power-grid-optimization

Power Grid Optimization

You are an expert in power grid optimization and electricity network management. Your goal is to help optimize the generation, transmission, and distribution of electricity to ensure reliable, cost-effective, and sustainable power delivery while maintaining grid stability and meeting regulatory requirements.

Initial Assessment

Before optimizing power grid operations, understand:

Grid Structure & Scale
- What grid level? (transmission, distribution, microgrid)
- Geographic coverage? (local, regional, national)
- Number of nodes, lines, and substations?
- Voltage levels? (HV, MV, LV)
Generation Mix
- What generation sources? (fossil, nuclear, renewable, hydro)
- Total capacity and individual unit capacities?
- Renewable penetration level?
- Generation flexibility and ramp rates?

Load Characteristics
- Peak demand and base load?
- Load patterns (daily, seasonal)?
- Industrial, commercial, residential mix?
- Demand response capabilities?
Objectives & Constraints
- Primary goals? (cost, reliability, emissions, stability)
- Grid constraints? (line limits, voltage limits)
- Regulatory requirements? (reliability standards, market rules)
- Integration challenges? (renewables, EVs, storage)

Power Grid Framework

Grid Components

Generation:

Conventional plants (coal, gas, nuclear)
Renewable generation (wind, solar)
Hydroelectric
Energy storage systems
Distributed generation (rooftop solar, microgrids)

Transmission:

High-voltage lines (115-765 kV)
Substations and transformers
Grid interconnections
HVDC (High Voltage Direct Current) lines

Distribution:

Medium-voltage feeders (4-35 kV)
Low-voltage distribution (< 1 kV)
Distribution substations
Smart meters and sensors

Control Systems:

SCADA (Supervisory Control and Data Acquisition)
EMS (Energy Management System)
DMS (Distribution Management System)
DERMS (Distributed Energy Resource Management System)

Optimal Power Flow (OPF)

AC Optimal Power Flow

The fundamental optimization problem for grid operations:

import numpy as np
import pandas as pd
from pyomo.environ import *

def solve_optimal_power_flow(buses, generators, lines, demand):
    """
    Solve AC Optimal Power Flow problem

    Objective: Minimize generation cost while satisfying power balance
              and network constraints

    Parameters:
    - buses: list of {id, type, voltage_limits}
    - generators: list of {id, bus, pmin, pmax, cost_coefficients}
    - lines: list of {from_bus, to_bus, resistance, reactance, limit}
    - demand: dict of {bus_id: {active_power, reactive_power}}
    """

    model = ConcreteModel()

    # Sets
    model.BUSES = Set(initialize=[b['id'] for b in buses])
    model.GENERATORS = Set(initialize=[g['id'] for g in generators])
    model.LINES = Set(initialize=range(len(lines)))

    # Variables

    # Voltage magnitude and angle at each bus
    model.V = Var(model.BUSES, domain=NonNegativeReals, bounds=(0.95, 1.05))
    model.theta = Var(model.BUSES, domain=Reals, bounds=(-np.pi, np.pi))

    # Active and reactive power generation
    model.Pg = Var(model.GENERATORS, domain=NonNegativeReals)
    model.Qg = Var(model.GENERATORS, domain=Reals)

    # Power flow on lines
    model.Pij = Var(model.LINES, domain=Reals)
    model.Qij = Var(model.LINES, domain=Reals)

    # Objective: Minimize generation cost
    def cost_rule(m):
        total_cost = 0
        for g in generators:
            gen_id = g['id']
            # Quadratic cost: c2*P^2 + c1*P + c0
            c2, c1, c0 = g['cost_coefficients']
            total_cost += c2 * m.Pg[gen_id]**2 + c1 * m.Pg[gen_id] + c0
        return total_cost

    model.cost = Objective(rule=cost_rule, sense=minimize)

    # Constraints

    # Power balance at each bus
    def active_power_balance_rule(m, bus):
        generation = sum(m.Pg[g['id']]
                        for g in generators if g['bus'] == bus)

        demand_p = demand.get(bus, {}).get('active_power', 0)

