Integrated Energy Management System Research Articles

Development of an integrated energy management system for off-grid solar applications with advanced solar forecasting, time-of-use tariffs, and direct load control

Effectively managing and maximizing the integration of renewable energy sources is essential for a sustainable power grid due to the stochastic and intermittent nature of renewable energy generation. This study develops a comprehensive Integrated Energy Management System incorporating supply-demand side management in the form of time-of-use credit, direct load control, and generator control to enhance photovoltaic utilization in off-grid applications. A novel three-step solar energy forecasting approach is proposed in this paper, utilizing low-level data fusion and regression models to predict next-day photovoltaic generation with improved accuracy, and a rule-based decision algorithm is developed to correct forecast errors and manage loads dynamically. A techno-economic analysis covering a 20-year duration is carried out for scenarios with and without the integrated energy management system; three configurations are investigated for supplying an off-grid residential home, including diesel generator, diesel generator/photovoltaic system, and diesel generator/photovoltaic system/integrated energy management system. Results reveal that the hybrid configuration with integrated energy management system achieved 44 % and 46 % reductions in costs and carbon dioxide emissions compared to the diesel generator alone, and 8 % and 9 % compared to the diesel generator/photovoltaic setup respectively. The Integrated Energy Management System further enhanced photovoltaic utilisation rate by over 113 % when compared to the diesel generator/photovoltaic system. Further evaluations include customer behaviour impacts, demonstrating that a fully automated system with 100 % compliance significantly outperforms systems with manual customer control, highlighting the detrimental effect of overrides on the efficiency of direct load control. The flexibility of the Integrated Energy Management System framework allows potential adaptation for on-grid applications, showcasing its utility in diverse operational contexts.

Two-stage energy management framework of the cold ironing cooperative with renewable energy for ferry

The cold ironing system is gaining interest as a promising approach to reduce emissions from ship transportation at ports, enabling further reductions with clean energy sources coordination. While cold ironing has predominantly been applied to long-staying vessels like cruise ships and containers, feasibility studies for short-berthing ships such as ferries are limited. However, the growing demand for short-distance logistics and passenger transfers highlights the need to tackle emissions issues from ferry transportation. Incorporating electrification technology together with integrated energy management systems can significantly reduce emissions from ferry operations. Accordingly, this paper proposes a cooperative cold ironing system integrated with clean energy sources for ferry terminals. A two-stage energy management strategy combining sizing and scheduling optimization is employed to reduce the port’s emissions while minimizing system and operational costs. The proposed system configuration, determined through the sizing method, yields the lowest net present cost of $9.04 M. The applied energy management strategy managed to reduce operational costs by up to 63.402 %, while significantly decreasing emissions from both shipside and shoreside operations. From the shipside, emissions reductions of 38.44 % for CO2, 97.7 % for NOX, 96.69 % for SO2, and 92.1 % for PM were achieved. From the shoreside, the approach led to a 28 % reduction across all emission types. Thus, implementing cold ironing powered by clean energy sources is a viable solution for reducing emissions generated by ferry operations. The proposed energy management approach enables emissions reduction and delivering cost-effectiveness at ferry terminals.

Research on Development and Implementation of Integrated Energy Management System for Buildings

Research on Development and Implementation of Integrated Energy Management System for Buildings

Towards a low-carbon future for offshore oil and gas industry: A smart integrated energy management system with floating wind turbines and gas turbines

Decarbonizing offshore oil and gas fields is crucial in the global fight against climate change. To achieve this objective, the offshore oil and gas industry has embraced innovative energy systems, including microgrids that seamlessly integrate renewable energy sources like floating wind turbines. This study presents a comprehensive investigation into an integrated energy management system for an offshore microgrid, encompassing three platforms and a floating wind farm, along with green hydrogen production and storage facilities.The operational decision-making process for such a complex microgrid, involving numerous assets, presents notable challenges. To address this, a sophisticated smart management system is employed, enabling efficient optimization with advanced forecasting capabilities to identify the most cost-effective and environmentally friendly version of the microgrid's operation. To overcome the intricacies of optimization and computational constraints, a novel hybrid optimization approach, with a platform-centric strategy, is utilized. Leveraging real-world operational data, the study harnesses an innovative online optimization method fortified with state-of-the-art AI algorithms.The results of the optimization are benchmarked against a rule-based operation, wherein no formal optimization occurs, but the most economically viable decisions are made. The findings underscore the effectiveness of the developed optimization method, leading to a significant 16% reduction in operational costs and carbon-based emissions compared to the rule-based approach.This study effectively demonstrates the real-world applicability of the developed method by applying and testing the smart management system on an actual offshore platform with minimal simplifications. The investigation provides valuable evidence of the method's adaptability to complex operational scenarios, highlighting its potential for practical implementation in the offshore oil and gas industry.

