Grid Energy Consumption Research Articles

Cost-efficient microgeneration renewable energy provision dimensioning for sustainable 5G heterogeneous network

The deployment of mobile networks has imposed an urgent requirement for the pursuit of low-carbon communication infrastructures. The increasing energy consumption of mobile networks has brought about challenges of techno-economic and environmental sustainability. Renewable energy-enabled mobile networks have received a lot of attention due to their capability to evade greenhouse gas emissions and easy availability. Microgeneration-based renewable energy provision is a feasible and effective solution for 5G networks. Dimensioning of microgeneration renewable energy power supply is an essential issue to make the system operate for a long period cost-effectively with a minimum amount of grid energy consumption. For effective deployment of microgeneration renewable energy system, it is essential to provision it with adequate PV panel capacity and storage devices. This work attempts to identify the cost-effective, energy-efficient, and emissions-aware sizing of PV panels and storage for 5G HetNet. An energy-saving strategy based on an optimal policy of advanced sleep modes and traffic-aware load offloading is developed and the interaction of the energy-saving strategy on the system dimensioning is explicitly examined. The proposed solution aims to ensure the communication quality of service whilst keeping the optimal cost-effective deployment and network operation. The system performance in terms of grid energy consumption, empty storage probability, emission performance, and total cost of the system is extensively assessed through experiments for a range of operational scenarios. The numerical results demonstrated that the proposed sustainable energy system dimensioning and operation integrated with an energy-saving strategy is energy-efficient and cost-effective.

Distributed Heterogeneous Transfer Learning

Transfer learning has proved to be effective for building predictive models even in complex conditions with a low amount of available labeled data, by constructing a predictive model for a target domain also using the knowledge coming from a separate domain, called source domain. However, several existing transfer learning methods assume identical feature spaces between the source and the target domains. This assumption limits the possible real-world applications of such methods, since two separate, although related, domains could be described by totally different feature spaces. Heterogeneous transfer learning methods aim to overcome this limitation, but they usually i) make other assumptions on the features, such as requiring the same number of features, ii) are not generally able to distribute the workload over multiple computational nodes, iii) cannot work in the Positive-Unlabeled (PU) learning setting, which we also considered in this study, or iv) their applicability is limited to specific application domains, i.e., they are not general-purpose methods.In this manuscript, we present a novel distributed heterogeneous transfer learning method, implemented in Apache Spark, that overcomes all the above-mentioned limitations. Specifically, it is able to work also in the PU learning setting by resorting to a clustering-based approach, and can align totally heterogeneous feature spaces, without exploiting peculiarities of specific application domains. Moreover, our distributed approach allows us to process large source and target datasets.Our experimental evaluation was performed in three different application domains that can benefit from transfer learning approaches, namely the reconstruction of the human gene regulatory network, the prediction of cerebral stroke in hospital patients, and the prediction of customer energy consumption in power grids. The results show that the proposed approach is able to outperform 4 state-of-the-art heterogeneous transfer learning approaches and 3 baselines, and exhibits ideal performances in terms of scalability.

A Study Article of DLMS to IOT Protocol

Abstract: The DLMS (Device Language Message Specification) protocol plays a crucial role in energy metering and management. It provides a standardized communication framework for exchanging data between utility meters and data collection devices. The protocol enables interoperability, remote meter reading, and supports efficient management of energy consumption, billing, and grid operations. The DLMS protocol is essential for Internet of Things (IoT) applications as it provides a standardized method for communication between Internet of Things devices and energy meters. This facilitates the seamless integration of energy data into IoT platforms, enabling efficient monitoring, analysis, and management of energy consumption in smart grid and smart city deployments. To this end, we have developed a prototype DLMS to IoT gateway that is based on either STM32 or any other IoT module. However, we encountered a challenge in testing the prototype since we couldn't get any digital meter to conduct hands-on testing..

Minimizing grid energy consumption in wastewater treatment plants: Towards green energy solutions, water sustainability, and cleaner environment

