Project

INCITE: Innovative controls for renewable source integration into smart energy systems

Goal: INCITE is Marie Sklodowska-Curie European Training Network (ITN-ETN) funded by the HORIZON 2020 Programme that brings together experts on control and power systems, from academia and industry with the aim of training fourteen young researchers capable of providing innovative control solutions for the future electrical networks.
New smart meters, distributed generation, renewable energy sources and the concern about the environment are redefining electrical networks. Now, both consumers and generators are active agents, capable of coordinating the power exchange in the electrical grids depending on multiple factors. To take full advantage of the new electrical networks, it is necessary a coordinated and harmonic interaction of the all actors in the network. Control algorithms are intended for this purpose; to act at several levels to conduct the electrical power exchange and improve efficiency, reliability and resilience of the network. INCITE seeks new control algorithms with an integral view of the future electrical networks, covering aspects like energy management, stability of electrical variables, monitoring and communication implementation, energy storage, among others.
http://www.incite-itn.eu

Date: 1 December 2015 - 30 November 2019

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Nikolaos Sapountzoglou
added a research item
The evolution of the conventional power systems to smart grids has changed the way to conceive and operate them. The part of the grid evolving the most is the distribution grid where the installation of additional sensors and actuators has increased its observability and controllability. These have enabled the development of more accurate and automated processes including some critical ones such as the fault detection, isolation and restoration techniques. In this direction, unconventional methods, e.g. artificial intelligence, have been increasing in popularity over the last years. In this paper, fault location and fault classification methods are reviewed for both medium–voltage and the until recently unexplored case of low–voltage distribution grids. Different methods applied for both fault location and fault classification are being classified by the implemented technique. Such methods are explained and analyzed providing the main advantages and disadvantages of each category. Additionally, the research trends in both fields are analyzed and state–of–the–art methods from each category are thoroughly compared. Finally, the research gaps are identified.
Wicak Ananduta
added a research item
This book describes the development of innovative non-centralized optimization-based control schemes to solve economic dispatch problems of large-scale energy systems. Particularly, it focuses on communication and cooperation processes of local controllers, which are integral parts of such schemes. The economic dispatch problem, which is formulated as a convex optimization problem with edge‐based coupling constraints, is solved by using methodologies in distributed optimization over time-varying networks, together with distributed model predictive control, and system partitioning techniques. At first, the book describes two distributed optimization methods, which are iterative and require the local controllers to exchange information with each other at each iteration. In turn, it shows that the sequence produced by these methods converges to an optimal solution when some conditions, which include how the controllers must communicate and cooperate, are satisfied. Further, it proposes an information exchange protocol to cope with possible communication link failures. Finally, the proposed distributed optimization methods are extended to the cases with random communication networks and asynchronous updates. Overall, this book presents a set of improved predictive control and distributed optimization methods, together with a rigorous mathematical analysis of each proposed algorithms. It describes a comprehensive approach to cope with communication and cooperation issues of non-centralized control schemes and show how the improved schemes can be successfully applied to solve the economic dispatch problems of large-scale energy systems.
Wicak Ananduta
added 2 research items
A multi-agent optimization problem motivated by the management of energy systems is discussed. The associated cost function is separable and convex although not necessarily strongly convex and there exist edge-based coupling equality constraints. In this regard, we propose a distributed algorithm based on solving the dual of the augmented problem. Furthermore, we consider that the communication network might be time-varying and the algorithm might be carried out asynchronously. The time-varying nature and the asynchronicity are modeled as random processes. Then, we show the convergence and the convergence rate of the proposed algorithm under the aforementioned conditions.
A non-centralized model predictive control (MPC) scheme for solving an economic dispatch problem of electrical networks is proposed in this paper. The scheme consists of two parts. The first part is an event-triggered repartitioning method that splits the network into a fixed number of non-overlapping sub-systems (microgrids). The objective of the repartitioning procedure is to obtain self-sufficient microgrids, i.e., those that can meet their local loads using their own generation units. However, since the algorithm does not guarantee that all the resulting microgrids are self-sufficient, the microgrids that are not self-sufficient must then form a coalition with some of their neighboring microgrids. This process becomes the second part of the scheme. By performing the coalition formation, we can decompose the economic dispatch problem of the network into coalition-based sub-problems such that each subproblem is feasible. Furthermore, we also show that the solution obtained by solving the coalition-based sub-problems is a feasible but sub-optimal solution to the centralized problem. Additionally, some numerical simulations are also carried out to show the effectiveness of the proposed method.
Wicak Ananduta
added a research item
A multi-agent optimization problem motivated by the management of energy systems is discussed. The associated cost function is separable and convex although not necessarily strongly convex and there exist edge-based coupling equality constraints. In this regard, we propose a distributed algorithm based on solving the dual of the augmented problem. Furthermore, we consider that the communication network might be time-varying and the algorithm might be carried out asynchronously. The time-varying nature and the asynchronicity are modeled as random processes. Then, we show the convergence and the convergence rate of the proposed algorithm under the aforementioned conditions.
Wicak Ananduta
added a research item
In this paper, we propose a distributed model predictive control (MPC) scheme for economic dispatch of energy systems with a large number of active components. The scheme uses a distributed optimization algorithm that works over random communication networks and asynchronous updates, implying the resiliency of the proposed scheme with respect to communication problems, such as link failures, data packet drops, and delays. The distributed optimization algorithm is based on the augmented Lagrangian approach, where the dual of the considered convex economic dispatch problem is solved. Furthermore, in order to improve the convergence speed of the algorithm, we adapt Nesterov's accelerated gradient method and apply the warm start method to initialize the variables. We show through numerical simulations of a well-known case study the performance of the proposed scheme.
Unnikrishnan Raveendran Nair
added a research item
Increasing integration of photovoltaic (PV) system in electric grids cause congestion during peak power feed-in. Battery storage in PV systems increases self-consumption, for consumer's benefit. However with conventional maximising self consumption (MSC) control for battery scheduling, the issue of grid congestion is not addressed. The batteries tend to be fully charged early in the day and peak power is still fed-in to grid. This also increases battery degradation due to increased dwell time at high state of charge (SOC) levels. To address this issue, this work uses a model predictive control (MPC) for scheduling in PV system with battery storage to achieve multiple objectives of minimising battery degradation, grid congestion, while maximising self consumption. In order to demonstrate the improvement, this work compares the performances of MPC and MSC schemes when used in battery scheduling. The improvement is quantified through performance indices like self consumption ratio, peak power reduction and battery capacity fade for one-year operation. An analysis on computation burden and maximum deterioration in MPC performance under prediction error is also carried out. It is concluded that, compared to MSC, MPC achieves similar self consumption in PV systems while also reducing grid congestion and battery degradation.
Jesus Lago
added a research item
Seasonal thermal energy storage systems (STESSs) can shift the delivery of renewable energy sources and mitigate their uncertainty problems. However, to maximize the operational profit of STESSs and ensure their long-term profitability, control strategies that allow them to trade on wholesale electricity markets are required. While control strategies for STESSs have been proposed before, none of them addressed electricity market interaction and trading. In particular, due to the seasonal nature of STESSs, accounting for the long-term uncertainty in electricity prices has been very challenging. In this article, we develop the first control algorithms to control STESSs when interacting with different wholesale electricity markets. As different control solutions have different merits, we propose solutions based on model predictive control and solutions based on reinforcement learning. We show that this is critical since different markets require different control strategies: MPC strategies are better for day-ahead markets due to the flexibility of MPC, whereas reinforcement learning (RL) strategies are better for real-time markets because of fast computation times and better risk modeling. To study the proposed algorithms in a real-life setup, we consider a real STESS interacting with the day-ahead and imbalance markets in The Netherlands and Belgium. Based on the obtained results, we show that: 1) the developed controllers successfully maximize the profits of STESSs due to market trading and 2) the developed control strategies make STESSs important players in the energy transition: by optimally controlling STESSs and reacting to imbalances, STESSs help to reduce grid imbalances. Index Terms-Demand response, electricity markets, model predictive control (MPC), optimal control, reinforcement learning (RL), seasonal storage systems.
Wicak Ananduta
added a research item
