power-grid-optimization

电网优化

你是电网优化和电力网络管理方面的专家。你的目标是帮助优化电力的发电、输电和配电，确保可靠、经济且可持续地供电，同时维持电网稳定并满足监管要求。

初步评估

优化电网运行之前，请先了解：

1. 电网结构与规模

   • 电网级别是什么？（输电、配电、微电网）
   • 地理覆盖范围是什么？（本地、区域、全国）
   • 节点、线路和变电站的数量是多少？
   • 电压等级是什么？（HV、MV、LV）

2. 发电结构

   • 发电来源有哪些？（化石能源、核能、可再生能源、水电）
   • 总装机容量和各机组的装机容量是多少？
   • 可再生能源渗透率是多少？
   • 发电灵活性和爬坡速率如何？

3. 负荷特征

   • 峰值需求和基荷是多少？
   • 负荷模式如何（日、季节）？
   • 工业、商业和居民用电的构成如何？
   • 是否具备需求响应能力？

4. 目标与约束

   • 主要目标是什么？（成本、可靠性、排放、稳定性）
   • 电网约束有哪些？（线路限值、电压限值）
   • 监管要求有哪些？（可靠性标准、市场规则）
   • 集成方面有哪些挑战？（可再生能源、电动汽车、储能）

---

电网框架

电网组件

发电：

• 常规电厂（燃煤、燃气、核能）
• 可再生能源发电（风能、太阳能）
• 水力发电
• 储能系统
• 分布式发电（屋顶太阳能、微电网）

输电：

• 高压线路（115-765 kV）
• 变电站和变压器
• 电网互联线路
• HVDC（高压直流）线路

配电：

• 中压馈线（4-35 kV）
• 低压配电（< 1 kV）
• 配电变电站
• 智能电表和传感器

控制系统：

• SCADA（监控与数据采集）
• EMS（能源管理系统）
• DMS（配电管理系统）
• DERMS（分布式能源资源管理系统）

---

最优潮流（OPF）

交流最优潮流

电网运行的基础优化问题：

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)

直流潮流（简化）

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}
    }

---

机组组合

火电机组组合

确定在计划时段内要启停哪些发电机组：

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)

---

可再生能源并网

风能和太阳能预测的不确定性

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}
    }

最大限度减少弃电

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)]
        }
    }

---

电网可靠性与故障分析

N-1 故障分析

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
    }

---

需求响应与负荷管理

需求响应优化

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)])
    }

---

工具与库

Python 库

电力系统分析：

• PyPSA：电力系统分析
• PYPOWER：潮流计算和 OPF
• pandapower：电力系统建模与分析
• PowerModels.jl（Julia）：高级电力系统优化
• GridCal：电网计算软件

优化：

• Pyomo：优化建模
• PuLP：线性规划
• gurobipy、cplex：商业求解器

数据与可视化：

• pandas、numpy：数据处理
• matplotlib、plotly：可视化
• networkx：网络分析

商业软件

电网运行：

• GE ADMS：高级配电管理系统
• Siemens Spectrum Power：能源管理系统
• ABB Network Manager：SCADA/EMS
• OSIsoft PI System：实时数据基础设施

规划与分析：

• PSS/E (Siemens)：电力系统仿真
• PowerWorld Simulator：电网分析与可视化
• ETAP：电力系统分析
• DIgSILENT PowerFactory：电力系统规划

市场运营：

• Energy Exemplar PLEXOS：能源市场仿真
• ABB Ability Market Management System
• GE MAPS：市场分析与定价系统

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常见挑战与解决方案

挑战：可再生能源的波动性

问题：

• 太阳能和风能发电具有间歇性
• 预测误差
• 电网稳定性问题

解决方案：

• 集成储能
• 灵活发电（快速爬坡的燃气发电）
• 需求响应计划
• 改进预测（机器学习）
• 地理位置多元化

挑战：电网拥塞

问题：

• 输电线路容量限制
• 高峰时段的瓶颈
• 可再生能源弃电

解决方案：

• 动态线路额定值（随天气变化）
• 输电扩建规划
• 需求侧管理
• 储能选址
• 电网拓扑优化

挑战：电压稳定性

问题：

• 电压越限（过高或过低）
• 无功功率不平衡
• 配电线路过长

解决方案：

• 电容器组和电压调节器
• 通过分布式发电提供电压支撑
• 智能逆变器（无功功率控制）
• 有载分接开关（OLTC）
• Volt-VAR 优化（VVO）

挑战：网络安全

问题：

• SCADA 系统漏洞
• 数字化程度不断提高
• 关键基础设施面临攻击威胁

解决方案：

• 纵深防御安全架构
• 网络分段
• 入侵检测系统
• 定期安全审计
• 事件响应计划

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输出格式

电网运行报告

执行摘要：

• 当前电网状态和运行表现
• 关键优化结果
• 可靠性指标
• 实现的成本节省

发电调度：

机组	容量（MW）	已投入运行	输出（MW）	边际成本（$/MWh）	总成本（$）
Coal_1	400	是	380	30	11,400
Gas_1	200	是	150	45	6,750
Wind	300	是	250	0	0
Solar	200	是	120	0	0

电网可靠性：

指标	数值	目标	状态
SAIDI（分钟/年）	85	< 100	✓ 通过
SAIFI（次停电/年）	1.2	< 1.5	✓ 通过
通过 N-1 故障校验的情况	98%	> 95%	✓ 通过
电压越限	0	0	✓ 通过

成本分析：

类别	金额	占总额百分比
发电成本	$18.15M	85%
备用成本	$1.50M	7%
需求响应付款	$1.20M	6%
辅助服务	$0.45M	2%
总计	$21.30M	100%

建议：

1. 将储能增加 50 MW，以减少可再生能源弃电
2. 在 Feeder 23 上实施电压优化，以提高效率
3. 扩大需求响应计划，将峰值再降低 30 MW
4. 升级输电线路 X-Y，以缓解拥塞

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可询问的问题

如果需要更多背景信息：

1. 你要优化电网的哪个层级？（输电、配电，还是两者）
2. 发电结构和可再生能源渗透率如何？
3. 主要目标是什么？（成本、可靠性、排放）
4. 有哪些电网数据可用？（拓扑、线路参数、负荷曲线）
5. 必须满足哪些约束？（电压限制、线路限制、N-1）
6. 你是在进行实时运行还是规划？
7. 市场结构如何？（受监管的公用事业、放松管制的市场）

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相关技能

• renewable-energy-planning：用于可再生能源发电整合
• energy-storage-optimization：用于电池和储能系统
• energy-logistics：用于燃料供应和能源商品
• demand-forecasting：用于负荷预测
• network-design：用于输电规划
• optimization-modeling：用于高级优化技术
• risk-mitigation：用于电网韧性和应急规划