ABSTRACT. This Panel Session, organised by EU H2020 project JUMP2Excel consortium, addresses different aspects of the integration of PV generation and related supporting technologies, energy storage and demand-side management, in power networks and discusses associated network operational, planning and economic impacts and developments, including the provision of ancillary services and electricity markets. The panellist will discuss challenges and future aspects from PV prosumers within the Energy Communities, grid services from energy storage systems, integration of PV modules within commercial vehicles, demand-side management under variable PV generation and the role of network and demand-side flexibility to minimise impacts of PV integration into the distribution system.

ABSTRACT. As of 2019, the world population without electricity access is estimated to 770 million with most of these communities residing in sub-Saharan Africa. Nevertheless, between 2000 and 2019 the Indian population with electricity access has grown from 43% to 99%. Minigrids have played a major role in the efforts of increasing access to electricity in rural areas. However, interconnecting minigrids to each-other or to the main grid remains still a challenge both due to lack of clear protocols and of technically matured controllers to manage the synchronization. In this session, existing interconnection guidelines and their relevance for the interconnection of minigrids will be discussed. The panelists will discuss the role synchronization controllers can play for efficient utilization of minigrids assets and facilitate bottom-up electrification process.

ABSTRACT. The introduction of variable generation and environmental dependency due to the integration of renewable energy sources (RES) changes the dynamics and priorities of generation expansion planning. This paper aims to develop and propose an optimal strategy for generation expansion model considering wind turbine (WTG) generation system. The problem is formulated on the concept of multi-area power system standards and the derivation of an optimal location of the WTG with compressed air energy storage system (CAESS) in the energy mix of the power network. Ensuring that a maximum capacity credit is achieved with WTG incorporation. The objective function in this study, is based on the effective load carrying capability (ELCC) that is utilized to quantify the efficacy of the capacity credit methodology. The ELCC parametric index ensures the reliability standards of the existent system in concurrence with the introduction of additional loads, that is basically grid expansion. Therefore, an evaluative analytical study is performed to increase the dispatchability and availability of the renewable energy sources. The proposed methodology is evaluated and validated on a multi-area system wherein, each area has a pre-existing capacity, load profile, wind generation profile, and reliability data sets.

ABSTRACT. Embedded with producers, consumers, and prosumers, active Low-Voltage Distribution Networks (LVDNs) with bi-directional power flows are rising to over-shadow the investment and operation planning in power systems. The Optimal Power Flow (OPF) has been extensively used in the recent years to solve different investment and operation planning problems in LVDNs. However, OPF is inherently a complex non-linear and non-convex optimization problem. Hence, different linearization and convexification models have been introduced in the literature to enhance the modeling accuracy and computational tractability of the OPF problem in LVDNs. In this paper, five multi-period OPF models (including the basic non-linear and non-convex one) are presented, with different linearizations/convexifications for the power flow equations. The proposed models are implemented on the IEEE 34-bus test system and their modeling accuracy and computational complexity are compared and discussed.

ABSTRACT. In order to prevent conflicting or counteracting use of flexibility options, the coordination between distribution system operator and transmission system operator has to be strengthened. For this purpose, methods for the standardized description and identification of the aggregated flexibility potential of distribution grids are developed. Approaches for identifying the feasible operation region (FOR) of distribution grids can be categorized into two main classes: Random sampling/stochastic approaches and optimization-based approaches. While the latter have the advantage of working in real-world scenarios where no full grid models exist, when relying on naive sampling strategies, they suffer from poor coverage of the edges of the FOR due to convoluted distributions. In this paper, we tackle the problem from two different angles. First, we present a random sampling approach which mitigates the convolution problem by drawing sample values from a multivariate Dirichlet distribution. Second, we come up with a hybrid approach which solves the underlying optimal power flow problems of the optimization-based approach by means of a stochastic evolutionary optimization algorithm codenamed REvol. By means of synthetic feeders, we compare the two proposed FOR identification methods with regard to how well the FOR is covered and number of power flow calculations required.

ABSTRACT. One significant challenge in Non-Intrusive Load Monitoring (NILM) is to identify and classify active appliances used in a building. This research focuses on the classifying process, exploring different approaches for the feature extraction of the appliances' power load to improve the classification accuracy. In this paper, we present a new method - Spectral Entropy and Instantaneous Frequency-based Bidirectional Long Short Term Memory (SE-IF BiLSTM). It uses feature extraction from the power load to obtain information, such as instant frequency, spectral entropy, spectrogram, Mel spectrogram and signal variation, to feed BiLSTM Neural Network. We also test different options for the BiLSTM to decide the most optimal settings. This method improves the classification performance, achieving up to 98.57\% classification accuracy.

ABSTRACT. The fluctuant nature of wind power constitutes a major challenge that needs to be addressed in smart grids. Fuel cells provide vast opportunities for wind power smoothing and control. Specifically, ammonia fuel cells provide solutions to various problems associated with hydrogen fuel cells. Hence, in this study, the implementation of ammonia fuel cells is investigated for wind power smoothing and control. The moving average (MA), Savitzky-Golay (SG), and moving median (MM) smoothing filters are utilized to smooth fluctuant wind power outputs. An electrochemical ammonia synthesizer (EAS) is utilized to synthesize ammonia when excess power is available. A direct ammonia fuel cell (DAFC) is employed for mitigating power deficits to attain smooth power outputs. Daily EAS and DAFC capacity requirements are found to reach 16.3 kmol and 59.8 MWh respectively for the SG filtered smooth power. The developed energy storage system provides an effective methodology for smoothing and control of fluctuant wind power.