A non-centralized model predictive control (MPC) scheme for solving an economic dispatch problem of electrical networks is proposed in this paper. The scheme consists of two parts. The first part is an event-triggered repartitioning method that splits the network into a fixed number of non-overlapping sub-systems {(microgrids)}. The objective of the repartitioning procedure is to obtain self-sufficient microgrids, i.e., those that can meet their local loads using their own generation units. However, since the algorithm does not guarantee that all the resulting microgrids are self-sufficient, the microgrids that are not self-sufficient must then form a coalition with some of their neighboring microgrids. This process becomes the second part of the scheme. By performing the coalition formation, we can decompose the economic dispatch problem of the network into coalition-based sub-problems such that each subproblem is feasible. Furthermore, we also show that the solution obtained by solving the coalition-based sub-problems is a feasible but sub-optimal solution to the centralized problem. Additionally, some numerical simulations are also carried out to show the effectiveness of the proposed method.
Nikolaos Sapountzoglou
added a research item
Power outages in electrical grids can have very negative economic and societal impacts rendering fault diagnosis paramount to their secure and reliable operation. In this paper, deep neural networks are proposed for fault detection and location in low-voltage smart distribution grids. Due to its key properties, the proposed method solves some of the drawbacks of the existing literature methods, namely a method that: 1) is not limited by the grid topology; 2) is branch-independent; 3) can localize faults even with limited data; 4) is the first to accurately detect and localize high-impedance faults in the low-voltage distribution grid. The generalizability of the method derives from the non-grid specific nature of the inputs that it requires, inputs that can be obtained from any grid. To evaluate the proposed method, a real low-voltage distribution grid in Portugal is considered and the robustness of the method is tested against several disturbances including large fault resistance values (up to 1000 Ω). Based on the case study, it is shown that the proposed methodology outperforms conventional fault diagnosis methods: it detects faults with 100% accuracy, identifies faulty branches with 83.5% accuracy, and estimates the exact fault location with an average error of less than 11.8%. Finally, it is also shown that: 1) even when reducing the available measurements to the bare minimum, the accuracy of the proposed method is only decreased by 4.5%; 2) while deep neural networks usually require large amounts of data, the proposed model is accurate even for small dataset sizes.
Nikolaos Sapountzoglou
added a research item
Results of a fault detection and localization algorithm for low voltage smart distribution grids
Unnikrishnan Raveendran Nair
added a research item
Model predictive control (MPC) facilitates online optimal resource scheduling in electrical networks, thermal systems, water networks, process industry to name a few. In electrical systems, the capability of MPC can be used not only to minimise operating costs but also to improve renewable energy utilisation and energy storage system degradation. This work assesses the application of MPC for energy management in an islanded microgrid with PV generation and hybrid storage system composed of battery, supercapacitor and regenerative fuel cell. The objective is to improve the utilisation of renewable generation, the operational efficiency of the microgrid and the reduction in rate of degradation of storage systems. The improvements in energy scheduling, achieved with MPC, are highlighted through comparison with a heuristic based method, like Fuzzy inference. Simulated behaviour of an islanded microgrid with the MPC and fuzzy based energy management schemes will be studied for the same. Apart from this, the study also carries out an analysis of the computational demand resulting from the use of MPC in the energy management stage. It is concluded that, compared to heuristic methods, MPC ensures improved performance in an islanded microgrid.
Wicak Ananduta
added a research item
We consider the economic dispatch problem for a day-ahead, peer-to-peer (P2P) electricity market of prosumers (i.e., energy consumers who can also produce electricity) in a distribution network. In our model, each prosumer has the capability of producing power through its dispatchable or non- dispatchable generation units and/or has a storage energy unit. Furthermore, we consider a hybrid main grid & P2P market in which each prosumer can trade power both with the main grid and with (some of) the other prosumers. First, we cast the economic dispatch problem as a noncooperative game with coupling constraints. Then, we design a fully-scalable algorithm to steer the system to a generalized Nash equilibrium (GNE). Finally, we show through numerical studies that the proposed methodology has the potential to ensure safe and efficient operation of the power grid.
Nikolaos Sapountzoglou
added a research item
A fault localization method for single-phase to ground short-circuit (SC) faults in low voltage (LV) smart distribution grids is presented in this paper. Both the use of rms voltage phase measurements and an analysis of symmetrical components of the voltage were investigated and compared in this study. Phase measurements were found to be more suitable for single-phase to ground faults. The described method is a three-step process beginning with the identification of the faulty branch, followed by the localization of the sector in which the fault occurred and concluding with the estimation of the fault distance from the beginning of the feeder. Fault resistance values of 0.1, 1, 5, 10, 50, 100, 500 and 1000 Ω were tested. An heterogeneity analysis was performed to test the effect of the use of various conductors on the method. Faults in all three phases were implemented and simulated on a real case of a semi-rural LV distribution network of Portugal, provided by Efacec. Finally, the method presented an average estimation accuracy of 89.33% and an increased accuracy of 93.11% for low impedance faults (up to 10 Ω of fault resistance).
Konstantinos Kotsalos
added a research item
The large number of small scale Distributed Energy Resources (DER) such as Electric Vehicles (EVs), rooftop photovoltaic installations and Battery Energy Storage Systems (BESS), installed along distribution networks, poses several challenges related to power quality, efficiency, and reliability. Concurrently, the connection of DER may provide substantial flexibility to the operation of distribution grids and market players such as aggregators. This paper proposes an optimization framework for the energy management and scheduling of operation for Low Voltage (LV) networks assuring both admissible voltage magnitudes and minimized line congestion and voltage unbalances. The proposed tool allows the utilization and coordination of On-Load Tap Changer (OLTC) distribution transformers, BESS, and flexibilities provided by DER. The methodology is framed with a multi-objective three phase unbalanced multi-period AC Optimal Power Flow (MACOPF) solved as a nonlinear optimization problem. The performance of the resulting control scheme is validated on a LV distribution network through multiple case scenarios with high microgeneration and EV integration. The usefulness of the proposed scheme is additionally demonstrated by deriving the most efficient placement and sizing BESS solution based on yearly synthetic load and generation data-set. A techno-economical analysis is also conducted to identify optimal coordination among assets and DER for several objectives.
Adedotun J Agbemuko
added 2 research items
Despite attempts to increase the active power capability of vector-controlled voltage source converters (VSCs) connected to very weak grids, the interaction between the control dynamics and physical system is not completely understood. The result is often complex strategies that are difficult to implement. This paper proposes an intuitive modification of the VSC control based on physical considerations and dynamics of existing control. Several physical variables as seen from the point of common coupling (PCC) are found to contribute to the detrimental behavior of the VSC under weak connections. Hence, eliminating the impacts of these variables through feedforward eliminates their influence and significantly improves the active power capability. Notably, the basic structure of the vector controlled VSC is kept and its output-impedance is effectively reshaped. The proposed modification is validated through nonlinear time-domain simulations in MATLAB/Simulink Simscape Power System and results demonstrate the simplicity and intuitiveness of the modified structure.
Adedotun J Agbemuko
added 5 research items
An analysis of the impact of network and control in an interconnected system have been studied in this paper. Particularly, a simplified integrated approach is considered by modelling the entire network of devices and control in a multivariable feedback manner. In this way, the contribution of each device (via its impedance and/or control) and the network connection to responses observed can be understood in a tractable manner. The presented approach potentially provides a method that allows for manipulating observed responses via multivariable approaches. Results show the efficacy of the approach in providing a system perspective to dynamic responses by taking into account contributions. Analysis is carried out in frequency domain and validated with the physical model of the system built in SimscapeTM Power SystemsTM and control systems in MATLAB/Simulink®.
This paper presents an impedance-based interaction and stability analysis of multi-terminal HVDC systems. First, an analytical derivation procedure is presented to obtain the feedback impedance models of a voltage source converter (VSC) as a subsystem, with particular interest on the DC side impedance. Subsequently, in addition to the impedance models of other subsystems, an impedance aggregation method is applied to derive the dynamic closed-loop impedance matrix of the system considering the interconnected structure of the grid. Given the closed-loop impedance matrix, multi-input multi-output (MIMO) relative gain array (RGA) formulation is proposed to analyse nodal interactions between converters and the network. Furthermore, the impedance ratio matrix is proposed for HVDC stability analysis based on impedance models. Remarkably, these formulations are derived compactly without any knowledge of the internal states of any converter and therefore allows the ease of interoperability analysis. Finally, the impact of control architecture and strategy on the dynamic responses, interaction, and stability analysis from an impedance perspective is conducted as a case study. The analysis is done in the frequency domain and validated with the physical model of a three-terminal DC test grid built in SimscapeTM MATLAB/Simulink®.
A progressive interconnection of existing HVDC links to form grids and the development of completely new HVDC grids from different vendors are expected shortly. One of the current challenges of such endeavour is unintended interactions due to independently designed controllers. This article proposes a design methodology for decentralized controllers to mitigate such interactions in multi-vendor voltage source converter (VSC)-HVDC grids. The approach presented relies on the unique stand-alone input-output impedance transfer function of each VSC, and the global impedance transfer function as seen from each terminal after interconnection with other VSCs. Subsequently, network-level controllers are designed by attempting to match the global responses at selected locations based on a novel interaction analysis, to the unique transfer function model of the vendors at the corresponding location. This approach reduces the entire problem to an impedance matching problem. We demonstrate the efficacy and flexibility of both the methodology and the designed controllers in mitigating interactions due to the independent design of VSC controllers through nonlinear simulations on a four-terminal droop controlled HVDC grid.