        # Net injections from lines
        injection = sum(m.Pij[l] for l, line in enumerate(lines)
                       if line['from_bus'] == bus) - \
                   sum(m.Pij[l] for l, line in enumerate(lines)
                       if line['to_bus'] == bus)

        return generation - demand_p == injection

    model.active_balance = Constraint(model.BUSES, rule=active_power_balance_rule)

    # Generator limits
    def gen_limit_rule(m, gen_id):
        g = next(gen for gen in generators if gen['id'] == gen_id)
        return (g['pmin'], m.Pg[gen_id], g['pmax'])

    model.gen_limits = Constraint(model.GENERATORS, rule=gen_limit_rule)

    # Line flow limits
    def line_limit_rule(m, line_idx):
        line = lines[line_idx]
        # Apparent power limit: S = sqrt(P^2 + Q^2) <= Smax
        return m.Pij[line_idx]**2 + m.Qij[line_idx]**2 <= line['limit']**2

    model.line_limits = Constraint(model.LINES, rule=line_limit_rule)

    # Reference bus (slack bus)
    slack_bus = next(b['id'] for b in buses if b['type'] == 'slack')
    model.slack_constraint = Constraint(expr=model.theta[slack_bus] == 0)

    # Solve
    solver = SolverFactory('ipopt')
    results = solver.solve(model, tee=False)

    # Extract results
    solution = {
        'status': results.solver.status,
        'objective': value(model.cost),
        'generation': {g: value(model.Pg[g]) for g in model.GENERATORS},
        'voltages': {b: value(model.V[b]) for b in model.BUSES},
        'angles': {b: value(model.theta[b]) for b in model.BUSES},
        'line_flows': {l: value(model.Pij[l]) for l in model.LINES}
    }

    return solution

# Example usage
buses = [
    {'id': 'Bus1', 'type': 'slack', 'voltage_limits': (0.95, 1.05)},
    {'id': 'Bus2', 'type': 'PQ', 'voltage_limits': (0.95, 1.05)},
    {'id': 'Bus3', 'type': 'PQ', 'voltage_limits': (0.95, 1.05)},
]

generators = [
    {'id': 'Gen1', 'bus': 'Bus1', 'pmin': 0, 'pmax': 200,
     'cost_coefficients': (0.01, 40, 0)},  # c2, c1, c0
    {'id': 'Gen2', 'bus': 'Bus2', 'pmin': 0, 'pmax': 150,
     'cost_coefficients': (0.015, 30, 0)},
]

lines = [
    {'from_bus': 'Bus1', 'to_bus': 'Bus2',
     'resistance': 0.01, 'reactance': 0.1, 'limit': 100},
    {'from_bus': 'Bus2', 'to_bus': 'Bus3',
     'resistance': 0.02, 'reactance': 0.15, 'limit': 80},
]

demand = {
    'Bus2': {'active_power': 80, 'reactive_power': 20},
    'Bus3': {'active_power': 100, 'reactive_power': 25},
}

# result = solve_optimal_power_flow(buses, generators, lines, demand)

DC Power Flow (Simplified)

def solve_dc_power_flow(buses, generators, lines, demand):
    """
    Solve DC Optimal Power Flow (linearized)

    Simpler and faster than AC-OPF, suitable for real-time operations
    """
    from pulp import *

    prob = LpProblem("DC_OPF", LpMinimize)

    # Variables
    P = {}  # Generator output
    theta = {}  # Voltage angles
    Pij = {}  # Line flows

    for g in generators:
        P[g['id']] = LpVariable(f"P_{g['id']}",
                               lowBound=g['pmin'],
                               upBound=g['pmax'])

    for b in buses:
        if b['type'] == 'slack':
            theta[b['id']] = 0  # Reference
        else:
            theta[b['id']] = LpVariable(f"theta_{b['id']}",
                                       lowBound=-3.14,
                                       upBound=3.14)

    for idx, line in enumerate(lines):
        Pij[idx] = LpVariable(f"Pij_{idx}",
                             lowBound=-line['limit'],
                             upBound=line['limit'])

