Energy Management Method Research Articles

Distributed Low-Carbon Energy Management of Urban Campus for Renewable Energy Consumption

In order to solve the mismatch between renewable energy and load in urban building microgrids, that is, the problem of renewable energy consumption in building microgrid clusters, while preserving the privacy of each user, this paper proposes a distributed low-carbon energy management method for urban building microgrid clusters. First, a low-carbon energy management method for the urban building microgrid is proposed in order to coordinate the power sharing of various subjects to minimize the total economic cost, unleash the consumption potential of low-carbon building clusters for renewable energy, and reduce carbon emissions on the spatial and time scale. Second, an ADMM-based distributed optimal energy management method is proposed to meet user energy needs while preserving local privacy; this includes energy storage systems, renewable energy generation, and the loads within each urban building microgrid. Finally, simulation experiments are conducted based on actual data from a certain area in Hangzhou, China, and the results verify the effectiveness of the model.

Research on the design of prefabricated curved structure production capacity residential energy system: a case study of an entry of 2022 China International Solar Decathlon Competition- 'Solar Ark 3.0'

Under the severe situation of global warming, the problem of high energy consumption and carbon emissions in the construction industry need to be addressed urgently, and the method to promote the development of the construction industry in the direction of low-carbon sustainability has become a problem to be solved. Taking ‘Solar Ark 3.0’, the entry of Southeast University United Team in the Third China International Solar Decathlon Competition (SDC2022), as an example. This study proposes the wind and solar complementary capacity design, multi-mode energy storage design, intelligent energy use design and production, storage and use of multi-energy complementary intelligent energy management methods through the exploration of the integrated design and construction of curved structure housing and energy system. These designs of ‘Solar Ark 3.0’ improve the utilization efficiency of renewable energy in buildings and also provide methods for the integrated design of curved structural housing and energy systems.

A smart home energy management system based on human activity recognition and deep reinforcement learning

Smart home energy management systems (SHEMS) can help users save on energy costs by controlling various household loads in response to environmental changes. However, human activity is closely related to household energy consumption, which can lead to potential energy waste. Additionally, smart homes often feature multiple types of distributed energy resources (DER), and how to leverage their potential for participating in grid scheduling without significantly impacting users remains an unresolved issue. This paper proposes an intelligent home energy management method based on human activity recognition (HAR) and deep reinforcement learning. By using activity recognition results derived from intrusive load monitoring (ILM), we categorize contexts into two groups. The agent employs the soft actor-critic (SAC) algorithm for real-time control. In the first context, the goal is to ensure user satisfaction while reducing energy costs. In the second context, the focus is on controlling DER to participate in grid ancillary services to generate revenue. The proposed agent is trained using a real dataset, and simulations validate the effectiveness of the algorithm in home energy management.

Hierarchical Game Theory-Based Optimal Power Management for Both Seaports and Ships

Unlike land-based power systems, a seaport microgrid is not only equipped with extensive electrification but also connected with ships, which turns maritime power management into a comprehensive transportation-power coordination problem. To fully exploit the potential of shipboard power systems for energy efficiency improvement of seaports, a hierarchical Stackelberg game-based framework is developed in this paper to achieve efficient power management for both seaports and ships. In this framework, the seaport microgrid serves as a leader to manage the local sources and determine the price for ships to maximize revenue, considering the influence of both the power side and transportation side. On the other hand, the ships act as followers to decide the charging and service strategies for an optimal trade-off between the benefits from onboard battery charging and the ship service cost. The proposed game is solved as a bilevel optimization problem via mathematical programming with the equilibrium constraints method. The proposed algorithm's efficiency is evaluated through numerical simulations. The simulation results demonstrate that the proposed power management method can simultaneously increase the economic profits of seaports and improve the service of ships at berth. Compared to traditional energy management methods, the comprehensive benefits can be increased by approximately 36%.

