Publication record · 18.cifr/2020.opathella.merchant-ess-scheduling

Optimal Scheduling of Merchant-Owned Energy Storage Systems With Multiple Ancillary Services

v1.0.0

C. Opathella (Ryerson University), Ayman Elkasrawy (Ryerson University), Amr A. Mohamed (Ryerson University), B. Venkatesh (Ryerson University)

RAI18.cifr/2020.opathella.merchant-ess-scheduling

IEEE Open Access Journal of Power and Energy· 2020· doi:10.1109/OAJPE.2019.2952811

This paper presents an optimal scheduling framework for merchant-owned energy storage systems (ESS) participating in multiple ancillary service markets alongside energy arbitrage. The proposed mixed-integer linear program maximises expected daily profit by co-optimising bids for energy, spinning reserve, regulation up, and regulation down over a 24-hour horizon while respecting physical ESS constraints including state-of-charge limits, power limits, round-trip efficiency, and battery degradation costs.

energy storageancillary servicesoptimal schedulingspinning reserveregulationlinear programmingmerchant storage

✦ Research context

What this agent contributes to the literature.

Problem solved

Merchant ESS owners lack a unified scheduling tool covering energy and multiple ancillary service markets together.
Sequential or siloed bidding across markets leaves profit on the table and ignores cross-market physical conflicts.
The 24-hour horizon model enforces state-of-charge, power limits, and round-trip efficiency simultaneously across all markets.

Novelty

Co-optimises bids across energy arbitrage, spinning reserve, regulation up, and regulation down simultaneously in one MILP.

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Canvas contract1-in / 1-out · unpacked into market_data, ess_params legacy ports

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sha256:2bf0953a15f928eaad18df7dcb2167efd516b2ba4904cae34a0dbc6a480e3d00

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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: market_data, ess_params

Leave empty to run the paper's canonical scenario.

{
  "market_data": {
    "lambda_energy": [
      28.5,
      25,
      22.3,
      20.1,
      21,
      24.5,
      35,
      52,
      68,
      72,
      70,
      65,
      60,
      58,
      62,
      75,
      85,
      92,
      88,
      78,
      65,
      52,
      40,
      32
    ],
    "lambda_sr": [
      5,
      5,
      4.5,
      4.5,
      4.5,
      5,
      6,
      7.5,
      8,
      8,
      7.5,
      7,
      7,
      7,
      7.5,
      8.5,
      9.5,
      10,
      9.5,
      8.5,
      7.5,
      6.5,
      6,
      5.5
    ],
    "lambda_ru": [
      4,
      4,
      3.5,
      3.5,
      3.5,
      4,
      5.5,
      7,
      8.5,
      9,
      8.5,
      8,
      7.5,
      7.5,
      8,
      9.5,
      11,
      12,
      11.5,
      10,
      8,
      6.5,
      5.5,
      4.5
    ],
    "lambda_rd": [
      3,
      3,
      2.5,
      2.5,
      2.5,
      3,
      4,
      5.5,
      6.5,
      7,
      6.5,
      6,
      5.5,
      5.5,
      6,
      7,
      8.5,
      9,
      8.5,
      7.5,
      6,
      5,
      4,
      3.5
    ],
    "prob_sr": [
      0.25,
      0.25,
      0.2,
      0.2,
      0.2,
      0.25,
      0.3,
      0.35,
      0.4,
      0.4,
      0.38,
      0.35,
      0.33,
      0.33,
      0.35,
      0.4,
      0.45,
      0.5,
      0.48,
      0.42,
      0.35,
      0.3,
      0.28,
      0.25
    ],
    "prob_ru": [
      0.45,
      0.45,
      0.4,
      0.4,
      0.4,
      0.45,
      0.5,
      0.55,
      0.6,
      0.6,
      0.58,
      0.55,
      0.52,
      0.52,
      0.55,
      0.6,
      0.65,
      0.7,
      0.68,
      0.62,
      0.55,
      0.5,
      0.48,
      0.46
    ],
    "prob_rd": [
      0.45,
      0.45,
      0.4,
      0.4,
      0.4,
      0.45,
      0.5,
      0.55,
      0.6,
      0.6,
      0.58,
      0.55,
      0.52,
      0.52,
      0.55,
      0.6,
      0.65,
      0.7,
      0.68,
      0.62,
      0.55,
      0.5,
      0.48,
      0.46
    ]
  },
  "ess_params": {
    "capacity_mwh": 4,
    "max_power_mw": 1,
    "efficiency_charge": 0.95,
    "efficiency_discharge": 0.95,
    "soc_min": 0.1,
    "soc_max": 0.95,
    "soc_initial": 0.5,
    "degradation_cost_per_mwh": 2.5
  }
}

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