A fuel cell range extender integrating with heat pump for cabin heat and power generation

Batteries, Heat Pumps (HPs), and fuel cells (FCs) are critical for transport decarbonization and a net zero future. However, cabin heating in extreme conditions leads to severe driving range reduction in current Electric Vehicles (EVs). The performance of the heat pump (HP) in EVs and its performance enhancement technologies are widely investigated but cannot, simultaneously, provide sufficient heat and high COP. The source and amount of the waste heat within a vehicle for the heat pump integrated system is a crucial challenge to improve performance. The structure becomes increasingly complicated, but the benefits are not significant. Therefore, in this study, a small Fuel Cell, battery and heat pump integrated energy management system for range extended EVs (FCBEEV) is designed. The cogeneration characteristic of the fuel cell and waste heat from battery pack are utilised by the heat pump to ensure a high-level of cabin comfort in extremely cold temperatures and an extension of the driving range. A numerical model was established in MATLAB and the results were analysed from energy, exergy, environment, and economic (4E) perspectives. In this study, we show that the highest COPsys of the proposed system is 5.8 and can improve the driving range (DR) by 65% to 110% compared to the reference systems. The exergy efficiency of the suggested system is 75% at −10 °C and the fuel cell and internal condenser are the primary causes of the exergy destruction. The environmental impact decreases by 13 kg/year per car compared to current EVs with a Positive Temperature Coefficient (PTC) and Air Source Heat Pump (ASHP) system, and the reduction is primarily sourced from the indirect emissions. The operating cost which includes driving and heating is 28.9% higher than cited for an ASHP and PTC system and 41% higher than the PTC baseline system. The payback duration is 300,000 km at current market prices, and it is predicted to be shorter to 100,000 km, if the cost of the fuel cell stack is estimated at £4000 and the H2 price is the same as electricity. We anticipate that the proposed system can significantly improve cabin comfort and driving range anxieties, as well as promote the decarbonization of transport.

Integrated Energy Management System for Microgrid based on Renewable Energy Sources

An effective energy management strategy is crucial to ensure highest system reliability, stability, operation efficiency and cost-effective operation of renewable energy sources based standalone microgrid. This paper presents an efficient energy management system for microgrid incorporated with Photovoltaic system, PMSG based wind turbine and energy storages including battery, fuel cell-Electrolyzer. Implemented hybrid modified invasive weed optimization with perturbed and observed method for PV systems to harvest maximum energy during partial shading condition. A sliding mode controller is implemented for boost converter to work as maximum power point tracker for wind turbine. Three solar plants and three wind farms are considered in this paper to establish 1 MW microgrid. Each wind farm is established with multiple wind turbines and similarly each solar plant having multiple PV modules. Each wind turbine and solar plant has their own inverter to synchronize at point of common coupling (PCC). Effective controllers are proposed to supply quality power at PCC under linear status and nonlinear status of single and three phase loads. Small size battery is considered to work under transient time and electrolyzer-fuel cell set will be working under steady state condition to reduce the cost of the system. TS-Fuzzy based controllers are designed for all the converters and implemented hardware-in-loop on a Real Time Simulator (RTS) by using OPAL RT technology/modules. The results unveiled that the RTS precisely emulate the dynamics of the microgrid with proposed controllers.