ABSTRACT. Grid battery energy storages require accurate and robust state of charge estimation to maximize the amount of power and energy offered to the grid. In literature, Kalman filter based state of charge estimation is extensively discussed and shown to provide accurate estimates. Tuning of Kalman filter noise parameters is crucial for estimation accuracy and robustness. Conventionally, noise parameters are set to constant values based on a training dataset for which the state of charge estimation accuracy was manually or programmatically optimized. This publication provides an analytical Kalman filter tuning method which does not require training data. Noise parameters are computed dynamically based on the measurements, measurement uncertainties, model states, and approximate model parameter uncertainties. The method is validated using battery cell measurements representing use cases of grid battery energy storages. Model error and sensor error scenarios are defined and tested to ensure robustness. For all validation scenarios, the RMSE was below 1.9%.

ABSTRACT. Aging may increase the number of failures that underground power distribution cables undergo. Recent works have investigated this problem and proposed repairable failure models that consider cables’ aging and loading conditions. This paper examines and assesses the performances of two models, one of them is called IEC-Arrhenius-Weibull because it combines the failure rate function of the Weibull distribution with the Arrhenius relationship and the standard IEC 60287-1-1. The first part of the investigation consists of analyzing the modeling assumptions, the methods for incorporating loading conditions, and the approaches for calculating the model parameters. In the second part, the performance of the two models is evaluated through computer simulations, sensitivity analysis, and cable rankings. The results strongly suggest that the use of the Arrhenius relationship to calculate the scale parameter of the IEC-Arrhenius-Weibull model leads to provide reduced failure rates that may affect cable maintenance plans, and that the model significantly depends on its shape parameter to describe different degrees of aging. Thus, this paper helps stakeholders to identify a realistic model for underground cable management.

ABSTRACT. To meet its target of 30% renewable energy integration by 2030, Israel must considerably develop its transmission grid. One idea that may reduce the costs of grid development is to use energy storage for grid deferral, that is, to locally store and time shift energy that cannot be transmitted due to grid congestion. For Israel this function would be most beneficial at noon when the grid is expected to be most congested due to high shares of solar energy. To study this idea, in this paper we estimate the required storage capacity as a function of renewable energy generation and grid capacity in Israel, and use the results to calculate the current required storage costs, which is then compared to the expected costs of grid development. We also analyze the added value of storage for generation capacity replacement, and show the conditions under which value stacking can increase the attractiveness of storage for grid deferral. Two main findings are that the storage capacity needed to enable connection of additional PV plants to the existing grid grows non-linearly, and that the use of storage for grid deferral is limited to an addition of 170% over the current PV capacity.

ABSTRACT. To enable an increasing share of renewable energy sources (RES) into distribution grids, suitable technical solutions, which improve grid integration, are necessary. This paper presents new simulation results regarding the hosting capacity (HC) of low voltage (LV) distribution grids for the integration of Photovoltaic (PV) generators considering the use of smart grid technologies with focus on combinations thereof. The covered technologies include reactive power control by Q(V) and cosϕ(P), on load tap changers (OLTC) with different control schemes and battery energy storage systems (BESS) for transformer load reduction, which are already known to increase the HC. These smart grid technologies are applied on different scenarios to obtain robust results. Addtionally, a performance benchmark consisting of a centralized control scheme with a MILP optimization is introduced, which optimizes all assets and provides an upper theoretical performance bound. The ability of smart grid technologies to increase the HC is investigated for different LV grids and different local distributions of PV generators. Among others, the results show that combinations of decentral smart grid technologies can significantly improve the HC by more than 100% by overcoming both voltage constraints as well as transformer loading limitations and reach the same HC as the MILP optimization.

ABSTRACT. High penetration of solar photovoltaics (PVs) in distribution systems necessitates the need for embedding an efficient control strategy for reducing loss and confining nodal voltage within an acceptable range. In this study, a multi-timescale Volt/VAR control model is proposed to co-optimize the traditional capacitor banks with off-unity power factor inverters of PVs and electric vehicles (EVs). First, in an inter-hour scale, capacitor banks are scheduled. Next, in an intra-hour scale, the inverters and EVs provide power support to supplement the inter-hours decision. The coordination of resources between two different time scales is performed using a risk-constrained stochastic problem with uncertainty modelling. The prediction errors associated with PV generation are modeled by Gaussian Mixture model (GMM), capturing the drastic changes of solar generation due to the weather-related effects. Finally, the merits of the proposed model are shown on a modified IEEE 33 bus system.

ABSTRACT. This paper presents a comparison of the necessary grid reinforcements of a distribution grid by considering the approaches for application of different performance indices. The aim is to enable an optimal integration of renewable energies sources into the existing grid structure and to utilize the grid in the best possible way. In addition, the necessary grid reinforcements should be targeted to be as low as possible. For this purpose, the required grid expansion will be estimated by applying the grid planning method, which will be presented in this paper, under application of the performance indices. These performance indices can be determined from the results of the contingency analysis in order to evaluate the impact of line outage on the other lines. This enables a targeted identification of grid reinforcements and expansion measures using the developed method in the time-series-based probabilistic grid planning.

