Ingo Mauser

Evolutionary Optimization of Smart Buildings with Interdependent Devices

To enable a more efficient utilization of energy carriers, energy management systems (EMS) are designed to optimize the usage of energy in future smart buildings. In this paper, we present an EMS for buildings that uses a novel approach towards optimization of energy flows. The system is capable of handling interdependencies between multiple devices consuming energy, while keeping a modular approach towards components of the EMS and their optimization. Evaluations of the EMS in a realistic scenario, which consists of a building with adsorption chiller, hot and cold water storage tanks as well as combined heat and power plant, show the ability to reduce energy consumption and costs by an improved scheduling of the generation of hot and chilled water for cooling purposes.

Customizable Energy Management in Smart Buildings Using Evolutionary Algorithms

Various changes in energy production and consumption lead to new challenges for design and control mechanisms of the energy system. In particular, the intermittent nature of power generation from renewables asks for significantly increased load flexibility to support local balancing of energy demand and supply. This paper focuses on a flexible, generic energy management system for Smart Buildings in real-world applications, which is already in use in households and office buildings. The major contribution is the design of a “plug-and-play”-type Evolutionary Algorithm for optimizing distributed generation, storage and consumption using a sub-problem based approach. Relevant power consuming or producing components identify themselves as sub-problems by providing an abstract specification of their genotype, an evaluation function and a back transformation from an optimized genotype to specific control commands. The generic optimization respects technical constraints as well as external signals like variable energy tariffs. The relevance of this approach to energy optimization is evaluated in different scenarios. Results show significant improvements of self-consumption rates and reductions of energy costs.

Organic Architecture for Energy Management and Smart Grids

An unprecedented rise of renewable and distributed energy resources imposes unprecedented challenges in terms of complexity to power grids. Multitudes of devices are not only connected to the electricity grid but need appropriate information and communication technologies for proving their services. These devices ask for novel control mechanisms on different levels and regional scales. In this paper, we show how concepts from Organic Computing may support the controlled self-organization of the future smart grid. We propose a generic hierarchical architecture as a framework for various energy management systems. This architecture is able to reflect the physical power grid structure as well as the interdependencies of its stakeholders, user objectives, subsystems, and devices. It enables adaptive responses to changing objectives as well as disturbances in the system. Various simulations of systems based on the proposed architecture show the applicability of the proposed architecture to domains of energy management in smart grids.

Building Energy Management in the FZI House of Living Labs

The FZI House of Living Labs is a research and demonstration environment that facilitates interdisciplinary research, development, and evaluation in real-life scenarios. It consists of various Living Labs addressing different research topics. In the Living Lab smartEnergy, solutions for the energy system of the future are investigated. For this reason, the whole FZI House of Living Labs has been equipped with building automation, distributed generation, thermal and electrical storage, and technologies that enable the flexibilization of energy supply and demand. The equipment, among others, includes a photovoltaic and battery storage system, a micro combined heat and power plant, and an adsorption chiller. A building energy management system was developed that integrates various communication technologies, and hence enables monitoring, data recording, visualization, and the integrated optimization of the devices and systems. This way, flexibilities can be utilized with regard to different optimization goals such as an increased self-consumption, or the provisioning of grid-supporting services.

Comparison of Multi-objective Evolutionary Optimization in Smart Building Scenarios

The optimization of operating times and operation modes of devices and systems that consume or generate electricity in buildings by building energy management systems promises to alleviate problems arising in today’s electricity grids. Conflicting objectives may have to be pursued in this context, giving rise to a multi-objective optimization problem. This paper presents the optimization of appliances as well as heating and air-conditioning devices in two distinct settings of smart buildings, a residential and a commercial building, with respect to the minimization of energy costs, CO2 emissions, discomfort, and technical wearout. We propose new encodings for appliances that are based on a combined categorization of devices respecting both, the optimization of operating times as well as operation modes, e.g., of hybrid devices. To identify an evolutionary algorithm that promises to lead to good optimization results of the devices in our real-world lab environments, we compare four state-of-the-art algorithms in realistic simulations: ESPEA, NSGA-II, NSGA-III, and SPEA2. The results show that ESPEA and NSGA-II significantly outperform the other two algorithms in our scenario.

Optimization of Operation and Control Strategies for Battery Energy Storage Systems by Evolutionary Algorithms

To support the utilization of renewable energies, an optimized operation of energy systems is important. Often, the use of battery energy storage systems is stated as one of the most important measures to support the integration of intermittent renewable energy sources into the energy system. Additionally, the complexity of the energy system with its many interdependent entities as well as the economic efficiency call for an elaborate dimensioning and control of these storage systems. In this paper, we present an approach that combines the forward-looking nature of optimization and prediction with the feedback control of closed-loop controllers. An evolutionary algorithm is used to determine the parameters for a closed-loop controller that controls the charging and discharging control strategy of a battery in a smart building. The simulation and evaluation of a smart residential building scenario demonstrates the ability to improve the operation and control of a battery energy storage system. The optimization of the control strategy allows for the optimization with respect to variable tariffs while being conducive for the integration of renewable energy sources into the energy system.

Smart grid services provided by building energy management systems

The integration of decentralized energy resources is associated with new challenges for the operation of low voltage distribution grids. At the same time, the interconnection of these systems offers large potential for providing smart grid services to increase power quality and grid stabilization in areas with a high penetration of renewable energy sources. Therefore, new operation strategies for photovoltaic inverters are presented in combination with building energy management systems. The effect of a locally controlled feed-in is discussed based on simulation results of exemplary rural grid structures.

Run-Time Parameter Selection and Tuning for Energy Optimization Algorithms

Energy Management Systems (EMS) promise a great potential to enable the sustainable and efficient integration of distributed energy generation from renewable sources by optimization of energy flows. In this paper, we present a run-time selection and meta-evolutionary parameter tuning component for optimization algorithms in EMS and an approach for the distributed application of this component. These have been applied to an existing EMS, which uses an Evolutionary Algorithm. Evaluations of the component in realistic scenarios show reduced run-times with similar or even improved solution quality, while the distributed application reduces the risk of over-confidence and over-tuning.

A Privacy-Aware Architecture for Energy Management Systems in Smart Grids

Electrical power grids are in a phase of transition. Therefore, control mechanisms are needed that allow the integration of renewable energies, decentralized generation and storage systems, as well as electric vehicles into the arising smart grid. In this paper, we propose a privacy-aware architecture based on the principles of Organic Computing to tackle the aforementioned challenges. It enables controlled self-organization on all levels, from in-house optimization through demand side management to mains utility and management of entire smart grids. Physical as well as virtual devices and entities can be incorporated flexibly into the control loop of the proposed architecture. Tofu fill privacy requirements, a component named Data Custodian Service is introduced. It handles data exchange across system borders while protecting privacy and interests of data owners. Privacy is preserved by giving the data owner the choice to control data access based on temporal and spatial resolution and on precision of the requested data sets. The application to real world scenarios is shown using the example of a building energy management system that implements the suggested architecture.