Reinforcement Learning (RL) has emerged as a promising solution for defining the optimal dispatch of Energy Storage Systems (ESS) in distributed energy systems. However, a notable gap exists in the literature: a lack of comprehensive and fair comparisons between different RL algorithms, particularly between linear and nonlinear approaches. This study critically evaluates the trade-offs between computational efficiency and operational accuracy among various Linear RL (LRL) strategies and compares them against the nonlinear Deep-Q-Network (DQN) algorithm. Through a comprehensive analysis, this study benchmarks the model-based Mixed-Integer Linear Programming (MILP) results to assess and compare these algorithms’ convergence, training efficiency, and optimization accuracy. Results indicate that while LRL approaches the operational cost accuracy of DQN, it faces significant trade-offs in computational efficiency and struggles with generalization across larger and varied datasets. The results illuminate critical areas for further development in LRL methodologies, particularly in enhancing their adaptability and generalization capabilities.
The rising need for energy flexibility has paved the way to explore different solutions in various sectors. The residential sector represents one group of consumers who have very low individual flexibility, but due to their large numbers when coupled together, can help the energy system during times of need. This study investigates residential consumers’ preferences for Demand Response (DR) programs, aiming to debunk prevalent myths and provide insights for stakeholders. Analyzing data across demographic divisions like gender, age, education level, household composition, and type, our findings challenge the existing literature, highlighting the need for tailored strategies to effectively promote residential DR adoption. This study not only offers valuable insights into residential DR motivators but also underscores the importance of adapting strategies to reflect the consumer background and meet evolving consumer needs.
This work primarily focuses on investigating how the frequency response of a power system may change due to the addition or failure of tie-lines. The proposed method enables the analysis of two specific scenarios: firstly, assessing the potential enhancement in frequency response by adding an extra tie-line between two areas, and secondly, evaluating the deterioration in frequency response when one tie-line is disconnected. One notable advantage of the proposed method is its reliance solely on actual frequency response measurements from individual areas, thereby eliminating the need for extensive knowledge of system parameters. The effectiveness of this method is evaluated by comparing the analytically derived frequency responses with those obtained through simulation.
Wireless long-term evolution (LTE) networks are considered as a viable option for deployment in smart distribution systems due to their reliability, scalability, and cost-effectiveness. Yet, co-simulation platforms with the ability to analyze the performance of wireless LTE networks are lacking in the existing literature. This paper, for the first time, presents a real-time co-simulation platform based on Typhoon HIL and OMNeT++ for wireless LTE networks in smart distribution systems. The performance of wireless LTE networks in smart distribution systems is investigated using the proposed co-simulation platform. A case study related to distribution automation is used to demonstrate the usefulness of the proposed co-simulation platform for examining the performance of wireless LTE networks.
Tidal range energy is a renewable energy source that harvests the gravitational potential energy of seawater derived from tide level variation. Tidal energy generation is highly predictable and can be used as a reliable energy source. The generation capability of a tidal range scheme could be increased by utilising reversible turbines with pumping functions that increase the water level difference. Furthermore, adopting an adjustable number of active turbines during generating and pumping can help the tidal range scheme better regulating its electricity generation. Therefore, this paper proposed an optimal operation model of a tidal range scheme with reversible turbines, in which the tidal range scheme could work with a flexible generation plan to maximise its total energy generation or daily revenue. A case study is conducted to demonstrate how the operation profiles, generation and revenue vary when the tidal range scheme incorporates the pumping turbines and adopts a flexible operation plan.
Green ammonia production stands as a pivotal component in the transition towards sustainable energy and agriculture, poised to revolutionize numerous industries. This paper presents an optimization control framework for industrial green ammonia fuel hubs to engage in electricity, hydrogen, and oxygen markets, addressing both economic and technical considerations. By evaluating scenarios with and without battery storage, this study demonstrates the potential for increased profitability and energy independence through secondary reserve market participation, alongside insights into the economic viability of photovoltaic investments. These findings underscore the importance of considering market dynamics and technological integration in the sustainable operation of green ammonia production hubs.
Efficiency and safety are paramount in the ever-changing world of energy supply. Modernising power systems requires the development of new features and extensive testing. Ideally, this should be done under real conditions. Current energy system automation focuses mainly on grid protection, grid frequency control and voltage regulation and is mainly found in high and extra-high voltage grids. However, there is an urgent need for further automation in medium and low voltage grids. Alternatives such as a massive increase in staff and costly grid expansion are difficult to implement. Legal requirements such as German Energy Industry Act (EnWG) also underline the urgency of digitising the energy transition. This paper therefore presents the concept of a test bench for researching and validating automatic resupply for a specific application in the medium-voltage grid. This automation solution automatically restores power to an electrical station following a fault shutdown.