ABSTRACT. Regarding the weaknesses accounted for the centralized distribution state estimation (DSE), proposing decentralized formulations have been remarked in large distribution systems. However, predefined network division is usually assumed in the previous work and the effect of zone size (or the number of network zones) on the performance of the multi-area DSE procedures has not been investigated. In response, this paper presents a procedure to determine the optimum network division scheme for the decentralized execution of DSE in the active distribution networks. For this purpose, an algorithm for dividing the network into all possible zone sizes is proposed. As the first step, the network is divided into the maximum number of zones, and then neighboring zones are grouped step by step to increase the network zone sizes and obtain all network division scenarios. Finally, the hierarchically distributed DSE method is executed to estimate the network condition in all of these scenarios and the cost-effective scenario is recognized based on the newly introduced performance index composed of accuracy and runtime indices. The performance of the proposed method is tested on the revised generic 77-bus UK distribution network as an active network, and the results are presented and discussed.

ABSTRACT. The new global trend in the field of electrical energy supply is the application of artificial intelligence both for monitoring and controlling energy systems. High-performance power system state estimation is essential to ensure safe network operation. The approach presented here describes a method based on artificial neural networks to perform a state estimation for different switching states in distribution grids with missing input data. The approach is data-driven and based on synthetically generated data without consideration of historical measurements. With appropriate training, the method can predict grid variables, e.g., voltage magnitudes, voltage phase angles or line loadings, with high accuracy. Furthermore, the introduced procedure considers a high penetration of electric vehicles and photovoltaik systems. For measurement infrastructure planning, various configurations are presented to determine suitable measurement locations. The proposed concept is finally demonstrated using a synthetic reference grid.

ABSTRACT. This paper is concerned with solving the phase identification problem in a real-world smart grid project; where there is only a few smart meters available on each of the five power distribution feeders in the test site in Riverside, CA. The main idea is to develop and use two reliability criteria that can identify the most reliable components in a broken-down phase identification analysis; thereby significantly improving the accuracy of phase identification. The proposed method consists of three steps. The results from field implementation reveal the accuracy and consistency of the proposed method in practice, in correctly and reliability identifying the phase connectivity.

ABSTRACT. Vibration analysis is a powerful tool for transformer assessment. Knowledge about functional relationships between vibration and influence factors is essential for a successful assessment. One of these influence factors is the power factor, which may vary due to day-dependent changes in the electric load. In this paper, the functional relationship between the power factor and the vibration is investigated. First, a theoretical analysis is conducted on the superposition of core- and winding vibrations in dependence of the power factor. The theoretical analysis concludes that the highest 100 Hz vibration amplitude is measured when the phi angle of the load current equals the phi angle of the transformer in open-circuit test. The results from the theoretical analysis are then experimentally investigated in single-phase loads for different power factors and apparent power scenarios. The laboratory experiment is conducted with a transformer for primary and secondary distribution. The results from the laboratory experiment support the findings from the theoretical analysis.

ABSTRACT. Non-intrusive load monitoring (NILM) techniques represent an opportunity to increase the flexibility and resilience of the electrical system. However, there is limited research and practical work focused on implementing these methods for distributed energy resources on distribution networks. Non-intrusive load monitoring can contribute to demand-side management strategies required for real-time operation of smart grids as well as reducing the negative impacts of distributed generation and low carbon technologies in low voltage networks. In this paper, a unique supervised machine learning algorithm to disaggregate electric power generation of photovoltaic systems from the main power feeder of a residential dwelling is proposed. The algorithm uses low complexity sliding windows and conventional machine learning techniques applied to real residential households’ data obtained from the dataset of the Smart* project. The results exhibit an accurate performance of the proposed NILM in fast computation time with a mean average error below 5.2%.

ABSTRACT. The identification of price zones is a critical issue for designing efficient mechanisms in network-constrained electricity markets. It consists of identifying nodes in zones that share similar Locational Marginal Prices (LMPs) and are physically connected by transmission lines. This paper proposes a methodology based on graph theory to identify price zones and the corresponding transmission tie-lines interconnecting them considering the changing pattern of LMPs. A mathematical characterization of network-constrained electricity market based on LMPs is proposed to construct a market-aware graph. Then, a spectral clustering methodology is applied to identify the price zones. The effectiveness of the proposed approach is illustrated on a 118-node test system.

ABSTRACT. The increased penetration of renewable energy sources and the advancing sector coupling induce a changing supply task within distributions grids. In combination with delayed grid expansion, this leads to increased congestion. To lower grid expansion costs, grid-supporting flexibility can be used to reduce load and generation peaks. In the future, this flexibility could be offered on a local flexibility market in the case of market-based redispatch procurement. However, flexibility procurement in such a scenario requires detailed knowledge of grid user behavior. For this purpose, this paper models the market-oriented grid user behavior in active distribution grids within a Markov decision process solved by a deep reinforcement learning algorithm. We compare our implementation to an iterative optimization algorithm, which iteratively optimizes the zonal and local flexibility market bidding. Exemplary results prove the functionality of the implemented model. The analysis of the computed market-oriented grid user behavior shows a changing bidding behavior of market-participants due to the local flexibility market, as the opportunity is priced into the zonal market bids. Further results indicate that the presented model is capable of exploring gaming strategies to maximize the reward from both markets. This increases congestion.

