Optimization approaches for exploiting the load flexibility of electric heating devices in smart grids

Energy systems all over the world are undergoing a fundamental transition to tackle climate change and other environmental challenges. The share of electricity generated by renewable energy sources has been steadily increasing. In order to cope with the intermittent nature of renewable energy sources, like photovoltaic systems and wind turbines, the electrical demand has to be adjusted to their power generation. To this end, flexible electrical loads are necessary. Moreover, optimization approaches and advanced information and communication technology can help to transform the traditional electricity grid into a smart grid. To shift the electricity consumption in time, electric heating devices, such as heat pumps or electric water heaters, provide significant flexibility. In order to exploit this flexibility, optimization approaches for controlling flexible devices are essential. Most studies in the literature use centralized optimization or uncoordinated decentralized optimization. Centralized optimization has crucial drawbacks regarding computational complexity, privacy, and robustness, but uncoordinated decentralized optimization leads to suboptimal results. In this thesis, coordinated decentralized and hybrid optimization approaches with low computational requirements are developed for exploiting the flexibility of electric heating devices. An essential feature of all developed methods is that they preserve the privacy of the residents. This cumulative thesis comprises four papers that introduce different types of optimization approaches. In Paper A, rule-based heuristic control algorithms for modulating electric heating devices are developed that minimize the heating costs of a residential area. Moreover, control algorithms for minimizing surplus energy that otherwise could be curtailed are introduced. They increase the self-consumption rate of locally generated electricity from photovoltaics. The heuristic control algorithms use a privacy-preserving control and communication architecture that combines centralized and decentralized control approaches. Compared to a conventional control strategy, the results of simulations show cost reductions of between 4.1% and 13.3% and reductions of between 38.3% and 52.6% regarding the surplus energy. Paper B introduces two novel coordinating decentralized optimization approaches for scheduling-based optimization. A comparison with different decentralized optimization approaches from the literature shows that the developed methods, on average, lead to 10% less surplus energy. Further, an optimization procedure is defined that generates a diverse solution pool for the problem of maximizing the self-consumption rate of locally generated renewable energy. This solution pool is needed for the coordination mechanisms of several decentralized optimization approaches. Combining the decentralized optimization approaches with the defined procedure to generate diverse solution pools, on average, leads to 100 kWh (16.5%) less surplus energy per day for a simulated residential area with 90 buildings. In Paper C, another decentralized optimization approach that aims to minimize surplus energy and reduce the peak load in a local grid is developed. Moreover, two methods that distribute a central wind power profile to the different buildings of a residential area are introduced. Compared to the approaches from the literature, the novel decentralized optimization approach leads to improvements of between 0.8% and 13.3% regarding the surplus energy and the peak load. Paper D introduces uncertainty handling control algorithms for modulating electricheating devices. The algorithms can help centralized and decentralized scheduling-based optimization approaches to react to erroneous predictions of demand and generation. The analysis shows that the developed methods avoid violations of the residents\u27 comfort limits and increase the self-consumption rate of electricity generated by photovoltaic systems. All introduced optimization approaches yield a good trade-off between runtime and the quality of the results. Further, they respect the privacy of residents, lead to better utilization of renewable energy, and stabilize the grid. Hence, the developed optimization approaches can help future energy systems to cope with the high share of intermittent renewable energy sources

Demand response through decentralized optimization in residential areas with wind and photovoltaics

A paradigm shift has to be realized in future energy systems with high shares of renewable energy sources. The electrical demand has to react to the fluctuating electricity generation of renewable energy sources. To this end, flexible electrical loads like electric heating devices coupled with thermal storage or electric vehicles are necessary in combination with optimization approaches. In this paper, we develop a novel privacy-preserving approach for decentralized optimization to exploit load flexibility. This approach, which is based on a set of schedules, is referred to as SEPACO-IDA. The results show that our developed algorithm outperforms the other approaches for scheduling based decentralized optimization found in the literature. Furthermore, this paper clearly illustrates the suboptimal results for uncoordinated decentralized optimization and thus the strong need for coordination approaches. Another contribution of this paper is the development and evaluation of two methods for distributing a central wind power profile to the local optimization problem of distributed agents (Equal Distribution and Score-Rank-Proportional Distribution). These wind profile assignment methods are combined with different decentralized optimization approaches. The results reveal the dependency of the best wind profile assignment method on the used decentralized optimization approach