Nikolaos Sapountzoglou
added a research item
In this paper, a gradient boosting tree model is proposed to detect, identify and localize single-phase-to-ground and three-phase faults in low voltage (LV) smart distribution grids. The proposed method is based on gradient boosting trees and considers branch-independent input features to be generalizable and applicable to different grid topologies. Particularly, as it is shown, the method can be estimated in a specific grid topology and be employed in a different one. To test the algorithm, the method is evaluated in a simulated real LV distribution grid of Portugal. In this case study, different fault resistances, fault locations and hours of the day are considered. In detail, the algorithm is evaluated at eighteen fault resistance values between 0.1 and 1000 ; similarly, nine fault locations are considered within each one of the 32 sectors of the grid and the faults are simulated across different hours of a day. The developed algorithm showed promising results in both out-of-sample branch and fault resistance data especially for fault detection, demonstrating a maximum fault detection error of 0.72%.
Adedotun J Agbemuko
added a research item
The conventional power system is rapidly evolving due to changes in energy landscape and regions around the world are beginning to depend heavily on renewable energy sources, and wind energy is one of the most viable. Due to increasing public opposition and other issues, energy providers are starting to explore offshore locations where wind energy is seemly abundant. However, such locations bring about peculiar technical challenges due to location and distance to load consumption centers. Several proposals have been made over the last decade to solving these issues and most proposals involve the use of VSC-HVDC technologies (links and multi-terminals) for integration to significantly improve security of supply. These links will invariably be from several different vendors, manufacturers, and potentially different network operators, each with different experiences, and design strategies. Given the low maturity of VSC-HVDC technologies compared to the conventional LCC-HVDC, manufacturers are reluctant to share information about devices and interactions between multi-vendor systems are expected. The extent of potential interactions and interoperability issues are only starting to emerge. There is a lack of knowledge on analytical methods for detection of interactions, interoperability issues, and mitigation. This paper aims to shed some light on how analytical methods can be applied to mitigate interactions between multi-vendor HVDC grids and improve interoperability without requiring knowledge of the internal structure of converters. The responsibility is clearly on the system operator to keep the system behaviour acceptable at all times during operation. Therefore, this paper is devoted to system-level analysis and control design to improve robustness to disturbances, based on system-level input-output behaviour without requiring internal knowledge of converters.
Jesus Lago
added 3 research items
Due to the increasing integration of renewable sources in the electrical grid, electricity generation is expected to become more uncertain. In this context, seasonal thermal energy storage systems (STESSs) are key to shift the delivery of renewable energy sources and tackle their uncertainty problems. In this paper, we propose an optimal controller for STESSs that, using reinforcement learning, builds bidding functions for the day-ahead market. In detail, considering that there is an uncertain energy demand that the STESS has to satisfy, the controller buys energy in the day-ahead market so that the uncertain demand is satisfied while the profits are maximized. Since prices are low during periods of large renewable energy generation (and vice versa), maximizing the profit of a STESS indirectly shifts the delivery of renewable energy to periods of high energy demand while reducing their uncertainty problems. To evaluate the proposed algorithm, we consider a real STESS providing different yearly-demand levels; then, we compare the performance of the controller to the theoretical upper bound, i.e. the optimal cost of buying energy given perfect knowledge of the demand and prices. The results indicate that the proposed controller performs reasonably well: despite the large uncertainty in prices and demand, the proposed controller obtains 70%-50% of the maximum gains given by the theoretical bound.
To mitigate the effects of the intermittent generation of renewable energy sources, reliable and efficient energy storage is critical. Since nearly 80% of households energy consumption is destined to water and space heating, thermal energy storage is particularly important. In this context, we propose and validate a new model for one of the most efficient heat storage systems: stratified thermal storage tanks. The novelty of the model is twofold: first, unlike the non-smooth models from the literature, it identifies the mixing and buoyancy dynamics using a smooth and continuous function. This smoothness property is critical to efficiently integrate thermal storage vessels in optimization and control problems. Second, unlike models from literature, it considers two types of buoyancy: slow, linked to naturally occurring buoyancy, and fast, associated with charging/discharging effects. As we show, this distinction is paramount to identify accurate models. To show the relevance of the model, we consider a real tank that can satisfy heat demands up to 100 kW. Using real data from this vessel, we validate the proposed model and show that the estimated parameters correctly identify the physical properties of the vessel. Then, we employ the model in a control problem where the vessel is operated to minimize the cost of providing a given heat demand and we compare the model performance against that of a non-smooth model from literature. We show that: (1)the smooth model obtains the best optimal solutions; (2)its computation costs are 100 times cheaper; (3)it is the best alternative for use in real-time model- based control strategies, e.g. model predictive control.
Konstantinos Kotsalos
added a research item
The last decade the continuous integration of Distributed Energy Resources (DER) along distribution networks, follows an uncoordinated fashion posing manifold technical challenges for the operation of the grid. However, DER could be actively incorporated in the operation of distribution networks providing certain flexibility. Such DER flexibility is generally associated with temporal shifting of energy (i.e. for consumption or injection). This work assesses the active management of multiple DER in a coordinated manner in Low Voltage (LV) distribution networks. The flexible use of DER is hereby regarded for the provision of support to the LV grid, mainly for voltage regulation and phase balancing, line congestions management as well as to ensure rated power for the transformer of the secondary substation. A control framework is proposed for the management of DER flexibilities, which relies on a three phase multi-period optimal power flow. A study based on a real-IEEE benchmark-LV distribution network is presented to demonstrate and quantify the importance of active management of DER such as battery storage system, electric vehicles (with vehicle to grid operation) and microgeneration.
Konstantinos Kotsalos
added a research item
This paper proposes a secondary substation centered control approach which deals with the effective coordination of multiple Distributed Energy Resources (DER) connected along the LV grid. The presented tool manages and schedules the flexibilities (i.e. active and reactive power injection or absorption) provided by DER in order to address overvoltages or voltage sags, while minimizing operational costs. The methodology is based on a three-phase multiperiod optimal power flow. The proposed control scheme is assessed within an IEEE-LV benchmarked network considering different scenarios of Electric Vehicle and microgeneration integration as well as a centralized battery owned by the grid operator.
Unnikrishnan Raveendran Nair
added 2 research items
Power converters in grid connected systems are required to have fast response to ensure the stability of the system. The standard PI controllers used in most power converters are capable of fast response but with significant overshoot. In this paper a hybrid control technique for power converter using a reset PI+CI controller is proposed. The PI+CI controller can overcome the limitation of its linear counterpart (PI) and ensure a fast flat response for power converter. The design, stability and cost of feedback analysis for a DC-DC boost converter employing a PI + CI controller is explored in this work. The simulation and experimental results which confirm the fast, flat response will be presented and discussed.
Wicak Ananduta
added a research item
Distributed energy management of interconnected microgrids that is based on Model Predictive Control (MPC) relies on the cooperation of all agents (microgrids). This paper discusses the case in which some of the agents might perform one type of adversarial actions (attacks) and they do not comply with the decisions computed by performing a distributed MPC algorithm. In this regard, these agents could obtain a better performance at the cost of degrading the performance of the network as a whole. A resilient distributed method that can deal with such issues is proposed in this paper. The method consists of two parts. The first part is to ensure that the decisions obtained from the algorithm are robustly feasible against most of the attacks with high confidence. In this part, we formulate the economic dispatch problem, taking into account the attacks as a chance-constrained problem and employ a two-step randomization-based approach to obtain a feasible solution with a predefined level of confidence. The second part consists in the identification and mitigation of the adversarial agents, which utilizes hypothesis testing with Bayesian inference and requires each agent to solve a mixed-integer problem to decide the connections with its neighbors. In addition, an analysis of the decisions computed using the stochastic approach and the outcome of the identification and mitigation method is provided. The performance of the proposed approach is also shown through numerical simulations.
Nikolaos Sapountzoglou
added 2 research items
In this paper, a fault detection and localization method for a Low Voltage (LV) distribution grid are presented. Two fault detection approaches were examined both suitable only for low impedance faults (up to 10 Ω of fault resistance). The first one was based on current measurements at the beginning of the feeder and the second one was based on the highest voltage drop across the feeder branches. The localization method was based solely on nodal rms voltage measurements across the grid. The localization method was divided in three steps: a) faulty branch identification, b) faulty sector localization and c) fault distance estimation. Two categories of faults were examined: single-phase to ground short-circuit (SC) faults and three-phase SC faults. Faults were divided in two major categories: a) faults in the beginning of a branch and b) faults in the middle or towards the end of a branch. Additionally, in order to study the effects of loads and microgeneration units, four different hours in a day were chosen. For all of the above cases both low and high impedance faults were studied with fault resistance values ranging from 0.1 Ω to 1 kΩ. Finally, a preliminary study with less available measurements was made and presented in this paper. The results have been validated by simulation means on a real semi-rural LV distribution network of Portugal.