ABSTRACT. Due to the current policies of the EU on climate change, the energy generation mix of the future power system will be dominated by variable renewable energy (VRE) sources, such as wind power. These sources have a highly intermittent output in real-time and will significantly affect the equilibrium of day-ahead (DA) markets. This work simulates the future DA prices of the Nord-Pool Elspot market considering high penetrations of VRE added to the existing generation structure. The historical demand and supply curves are reconstructed, and the new equilibrium follows the parallel shifting of the supply curve in accordance with the hourly generation level of VRE sources. Moreover, the effect of demand response (DR) is also investigated and renders the demand curve shift able in both directions. Results demonstrate that even a moderate level of VRE drives the prices into high volatility while activating DR alleviates the situation to some extent.

ABSTRACT. Over the next decade, fossil fuel electricity generators will remain as important as zero-carbon renewable generators from a grid reliability perspective. Such fossil fuel generators often have operating constraints in terms of ramp up/down speeds, minimum down times, and minimum generation limits. In this paper, we present a methodology for generators with such constraints to participate in a two-sided day-ahead electricity market. Specifically, our approach determines the sell bids for a generator so that the likelihood of the generator’s market cleared volume to satisfy its own operating constraints is increased without significantly reducing the generator’s profitability. We achieve this through an iterative mathematical programming procedure that makes use of the forecasted market behavior. Ours is one of the first few works to focus on this problem. We simulate the performance of our approach on real world market data logs obtained from the European Power Exchange (EPEX). We find that the proposed strategy gives a better trade-off between profit and constraint violations in comparison to naive baselines – our strategy decreases the constraint violations by as much as 45% while marginally affecting the profits by just 6%.

ABSTRACT. The structure of the day-ahead electricity market obliges wind farm owners (WFOs) to make commitments hours before delivery. Due to the uncertainty of wind generation, WFOs bid in the electricity market with the prediction of its generation that, more often than not, is different from the actual generation. Therefore, WFOs experience deviations between their commitment to the electricity market and the actual generation, namely overproduction and underproduction, which are subject to penalties. This paper investigates solutions to decrease such deviation and increase the profit of the WFO. To this end, the joint planning and operation of electrical energy storage (EES) and power-to-gas (P2G) units to be paired with wind farms are evaluated while considering both the electricity and gas markets. Two case studies, with only EES and with both the EES and P2G units, are conducted to reveal the potential of the proposed approach considered, while risk analyses are performed to study the impact of different risk criteria on the decisions of WFO. This problem is formulated as an MINLP and then recast into MILP to obtain global solutions. Results offer the best strategies for WFOs to enhance their profit under the existing uncertain conditions.

ABSTRACT. Large-scale integration of intermittent renewable energy resources into power systems increases the need for flexibility services such as frequency and voltage control. In the future, system operators need to utilize more flexible energy resources from all levels of the system in order to fulfill flexibility needs. Aggregated customers in form of a local energy community (LEC) are potential resources which can provide a part of the required flexibility. In this regard, accurate forecasting of flexible capacities of a LEC is essential. This paper proposes a methodology to estimate the flexibility of a LEC based on the LEC’s predicted consumption. In addition, the paper suggests a novel prediction method which is based on a three-branch architecture using recurrent neural networks (RNN) and long short-term memory (LSTM) units to forecast the consumption of the LEC considering its temporal dependencies. Finally, the proposed prediction methods are implemented on a case study and the results are compared with each other.

ABSTRACT. The paper at hand deals with an intelligent trolleybus guidance system. For this purpose, the first step is the generation of pseudo-measurements accumulated by the trolleybus guidance system. Based on the grid components' pseudo-measurements' information, a short-term grid state forecast occurs from this point in time. This forecast results in a wholly determined grid state for each point in time of the forecast. The generation of individual forecasts for all grid components with a special developed and adapted power flow calculation ensures a highly accurate forecast. Finally, the paper evaluates the forecast accuracy by comparing the pseudo-measurements with the generated forecasts.

ABSTRACT. Residential thermostatically controlled loads are good candidates to provide ancillary services to the power grid. However, some difficulties need to be overcome to harvest their flexibility. One of the challenges involved is to ensure that all controlled loads stay within their operational limits while being subject to uncertainties. In this paper, we examine the provision of frequency containment reserves with electric water heaters. We use an aggregate-and-dispatch method along with a chance-constrained optimization problem to manage uncertain frequency deviations. We reformulate the problem both in an analytical way and based on robust optimization. The approaches are then validated and compared in a simulation study. Compared to the analytical approach, we find that the robust optimization approach results in a similar average energy consumption, while considerably increasing the bidding potential of the cluster of appliances considered.

ABSTRACT. A central issue that was recently discussed within the power systems community is how the COVID-19 pandemic, and possibly other future pandemics, will affect the integration of renewable energy sources in the long-term. An interesting idea that may shed light on this question is the one of ''economic shocks'', according to which, if the integration of renewable sources can be described as a dynamic system operating on time-scales of years, then several months of low consumption may be viewed as a negative impulse signal (a shock), which causes reactions and counter-reactions that evolve in a closed feedback loop. In this paper we explore this idea by means of a regression model, which attempts to reflect the relations between fossil-fuel based generation and day-ahead electricity prices and demand in the European market. The results point out to an interesting phenomenon of instability in fossil-fuel based generation following a shock in consumption, which may support the claim that pandemics and other economic shocks may promote the future integration of renewable energy sources.