Integrated model for optimal energy management and demand response of microgrids considering hybrid hydrogen-battery storage systems

Hybrid hydrogen-battery (HHB) based energy storage technologies have recently become matters of significant interest to enable sustainable and net zero-emission microgrids characterized with high penetration levels of variable renewable energy sources (RES) such as solar and wind. In this regard, there are multiple objectives and constraints controlling the scheduled decision-making framework that manages these microgrid resources while supplying the demand. As such, this paper introduces an integrated energy management system (IEMS) to concurrently optimize the operation schedule of the HHB storage system and provide demand response (DR) via adequate shifting slots for the elastic loads of a microgrid setup. The proposed IEMS aims to optimally manage the microgrid’s energy by minimizing three conflicting objective functions: the electricity and battery degradation costs, customers’ discomfort, and peak-to-average ratio (PAR). The proposed IEMS is developed for microgrids containing a mix of variable RES, an HHB storage system, and a local power consumption considering dynamic grid tariff. The IEMS is formulated as a mixed-integer nonlinear problem and is solved using a multi-objective artificial hummingbird optimizer (MOAHA). To account for the variability and uncertainty of RES, the proposed IEMS is tested under four scenarios: good, bad, average weather scenarios, and a forecasted weather profile based on a stochastic model of RES. The performance of the proposed IEMS is evaluated via comprehensive comparative analyses with the conventional energy management strategy (CEMS) without and with consideration of the HHB storage system and DR. Moreover, the performance of MOAHA has been assessed versus the state-of-the-art multi-objective particle swarm optimizer (MOPSO). For the purpose of these comparisons, four quality metrics have been utilized to quantify the benefits of the proposed IEMS: renewable contribution factor (RCF), greenhouse gas (GHG) emission, loss of power supply probability (LPSP), and saving cost (SC). The results show that the proposed IEMS yields better-optimized values that help the microgrid operator identify the sweet spot of the conflicting objectives, where it has increased the customer saving to be (82.95%, 29.65%, 33.33%, and 33.00 %), (73%,23.94%, 25.49%, and 31.65%) and (10.69%,3.62%, 1.59%, and 1.67%) compared to the CEMS without/with including HHB. and IEMS based-MOPSO over the four studied scenarios, respectively. Also, the results show the superiority of the proposed IEMS in improving the identified four quality metrics compared to the CEMS without/with considering HHB storage systems and IEMS-based-MOPSO. Accordingly, integrating HHB and implementing optimized IEMS based-MOAHA with considering the DR approach plays a vital role in providing a profit for the customer, protecting the environment from emissions, and enhancing the system’s reliability with considering the weather uncertainty.

Development of fuzzy logic-based demand-side energy management system for hybrid energy sources

Demand-side energy management techniques, such as load shielding, shifting, and delaying appliance operation during peak periods, are typically used to reduce electricity costs at the expense of users’ comfort. To address these challenges, especially where operational delays are unacceptable, distributed generation (DG) is frequently incorporated into the grid system to improve power balance and total energy costs. However, dynamic load demands and varying outputs from renewable DG sources such as solar photovoltaic (PV) systems make energy management in microgrids (MGs) extremely challenging. Moreover, most of the existing studies in this domain focus on objective functions that are geared on optimising the economic balance between cost and value of MG operation over a certain time period. Nevertheless, research that took into consideration the stochastic behaviour of DG’s subsystems in addition to cost and benefit of MG operation are still limited. This current study proposed a fuzzy logic control (FLC) integrated energy management system (EMS) for commercial loads with hybrid grid-solar PV/battery energy system. The proposed technique intelligently selects energy sources using the grid energy cost and the state of charge (SoC) of the solar PV/battery at any time of the day. The EMS operate the loads at a reduced cost without any operational delay or shifting. The system was implemented in the MATLAB/Simulink environment, and the techno-economic feasibility of energy cost savings was investigated by comparing the developed scheme with the Homer hybrid energy system model for a hotel building. The developed EMS reduced energy costs by an average of 11.87 % per day and 7.94 % over a 20-year lifetime.