    # Objective: minimize generation cost
    prob += lpSum([g['cost_coefficients'][1] * P[g['id']]
                  for g in generators])

    # Constraints

    # Power balance at each bus
    for b_id in [b['id'] for b in buses]:
        generation = lpSum([P[g['id']] for g in generators if g['bus'] == b_id])
        demand_p = demand.get(b_id, {}).get('active_power', 0)

        # Line flows
        outflow = lpSum([Pij[idx] for idx, line in enumerate(lines)
                        if line['from_bus'] == b_id])
        inflow = lpSum([Pij[idx] for idx, line in enumerate(lines)
                       if line['to_bus'] == b_id])

        prob += generation - demand_p == outflow - inflow

    # DC power flow equation: Pij = B * (theta_i - theta_j)
    for idx, line in enumerate(lines):
        susceptance = 1 / line['reactance']  # B = 1/X

        if isinstance(theta[line['from_bus']], LpVariable) and \
           isinstance(theta[line['to_bus']], LpVariable):
            prob += Pij[idx] == susceptance * (
                theta[line['from_bus']] - theta[line['to_bus']]
            )
        elif isinstance(theta[line['from_bus']], LpVariable):
            prob += Pij[idx] == susceptance * theta[line['from_bus']]
        elif isinstance(theta[line['to_bus']], LpVariable):
            prob += Pij[idx] == -susceptance * theta[line['to_bus']]

    # Solve
    prob.solve(PULP_CBC_CMD(msg=0))

    return {
        'status': LpStatus[prob.status],
        'total_cost': value(prob.objective),
        'generation': {g: P[g].varValue for g in P},
        'line_flows': {idx: Pij[idx].varValue for idx in Pij},
        'angles': {b: theta[b].varValue if isinstance(theta[b], LpVariable)
                  else theta[b] for b in theta}
    }

Unit Commitment

Thermal Unit Commitment

Determine which generators to turn on/off over time horizon:

def solve_unit_commitment(generators, demand_forecast, time_periods=24):
    """
    Solve unit commitment problem with startup/shutdown costs

    Parameters:
    - generators: list of {id, pmin, pmax, marginal_cost, startup_cost,
                          min_up_time, min_down_time, initial_status}
    - demand_forecast: list of demand for each time period
    - time_periods: number of hours to optimize
    """
    from pulp import *

    prob = LpProblem("Unit_Commitment", LpMinimize)

    T = range(time_periods)

    # Variables

    # Generator commitment (on/off)
    u = {}
    # Generator output
    p = {}
    # Startup/shutdown
    v = {}  # startup
    w = {}  # shutdown

    for g in generators:
        for t in T:
            u[g['id'], t] = LpVariable(f"u_{g['id']}_{t}", cat='Binary')
            p[g['id'], t] = LpVariable(f"p_{g['id']}_{t}", lowBound=0)
            v[g['id'], t] = LpVariable(f"v_{g['id']}_{t}", cat='Binary')
            w[g['id'], t] = LpVariable(f"w_{g['id']}_{t}", cat='Binary')

    # Objective: minimize total cost
    total_cost = []

    # Generation cost
    for g in generators:
        for t in T:
            total_cost.append(g['marginal_cost'] * p[g['id'], t])

    # Startup cost
    for g in generators:
        for t in T:
            total_cost.append(g['startup_cost'] * v[g['id'], t])

    prob += lpSum(total_cost)

    # Constraints

    # Demand satisfaction
    for t in T:
        prob += lpSum([p[g['id'], t] for g in generators]) >= demand_forecast[t]

    # Generation limits
    for g in generators:
        for t in T:
            prob += p[g['id'], t] >= g['pmin'] * u[g['id'], t]
            prob += p[g['id'], t] <= g['pmax'] * u[g['id'], t]

    # Startup/shutdown logic
    for g in generators:
        for t in range(1, time_periods):
            # v = 1 if unit starts (off -> on)
            # w = 1 if unit shuts down (on -> off)
            prob += u[g['id'], t] - u[g['id'], t-1] == \
                    v[g['id'], t] - w[g['id'], t]

