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Deploying renewable energy to unit design energy-efficient technologies may fulfill the additional demand of various energy-intensive industries for their sustainability and resiliency. Multiple Stage Evaporator (MSE) is one of the most energy-intensive units used to extract the water content of the weak liquor in various industries, including the paper industry. This energy intensiveness may be reduced by integrating various Energy Reduction Schemes (ERSs). Hence, this work proposes a ERSs integrated MSE model to analyze its energy efficiency. These ERSs includes Thermo-Vapor Compressor, Steam-, Feed- Split, Feed Preheater, and Flash Tanks. Further, the performance of the proposed model is investigated under two important real-time plant complexities: Boiling Point Elevation and Fouling to achieve more realistic results. The performance analysis is initiated with the formulation of a nonlinear constrained optimization problem to increase the steam economy (SE). Also, the simulation is extended to validate at two different product concentrations (52% and 65%) by employing state-of-art optimization algorithms: CONOPT, and SCA in GAMS and MATLAB respectively. The simulated results shows an increment 1.64% and 1.37% of SE for both 52% and 65% concentration of weak liquor respectively in case of CONOPT than SCA. Also, the waste heat of the condensate, feed, and product may be further utilized for the heat recovery by incorporating the flash tanks which leads to a countable energy saving. Eventually, integrating the solar fields: PTC and LFR ensures a notable reduction in conventional heat utilization by 85.96% and 92.85%, respectively and hence, enhance the energy efficiency. • Expounded energy modeling of ERSs based MSE with BPE and Fouling. • Implementation of NLP and metaheuristic approaches for performance optimization. • Reutilization of waste heat to maximize the energy efficiency. • Integration of solar fields (PTC and LFR) to reduce conventional fuel utilization. • Prospecting the challenges and direction of energy management optimization. [ABSTRACT FROM AUTHOR]

In the context of renewable energy systems, microgrids (MG) are a solution to enhance the reliability of power systems. In the last few years, there has been a growing use of energy storage systems (ESSs), such as hydrogen and battery storage systems, because of their environmentally-friendly nature as power converter devices. However, their short lifespan represents a major challenge to their commercialization on a large scale. To address this issue, the control strategy proposed in this paper includes cost functions that consider the degradation of both hydrogen devices and batteries. Moreover, the proposed controller uses scenarios to reflect the stochastic nature of renewable energy resources (RESs) and load demand. The objective of this paper is to integrate a stochastic model predictive control (SMPC) strategy for an economical/environmental MG coupled with hydrogen and battery ESSs, which interacts with the main grid and external consumers. The system's participation in the electricity market is also managed. Numerical analyses are conducted using RESs profiles, and spot prices of solar panels and wind farms in Abu Dhabi, UAE, to demonstrate the effectiveness of the proposed controller in the presence of uncertainties. Based on the results, the developed control has been proven to effectively manage the integrated system by meeting overall constraints and energy demands, while also reducing the operational cost of hydrogen devices and extending battery lifetime. • Implementation of an SMPC strategy based on a scenario-based approach. • Reduction of operation and maintenance costs and integration of uncertainties. • Minimization of the overall operational costs and maximization the profits. • Reduction of complexity and computational time. • Implementation of a unique control architecture to manage a microgrid in different modes. [ABSTRACT FROM AUTHOR]

This paper presents an innovative Fuel Cell Combined Heat and Power (FC–CHP) system designed to enhance energy efficiency in hospital settings. The system primarily utilizes solar energy, captured through photovoltaic (PV) panels, for electricity generation. Excess electricity is directed to an electrolyzer for water electrolysis, producing hydrogen which is stored in high-pressure tanks. This hydrogen serves a dual purpose: it fuels a boiler for heating and hot water needs and powers a fuel cell for additional electricity when solar production is low. The system also features an intelligent energy management system that dynamically allocates electrical energy between immediate consumption, hydrogen production, and storage, while also managing hydrogen release for energy production. This study focuses on optimization using genetic algorithms to optimize key components, including the peak power of photovoltaic panels, the nominal power of the electrolyzer, fuel cell, and storage tank sizes. The objective function minimizes the sum of investment, and electricity costs from the grid, considering a penalty coefficient. This approach ensures optimal use of renewable energy sources, contributing to energy efficiency and sustainability in healthcare facilities. • Investigation of innovative FC Micro combined heat and power unit for energy efficiency in a hospital settings. • Intelligent energy management strategy is proposed basing on the energy need, hydrogen production, release, and storage. • Genetic algorithms is applied for optimal use and storage of energy, increasing energy efficiency and sustainability. [ABSTRACT FROM AUTHOR]

This research paper presents an comprehensive energy management system (EMS) for standalone renewable energy systems, integrating photovoltaic (PV) panels, fuel cells (FC), batteries, and supercapacitors (SC). The study evaluates four EMS strategies: Proportional-Integral (PI) approach, an Equivalent Consumption Minimization Strategy (ECMS), an ECMS optimized with Harris Hawks Optimization (ECMS_HHO), and State Machine Control (SMC) Strategy. The main goal is to minimize fuel consumption and boost system efficiency. Simulations results show that ECMS- based on Harris Hawks Optimization excels, achieving the lowest hydrogen consumption and highest efficiency: 7.1797 g at 86.8601% efficiency in spring, and 8.5521 g at 88.3974% in summer. SMCS, however, has the highest hydrogen consumption, indicating lower efficiency. ECMS_HHO's strategic battery discharge yields a final state of charge of 44.1051% in spring and 44.5713% in summer, emphasizing hydrogen conservation. Thus, ECMS_HHO is identified as a superior, sustainable solution for efficient energy management in the standalone renewable systems. • Optimal energy management of DC microgrid is proposed. • The consumed hydrogen is optimally managed and decreased. • The overall efficiency of the system is increased. [ABSTRACT FROM AUTHOR]

