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GP-EE-023V7AWElectricalOpen for request

Indian microgrid dispatch optimiser

A scenario-based optimiser for scheduling solar generation, battery storage, grid exchange, and backup supply in an Indian microgrid model.

  • Nuxt
  • Vue
  • FastAPI
  • PostgreSQL
  • Python
  • Pyomo
  • HiGHS
  • Docker

Project definition

Problem statement

A microgrid must balance changing demand, solar availability, battery limits, tariffs, outages, and backup generation while maintaining the electrical balance at every time step.

The engineering problem is to formulate a transparent constrained dispatch model, compare it with simple operating rules, and explain the tradeoff between cost, renewable use, battery cycling, and unmet demand.

Project objectives

  • Model selected loads, solar generation, battery storage, grid limits, tariffs, and backup supply.
  • Optimise time-step dispatch under power, energy, efficiency, reserve, and availability constraints.
  • Compare the optimiser with rule-based and no-storage baselines.
  • Test grid-connected, outage, tariff, and renewable-variation scenarios.
  • Measure cost, renewable utilisation, curtailment, battery cycling, and unmet energy.

System design

System modules

01

Microgrid modeller

Defines components, capacities, efficiencies, operating limits, and the electrical balance.

02

Scenario manager

Imports prepared load, solar, tariff, grid-availability, and initial battery-state profiles.

03

Dispatch optimiser

Builds and solves the time-indexed objective and constraints using a recorded solver configuration.

04

Baseline simulator

Runs documented priority-based control strategies for direct comparison.

05

Results dashboard

Shows power flows, state of charge, costs, curtailment, shortages, constraints, and scenario differences.

Methodology

System workflow

  1. 01
    Configure system

    The student selects a prepared microgrid and its component limits.

  2. 02
    Load scenario

    Demand, solar, tariff, grid availability, and initial state are validated on one time base.

  3. 03
    Run baseline

    A fixed dispatch rule produces the reference result.

  4. 04
    Optimise dispatch

    The mathematical model solves the same scenario and records status and constraint checks.

  5. 05
    Compare and test

    Cost, reliability, renewable use, and battery behaviour are compared across sensitivity cases.

Demonstration scenario

A campus microgrid with solar, a battery, grid supply, and a backup generator is simulated for one day. The rule-based controller charges immediately, while the optimiser saves capacity for an evening tariff peak and a prepared outage. The dashboard compares cost, state of charge, generator use, and unmet demand.

Engineering

Technical architecture

Web application
Nuxt and Vue for component setup, time-series charts, scenario comparison, and result inspection.
Optimisation API
FastAPI for model inputs, solver jobs, baseline simulations, feasibility checks, and exports.
Data layer
PostgreSQL for components, profiles, tariffs, scenarios, configurations, dispatch values, and metrics.
Mathematical model
Python and Pyomo with a HiGHS-compatible linear or mixed-integer formulation and explicit units.
Verification
Energy-balance checks, bound checks, hand-calculated cases, baseline comparisons, and infeasibility explanations.

Testing

Evaluation

Evaluation measures

  • Energy-balance and constraint satisfaction at every time step
  • Objective value agreement with manually solvable prepared cases
  • Cost reduction relative to rule-based and no-storage baselines
  • Renewable utilisation, curtailment, battery throughput, and unmet energy
  • Sensitivity to tariffs, outages, initial charge, forecast error, and component size
  • Solver time and optimality status across scenario sizes

System boundaries

  • The system is an offline planning and simulation tool and does not control live electrical equipment.
  • Results depend on the prepared profiles, tariff assumptions, and simplified component models.
  • Protection coordination, transient stability, and detailed power flow are outside the first scope.
  • Operational decisions require validation by qualified electrical engineers and the relevant utility rules.

Included

  1. 01Microgrid configuration and scenario interface
  2. 02Dispatch optimisation and baseline simulation engine
  3. 03Energy-flow, cost, reliability, and sensitivity dashboard
  4. 04Prepared load, solar, tariff, and outage scenarios
  5. 05Complete source code in a private GitHub repository
  6. 06Complete project documentation with synopsis, abstract, methodology, single-line and energy-flow diagrams, optimisation results, screenshots, and conclusion
  7. 07Setup and usage guide

Project record

No buyer information is collected on this page.

Permanent project ID
GP-EE-023V7AW
Catalogued
21 Aug 2026
Completed
Pending
Verified
Pending
Demonstration
Added when ready

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.