Integrated Energy Management System Research Articles

Optimising Energy Efficiency in Offshore Buildings: Supporting SDG 7 - Clean and Affordable Energy

The offshore oil and gas sector faces significant challenges in reducing its carbon footprint while meeting global energy demand. Offshore structures consume up to 500 MW of energy per day. Despite the potential for 30-40% energy savings with efficiency technologies, only 25% of global platforms implement integrated energy management systems. This study aims to optimize energy efficiency in offshore structures to support SDG 7 - Affordable and Clean Energy. The study uses a multi-method approach, analyzing data from 10 offshore platforms, conducting energy modeling, and cost-benefit analysis. The results show variability in daily energy consumption (423.7 ± 32.5 MW) with key factors such as hydrocarbon production and environmental conditions. Energy efficiency technologies such as hybrid power generation systems and Integrated Energy Management Systems (SMET) show significant potential for savings. The optimization model shows a total potential energy savings of 37.2 ± 2.1%. Economic analysis using Monte Carlo simulations confirms the feasibility of investment with positive NPV for all evaluated technologies. SMET has the most favorable risk-return profile with a mean NPV of 5.8 ± 0.7 million USD. These findings provide important insights for operators and policymakers, emphasizing the importance of a holistic approach to energy management in offshore structures. Implementation of the proposed energy efficiency strategies can contribute significantly to climate change mitigation and the achievement of SDG 7.

Data Modeling and Synchronization Method to Align Power Trading Rules for Integrated Energy Management Systems

Integrated energy systems (IESs) couple multiple energy sources to promote clean energy and reduce emissions. IESs need to participate in business activities, such as power trading, aided by automated data-driven systems to achieve optimal and economical operation. However, challenges arise due to the lack of unified data model standards and the semantic ambiguity of rules, on top of the difficulty of synchronizing data across heterogeneous subsystems of integrated energy management systems (IEMSs). Previous research on power trading data models was limited to certain application scenarios and heterogeneities. This study pivots from proposing model standards to a standard neutral way to align power trading rules across IEMS subsystems. The method features a framework for power trading rules and a software platform called the power trading rule synchronizer. The proposed approach can minimize semantic ambiguity and ensure the automated rule synchronization across subsystems of IEMSs. A case study demonstrated the application of the proposed method, saving an estimated 672 man-days in implementing the rules in 16 subsystems of the IEMS, contributing directly and indirectly to reducing emissions. This study provides a foundation for aligning other rules in IEMSs, such as carbon and gas trading rules.

Collaborative optimization of multi-energy multi-microgrid system: A hierarchical trust-region multi-agent reinforcement learning approach

In the context of the expanding diversity of energy demands, an increasing number of heterogeneous Multi-energy Microgrids (MEMGs) are engaging in the collaborative framework of the Multi-energy Multi-microgrid System (MEMMG). However, following this trend, the existing centralized Integrated Energy Management System (IEMS) control strategy is unreliable for future energy systems, characterized by more complex optimization control and a flexible system structure. This paper introduces a hierarchical Multi-agent Deep Reinforcement Learning (HMADRL) approach for distributed IEMS in MEMMG. Firstly, by employing a hierarchical approach, this method simplifies control complexity by segmenting the overarching control challenge into tasks of collaborative planning and action control, which are distributed across varied multi-agent scenes. Considering both macro and microeconomic factors, alongside carbon emissions, the optimal operation of MEMMG is realized through a bottom-up edge multi-agent control approach, in contrast to traditional top-down centralized methods. Secondly, in the phase of the inter-MEMG collaborative strategy, the Centralized Training Decentralized Execution (CTDE) framework is adopted to overcome the problems of unstable training environments and large-scale agent training, and each heterogeneous microgrid can develop local strategies independently with the assurance that their internal data will not be overly exposed. Thirdly, within each MEMG, the Trust-Region (TR) model is introduced for multi-agent action control, adeptly addressing the effects of mutual exclusion in decision-making time series. Simultaneously, an initialized Hot Experience Pool (HEP) is implemented, efficiently reducing exploration in complex, high-dimensional spaces. Finally, the off-time agent model is integrated into the HMADRL environment and undergoes secondary training based on real interactions, thereby deriving the optimal energy management policy. The proposed method markedly reduces reliance on exact physical modeling systems. Comparative simulations validate the proposed control scheme’s efficacy.

Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation

Considering the rapidly evolving microgrid technology and the increasing complexity associated with integrating renewable energy sources, innovative approaches to energy management are crucial for ensuring sustainability and efficiency. This paper presents a novel Fuzzy Logic-Based Particle Swarm Optimization (FLB-PSO) technique to enhance the performance of hybrid energy management systems. The proposed FLB-PSO algorithm effectively addresses the challenge of balancing exploration and exploitation in optimization problems, thereby enhancing convergence speed and solution accuracy with robustness across diverse and complex scenarios. By leveraging the adaptability of fuzzy logic to adjust PSO parameters dynamically, the method optimizes the allocation and utilization of diverse energy resources within a grid-connected microgrid. Under fixed grid tariffs, the investigation demonstrates that FLB-PSO achieves grid power purchase and battery degradation costs of $1935.07 and $49.93, respectively, compared to $2159.67 and $61.43 for the traditional PSO. This results in an optimal cost of $1985.00 for FLB-PSO, leading to a cost saving of $236.09 compared to the $2221.10 of PSO. Furthermore, under dynamic grid tariffs, FLB-PSO incurs grid power purchase and battery degradation costs of $2359.20 and $64.66, respectively, in contrast to $2606.47 and $54.61 for PSO. The optimal cost for FLB-PSO is $2423.86, representing a cost reduction of $237.23 compared to the $2661.08 of PSO. The FLB-PSO algorithm proficiently manages energy sources while addressing complexities associated with battery storage degradation. Overall, the FLB-PSO algorithm outperforms traditional PSO in terms of robustness to system dynamics, convergence rate, operational cost reduction, and improved energy efficiency.

Integrated energy hub dispatch with a multi-mode CAES–BESS hybrid system: An option-based hierarchical reinforcement learning approach

The high penetration of renewable energy sources (RES) in power generation has driven demand for advanced integrated energy management systems (IEMS). In this study, to address the challenges of insufficient adaptability to dynamic supply–demand, a multi-type energy IEMS combining compressed air energy storage (CAES) and a battery energy storage system (BESS) is proposed, which operates under a multi-mode energy storage (MES) mechanism with rapid response, long-term balance, and synergic adjustment modes. To address the complexity of sequential decisions, an option-critic based twin delayed deep deterministic policy gradient (OCTD3) algorithm is firstly proposed within the hierarchical reinforcement learning (HRL) framework, enhancing efficiency through encapsulation of subtasks within ”options”. Additionally, model precision is refined by fitting the electricity–gas–heat conversion dynamics of CAES under off-design conditions. Dispatch tasks are modeled as an option-based Semi-Markov Decision Process (SMDP) and optimized by the OCTD3 to improve the power fluctuations index (PFI), comprehensive costs index (CCI), and system response synergy index (SRSI). Comparative simulations reveal that the MES mechanism boosts SRSI by 91.8%, showcasing high adaptability to varied supply–demand scenarios. The OCTD3 algorithm develops five hybrid strategies for CAES–BESS across three modes, effectively cutting costs by reducing electricity purchases and fluctuations expenses, and lowering PFI by 42.2% through balancing peak–valley loads and swiftly responding to transient shifts.

Integrated Power and Thermal Management System for a Hybrid-Electric Aircraft: Integrated Modelling and Passive Cooling Analysis

Abstract Aircraft electrification introduces challenges in power and thermal management. In a hybrid-electric aircraft (HEA), the additional heat loads generated by the high-power electrical components in the propulsion system can negate the benefits of the HEA. Consequently, an integrated energy management system is required for the HEA to reject the additional heat loads while minimizing energy consumption. This paper presents the integrated modelling method for an integrated power and thermal management system (IPTMS) for HEA. With this method, a platform can be developed to assess the varying efficiencies of the components in the electrical propulsion system (EPS), and the performance of the thermal management system (TMS), such as passive cooling, during a flight mission. This makes it applicable to modular designs and optimizations of the IPTMS. A small/medium range (SMR) aircraft similar to ATR72 is studied. In this study, the EPS operates only during take-off and climb. Therefore, the platform assesses the heat and power loads of the IPTMS for a typical flight mission (take-off and climb) in this study. The performance of passive cooling is also analysed across this typical flight mission and under normal, hot-day, and cold-day conditions. It was found that passive cooling is sufficient under these three conditions, and the active temperature control is requried to ensure the components' temperatures are above the minimum temperatures. These findings imply the potential to minimize TMS weight and energy consumption, providing an insight for further research on IPTMS.

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.