Dual-Actor Critic Adaptive Energy Management Method for EH-WSN Based on Battery Energy Neutral Operation

Dual-Actor Critic Adaptive Energy Management Method for EH-WSN Based on Battery Energy Neutral Operation

Combined application of life cycle assessment and neural network model for modeling energy and environmental emissions in sugar beet production

Current agricultural energy management methods do not sufficiently account for the environmental impacts related to farm size. This gap limits the ability to accurately predict the ecological effects of sugar beet cultivation, particularly regarding climate change, eutrophication, acidification, human toxicity, and terrestrial and aquatic ecotoxicity. Moreover, optimizing agricultural productivity and integrating artificial intelligence (AI) are necessary to improve energy management and anticipate future consumption patterns, considering current constraints. Our innovative solution combines LCA with AI to better forecast the ecological impacts of sugar beet cultivation across farms of different sizes. By using historical data and advanced neural models, this method anticipates future energy consumption while integrating environmental and energy constraints. The study highlights that medium-sized farms (MF) show the lowest environmental impacts, according to the CML and USEtox methodologies, unlike small (SF) and large farms (LF), which do not significantly differ in their contribution to pollutants. The average energy consumption for beet production was 1,092,000 MJ/ha on small farms, 1,126,100 MJ/ha on medium farms, and 1,107,080 MJ/ha on large farms. The key critical points identified were related to the use of diesel, machinery, seeds, and pesticides. In terms of energy distribution, chemical fertilizers accounted for 41 % of the total energy used, followed by fuel (24 %), pesticides (18 %), and machinery (7 %). Neural models revealed correlation coefficients (R2) ranging from 0.634 to 0.945 for testing, 0.861 to 0.967 for validation, and 0.944 to 0.982 for training. These results suggest good predictive accuracy for our AI-based approach. Finally, to improve energy efficiency, the study proposes modernizing agricultural equipment and developing more sustainable technologies, such as hybrid tractors, robotic and automated weeding systems, and cultivation techniques that reduce carbon emissions.

Resilient energy management of a multi-energy building under low-temperature district heating: A deep reinforcement learning approach

The corrective control of a building-level multi-energy system (MES) for emergency load shedding is essential to optimize the operating cost after contingency. For a Danish case, the heating devices in the building are connected to a developing low-temperature district heating (LTDH) system and operated under a heat market. Due to the coupling between the electrical power and heating system, an electricity outage can be propagated to the heating network, and heat prices as well as tariffs can impact the MES operating cost. In the previous studies, only electrical load shedding is modeled, while the impact of electricity outages on heating system operation and heat load control is ignored. On the other hand, the problem is traditionally solved by model-based optimization methods which are highly nonconvex leading to high computing complexity. Moreover, operating uncertainties can lead to infeasible solutions. To address these challenges, this paper proposes a deep reinforcement learning-based corrective control method for the resilient energy management of a building-level MES. In the method, the proximal policy optimization algorithm is applied, where multiple uncertainties, system dynamics, and operating constraints are considered. A case study of a real-life residential building connected to the LTDH system in Denmark is carried out, where electricity outages are simulated. The results verify the performance of the proposed method in achieving resilient energy management of the MES.

An energy management method for combined load forecasting of fuel cell ships

Abstract The article proposes an energy management method (EMM) for fuel cell ships, which consists of two parts: load forecasting (LF) and real-time optimized scheduling (RTOS). LF is composed of Long Short-Term Memory neural networks, which are capable of being trained to predict future loads based on historical ship load information. RTOS consists of model predictive control and can allocate energy in real time. Initially, shiploads and battery status are collected in real time, and then LF passes the predicted load sequence to RTOS. RTOS optimizes real-time energy allocation based on the predicted load and the current operating status of two sets of batteries, while meeting system constraints and maintaining the battery SOC at a healthy level, to minimize operational cost consumption. The simulations demonstrate that the EMM can consistently maintain SOC at a healthy level and reduce fuel cell ship operating costs to some extent, thereby improving economic efficiency.

Research progress on topology and energy management of fuel cell hybrid power ships

Abstract With the increasing awareness of environmental protection, the maritime industry is moving towards green and low-carbon development. Fuel cells, due to their pollution-free and high-efficiency characteristics, are one of the key technologies driving the green transformation of traditional ships. However, fuel cells have relatively soft output characteristics and often require other energy storage components to provide supplementary power. This paper reviews the types, advantages, and disadvantages of the topologies of fuel cell hybrid power ships, analyses the current development status of energy management goals and methods, and finally provides an outlook on the development of fuel cell hybrid power ships.

Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction

Integrating Big Data and Internet of Things (IoT) platforms is the focus of this research, which aims to improve energy management. The problem statement is centered on the potential for development through advanced technologies and the inefficiencies in traditional energy management methods. The objectives are to analyze energy consumption patterns, develop an innovative Home Energy Management System (HEMS) architecture, and offer energy-saving solutions. Synthetic energy consumption data is generated, normalized, and divided into training and testing sets from a methodological perspective. K-nearest neighbors, Decision Trees, Support Vector Regression, and Random Forest are the machine learning models trained and evaluated. The Random Forest model outperforms other models in terms of the accuracy of its predictions of energy consumption. The integration of renewable energy sources with cutting-edge technology to revolutionize energy management practices is the essence of novelty. In conclusion, this investigation underscores the importance of utilizing advanced technologies to promote sustainable energy management, providing practitioners and policymakers with practical insights.

Monitoring the impact of micro-photovoltaic production on a real residential case study

Abstract In this paper a case study of micro-photovoltaic (MPV) production impact on real houses in Rome area is presented. The experimentation was conducted as part of the “Electrical System Research” Program, funded by the Italian Ministry for the Environment, and thanks to a Collaboration Agreement between ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development) and ARETI (the Distributor System Operator in Rome urban area). During this experimental activity, it was possible to carry out a few pilot cases and integrate the Smart Home Kit, supplied by ENEA, with the new generation electricity meters, supplied by ARETI. Different types of users were involved, both simple consumers and prosumers equipped with micro-photovoltaic and photovoltaic (PV) panels. Starting from the collected data, some considerations were made regarding the percentage of self-sufficiency of the users. The common goal was to test innovative energy management methods aimed at increasing users’ energy awareness and encouraging a more virtuous and flexible use of energy, also thanks to a greater diffusion of the electric vector.

Optimized Energy Management for PV Hybrid Power Systems with DC Bus Voltage Control

The growing popularity of direct current (DC) power sources, energy storage systems, and DC loads has recently shifted the focus away from alternating current (AC) microgrids and towards DC-only systems. However, smart and energy-efficient building integration and effective microgrid administration are prerequisites. Direct current microgrids, which include solar modules as their principal power source, an energy storage device (battery), and an essential DC load, may have their energy consumption managed with the help of our study. Within the microgrid (MG) architecture, the DC-DC boost converter enables the PV module to operate in many modes, one of which is Maximum Power Point Tracking (MPPT). In order to link the battery and supercapacitor to the DC bus, the system also makes use of a DC-DC bidirectional converter. By restricting the charging/discharging currents and state of charge (SoC) of the batteries and supercapacitors, the proposed control approach seeks to manage power flow within the microgrid while considering their lifespan. This method simplifies and improves upon previous approaches to DC load supply by doing away with complicated energy management or maximum power point tracking methods. We conceived and simulated the DC microgrid under investigation thanks to Matlab/Simulink. We fulfil the load demand while maintaining system stability and performance. We provide a presentation and discussion of findings from controller design, analysis, and simulation validation for several operating modes.

Energy management method for battery swapping station combining reinforcement learning and mathematical optimization

Abstract The rapid proliferation of electric vehicles has spurred the expansion of scalable Battery Swapping Station (BSS) to cater to the demand for swift charging. However, the current energy management faces challenges in coping with the fluctuation of charging batteries in scalable BSS, the unpredictability of electricity prices and battery demand, as well as the intricacy of demand response. So, this paper presents a two-layer optimization framework for energy management in scalable BSS. The framework decomposes the power scheduling problem in BSS into two subproblems and solves them with deep reinforcement learning and mathematical optimization. The upper layer uses deep reinforcement learning to schedule the BSS’s total power, while the lower layer uses mathematical optimization to allocate the power to each charging pile. The two layers cooperate to achieve an efficient solution. The experiments demonstrate that the proposed method can reduce the operating cost of the BSS, satisfy the safety and user’s demand, and facilitate grid demand response, in BSSs of different sizes. The method is an effective solution for power scheduling of scalable BSS.