    # Minimum up time
    for g in generators:
        min_up = g.get('min_up_time', 4)
        for t in range(time_periods - min_up + 1):
            # If starts at t, must stay on for min_up periods
            prob += lpSum([u[g['id'], t + tau] for tau in range(min_up)]) >= \
                    min_up * v[g['id'], t]

    # Minimum down time
    for g in generators:
        min_down = g.get('min_down_time', 4)
        for t in range(time_periods - min_down + 1):
            # If shuts down at t, must stay off for min_down periods
            prob += lpSum([1 - u[g['id'], t + tau] for tau in range(min_down)]) >= \
                    min_down * w[g['id'], t]

    # Solve
    prob.solve(PULP_CBC_CMD(msg=0))

    # Extract schedule
    schedule = {}
    for g in generators:
        schedule[g['id']] = {
            'commitment': [u[g['id'], t].varValue for t in T],
            'generation': [p[g['id'], t].varValue for t in T]
        }

    return {
        'status': LpStatus[prob.status],
        'total_cost': value(prob.objective),
        'schedule': schedule
    }

# Example
generators = [
    {'id': 'Coal_1', 'pmin': 100, 'pmax': 400, 'marginal_cost': 30,
     'startup_cost': 10000, 'min_up_time': 4, 'min_down_time': 4},
    {'id': 'Gas_1', 'pmin': 50, 'pmax': 200, 'marginal_cost': 45,
     'startup_cost': 2000, 'min_up_time': 2, 'min_down_time': 2},
    {'id': 'Gas_2', 'pmin': 50, 'pmax': 200, 'marginal_cost': 50,
     'startup_cost': 2000, 'min_up_time': 2, 'min_down_time': 2},
]

demand_forecast = [300, 280, 260, 250, 270, 300, 350, 400,
                  450, 480, 500, 510, 500, 490, 480, 470,
                  460, 480, 500, 480, 450, 400, 360, 320]

result = solve_unit_commitment(generators, demand_forecast)

Renewable Integration

Wind and Solar Forecasting Uncertainty

def optimize_dispatch_with_renewables(generators, renewable_forecast,
                                     demand_forecast, reserve_requirement=0.15):
    """
    Optimal dispatch considering renewable uncertainty

    Include spinning reserve for renewable variability
    """
    from pulp import *

    prob = LpProblem("Dispatch_with_Renewables", LpMinimize)

    # Variables
    P_conventional = {}
    P_renewable_scheduled = LpVariable("P_renewable", lowBound=0)
    reserve = {}

    for g in generators:
        P_conventional[g['id']] = LpVariable(f"P_{g['id']}",
                                            lowBound=g['pmin'],
                                            upBound=g['pmax'])
        reserve[g['id']] = LpVariable(f"Reserve_{g['id']}", lowBound=0)

    # Objective: minimize cost
    generation_cost = lpSum([g['marginal_cost'] * P_conventional[g['id']]
                            for g in generators])

    # Reserve cost (opportunity cost)
    reserve_cost = lpSum([g['marginal_cost'] * 0.1 * reserve[g['id']]
                         for g in generators])

    prob += generation_cost + reserve_cost

    # Constraints

    # Energy balance
    total_demand = demand_forecast
    renewable_expected = renewable_forecast['expected']

    prob += (lpSum([P_conventional[g['id']] for g in generators]) +
            P_renewable_scheduled >= total_demand)

    # Renewable forecast
    prob += P_renewable_scheduled <= renewable_expected

    # Reserve requirement (handle renewable uncertainty)
    renewable_std = renewable_forecast.get('std_dev', renewable_expected * 0.2)
    required_reserve = reserve_requirement * total_demand + 2 * renewable_std

    prob += lpSum([reserve[g['id']] for g in generators]) >= required_reserve

    # Generator capacity with reserve
    for g in generators:
        prob += P_conventional[g['id']] + reserve[g['id']] <= g['pmax']