Frequency Response (FR) strategies play a pivotal role in modern power systems by enabling grid operators to manage electricity production patterns and balance supply and demand in real time. This paper investigates the potential of hybrid wind power plants with integrated energy storage systems and their efficacy in enhancing grid resilience during high load demand. Simulation studies and case analyses explore the dynamics of high and low load demand and their impact on system reliability and renewable energy integration. The results are validated using the Nordic 44 model and underscore the importance of frequency response from wind power plants as a flexible tool for addressing grid challenges during periods of high load demand and promoting the efficient utilization of renewable energy resources.
The electrification of various sectors and the expansion of renewable energy resources (RES) leads to a change from the historically established and planned vertical load flow in the electrical power system to a horizontal one. This is placing a particular strain on the distribution grids to which the new loads and decentralized generators are connected. The cellular energy system approach is expected to work with a high proportion of RES and ensure a high level of supply security. This paper investigates the autonomous control of vehicle batteries for a cellular grid approach. The hierarchically lowest cell manages loads at the household level, in this case, the bidirectional charging of electrical vehicles (EVs). The higher entity ensures safe grid operation at the transformer level. The aim is to optimize the utilization at the transformer and supply line level to avoid congestion. The concept is validated on a test low voltage (LV) grid using three scenarios based on the grid expansion plan in Germany. The results show that a cellular grid approach can help reduce the utilization of operational resources and avoid overload with an unknown action space at the household level. For a future scenario (2037), the overload at the transformer is reduced by 0.72 pu and the overload at the critical supply line is reduced by 0.70 pu.
European low voltage distribution systems are primarily three-phase four-wire networks, where the three phases have uneven load connections. With the rising number of electric vehicles (EVs) in the market, the charging behaviors of the single-phase EVs are inevitably leading to a more severe phase unbalance issue in the grid. To address such concerns, this paper proposes a smart charging control scheme that utilizes phase mode switching functions of electric vehicle supply equipment (EVSE). The connected three-phase EVs can be switched to single-phase charging mode by charging only at the first phase, hence alleviating the congestion on the other phases of the cluster. A case study using charging data from a real-world situation in a public working place in Athens is conducted, to demonstrate the functionality of the proposed phase mode switching method. The simulation outcomes reveal that the phase mode switching scenario reduces the collective charging time by 8.4% and improves maximum phase power unbalance by 31% compared to the benchmark scenario. The results indicate that the smart charging control manages to effectively mitigate the phase power unbalance issues among three phases and allows the cluster to charge with more flexibility.
Grid operations plays a critical role in ensuring a reliable power supply ranging from the control center to the field units. Developments due to the decarbonization of the energy, transport and heat sectors are leading to a more complex power grid and an increasing workload for grid operations staff. The increasing workload needs to be addressed by optimizing the grid operations work processes. This can be done by assessing the optimization potential of each process with all the underlying process steps. An important step in this assessment is the description and analysis of the work processes. This paper presents an approach to model work processes in grid operations. The model aims to describe work processes, their components and function in the context of grid operations. The model consists of a mathematical definition, a visualization approach and a decision model. The decision model allows the estimation of decision outcomes based on the available information. Accompanying our model, we introduce an evaluation method by defining different metrics for evaluating and comparing multiple processes. Finally, we demonstrate the application of our model on a case study. For our case study we collaborated with two grid operators and created a model for the fault management process in distribution grids. Based on that, we evaluate the influence of information source outages on the process.
This paper evaluates the participation of a grid-connected BESS Energy Storage System (BESS), in the Day ahead (DA) and Frequency Containment Reserve (FCR) markets in Europe. Through annual simulations, the study demonstrates the substantial revenue potential of providing multiple services (DA+FCR) by a single BESS, resulting in a significant 74% increase compared to the sum of individual markets’ participation. Furthermore, a comprehensive sensitivity analysis is conducted to determine the optimal sizing of the BESS, revealing the advantages of higher power-to-energy ratios in terms of profitability and payback periods. An oscillation penalty is introduced to mitigate the BESS degradation by reducing the BESS’s depth of discharge. This leads to a reduction of BESS capacity fade behavior by 7 % while incurring only a marginal 9 % loss in the annual revenues. These findings contribute valuable insights for decision-making in deploying grid-connected batteries, considering revenue optimization and BESS performances.
Data analysis and intelligent monitoring hold notable significance in the new generation of industry, aiming to enhance predictive maintenance and fault detection. This study introduces a predictive system for control within hydropower plants. The approach involves the integration of data gathered from various sensors, based on data processing techniques and deep learning (DL) algorithms. The study explores the efficacy of Long Short-Term Memory (LSTM) algorithms, renowned for their precision in time series prediction, across two different structural configurations.
