2,234 research outputs found
Optimal load management of autonomous power systems in conditions of water shortage
The issues of optimizing the operation of micro hydropower plants in conditions of water scarcity, performed by additional connection to the grid of an energy storage system and wind power turbine, as well as optimal load management, are considered. It is assumed that the load of the system is a concentrated autonomous power facility that consumes only active power. The paper presents a rigorous mathematical formulation of the problem, the solution of which corresponds to the minimum cost of an energy storage system and a wind turbine, which allows for uninterrupted supply of electricity to power facilities in conditions of water shortage necessary for the operation of micro hydropower plants (under unfavorable hydrological conditions). The problem is formulated as a nonlinear multi-objective optimization problem to apply metaheuristic stochastic algorithms. At the same time, a significant part of the problem is taken out and framed as a subproblem of linear programming which will make it possible to solve it by a deterministic simplex method that guarantees to find the exact global optimum. This approach will significantly increase the efficiency of solving the entire problem by combining metaheuristic algorithms and taking into account expert knowledge about the problem being solved
Multi-objective particle swarm optimization for optimal scheduling of household microgrids
Addressing the challenge of household loads and the concentrated power consumption of electric vehicles during periods of low electricity prices is critical to mitigate impacts on the utility grid. In this study, we propose a multi-objective particle swarm algorithm-based optimal scheduling method for household microgrids. A household microgrid optimization model is formulated, taking into account time-sharing tariffs and users’ travel patterns with electric vehicles. The model focuses on optimizing daily household electricity costs and minimizing grid-side energy supply variances. Specifically, the mathematical model incorporates the actual input and output power of each distributed energy source within the microgrid as optimization variables. Furthermore, it integrates an analysis of capacity variations for energy storage batteries and electric vehicle batteries. Through arithmetic simulation within the Pareto optimal solution set, the model identifies the optimal solution that effectively mitigates fluctuations in energy input and output on the utility side. Simulation results confirm the effectiveness of this strategy in reducing daily household electricity costs. The proposed optimization approach not only improves the overall quality of electricity consumption but also demonstrates its economic and practical feasibility, highlighting its potential for broader application and impact
Electrified heat and transport : energy demand futures, their impacts on power networks and what it means for system flexibility
Demand electrification, system flexibility and energy demand reduction (EDR) are three central tenets of most energy system decarbonisation pathways in the UK and other high-income countries. However, their combined impacts on local energy systems remain understudied. Here, we investigate the impact of different UK energy demand future scenarios on the loading of local electricity networks, and the ability of electrified demand to act flexibly in (i) mitigating the need for network reinforcement and (ii) shifting demand around according to variable tariffs reflecting wider system needs. These scenarios are used to drive spatially- and temporally-explicit technology uptake and energy demand modelling for heating and transport in a localised context, for application to a local electricity network. A particular case study energy network in Scotland, representative of many networks in the UK and Northern Europe, is selected to demonstrate the method. On the basis of the presented case study, which considered a typical winter demand day, energy futures based on EDR policies were found on average to reduce evening transformer loading by up to 16%. Further reductions of up to 43% were achieved with flexible smart charging and up to 69% with the use of vehicle-to-grid. Therefore, we find that policies focused on EDR can mitigate the need for reinforcement of electricity networks against the backdrop of demand electrification. However, flexibility in electricity demand contributes a larger difference to a network’s ability to host electrified heat and transport than relying solely on EDR. When used in tandem, policies that simultaneously pursue EDR and electricity system flexibility are shown to have the greatest benefits. Despite these benefits, peak electricity demand is very likely to increase significantly relative to the current baseline. Therefore, widespread reinforcement is required to local electricity networks in the net-zero transition and, accordingly, urgent investment is required to support the realisation of the UK’s legally-binding climate goals