ABSTRACT. With the expansion of renewable energies, more grid transparency is necessary in order to continue to guarantee a stable grid operation. In transmission grids, state estimation has been successfully used to estimate the grid state based on available measurement data. However, distribution grids are not completely permeated with sensor technology, primarily due to historical and cost reasons. Installing sensor technology at each node to be observed is economically not feasible, which makes it difficult to impossible to transfer state estimation technologies to the distribution grids. Therefore, an intelligent solution approach to create transparency is needed. In this paper, we analyze the pros and cons of existing approaches for distribution system state estimation. We also show how Artificial Neural Networks have been applied for state estimation and propose an approach that combines proven solutions with Transfer Learning to make this Neural State Estimation applicable to any distribution grid.

ABSTRACT. This work focuses on calculating the available amount of frequency containment reserve (FCR) for a hybrid power plant containing a wind or solar power plant and a supporting storage. The amount of FCR has to be determined day-ahead and is linked to a high reliability requirement by the transmission system operators which is especially challenging for renewable power plants due to their weather dependency. To overcome this challenge, probabilistic forecasts are needed. A probabilistic forecast includes the current weather forecast and information about forecast errors. The determination of the forecast error is even more challenging if a storage is included because of the temporal dependencies between the errors of consecutive hours. The presented method firstly uses copula theory to model the power forecast error of a renewable power plant and the FCR demand including temporal dependencies. Secondly the amount of FCR which can be provided by the hybrid power plant is optimized based on power output and FCR demand day-ahead scenarios. The genetic algorithm is used to solve the problem and a wind and solar power plant are compared regarding their potential to provide FCR. In addition, the potential of different product lengths for the FCR bids is investigated.

ABSTRACT. For risk assessment purposes, we study how economic dispatch decisions vary with the uncertain input factors that may arise, e.g., from the use of variable renewable energies. Given a known random input distribution and linear programming (LP)-based dispatch, we aim to describe the distribution of the resulting variables and objective values. Relying on Monte Carlo simulation (MCS) is computationally expensive, especially if the uncertain factors are high dimensional. In this paper we evaluate an algorithm using multiparametric linear programming (MPLP) for this purpose. It avoids solving an LP for every sample of the random vector by characterizing the parametric LP solution as a piece-wise linear function whose pieces can be stored for repeated use. We compare the algorithm with MCS and other quasi-Monte Carlo sampling approaches for three economic dispatch use cases with varying complexity. The MPLP approach is as accurate as MCS, but up to 300 times faster for the merit order use case.

ABSTRACT. The integration of electric vehicle (EV) charging and Photovoltaic (PV) systems at residential buildings has increased in recent years and poses new challenges for the power system. Smart charging of EVs is believed to be one of the solutions to problems arising from PV and EV integration since it can improve the synergy between PV generation and EV charging. Accurate forecasts of PV generation plays an important role in smart charging schemes to optimally utilize the PV electricity for EV charging.

This paper presents an assessment of a direct forecasting method applied to an EV smart charging scheme with an objective to minimize the net-load variability. Minimizing the net-load variability implies increasing the self-consumption of PV electricity and reducing the peak loads. The PV self-consumption ratios in different forecast scenarios are compared. Results show that the smart charging with the direct forecast can achieve up to 89% of the PV self-consumption performance of the scheme with perfect forecast.

ABSTRACT. In this paper, we investigate the management of energy storage control and load scheduling in scenarios considering a grid-connected photovoltaic (PV) system using packetized energy management. The aim is to reduce an average aggregated system cost through the proposed packetized energy management controller considering household energy consumption, procurement price, load scheduling delays, PV self-sufficiency via generated renewable energy, and battery degradation. The proposed approach solves the joint optimization problem using established heuristics, namely genetic algorithm (GA), binary particle swarm optimization (BPSO), and differential evolution (DE). Additionally, the performances of heuristic algorithms are also compared in terms of the effectiveness of load scheduling with delay constraints, packetized energy transactions, and battery degradation cost. Case studies have been provided to demonstrate and extensively evaluate the algorithms. The numerical results show that the proposed packetized energy management controller can considerably reduce the aggregated average system cost up to 4.7%, 5.14%, and 1.35% by GA, BPSO, and DE, respectively, while meeting the packetized energy demand and scheduling delays requirements

ABSTRACT. This paper proposes a method of migrating workload among geo-distributed data centres that are equipped with on-site renewable energy sources (RES), such as solar and wind energy, to decarbonise data centres. It aims to optimise the performance of such a system by introducing a tunable Reinforcement Learning (RL) based load-balancing algorithm that implements a Neural Network to intelligently migrate workload. By migrating workload within the network of geo-distributed data centres, spatial variations in electricity price and intermittent RES can be capitalised upon to enhance data centres' operations. The proposed algorithm is evaluated by running simulations using real-world data traces. It is found that the proposed algorithm is able to reduce costs by 6.1% whilst also increasing the utilisation of RES by 10.7%.