Wastewater treatment plants (WWTPs) consume significant amount of energy to sustain their operation. From this point, the current study aims to enhance the capacity of these facilities to meet their energy needs by integrating renewable energy sources. The study focused on the investigation of two primary solar energy systems in As Samra WWTP in Jordan. The first system combines parabolic trough collectors (PTCs) with thermal energy storage (TES). This system primarily serves to fulfill the thermal energy demands of the plant by reducing the demands from boiler units, which allows more biogas for electricity generation. The second system is a photovoltaic (PV) system with Lithium-Ion batteries, which directly produces electricity that will be used to cover part of the electrical energy demands of plant. To assess the optimal configuration, two distinct scenarios have been formulated and compared to the current case scenario (SC#1). The first scenario focuses on maximizing the net present value (NPV) and minimizing the levelized cost of electricity (LCOE). The second scenario is centred on minimizing the levelized cost of heat (LCOH). The findings indicate that both scenarios succeeded in reducing the reliance on the grid to a value that reach 1 %. Moreover, they both reduced biogas percentage in energy production from 88 % to approximately 65 % through the integration of the PV system. In terms of thermal demand, SC#2 reduced the reliance on biogas boiler units from 100 % to 25 %, while SC#3 achieved an even more impressive reduction to just 8 %. The best LCOE value was attained in SC#2, at 0.0895 USD/kWh, with an NPV of 10.54 million USD. Conversely, SC# 3 yielded an LCOH value of 0.0432 USD/kWhth compared to 0.0534 USD/kWhth USD for SC#2. Despite their relatively high capital and operating costs, SC#2 and SC#3 managed to substantially decrease the annual electricity expenditure from approximately 2 million USD to 86,000 USD and 0 USD, respectively.

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Efficient energy management is crucial for optimizing greenhouse (GH) operations and promoting sustainability. This paper presents a novel multi-objective optimization approach tailored for GH energy management, aiming to minimize grid energy consumption while maximizing battery state of charge (SOC) within a specified time frame. The optimization problem integrates decision variables such as network power, battery power, and battery energy, subject to constraints based on battery capacity and initial energy, along with minimum and maximum energy from the battery storage system. Through the comparison of a smart energy management system (EMS) with traditional optimization algorithms, the study evaluates its efficiency. Key hyperparameters essential for the optimization problem, including plateau time, prediction time, and optimization time, are determined using the ellipse optimization method. Treating the GH as a microgrid, the analysis encompasses energy management indicators and loads. A simulation conducted via Simulink in MATLAB software (R2021b) demonstrates a significant enhancement, with the smart EMS achieving a more than 50% reduction in the objective function compared to conventional EMS. Moreover, the EMS exhibits robust performance across variations in the load power and irradiation profile. Under partial shading conditions, the EMS maintains adaptability, with a maximum objective function increase of 0.35553%. Aligning the output power of photovoltaic (PV) systems with real-world conditions further validates the EMS’s effectiveness in practical scenarios. The findings underscore the efficiency of the smart EMS in optimizing energy consumption within GH environments, offering promising avenues for sustainable energy management practices. This research contributes to advancing energy optimization strategies in agricultural settings, thereby fostering resource efficiency and environmental stewardship.

Load Forecasting and Operation Optimization of Ice-Storage Air Conditioners Based on Improved Deep-Belief Network

The prediction of cold load in ice-storage air conditioning systems plays a pivotal role in optimizing air conditioning operations, significantly contributing to the equilibrium of regional electricity supply and demand, mitigating power grid stress, and curtailing energy consumption in power grids. Addressing the issues of minimal correlation between input and output data and the suboptimal prediction accuracy inherent in traditional deep-belief neural-network models, this study introduces an enhanced deep-belief neural-network combination prediction model. This model is refined through an advanced genetic algorithm in conjunction with the “Statistical Products and Services Solution” version 25.0 software, aiming to augment the precision of ice-storage air conditioning load predictions. Initially, the input data undergo processing via the “Statistical Products and Services Solution” software, which facilitates the exclusion of samples exhibiting low coupling. Subsequently, the improved genetic algorithm implements adaptive adjustments to surmount the challenge of random weight parameter initialization prevalent in traditional deep-belief networks. Consequently, an optimized deep-belief neural-network load prediction model, predicated on the enhanced genetic algorithm, is established and subjected to training. Ultimately, the model undergoes simulation validation across three critical dimensions: operational performance, prediction evaluation indices, and operating costs of ice-storage air conditioners. The results indicate that, compared to existing methods for predicting the cooling load of ice-storage air conditioning, the proposed model achieves a prediction accuracy of 96.52%. It also shows an average improvement of 14.12% in computational performance and a 14.32% reduction in model energy consumption. The prediction outcomes align with the actual cooling-load variation patterns. Furthermore, the daily operational cost of ice-storage air conditioning, derived from the predicted cooling-load data, has an error margin of only 2.36%. This contributes to the optimization of ice-storage air conditioning operations.