A fault localization method for single-phase to ground short-circuit (SC) faults in low voltage (LV) smart distribution grids is presented in this paper. Both the use of rms voltage phase measurements and an analysis of symmetrical components of the voltage were investigated and compared in this study. Phase measurements were found to be more suitable for single-phase to ground faults. The described method is a three-step process beginning with the identification of the faulty branch, followed by the localiza-tion of the sector in which the fault occurred and concluding with the estimation of the fault distance from the beginning of the feeder. Fault resistance values of 0.1, 1, 5, 10, 50, 100, 500 and 1000 Ω were tested. An heterogeneity analysis was performed to test the effect of the use of various conductors on the method. Faults in all three phases were implemented and simulated on a real-case of a semi-rural LV distribution network of Portugal, provided by Efacec. Finally, the method presented an average estimation accuracy of 89.33% and an increased accuracy of 93.11% for low impedance faults (up to 10 Ω of fault resistance).
Thibault Péan
added 2 research items
Model predictive controllers (MPC) have shown great potential for activating the energy flexibility of thermal loads, especially in buildings equipped with heat pump systems. In this work, an MPC controller is developed and tested within a co-simulation framework which couples an optimization software with a dynamic building simulation tool. The development phase is described in detail, in particular the methods to obtain simplified models to be used by the controller. The building envelope and the heat pump performance (based on experimental data) were thus modelled, both in heating and cooling seasons. Three different objective functions of the MPC are tested on a study case consisting of a Spanish residential building: promising results are obtained when the controller aims at minimizing operational costs (savings of 13–29%) or CO2 marginal emissions (savings of 19–29%). The development efforts, the required tuning and sensitivity of the MPC algorithm parameters, the adaptations needed between the cooling and heating operations are also discussed and put into perspective with the obtained benefits in terms of savings, comfort and load-shifting.
Thermal mass of buildings as well as domestic hot water tanks represent interesting sources of thermal energy storage readily available in the existing building stock. To exploit them to their full potential, advanced control strategies and a coupling to the power grid with heat pump systems represent the most promising combination. In this work, model predictive control (MPC) strategies are developed and tested in a semi-virtual environment laboratory setup: a real heat pump is operated from within a controlled climate chamber, and coupled with the loads of a virtual building, i.e. a detailed dynamic building simulation tool. Different MPC strategies are tested in this laboratory setup, with the goals to minimize either the delivered thermal energy to the building, the operational costs of the heat pump, or the CO2 emissions related to the heat pump use. The results highlight the ability of the MPC controller to perform load-shifting by charging the thermal energy storages at favorable times, and the satisfactory performance of the control strategies is analyzed in terms of different indicators such as costs, comfort, carbon footprint, energy flexibility. The practical challenges encountered during the implementation with a real heat pump are also discussed and provide additional valuable insights.
Wicak Ananduta
added 2 research items
Economic dispatch of interconnected microgrids that is based on distributed model predictive control (DMPC) requires the cooperation of all agents (microgrids). This paper discusses the case in which some of the agents might not comply with the decisions computed by performing a DMPC algorithm. In this regard, these agents could obtain a better performance at the cost of degrading the performance of the network as a whole. A resilient distributed method that can deal with such issues is proposed and studied in this paper. The method consists of two parts. The first part is to ensure that the decisions obtained from the algorithm are robustly feasible against most of the attacks with high confidence. In this part, we employ a two-step randomization-based approach to obtain a feasible solution with a predefined level of confidence. The second part consists in the identification and mitigation of the adversarial agents, which utilizes hypothesis testing with Bayesian inference and requires each agent to solve a mixed-integer problem to decide the connections with its neighbors. In addition, an analysis of the decisions computed using the stochastic approach and the outcome of the identification and mitigation method is provided. The performance of the proposed approach is also shown through numerical simulations.
In this paper, we propose a decentralized model predictive control (MPC) method as the energy management strategy for a large-scale electrical power network with distributed generation and storage units. The main idea of the method is to periodically repartition the electrical power network into a group of self-sufficient interconnected microgrids. In this regard, a distributed graph-based partitioning algorithm is proposed. Having a group of self-sufficient microgrids allows the decomposition of the centralized dynamic economic dispatch problem into local economic dispatch problems for the microgrids. In the overall scheme, each microgrid must cooperate with its neighbors to perform repartitioning periodically and solve a decentralized MPC-based optimization problem at each time instant. In comparison to the approaches based on distributed optimization, the proposed scheme requires less intensive communication since the microgrids do not need to communicate at each time instant, at the cost of suboptimality of the solutions. The performance of the proposed scheme is shown by means of numerical simulations with a well-known benchmark case.
Jesus Lago
added a research item
Urban building energy models (UBEMs) are expected to play a key role in the integrated assessment of sustainability measures on both district and city level. However, due to limited availability of data sources, those models are often created through an archetype approach, which is a deterministic method to allocate building envelope characteristics to building groups. Unfortunately, this deterministic approach may underestimate the variability of the existing building stock, which is important when designing district energy systems to optimise the location of production and storage units within the system. In contrast to the deterministic approach, this work presents a new probabilistic approach to allocate building envelope characteristics within UBEMs that in combination with stochastic occupants enables to include the variability of existing districts. A thorough comparison of the deterministic and the probabilistic method is established for 820 buildings of the Boxbergheide district in Genk by performing dynamic energy simulations in the IDEAS Modelica library. For the studied district, a probabilistic building envelope characterisation with standard occupants increases the coefficient of variation (CV) on the energy demand for space heating, compared to a deterministic approach with standard occupants, from 17.8% to 46.4%. Including a probabilistic building envelope characterisation increases the variability on the energy demand for space heating to a larger extent than including stochastic occupants, which increases the CV to only 29.6%.
Konstantinos Kotsalos
added 2 research items
The goal of this work is to propose a tool that optimizes the operational planning of the Low Voltage (LV) grid at the day-ahead stage. The multiple Distributed Energy Resources (DER) are incorporated within a three-phase Optimal Power Flow (OPF), which is executed sequentially considering future grid states, based on forecasted information for load and renewable generation. Relying on the fact that the DER units can provide a certain degree of flexibility for the operation of the grid, such assets are being coordinated within a top-level centralized approach. The scheme is assessed within an IEEE-LV benchmarked network, and compared with different scenarios of DER integration and local controls.
In recent years, the installation of residential Distributed Energy Resources (DER) that produce (mainly rooftop photovoltaics usually bundled with battery system) or consume (electric heat pumps, controllable loads, electric vehicles) electric power is continuously increasing in Low Voltage (LV) distribution networks. Several technical challenges may arise through the massive integration of DER, which have to be addressed by the distribution grid operator. However, DER can provide certain degree of flexibility to the operation of distribution grids, which is generally performed with temporal shifting of energy to be consumed or injected. This work advances a horizon optimization control framework which aims to efficiently schedule the LV network’s operation in day-ahead scale coordinating multiple DER. The main objectives of the proposed control is to ensure secure LV grid operation in the sense of admissible voltage bounds and rated loading conditions for the secondary transformer. The proposed methodology leans on a multi-period three-phase Optimal Power Flow (OPF) addressed as a nonlinear optimization problem. The resulting horizon control scheme is validated within an LV distribution network through multiple case scenarios with high microgeneration and electric vehicle integration providing admissible voltage limits and avoiding unnecessary active power curtailments.
Wicak Ananduta
added 2 research items
A novel partitioning approach for linear switching large-scale systems is presented. We assume that the modes of the switching system are unknown a priori but can be detected. We propose an online partitioning scheme that can partition the system when the mode switches, thus adapting the partition to the mode. Moreover, after the system has been partitioned, we apply a decentralized state-feedback control scheme to stabilize the system. We also apply a dwell time stability scheme to prove that the closed-loop system remains stable even after both the mode and partition changes. The proposed approach is illustrated by means of an automatic generation control problem related to frequency deviation regulation in a large-scale power network.
Marta Fonrodona
added an update
New INCITE blog contribution by Unnikrishnan Raveendran Nair: "An evolution towards smart grids: the role of storage systems" http://www.incite-itn.eu/blog/an-evolution-towards-smart-grids-the-role-of-storage-systems/
 