Developing and testing an “Integrated Energy Management System” in a ski resort: The “Living Lab Madonna di Campiglio”

Nowadays, ski resorts represent an energy-intensive industry in the mountain environment. On the one hand, their energy consumptions (and costs) have greatly increased in the last 40 years, linked to growing: (I) snow production and grooming demands, facing climate change and ski season extension, (II) number of ski lifts, with a share expansion of the energy-intensive ones (e.g. gondola), (III) need of operational buildings (e.g. warehouses, workshops). On the other hand, their energy transition calls for energy efficiency improvement and renewable sources integration, to be part of cleaner energy systems with low emissions and low environmental impacts (e.g. water use). This paper describes the developing and testing of an “Integrated Energy Management System - IEMS” in the “Living Lab Madonna di Campiglio”, as part of the Interreg Alpine Space - Smart Altitude project. After a detailed characterization of the case study, this paper describes the development of the new IEMS as an integration of existing and new monitoring systems and platforms. This IEMS aims to facilitate and stimulate ski resort technicians and managers in the continuous analysis of energy, environmental and economic performance, paving the way for greater awareness and targeted interventions.

Energy-Circuit-Based Integrated Energy Management System: Theory, Implementation, and Application

Integrated energy systems (IESs), in which various energy flows are interconnected and coordinated to release potential flexibility for more efficient and secure operation, have drawn increasing attention in recent years. In this article, an integrated energy management system (IEMS) that performs online analysis and optimization on coupling energy flows in an IES is comprehensively introduced. From the theory perspective, an energy circuit method (ECM) that models natural gas networks and heating networks in the frequency domain is discussed. This method extends the electric circuit modeling of power systems to IESs and enables the IEMS to manage large-scale IESs. From the implementation perspective, the architecture design and function development of the IEMS are presented. Tutorial examples with illustrative case studies are provided to demonstrate its functions of dynamic state estimation, energy flow analysis, security assessment and control, and optimal energy flow. From the application perspective, real-world engineering demonstrations that apply IEMSs in managing building-, park-, and city-scale IESs are reported. The economic and environmental benefits obtained in these demonstration projects indicate that the IEMS has broad application prospects for a low/zero-carbon future energy system.

Investigation of ZVS Criteria and Optimization of Switching Loss in a Triple Active Bridge Converter Using Penta-Phase-Shift Modulation

Triple active bridge (TAB) as an isolated multiport dc–dc converter is a promising solution for integrated energy management systems to maintain an efficient power flow between the ports. This article presents the design, modeling, and switching loss optimized phase-duty-controlled PWM modulation scheme of a TAB converter composed of three full-bridge modules and a high-frequency planar transformer. The work aims at the derivation and analysis of the zero-voltage switching (ZVS) conditions for a TAB, where the turn-on switching loss is mitigated along with attaining an improved EMI performance, comprising of reduced noise peaks. Moreover, an optimized five-variable control-based TAB PWM modulation technique is proposed in this article in order to achieve minimized switching loss and the overall system loss for a wide voltage gain and load range. The instantaneous switching currents, responsible for the switching losses at the devices, are derived from the transformer winding current expressions, formulated employing the generalized harmonic approximation (GHA) technique. The various loss minimization techniques and the theoretically obtained criteria for ZVS are experimentally verified using a laboratory-developed prototype of an 800-W TAB converter. With the implementation of the proposed optimal phase-duty control, the experimental results show a nonunity gain light-load efficiency increment up to 10% compared to the conventional phase-shift modulation alone.

An integrated energy management system using double deep Q-learning and energy storage equipment to reduce energy cost in manufacturing under real-time pricing condition: A case study of scale-model factory

Reducing energy costs is an emerging aspect in the research on the economic and environmental dimensions of manufacturing systems. The share of electricity cost accounts for approximately 60 % of the total energy cost of a manufacturing system, whereas the share of oil, coal, and gas accounts for the remaining 40 %. The electricity cost is dependent on the electricity price and usage. In terms of the electricity price, one of the pricing strategies widely used in the USA and Europe is called real-time pricing (RTP), which is characterised by hourly price changes. Compared to other pricing strategies, RTP yields the highest reward and the highest risk. In the RTP strategy, the electricity price is influenced by the supply and demand of the energy market. Hence, the energy cost of manufacturing cannot be determined by the manufacturing companies, implying a high level of risk. However, if manufacturing companies seize the opportunity to perform more manufacturing tasks when the energy price is low, the cost-savings will be significant, implying a high level of reward. In this study, we propose an integrated energy management system (IEMS) to reduce the energy cost of manufacturing systems. The IEMS consists of an energy storage equipment and an intelligent switch mechanism. When the electricity price is high, the manufacturing system is powered by the energy storage equipment. When the electricity price is low, the manufacturing system is powered by the public electricity grid, and the energy storage equipment is charged. The decision-making of these operations is performed by the intelligent switch mechanism based on double deep Q-learning. To validate this framework, a case study is conducted, in which an IEMS is developed to reduce the electricity cost of a scale-model factory. Based on an online test of the IEMS in different manufacturing cycles, it is concluded that the proposed IEMS approach achieves a cost reduction of approximately 57.21 %.

