Abstract High-temperature waste heat gases above 700°C represent a recoverable heat source for power cycles. Conversion efficiency is governed mainly by temperature. Still, it can be enhanced by systematically formulating the cycle. This paper proposes a new concept for efficiency improvement of inverted gas turbines through ejector-assisted steam injection. Hereby, the waste gas enters an ejector, where it is compressed using high-pressure, high-temperature steam. The resulting mixture expands from higher-than-atmospheric to sub-atmospheric pressure in a turboexpander. The low-pressure mixture is cooled first in steam-generating heat exchangers. Then, an additional heat exchanger is proposed to cool the wet waste gases below the water saturation temperature, where the injected steam condenses. This reduces turbocompressor flow rate, resulting in lower power consumption. Finally, the condensed water is pressurized in a pump and heated and evaporated in the steam generator. The high-pressure steam is used in the ejector. To analyze system performance under varying conditions of waste gas inlet temperature, vacuum intensity, steam pressure, and steam injection fraction, a thermodynamic model is developed. Results show that thermal efficiency of 0.3 at 1300°C waste heat temperature, 0.26 at 1100°C, 0.21 at 900°C, and 0.17 at 700°C is achievable. Recommendations are provided for vacuum and steam parameters that maximize thermal efficiency. In this way, the study makes a broader scientific contribution by establishing a conceptual framework applicable to advanced cycle investigations for the systematic integration of waste heat.

The adoption of Distributed Energy Resources (DERs) causes fluctuating peak supply at certain times of the day, leading to grid congestion and energy shortages in community micro-grids during other periods. However, the rise of DERs has led to community-based microgrids called Energy Communities (ECs), which consist of flexible sources like DERs, energy storage systems, and electric vehicles. Transactive Energy Management (TEM) approaches are essential for leveraging the flexibility of ECs to support various grid objectives, including congestion management, peak shaving, and cost minimization. This paper presents a survey of TEM approaches with a focus on ECs. The TEM approaches from the surveyed article are placed in one of six clusters: Energy management goals, robustness, computational nature, privacy, scalability, and compatibility for isolated ECs. This survey presents the direction in which research in TEM approaches for ECs is heading and, combined with the needs of the ongoing energy transition, suggests directions for future research efforts. Specifically, it is observed that while the initial energy management approaches consisted of centralized solvers dependent on perfect predictions, there is a gradual shift towards investigating decentralized and robust approaches to energy management. Drawing upon the inferences from the clustering, this paper presents a discussion on the need for agent-based approaches evolving into a multi-agent energy system and eventually transforming into a scalable approach consisting of a system of systems.

Reaching global climate change targets requires new concepts for industrial energy systems. In this context, high-temperature heat pumps (HTHPs) are efficient technologies for waste heat recovery and process heat generation. Their integration into grid-connected multi-energy systems (MES) offers operational flexibility but also increases complexity due to coupled thermal and electrical energy flows. To address this complexity, HTHP operational optimization has recently gained attention but has typically been limited to single-temperature, single-stage heat pump configurations. This paper introduces a component-level operational optimization methodology for grid-connected industrial MES. The operating load of each component is optimized to meet diverse thermal demands and improve grid interaction. The methodology is demonstrated on a HTHP-integrated industrial MES. The MES includes a novel multi-temperature multi-stage R718 HTHP configuration, thermal energy storage (TES), and photovoltaics. A key novelty is the coherence of HTHP and TES via direct flash evaporation and condensation, enabling cascade arrangement of single-stage centrifugal compression units to upgrade waste heat across multi-temperature levels. A physics-based model is developed to assess performance in design conditions and generate simulation results. Surrogate models are then derived for each HTHP component and combined with a physics-based TES model to optimize the system operation. The results show that the HTHP achieves a high COP per stage, high exergy efficiency, and improved flexibility. Compared to rule-based operation, the optimized operation increases renewables share and reduces grid stress and peaks. The findings demonstrate that the component-level approach captures internal thermodynamic interactions and multi-temperature energy flows while remaining tractable for operational optimization.

Present-day cyber-physical systems, such as the Smart Grid, lead to the integration of multiple sub-systems into one single intertwined system. Such systems are characterized by many inter-dependencies between these sub-systems. This makes it complex to correctly assess the impact of new defense mechanisms with respect to the safety and security of the system as a whole. Existing formalisms, such as fault and attack trees, cannot describe the full system complexity. This paper presents a novel integrated model, namely the Attack-Fault-Defense Tree (AFDT), and tools to analyze such cyber-physical systems. The presented visual representation allows experts from various disciplines to discuss system dependencies together. In addition, we also present how minimum cut sets can be derived to formally quantify how the safety and security of the overall system is enhanced with the implementation of new defenses. We furthermore extend this to quantitative analysis by assigning safety and security metrics to these minimal cut sets. The presented AFDT is applied to the Gridshield concept, a novel defense mechanism to prevent grid overloading in power grids due to simultaneous charging of electric vehicles. One sentence summary: We introduce Attack-Fault-Defense Trees (AFDTs) and apply qualitative and approximate quantitative analysis to the Gridshield smart-grid defense mechanism.

The growing complexity of micro-grid management and the demand for resilient, sustainable energy systems require solutions that go beyond traditional management strategies. This paper introduces a Multi-Objective Energy Management System (MOEMS) designed for micro-grids and energy communities, emphasizing resilience and sustainability. Unlike conventional Energy Management Systems (EMS), which mainly focus on cost and efficiency, MOEMS takes a user-centered approach. It incorporates a democratic decision-making process that involves all stakeholders, enabling personalized energy management tailored to user preferences and environmental considerations. MOEMS is used to address grid challenges like power congestion and voltage issues while balancing diverse stakeholder goals. The optimization problem is formulated as a mixed-integer non-linear program and adopted to be solved using a free and open-source solver. The proposed framework leverages the results of a multi-objective optimization model, allowing users to define their preferences. By specifying an acceptable solution space, the central controller in the micro-grid can optimize operations while ensuring that the selected solutions align with user expectations. The system is validated through simulations and a real-world micro-grid case study, demonstrating its adaptability to different setups. To evaluate its effectiveness, MOEMS is compared with traditional EMS approaches, including profile steering and methods that prioritize economic and environmental factors. A real-time implementation in the Kezo micro-grid further demonstrates its capability to dynamically manage energy flows, meet user energy demands, and adapt to real-time fluctuations in supply and demand. Significantly, MOEMS achieved up to 22% higher annual electricity cost savings and a 37% reduction in CO2 emissions compared to traditional EMS methods.

Energy storage is crucial for reducing the imbalance between energy demand and generation and thus an important asset for increasing the share of renewable energy. This work proposes an integrated energy system that stores excess renewable electrical energy by producing renewable methanol and generates electrical energy in a supercritical CO2 gas turbine system. Hereby, the supercritical CO2 gas turbine is an essential component of the integrated energy system, allowing self-sufficiency and high round-trip efficiency. To assess the thermal characteristics of supercritical CO2 gas turbines, a thermodynamic model is developed in MATLAB/CoolProp, with a focus on the following aspects: heat recuperation with pinch analysis, thermodynamic properties of CO2 including the variations in the critical region, thermodynamic properties of the CO2/H2O mixture and condensation of water by cooling below the saturation temperature for the partial pressure of water in the mixture. A multi-parameter analysis shows the influence of various operating conditions, such as (i) compressor outlet pressure, (ii) gas turbine inlet temperature, (iii) regenerator temperature difference, (iv) compressor isentropic efficiency, (v) gas turbine isentropic efficiency, and (vi) pressure drop in heat exchangers. The results of the investigation show that high thermal efficiency of the supercritical CO2 gas turbine (above 60 %) can be achieved. Hereby, the low compression work near the critical point, operation at high temperature and high pressure, and effective heat recuperation in the cycle are essential to achieve this high efficiency. The mass and energy flow balances of the integrated energy system lead to self-sufficiency and high round-trip efficiency (37 %).

This paper presents a congestion-aware Energy Management System (EMS) for Electric Vehicle (EV) charging hubs with on-site PV generation and energy storage in the built environment. The concept of the system is based on a time-discretized scheduling approach that incorporates all relevant assets at the hub to charge the EVs without creating grid congestion problems. Since EV charging schedules in general have to be determined based on incomplete and often inaccurate forecasts and information, the scheduler is combined with an online control policy that operates to compensate for forecast errors or that can be used to react on external market price signals or congestion information. A simulation study of the scheduling concept and its underlying model shows that incorporating the current peak-tariff structure used in the Netherlands into the EMS can contribute to reducing the peak demand on the grid throughout the year for the proposed charging hub by 8.4%. The simulations furthermore show that the application of a peak shaving approach can lead to a peak load reduction of up to 36%, at only a 2.6% increase in total operational costs. The research objective of this study is to investigate how the theoretical model of the EMS can be applied in a real-world implementation. For this, the EMS is implemented in a real-life operational demonstration of the concept with real devices, data and users. The demonstration of the EMS in a real-world implementation advances the state of knowledge on these topics by demonstrating conflicting interests the lack of information-exchange between different stakeholders and the effect of this on the EV charging ecosystem. Further research is necessary, specifically on the interaction between EVs, Charge Point Operators, Mobility Service Providers and other relevant market parties such as energy traders.