Thibault Péan
added a research item
The present work develops research to exploit the energy flexibility of buildings through rule-based controls. A novel signal representing the marginal CO2 emissions of the electricity grid is created, and its calculation methodology detailed, so that it can be applied to other energy systems. This signal is used as an input by a rule-based controller acting on the indoor temperature set-point of a residential building equipped with a heat pump. Through this set-point modulation, the energy use of the heat pump is displaced towards periods of lower CO2 intensity. A similar method is applied with an electricity price signal, and both strategies are compared in terms of energy, CO2 emissions and monetary costs. The two rule-based controls perform in a relatively similar way in the heating season (although with improvements of different amplitudes), while especially the price-based modulation produces adverse effects in the cooling season.
Wicak Ananduta
added 2 research items
Information sharing among local controllers is the key feature of any distributed model predictive control (DMPC) strategy. This study addresses the problem of communication failures in DMPC strategies and proposes a distributed solution to cope with them. The proposal consists in an information-exchange protocol that is based on distributed projection dynamics. By applying this protocol as a complementary plug-in to a DMPC strategy, the controllers improve the resilience against communication failures and relax the requirements of the communication network. Furthermore, a reconfiguration algorithm, which is a contingency procedure to maintain the connectivity of the network, and a discussion on the selection criteria of the information-sharing network are also presented. In order to demonstrate the performance and advantages of the proposed approach when it is applied to a DMPC strategy, a case study of a power-network control problem is provided.
In this paper, distributed energy management of interconnected microgrids, which is stated as a dynamic economic dispatch problem, is studied. Since the distributed approach requires cooperation of all local controllers, when some of them do not comply with the distributed algorithm that is applied to the system, the performance of the system might be compromised. Specifically, it is considered that adversarial agents (microgrids with their controllers) might implement control inputs that are different than the ones obtained from the distributed algorithm. By performing such behavior, these agents might have better performance at the expense of deteriorating the performance of the regular agents. This paper proposes a methodology to deal with this type of adversarial agents such that we can still guarantee that the regular agents can still obtain feasible, though suboptimal, control inputs in the presence of adversarial behaviors. The methodology consists of two steps: (i) the robustification of the underlying optimization problem and (ii) the identification of adversarial agents, which uses hypothesis testing with Bayesian inference and requires to solve a local mixed-integer optimization problem. Furthermore, the proposed methodology also prevents the regular agents to be affected by the adversaries once the adversarial agents are identified. In addition, we also provide a sub-optimality certificate of the proposed methodology.
Nikolaos Sapountzoglou
added a research item
A fault detection algorithm for grid-connected photovoltaic (GCPV) systems is presented in this paper. After a synthetic description of the most important fault detection techniques up to date, the selection of the signal approach and of the output of the inverter as a measurement point for the monitored electrical variables, for this study, are explained. In Section 2, the procedure that was followed to build a GCPV model is discussed. In Section 3, the different sources of faults that were studied are presented and the necessary steps to construct the fault signature table are demonstrated. In Section 4, the developed algorithm and its threshold crossing parameters are analyzed. The algorithm is able to identify the different types of faults or groups of them in less than 100 ms under the assumption that only one fault is occurring at a time. Finally, the robustness of the algorithm for various irradiance levels was validated via simulations.
Marta Fonrodona
added an update
New INCITE blog contribution by Konstantinos Kotsalos: "Introducing Microgrids & Local Energy Communities" http://www.incite-itn.eu/blog/introducing-microgrids-local-energy-communities/
 