Occupant-Oriented Demand Response with Room-Individual Building Control

In future energy systems with high shares of renewable energy sources, the electricity demand of buildings has to react to the fluctuating electricity generation in view of stability. As buildings consume one-third of global energy and almost half of this energy accounts for Heating, Ventilation, and Air Conditioning (HVAC) systems, HVAC are suitable for shifting their electricity consumption in time. To this end, intelligent control strategies are necessary as the conventional control of HVAC is not optimized for the actual demand of occupants and the current situation in the electricity grid. In this paper, we present the novel multi-zone controller Price Storage Control (PSC) that not only considers room-individual Occupants' Thermal Satisfaction (OTS), but also the available energy storage, and energy prices. The main feature of PSC is that it does not need a building model or forecasts of future demands to derive the control actions for multiple rooms in a building. For comparison, we use an ideal, error-free Model Predictive Control (MPC), a simplified variant without storage consideration (PC), and a conventional hysteresis-based two-point control. We evaluate the four controllers in a multi-zone environment for heating a building in winter and consider two different scenarios that differ in how much the permitted temperatures vary. In addition, we compare the impact of model parameters with high and low thermal capacitance. The results show that PSC strongly outperforms the conventional control approach in both scenarios and for both parameters. For high capacitance, it leads to 22 % costs reduction while the ideal MPC achieves cost reductions of more than 39 %. Considering that PSC does not need any building model or forecast, as opposed to MPC, the results support the suitability of our developed control strategy for controlling HVAC systems in future energy systems.Comment: Paper revisio

Occupant-oriented demand response with multi-zone thermal building control

In future energy systems with high shares of renewable energy sources, the electricity demand of buildings has to react to the fluctuating electricity generation in view of stability. As buildings consume one-third of global energy and almost half of this energy accounts for Heating, Ventilation, and Air Conditioning (HVAC) systems, HVAC are suitable for shifting their electricity consumption in time. To this end, intelligent control strategies are necessary as the conventional control of HVAC is not optimized for the actual demand of occupants and the current situation in the electricity grid. In this paper, we present the novel multi-zone controller Price Storage Control (PSC) that not only considers room-individual Occupants’ Thermal Satisfaction (OTS), but also the available energy storage, and energy prices. The main feature of PSC is that it does not need a building model or forecasts of future demands to derive the control actions for multiple rooms in a building. For comparison, we use an ideal, error-free Model Predictive Control (MPC), a heuristic control approach from the literature (PC), and a conventional hysteresis-based two-point control as upper and lower benchmarks. We evaluate the four controllers in a multi-zone environment for heating a building in winter and consider two different scenarios that differ in how much the permitted temperatures vary. In addition, we compare the impact of model parameters with high and low thermal capacitance. The results show that PSC strongly outperforms the conventional control approach and PC in both scenarios and for both parameters concerning the electricity costs and OTS. For high capacitance, it leads to 22 % costs reduction while the ideal MPC achieves cost reductions of more than 39 %. Considering that PSC does not need any building model or forecast, as opposed to MPC, the results support the suitability of our developed control strategy for controlling HVAC systems in future energy systems

Using Eddy Covariance Sensors to Quantify Carbon Metabolism of Peatlands: A Case Study in Turkey