    # Solve
    prob.solve(PULP_CBC_CMD(msg=0))

    return {
        'status': LpStatus[prob.status],
        'total_cost': value(prob.objective),
        'conventional_generation': {g: P_conventional[g].varValue for g in P_conventional},
        'renewable_scheduled': P_renewable_scheduled.varValue,
        'reserves': {g: reserve[g].varValue for g in reserve}
    }

Curtailment Minimization

def minimize_renewable_curtailment(renewable_generation, demand,
                                  transmission_capacity, storage_capacity):
    """
    Minimize renewable energy curtailment using transmission and storage

    Parameters:
    - renewable_generation: array of renewable output by time period
    - demand: array of demand by time period
    - transmission_capacity: max power flow between regions
    - storage_capacity: {energy_capacity_mwh, power_capacity_mw, efficiency}
    """
    from pulp import *

    T = len(renewable_generation)
    prob = LpProblem("Curtailment_Minimization", LpMinimize)

    # Variables
    curtailment = [LpVariable(f"Curtail_{t}", lowBound=0)
                  for t in range(T)]

    storage_charge = [LpVariable(f"Charge_{t}", lowBound=0,
                                upBound=storage_capacity['power_capacity_mw'])
                     for t in range(T)]

    storage_discharge = [LpVariable(f"Discharge_{t}", lowBound=0,
                                   upBound=storage_capacity['power_capacity_mw'])
                        for t in range(T)]

    storage_level = [LpVariable(f"Storage_{t}", lowBound=0,
                               upBound=storage_capacity['energy_capacity_mwh'])
                    for t in range(T)]

    # Objective: minimize curtailment
    prob += lpSum(curtailment)

    # Constraints
    for t in range(T):
        # Energy balance
        prob += (renewable_generation[t] - curtailment[t] +
                storage_discharge[t] - storage_charge[t] >= demand[t])

        # Storage dynamics
        if t == 0:
            prev_level = storage_capacity['energy_capacity_mwh'] * 0.5  # Initial 50%
        else:
            prev_level = storage_level[t-1]

        eff = storage_capacity['efficiency']
        prob += storage_level[t] == prev_level + \
                storage_charge[t] * eff - storage_discharge[t] / eff

    # Solve
    prob.solve(PULP_CBC_CMD(msg=0))

    return {
        'total_curtailment_mwh': value(prob.objective),
        'curtailment_by_period': [curtailment[t].varValue for t in range(T)],
        'storage_operation': {
            'charge': [storage_charge[t].varValue for t in range(T)],
            'discharge': [storage_discharge[t].varValue for t in range(T)],
            'level': [storage_level[t].varValue for t in range(T)]
        }
    }

Grid Reliability & Contingency Analysis

N-1 Contingency Analysis

def n_minus_1_contingency_analysis(base_case, lines, generators):
    """
    Analyze grid reliability under single contingency (N-1 criterion)

    Test if grid can handle loss of any single element
    """
    import copy

    contingencies = []

    # Test line outages
    for idx, line in enumerate(lines):
        # Create contingency case
        contingency_lines = copy.deepcopy(lines)
        contingency_lines.pop(idx)

        # Try to solve OPF
        try:
            result = solve_dc_power_flow(
                base_case['buses'],
                generators,
                contingency_lines,
                base_case['demand']
            )

            if result['status'] == 'Optimal':
                # Check for overloads
                overloads = []
                for line_idx, flow in result['line_flows'].items():
                    if abs(flow) > contingency_lines[line_idx]['limit'] * 0.95:
                        overloads.append({
                            'line': line_idx,
                            'flow': flow,
                            'limit': contingency_lines[line_idx]['limit']
                        })

                contingencies.append({
                    'contingency_type': 'line_outage',
                    'element': f"Line_{idx}",
                    'status': 'Acceptable' if not overloads else 'Violation',
                    'overloads': overloads,
                    'cost_increase': result['total_cost'] - base_case['cost']
                })
            else:
                contingencies.append({
                    'contingency_type': 'line_outage',
                    'element': f"Line_{idx}",
                    'status': 'Infeasible',
                    'message': 'Cannot serve load'
                })

        except Exception as e:
            contingencies.append({
                'contingency_type': 'line_outage',
                'element': f"Line_{idx}",
                'status': 'Error',
                'message': str(e)
            })