Influence of metal encapsulation on thermophysical properties and heat transfer in salt hydrate phase change material for air conditioning system

Phase change materials (PCM) are gaining increasing attention as a cold energy storage technology for air conditioning systems due to their potential to reduce electrical grid loading, energy consumption, and electricity costs. However, realizing the full potential of PCM-based cold energy storage in practical applications is constrained by low cold energy transfer efficiency within air conditioning systems. This study addresses this challenge by exploring the encapsulation of salt hydrate PCM using six different metals to enhance heat transfer efficiency. Additionally, the corrosion behaviors of the PCM towards these metals were thoroughly evaluated. Notably, copper demonstrated the highest corrosion rate, and the resulting corrosion products, particularly from copper and brass, were found to substantially reduce the energy storage capacity of PCM. Moreover, PCM encapsulated into aluminum alloy exhibited higher heat transfer efficiency than that of stainless steel. In light of these findings, aluminum alloy is recommended as the optimal choice for PCM encapsulation, given its corrosion resistance and minimal effect on thermal properties. This research provides crucial insights for selecting appropriate metals to enhance the efficiency and durability of air conditioning systems utilizing salt hydrate PCM.

Method of electric vehicle braking energy recovery

Currently, the issue of the use of network power storage devices is increasingly being discussed. A promising technical solution is to combine a flywheel and a motor-generator motor on one shaft. Such an association can be called electromachine energy accumulators. Its main disadvantage should be considered the management difficulties. In this regard, it is possible to use an energy recovery system to reduce energy consumption and increase stability in the power grid. Such devices can reduce energy costs and increase the efficiency of vehicles. Such a solution can be useful and effective for the development of electric transport and reducing energy costs.

DRL-Based Computation Offloading and Resource Allocation in Green MEC-Enabled Maritime-IoT Networks

The maritime Internet of Things (MIoT), a maritime version of the Internet of Things (IoT), is envisioned as a promising solution that can provide ubiquitous connectivity over land and sea. Due to the rapid development of maritime activities and the maritime economy, there is a growing demand for computing-intensive and latency-sensitive maritime applications requiring various energy consumption, communication, and computation resources, posing a significant challenge to MIoT devices due to their limited computational ability and battery capacity. Mobile Edge Computing (MEC), which can handle computation tasks at the network’s edge more efficiently and with less latency, is emerging as a paradigm for fulfilling the ever-increasing demands of MIoT applications. However, the exponential increase in the number of MIoT devices has increased the system’s energy consumption, resulting in increased greenhouse gas emissions and a negative impact on the environment. As a result, it is vital for MIoT networks to take traditional energy usage minimization into account. The integration of renewable energy-harvesting capabilities into base stations or MIoT devices possesses the potential to reduce grid energy consumption and carbon emissions. However, making an effective decision regarding task offloading and resource allocation is crucial for maximizing the utilization of the system’s potential resources and minimizing carbon emissions. In this paper, we first propose a green MEC-enabled maritime IoT network architecture to flexibly provide computing-intensive and latency-sensitive applications for MIoT users. Based on the architecture, we formulate the joint task offloading and resource allocation problem by optimizing the total system execution efficiency (including the total size of completed tasks, task execution latency, and the system’s carbon emissions) and then propose a deep-deterministic-policy-gradient-based joint optimization strategy to solve the problem, eventually obtaining an effective resolution through continuous action space learning in the changing environment. Finally, simulation results confirm that our proposal can yield good performance in system execution efficiency compared to other benchmarks; that is, it can significantly reduce the system’s carbon emissions and tasks’ delay and improve the total size of completed tasks.

Maximising the benefit of variable speed heat-pump water heater with rooftop PV and intelligent battery charging

Domestic applications of thermal energy storage, interacting with rooftop PV and batteries can substantially reduce consumer electricity costs, whilst assisting grid stability. Air source heat pump water heaters (HPs) with inherent thermal storage offer significant purchased energy savings, however, their typical control only considers stored water temperature. We demonstrate that further energy and cost reductions are possible using variable compressor speed controls that optimise thermal performance in real-time, whilst coordinating energy consumption to match available PV or shifting consumption to off-peak hours. The incremental complexity of variable-speed HP water heaters can reduce HP electricity consumption from the grid by up to 28% without PV or battery, 88% with PV, and 99% using PV and battery storage. By integrating the HP and all other domestic electrical loads, we exploit the combination of thermal and battery energy storage using rules-based and machine-learnt hierarchical controls to reduce the net household energy cost. Machine-learnt grid battery charging is shown to reduce annual grid energy consumption by 84%, delivering 99% of the potential energy cost savings. We conclude that a HP with inherent thermal storage, interacting with rooftop PV offers the most cost-effective home energy storage solution, lowering net lifetime costs by up to 27%.