Voltage calculations are key for most distribution network analyses. However, the challenge in low voltage (LV) networks is that electrical models are not readily available and, therefore, accurate voltage calculations (via power flows) are not possible. Alternatively, regression methods can be used to capture the relationships among the historical smart meter data of customers (P, Q, and V) and the corresponding LV network. This paper improves a previously proposed methodology to carry out electrical model-free voltage calculations based on Neural Networks (NNs) so it can be used with real smart meter data. The effectiveness of the improved methodology is demonstrated using data from 6 Australian LV networks. The results show that the improved methodology significantly reduces the computing time required to produce the NN, making it an accurate and extremely fast alternative for distribution companies to calculate voltages without electrical models.
In this paper we analyze potential economic benefits of a collocated renewable energy production facility containing photovoltaic generators with hydrogen energy storage (HES). We consider a data modelling approach which is based on real-world company data from a local commercial PV installation in NRW, Germany. Using non-linear programming, we model potential profit gains from the generator perspective. A retrospective optimization is conducted based on the German-Luxembourg intraday continuous price index from EPEX and company generation data. The optimization aims to determine the best possible profit that a company could have achieved with a 200.00 MW HES availability in 2023. Our results indicate an extra profit premium of 4.86 percent from the reference. Despite positive financial results, the intensity of HES utilization appeared to be moderate, as the HES was operated on 13.70 percent of the days in the year. Our analysis suggests a theoretical possibility of the usage of HES as energy storage for generating elevated profits. However, these results were generated under an optimistic assumption of the relative maturity of the technology. This study offers insights for strategic aspects of the integration of hydrogen energy systems into the national energy grid, optimizing resource allocation, and aligning with Germany's long-term sustainability goals. The analysis also contributes to the literature that so far remains relatively scarce in the domain of the commercialized HES dispatch optimization.1
Electrical grids are increasingly congested, which causes stability and safety risks. To get more insights in how congestion can be managed, this paper analyses whether bus characteristics exist that have a consistent influence on grid loading. To study this, a metric for quantifying bus influence on grid loading is introduced and applied in two case studies. The first case study investigates if there are characteristics that correlate with congestion consistently, regardless of grid topology. The results from this case study suggest that none of the evaluated characteristics consistently correlate with grid loading. These results imply that topology should be explicitly considered in congestion management. The second case study investigates the variance in the correlation between bus characteristics and influence, within a single topology, over a variety of conditions. The results of the second case study suggest that this correlation is robust to reasonable changes in bus loads.
The electrification of energy demand and the challenges associated with the energy transition pose significant uncertainties for rural distribution system operators (DSOs). Digital twin (DT) frameworks can provide real-time analysis, modeling, and simulation capabilities. However, traditional DT applications rely heavily on data, presenting challenges for rural DSOs with limited monitoring coverage and constrained data availability. The proposed load shedding application combines the DT framework’s data-driven load estimation and graphbased grid modeling offering operator assistance in reduced data availability scenarios. This is exemplified with a case study using data from a North-Eastern Spain Rural DSO.
A probabilistic method has been proposed to study the interaction between the active distribution network and the transmission network at the grid supply point. The Monte Carlo method has been adopted to account for uncertainty arising from renewable energy generation. Subsequently, the probabilistic load flow has been run to identify all operating points in terms of active and reactive power. Following this, the operating envelope at the point of interaction (grid supply Point) is plotted in the P-Q plane using the convex hull method. Four types of load models (static, dynamic, and composite) have been considered to observe the importance of choosing a realistic load model and its influence on the operation envelope. The results show that the proposed method is highly efficient for creating the parameter space to assist with studying distribution-transmission network interactions at the interface point.
Power systems are shifting towards a sustainable and interconnected future, where renewable power is transmitted over long distances to demand centers. This paper proposes a High Voltage Direct Current (HVDC) grid over a 6-zone AC/DC grid inspired by the European power system. The test case is analyzed with Optimal Power Flow (OPF) and Electromagnetic Transient (EMT) models in normal operations and for two contingency scenarios, namely loss of an HVDC link in the meshed AC/DC grid and loss of a radial HVDC link both in a congested hour. The results show how the HVDC grid helps avoid load curtailment when a link is lost in its meshed part. Furthermore, a robust control philosophy to maintain the power balance in hybrid AC/DC grids is discussed.
The green shift has resulted in a growing volume of electricity connection requests for power grid operators in Norway. The grid is however in most areas congested, and investments are needed. This paper utilises data-driven approaches to investigate the feasibility of co-localising high electricity demand customers in grid connection request queues to reduce grid investments in congested grids for a distribution grid operator in Norway. This is performed by matching clusters of real electricity demand profiles to investigate the compatibility between the different customer groups. The results show promising opportunities for co-localising different customer groups. Especially the demand profile for charging stations is compatible with the demand profiles of other sectors.
