What it is
EcoSizing is a sizing and dispatch optimisation platform for microgrid projects. It takes site resource data, load profiles, tariff or fuel-cost structures and equipment libraries as input, and returns a device-level system configuration together with an hourly operating schedule and a full-lifecycle financial model.
The engine is a mixed-integer linear program solved over the full 8760-hour chronological sequence, with configurable time steps ranging from 1 hour down to 15 minutes, and a multi-year financial horizon layered on top of the operating solution.
The platform was built for projects where configuration cannot be settled by rule of thumb: off-grid systems that must guarantee supply hours, weak-grid sites that must remain stable through disturbances, and grid-connected systems where several assets are coupled to a tariff structure with more than one revenue mechanism.
Inputs
Resource data. Typical Meteorological Year data is retrieved from the Meteonorm database by latitude and longitude, covering irradiance, temperature and wind speed. Site-specific measured generation data can be imported in place of TMY data where it exists.
Load. Hourly or sub-hourly load profiles, together with supply-hour requirements and, where flexible shedding is in scope, load priority tiers.
Commercial structure. Time-of-use tariffs, demand charges, fuel prices, PPA pricing and discount rates, project lifetime and discount rate. Tariff and PPA assumptions can be varied year by year across project life rather than held flat.
Equipment. DOGO's grid-forming storage products are modelled natively. A vendor whitelist provides parameter loading for common third-party equipment, and an open interface allows PV modules, inverters, battery systems, power batteries and gensets to be added with user-supplied parameters.
Constraints. Any of the following can be fixed by the user before optimisation: investment budget, maximum installable PV capacity, PV-to-battery ratio, constant storage power, supply hours, renewable energy share.
Outputs
-
Configuration at device level — equipment counts and model selection for PV modules, inverters, storage units
-
8760-hour operating curves — PV, storage, genset and load interaction resolved hour by hour across the year
-
Year-by-year cash flow — CAPEX, OPEX, replenishment and replacement, debt service, depreciation, tax and residual value
-
Economic indicators — NPV, IRR, equity IRR, LCOE and payback period, before and after tax
-
Formatted design report
Method
Decision variables
Integer and binary sizing variables covering equipment counts and model selection, together with continuous and binary operating variables covering grid import and export, battery charge, discharge and state of energy, load served, and supply status.
Constraints
Hourly power balance; equipment nameplate limits; inter-temporal battery state-of-charge dynamics; demand-charge and interconnection bounds; genset operating limits; annual service-level requirements.
Objective by run mode
| Mode | Objective |
| Operation | Minimise annual operating cost — grid purchase and demand charges plus fuel cost, net of supply benefit and dispatch behaviour reward |
| Design, PPA | Maximise project NPV, net of CAPEX, battery replacement and replenishment cost, with terminal salvage value |
| Design, self-investment | Minimise lifecycle net present cost on the same cost basis |
The choice of objective changes the resulting configuration. A system optimised to minimise lifecycle cost under self-investment will differ from one optimised to maximise NPV under a PPA, on the same site with the same load.
Time resolution
The model runs the full 8760-hour chronological sequence rather than extracting typical days，supporting granularity from hourly down to 15-minute intervals. Each step resolves generation from irradiance, storage charge and discharge, tariff settlement and PCS spinning reserve requirement. Monthly and annual maximum demand, cumulative supply hours and renewable penetration limits are validated across the sequence rather than at a representative point.
Typical-day extraction fails on two specific things. Demand charges are set by the single highest interval in a billing period, which by construction does not appear in an averaged profile. And curtailment concentrates in seasonal mismatches — high irradiance against a shut plant — which averaging removes.
Degradation and replacement
Battery ageing is modelled as a function of depth of discharge, discharge rate, cycle life and calendar life. When available capacity falls below the replenishment threshold, or end of life is reached, a CAPEX event is triggered and dispatch continues against the revised capacity for the remainder of the project life.
Modelling assumptions
A mixed-integer linear program requires that every relationship in the model be expressed linearly. Several physical relationships in a microgrid are not linear, and are approximated. The optimum returned is optimal with respect to the linearised model, and the distance between that model and the physical system is a property worth stating explicitly.
Scope
Off-grid systems. Mining operations and industrial facilities beyond grid reach, island utilities, and sites presently served by diesel generation. Configuration here is driven by supply-hour guarantees and fuel displacement rather than tariff arbitrage, and the binding constraint is usually the longest low-resource window rather than the annual energy balance.
Weak-grid systems. Sites with an unreliable or capacity-limited interconnection, where the configuration has to hold through grid disturbances and support island operation.
Grid-connected commercial and industrial systems. Peak shaving, demand-charge management and time-of-use arbitrage, under self-investment or PPA structures.
Grid-forming verification
For off-grid and weak-grid configurations, stability is verified against the actual capability of the grid-forming storage rather than assumed from the PV-to-battery ratio.
Related
The full method documentation, including the parameter set and the worked example, is in the White Paper on Intelligent Microgrid Planning and Sizing.
-
Wind capacity as a substitute for battery capacity in off-grid systems
-
Off-grid sizing: genset dispatch, load shedding and capacity assurance
-
Grid-connected C&I sizing: demand charges and the limits of typical-day methods
