
"egypt": {
"total_generation_twh": 215.8,
"distribution_losses_twh": 23.74,
"potential_savings_twh": 7.39,
"potential_savings_gwh": 7394.4,
"co2_saved_million_tonnes": 3.697,
"cost_saved_usd_subsidised": 221831610.0,
"cost_saved_usd_real": 591550960.0,
"impact_pct_of_generation": 3.43,
"emission_factor": 0.5
},
"cairo": {
"potential_savings_twh": 1.996,
"co2_saved_million_tonnes": 0.9982,
"share_of_national": 0.27
},
"global": {
"total_generation_twh": 30000.0,
"distribution_losses_twh": 1500.0,
"potential_savings_twh": 467.2,
"co2_saved_million_tonnes": 221.9,
"impact_pct_of_generation": 1.558
},
"implementation_plan": {
"target_partners": [
"North Cairo Electricity Distribution Company (NCEDC) \u2014 already deploying 500,000 smart meters with Iskraemeco",
"South Cairo Electricity Distribution Company",
"Egyptian Electricity Holding Company (EEHC) \u2014 parent of all 9 regional companies"
],
"phase_0_mvp": {
"timeline": "Now (completed)",
"deliverable": "IEEE benchmark validated, matches published global optimal",
"cost": "$0 (open-source tools, no hardware)"
},
"phase_1_pilot": {
"timeline": "3-6 months",
"scope": "5-10 feeders in one NCEDC substation",
"steps": [
"1. Partner with NCEDC (they already have SCADA + smart meters)",
"2. Get read-only access to SCADA data for 5-10 feeders (bus loads, switch states, voltage readings)",
"3. Map their feeder topology to pandapower format (line impedances from utility records, bus loads from SCADA)",
"4. Run OptiQ in shadow mode: compute optimal switch positions but do NOT actuate \u2014 compare recommendations vs operator decisions",
"5. After 1 month of shadow mode proving accuracy, actuate switches on 1-2 feeders with motorised switches"
],
"hardware_needed": "None \u2014 uses existing SCADA. Runs on a standard cloud VM.",
"cost": "$10,000-20,000 (cloud hosting + integration labour)"
},
"phase_2_district": {
"timeline": "6-12 months after pilot",
"scope": "100+ feeders across one distribution company",
"steps": [
"1. Automate SCADA data pipeline (real-time feed every 15 min)",
"2. Deploy on all feeders in one NCEDC district",
"3. Add motorised switches where manual-only exists (~$2,000 per switch)",
"4. Measure and verify savings against utility billing data"
],
"cost": "$50,000-100,000 (software + switch upgrades where needed)"
},
"phase_3_city": {
"timeline": "1-2 years",
"scope": "City-wide Cairo (~5,000+ feeders across NCEDC + SCEDC)",
"cost": "$500,000-1,000,000 (enterprise license + integration)"
},
"phase_4_national": {
"timeline": "2-3 years",
"scope": "All 9 distribution companies across Egypt",
"cost": "$2-5 million (national enterprise license)"
}
}
},
"variables": {
"physical_variables": {
"bus_loads_p": 33,
"bus_loads_q": 33,
"line_resistance": 37,
"line_reactance": 37,
"switch_states_binary": 5,
"bus_voltages_state": 33
},
"algorithmic_hyperparameters": {
"quantum_reps": 1,
"quantum_shots": 1,
"quantum_top_k": 1,
"quantum_penalties": 2,
"quantum_sa_iters": 1,
"quantum_sa_restarts": 1,
"quantum_sa_temperature": 2,
"gnn_hidden_dim": 1,
"gnn_layers": 1,
"gnn_dropout": 1,
"gnn_lr": 1,
"gnn_epochs": 1,
"gnn_batch_size": 1,
"physics_loss_weights": 3,
"dual_lr": 1,
"n_scenarios": 1
},
"external_assumptions": {
"emission_factor": 1,
"electricity_price": 1,
"hours_per_year": 1
},
"totals": {
"physical": 178,
"algorithmic": 20,
"external": 3,
"grand_total": 201
},
"decision_variables": 5,
"note": "Of ~200 total variables, only 5 are decision variables (which lines to open/close). The rest are grid physics parameters (~178) and tunable hyperparameters (~20)."
},
"business_model": {
"usage_model": {
"type": "Recurring SaaS \u2014 NOT one-time",
"unit": "Per feeder (a feeder is one radial distribution circuit, typically 20-40 buses, serving 500-5,000 customers)",
"frequency": "Continuous \u2014 runs every 15-60 minutes with live SCADA data",
"why_recurring": "Load patterns change hourly (morning peak, evening peak), seasonally (summer AC in Egypt doubles demand), and with new connections. The optimal switch configuration changes with load. Static one-time reconfiguration captures only ~40% of the benefit vs dynamic recurring optimisation."
},
"savings_per_feeder": {
"energy_saved_kwh_year": 553020.0,
"cost_saved_year_subsidised_usd": 16591.0,
"cost_saved_year_real_cost_usd": 44242.0,
"co2_saved_tonnes_year": 262.68
},
"pricing_models": {
"model_a_saas": {
"name": "SaaS Subscription",
"price_per_feeder_month_usd": 200,
"price_per_feeder_year_usd": 2400,
"value_proposition": "Feeder saves $44,242/year at real cost. License costs $2,400/year = 5.4% of savings. Payback: immediate."
},
"model_b_revenue_share": {
"name": "Revenue Share",
"share_pct": 15,
"revenue_per_feeder_year_usd": 6636.0,
"value_proposition": "No upfront cost. Utility pays 15% of verified savings."
},
"model_c_enterprise": {
"name": "Enterprise License",
"price_per_year_usd": 500000,
"covers_feeders_up_to": 1000,
"effective_per_feeder_usd": 500,
"value_proposition": "Flat annual license for large utilities."
}
},
"revenue_projections": {
"pilot_phase": {
"n_feeders": 10,
"annual_revenue_saas": 24000,
"annual_savings_to_utility_real": 442416.0
},
"city_phase_cairo": {
"n_feeders": 5000,
"annual_revenue_saas": 12000000,
"annual_savings_to_utility_real": 221208000.0
}
},
"comparison_to_alternatives": {
"manual_switching": {
"method": "Operator manually changes switch positions quarterly/yearly",
"loss_reduction": "5-10%",
"cost": "Zero software cost, but high labour + suboptimal results",
"limitation": "Cannot adapt to load changes. Human error. Slow."
},
"full_adms": {
"method": "ABB/Siemens/GE Advanced Distribution Management System",
"loss_reduction": "15-25%",
"cost": "$5-50 million for full deployment + annual maintenance",
"limitation": "Massive CAPEX. 12-24 month deployment. Requires new SCADA hardware. Reconfiguration is one small module in a huge platform."
},
"optiq": {
"method": "OptiQ Hybrid Quantum-AI-Classical SaaS",
"loss_reduction": "28-32% (matches published global optimal)",
"cost": "$200/feeder/month or 15% revenue share",
"advantage": "Software-only \u2014 works on existing SCADA infrastructure. No CAPEX. Deploys in weeks, not years. Achieves global optimum via physics-informed AI + quantum-inspired search, while ADMS typically uses simple heuristics. 10-100x cheaper than full ADMS deployment."
}
}
}
} |