Smelling-based hunting optimization for battery–Super/Ultracapacitor hybrid energy storage station in wind/solar generation system using Siamese neural network

To ensure the continuous supply of power in remote areas utilizing renewable energy resources is significant. Hence, in this research, an effective energy management method for a small-scale hybrid wind-solar-battery-Ultra-capacitor-based microgrid is proposed. Hybrid Energy Storage System (HESS) has been presented in conjunction with wind and solar energy conversion technologies characterized by coupling of power electronic converters, neural networks, optimization, battery, controllers, and ultra-capacitor storage systems to attain the intended performance. The power balance is regulated via an energy management system by considering the fluctuation in load demand and renewable energy power generation. Further, the voltage controller utilizes the deep Siamese neural network (deep SNN) that effectively carries out energy management among the hybrid renewable energy sources. The smelling-based hunting optimization assists in optimal parameter tuning and training of the deep SNN for enhancing the HESS’s efficiency. In addition, the microgrid operates independently and offers a testing area for different energy management systems and testing scenarios. The proposed small-scale microgrid, which is based on renewable energy, can serve as a significant testing area for methods utilized in smart grid applications. The proposed SBHO model’s efficiency is determined by varying the voltage, current, and power of the wind, solar, battery, and ultra-capacitor measurements. The current capacity of the battery reaches −11.06A and the battery voltage reaches 259.831 V in 0.82 s. The DC load measurement utilizing the SBHO approach obtained a DC bus voltage of 357.11V, a load current of 3.348 A within 0.82 s, and in DC power load attained the 1195.58 W within 0.82 s. The battery’s SOC by applying the smelling-based hunting optimization is gradually increased to 50.008% in 0.82s.In terms of the PV measurement, the PV current, PV voltage, and PV power are obtained as 4.238A,241.08V, and 1021.64W for the SBHO approach which surpasses other competent techniques. The ultra-capacitor current ranges from 35A to 40A with reduced heavy discharge of SOC from 98% obtained on evaluating the performance. The output power of wind using a boost converter remains 3000 W with few harmonics.

Deep learning-based post-disaster energy management and faster network reconfiguration method for improvement of restoration time

Natural disasters in the world often cause power outages. To improve post-disaster response and restoration time, a method for energy management and network reconfiguration in emergencies has been proposed, where energy storages systems (ESSs) in distribution networks are used to support the system balance immediately and then the network reconfiguration is accelerated based on deep learning techniques. First, when power failures occur, the outputs of ESSs are calculated optimally in terms of the proposed optimization model under the constraints of minimizing line losses. After the injections of ESSs in emergencies, the network reconfiguration is still needed due to the limitation of ESS capacities. To avoid calculations of power flow repeatedly, a deep-learning based reconfiguration method for distribution networks has been proposed, where the deep-learning model is trained offline and can output the parameters of power flow immediately when a state combination of distribution generators (DGs), loads and power lines, which is an effective way to accelerate the network reconfiguration. If many reconfiguration schemes are found, a decision-making method with preferences are used to select one according to different situations. Finally, cases are designed, and simulation results show that the calculation time of a reconfiguration schemes are reduced significantly by almost one hundred multiples.

Energy management optimization of hybrid electric vehicles based on deep learning model predictive control