ABSTRACT. The port industry is transforming into a smart port thanks to technological advancements and environmental expectations. Developing a sustainable maritime transportation system and its beneficial electrification as a proven approach in emissions reduction are gathering momentum due to technological growth. Global containerization leads to high electricity demand at container terminals, and the electricity demand is highly dynamic and dependent on different operation processes. The approach of this paper is to forecast the hourly peak load demand and short-term electricity demand profile in a container terminal. The correctly forecasted electricity demand profile is crucial for less expensive and reliable power operation and planning. First, Artificial Neural Network (ANN) method is used to predict the container terminal baseload demand. Second, worst-case simultaneous peak load is estimated. Third, the day-ahead load profile is modeled based on the handling operation scheduled for the day. The approach is implemented at the container terminal in Port of Gävle, and the results have been used in dialogue with the local energy company for the future predicted need of load.

ABSTRACT. Conventional steady-state estimates using SCADA systems focus on optimal network topologies to make them observable in steady-state. However, approaches based on steady-state or quasi-steady-state operating conditions are not applicable for power systems experiencing fast and dynamic changes. For these systems, we study power systems monitoring using of phasor measurement unit (PMU). Thanks to these devices, we can obtain fast, precise, and synchronized measurements, making possible dynamic monitoring instead of steady-state estimation only. With these devices, we can exploit simultaneous input and state estimation for a partly unknown power system. However, due to its considerable costs, the use of PMU should be selective. This paper proposes a greedy approach to solve optimal sensor placement for joint input and state estimation of partially known power grids.

ABSTRACT. The increasing penetration of renewables in distribution networks calls for the development of new technologies allowing for secure and stable operation of the power grid. Smart DSM and sector coupling are options with growing potential to integrate flexibilities into the grid, supporting decarbonization, decentralization, and digitization. Advancing the available flexibility of smart demand side applications, the energy system will be enabled to operate in a more sustainable and reliable way. The German-Finnish R&D project FUSE (FUture Smart Energy) attempts to increase the resilience of distribution grids by developing and investigating methods based on Artificial Intelligence (AI) for integrated planning and operation of multi-energy applications, supply infrastructure and local network conditions. For performance testing and assessment, the applied methods are implemented in a Smart Grid laboratory integrating process and/or buffer storage flexibility. An optimization framework is established to find the optimum system design and operation of resources. With the developed methods soft-control signals are generated automatically for available resources, enabling dynamic and decentralized control of demand side applications, while preserving independence of stakeholders. First performance testing runs show promising results. Ongoing development activities include improvement of system forecasts and determination of dynamically changing flexibility levels of participating devices.

ABSTRACT. In future power systems the share of power electronic interfaced generation will increase and respective challenges related to power system protection will emerge. In this paper the performance of different out-of-step protection algorithms is assessed using actual relays from the network and real-time digital simulator RTDS. For the performance analysis various grid conditions were considered, e.g. system strength, line lengths and generation mix between renewables and conventional synchronous units. The results indicate that there are performance differences between the algorithms (relays), therefore analysis considering the specific system characteristics should be made. Information obtained from this analysis is essential input for transmission system operators when planning their system operation and protection for future networks.

ABSTRACT. In this paper, we propose a modified data-enabled predictive control (DeePC) algorithm to solve load frequency control (LFC) problem of power systems. Compared with the existing DeePC algorithm, which is based on behavioral system theory, the following three aspects of modifications are made based on the characteristics of LFC problem of power systems with high penetration of renewable energy sources. First, the external input signal, i.e., the net load demand, to the system is considered also as the control input signal so that predictive control can be achieved for LFC only using input/output data. Second, the l2 regularization term and slack variables are added on the DeePC algorithm to address the uncertainty of net load demand. Third, the mechanical power input of the generator is regarded as an output of the power system model so that generation rate constraints (GRC) can be dealt with by making some constraints on the output. By applying the modified DeePC algorithm, effective control for LFC can be achieved in a model-free and receding horizon control framework. Simulation results on a power system with two control areas demonstrate the effectiveness of the DeePC based method.

ABSTRACT. The level of penetration of distributed energy sources in distribution networks has been steadily increasing in the recent years causing significant changes and fluctuations in the voltage profiles. For this reason, distributed generators are being asked to participate in the voltage regulation. This task is commonly achieved by applying additional droop controllers to amend the active and reactive power references. However, in weak and resistive grids the coupling between the two powers reduces the efficacy of this approach. In this paper, a controller that emulates the response of an admittance is used to provide voltage support to the distribution network. With this control strategy, voltage support can be provided by taking advantage of the existing coupling. The controller performance is electrically equivalent to an admittance, simplifying the understanding of the proposed concept. The inclusion of power and current restrictions in the control scheme is discussed. Simulation results from Matlab/Simulink and continuation analysis from Matcont are both used to validate the contributions of this work.

ABSTRACT. Distance protection is extensively used in distribution and transmission grids world wide. However, due to the increased uptake of power electronic converters, traditional distance protection can fail. The present paper proposes a novel fault distance estimation method for lines linked to power electronic converters. By first eliminating the fundamental frequency components from voltage and current measurements, the method calculates different possible fault locations. The true fault location is found where the calculated fault impedance is purely resistive. First the concept of the proposed method is described after which the mathematical calculations behind the method are elaborated. Finally, transient simulations show the potential of this novel method.

ABSTRACT. Grid-forming inverters-based autonomous microgrids present new operational challenges as the stabilizing rotational inertia of synchronous machines is absent. We propose in the paper a control architecture for frequency and voltage control with good scalability properties. At slower timescales it allows to incorporate a distributed secondary control policy for which we provide a stability result with line conductances taken into account. At faster timescale this control architecture satisfies a passivity property for a wide range of parameters. The distinctive feature of the voltage control scheme is that it has a double loop structure that uses the DC voltage in the feedback control policy to improve performance. The frequency control policy employs the inverter output current and angle to provide an improved angle droop policy. The performance of the control schemes is illustrated via advanced simulations.

ABSTRACT. This paper addresses issues for the classes of protection relays defined in the Common Information Model (CIM) and identifies the advantages and issues for extension cases of the CIM classes proposed in previous studies. Then, in order to solve the issues, this paper systematically designs classes that represent functions of protection relays and proposes other classes that can flexibly support data exchanges between various applications for power system protections. Finally, it shows that the designed classes of protection relays can be mapped onto combinations of protection relays and those setting values in the sequence diagram for the line protection based on distance protection of the direct grounding system.

ABSTRACT. In this paper, we propose a bilateral peer-to-peer (P2P) energy trading scheme for residential prosumers with a simplified entry to the market. We formulate the market as an assignment game, a special class of coalitional games. For solving the resulting decision problem, we design a bilateral negotiation mechanism that enables matched buyer-seller pairs to reach a consensus on a set of ``stable" and ``fair" trading contracts. The proposed negotiation process can be executed on possibly time-varying communication networks with virtually minimal information requirements that in turn preserves privacy among prosumers. Numerical simulations illustrate the beneficial features of our P2P market model and negotiation protocol.

ABSTRACT. Introducing the distributed generation, namely renewable-based technologies such as wind and solar, increases the complexity of the management due to intermittent behaviors. So, the focus must be turned to the consumers' side through demand-side management. The authors proposed a methodology able to deal with the uncertainty from the active consumers' response to Demand Response events. The creation of a trustworthy rate defines the availability and willingness, considering previous experiences, from a consumer to a certain context. With this, the aggregator will be able to select the optimal consumers when a reduction is needed. The study presented varies the minimum rate on selecting consumers for scheduling along with the remuneration value. The importance of the right incentive on reducing the response uncertainty is proved.

ABSTRACT. As the distributed renewable energy and smart meters develop rapidly, the value of flexibility from the prosumers’ end gets more attention. However, the heterogeneous nature of flexible household devices is often overlooked. Neither their unique physical feature nor their impact on users’ comfort level is investigated. Besides that, the flexibility providers are often treated as price takers. They do not show their deserved market power during the pricing period. In this paper, we propose a prosumer-centric competitive day-ahead flexibility market. Based on the Monte Carlo method, the Geometric Brownian Motion is used to simulate the price path to determine the value of flexibility as a commodity. With a naïve seasonal neural network, the Home Energy Management System will generate flexibility bids locally and submit them to the market while considering prosumers’ comfort level as an important constraint. A series of household consumption profiles from Germany is used as a study case. The simulation results show that the proposed flexibility market is capable of providing needed service for distribution system operator. The market can also provide a noticeable financial benefit for prosumers.

ABSTRACT. This paper presents a novel method to optimally place phasor measurement units (PMUs) and Photovoltaic power nowcasting devices (PNDs) in active distribution systems (ADS) with high PV penetration. The paper solves two placement problems for active distribution systems, i.e., achieving observability and desired measurement redundancy and probabilistically ensuring that the ADS will remain observable despite topological changes. The Genetic Algorithm (GA) is implemented to solve both non-linear optimization problems.

ABSTRACT. This paper proposes the parameter tuning methods for building the dynamic Digital Twin of generation unit in the power grid. Recently, the Digital Twin is paid more attention to its application in the digitalization of power grid, which could improve the accuracy for state monitoring, support the security assessment and operate the predictive simulation to assist the operator for the optimization of the power system. To achieve them, the investigation of the method for building the Digital Twin is essential. This paper provides four methods for dynamically parameterizing the Digital Twin to keep its adaptivity. The parameter tuning is the sine qua non to keep the Digital Twin alive with the dynamically regulating the parameters for the replication of the physical system feature. To investigate the efficiency of building the Digital Twin by these methods, the Cigré benchmark is utilized as the standard physical power grid. The fourth-order synchronous generator is applied for building the Digital Twin with the parameter estimation methods.

ABSTRACT. Phasor Measurement Units offer considerable insights into system disturbances whether they are connected to the transmission or distribution system. Multiple PMUs provide a large amount of streaming data that can reveal an event’s severity, duration, and location. This paper discusses the development of a passive MQTT event-stream detection framework between 2 PMUs on the island of Ireland. It presents a simple and straight forward methodology of event detection and event classification. Applying simple query functions to PMU streams such as system frequency, RoCoF, and symmetrical components provides an efficient way of detecting events on a power grid. Power system events such as short circuits, frequency transients and Very Low Frequency Oscillations captured on the Irish Electrical grid are discussed to validate the proposed framework.

