Increasing electricity demand and upcoming shortages of resources, make on-line energy management like peak-load reduction and control energy provision by the demand side of the electric power system a valuable method for keeping the grid stable and efficient. For developing the next generation if these methods, a simulation environment for studying demand response (DR) algorithms with large-scale and detailed grid simulations is currently developed. An important part of this is an accurate but computationally inexpensive dynamic model for domestic housing and small business loads. This paper presents the design of this model and the deduction of the model parameters. As a determining factor for domestic loads, the power consumption of heating, ventilation and air conditioning (HVAC) systems has been identified. The model is partly based on earlier proposals that simulate the state changes of thermostatically controlled processes, but the complexity is further reduced to an absolute minimum. The result can be used as one of the basic building blocks required to set up a comprehensive simulation of power consumption in electric power grids under DR conditions.

Within the field of Distributed Generation (DG), stand-alone Hybrid Power Systems (HPS) are a suitable solution to provide energy to isolated facilities where the connection to a centralized grid is not affordable. The logical evolution of such systems involves the optimization of power resources and related control strategies, but also enhancements concerning the management of energy loads. This paper introduces Demand Side Management (DSM) strategies specially designed for HPS. They are applied on a real and patented HPS that consists of PV panels, a diesel generator, an inverter and a set of batteries. DSM strategies are built up on a framework of distributed endpointdevices connected to a central control application where loads are identified according to their behavior. System network components, load definitions, the control application and DSM strategies are depicted. Finally, simulations show illustrative savings achieved by the application of some of the proposed strategies.

The transition of classical power distribution grids towards actively operated smart grids locates new functionality into intelligent secondary substations. Increased computational power and newly attained communication infrastructure in thousands of secondary substations allow for the distributed realization of sophisticated functions, which were inconceivable a few years ago. These novel functions (e.g., voltage and reactive power control, distributed generation optimization or decentralized market interaction) can primarily be realized by software components operated on powerful automation devices located on secondary substation level. The effective and safe operation of such software is crucial and has a broad set of requirements. In this paper, we present a flexible and modular software ecosystem for automation devices of substations, which is able to handle these requirements. This ecosystem contains means for high performance data exchange and unification, automatic application provisioning and configuration functions, dependency management, and others. The application of the ecosystem is demonstrated in the context of a field operation example, which has been developed within an Austrian smart grid research project.

Distribution networks are typically unbalanced due to loads being unevenly distributed over the three phases and untransposed lines. Additionally, unbalance is further increased with high penetration of single-phased distributed generators. Load and optimal power flows, when applied to distribution networks, use models developed for transmission grids with limited modification. The performance of optimal power flow depends on external factors such as ambient temperature and irradiation, since they have strong influence on loads and distributed energy resources such as photo voltaic systems. To help mitigate the issues mentioned above, the authors present a novel class of optimal power flow algorithm which is applied to low-voltage distribution networks. It involves the use of a novel three-phase unbalanced holomorphic embedding load flow method in conjunction with a non-convex optimization method to obtain the optimal set-points based on a suitable objective function. This novel three-phase load flow method is benchmarked against the well-known power factory Newton-Raphson algorithm for various test networks. Mann-Whitney U test is performed for the voltage magnitude data generated by both methods and null hypothesis is accepted. A use case involving a real network in Austria and a method to generate optimal schedules for various controllable buses is provided.

Most typical distribution networks are unbalanced due to unequal loading on each of the three phases and untransposed lines. In this paper, models and methods which can handle three-phase unbalanced scenarios are developed. The authors present a novel three-phase home energy management system to control both active and reactive power to provide per-phase optimization. Simplified single-phase algorithms are not sufficient to capture all the complexities a three-phase unbalance system poses. Distributed generators such as photo-voltaic systems, wind generators, and loads such as household electric and thermal demand connected to these networks directly depend on external factors such as weather, ambient temperature, and irradiation. They are also time dependent, containing daily, weekly, and seasonal cycles. Economic and phase-balanced operation of such generators and loads is very important to improve energy efficiency and maximize benefit while respecting consumer needs. Since homes and buildings are expected to consume a large share of electrical energy of a country, they are the ideal candidate to help solve these issues. The method developed will include typical distributed generation, loads, and various smart home models which were constructed using realistic models representing typical homes in Austria. A control scheme is provided which uses model predictive control with multi-objective mixed-integer quadratic programming to maximize self-consumption, user comfort and grid support.

Smart Grids ideally interconnect intelligent grid members. One big share of grid presence is with buildings. Flexible and grid-friendly buildings would improve grid management and are an important contribution to the integration of renewable energy sources. Classical buildings, however, are passive and not cooperative. This article describes how electro-thermal processes in buildings can be used for demand response and how such intelligent behavior can be enabled via communication technology. Experiments and simulations on typical mid-European buildings were done to estimate the potential time constants.