Jesus Lago
added a research item
Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g. operational planning, power purchases, reserve activation, etc. In this context, as solar generators are geographically dispersed and ground measurements are not always easy to obtain, it is very important to have general models that can predict solar irradiance without the need of local data. In this paper, a model that can perform short-term forecasting of solar irradiance in any general location without the need of ground measurements is proposed. To do so, the model considers satellite-based measurements and weather-based forecasts, and employs a deep neural network structure that is able to generalize across locations; particularly, the network is trained only using a small subset of sites where ground data is available, and the model is able to generalize to a much larger number of locations where ground data does not exist. As a case study, 25 locations in The Netherlands are considered and the proposed model is compared against four local models that are individually trained for each location using ground measurements. Despite the general nature of the model, it is shown show that the proposed model is equal or better than the local models: when comparing the average performance across all the locations and prediction horizons, the proposed model obtains a 31.31% rRMSE (relative root mean square error) while the best local model achieves a 32.01% rRMSE.
Marta Fonrodona
added an update
New INCITE blog contribution by Miguel Picallo: "The odyssey of monitoring distribution networks" http://www.incite-itn.eu/blog/the-odyssey-of-monitoring-distribution-networks/
 
Marta Fonrodona
added a research item
Nowadays, in many countries wind energy is responsible for a significant part of the electricity generation. For this reason, Transmission System Operators (TSOs) are now demanding the wind power plants (WPPs) to contribute with ancillary services such as frequency support. To this end, WPPs must be able to temporally increase the active power delivered into the grid to compensate consume and demand imbalances. This implies that WPPs now work below their maximum capacity to keep some power reserve to be able to inject extra power into the grid when needed. This reserve depends on the available wind power, which is directly connected with the wind speed faced by each turbine within the WPP. However, wind speed is negative affected by the wakes caused by the upstream turbines. This paper proposes a control algorithm to distribute the power contribution of each turbine seeking to minimize the wake effects and thus maximize the power reserve. The proposed algorithm is evaluated by simulations for the case of a WPP of 12 wind turbines.
Unnikrishnan Raveendran Nair
added 2 research items
The goal of this work is to apply the framework of reset systems, in particular PI+CI controllers, in the controller design for power converters. While the PI+CI controller has been applied in several industrial applications, the application of such controllers in fast electrical systems especially power electronic converters appears to be new. The main motivations for this proposal are performance superiority of these controllers and the ability to produce a fast flat response without any overshoot for a step input. Another factor that influenced the use of such controller is the relatively simple design equations, which enables plug and play capability. The flat responses are highly interesting from the perspective of power converters especially when they are connected to power grids.
Hybrid controllers are capable of improved performance over their linear counterparts. In particular, reset controllers like the PI+CI are capable of fast flat response for lag dominant plants. Grid connected power converters especially interfacing energy storage systems to grids are required to have fast response to varying load demands to ensure minimum variation in grid parameters. Application of PI+CI controllers in such systems can improve their performance. In this work the improvement brought about by use of PI+CI controller employed for energy storage system power converters is highlighted by comparing it with PI controller based system under load variations. A DC microgrid with Fuel cell-supercapacitor based storage elements are considered here. The design criteria and simulation results are presented here.
Jesus Lago
added a research item
Fast online generation of feasible and optimal reference trajectories is crucial in tracking model predictive control, especially for stability and optimality in presence of a time varying parameter. In this paper, in order to circumvent the operational efforts of handling a discrete set of precomputed trajectories and switching between them, time warping of a single trajectory is proposed as an alternative concept. In particular, the conceptual ideas of warping theory are presented and illustrated based on the example of a tethered kite system for airborne wind energy. In detail, for warpable systems, feasibility and optimality of trajectories are discussed. Subsequently, the full algorithm of a nonlinear model predictive control implementation based on warping a single precomputed reference is presented. Finally, the warping algorithm is applied to the airborne wind energy system. Simulation results in presence of real world perturbations are evaluated and compared.
Marta Fonrodona
added an update
New INCITE blog contribution by Adedotun J Agbemuko : "Resonances, Interactions, and Stability of Future Power Systems" http://www.incite-itn.eu/blog/resonances-interactions-and-stability-of-future-power-systems/
#PowerElectronics #PowerSystems #Stability #Harmonics #Resonances
 
