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  1. KNX cityPart 6: Professional

  2. Current situation:Example Germany • In 2011 only 20% of the total energy generation were covered by renewable energies, although renewable energies have been greatly expanded in the last years  Why? Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  3. Safe power supplyCapacity credit and the secured power 119,4 GW Total power of all power plants Example of Germany: The secured power from all power plants and renewable energies in Germany in 2005 -22,8 GW not usable power -4,1 GW outages The secured power is the power which can be generated at every time with a probability of 99% -2,7 GW revisions 82,7 GW secured power 6 GW residual power 76,7 GW annual maximum load Security of power supply Yes: secured power > annual max. load No: secured power < annual max. load Source: dena Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  4. A secure power supplyRenewable Energies reduce the secured power 119,4 GW from all power plants -22,8 GW not usable -4,1 GW Black outs -2,7 GW Revisions 82,7 GW Secured power 6 GW Reserve 76,7 GW max. annual load  One by one substitution means decrease of secured power Secured power > max. annual load  Yes Secured power< max. annual load No Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  5. Expansion of Renewable EnergiesA secure power supply means as well surplus generations Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner 9 MW inst. renewable power means about 0,9MW secured power = 0,9 MW = 8,1 MW • Min. generation = 0,9 MW • Max. generation= 9 MW • The band in which the generation fluctuates will increase • In future the amount of surplus energy generation increases for keeping the security of supply

  6. Example Germany • The expansion of renewable energies in the amount of 81% of the annual peak load leads only to a 20% coverage of the annual electricity consumption • Expansion alone is not a solution Installed Power Ren. En. Secured Power Ren. En. Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  7. Smart Gridsas a solution Smart Grids are seen in world wide initiatives and pilot projects as a solution for todays energy challenges in order to provide a secure power supply 380/220 kV 110 kV VPP 20/10 kV 0,4 kV Demand Side Management EV Micro Grid Load Management Cooperation

  8. Smart Grids as a solutionWhat are Smart Grids Definition • A Smart grid is defined as an electrical power supply combined with Information and Communication technology (ICT) Drivers for Smart Grids • Generation side • Integration of renewable Energies • Reduction of carbon dioxides • Consumption side • Increasing Energy efficiency, especially in Mega cities • Control of Energy Demand  The idea of a Smart Grid is to adapt loads to the renewable generation in order to increase the coverage of the annual electricity consumption

  9. Sustainable cities and Smart Grids……require more than single solutions A “Single solution” doesn’t meet Smart Grid objectives KNX city solutions shall • …be combined • …interact with Smart Grid • …offer interfaces to the grid • …focus on the total building environment • …involve all fields which affect living • …  Systemic approaches who consider the interaction of different fields are necessary

  10. KNX city projectSolutions for sustainable cities and renewable energies The KNX city initiative brings mobility, building, infrastructure and energy generation with a single communication together: the KNX communication standard

  11. KNX cityFrom Smart Buildings to Sustainable Cities • KNX city provides solutions in the building who affect cities and Smart grids • Smart Metering • Demand Response • Demand Side Management • Tariff management • White good automation • Heating and air conditioning controls • Energy efficiency • KNX city provides • 1. Energy efficiency in builidngs • 2. Interaction with smart grids • 3. Interaction of generation, building, mobility and infrastructure

  12. KNX has its focus in the building…… but considers Smart grid and city issues • Smart grids require buildings • „Energy generation“ affects buildings, e.g. decentralized generation on roofs of “buildings”. • “Mobility” affects “buildings”, e.g. charging of electric vehicles • The building affects the “City”, e.g. with feeding surplus energy into the grid. • A systemic solution can be only realized, if many fields interact with each other • KNX city sets a new focus with existing KNX technologies