ABSTRACT. The increase of distributed energy resources, namely rooftop photovoltaic (PV) systems and electric vehicles (EV), has brought new technical challenges related to the operation and planning in low-voltage distribution networks. The modification of electrical network dynamics has exposed a lack of observability in this side of the electric system requiring innovative techniques to increase flexibility, reliability and security of supply in power networks. A supervised non-intrusive load monitoring method commonly used for the identification of traditional electrical appliances connected behind-the-meter in household distribution boards is proposed in this research study. The work is based on a low-complexity, effective machine learning algorithm to identify the presence of PV generation or EV power consumption in aggregated measurements of low-voltage networks. The model is developed in the IEEE European low voltage test feeder and it evaluates several scenarios using data with 1-minute sampling simulated with the tool OpenDSS and with k-nearest neighbour as classification algorithm. The results of the proposed method exhibit high performance for both PV and EV identification and illustrate the potential for these non-intrusive monitoring solutions in supporting the integration of distributed resources in low-voltage power networks.

ABSTRACT. The proliferation of smart meters has led to the development of data-driven control algorithms in power distribution systems. However, many of these algorithms rely on accurate topological data. Network reconfiguration can change the topology of the system. It is critical for the system operators to quickly identify the updated topology and take appropriate control actions accordingly. This paper develops an algorithm based on graph signal processing to detect changes in the topology and to identify the new topology after reconfiguration using smart meter voltage magnitude measurements by comparing the smoothness of the signal on the possible topologies. The algorithm is fast, online, and requires only voltage magnitude measurements. Numerical results are presented on 16, 33, and 70-bus test cases. The algorithm achieves good performance for both detecting changes and identifying topology across all test cases.

ABSTRACT. This paper presents a three-phase linear state estimation (LSE) algorithm for power networks observed by enough supervisory control and data acquisition system (SCADA) measurements and a few phasor measurements provided by a limited number of phasor measurement units (PMU). Since SCADA measurement equations are nonlinear, three-phase state estimation (SE) is computationally costly. Furthermore, incorporating SCADA and PMU measurements in the same solution requires a new formulation. Both of these challenges are addressed in this paper by iteratively transforming SCADA measurements into equivalent phasor measurements with the help of few existing PMU measurements. Computational efficiency is further improved by employing previously developed modal decoupling which facilitates the use of single-phase SE algorithm instead of the fully coupled three-phase solution. Simulation results on two IEEE test cases show the high efficiency and accuracy of the method.

ABSTRACT. As we move more towards integrating more distributed energy resources (DERs) in the distribution grid, state estimation plays a crucial role in monitoring the grid. In this study, the approximated Distflow model has been shown. This formulation is used to include the line losses in the newly proposed distribution system state estimation (DSSE). The newly proposed DSSE model is evaluated with other DSSE models on the IEEE-37 node test feeder. All of the models are solving a weighted least square problem using the Gauss-Newton method. In all the cases the proposed DSSE model outperforms the other models. Furthermore, it is recommended to have a power flow measurement from 0 to 1 for better estimates for future DSSE methods which can be easily obtained from the measuring device at the transformer.

ABSTRACT. An incremental learning system has been developed to reduce the time to begin operations of a monitoring system. The system consists of weather stations installed at two electrical towers at different topographical locations. Based on weather simulations and numerical weather predictions, a machine learning ampacity forecasting system was pre-trained. Afterwards, monthly weather measurements were added to the training process to analyze the accuracy improvement of the ampacity predictions. In this study, feedforward neural networks and gradient boosting regressors are the base of the machine learning system. The models achieve an improvement of up to 20% after six months of incremental dataset expansion. Machine learning has shown accuracy improvements of the ampacity predictions especially when the considered place is surrounded by trees. The implementation of incremental learning reduces the time into operations of a just installed weather based overhead line monitoring system. This fosters its implementation and use as an optimization method of electrical networks.

ABSTRACT. The dynamic performance and stability of LCC HVDC is dependent upon the grid impedance seen by the converter. The performance robustness of LCC HDVC can benefit from additional information delivered by the online measurement of grid impedance/fault-level. With the accelerating transformation of the power systems towards a renewable generation-based one, it is important to include a measure of the amount of nearby voltage controlling devices to the concept of fault level. Another challenge faced by the LCC converters is big sudden changes in grid impedance, like the ones experienced after the trip of important AC network assets. This paper addresses these challenges by using a more comprehensive concept of network strength which defines the fault level considering nearby voltage controlling devices. Furthermore, the paper proposes a method to quickly estimate the resulting fault level after a sudden change in grid impedance. The task of network strength estimation is handled by using Artificial Neural Networks (ANN). The results confirm that this method not only provides a reliable steady-state estimation but also a fast and accurate estimate of the resulting fault level after an AC fault clearing.

ABSTRACT. Low-frequency oscillations in power systems have been commonly damped by using additional loops in the control system of synchronous generators. With the massive integration of converter-interfaced generators (CIGs), power system operators have recently started requesting the same power oscillation damping (POD) capability from those power plants as well. CIGs are known to have a flexible active and reactive control for providing POD services. However, their active and reactive power injection is highly constrained by the CIG operational limits. In this paper, a method for designing a high-performance POD algorithm for CIGs taking into consideration active and reactive power constraints is presented. This method uses all the available active and reactive power to maximise the POD effectiveness. The design procedure is based on frequency response techniques and only an estimate of the frequency response at the frequency of power oscillation is required. The proposed methodology is tested on a two-area power-system model, with an additional CIG. Electromagnetic simulations performed in Matlab and SimPowerSystems are used to validate the main contributions of this work.