Case Study

Intelligent District Management

This study isolates potentials to improve operation of a fully electrified quarter with power producers (PV systems) and consumers (heating, production sites, infrastructure, etc.). Using a control system, the degree of self-sufficiency, i. e. the degree of the total energy demand that is covered from within the district itself, and the resulting total operating costs can be balanced.

Fig. 1: The simulation model of the potential study. The grid stabilizes energy deficits and surpluses. Producers feed consumers and storage. An AI-powered logic controls how energy flows.

Details

  • A district with the structure shown in Fig. 1 was simulated over a 10-year period. Varying weather with persistent bad weather, weekends, holidays, various production schedules, electricity price fluctuations, and storage losses were taken into account.
  • A control model without storage serves as a base scenario for correct scaling (Ø) in Fig. 2.
  • Two optimized reactive control logics serve as a reference, which operate the district as self-sufficiently as possible (I) or as cost-effectively as possible (II).
  • The intelligent control (III) combines forecasts with a dynamic view in order to always make the best choice with foresight.
  • An extension (IV) also involves altering a limited volume of production and heating sequences to favorably schedule consumers with high consumption.
  • 3 and 4 show strategies taken by the intelligent logic.
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Findings

  • The analysis shows clear advantages for the intelli-gent control logic over reactive strategies.
  • In the given model, electricity costs can be reduced by 4-9% and the degree of self-sufficiency can be increased by 1-3% at the same time.
Fig. 2: Relative costs and degree of self-sufficiency in compari-son. Values were normalized to the case Ø. Effectively, the degree of self-sufficiency in this case was 41.7% for Ø.

Fig. 3: Logic III (green) prepares a higher state of charge compared to I to better satisfy a peak demand using internal storage, as electricity prices are high at this point in time.

Abb. 4: Logic III pre-charges the energy storage from the grid at favorable times. The higher initial costs compared to II pay off quickly.

Potentials

  • Expansion with detailed model for a concrete district
    –> gain robust, quantitative results
  • Incorporation of other factors, such as
    –> Electric-cars, battery-saving operation, more storage technologies
  • Applying this simulation to answer deeper questions:
    • How can battery and PV capacities be effectively utilized?
    • Which influences are relevant & can be controlled?
  • Development of an operational intelligent control system

Your Contact

Carlos Ayala Jiménez

Carlos Ayala Jiménez

Phone:  +49 1516 2 60 74 21

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