Adaptive control and dynamic demand response for the stabilization of grid frequency

Over the past few years, there has been a marked increase in the output from wind and solar generation in many countries. High levels of distributed generation provide variable energy and the increasing share of converter-connected plant results in a reduction in system inertia. Consequently, the rate of change of frequency, especially during and after severe faults, becomes more rapid. This thesis describes the use of heat pumps and fridges to provide ancillary services of frequency response so that to continuously balance the supply with demand. A decentralized digital controller namely: Adaptive DeadBeat (ADB) is designed to improve the frequency behaviour in an interconnected power system during and after faults. Simulation results show that the ADB controller can be considered as a contribution of digital control application to improve the frequency behaviour in an interconnected power system with reduced system inertia. The thermal performance of domestic buildings using heat pumps, and of fridges using thermostat temperature control is modelled. A dynamic frequency control (DFC) algorithm is developed to control the power consumption of the load in response to the grid frequency without affecting the overall performance of the load. Then, the dynamic frequency control algorithm is applied to a population of over 10 million aggregated units that represent the availability of load to provide frequency response. A dynamic relationship between the temperature and pre-defined trigger frequencies is given to ensure smooth and gradual load switching. A simulation is undertaken by connecting the controllable heat pumps to the reduced dynamic model of the Great Britain power system. Following a loss of 1,800 MW of generation, it is shown that the DFC reduces 1,000 MW of heat pumps demand and hence the frequency deviation is maintained within acceptable limits. In addition, a population of heat pumps and fridges are connected to the electrodynamic master model of the GB power system that is at present used by the ii GB transmission system operator, National Grid plc. Results show that the aggregated domestic heat pumps and fridges controlled by the DFC algorithm can participate in the Firm Frequency Response (FFR) service and provide rapid frequency response to the GB power system, mimicking the behaviour of the frequency-sensitive generators

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

Domestic Space Heating Load Management in Smart Grid - Potential Benefits and Realization

In future power systems, intermittent renewable generation sources are expected to have a considerable segment in the total generation assortment. Given the inconsistency and unpredictability of intermittent renewable energy sources, the fast growing integration of intermittent renewable generation could negatively affect the operations of power system. Since demand response (DR) is a flexible load shaping tool, it is viewed as a practicle solution to enhance the overall system efficiency in future smart grids. The overall objective of this dissertation is to evaluate the possible advantages of responsive domestic heating, ventilation, and air conditioning (HVAC) loads for DR applications and the development of practical frameworks to realize them. Due to its considerable share in energy consumption profile and operational flexibility, the DR treatment is restricted to the HVAC load. The DR applications include the minimization of customer energy cost and increased utilization of intermittent generation while taking into account customers' thermal comfort. The goal of this dissertation is divided into three major tasks so as to describe the DR benefits for various applications. A comprehensive assessment of HVAC DR potential for up/down ramping is suggested in the first task. The second task proposes generic frameworks for HVAC load management that are directed towards minimizing customer energy payments while taking customer's preferences into consideration. Finally, the last task establishes tools for increased utilization of wind generation by optimally managing the cyclic operation of responsive HVAC loads. To accomplish this dissertation objective, simulations are conducted using the proposed frameworks for Finnish systems. The following significant deductions are indicated in the results. The flexibility to unleash DR for up/down ramping is affected by the heat demand requirements, while upward DR is strongly limited by HVAC power ramping capability and allowed thermal comfort limits. Furthermore, utilization of DR will greatly shrink customer energy payments which mainly depends on the permissible indoor temperature deviation. The monetary savings are value added when DR is jointly activated in both energy and balancing market using the proposed model. Additionally, it is revealed that joint optimization of DR and RTTR will attain greater utilization of wind generation in distribution networks as weighed against DR activation alone. The developed models can be utilized by power system operators and stake holders to enhance the system operation. Consequently, the developed tools will help to achieve a better understanding of HVAC DR potential and advantages and will act as support to maximize the DR enrolment at the end user level

Opening of Ancillary Service Markets to Distributed Energy Resources: A Review

Electric power systems are moving toward more decentralized models, where energy generation is performed by small and distributed power plants, often from renewables. With the gradual phase out from fossil fuels, however, Distribution Energy Resources (DERs) are expected to take over in the provision of all regulation services required to operate the grid. To this purpose, the opening of national Ancillary Service Markets (ASMs) to DERs is considered an essential passage. In order to allow this transition to happen, current opportunities and barriers to market participation of DERs must be clearly identified. In this work, a comprehensive review is provided of the state-of-the-art of research on DER integration into ASMs. The topic at hand is analyzed from different perspectives. First, the current situation and main trends regarding the reformation processes of national ASMs are analyzed to get a clear picture of the evolutions expected and adjustment required in the future, according to the scientific community. Then, the focus is moved to the strategies to be adopted by aggregators for the effective control and coordination of DERs, exploring the challenges posed by the uncertainties affecting the problem. Coordination schemes between transmission and distribution system operators, and the implications on the grid infrastructure operation and planning, are also investigated. Finally, the review deepens the control capabilities required for DER technologies to perform the needed control actions

Optimized Operation of Local Energy Community Providing Frequency Restoration Reserve

In order to unlock the maximum flexibility potential of all levels in the power system, distribution-network-located flexible energy resources (FERs) should play an important role in providing system-wide ancillary services. Frequency reserves are an example of system-wide ancillary services. In this regard, this paper deals with the optimal operation of a local energy community (LEC) located in the distribution network. The LEC is proposed to participate in providing manual frequency restoration reserves (mFRR) or tertiary reserves. In addition, the community is supposed to have a number of electric vehicles (EVs) and a battery energy storage system (BESS) as FERs. The scheduling of the community, which is fully compliant with the existing balancing market structure, comprises two stages. The first stage is performed in day-ahead, in which the energy community management center (ECMC) estimates the amount of available flexible capacities for mFRR provision. In this stage, control parameters are deployed by the ECMC in order to control the offered flexibility of the BESS. In the second stage, the real-time scheduling of the community is performed for each hour, taking into account the assigned and activated amount of reserve power. The target of the real-time stage is to maximize the community’s profit. Finally, the model is implemented utilizing a case study considering different day-ahead control parameters of the BESS. The results demonstrate that the proposed control parameters adopted in the day-ahead stage considerably affect the realtime profitability of the LEC. Moreover, according to the simulation results, participating in the mFRR market can bring additional profits for the LEC.© Firoozi, Hooman; Khajeh, Hosna; Laaksonen, Hannu. This work is licensed under a Creative Commons Attribution 4.0 License.fi=vertaisarvioitu|en=peerReviewed

Future Smart Grid Systems

This book focuses on the analysis, design and implementation of future smart grid systems. This book contains eleven chapters, which were originally published after rigorous peer-review as a Special Issue in the International Journal of Energies (Basel). The chapters cover a range of work from authors across the globe and present both the state-of-the-art and emerging paradigms across a range of topics including sustainability planning, regulations and policy, estimation and situational awareness, energy forecasting, control and optimization and decentralisation. This book will be of interest to researchers, practitioners and scholars working in areas related to future smart grid systems