In this paper, the hybrid electric vehicle (HEV) energy management optimization method is proposed based on deep learning (DL) model predictive control. Through empirical research combined with the questionnaire survey, this article not only provides a new perspective and practical basis but also improves the efficiency and accuracy of the model by improving the relevant algorithms. The study first analyzes the importance of HEV energy management and reviews the existing literature. Then, the optimization method of HEV energy management based on the deep learning model is introduced in detail, including the composition of energy management for hybrid electric vehicles, the structure and working principle of the deep learning model, especially the backpropagation neural network (BPNN) and the convolutional neural network (CNN), and the steps of application of deep learning in energy management. In the experimental part, questionnaire data from 1,500 consumers were used to design the HEV energy management optimization scheme, and consumers’ attitudes and preferences towards HEV energy optimization were discussed. The experimental results show that the proposed model can predict HEV energy consumption under different road conditions (urban roads, highways, mountain areas, suburban areas, and construction sites), and the difference between the average predicted energy consumption and the actual energy consumption is between 0.1KWH and 0.3KWH, showing high prediction accuracy. In addition, the deep learning-based energy management strategy outperforms traditional control strategies in terms of fuel consumption (6.2 L/100 km), battery charge and discharge times (814), battery life, and CO2 emissions, significantly improving the efficiency of HEV energy. These results demonstrate the great potential and practical application value of deep learning models in the optimization of energy management of HEVs, helping to drive the development of more sustainable and efficient transportation systems.

Optimised rule-based peak shaving and its impact on annual energy cost reduction

This paper presents an optimised rule-based peak-shaving method which is tested on an educational institute to know its impact on annual energy cost reduction. In recent years, energy management in low-voltage distribution systems has drawn more attention. Peak shaving is an important energy management method which helps consumers and grid operators. Peak shaving reduces voltage drop and voltage-rise issues, ratings of the different components and energy consumption costs and power losses. In this work, some of the most effective peak-shaving techniques to handle the problem of peak shaving and a few real-world implementations are discussed. Furthermore, an optimised rule-based peak-shaving (RBPS) method, which considers grid import and export power limits, is presented. The impact of this peak-shaving method, on a case study of an educational institute with a photovoltaic (PV)-battery energy storage system (BESS) based on its net load data over an annum for annual operating energy cost reduction, is presented. The results indicate that the total cost reduction for a year is 68.48% using PV and BESS with the peak-shaving application.

Real-Time Energy Management Method for Data Center Considering Shiftable Workload and Renewable Energy

Aim: This study aimed to improve the electricity economy of data centers. Background: Data centers have been rapidly developed over the last decade to balance increasing computing workloads. Such a rapid development has been found to result in a significant increase in higher electricity expenses for data centers. Objective: The objective of this study was to improve the operation efficiency of a data center by developing a data center energy management model considering shiftable data workloads, energy storage, and renewable energy. Method: First, a data center workload model has been established to describe its temporal shifting characteristics. Then, a data center power consumption model has been developed to describe the energy usage of IT equipment and cooling systems. Finally, the energy management model for the data center has been developed to reduce the electrical energy costs of the data center. Results: Case studies have been performed on a trial system to illustrate the validity of the proposed model, which has been found to meet the requirements. Conclusion: Rationally re-scheduling the delay-tolerant workloads and making full use of energy storage can enhance the flexibility of the power system and reduce electricity costs by 19.06%.

Analysis of the Use of Energy Storage in the Form of Concrete Slabs as a Method for Sustainable Energy Management in a System with Active Thermal Insulation and Solar Collectors

One effective approach to reducing the energy required for heating buildings is the use of active thermal insulation (ATI). This method involves delivering low-temperature heat to the exterior walls through a network of pipes carrying water. For ATI to be cost-effective, the energy supply must be affordable and is typically derived from geothermal or solar sources. Solar energy, in particular, requires thermal energy storage (TES) to manage the gap between summer and the heating season. A building that integrates various renewable energy systems and heating/cooling technologies should be managed efficiently and sustainably. The proper integration of these systems with smart management strategies can significantly lower a building’s carbon footprint and operational costs. This study analyzes the use of concrete slabs as a method for sustainable energy management in a system incorporating active thermal insulation and solar collectors. Using ambient temperature and solar radiation data specific to Cracow, Poland, the simulations evaluate the feasibility of employing a concrete slab positioned beneath the building as a thermal storage tank. The results reveal some drawbacks of using concrete slabs, including high temperatures that negatively affect system efficiency. Increased temperatures lead to higher heat losses, and during summer, inadequate insulation can cause additional heat leakage into the building. The findings suggest that water may be a more effective alternative for thermal energy storage.

Integrated Optimal Energy Management of Multi-Microgrid Network Considering Energy Performance Index: Global Chance-Constrained Programming Framework

Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG’s inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed.