The paper introduces an advanced Decentralized Energy Marketplace (DEM) integrating blockchain technol-ogy and artificial intelligence to manage energy exchanges among smart homes with energy storage systems. The proposed framework uses Non-Fungible Tokens (NFTs) to represent unique energy profiles in a transparent and secure trading environment. Leveraging Federated Deep Reinforce-ment Learning (FDRL), the system promotes collaborative and adaptive energy management strategies, maintaining user privacy. A notable innovation is the use of smart contracts, ensuring high efficiency and integrity in energy transactions. Extensive evaluations demonstrate the system's scalability and the effectiveness of the FDRL method in optimizing energy distribution. This research significantly contributes to developing sophisticated decentralized smart grid infras-tructures. Our approach broadens potential blockchain and AI applications in sustainable energy systems and addresses incentive alignment and transparency challenges in traditional energy trading mechanisms. The implementation of this paper is publicly accessible 1 ,
To reduce greenhouse gas emissions, volatile energy production from renewable sources is highly encouraged by international agreements. This leads to balancing challenges of demand and supply which can be addressed with smart grids or even smart city concepts. Demand side management control strategies for flexibility harvesting often include energy storage systems, like flywheel- (FESS) and battery (BESS) storages. To investigate different control strategies for a hybrid energy storage system with a flywheel and battery storage in an islanded microgrid, an existing flywheel is modernized with state-of-the-art components to support real time power hardware in the loop simulations. Testing a load levelling control strategy with this test bench showed that the cyclic lifetime of the battery storage system could be increased with peak shaving due to a reduced amount of charging and discharging operations. An excessive energy buffering control method could increase the islanded operation time by using nearly 10% of the otherwise lost energy. However, these results with the testbench showed limited use for research with the current setup due to low capacity and high self-discharge rate of the existing FESS. But due to the MATLAB-based programming interface, it is perfectly suitable as an educational setup for the demonstration of possible implementations of the European Green Deal.
Monitoring of photovoltaic (PV) plants is crucial for optimal operation and timely fault diagnosis as well as system maintenance. In this work, we propose a telemetry system, based on IoT open technologies, to support the work of specialized personnel in charge of monitoring the operation of various PV plants. PV installations use various types of inverters and technologies which vary between providers as well as a variety of communication methods. The proposed system collects all data concerning the production and operation of several PV plants in one platform, with a user-friendly interface and mobile application. Data from different telemetry systems is integrated and displayed in a unified manner, allowing monitoring at a glance, reduced entry delays when using different proprietary platforms and providing the basis for remote fault detection systems using advanced machine learning techniques. The architecture of the system is described and results are shown from its implementation in five PV plants with different generation capacities, topologies, and communication systems.
In this paper, we investigate how experts evaluate peer-to-peer (P2P), community self-consumption (CSC), and transactive energy (TE) market models compared to a traditional market model. The different models are evaluated on their capacities to generate different types of values in the electricity market. To facilitate the evaluations, we adopted the Analytic Hierarchy Process (AHP). The AHP is a quantitative multiple criteria decision-making tool facilitating experts to make a difficult choice between various options along a set of distinct evaluation criteria by a sequence of pairwise comparisons. So far, the AHP has not yet been applied in the context of energy transaction markets. Results show that experts prefer the community self-consumption and transactive energy market models because they might be most successful in generating green energy. These results help policy makers to better understand the heterogeneous capacities of the market models.
This Work studies the decentralized and uncoordinated energy source selection problem for smart-grid consumers with heterogeneous energy profiles and risk attitudes: they compete for a limited amount of renewable energy in their local community, at the risk of paying a higher cost if that energy is not enough to supply all such demand. We model this problem as a non-cooperative game and study the existence of mixed-strategy Nash equilibria (NE) under the proportional allocation policy employed when the total demand for renewable energy exceeds the available one. We derive under NE closed-form expressions for the resulting total renewable energy demand and social cost under varying consumer profiles, energy costs and availability. The analysis also provides useful guidelines as to What consumers should do (compete or not) based on their risk attitude or if they should be more risk-taking, under certain conditions. Finally, we study numerically the efficiency of this decentralized scheme compared to a centralized one via the price-of-anarchy metric.
The article deals with the problem of analyzing the controllability of an unmanned aerial vehicle based on interval analysis, computer algebra. On the basis of interval mathematics, a criterion for the controllability of the system under study was obtained. When developing the software, the library of interval mathematics was used. The developed software that implements the introduced interval arithmetic operations can be used in the study of the dynamic properties of automatic control systems, energy, economic and other nonlinear systems.