Jesus Lago
added a research item
Recent research has seen several forecasting methods being applied for heat load forecasting of district heating networks. This paper presents two methods that gain significant improvements compared to the previous works. First, an automated way of handling non-linear dependencies in linear models is presented. In this context, the paper implements a new method for feature selection based on [1], resulting in computationally efficient models with higher accuracies. The three main models used here are linear, ridge, and lasso regression. In the second approach, a deep learning method is presented. Although computationally more intensive, the deep learning model provides higher accuracy than the linear models with automated feature selection. Finally, we compare and contrast the proposed methods with earlier work for day-ahead forecasting of heat load in two different district heating networks.
Jesus Lago
added a research item
In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and we show how they lead to improvements in predictive accuracy. In addition, we also consider that, despite the large number of proposed methods for predicting electricity prices, an extensive benchmark is still missing. To tackle that, we compare and analyze the accuracy of 27 common approaches for electricity price forecasting. Based on the benchmark results, we show how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant. Finally, using the same results, we also show that: (i) machine learning methods yield, in general, a better accuracy than statistical models; (ii) moving average terms do not improve the predictive accuracy; (iii) hybrid models do not outperform their simpler counterparts.
Marta Fonrodona
added an update
New INCITE blog contribution by Jesus Lago : "Forecasting in the electrical grid" http://www.incite-itn.eu/blog/forecasting-in-the-electrical-grid/
You can find out more about Jesus' IRP "Development of non-intrusive and intrusive energy-management" at http://www.incite-itn.eu/network/irp14-development-of-non-intrusive-and-intrusive-energy-management/
 
Thibault Péan
added a research item
The present work constitutes a review of the existing literature on supervisory control for improving the energy flexibility provided by heat pumps in buildings. A distinction was drawn between rule-based controls (RBC) and model predictive controls (MPC),given the clear differences in their concept and complexity. For both kinds, the different objectives claimed by these strategies have been reviewed, as well as the control inputs, disturbances and constraints. Notably in MPC, the monetary objective (reduction of the energy costs) has been the most utilized in the literature, therefore the authors advocate for the further study of other objectives related to energy flexibility. Further than the control strategies themselves, the different thermal storage options (necessary to activate the flexibility) have also been reviewed, the built-in thermal mass seeming more cost-effective than water buffer tanks in this regard. Based on these conclusions, recommendations for further research topics are drawn.
Adedotun J Agbemuko
added a research item
Pervasiveness of power converters in the electric power system is expected in the future. Such large penetration will change the current power system dynamics leading to uncertain, unexpected, and potentially critical responses. This paper investigates the stability and resonance of a VSC-HVDC (Voltage Source Converter High Voltage Direct Current) link within an AC grid, whilst providing insights into resonances having a role on the grid. This is studied through the impedance-based modelling of the entire system (AC and DC grids), including controls of converters. Additionally, the impact of the different parameters of the hybrid AC-DC power system such as control systems and grid components on the system dynamics and stability is investigated. From this study, the impact of the system components and the controls of the converter on overall resonance response and stability is shown, including potential undesired sub-synchronous and harmonic resonances due to AC-DC system interactions. The analytical impedance-based models developed and obtained is validated through time-domain simulations, the physical model of the whole system is built in Simscape™ Power Systems™ and control systems in MATLAB/Simulink® (R2017b). This has demonstrated the validity of the model to deal with and detect such dynamics.
Wicak Ananduta
added 2 research items
Distributed Model Predictive Control (DMPC) strategies require local controllers to share information among each other. Considering the importance of communication in such control strategies and the failures that may occur in the information-sharing network, this paper proposes to apply the distributed consensus algorithm as an information-exchange protocol for DMPC controllers. The advantage of the proposed protocol is twofold. First, it relaxes some communication assumptions usually made for DMPC controllers. Second, under some assumptions, it provides resilience against some communication failures such that the performance and the features of the implemented distributed controller are preserved. A case study of a microgrid system is provided as an example in which some simulations are carried out to illustrate the aforementioned advantages.
Marta Fonrodona
added an update
New INCITE blog contribution by Wicak Ananduta : "Distributed control approach in electrical networks: advantages and challenges" http://www.incite-itn.eu/blog/distributed-control-approach-in-electrical-networks-advantages-and-challenges/
You can find out more about Wicak's IRP "Partitioning and optimisation-based non-centralised control of dynamical energy grids" at http://www.incite-itn.eu/network/irp11-partitioning-and-optimisation-based-non-centralised-control-of-dynamical-energy-grids/
 
Thibault Péan
added a research item
A series of experiments were performed in a semi-virtual environment to investigate the performance of a gas boiler under dynamic operation conditions. The real condensing boiler was placed in the laboratory, and connected to thermal benches which emulate the thermal loads generated by a virtual building model. The results revealed first that the boiler efficiency drops when the Domestic Hot Water (DHW) needs prevail over space heating, due to the start-up losses provoked by a more frequent switching. The heating curve control enables to save energy and to reach higher efficiency levels in spring season, but this was not entirely verified in winter (full load heating). The impact of an increased thermal mass and insulation level was also experimented, as a preliminary step towards investigating energy flexibility in a nZEB-type building.
Marta Fonrodona
added an update
New INCITE blog contribution by Camilo Orozco: "Integrated simulation and design optimisation tools" http://www.incite-itn.eu/blog/integrated-simulation-and-design-optimisation-tools/
You can find out more about Camilo's IRP "Integrated simulation and design optimisation tools" at http://www.incite-itn.eu/network/irp41-integrated-simulation-and-design-optimisation-tools/
 