A Review of Mathematical Models of Building Physics and Energy Technologies for Environmentally Friendly Integrated Energy Management Systems

The Energy Management System (EMS) is an efficient technique to monitor, control and enhance the building performance. In the state-of-the-art, building performance analysis is separated into building simulation and control management: this may cause inaccuracies and extra operating time. Thus, a coherent framework to integrate building physics with various energy technologies and energy control management methods is highly required. This framework should be formed by simplified but accurate models of building physics and building energy technologies, and should allow for the selection of proper control strategies according to the control objectives and scenarios. Therefore, this paper reviews the fundamental mathematical modeling and control strategies to create such a framework. The mathematical models of (i) building physics and (ii) popular building energy technologies (renewable energy systems, common heating and cooling energy systems and energy distribution systems) are first presented. Then, it is shown how the collected mathematical models can be linked. Merging with two frequently used EMS strategies, namely rule-based and model predictive controls, is discussed. This work provides an extendable map to model and control buildings and intends to be a foundation for building researchers, designers and engineers.

Integrated Renewable Energy Management System for Reduced Hydrogen Consumption using Fuel Cell

The hybrid energy sources and their behavior may be controlled by monitoring and sensing with the help of a single or multiple control strategies incorporated in the energy management system. Utilization of the battery state of charge (SOC) and reduction in the consumption of hydrogen are the main objectives of battery and fuel cell (FC) based renewable hybrid power systems. The lifespan of the hydrogen storage as well as battery may be improved while improving the cost reduction benefits using these parameters. These objectives are achieved by designing an integrated energy management system (IEMS). A battery, supercapacitor (SC), proton-exchange membrane fuel cell (PEMFC) and Photovoltaic (PV) cell are combined to provide the required power to a predetermined load to form a renewable hybrid power system (RHPS). During daylight, PV is the master power source in RHPS. During the shading or night time, FC is the secondary power source. When high load power is required, the FC is supported by the battery. Load fast change or load transient operation is performed by the SC. Maximum SOC value and minimum hydrogen consumption value is obtained simultaneously based on predetermined functions that aids in switching between the state machine control, frequency decoupling and fuzzy logic based integrated strategies in the proposed energy management model. When compared to the stand-alone strategies, the integrated model achieves increased SOC and reduced hydrogen consumption. When maximum value of PV power is attained, the surplus power is displayed at the load. The battery is charged using this surplus power. The stand-alone strategies and integrated strategy results are compared. The attainment of the goal of IEMS is confirmed from this comparison.

Integrated energy management system for Microgrids of building prosumers

In this paper, an easy to apply and computationally efficient energy management system (EMS) for microgrids of building prosumers is proposed. It is based on a hierarchical multi-agent system (MAS), aiming to minimize the operation cost of a microgrid comprising a group of buildings, renewable energy sources (RES), energy storage systems (ESS) and plug-in electric vehicles (PEVs). The optimization problem is suitably set up to ensure low computation requirements and it is solved with particle swarm optimization (PSO). Optimal operation scheduling for grid-connected and autonomous operation of microgrids of building prosumers, considering their thermal and electrical systems, integrated RES, conventional and dynamic ESS is proposed. The models used are not computationally demanding, allowing the application to complex systems and they are not being limited to one building scale which is the case for the majority of relative research. Another innovative feature of the proposed algorithm, among others, is that it ensures autonomous operation at certain time periods without the use of any emergency generator sets. Detailed simulation results of different operation scenarios, showed that cost savings in range of 11% can be achieved while a large number of operation and technical constraints are satisfactorily met in grid-connected and autonomous operation.