With further penetration of low-carbon energy conversion, microgrids (MGs) have become a necessary tool for expanding the consumption of renewable energies. In this paper, an optimal operation model for a microgrid-based multi-agent system is proposed. The goal is to save the total energy cost, which is expressed as a sum of locally observable convex functions. Therefore, improving the operational efficiency of microgrids is the key to promoting renewable energy development. This paper develops a three-layer multi-agent system model considering energy storage system and power thermal load demand response to solve the energy management problem of microgrids. In order to investigate the effect of energy storage system and demand response in microgrids, this paper designs three simulation cases, namely infrastructure case, energy storage case and demand response case. In order to prove the effectiveness of the proposed method, this paper uses the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) the multi-agent system model can realize the joint optimization of "resource, network, load and storage". (2) The introduction of energy storage and demand response system in microgrids can stabilize the output. Renewable energy units promote the use of renewable energy and reduce the overall operating cost of microgrids. Moreover, the results clearly demonstrate that the proposed algorithm has far better performance than other optimization methods. Also, the analysis obtained from the results shows that the cost is reduced by 1.82%. PV and WT output increased by 14.54% and 2.42%. In addition, their standard deviation decreases after ESS participation. The proposed approach is very effective through a simulation case study, which shows high potential for applications. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system. • An efficient energy storage system is essential for managing intermittent energy supply. • The fuel cell can generate DC power by combining hydrogen with oxygen. • The amount of power generated by a PV panel depends on temperature and solar radiation intensity. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents economically optimized energy and power management strategies for grid-friendly hydrogen based Zero Energy Buildings (ZEBs). The proposed energy management strategy is an adaptative optimization-based strategy that minimizes the operation cost of the facility taking into account RES generation prediction errors. It is shown that with an Adaptative Optimized Five-step Charge Controller (AOFC2) the use of the different equipment is optimized and the overall operation cost is minimized considering the entire life of the facility. The proposed energy management strategy is coordinated with power management strategies to offer advanced functionalities (peak-shaving, reactive power control and back-up service) that provide added-value to the facility. The paper demonstrates by means of offline and real-time simulations, that an adequate energy and power management structure permits the optimal economic exploitation of an advanced ZEB (that includes an energy storage system), providing not only a zero energy annual balance but also interesting added-value features to the grid and to the local consumers. [Copyright &y& Elsevier]

With the high penetration of renewable energy, its intermittency and volatility also bring challenges to traditional power system such as maintaining reliable operation of system and improving the utilization of renewable energy. Under the background of Energy Internet (EI), energy router (ER) emerges as the times require. Aiming to improve the ability of support of energy storage units to DC buses and suppressing power shocks both inside and outside the ER, in this paper, an ER based on multi-hybrid energy storage system (MHESS) is proposed. As the principle of maximizing the utilization of renewable energy, a corresponding energy coordinated management strategy is proposed. Charging/discharging timespan optimization model is established to ensure MHESS as available as possible. The reference power of MHESS is allocated based on the proposed optimization model. Simulation analysis of different working conditions in 4 operation scenarios are carried out and the results show that the power oscillation on different buses of ER can be dampened within 0.1s and the bus voltages fluctuate within 2% while operation scenarios are switched, which verifies the feasibility and effectiveness of the proposed energy coordinated management strategy. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources is increasing worldwide to achieve energy production sustainability. To reach this goal and mitigate against climate changes all energy resources must be optimally managed. In this paper, an innovative multi agent-based heuristic optimisation system is developed to address renewable energy systems' challenges associated with the management of renewables including storage devices, planning of energy distribution, and consumption flexibility. A new heuristic algorithm is developed to introduce an efficient and sustainable storage strategy of surplus energy generated by different renewable resource options. This leads to archiving optimised storage levels of energy across different located storage devices in order to face risk of production shortages due to weather conditions. In order to test the behaviour of the developed system and address its benefits, a case study in the Republic of IRAQ is developed. Twelve major cities (in order of population size) distributed in 5 main regions including North, South, East, West and Middle parts of the country are selected, and hybrid renewable power generation sources such as Wind, PV-solar and Hydro are implemented based on weather changes. The results highlight the effect of high suppliers' rate on electricity exchange and production planning from differently used renewable technologies. A sensitivity analysis is conducted to verify the behaviour of the developed model on different demand behaviour types. • This paper considers a complex renewable energy production-distribution problem. • This study proposes an agent-based model to satisfy consumers' demand of energy. • A new heuristics algorithm to provide a sustainable storage strategy of energy. • Simulation result presents optimal energy production and minimum level of shortage. [ABSTRACT FROM AUTHOR]

• Accurately modeling the blockchain system based on the miners' energy consumption during the data transactions process. • Offering a two-stage structure of energy consumption management of miners through an IPS-based placement algorithm. • Developing a distributed energy architecture among blockchain miners and the electrical grid and renewable energy resources located on the grid inspired by the PDMM concept. Addressing the blockchain technology (BCT) challenges, such as the high energy consumption is unavoidable in smart power systems to take maximum advantage. As an antithetical point, there is a structural duality between using BCT in the electrical grid due to its advantages and energy consumption. To this end, this research work is an inclination toward controlling the miner's energy consumption based on a realistic and efficacious two-stage energy mechanism toward the distributed energy concept and optimal placement of data mining devices. In this circumstance, such a new structure allows miners to mainly sustain their hash rates as a prominent index for security achievement and optimally decrease their cost. This paper suggests a novel distributed model for miners coincided with the smart electrical grid getting inspired by the Prima-Dual Method of Multipliers (PDMM) based distributed approach to settle down the needed power bartering between miners and energy supply resources. In addition, for a similar purpose, it seems to reach the ideal resolution needs to perform the modified reaffirmation of miners (the mining pool) within the secured grid. To execute this task, this research recommends the Intelligent Priority Selection (IPS) techniques as well. When all is said and done, the result of this approach can aid researchers with performing in the field of improving social advantages by the usage of the suggested method. [ABSTRACT FROM AUTHOR]