In this paper, the analysis of peer-to-peer energy trading with smart contracts, which are applications run on blockchains, is presented. The software architecture and algorithm of the smart contracts that run on the Ethereum Virtual Machine are mentioned. The smart contracts are applied transactions that guarantee the information of both parties in a peer-to-peer transmission and are verified by the logic structure that operates within itself and do not cause any vulnerabilities in the system. In addition, a software background with an instrument panel in the user interface and offering prices within the framework of the supply-demand relationship is presented. In addition to these, the paper aims to promote decentralisation by integrating renewable energy sources with the framework of peer-to-peer trading methods on a Blockchain.
This paper explores variables for NILM dataset creation, focusing on the relationship between measurement frequency, dataset weight, micro and macro characteristics, and prepossessing. Measurements show that a frequency of 2 kHz allows micro analysis by FFT decomposition and high resolution macro analysis by 0.02 s resampling. Storing only features of interest such as events or harmonic frequencies show potential for diminishing the weight of the dataset while keeping insightful data.
It is now very important to adopt and continue to improve on the technologies that support the ideas of smart management of energy and integration of renewable energy into the traditional power distribution systems. Smart energy management is one of the major ways to solve the numerous challenges associated with energy and power, especially in Nigeria and other African Countries. Introduction of smart embedded devices will avail power distribution systems the ability to be monitored and controlled for necessary protection, and also assist in making informed decisions on efficient management of energy resources, cost and future expansion. The implementation of IoT system requires various sensors and microcontrollers that will route captured data to a cloud-based platform. A limitation of this approach is the latency involved in the transfer and management of large amount of data from sensors to cloud. Another limitation is the high bandwidth requirement. The third limitation of an IoT enabled system is the issue of security and privacy of data. This study provides a breakdown of some recent IoT-based techniques with the view to highlight the prominent associated issues and offer relevant suggestions towards further improvement on the existing techniques.
With increasing electricity tariffs for African farmers, as well as pressure on global energy sectors to end their reliance on coal, a renewable system that has the potential to cut both costs and emissions would be a valuable resource. This paper details a waste-to-energy system intended for African animal farms. A focus on animal farms in Africa is important as many African power supply utilities are unreliable and rural areas are often at a loss for power. Africa, as a largely developing continent, is well suited to lead the way in a novel system of on-farm energy production. This paper shows that a system with 84% biomass and 16% PV supply provides an African farmer with the ability to save more than R6 million over the project’s lifespan of 20 years, while also recycling a waste resource and cutting methane emissions.
Electric Vehicles (EVs) have been identified as a significant solution for reducing carbon emissions from the transportation sector by replacing internal combustion engine vehicles. EVs improve air quality, resulting in yearly carbon emissions reductions of up to 1.5 gigatons. The establishment of a prominent EV market and the construction of a robust charging infrastructure are critical components for the success of electromobility. Slow, fast, and rapid chargers may all be used to charge EVs. Rapid chargers draw large quantities of energy from the grid within a short period of time. If a significant number of rapid chargers are used concurrently during the network’s peak load, for example, network may be subjected to critical conditions. Using Deno runtime based on the JavaScript programming language, this paper presents an algorithm for estimating the energy consumption and demand of common EV models capable of rapid charging. The PSCAD/EMTDC is also used to analyse the effect of chargers on distribution cable and substation transformer loading. The paper’s findings show that battery energy storage units may be used to reduce the loading on the distribution cables and transformers.
This study describes the developed method intended for dynamic modelling of a country’s economic and demographic processes related to the transport market, vehicle and fuel consumption. The method of prediction used in this model is artificial life. The situation of energy consumption for motor transport in Latvia is considered. The model being developed examines the factors influencing energy consumption in road transport. The model takes into account the influence of various factors on energy consumption in transport population, income rates, number of vehicles, countries of the region, taxes, economic factors. The energy forecasting model focuses on a set of planning and forecasting practices that take into account micro- and macroeconomic variables. Since forecasting is a scientific study of specific development prospects based on a system of qualitative and quantitative research aimed at identifying trends in the development of desired indicators, it is necessary to compile statistics on micro and macroeconomics. Data from various fields related to various scopes of human activity have been collected and analysed.
Increasing electricity consumption, especially in residential buildings, poses significant challenges to the management of the electric grid. These challenges can be mitigated by gains in energy efficiency and by the development of intelligent energy management applications to anticipate and prevent peaks. Both energy efficiency and energy management require a deep understanding of detailed individual load levels within a house. In this work, a large real world residential dataset of US residential buildings is developed and used as the foundation for addressing the data gaps that impair current state-of-the-art electricity consumption analysis algorithms. This dataset is the first known database that includes non-intrusive load monitoring (NILM), meaning that individual appliance consumption within each house, including any electric vehicles garaged in that residence is measured. The interactions between electricity consumption, weather and size of houses is investigated and compared to current state-of-the-art methods to demonstrate the performance and more accurate prediction of future usage. The approach extracts important time periodicities, performs a deep data loss analysis and develops a long-short term memory (LSTM) model based forecast for next half hour consumption in a house. The results demonstrate the impact of adding finer level data of NILM information to improve the LSTM forecasts by over 5%.