Achievable Energy Flexibility Forecasting of Buildings Equipped With Integrated Energy Management System

Buildings’ achievable energy flexibility refers to the real load reduction amount in an incentive-based demand response (DR) event, which presents dynamic, subjective, and uncertain characteristics. It is different from the buildings’ theoretical energy flexibility, which refers to its physical load reduction potential during a certain time period and is static and certain. The former serves as a foundation in a DR aggregator’s market transaction strategy formulation process and thereby calls on the necessity of its accurate forecasting. However, most of the existing literature focuses on the latter one, which is not necessarily equal to the real achievable flexibility. Therefore, to help DR aggregators bid accurately in the ancillary service market, this paper proposes an achievable energy flexibility forecasting method for a building equipped with an integrated energy management system based on the decision tree model. The impact of gas-fired equipment on buildings’ achievable-energy-flexibility is also taken into account in the proposed method. The case study indicates that the proposed method exhibits promising performance in forecasting the achievable energy flexibility of a building.

An intelligent load priority list–based integrated energy management system in microgrids

In contrast to most contemporary literature on demand-side management (DSM) in microgrids (MG), which often neglects the granularity of the load importance degree prior to the shedding time or at best arbitrarily fixes a certain load priority list (LPL), this paper introduces spatially and temporally varying LPL-based DSM that determines the load category and corresponding amount that should be curtailed. The high penetration of the intrinsically stochastic renewable resources in MGs elevates some reliability problems and supply–demand balance instabilities in MGs, raising the necessity for applying robust and prompt demand response (DR) actions to avoid purchasing expensive energy from the grid, especially during peak hours. The proposed neuro-fuzzy LPL-based DSM is developed and implemented to offer smarter and less severe DR actions that will ensure a minimised operational cost, without shedding critical or essential loads. By applying the methodology on an MG testbed with real and local multi-category demand and hybrid renewable generation data, the results showed the effective optimization of the MG self-generation adequacy through DSM, especially during grid’s high-tariff periods.

Integrated Energy Management System in SupermarketAssuming Utilization of Exhaust Heat for Subcooling by PCM to Achieve Sustainable Cities and Communities

Integrated Energy Management System in SupermarketAssuming Utilization of Exhaust Heat for Subcooling by PCM to Achieve Sustainable Cities and Communities

Energy Management Practices’ Determinants in Greek Enterprises

The implementation of energy management over recent decades has been considered to be particularly important for companies wishing to save energy resources, to tackle the issue of climate change, and, at the same time, maintain their competitive edge in an ever-changing environment. The main objective of this article is to identify and analyse all of those critical factors that may affect the implementation of effective energy management practices by Greek enterprises. A questionnaire survey was conducted to investigate the implementation of energy management in Greek enterprises. The survey was conducted between May and September 2016. Out of the 649 questionnaires sent, 132 were collected (a response rate of 20.3%). The implementation of energy management in Greek enterprises is affected particularly positively by the implementation of a structured and integrated energy management system as far as the main findings of the survey are concerned. The use of modern technology, the involvement of the employees, and the commitment of the senior management proved to be additional critical determinants in the implementation of energy management in the operation of businesses.

Integrated energy management system employing pre-emptive priority based load scheduling (PEPLS) approach at residential premises

Human work has been automated and replaced by various smart appliances at residential buildings with an objective to save time and energy. So, there is a massive upsurge of energy consumption everywhere and in due course, it resulted in increased electrical bills than before. In order to meet the growing utilization of more electricity by consumers at residences, internalization of renewable energy systems are firmly recommended for a better transformation. Hither, the IEMS is principally aiming to truncate the rate of electricity bills to consumers. Likewise, scheduling the usage of smart appliances and implementing load management practices prudently, a peak load reduction can be certainly accomplished by effectively using Solar Power, Batteries and Utility Grid. Already existing TOU tariff is employed in the process along with restructured components and renewable resources to manage the loads of different smart appliances according to their usage priority at different times of a day. This automated system is able to manage multiple heavy loads smartly with the help of renewable energy resources even at peak hours of a day.