FlexECO

An optimal dispatch tool for large-scale interconnected multi-energy systems

FlexECO is a flexible and modular energy system modelling framework that follows the cost-minimisation principle and performs an optimal dispatch of different energy supply, conversion and storage technologies using different primary energy sources (e.g. fuels, methane, coal, PV, wind, water, hydrogen) so that the final energy demand (e.g. electricity, heating, cooling, transport) is covered (as far as possible) at all time steps.

Terminology

The terminology defined here is used throughout the documentation:

Technology: a technology produces, consumes, stores, or converts or energy
Node: a region, location, or site which can contain multiple technologies
Resource: a source of energy that is be used by a technology to supply energy to the system (e.g. Coal, Uranium, PV, Wind, Methane) or a sink of energy that is used by a technology to cover a type of demand (e.g. heat, cooling, transport).
Energy carrier: An energy carrier is a substance that contains energy which can be later converted to other forms such as mechanical work or heat.

Component Models

In FlexECO, an energy system is decomposed into different sectors, as shown in Figure 1. Within a sector, the type of energy carrier that is consumed, supplied, stored or transported is always the same. To take into account the spatial dimension of the system under consideration, the system can be divided into several regions or locations, called nodes. A sector can be modelled by (i) demand technologies, (ii) supply technologies, (iii) storage technologies and (iv) transport infrastructure. Coupling between different sectors can be achieved by using conversion technologies.

A supply technology with an efficiency ηgen can take a resource (ξ) from outside the sector and turns it into energy carrier in the same sector (ugen) at a given location/node. For example, a coal-fired power plant burns coal (ξ) to produce electricity (ugen), which is fed into the system.

A demand technology with an efficiency ηload takes out energy from the system (uload) to cover a specified demand (ξ)
at a given location/node.

A storage technology with energy capacity of C with a charging efficiency (ηload) and discharging efficiency (ηgen) can
store or feed energy at a given location.

By extending the number of variables by (i) a waste term (ω) and (ii) a loss term (v), one could generalise the representation of demand, supply and storage technologies by the so-called Power Nodes Formulation (PNF)[1].

All PNF variables (u_load, u_gen, x, ξ, ω, v) have (i) minimum and maximum values (u_load^min, u_load^max , u_gen^min, u_gen^min, . . . ) so that constraining different variables in PNF can mimic different behaviours of different supply, demand and storage technologies relevant to an optimal dispatch problem and (ii) constant cost term (e.g., u_gen^c0), linear cost term (e.g., u_gen^c1) and , quadratic cost term (e.g., u_gen^c2), which are directly included in the objective function of the system.

Table 1: Examples of different constraints for different technology types

A conversion technology with an efficiency η converts one (or more) carrier P₁ to another (or more) P₂ at the same node between different sectors. In the framework of FlexECO, these technologies are modelled using the Energy-Hub Formulation [2].

A transport infrastructure can move energy of the same carrier from one location (P₁) to another (P₂)

Transport infrastructure can be modelled by:

symmetrical transport model

asymmetrical transport model

physical linear transport model

where x is stands for a system state of the physical system (e.g. voltage, pressure) and B for transport characteristics (e.g., admittance). A transport infrastructure can be made up of a combination of the models described above.

Model Input

A model based on FlexECO consists of (i) a JSON file, in which the topology ot the system, the technologies with their parameters and location and (ii) a set of MAT files with profiles as time series. FlexECO takes these files, constructs an optimisation problem, solves it, and reports the hourly dispatch results in the form of CSV Files.

To make the model input easier for the user, a model can also be defined in Excel spreadsheet format.

References

[1] Kai Heussen, Stephan Koch, Andreas Ulbig, and Göran Andersson. Unified system-level modeling of intermittent renewable energy sources and energy storage for power system operation. IEEE Systems Journal, 6(1):140–151, 2012.

[2] Martin Geidl and Göran Andersson. Optimal power flow of multiple energy carriers. IEEE Transactions on Power Systems, 22(1):145–155, 2007.

Figure 1. Conceptual representation of the energy system in FlexECO FEN

Example with interactive tool