In recent years, the severe environmental degradation and high levels of fossil fuel consumption linked to conventional ship energy systems have drawn attention to the advancement of alternative ship energy systems. Consequently, ship energy systems based on the use of an electrical microgrid are coming to the fore as an increasingly popular alternative solution. However, managing the energy flows within a shipboard microgrid can be highly challenging due to the multiple energy sources (including renewable energy sources) and types of loads involved. Additionally, the integration of an energy storage system has been identified as an effective solution for improving the reliability of shipboard power systems, pointing out the important role of energy storage systems in maritime microgrids and their potential to enhance the energy management process. As such, effective energy management strategies are necessary to optimize performance and ensure that the vessel's energy demands are met while minimizing fuel consumption and reducing cost and environmental impacts. This paper presents a comprehensive review of such strategies and methods recently presented in the literature associated with energy management in shipboard microgrids integrating energy storage systems and examine the different techniques that can be utilized to achieve optimal system performance. Moreover, the paper also sheds light on the future challenges and perspectives imposed by the modern shipboard microgrids operation on the energy/power management of these systems. The aim of this paper is to provide a helpful guideline for researchers and engineers conducting research in the field of electrically powered ships. [Display omitted] • A comprehensive review of EMS for electrical and hybrid ship is presented. • A comprehensive review of ESS which can be used in such ships is presented. • Technologies of ESS and their suitability to ships are discussed and commented. • Future challenges and perspectives imposed by modern SMG operation are presented. [ABSTRACT FROM AUTHOR]

• Providing an inclusive, up-to-date, and organized review of the published research. • Taxonomy of microgrid based on control structures, specifications and components. • Energy management summation and classification based on several significant factors. • Discussing some of the skilled methodologies and techniques developed or adopted. • Taxonomy of the different modeling and handling methods of the uncertainty. Energy is one of the essential foundations for the sustainable development of human society, so its management is necessary. Energy management system (EMS) can be explained as the procedure of optimizing, planning, controlling, monitoring, and saving energy to maximize operations and efficiency and minimize consumption. Microgrid (MG) requires EMS as an efficient and optimal tool owing to the stochastic nature of electrical loads and renewable sources. Moreover, energy management system is responsible for operation of a MG in reliable, secure and economical manner in either states of grid-connected or disconnected. Many literatures have recently focused on the expansion of advanced strategies of the MG energy management for establishing a self-sustained MG in both industrial and academic research. Thus, a comparative research is needed for having a 360° viewpoint of the energy management domain in MGs. In this regard, this research investigates a comparative and critical analysis of the developed strategies of the energy management for the MGs from different views and aspects from 2009 to 2022. The review strategy systematically adopted by the author includes: (i) Extracting research papers relevant to energy management in MGs; (ii) Filtering the significant papers to prepare a database of related research papers (iii) Classifying the used methods for EMS based on the technique, control strategies, and structure; (iv) Discussing potential directions for future studies. In a wider outlook, this research provides a systematic and updated review of energy management strategies for MGs developed by different researchers. The author hopes that academicians and practitioners can use the suggested framework as well as the offers presented for further studies on this significant yet sophisticated issue. [ABSTRACT FROM AUTHOR]

In this paper, we address the increasing focus on Renewable Energy Sources (RES) and energy policies in S. Korea, advocating for a shift from large, centralized power systems to decentralized Local Power Systems (LPS). This transition emphasizes the integration of Integrated Energy Systems (IES), which significantly enhances the target benefits for stakeholders in the power system. We have also adopted a Distributionally Robust Optimization (DRO) approach to account for the output uncertainty of RES and developed and applied new ambiguity sets tailored to each RES, enhancing the extensive robust design previously utilized with DRO. By applying our proposed ambiguity set-based DRO, we avoid excessive robustness and achieve superior optimal outcomes compared to the general DRO methodology, potentially making a significant contribution to business investment decisions. We have categorized the main stakeholders of the power system as Independent Systems Operators (ISO), Renewable Energy Providers (REP), Energy Exchange Aggregators (EXA), and End-users. The integration of IES leads to a benefit improvement rate of up to 34.8 %, 23.0 %, and 24.2 % for ISOs, REPs, and End-users, respectively, on a global scale. EXAs can secure an annual economic profit of up to $191,362. • Novel integrated energy system enhances renewable energy utilization efficiency. • Virtual energy management system optimizes power, heat, and hydrogen integration. • New ambiguity set based distributionally robust optimization. • Stakeholder cooperation framework simplifies multi-objective optimization processes. [ABSTRACT FROM AUTHOR]

This paper presents a pioneering approach to enhance energy efficiency within distributed energy systems by integrating hybrid energy storage. Unlike prior research, our study focuses on synergizing two distinct energy storage systems while concurrently optimizing system configuration and operational strategies to enhance performance and reduce costs. The employed distributed energy system incorporates hybrid energy storage, merging thermal energy storage with power storage technologies such as supercapacitors and lithium batteries. We conduct a comprehensive investigation into the impact of this innovative system on distributed energy systems, employing a dual-objective cooperative optimization method that addresses energy efficiency and economic factors alongside environmental considerations. This approach is applied to an almost zero-energy community, demonstrating its efficacy in improving load profiles and optimizing energy management through demand-side management. Additionally, we incorporate stochastic energy management, renewable energy resource planning, and dynamic storage to account for uncertainties in energy sources and demand, utilizing the Two-Point Estimate Method (2P + E PEM). To address the complexity of the problem, we introduce an advanced multi-objective optimization algorithm, the Horse Herd Optimization Algorithm (HOA), inspired by the movement behavior of horses in a herd. This algorithm exhibits superior performance with various operators including elite selection and crossover. In conclusion, our contributions include the introduction of a distributed energy system with hybrid storage, a dual-objective cooperative optimization method, and the application of advanced algorithms. Our results demonstrate significant reductions, with primary energy consumption decreasing by nearly 54.8 % and equivalent pollutant emissions by 63.6 %, highlighting the significance and effectiveness of our proposed approach. • Application of groundbreaking approach by hybrid energy storage, & thermal storage. • Considering optimal system configuration and operational strategies concurrently. • Application of new dual-objective cooperative optimization method in mentioned problem. • Improving load profiles & achieving superior energy management through DR strategies. • Application of HOA to solve the mentioned optimization problem. [ABSTRACT FROM AUTHOR]