1. Helgoland Power and Energy Conference - 24. Dresdener Kreis 2023
Der Sammelband "1. Helgoland Power and Energy Conference" beinhaltet neben einem kurzen Bericht zum 24. Treffen des Dresdener Kreises 2023 wissenschaftliche Beiträge von Doktoranden der beteiligten Hochschulinstitute zum Thema Elektroenergieversorgung. Der Dresdener Kreis setzt sich aus der Professur für Elektroenergieversorgung der Technischen Universität Dresden, dem Fachgebiet Elektrische Anlagen und Netze der Universität Duisburg-Essen, dem Fachgebiet Elektrische Energieversorgung der Leibniz Universität Hannover und dem Lehrstuhl Elektrische Netze und Erneuerbare Energie der Otto-von-Guericke Universität Magdeburg zusammen und trifft sich einmal im Jahr zum fachlichen Austausch an einer der beteiligten Universitäten
Thermal energy storage in concrete: A comprehensive review on fundamentals, technology and sustainability
This comprehensive review paper delves into the advancements and applications of thermal energy storage (TES) in concrete. It covers the fundamental concepts of TES, delving into various storage systems, advantages, and challenges associated with the technology. The paper extensively explores the potential of concrete as a medium for thermal energy storage, analysing its properties and different storage methods. Additionally, it sheds light on the latest developments in concrete technology specifically geared towards thermal energy storage. The evaluation section discusses measurement techniques, experimental evaluations and performance metrics. Environmental and economic aspects, including sustainability and cost analysis, are thoughtfully addressed. The review concludes by underlining the significance of thermal energy storage in concrete, emphasizing its role in efficient energy management and the promotion of sustainable practices
Dynamic analysis and energy management strategies of micro gas turbine systems integrated with mechanical, electrochemical and thermal energy storage devices
The growing concern related to the rise of greenhouse gases in the atmosphere has led to an increase of share of renewable energy sources. Due to their unpredictability and intermittency, new flexible and efficient power systems need to be developed to compensate for this fluctuating power production. In this context, micro gas turbines have high potential for small-scale combined heat and power (CHP) applications considering their fuel flexibility, quick load changes, low maintenance, low vibrations, and high overall efficiency. Furthermore, the combination of micro gas turbines with energy storage systems can further increase the overall system flexibility and the response to rapid load changes. This thesis aims to analyse the integration of micro gas turbines with the following energy storage systems: compressed air energy storage (CAES), chemical energy storage (using hydrogen and ammonia), battery storage, and thermal energy storage.
In particular, micro gas turbines integrated with CAES systems and alternative fuels operate in different working conditions compared to their standard conditions. Applications requiring increased mass flow rate at the expander, such as CAES and the use of fuels with low LHV, such as ammonia, can potentially reduce the compressor surge margin. Conversely, sudden composition changes of high LHV fuels, such as hydrogen, can cause temperature peaks, detrimental for the turbine and recuperator life. A validated model of a T100 micro gas turbine is used to analyse transitions between different conditions, identify operational limits and test the control system.
Starting from the dynamic constraints defined in the related chapters, in the final part, an optimisation tool for energy management is developed to couple the micro gas turbine with energy storage systems, maximizing the plant profitability and satisfying the local electrical and thermal demands. For the modelling of the CAES system and alternative fuels, the operating constraints obtained from the initial analyses are implemented in the optimisation tool. In addition, a battery and thermal energy storage system are also considered.