Marta Fonrodona
added an update
New INCITE blog contribution by Shantanu Chakraborty : "Reconnecting the dots – Closing the gap between markets and the physical grid" http://www.incite-itn.eu/blog/reconnecting-the-dots-closing-the-gap-between-markets-and-the-physical-grid/
You can find out more about Shantanu's IRP "Hybrid agent-based optimisation model for self-scheduling generators in a market environment" at http://www.incite-itn.eu/network/irp13-hybrid-agent-based-optimisation-model-for-self-scheduling-generators-in-a-market-environment/
 
Marta Fonrodona
added an update
New INCITE blog contribution by Sara Sinisiscalchi Minna : "Wind energy for grid support" http://www.incite-itn.eu/blog/wind-energy-for-grid-support/ You can find out more about Sara's IRP " Distributed control strategies for wind farms for grid support" at http://www.incite-itn.eu/network/irp33-distributed-control-strategies-for-wind-farms-for-grid-support/
 
Marta Fonrodona
added an update
New INCITE blog contribution by Tomas Pippia: "Distributed generation in houses with µCHPs and the role of control for reduced costs" http://www.incite-itn.eu/blog/distributed-generation-in-houses-with-micro-chps-and-the-role-of-control-for-reduced-costs/
You can find out more about Tomas' IRP "Robust management and control of smart multi-carrier energy systems" at http://www.incite-itn.eu/network/irp23-title-robust-management-and-control-of-smart-multi-carrier-energy-systems/
 
Adedotun J Agbemuko
added a research item
HVDC technology is being increasingly used to integrate RES, transmit power via long distance connections, interconnect regions to allow for cross-border sharing of resources and, strengthen AC networks among others. Such penetration will lead to the expectation of meshed HVDC grid constructions into the future. Converters have been known influence power quality and on the long term, supply security. More so as they are expected to dominate future grids. This paper aims to analyse the harmonic stability and interactions in meshed HVDC network by employing impedance models of the converters and grid distinctly, and applying the Z-bus concept to connect them together. Finally, classical frequency domain tools are applied to the derived closed-loop Z-bus matrix to analyse the shape of obtained impedances at each bus, and the transfer impedances to detect interaction. Additionally, the impact of control parameters is studied. Finally, the method is compared with the eigenvalues of the system to demonstrate the effectiveness of the analytical method.
Jesus Lago
added a research item
Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features.
Marta Fonrodona
added an update
New INCITE blog contribution by Private Profile: "Demand-side management by real-time market-based control" http://www.incite-itn.eu/blog/demand-side-management-by-real-time-market-based-control/
You can find out about Hazem's IRP "Decentralised control for RES by fast market-based MAS" at http://www.incite-itn.eu/network/irp12-decentralised-control-for-res-by-fast-market-based-mas/
 
Marta Fonrodona
added an update
Follow the updates of ESRs and supervisors in our blog: http://www.incite-itn.eu/blog/
The latest contribution is by Nikolaos Sapountzoglou: "Solar energy: the once and future king" http://www.incite-itn.eu/blog/solar-energy-the-once-and-future-king/
You can find out more about Nikolaos' IRP "Fault detection and isolation for renewable sources" at http://www.incite-itn.eu/network/irp42-fault-detection-and-isolation-for-renewable-sources/
 
Marta Fonrodona
added an update
Follow the updates of ESRs and supervisors in our blog: http://www.incite-itn.eu/blog/
The latest contribution is by Felix Koeth and talks about "Synchronization in dynamical systems and power systems" http://www.incite-itn.eu/blog/synchronization-in-dynamical-systems-and-power-systems/
You can find out more about Felix's IRP "A new modelling approach for stabilisation of smart grids" at http://www.incite-itn.eu/network/irp32-a-new-modelling-approach-for-stabilisation-of-smart-grids/
 
Marta Fonrodona
added an update
The Barcelona Summer School & 2nd INCITE Workshop took place at UPC in Barcelona 26-30 June 2017. The technical training focused on "Smart Energy Systems: Advanced Control and Safety Capabilities". During the Workshop, the INCITE ESRs presented the most recent advances on their research projects. Their presentations can be found in the INCITE webpage http://www.incite-itn.eu/training-events/incite-workshop-2/
 
Marta Fonrodona
added an update
Follow the updates of ESRs and supervisors in our blog: http://www.incite-itn.eu/blog/
The latest contribution is by Unnikrishnan Raveendran Nair and talks about "The role of Electrical storage systems in future grids" http://www.incite-itn.eu/blog/the-role-of-electrical-storage-systems-in-future-grids/
 
Thibault Péan
added a research item
In this study, simulation work has been carried out to investigate the impact of a demand-side management control strategy in a residential nZEB. A refurbished apartment within a multi-family dwelling representative of Mediterranean building habits was chosen as a study case and modelled within a simulation framework. A flexibility strategy based on set-point modulation depending on the energy price was applied to the building. The impact of the control strategy on thermal comfort was studied in detail with several methods retrieved from the standards or other literature, differentiating the effects on day and night living zones. It revealed a slight decrease of comfort when implementing flexibility, although this was not prejudicial. In addition, the applied strategy caused a simultaneous increase of the electricity used for heating by up to 7% and a reduction of the corresponding energy costs by up to around 20%. The proposed control thereby constitutes a promising solution for shifting heating loads towards periods of lower prices and is able to provide benefits for both the user and the grid sides. Beyond that, the activation of energy flexibility in buildings (nZEB in the present case) will participate in a more successful integration of renewable energy sources (RES) in the energy mix.
Fernando D. Bianchi
added a project goal
INCITE is Marie Sklodowska-Curie European Training Network (ITN-ETN) funded by the HORIZON 2020 Programme that brings together experts on control and power systems, from academia and industry with the aim of training fourteen young researchers capable of providing innovative control solutions for the future electrical networks.
New smart meters, distributed generation, renewable energy sources and the concern about the environment are redefining electrical networks. Now, both consumers and generators are active agents, capable of coordinating the power exchange in the electrical grids depending on multiple factors. To take full advantage of the new electrical networks, it is necessary a coordinated and harmonic interaction of the all actors in the network. Control algorithms are intended for this purpose; to act at several levels to conduct the electrical power exchange and improve efficiency, reliability and resilience of the network. INCITE seeks new control algorithms with an integral view of the future electrical networks, covering aspects like energy management, stability of electrical variables, monitoring and communication implementation, energy storage, among others.