Smart grid energy scheduling based on improved dynamic programming algorithm and LSTM.

The optimal scheduling of energy in a smart grid is crucial to the energy consumption of the entire grid. In fact, for larger grids, intelligent scheduling may result in substantial energy savings. Herein, we introduce an enhanced dynamic programming algorithm (DPA) that utilizes two state variables to derive the optimal power supply schedule. The algorithm accounts for the dynamic states of both batteries and supercapacitors in the power supply system to augment the performance of the dynamic programming model. Additionally, this study incorporates a long short-term memory (LSTM) deep learning model, which integrates various environmental factors such as temperature, humidity, wind, and precipitation to predict grid power consumption. This serves as a mid-point pre-processing step for smart grid energy consumption scheduling. Our simulation experiments confirm that the proposed method significantly reduces energy consumption, surpassing similar grid energy consumption scheduling algorithms. This is critical for the establishment of smart grids and the reduction of energy consumption and emissions.

Reliable Long-Term Energy Load Trend Prediction Model for Smart Grid Using Hierarchical Decomposition Self-Attention Network

Extending the length of time series forecasting has a long-term impact on smart grid energy consumption planning, residential electricity monitoring, extreme weather warning, and other real applications. This article studies the reliable long-term load trend forecasting approaches in smart grid environment. In recent years, the improved neural networks models based on self-attention mechanism show good performances in many sequence tasks, but most studies focus on reducing the complexity of networks layer, and can not restrain the increase of calculation error in longer distance forecasting scenarios. Also, most models lack the ability to mine potential high-dimensional features of time series. Based on these problems, we design a reliable hierarchical self-attention model named as long-term stability network (LTSNet), which adopts a tree-shaped decomposition neural network architecture based on hierarchical residual self-attention blocks, to top-down incrementally mine high-dimensional features of temporal components. At the same time, the attention matrix is used for feature interaction at each layer, to reduce the distribution gap between time series fragments in different domains. Compared with existing study models, LTSNet maintains stable forecasting performance and speed in long-term forecasting services, achieved the most reliable multivariate and univariate forecasting results in multiple domain scenarios. Compared with the latest models, the forecasting accuracy of the proposed model is improved by 22.8% and 13.8%, respectively, covering three applications: energy consumption, residential electricity consumption, and weather forecasting. At the same time, our experiments verify that the hierarchical decomposition networks can be used as a backbone architecture to effectively extended to longer dimensional load trend forecasting scenarios.

Assessment method of large-scale distributed power consumption capacity for rural grids

As the scale of photovoltaic (PV) access continues to expand, both transmission and distribution grids are experiencing systemic consumption problems, with rural distribution grids being particularly prominent. An accurate assessment of distributed power consumption capacity is crucial to the improvement of distributed power consumption capacity. Firstly, taking into account the uncertainty of the output of the distributed power supply and comprehensive consideration of power quality, reliability of the electricity supply and operational influences, A comprehensive index evaluation system has been established to assess the ability of distributed power sources to consume; secondly, hierarchical analysis and improved gray correlation analysis are used to assign weights to the indexes; finally, a Monte Carlo model is used to identify the consumption capacity of rural power grids. This method can provide a comprehensive and accurate assessment of the consumption capacity of large-scale distributed power sources in new rural power grids, and also provide data support for solving the problem of new energy consumption in power grids.

Intelligent Energy Management System for Smart Grids Using Machine Learning Algorithms

Smart grid technology is rapidly advancing and providing various opportunities for efficient energy management. To achieve the full potential of smart grids, intelligent energy management systems (IEMS) are required that can optimally manage and control the distributed energy resources (DERs). In this paper, proposed an IEMS using the Deep Reinforcement Learning (DRL) algorithm to manage the energy consumption and production in a smart grid. The proposed methodology aims to minimize the energy cost while maintaining the stability and reliability of the grid. The performance of the proposed IEMS is evaluated on a simulated smart grid, and the results show that it can effectively manage the energy resources while minimizing the energy cost.

Regional Electric Vehicle Energy Consumption and Carbon Emissions in Great Britain

This work presents the regional differences in electric vehicle (EV) real-world energy consumption and associated carbon emissions during charging in Great Britain (GB). A model was developed considering the variability in road traffic, ambient temperature, and electricity grid profile between the GB regions on EV carbon emissions under uncontrolled and smart scenarios. The results show the variations in EV energy consumption and carbon emissions impacted by where, when, and how an EV is driven and charged. Carbon emission reduction varies from 5% to 33% between the regions when switching to delayed smart charging, shifting the charging process outside peak hours. An optimised smart charging that moves the charging events to periods of low grid carbon intensity reduces carbon emissions from 6% to 55%, affected by region grid carbon intensity and energy consumption.