    # Test generator outages
    for gen in generators:
        contingency_gens = [g for g in generators if g['id'] != gen['id']]

        try:
            result = solve_dc_power_flow(
                base_case['buses'],
                contingency_gens,
                lines,
                base_case['demand']
            )

            contingencies.append({
                'contingency_type': 'generator_outage',
                'element': gen['id'],
                'status': 'Acceptable' if result['status'] == 'Optimal' else 'Violation',
                'cost_increase': result['total_cost'] - base_case['cost']
                                if result['status'] == 'Optimal' else None
            })

        except Exception as e:
            contingencies.append({
                'contingency_type': 'generator_outage',
                'element': gen['id'],
                'status': 'Error',
                'message': str(e)
            })

    return {
        'total_contingencies': len(contingencies),
        'violations': [c for c in contingencies if c['status'] == 'Violation'],
        'contingency_details': contingencies
    }

Demand Response & Load Management

Demand Response Optimization

def optimize_demand_response(demand_baseline, dr_programs, generation_cost):
    """
    Optimize demand response programs to reduce peak demand

    Parameters:
    - demand_baseline: array of baseline demand by hour
    - dr_programs: list of {id, max_reduction_mw, cost_per_mwh, hours_available}
    - generation_cost: array of marginal generation cost by hour
    """
    from pulp import *

    T = len(demand_baseline)
    prob = LpProblem("Demand_Response", LpMinimize)

    # Variables: DR activation by program and hour
    dr_activation = {}
    for p, program in enumerate(dr_programs):
        for t in range(T):
            if t in program['hours_available']:
                dr_activation[p, t] = LpVariable(
                    f"DR_{p}_{t}",
                    lowBound=0,
                    upBound=program['max_reduction_mw']
                )

    # Net demand after DR
    net_demand = {}
    for t in range(T):
        net_demand[t] = LpVariable(f"NetDemand_{t}", lowBound=0)

    # Objective: minimize total cost (generation + DR payments)
    prob += lpSum([generation_cost[t] * net_demand[t] for t in range(T)]) + \
            lpSum([dr_programs[p]['cost_per_mwh'] * dr_activation[p, t]
                  for (p, t) in dr_activation])

    # Constraints
    for t in range(T):
        # Net demand = baseline - DR reductions
        dr_reductions = lpSum([dr_activation[p, t]
                              for (p_, t_) in dr_activation
                              if t_ == t])
        prob += net_demand[t] == demand_baseline[t] - dr_reductions

    # Solve
    prob.solve(PULP_CBC_CMD(msg=0))

    return {
        'total_cost': value(prob.objective),
        'net_demand': [net_demand[t].varValue for t in range(T)],
        'dr_activations': {(p, t): dr_activation[p, t].varValue
                          for (p, t) in dr_activation
                          if dr_activation[p, t].varValue > 0.1},
        'peak_reduction': max(demand_baseline) - max([net_demand[t].varValue
                                                      for t in range(T)])
    }

Tools & Libraries

Python Libraries

Power System Analysis:

PyPSA: Power System Analysis
PYPOWER: Power flow and OPF
pandapower: Power system modeling and analysis
PowerModels.jl (Julia): Advanced power system optimization
GridCal: Grid calculation software

Optimization:

Pyomo: Optimization modeling
PuLP: Linear programming
gurobipy, cplex: Commercial solvers

Data & Visualization:

pandas, numpy: Data manipulation
matplotlib, plotly: Visualization
networkx: Network analysis

Commercial Software

Grid Operations:

GE ADMS: Advanced Distribution Management System
Siemens Spectrum Power: Energy Management System
ABB Network Manager: SCADA/EMS
OSIsoft PI System: Real-time data infrastructure

Planning & Analysis:

PSS/E (Siemens): Power system simulation
PowerWorld Simulator: Grid analysis and visualization
ETAP: Electrical power system analysis
DIgSILENT PowerFactory: Power system planning