Net ecosystem exchange (NEE) of carbon dioxide (CO2) was measured in a cool temperate peatland in northwestern Turkey on a continuous basis using eddy covariance (EC) sensors and multiple (non-)linear regression-M(N)LR-models. Our results showed that hourly NEE varied between −1.26 and 1.06 mg CO2 m−2 s−1, with a mean value of 0.11 mg CO2 m−2 s−1. Nighttime ecosystem respiration (RE) was on average measured as 0.23 ± 0.09 mg CO2 m−2 s−1. Two best-fit M(N)LR models estimated daytime RE as 0.64 ± 0.31 and 0.24 ± 0.05 mg CO2 m−2 s−1. Total RE as the sum of nighttime and daytime RE ranged from 0.47 to 0.87 mg CO2 m−2 s−1, thus yielding estimates of gross primary productivity (GPP) at −0.35 ± 0.18 and −0.74 ± 0.43 mg CO2 m−2 s−1. Use of EC sensors and M(N)LR models is one of the most direct ways to quantify turbulent CO2 exchanges among the soil, vegetation and atmosphere within the atmospheric boundary layer, as well as source and sink behaviors of ecosystems

Comparison of different methods of spatial disaggregation of electricity generation and consumption time series

Energy system models involve various input data sets representing the generation, consumption and transport infrastructure of electricity. Especially energy system models with a focus on the transmission grid require time series of electricity feed-in and consumption in a high spatial resolution. In general, there are two approaches to obtain regionalized time series: top-down and bottom-up. In many cases, both methodologies may be combined to aggregate or disaggregate input data. Furthermore, there exist various approaches to assign regionalized feed-in of renewable energy sources and electrical load to the model\u27s grid connection points. The variety in the regionalization process leads to significant differences on a regional scope, even if global values are the same. We develop a methodology to compare regionalization techniques of input data for photovoltaics, wind and electrical load between various models as well as data assignment techniques to the power grid nodes. We further define two invariants to evaluate the outcome of the regionalization process at the NUTS 3 level, one invariant for the annual profiles and one for the installed capacities. This methodology enabled us to compare different regionalization and assignment workflows using simple parameters, without explicit knowledge of grid topology. Our results show that the resolution of the input data and the use of a top-down or a bottom-up approach are the most determinant factors in the regionalization process

Demand Response through decentralized optimization in residential areas with wind and photovoltaics

A paradigm shift has to be realized in future energy systems with high shares of renewable energy sources. The electrical demand has to react to the fluctuating electricity generation of renewables. To this end, flexible electrical loads like electric heating devices and electric vehicles are necessary in combination with optimization approaches. In this paper, we develop a novel privacy-preserving approach for decentralized optimization to exploit load flexibility in residential areas. This approach, which is based on a set of schedules, is referred to as SEPACO-IDA. Compared to the approaches from the literature for decentralized optimization, SEPACO-IDA leads to improvements of between 0.8% and 13.3% regarding the surplus energy and the peak load. Furthermore, this paper illustrates the suboptimal results for uncoordinated decentralized optimization and thus the need for coordination approaches. Another contribution of this paper is the development and evaluation of two methods for distributing a central wind power profile to the local optimization problem of different buildings in a residential area (Equal Distribution and Score-Rank-Proportional Distribution). These wind profile assignment methods are combined with different decentralized optimization approaches. The results reveal the dependency of the best wind profile assignment method on the used decentralized optimization approach

Aggregating load shifting potentials of electric vehicles for energy system models

This paper presents and classifies different methodologies for aggregating the load shifting potentials of several plug-in electric vehicles (EVs) for consideration in energy system models. These approaches range from modeling single charging events to approximation of EV fleets. We conclude that most of the applied approaches show shortages and that their suitability depends on the number of time steps of the energy system model and the EV fleet size. For distributed systems and limited time span, modeling single EVs is suitable and can provide exact results when used in optimization models. For large EV fleets and centralized optimization approaches, approximation methods seem to be appropriate. Aggregating the flexibility of multiple EVs to polytopes exactly describes the solution space for the optimization but is an NP-hard task. Consequently, approximation methods to this so-called Minkowski sum are presented, which may improve the consideration of EVs’ flexibility in energy system models