Publication record · 18.cifr/2023.wang.ev-charging-renewable-planning

Coordinated Planning of Electric Vehicle Charging Infrastructure and Renewables in Power Grids

v1.0.0

Bo Wang (Amazon Web Services), Payman Dehghanian (George Washington University), Dongbo Zhao (Eaton)

RAI18.cifr/2023.wang.ev-charging-renewable-planning

IEEE Open Access Journal of Power and Energy· 2023· doi:10.1109/OAJPE.2023.3245993

This paper presents a coordinated planning framework for electric vehicle (EV) charging infrastructure and renewable energy sources in power grids. A two-stage stochastic mixed-integer linear programming (MILP) model is formulated to co-optimize investment decisions for EV charging stations and renewable generators, accounting for power flow constraints, voltage limits, line thermal limits, and budget constraints. The proposed approach captures the spatial and temporal coupling between EV charging demand and renewable generation, enabling cost-effective and reliable infrastructure expansion.

electric vehicle chargingrenewable energycoordinated planningstochastic optimizationMILPpower systems

✦ Research context

What this agent contributes to the literature.

Problem solved

Power system planners lack tools that jointly site EV charging and renewables rather than planning each separately.
Separate planning ignores coupling between EV load patterns and renewable output, leading to suboptimal or unreliable expansion.
The two-stage stochastic structure handles uncertainty in both EV demand and renewable generation across scenarios.

Novelty

Co-optimizes EV charging station siting/sizing and renewable generator placement in a single two-stage stochastic MILP.

Related research

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Canvas contract1-in / 1-out · unpacked into grid, planning legacy ports

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sha256:ad440d0e6817b12e3feae7971847707f44bee34783da8aecd34a73b2fa3db592

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python main.py

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input:application/json

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output:application/json

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Pre-filled with the paper's canonical scenario. Click Invoke agent to reproduce the original result, or edit the JSON below to run a counterfactual.

inputapplication/jsonoptional

Unified canvas input. The executor unpacks this JSON blob into legacy per-port files: grid, planning

Leave empty to run the paper's canonical scenario.

{
  "grid": {
    "base_mva": 100,
    "buses": [
      {
        "id": 1,
        "type": "slack",
        "p_load_mw": 0,
        "q_load_mvar": 0
      },
      {
        "id": 2,
        "type": "pq",
        "p_load_mw": 20,
        "q_load_mvar": 10
      },
      {
        "id": 3,
        "type": "pq",
        "p_load_mw": 30,
        "q_load_mvar": 15
      },
      {
        "id": 4,
        "type": "pq",
        "p_load_mw": 15,
        "q_load_mvar": 7
      },
      {
        "id": 5,
        "type": "pq",
        "p_load_mw": 25,
        "q_load_mvar": 12
      },
      {
        "id": 6,
        "type": "pq",
        "p_load_mw": 10,
        "q_load_mvar": 5
      }
    ],
    "branches": [
      {
        "from": 1,
        "to": 2,
        "r": 0.01,
        "x": 0.05,
        "b": 0.02,
        "capacity_mva": 80
      },
      {
        "from": 1,
        "to": 3,
        "r": 0.02,
        "x": 0.06,
        "b": 0.02,
        "capacity_mva": 80
      },
      {
        "from": 2,
        "to": 4,
        "r": 0.03,
        "x": 0.08,
        "b": 0.01,
        "capacity_mva": 50
      },
      {
        "from": 3,
        "to": 4,
        "r": 0.02,
        "x": 0.07,
        "b": 0.01,
        "capacity_mva": 50
      },
      {
        "from": 4,
        "to": 5,
        "r": 0.03,
        "x": 0.09,
        "b": 0.01,
        "capacity_mva": 40
      },
      {
        "from": 5,
        "to": 6,
        "r": 0.04,
        "x": 0.1,
        "b": 0.01,
        "capacity_mva": 30
      }
    ],
    "generators": [
      {
        "bus": 1,
        "p_min_mw": 0,
        "p_max_mw": 120,
        "cost_a": 30,
        "cost_b": 100
      },
      {
        "bus": 3,
        "p_min_mw": 0,
        "p_max_mw": 60,
        "cost_a": 40,
        "cost_b": 80
      }
    ]
  },
  "planning": {
    "planning_horizon_hours": 4,
    "budget_usd": 5000000,
    "cost_ev_per_mw_usd": 150000,
    "cost_res_per_mw_usd": 120000,
    "value_of_lost_load": 10000,
    "ev_candidates": [
      {
        "id": "EV1",
        "bus": 3,
        "max_size_mw": 8,
        "demand_profile_mw": [
          2,
          4,
          6,
          3
        ]
      },
      {
        "id": "EV2",
        "bus": 5,
        "max_size_mw": 6,
        "demand_profile_mw": [
          1.5,
          3,
          5,
          2.5
        ]
      }
    ],
    "res_candidates": [
      {
        "id": "RES1",
        "bus": 2,
        "type": "solar",
        "max_size_mw": 20
      },
      {
        "id": "RES2",
        "bus": 4,
        "type": "wind",
        "max_size_mw": 15
      }
    ],
    "scenarios": [
      {
        "id": "S1",
        "prob": 0.6,
        "load_factor": 1,
        "res_capacity_factors": {
          "RES1": [
            0.1,
            0.5,
            0.8,
            0.4
          ],
          "RES2": [
            0.6,
            0.65,
            0.55,
            0.7
          ]
        }
      },
      {
        "id": "S2",
        "prob": 0.4,
        "load_factor": 1.15,
        "res_capacity_factors": {
          "RES1": [
            0.05,
            0.3,
            0.6,
            0.2
          ],
          "RES2": [
            0.4,
            0.45,
            0.5,
            0.55
          ]
        }
      }
    ]
  }
}

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