The Dutch electric vehicle (EV) fleet is growing rapidly, which comes with challenges regarding the coordination of charging in locations like office parking lots. As the transition to EVs is at least partially motivated by sustainability goals, this work considers not only the omnipresent objective to flatten the aggregated power profile behind the transformer, but also to reduce (time-of-use) carbon emissions associated with the power drawn from the grid. Concretely, this paper presents results of a simulation study for a scenario with 400 EVs based on a real-world office parking lot. The analysis focuses on the impact of using flattening or carbon objectives for the coordination of EV charging, as well as the potential of optimizing for a weighted combination of both objectives. The findings clearly illustrate the trade-offs between flattening and carbon-minimization. While naively minimizing time-of-use carbon emissions may result in an increase of peak power drawn from the grid, assigning some weight to the flattening objective reduces the magnitude of peaks and allows to implicitly scale peaks to comply with the size of the available grid connection.

The flexibility of many electrical devices allows mechanisms to be deployed that mitigate grid congestion. For such mechanisms, control algorithms utilising uni-directional communication provide regulatory and privacy advantages over bi-directional approaches. This paper presents and examines the effectiveness of a novel uni-directional congestion mitigating measure named uni-directional auctions. This measure considers individual user requirements and gives priority to devices with a higher relative need for grid capacity. Simulations of 10 households show that the proposed uni-directional auctions are 94.3% as effective as their bi-directional counterpart.

This paper presents a decentralized energy management approach based on a Multi-Objective Energy Management System called DMOEMS, designed for Energy Communities (ECs), aiming to create resilient and sustainable energy systems. DMOEMS integrates a multi-objective optimization framework that aggregates conflicting goals-minimizing electricity cost and CO2, reducing Photovoltaic (PV) curtailment, and maximizing self-consumption-by converting them into a single objective using user-defined weight factors. Each local controller optimizes the operation of distributed assets based on localized constraints and user preferences, while an EC controller coordinates aggregated power profiles through an iterative feedback mechanism. This coordination dynamically adjusts weight factors and curtailment strategies to resolve grid congestion without compromising individual privacy. Simulation studies on the realistic Aardehuizen EC demonstrate that DMOEMS effectively mitigates overloading scenarios across diverse operating conditions (high EV charging, normal demand, and excess PV generation), enhances user satisfaction, reduces operational costs, and lowers CO2 emissions. The proposed framework highlights the potential of a democratic, decentralized approach to energy management in modern ECs. The numerical results for asset management using DMOEMS indicate improvements in different aspects such as reduction of 20 % in CO2 emissions, improvement of 4 % in electricity cost savings, and a 30 % reduction in PV curtailment relative to baseline scenarios. Furthermore, the proposed mechanism in the DMOEMS shows improvement in computational cost by converging faster to resolve grid congestion compared to conventional approaches.

In the electrical grid, operations such as averaging of local power profiles must be executed without relying on a central aggregator node. It is important to do this in a privacy -preserving way, meaning that devices do not reveal their sensitive information to others. This paper presents a method for devices in a distributed network to approximate the average state of devices without revealing their sensitive local power profiles to each other. We use a distributed load-balancing averaging algorithm from literature and enhance its privacy by locally injecting random zero mean Gaussian noise while providing a method to keep the noise in the noisy network average under a pre-determined tolerance bound Phi. We show that the added noise preserves nodal privacy but cancels out as the network size increases, preserving the practical utility of the network average. For example, in networks with 500 and 1000 devices, and a predefined permissible deviation threshold of 60% between the network average and the true average, our approach keeps the noisy network average within a deviation of just 4.32% and 2.92% from the true converged state respectively.

With an increase of electric vehicle (EV) charging demand, energy management systems in for example large parking lots increasingly apply optimization to coordinate the charging schedules of EVs. However, many of the used underlying objective functions do not result in a unique schedule, but rather in an entire space of optimal solutions. This work proposes LYNCS, a method to choose among those schedules in a way that increases service quality and robustities schedules against EVs departing earlier than expected. Numerical evaluation shows a significant improvement compared to a naive choice of schedule, and increased fairness of the resulting schedules.

The power grid is the largest human made system on earth. With the rise of smart grids, this system is being transformed into an increasingly intertwined and complex cyber-physical energy system. Among others, electrical engineers, computer scientists and governance scholars must work together to collectively transform this power system to support the energy transition. Separate diagrams exist for these fields, such as single-line diagrams and communication diagrams. However, no uniform modeling language exists to create an easy to understand high-level visual representation of cyber-physical energy systems that expose the intricate dependencies stemming from combining these fields. Therefore this paper proposes a novel modeling language and accompanying semantics to create visual diagrams to represent cyber-physical energy systems. Resources are made freely available to create these diagrams.