The exploitation and utilisation of solar energy is challenging because of both diurnal and seasonal variation. Seasonal thermal energy storage is a prominent solution to solve the problem of seasonal variation of solar production. This paper investigates both the optimal design and energy management of a renewable energy plant with seasonal thermal energy storage. As a case study, the thermal, cooling and electrical energy demands of a university campus during one year are considered. Three scenarios, characterized by a different available area (20,555 m2, area covered at present; 50,000 m2, potentially available area by exploiting the entire rooftop and parking area; unlimited area) for solar thermal collectors and photovoltaic panels are investigated. In the first two scenarios, the exploitation of solar energy allows a primary energy saving of 31% and 57%, respectively compared to the use of a conventional plant. The seasonal storage reaches an overall efficiency (thus considering both charging and discharging) up to 84%. Finally, the analysis of the third scenario shows that the feasibility of meeting the energy demands by only exploiting solar energy is technically challenging because a large area equal to about 100,293 m2 is needed for solar thermal collectors and photovoltaic panels. [ABSTRACT FROM AUTHOR]

Abstract As the smart grid paradigm is capable to encourage the active consumers for efficacious participation in increasing system efficiency, demand response programs (DRPs) have attracted much interest in the worldwide recently, especially in optimization of smart microgrids (MGs). Under this context, this paper proposes an integrated method relies on cleverly cooperation of time rate-based DRP and heterogeneous distributed energy resources (DERs) deployment with aim to reliability-oriented planning of multiple MGs. To do this, a novel two-stage decision making model is exploited in which at the first stage the MGs construction is formed by optimal dynamic planning of hybrid DERs simultaneously with section switch allocation considering a reliability criterion for MGs as loss of load expectation (LOLE) constraint. Subsequently, at the next stage the critical energy peak pricing-based program accomplishes in order to flatten the load profile as well as diminishing the investment costs of MGs. Besides, owing to the unpredictable nature pertaining to renewable power production, the uncertainty modeling is inevitable where in this paper, a novel pragmatic robust optimization approach has been employed to deal with intense uncertainty of the problem. Numerical results obtained from an illustrative case study elucidate how the proposed MGs planning and utilized DRP pairing significantly increases the expected profit of system and ameliorates the reliability of end-users. Highlights • An innovative two-stage framework is developed for DRP and MGs planning pairing. • A time-rate based DRP is employed to reduce and postpone the investment costs of MGs. • Dynamic model is used to design of multiple MGs considering reliability criterion. • A pragmatic robust optimization is proposed to cope with uncertainty of the problem. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources (DERs) such as wind, solar, batteries, etc. into the grid presents opportunities as well as challenges with regard to efficient coordination and trading. Virtual power plants (VPPs) have emerged as an important paradigm for aggregating and managing numerous DERs. This paper proposes a decentralized blockchain-based peer-to-peer (P2P) multilayer energy trading framework to facilitate optimized coordination of DERs within a VPP context. A control center, prosumers, and service operators constitute the model's multi-layered architecture. Profits from feed-in tariffs, ancillary services, and P2P sales are optimized for revenue while expenses are minimized using a mixed-integer linear programming formulation. Smart contracts enable prosumers to trade energy units in the Ethereum blockchain with ease via a participatory web platform. The effectiveness of the framework in synchronizing power supply and demand within the VPP by coordinating the charging and discharging of storage systems in accordance with consumption patterns is demonstrated through performance evaluation. In contrast to conventional centralized architectures, the proposed framework utilized decentralized architecture efficiently consolidates and optimizes distributed energy resources (DERs) to improve grid stability and renewable energy utilization. [Display omitted] • A novel multi-layer framework for peer-to-peer (P2P) energy trading, within VPP. • An interactive web-based trading platform using the Ethereum blockchain to facilitate exchange of energy units. • Implementation of MILP-based DER management model for optimal trading. [ABSTRACT FROM AUTHOR]

This paper represents a new framework to forecast electricity power net-load in renewable energy systems. Estimating electricity power net-load with high accuracy affects economic well-being, stability, and security of power networks. Despite this, large nonlinear variations make its estimation difficult and complicated. In this paper, a new, simple, robust, and straightforward method is proposed to predict signals with volatile characteristics to satisfy this need. This framework combines deep learning of the multi-input LSTM network type with fuzzy system and discrete wavelet transforms. Wavelet-based transforms provide insight into hidden details and aid in forecasting points with high chaos. Furthermore, technical indicators provide a way to determine the trend and momentum of data and to select the optimal time frame for estimation. Finally, the real case of Austrian data is selected, and the electricity power net-load is estimated. According to the results, the proposed framework can forecast the electricity power net-load with 97.7% accuracy. Furthermore, the forecast accuracy is improved to 99.5% by using wavelet transforms and fuzzy system simultaneously in the forecasting process. [Display omitted] • Electricity power net-load forecasting in renewable energy systems. • Combining deep learning with fuzzy and discrete wavelet transforms. • Improving the forecast accuracy to 99.5%. [ABSTRACT FROM AUTHOR]