  13. Implementation Demand Side Management Optimization problem The optimum for the load adaption can be described with a well known discrete optimization problem, the Knapsack-Problem ? highpriority P1 2 kW ~ mediumpriority ~ P2 lowpriority Pn Source: Wikipedia Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  14. Implementation Demand Side Management Mathematical model  Resultoftheoptimizationarethebestpossibleswitchingstatesof all participatingloads Goalfunction:total value(valueviofloadi) Boundaries:generation (G) ≥ consumption (ciPi) Loadiswitched on or off Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  15. Implementation Demand Side Management Simple example, and evaluation of the algorithm • Exampleoptimization • Konstant loads: P1=500W, P2=150W, P3=250W, P4=700W V1=6, v2=3, v3=5, v4=8 • Ideal PV generation (1 day) Pmax=2kWp Load-curve Ideal PV-generation Power in W Time in h Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  16. Implementation Demand Side Management Challenges: load profiles are not constant • Home appliances or loads in general don‘t have constant load profiles • Solution: Definition of reference constancies • Solution: Usage of average values for the load profiles (good results with 15min average values) Example: Washer Heating of water Rotation ofthe drum Power in W Time in h Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  17. Demand Side Management open-loop controlKNX day-ahead-prediction control (optimization) Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner • This control optimizes loads such as appliances to a prediction target curve • Target curve can be an time variable electricity tariff or e.g. an ideal photovoltaic generation curve • Objective: Setting starting times of loads with fixed running times, that could be controlled hardly by the real-time control KNX day-ahead-predictioncontrol ti,start PPV,pred Optimization KNX Load profiles

  18. Demand Side Management Controller: Challenges: control-derivation • In order to respond to current events such as e.g. suddenly started loads from a person the control can only hardly minimize the control deviation • Real time load adaption helps to compensate the prediction based load adaption errors coming from e.g. differences between the prediction photovoltaic generation target curve and the real photovoltaic generation curve Solution • Implementation of a real time load adaption control with special given loads (who have no fixed runtime) in order to compensate the prediction based load adaption errors Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  19. Demand Side Management controller KNX real-time control Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner • Objectives: • Compensation of control-derivation resulting from the day-ahead-prediction • Compensation of manual load changes Disturbance Pload,manual + Pload PPV,real Control KNX -

  20. Demand Side Management controller Scheme load adaption Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner KNX day-ahead-predictioncontrol ti,start PPV,pred Optimization Disturbance Pload,manual Load profiles + KNX real-time control Pload PPV,real Control KNX - PPV,realismeasuredby KNX measurmentdevices

  21. Demand Side Management setup Example 1 KNX IP Internet (Smart Grid) 3Loads Power KNX TP 8 Loads Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  22. Demand Side Management setup Example 2 KNX IP Internet (Smart Grid) 3Loads Power KNX TP 8 Loads Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  23. EvaluationValidation of the KNX day-ahead-prediction control • Boundary conditions • Loads which can be optimized (2x1kW, 2x500W, 6x100W) • Load can start or stop at every time depending from the optimization) • No minimum running times Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  24. Evaluation: Measured results, KNX day-ahead-prediction control  • KNX day-ahead-prediction control (loads with fixe running times) • Adaption of the loads to the ideal photovoltaic-generation • Objective: Starting selected loads at a good time (from the prediction prospective) at the next day • KNX real-time control (loads without fixed running times) • Compensation of the control-derivation between real photovoltaic generation and total load • Reacts to manual load changes from residents Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  25. Evaluation Total load in comparison to real photovoltaic generation Total load after load adaption with a small control-variable • KNX day-ahead-prediction control (Loads of washer, dryer,) • KNX real time control (EV, 2x1kW, 2x500W, 6x100W) Total load after load adaption with a small control-variable • KNX day-ahead-prediction control (Loads of washer, dryer,) • KNX real time control (EV, 3x100W) Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  26. Evaluation Conclusion • KNX day-ahead-prediction control • Control works very well, if the the difference between prediction target curve and real curves is small (e.g. tariff curves) • Tariff curves as prediction curves are better than photovoltaic curves • Good possibility to adapt loads with fixed running time and load profiles • KNX real-time control • Control works very well, but its efficiency depends from the amount of participating loads • Determination of the control parameters is difficult due to its discrete control-variable • Control works good if control-variable is large enough • Results are not optimum for one household • Better result for scenarios with more participating loads such as apartment buildings  Controls work good with large control-variables, focus will lie in the future on apartment house scenarios Source: Technische Universität Darmstadt, Dipl.-Ing. Lutz Steiner

  27. Contact Lutz Steiner lutz.steiner@knx.org KNX Association Brussels