In the first part, a comprehensive analysis of the T100 combined with a second-generation CAES system showed enhanced efficiency, reduced fuel consumption, reduced thermal power output and increased maximum electrical power output due to the reduction of the rotational speed. The study identified optimal air injection constraints, demonstrating a +3.23% efficiency increase at 80 kW net power with a maximum mass flow rate of 50 g/s. The dynamic analysis exposed potential instabilities issues during air step injections, mitigated by using ramps at a rate of +0.5 (g/s)/s for safe and rapid dynamic mode operation. The second part explored the effects of varying H2-NG and NH3-NG blends on the T100 mGT. Steady-state results showed increased power output with hydrogen or ammonia, notably +6.1 kW for 100% H2 and up to +11.3 kW for 100% NH3. Transient power steps simulations showed surge margin reductions, especially at lower power levels with high concentrations of ammonia, highlighting the need for controlled transitions. Controlled ramps were effective in preventing extreme temperature peaks during fuel composition changes. The final chapter focused on developing an energy scheduler for different plant setups, evaluating four configurations. For a typical day of the month of April of the Savona Campus, the integration of the CAES lead to relative savings of +8.1% and power-to-H2 of +5.3% when surplus electricity was not sold to the grid. Conversely, with the ability to sell excess electricity, CAES and battery energy storage (BES) systems exhibit modest savings of +1.2% and +2.4%, respectively, while the power-to-H2 system failed to provide economic advantages
An Improvement of Load Flow Solution for Power System Networks using Evolutionary-Swarm Intelligence Optimizers
Load flow report which reveals the existing state of the power system network under steady operating conditions, subject to certain constraints is being bedeviled by issues of accuracy and convergence. In this research, five AI-based load flow solutions classified under evolutionary-swarm intelligence optimizers are deployed for power flow studies in the 330kV, 34-bus, 38-branch section of the Nigerian transmission grid. The evolutionary-swarm optimizers used in this research consist of one evolutionary algorithm and four swarm intelligence algorithms namely; biogeography-based optimization (BBO), particle swarm optimization (PSO), spider monkey optimization (SMO), artificial bee colony optimization (ABCO) and ant colony optimization (ACO). BBO as a sole evolutionary algorithm is being configured alongside four swarm intelligence optimizers for an optimal power flow solution with the aim of performance evaluation through physical and statistical means. Assessment report upon application of these standalone algorithms on the 330kV Nigerian grid under two (accuracy and convergence) metrics produced PSO and ACO as the best-performed algorithms. Three test cases (scenarios) were adopted based on the number of iterations (100, 500, and 1000) for proper assessment of the algorithms and the results produced were validated using mean average percentage error (MAPE) with values of voltage profile created by each solution algorithm in line with the IEEE voltage regulatory standards. All algorithms proved to be good load flow solvers with distinct levels of precision and speed. While PSO and SMO produced the best and worst results for accuracy with MAPE values of 3.11% and 36.62%, ACO and PSO produced the best and worst results for convergence (computational speed) after 65 and 530 average number of iterations. Since accuracy supersedes speed from scientific considerations, PSO is the overall winner and should be cascaded with ACO for an automated hybrid swarm intelligence load flow model in future studies. Future research should consider hybridizing ACO and PSO for a more computationally efficient solution model
Multi-agent game operation of regional integrated energy system based on carbon emission flow
In the process of promoting energy green transformation, the optimization of regional integrated energy system faces many challenges such as cooperative management, energy saving and emission reduction, as well as uncertainty of new energy output. Therefore, this paper proposes a multi-agent game operation method of regional integrated energy system based on carbon emission flow. First, this paper establishes a carbon emission flow calculation model for each subject, and proposes a comprehensive tariff model based on the carbon emission flow, which discounts the carbon emissions from the power supply side to the power consumption side. Secondly, considering the interests of each subject, this paper establishes the decision-making model of each subject. And the new energy uncertainty, the cost of energy preference of prosumers, and the thermal inertia of buildings are considered in the decision model. Finally, the model is solved using differential evolution algorithm and solver. The case study verifies that the comprehensive electricity pricing model based on carbon emission flow developed in this paper can play a role in balancing economy and low carbon
Hybrid energy system integration and management for solar energy: a review
The conventional grid is increasingly integrating renewable energy sources like solar energy to lower carbon emissions and other greenhouse gases. While energy management systems support grid integration by balancing power supply with demand, they are usually either predictive or real-time and therefore unable to utilise the full array of supply and demand responses, limiting grid integration of renewable energy sources. This limitation is overcome by an integrated energy management system. This review examines various concepts related to the integrated energy management system such as the power system configurations it operates in, and the types of supply and demand side responses. These concepts and approaches are particularly relevant for power systems that rely heavily on solar energy and have constraints on energy supply and costs. Building on from there, a comprehensive overview of current research and progress regarding the development of integrated energy management system frameworks, that have both predictive and real-time energy management capabilities, is provided. The potential benefits of an energy management system that integrates solar power forecasting, demand-side management, and supply-side management are explored. Furthermore, design considerations are proposed for creating solar energy forecasting models. The findings from this review have the potential to inform ongoing studies on the design and implementation of integrated energy management system, and their effect on power systems
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