The increasing electrification due to Distributed Energy Resources (DERs), Electric Vehicles (EVs), and heat pumps has increased grid volatility. Demand-Side Management (DSM) approaches can help in solving the grid issues. However, DSM approaches require a group of energy agents to coordinate amongst themselves to achieve a global objective conducive to grid health. This paper extends upon previous work and presents an asynchronous and distributed coordination approach amongst a group of agents consisting of event-driven and periodic optimization processes. Specifically, in our approach agents formulate and constantly update a coordination tree in a distributed manner. We use the coordination tree to disseminate steering signals for optimization which the agents use to plan and update the aggregate group power profile. Our contribution deals with unplanned events, such as EV arrivals or departures or a loss in communication with an agent. For the evaluation, we use real EV charging data from a parking lot in the Netherlands. We compare our approach with the offline planning from greedy charging and an optimization algorithm called Profile Steering. Our approach achieves a solution with a deviation of 9.61% when compared to Profile Steering and has a maximum power of 44.64% less than that from greedy charging.

High temperature heat pumps (HTHPs) are essential technology for the electrification of industrial thermal processes. However, their widespread deployment is conditioned by the development of a framework for selecting the optimal HTHP configuration and refrigerant. In this paper, thermodynamic models are developed for estimating the performance of various HTHP configurations: single-stage, two-stage, cascade, Joule-Brayton, mechanical vapor re/compression HTHPs working with low-GWP refrigerants. The results are used to construct a new map of HTHP performance and selection. This represents a systematic, concise, and visual framework to guide the selection of the appropriate HTHP configuration that achieves a high COP for a given temperature lift and of the refrigerant that maximizes the COP. The map illustrates the influence of temperature conditions and the limitations imposed by compressors and refrigerants. Joint evaluation of HTHP configuration, refrigerant, and compressor technology extends achievable temperatures (evaporation temperature between 10 degrees C and 90 degrees C, condensation temperature up to 250 degrees C for closed vapor compression cycles, and up to 350 degrees C for open vapor compression cycles, and maximum temperature above 500 degrees C for gas compression cycles). Additionally, centrifugal compressors are shown to be applicable in HTHPs, considering the limitations in impeller peripheral speed (for refrigerants with small molecular mass) and fluid flow Mach number (for refrigerants with large molecular mass), as well as their implications on the achievable temperature lift. This indicates that high heating capacity HTHPs can be realized. An original system design diagram of industrial multi-energy systems shows the appropriate application of different HTHP configurations across suitable temperature levels.

Due to the ongoing electrification of transport in combination with limited power grid capacities, efficient ways to schedule the charging of electric vehicles (EVs) are needed for the operation of, for example, large parking lots. Common approaches such as model predictive control repeatedly solve a corresponding offline problem. In this work, we first present and analyze the Flow-based Offline Charging Scheduler (FOCS), an offline algorithm to derive an optimal EV charging schedule for a fleet of EVs that minimizes an increasing, convex and differentiable function of the corresponding aggregated power profile. To this end, we relate EV charging to processor speed scaling models with job-specific speed limits. We prove our algorithm to be optimal and derive necessary and sufficient conditions for any EV charging profile to be optimal. Furthermore, we discuss two online algorithms and their competitive ratios for a specific class objective functions. In particular, we show that if those algorithms are applied and adapted to the presented EV scheduling problem, the competitive ratios for Average Rate and Optimal Available match those of the classical speed scaling problem. Finally, we present numerical results using real-world EV charging data to put the theoretical competitive ratios into a practical perspective.

This study investigates the technical, financial and environmental effects of adding energy storage to a microgrid, while also considering flexible loads and storage sizing using commercially available devices. A simulation study is presented for the Dutch energy community the Aardehuizen, which aims to increase their grid autarky. Hereby, several scenarios are investigated, including a scenario when no flexible loads are controlled, when all flexible loads are controlled, or when only a single type of flexible load (heat pump, e-boiler, electric vehicle charging, whitegoods) is controlled. The results show that controlling only the flexible loads (i.e., no storage) increases the grid independence and reduces peaks. Here, the e-boilers as a group contribute the most to this reduction. When sizing storage, considering e-boiler control alone or in combination with other flexible loads leads to a reduction in the storage sizing required from 4 to 10 LiFePO4 4.32 kWh units in on-grid situations. Also, in all scenarios adding storage reduces the CO2 used in the neighbourhood. However, a yearly financial net profit is only achieved by adding storage and control over all flexible loads.

The adoption of renewables leads to an intermittent peak supply of energy during certain parts of the day which results in congestion in the distribution grid and energy scarcity scenarios for community micro-grids in other periods of the day. In this paper, we present a transactive mitigation scheme based on multi-objective steering signals shared between different grid entities to solve gird issues, with a focus on congestion. We evaluate the proposed approach using baseload data from 25 houses in New York by simulating a congestion scenario resulting from cost minimization optimization. We show that the proposed congestion mitigation approach resolves the congestion with minimal sacrifice of the comfort of the residents of the micro-grid.