This paper describes a novel energy management system for the optimized operation of the energy sources of a grid-connected hybrid renewable energy system (wind turbine and photovoltaic) with battery and hydrogen system (fuel cell and electrolyzer). A multi-objective optimization problem based on the weight aggregation approach is formulated by combining three objective functions (operating costs, efficiency and lifetime of the devices) that can conflict with each. The multi-objective function to be optimized by the energy management system is obtained by solving the problem for all the possible cases. Then, the weights that provide the minimum value of the multi-objective function are selected. As the results demonstrate, the multi-objective function becomes a single-objective function that differs according to the net power (power to be generated by/stored in the energy storage devices) and has to be solved in the energy management system of the hybrid system. It simplifies considerably the multi-objective problem implemented in the energy management system, while taking into account the three control objectives that can conflict with each other, which is the main contribution of this paper. This optimal energy management system is solved using the Particle Swarm Optimization (PSO) method, tested by simulations of the hybrid power generation system throughout 25 years (the expected lifetime of the system), and compared with the results obtained by the energy management systems based on optimizing each single-objective function separately, and by that based on optimizing the multi-objective function combining the three single-objective functions equally weighted. The results demonstrate that this energy management system achieves reasonable operating costs, efficiency and degradation of the devices. [ABSTRACT FROM AUTHOR]

Home energy consumption is increasing, and renewable energy sources are being deployed. The use of renewable energy sources as an alternative to fossil fuels and nuclear energy is a significant opportunity for reducing CO 2 emissions. However, carbon emissions from electricity generation are dependent on the type of renewable energy source and the quantity of the electricity that is produced. With the emergence of the smart grid, residents are able to reduce their electricity consumption by shedding the power usage in their homes. The objective of this study is to smooth power peak demand and diminish CO 2 emissions. This paper describes the power shedding benefits of reducing energy consumption and CO 2 emissions. This reduction of CO 2 emissions eliminates the need for auxiliary power plants in the grid in the case of peak demand points and the need to switch to renewable energy sources. In this study, we prove that emerging information and communication technology can reduce energy use and CO 2 emissions. Wireless communication architecture is introduced in this study to increase grid flexibility and rapidity in the data flow among all of the components in the proposed system. Our proposed system with local renewable generation can reduce CO 2 emissions nearly 72% of all CO 2 emissions when all appliances are supplied with the grid. With our load shedding algorithm, the CO 2 emissions decrease nearly 91%. The yearly estimation shows that our system can reduce 62% of CO 2 emissions and 37% of consumed power. In this paper, we introduce an efficient architecture of a smart home energy management system and propose a shedding algorithm for home energy usage. This system is based on wireless communication among home appliances, a home management and control system, a grid management and control system and domestic renewable energy sources. Our proposed system, which employs wireless communication, domestic renewable generation and a power shedding algorithm, can operate in conjunction with future smart grids by applying real-time data, such as peak demand points. Numerous real data sets permitted the development of some of the statistics in the system results. [ABSTRACT FROM AUTHOR]

Data analysis is one of the most important tools that can contribute to unveiling critical relationships. Econometric approaches are evolving continuously, while older methods although are not completely abandoned, they may be used vis-à-vis the new ones for comparison purposed. This special issue has called for papers that have used novel research methods in a panel data framework for the studying and modelling renewable energy use and management. This special issue is useful for readers because it provides them an overview of the those novel methodologies employed in the field of renewable energy economics and econometrics. [ABSTRACT FROM AUTHOR]

In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixed integer programming is formulated as a sequential model predictive control, which is then solved using genetic algorithm. The load shifting benefits obtained by deploying an advanced coordination strategy are compared against a baseline controller for various home characteristics, such as location, size and equipment. The simulation study shows that the deployment of the smart home energy management strategy achieves approximately 5% reduction in grid cost compared to a baseline strategy. This is achieved by deferring approximately 50% of the flexible loads, which is possible due to the use of the stationary energy storage. • Real-time load coordination strategy for optimizing electrical loads in smart homes. • Inclusion of renewable energy sources and stationary batteries in the coordination. • Definition of generalizable metrics for the evaluation of load shifting strategy. • Improvements in improved energy efficiency and reduced electricity costs. • Comprehensive analysis that considers diverse home characteristics such as location. [ABSTRACT FROM AUTHOR]

• HRES with PV-Wind and different types of energy storage systems. • Optimal size and efficient performance of HRES. • Bi-level mixed integer nonlinear programming optimization approach. • Real application of a stand-alone HRES in a real case study, Jordan. This paper aims to design and run a standalone hybrid renewable energy System (HRES) feeding residential building load. The suggested HRES encompasses photovoltaic panels and wind turbines as energy sources, combined with a hydrogen subsystem and a bank of batteries for energy storage. The meteorological input data includes irradiance, temperature, and wind speed for four seasonal weeks. These data sets are collected for a rural site situated in the town of Ras Munif, northern Jordan. The load profiles characterize a selected benchmark residential building. Consequently, a bi-level mixed integer nonlinear programming (BMINLP) problem is formulated to incorporate the designing problem with the energy management process. The upper level represents the sizing problem, while the lower level problem denotes the energy management strategy (EMS). Each problem has its independent objective function, constraints, and optimization variables. The EMS is an economic-based single objective optimization problem expressed as a mixed integer linear programming (MILP) problem. On the contrary, a mixed integer nonlinear programming (MINLP) approach frames the sizing problem to optimize a techno-economic-based multi-objective function. The interaction between the two problems is that the lower problem is an embedded constraint of the upper problem. Moreover, the candidate solutions from the upper problem are treated as fixed parameters in the lower problem. A multi-objective genetic algorithm is used to execute the overall problem by the MATLAB toolbox. The technical and economic performance parameters, including the investment cost, maintenance, operating costs, and others, are assessed based on the best findings. The key finding demonstrates that the summer week has the lowest total cost and energy loss factor, with around 132,000 $ and 0, respectively. Furthermore, the findings reveal that the entire system's energy loss factor for each sample period is less than 0.0009. This value is substantially less than the permitted limit of 0.01. [ABSTRACT FROM AUTHOR]