Experimental verification of a dynamic programming and IoT-based simultaneous load-sharing controller for residential homes powered with grid and onsite solar photovoltaic electricity

Quest for harnessing clean and affordable electricity has increased renewable energy installations, especially the rooftop solar photovoltaic (PV) systems in many residential homes; simultaneously, such homes are connected to the utility grid for reliable energy service, creating a hybrid power supply system (HPSS). However, the most stressful challenge with the HPSS is the confounding condition of connecting which load to which power source for optimal utilization of the energy sources. Hence, this paper proposes an optimum and sustainable solution with ease of implementation and simple operation for simultaneous load-sharing problems utilizing the dynamic programming (DP) and internet of things (IoT) concepts. In the proposed simultaneous load-sharing concept, the system decides which ON state loads of a household receive power from rooftop solar PV, considering the maximum power available at the rooftop solar plant. The critical condition arises during the absence of any power sources and/or the lack of both sources, which is also included in the system and resolved with the proposed solution. The IoT integration helps remote control through a customized mobile application, whose experimental verification ensured the effectiveness and real-time feasibility by reducing the grid energy consumption by 17.59% and related carbon dioxide (CO2) emissions thereby promoting low-carbon solar PV electricity consumption.

Power Grid Energy Consumption

Power Grid Energy Consumption

Technical-Economic Feasibility Study of a Tri-Generation System in an Isolated Tropical Island

Over the years, and despite the energy efficiency measures and possibilities, there has been an increase in energy consumption worldwide. However, the resources and primary energies are limited and short stocked, and the energy production technologies have environmental and social impacts on production and exploration. One alternative is to reuse the energy waste in the processes. In this study, a trigeneration system in a large scale out of grid consumption is analyzed and a technical-economic feasibility is elaborated. The case study is based on an isolated tropical island. For the baseline scenario, two traditional energy production systems that does not contain energy recovery in the engines are assumed to be implemented. For the improved scenario, a trigeneration system absorption chiller is analyzed. An economic analysis of this project was given the indicators obtained, it was possible to conclude that the use of a trigeneration system on an isolated large scale out of grid energy consumption system, is feasible and preferable.

Methodology to optimise electricity demand in the residential sector through efficient load management

The current energy model requires a transformation based on a sustainable model that is accessible to all, focused on the needs of citizens and committed to climate change. Moreover, the rising cost of non-renewable energy sources, and the growing presence of distributed renewable generation, has a major impact on the electricity sector, creating the need to develop mechanisms to help manage energy demand in order to converge towards an efficient, emission-free electricity system that is responsible for the environment and future generations. The main motivation is the recent change in the regulation of electricity tariff systems and the introduction of time discriminations in the residential sector. Based on the previous need and supported by a survey to find out society's predisposition and sensitivity to demand management, this paper presents the methodology developed as an active demand-side management mechanism for residential consumers, considering their load shifting priorities and prioritising the use of renewable energy if possible, reducing grid energy consumption. In a nutshell, the use of a tool capable of helping consumers to regulate their energy consumption, analyse their consumption pattern and plan their domestic loads to obtain the lowest energy and economic impact is developed. Key words. Active demand-side management, Load shedding, Energy optimisation, Renewable energy, Load prioritisation

LoRa-RL: Deep Reinforcement Learning for Resource Management in Hybrid Energy LoRa Wireless Networks

LoRa wireless networks are considered as a key enabling technology for next generation internet of things (IoT) systems. New IoT deployments (e.g., smart city scenarios) can have thousands of devices per square kilometer leading to huge amount of power consumption to provide connectivity. In this paper, we investigate green LoRa wireless networks powered by a hybrid of the grid and renewable energy sources, which can benefit from harvested energy while dealing with the intermittent supply. This paper proposes resource management schemes of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway energy efficiency. First, the problem of grid power consumption minimization while satisfying the system's quality of service demands is formulated. Specifically, both scenarios the uncorrelated and time-correlated channels are investigated. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Since the optimal solution is obtained with high complexity, online resource management heuristic algorithms that minimize the grid energy consumption are proposed. Finally, taking into account the channel and energy correlation, adaptable resource management schemes based on Reinforcement Learning (RL), are developed. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.