This paper introduces simple experiments on how to implement, test and evaluate “sampled measured values” in basic power system protection, automation and control education at undergraduate level. The focus is on basic system configuration and the use of commercially available measurement and testing solutions to illustrate the simplicity of using process bus. One of the outcomes of these experiments is to emphasize that in spite of the perceived complexity of using process bus, it is in fact simpler than the conventional approach.
The complexity of energy scheduling problems is increasing due to the energy transition. In recent research, Machine Learning (ML) has shown potential to contribute to the methodology for executing these tasks efficiently and reliably in future. This paper develops and compares three approaches for predicting binary decisions in Unit Commitment problems with network constraints: Two ML predictors using Random Forests and Graph Neural Networks are contrasted with a rule-based approach. On large datasets of realistic synthetic Unit Commitment problems, the performance criteria that need to be met for successful real-word application are evaluated: What is the speedup potential of using the predictions in the process? What is the risk of losing optimality or even feasibility? And what are the generalization capabilities of the predictors? We find that all three approaches have promising potential, each approach having its own pros and cons.
This paper proposes a five-level inverter. The proposed multilevel inverter is based on Switched Capacitor (SC) circuits. The charged capacitors act as a virtual dc link in the negative half-cycle. This feature leads to using a single dc source. Additionally, the proposed Multilevel Inverter (MLI) benefits from the common ground feature. The leakage current is eliminated fully in this structure which is of great importance in transformer-less systems. The proposed MLI is described comprehensively. The operational modes are discussed. The correct operation of the proposed structure is confirmed by MATLAB/Simulink.
Increasing penetration of DC-based electronics loads has highlighted the idea of DC -grid which is more efficient. Furthermore, DC -grid provides the distributed generators utilization that mostly is DC -based. As a result, in this study, we propose the combination of DC nano grid to the traditional AC-nano grid to the distribution system of a house using a power-electronics-interface called an energy router. However, interactions of multiple DC - DC and AC - DC converters in the nano grid make challenges in the stability of the system and control of it. Constant power loads are one of the main challenges since they are highly nonlinear and they behave as negative resistor from the feeder-side point of view. Consequently, we propose a nonlinear control technique to control the DC nano grid with the presence of constant power load in a residential application.
Smart grid networks require sufficient protection so as to safeguard the sensitive data that is exchanged among the smart meter, gateway node, data aggregators and utility service providers. To this end, numerous schemes have been presented based on a myriad of techniques such as public key cryptography, bilinear pairing operations, device identities and elliptic curve cryptography among others. However, many security loopholes still lurk in these protocols. In addition, performance is another serious issue that needs to be solved especially for smart gas meters. In this paper, a symmetric key based message verification protocol is presented. Extensive security evaluation is executed to show that the proposed protocol offers mutual authentication, key agreement and untraceability. It is also resilient against man-in-the-middle, verifier leakage, message replay, impersonation and known session key attacks. In terms of performance, the proposed protocol has the least communication costs and lower computation overheads compared with its peers.
Solar photovoltaic (PV) panels and wind turbines are by far the biggest drivers of the rapid increase in renewable energy electricity generation. Globally, in 2018, 100 gigawatts of solar PV were installed, contributing 55% of new renewable energy capacity; wind contributed the second largest share, with 28% of new renewable capacity. Both technologies are well established and feature heavily in decarbonisation scenarios as proven concepts to generate emission-free electricity. However, with this increasing trend of integrating renewable energy to the grid in the decarbonisation process entails lots of challenges among which stability of the grid plays a predominant role. Inertia plays an important role in the stability of the grid. Due to the penetration of the renewable energy sources (RES) to the grid, the system inertia is lowered because of the displacement of the synchronous generators along with the increasing use of the power electronics devices for synchronising the renewable power to the grid. This paper technically assesses the operational efficiency of both RES in terms of rotor angle stability. MATLAB/Simulink was used as the modelling tools. The results show that though wind turbines have a greater performance than the solar PV system, yet solar PV system can produce a more stable power to the grid than the wind turbine, which requires battery energy storage system (BESS) and additional frequency support to produce a stable power.
Due to the global competition in manufacturing, flexibility to provide for individually customized products is considered an important selling point. Constantly changing manufacturing processes face higher production costs than well known reoccurring schedules. To lower these costs in general, we propose a model predictive control concept to reduce manufacturing energy costs in particular, using an existing digital twin to estimate the load of the different manufacturing steps. Based on a mixed integer linear programming formulation of the battery-supported manufacturing process, the system makes optimum use of the on-site photovoltaic generation by production scheduling and adaptive battery control. A simulation study considering a time of use and a real-time pricing scenario provides a proof of concept.