As the proliferation of electric vehicles (EVs) continues to accelerate, the inherent attributes of EVs warrant meticulous consideration in the realm of energy dispatch. In order to evaluate the ability of EVs as mobile energy storage, this paper presents an energy management framework for the microgrids' online dispatch, which accounts for the spatio-temporal energy transmission of EVs between microgrids. The energy management framework contains two iterative processes: optimizing charging price and guiding charging dispatch. To sufficiently capture the uncertainties of the renewable energy and load demand, chance-constrained optimization is utilized to determine the charging price by reasonable power allocation. To achieve a continuous and efficient control policy, a normalized advantage function-deep Q learning network (NAF-DQN) is developed for EV dispatch under V2G technology. The above two processes as a coupled optimization problem are solved alternately until convergence. Numerical cases considering energy transmission of EVs between microgrids are studied to demonstrate the superiority of the proposed dispatch framework. The simulation results indicate improved computational efficiency and higher-quality solution. • A novel energy management framework for the microgrids' online dispatch is proposed. • The proposed method considers uncertainties introduced by PV, WT, and load. • A normalized advantage function-deep Q learning network is developed for EV dispatch. • The simulation results indicate improved computational efficiency and higher-quality solution. [ABSTRACT FROM AUTHOR]

The parametric uncertainties inherent in the models of renewable and sustainable energy systems (RSESs) make the associated decision-making processes of integrated resource operation, planning, and designing profoundly complex. Accordingly, intelligent energy management strategies are recognised as an effective intervention to efficiently accommodate the variability inherent in various input data and integrate distributed demand-side flexibility resources. To identify the key methodological and content gaps in the area of stochastic dispatch and planning optimisation of RSESs in the presence of responsive loads, this paper systematically reviews and synthetically analyses 252 relevant peer-reviewed academic articles. The review reveals that academic studies have utilised a wide variety of methods for the joint quantification of uncertainties and procurement of demand response services, while optimally designing and scheduling RSESs. However, to minimise simulation-to-reality gaps, research is needed into more integrated energy optimisation models that simultaneously characterise a broader spectrum of problem-inherent uncertainties and make behaviourally-founded use of flexible demand-side resources. More specifically, the review finds that while the research in this area is rich in thematic scope, it is commonly associated with strong simplifying assumptions that disconnect the corresponding approaches from reality, and thereby obscure the real challenges of transferring simulations into the real world. Accordingly, based on the descriptive analyses conducted and knowledge gaps identified, the paper provides useful insights into myriad possibilities for new research to more effectively utilise the potential of responsive loads, whilst simultaneously characterising the most salient problem-inherent parametric sources of uncertainty, during the investment planning and operational phases of RSESs. [Display omitted] • A systematic review of stochastic energy optimisation with demand response is conducted. • 252 relevant publications are identified in the literature and synthetically analysed. • Relevant research is rich in thematic scope, but involves strong simplifying assumptions. • Challenges ahead and opportunities for future research in the area are discussed. • Computational burden remains the most significant barrier to minimising reality gaps. [ABSTRACT FROM AUTHOR]

The complexity of power grids, the intermittent renewable energy generation and the uncertainty of load consumption bring great challenges to modern energy management systems (EMSs). To solve the energy optimization problem in the time-varying smart grid, this paper proposes a multi-timescale EMS based on the adaptive dynamic programming (ADP) algorithm and multi-neural-network fusion (MNNF) prediction technology. In detail, according to different power consumption characteristics, this paper uses fuzzy C -means (FCM) clustering algorithm to classify power users into industrial users, commercial users and residential users. Based on the classification results, an MNNF prediction method is proposed that can integrate different influencing factors to predict load consumption and renewable energy generation. Then a multi-timescale ADP optimization algorithm is proposed to maximize the utilization of renewable energy on daily, intra-day and real-time (i.e., three timescales) of energy behavior. The convergence of the multi-timescale ADP algorithm is proved mathematically when the initial value is a random semi-positive definite function. Finally, the proposed ADP with MNNF energy management system is verified on a hardware-in-the-loop (HIL) platform. [ABSTRACT FROM AUTHOR]

Abstract Energy in buildings covers a major and wide range of subject, which has attracted a great interest of the academic sector in recent years. Due to the high share of the global primary energy consumption associated with buildings, many studies have been published toward energy efficient buildings and the reduction of their primary energy consumption and energy management and optimization, for both Renewable Energy Sources (RES) and non-RES. As a result, a plethora of methods for managing the energy consumption and optimizing the integration of the RES in buildings have been proposed. The optimized dispatch of battery energy seems a promising method for managing the energy consumption profiles within buildings and achieving energy targets such as nearly Zero Energy Buildings (nZEBs). The current paper attempts a review of various studies related to convex mathematical optimization techniques for reducing the energy consumption in buildings, in real-time and via storage, either with or without integrated RES. Highlights • Mathematical optimization has been proved promising for the energy dispatch in buildings. • The optimized dispatch of battery seems a promising method for managing the building energy. • An important ingredient for reducing the energy needs of a building is the real-time energy management. • Global optimization guarantees energy related costs reduction in both short and long term. • In optimization theory the large border is not between linearity and non-linearity, but between convexity and non-convexity. [ABSTRACT FROM AUTHOR]

Future building energy management systems will have to be capable of adapting to variation in the rate of production of energy from renewable sources. Controllers employing a model predictive control (MPC) framework can optimise and schedule energy usage based on the availability of renewably generated energy. In this paper, an MPC using artificial neural networks (ANNs) was implemented in a residential building. The ANN-MPC was successfully tested and demonstrated good performance predicting the building's energy consumption. The controller was then modified to function as an economic MPC (EMPC) to optimise demand flexibility (i.e., the ability to adapt energy demands to fluctuations in supply). The operational costs of energy usage were associated with this demand flexibility, which was represented by three flexibility indicators: flexibility factor, supply cover factor, and load cover factor. The results from a day-long test showed that these flexibility indicators were maximised (flexibility factor ranged from −0.88 to 0.67, supply cover factor from 0.04 to 0.13, and load cover factor from 0.07 to 0.16) when the EMPC controller's demand flexibility was compared to that of a conventional proportional-integral (PI) controller. The EMPC framework for demand flexibility can be used to regulate on-site energy generation, grid consumption, and grid feed-in and can thus serve as a basis for overall optimisation of the operation of heating systems to achieve greater demand flexibility. • An ANN-MPC was developed and tested in a residential building. • ANN models and an MPC framework were validated against measurements in buildings. • An EMPC to maximise the demand flexibility of residential buildings was developed and tested. [ABSTRACT FROM AUTHOR]

