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Combined harnessing of electrical and thermal energies could leverage their complementary nature, inspiring the integration of power grids and centralized heating systems in future smart cities. This paper considers interconnected power distribution network (PDN) and district heating network (DHN) infrastructures through combined heat and power units and heat pumps. In the envisioned market framework, the DHN operator solves an optimal thermal flow problem given the nodal electricity prices and determines the best heat production strategy. Variate coefficients of performance of heat pumps with respect to different load levels are considered and modeled in a disciplined convex optimization format. A two-step hydraulic-thermal decomposition method is suggested to approximately solve the optimal thermal flow problem via a second-order cone program. Simultaneously, the PDN operator clears the distribution power market via an optimal power flow problem given the demands from the DHN. Electricity prices are revealed by dual variables at the optimal solution. The whole problem gives rise to a Nash-type game between the two systems. A best-response decentralized algorithm is proposed to identify the optimal operation schedule of the coupled infrastructure, which interprets a market equilibrium as neither system has an incentive to alter their strategies. Numeric results demonstrate the potential benefits of the proposed framework in terms of reducing wind curtailment and system operation cost.

Smart energy systems seem a promising choice for countries worldwide to realign their power systems to the challenges predicted for the next decades. With the will to participate in this class of systems, many solution providers design custom systems, which sometimes consist of similar parts, but are on the contrary hard to compare to each other. However, a reference describing existing commonalities is needed as a basis for many activities such as regulation design, legislation, national discussion or standardization. This paper illustrates the challenges connected with the creation of reference architectures for smart energy systems, delineates their benefits and suggests a model and method for their incremental, bottom-up development and validation through concrete system architectures.Index Terms-Reference Architecture; Smart Energy Systems; Smart Grid; Ontologies; Conceptual Modeling; Domain Modeling; Bottom-up design; I. INTRODUCTIONThe existing energy systems will go through major changes within the next decades. Facts like the increasing of renewable, decentralized energy sources, the growing number of electric cars, national efforts on market liberalization and reduction of CO 2 -emissions, integration of different energy grid types (e.g. electric power, district heating or gas grids) or the need for advanced monitoring systems and increased power stability will drive this change. Many countries discuss new concepts to solve problems like the fluctuating supply of renewable energy with smart systems while having to ensure power stability.As a reaction to this worldwide trend, many actors think of a new class of systems, often labeled as "Smart Energy Systems" (SES) and develop new systems inside this class. However these systems focus on different aspects of the energy system, involve different stakeholders, include new components, functions and data structures and use different technologies, concepts, terminology and infrastructure. The class of SES comprises a variety of systems used in home appliances, energy management, district heating, intelligent devices, virtual power plants, demand side management, market places, data platforms, metering infrastructure, field devices, portal software, weather forecasting or grid operations.Many countries, national and international organizations are interested in SES, as this class of systems is expected to have an impact on national grid infrastructure, markets, customers and industries. In contrast the sheer amount of existing systems and their different architectures complicate the comprehension and comparison of different solutions or the elaboration of an abstract view on SES.

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This article describes an approach for a system, which helps to increase energy efficiency in households by providing more transparency of energy consumption on single device level. The main idea is to analyze energy consumption using a smart meter and break it down into its individual characteristic components using an algorithm. Having such data, an online energy efficiency coach can provide households with personalized advice on saving energy on a single-device level with minimal time effort and great social experience. In order to develop such an algorithm, a simulation environment was created, which allows generating a huge number of virtual households based on real measurements of single devices. This article describes the simulation environment, how data is measured and collected and how the platform is set up. Smart home; energy transparency; smart meter; web interface; energy saving; social integration