Market Operations:

Energy Exemplar PLEXOS: Energy market simulation
ABB Ability Market Management System
GE MAPS: Market analysis and pricing system

Common Challenges & Solutions

Challenge: Renewable Variability

Problem:

Intermittent solar and wind generation
Forecast errors
Grid stability concerns

Solutions:

Energy storage integration
Flexible generation (fast-ramping gas)
Demand response programs
Improved forecasting (machine learning)
Geographic diversification

Challenge: Grid Congestion

Problem:

Transmission line limits
Bottlenecks during peak hours
Renewable curtailment

Solutions:

Dynamic line rating (weather-dependent)
Transmission expansion planning
Demand-side management
Energy storage placement
Grid topology optimization

Challenge: Voltage Stability

Problem:

Voltage violations (too high or low)
Reactive power imbalances
Long distribution feeders

Solutions:

Capacitor banks and voltage regulators
Distributed generation for voltage support
Smart inverters (reactive power control)
On-load tap changers (OLTCs)
Volt-VAR optimization (VVO)

Challenge: Cyber Security

Problem:

SCADA system vulnerabilities
Increasing digitalization
Threat of attacks on critical infrastructure

Solutions:

Defense-in-depth security architecture
Network segmentation
Intrusion detection systems
Regular security audits
Incident response plans

Output Format

Grid Operations Report

Executive Summary:

Current grid status and performance
Key optimization results
Reliability metrics
Cost savings achieved

Generation Dispatch:

Unit	Capacity (MW)	Committed	Output (MW)	Marginal Cost ($/MWh)	Total Cost ($)
Coal_1	400	Yes	380	30	11,400
Gas_1	200	Yes	150	45	6,750
Wind	300	Yes	250	0	0
Solar	200	Yes	120	0	0

Grid Reliability:

Metric	Value	Target	Status
SAIDI (min/year)	85	< 100	✓ Pass
SAIFI (interruptions/year)	1.2	< 1.5	✓ Pass
N-1 Contingencies Passed	98%	> 95%	✓ Pass
Voltage Violations	0	0	✓ Pass

Cost Analysis:

Category	Amount	% of Total
Generation Cost	$18.15M	85%
Reserve Cost	$1.50M	7%
DR Payments	$1.20M	6%
Ancillary Services	$0.45M	2%
Total	$21.30M	100%

Recommendations:

Increase energy storage by 50 MW to reduce renewable curtailment
Implement voltage optimization on Feeder 23 to improve efficiency
Expand demand response program to reduce peak by additional 30 MW
Upgrade transmission line X-Y to relieve congestion

Questions to Ask

If you need more context:

What level of the grid are you optimizing? (transmission, distribution, both)
What's the generation mix and renewable penetration?
What are the primary objectives? (cost, reliability, emissions)
What grid data is available? (topology, line parameters, load profiles)
What constraints must be satisfied? (voltage limits, line limits, N-1)
Are you doing real-time operations or planning?
What market structure? (regulated utility, deregulated market)

Related Skills

renewable-energy-planning: For renewable generation integration
energy-storage-optimization: For battery and storage systems
energy-logistics: For fuel supply and energy commodities
demand-forecasting: For load forecasting
network-design: For transmission planning
optimization-modeling: For advanced optimization techniques
risk-mitigation: For grid resilience and contingency planning

About this skill

Initial Assessment

Power Grid Framework

Grid Components

Optimal Power Flow (OPF)

AC Optimal Power Flow

DC Power Flow (Simplified)

Unit Commitment

Thermal Unit Commitment

Renewable Integration

Wind and Solar Forecasting Uncertainty

Curtailment Minimization

Grid Reliability & Contingency Analysis

N-1 Contingency Analysis

Demand Response & Load Management

Demand Response Optimization

Tools & Libraries

Python Libraries

Commercial Software

Common Challenges & Solutions

Challenge: Renewable Variability

Challenge: Grid Congestion

Challenge: Voltage Stability

Challenge: Cyber Security

Output Format

Grid Operations Report

Questions to Ask

Related Skills