Abstract: The Energy Strategy of the Republic of Croatia was adopted by the Croatian Parliament in 2009 for the period until 2020 in order to harmonize national energy goals with goals and time framework of strategic documents of the European Union. The adoption of the Energy Strategy Implementation Program with associated measures is still in process. The goal of the Strategy was to create sustainable energy system that will make a balanced contribution to the security of energy supply, competitiveness and environmental protection in Croatia. In this paper an overview of the energy production, final energy consumption and planned development of energy infrastructure is given. The intention of the paper was to research and stress out the abilities and opportunities of Croatian energy system and to give recommendations for Energy Strategy implementation and practical realization of planned energy infrastructure projects for improvement of security of energy supply and competitiveness of energy system of the Republic of Croatia. [Copyright &y& Elsevier]

Abstract: Heating/cooling cost allocation is of growing concern in many countries because it contributes much to the energy conservation of central air-conditioning system. This paper intends to summarize the current studies and techniques on the heating/cooling cost allocation and discuss about the key problems to them. The contents mainly include the following parts: (1) probing into the methods of heating/cooling measurement; (2) establishing the calculation model for thermal billing; (3) presenting several typical systems for heating/cooling cost allocation. The significance of the paper is to help the engineers in this field have a good idea of the current development of heating/cooling cost allocation and promote the application of the relevant technologies to practice. [Copyright &y& Elsevier]

Energy communities are at the heart of forming sustainable cities. Beside their important role in decarbonization, they can bring economic, and social benefits to the End-users. Based on the Clean Energy Package (CEP) definition in European Union, an energy community is categorized as: Citizen Energy Communities (CECs) and Renewable Energy Communities (RECs). In this regard, modeling an energy management system framework in a Smart Multi-Carrier Energy Network (SMCEN) associated with CECs and RECs is much of interest. In this paper, a two-stage risk-based optimization model is proposed to handle an energy management structure incorporating energy communities. To this purpose, Energy Community Managers (ECMs) of CECs and RECs concurrently solve the problem at the first stage and send the output data to the SMCEN operator. In the second stage, the SMCEN operator solves an optimization problem to maximize its profit considering the SMCEN's constraints. The uncertainties of energy demands as well as on-site generation are modeled by a scenario-based approach, while a risk-modeling technique is employed to handle the SMCEN risk attitudes in multi-carrier energy scheduling. The results verify that the risk attitude of the SMCEN operator is an influential factor to determine the profit of the SMCEN. • A novel mathematical model of RECs and CECs based on the CEP is proposed. • An integrated structure of SMCEN is modeled by the two-stage optimization approach. • The role of P2P energy trading, DRPs, and EVs in CEP is addressed. • The risk attitude of the SMCEN operator is addressed by the IGDT approach. [ABSTRACT FROM AUTHOR]

• We proposed an adaptive (two-stage) robust energy management for an isolated microgrid considering renewables uncertainty. • Minimization of the expected total operating costs of the microgrid is the objective function. • Reactive power costs from diesel generators are considered and are co-optimized with active power costs • The reactive power capability of the inverter-interfaced distributed energy resources is considered. • Detailed modeling of the diesel generators and inverter interfaced distributed energy resources by the consideration of their capability curves is provided. • The results obtained validate the effectiveness of the proposed solution. This paper presents an adaptive robust Energy Management (EM) model for an isolated Microgrid (MG) to decide for the day-ahead optimal dispatch to effectively manage the MG power sources considering uncertainties in Renewable Energy Sources (RESs). The aim of this model is to minimize the operation costs, reactive power costs, spinning reserve and load shedding. Usually, fuel consumption costs of diesel generators are considered to be dependent on active power generation only. However, neglecting the related reactive power costs might result in increased operation costs and deviations from optimal dispatches. Hence, this paper optimizes the costs related to both active and reactive powers of diesel generators. Furthermore, simultaneous active/reactive power dispatch can lead to accurate operation decisions compared to separate dispatch for active or reactive power alone. In addition, this study considers the reactive power capability of inverter-interfaced Distributed Energy Resources (DERs) so that reactive powers can be supplied from inverter-interfaced DERs. Moreover, detailed models for the different resources are presented, especially for diesel generators and inverter interfaced DERs where actual capability curves are used instead of the widely used box constraints. The problem is formulated as nonlinear programming problem using GAMS program and solved by the CONOPT solver. [ABSTRACT FROM AUTHOR]

