• Energy Research

Floor heating systems are important components of nowadays home-automation setups. The control of a floor heating system is a nontrivial task and the present solutions essentially implement variants of a simple bang-bang controller that opens for a hot water circulation in a room if its current temperature is below the user defined target temperature, otherwise it closes for the heating in the room. The disadvantage is that the heat exchange among the rooms, outside weather conditions, weather forecast and other factors are not considered. We propose a novel model-driven approach for intelligent floor heating control based on a chain of tools that allow us to gather the sensor readings from the actual hardware and use the state-of-the-art controller synthesis tool UPPAAL Stratego in order to synthesise abstract control strategies that are then executed on the real hardware platform provided by the company Seluxit. We have built a scaled demonstrator of the system and the experimental results document a 38% to 52 % increase in user satisfaction, moreover with additional energy savings between 2% to 12%.

Increasing the penetration of renewable power in Denmark, demand-side flexibility is offered as a workable solution to hedge against the intermittency and volatility of renewable energy. In the residential sector, heat pumps are economic alternatives for district heating to unlock heat flexibility in response to renewable power availability. This paper proposes a novel economic approach to optimize the heat consumption of a single responsive building with multiple rooms. The suggested approach makes it possible to set different temperature zones for the rooms based on the occupancy patterns. To provide power system flexibility, an Economic Model Predictive Control (EMPC) is designed. The EMPC adjusts heat consumption in response to the electricity price. Integration of the heat flexibility into the power system, two types of heat flexibilities are investigated, including thermal inertia of rooms and thermal storage of buffer. To increase the applicability of the problem, real sensor data of a Danish residential building is used to estimate the constant parameters of the thermal dynamic. To achieve the aim, a model identification method is developed to estimate the constant factors based on the maximum likelihood function. Finally, the performance of the suggested approach is examined in response to the day-ahead electricity price of the Danish Electricity Market. The results showed that the suggested approach not only ensures power system flexibility but also decreases the energy consumption cost of the building up to 37% a week.

An energy management system of a microgrid (MG) has several basic objectives; e.g. to maximize the utilization of renewable energy resources (RES), to protect the internal components from overloading, and to ensure that the MG operates reliably under any operating conditions. Although many control techniques are available in the literature to monitor and control the energy flows among distributed RES in MGs, formal verification of those techniques was not proposed yet. The emphasis of this paper is to design and validate energy management system for a MG which consists of a solar photovoltaic (PV) array, a pair of battery energy storage systems (BESes), a diesel generator (DG) and a load (LD). The physics and dynamics of the MG are defined as energy flow invariants and the designed behaviours are abstracted, modelled and validated in this work. Therefore, we have considered an invariant based flow technique to manage the energy flow in an MG. The results are validated and verified with UPPAAL, a powerful industrial tool which is commonly used to verify the correctness of real-time systems like supervisory controllers, communication protocols and others.

Increasing the penetration of Renewable Energy Sources (RES), the heat pumps are an alternative solution to facilitate the integration of RES into district heating. To hedge against the intermittency of RES, the flexibility potentials of the thermal inertia of buildings are unlocked and integrated into power systems. This paper suggests a novel structure to control the mixing loop of residential heating systems supplied by electrically operated district heating. The Economic Model Predictive Control (EMPC) is proposed to optimize the heat demand of the buildings in response to electricity market price. The EMPC controls the temperature and flow rate of forward/return water in the mixing loop to minimize the energy consumption cost of households. The thermal dynamics of the buildings are modeled by 3-state differential equations to integrate the flexibility potentials of thermal inertia into the district heating. The approach optimizes the heat consumption of the building with multi-temperature zones. The Continuous-Time Stochastic Model (CTSM) is addressed to determine the thermal dynamics of the building using sensor data. Finally, a 150 m2 Danish test house with four temperature zones is simulated. The simulation results show that the suggested approach not only minimizes the energy consumption cost but also provides flexibility for the Danish Electricity Market.

Energy systems worldwide are undergoing a major transformation as a consequence of the transition towards the widespread use of clean and sustainable energy sources [...]

Increasing the penetration of Renewable Energy Sources (RES), e.g. wind and solar, intermittency and volatility of the supply-side are increasing in power systems worldwide. Therefore, the power systems need alternative forms of flexibility potentials to hedge against the intermittent power. District Heating Systems (DHS), especially Heat Pump Systems (HPS), show true potentials to provide demand-side flexibility for power systems. This paper aims to survey the literature on the applications of DHS in power system flexibility. In this way, first of all, the basic structure of the DHS, including the heat resources, thermal units, and thermal storage, is surveyed to give insight into the DHS problem. Afterward, classifying flexibility potentials of the DHS, the conventional and advanced control strategies of the DHS are reviewed comprehensively to investigate the role of heat controllers on power system flexibility. To study the compatibility of the controllers to flexible energy markets, the roles of different control schemes in successive trading floors of the electricity markets, from the energy market to ancillary service markets, are investigated. Finally, the optimization solutions, including mathematical and heuristic approaches, with software tools are reviewed to give readers general insight into the way a DHS problem is modeled and solved.

The secure sharing of data is crucial for peer-to-peer energy trading. However, the vulnerability of Information and Communication Technology (ICT) infrastructures to cyberattacks, e.g., Denial of Service (DoS) attacks, poses a significant challenge. A possible solution is to use Digital Twin (DT) modeling of the physical system, which provides robust digital mapping and Big Data processing capabilities that facilitate data recovery. To this end, this paper proposes a DT-enabled energy trading framework for cyber-physical energy systems that offers data analytic and recovery capabilities to defend from DoS attacks. With this framework, a new distributed approximate-newton trading algorithm with a switched triggering control strategy is proposed. Therein, the DT model is employed to achieve data recovery and adjust the system evolution of trading trajectory during attack periods. This enables the proposed method to find optimal trading solutions even in the presence of DoS attacks. Theoretical analysis results demonstrate the correctness of the proposed method. Furthermore, numerical simulations are conducted to assess the effectiveness of the proposed method.