Energy hubs as a multi-carrier energy system with various energy carriers and conversions have been integrated with smart grids to increase the efficiency, flexibility, and reliability of the network. Today, scheduling multi-energy hubs (MEH) is one of the challenging issues in smart grids. In this paper, the optimal schedule of interconnected MEHs based on the robust optimization in the presence of electric vehicles (EVs) is proposed. A robust optimization approach is considered to deal with uncertainties of electrical price, renewable energy resources (RESs) generations, and electrical loads. In this paper, MEHs network include commercial, industrial, and residential energy hubs. The energy consumption of MEHs consist of electrical, water, gas, and heat. RESs, demand response (DR) programs, heat and electrical energy storage devices are used to supply the energy demand. The aim of the proposed model is reducing the total cost of operation and carbon emission. The price-based demand response program is applied to shift the power consumption from on-peak to off-peak times in response to the price signal. Finally, the problem is solved for the three cases with CPLEX under YALMIP MATLAB. The results illustrate the effectiveness of the proposed model to achieve optimal scheduling of MEHs. The proposed problem is organized in three scenarios, among which the scenario I determines optimal scheduling of MEHs, using robust optimization method. The scenario II investigates the problem with deterministic model. The scenario III evaluates the problem applying robust optimization method without DR program and energy storage device consideration in autonomous EHs. The numerical results show that the total operation cost is increased 15.46% in scenario I compared with the scenario II due to applying the worst-case of uncertainties in robust optimization approach. • An optimal scheduling of Multi-Energy Hubs is proposed with energy costs and emission. • Electrical, heat, gas and water demands, RESs, storages, and EVs is considered with DR. • A robust optimization is used to cover uncertainties of price, generation of RESs and loads. • Three interconnected EHs as MEHs including commercial, industrial and residential sectors. • The proposed problem is optimized in three cases and the results are compared with each other. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Energy demand side management is optimized to improve the system performance. • A game theoretic approach for distributed energy management is developed. • Energy supply constraints and storage facilities are evaluated. • Blockchain technology is applied for efficient and trustable micro-grid operation. • Decentralized energy supply and demand management is implemented in practice. Abstract The management of energy supply and demand is becoming more challenging, especially in regions where the demand continues to grow rapidly and more intermittent renewable supply sources are added to the energy infrastructure. In this context, Demand Side Management (DSM) can be employed to align supply and demand and improve reliability of the system under supply constraints and stretch the capacity limits of the existing grid infrastructure. A game theoretic approach for a DSM model incorporating storage components is suggested in this paper especially for environments with energy supply constraints. The proposed model is able to not only reduce the Peak-to-Average ratio to benefit the electrical grid, but can also smoothen the dips in the load profile caused by supply constraints. In a case study to demonstrate the value of the model presented, a variety of residential demand types are evaluated within a micro-grid to maximize the payoff utility of both individual users and the whole system. Moreover, emerging blockchain technologies are introduced to guarantee the seamless and secure implementation of the proposed scheme, illustrating how a decentralized demand side management approach could be implemented in practice to facilitate peer-to-peer transactions following optimized demand profiles. [ABSTRACT FROM AUTHOR]

Highlights • Scenario assessment method for realizing energy neutral neighborhoods is proposed. • A deterministic assessment is carried out to predict future demand variations. • Suggested LCPD based indicators provide valuable insights to decision-makers. • Monte Carlo simulations investigate uncertainty of the decision-maker’s choice. • A case study in the Netherlands to validate the methodology is demonstrated. Abstract Increasing demand for energy and emphasis on environmental sustainability has started to revolutionize the existing energy infrastructure within the built environment. In parallel, more distributed energy systems are rapidly springing up. These changes inevitably influence the design, operation and management of buildings. Recently, the energy and environmental evaluation of buildings for long-term decision-making and planning has shifted the boundaries from single buildings towards neighborhood scale. This is because buildings as a cluster can enhance the incorporation of distributed energy systems when realizing energy neutrality in the long run. However, when assessing the energy and environmental performance of infrastructural developments at the neighborhood level, the life-cycle aspect of energy systems is rarely considered. To understand the overall impacts from production to end-of-life stage, it is essential to assess the energy and environmental performance of clean energy initiatives from a life-cycle perspective. This paper proposes a novel decision support methodology by means of life cycle performance design-based approach to facilitate the planning process to realize energy neutral neighborhoods. The assessment methodology is developed based on scenario analysis through computational simulations. This is followed by a deterministic evaluation and the results let the decision-makers to select a suitable clean energy development scenario. The uncertainty of the selected scenario is scrutinized by performing a probabilistic sensitivity analysis using Monte Carlo simulations. A pragmatic case study has been analyzed and the results demonstrate the feasibility of exercising the proposed methodology in practice. The recommendations and limitations of realizing energy neutral neighborhoods are depicted subsequently. [ABSTRACT FROM AUTHOR]

Along with the popularity of distributed energy generations and hybrid energy appliances, the optimization of integrated energy system combining various kinds of energy has drawn more and more attention. In this paper, we establish a hierarchical model of integrated energy system, which composes of fundamental units such as energy converters, supply networks, and consumers. The fundamental units are modeled as individuals which operate independently and interact with others through mutual information. The fundamental units constitute low-level systems and the combinations of low-level systems make up high-level systems. In order to protect the information privacy of each individual and make full use of the limited mutual information, a decentralized optimization method is presented to optimize the operation strategy of the hierarchical system considering competitive activities between individuals. The individuals participate in an iterative process, interacting with others through an energy pricing mechanism until the system equilibrium achieved. Simulation studies are conducted on several integrated energy systems to investigate the performance of the proposed hierarchical model and decentralized optimization of coordinated electrical and thermal generations. Furthermore, the proposed method is applied to a practical case of Guangzhou Higher Education Mega Center, and the daily operation strategy of the energy supplier is optimized. [ABSTRACT FROM AUTHOR]

Unexpected outage of distribution network's branches due to load growth, component failures and high penetration of renewable energy resources, may affect the optimum energy management of the distribution network (DN) and decrease the expected profit. Therefore, distribution system operator (DSO) should contemplate the contingency problem in the day-ahead energy management. In this paper, contingency based energy management for the DN involving several microgrids (MGs) known as multi-microgrid (MMG) system has been proposed. In this approach, the DSO considers the probability of the contingency in the energy management of the network and adopts a stochastic optimization based on different scenarios of the contingencies. The capability of reconfiguring the network and presence of the MGs are utilized as two major tools for the DSO to handle unfavorable effects of the contingency problem, which stand on the power flow studies. The recommended energy management approach has been applied to the IEEE 33-bus distribution network modified by the MGs. The obtained results reveal the effectiveness of the proposed CBEM in preventing the network from economic loss. Moreover, this achievement not only is beneficial for the DN, but also advantageous from the MGs’ point of view. [ABSTRACT FROM AUTHOR]

Renewable energy resources are currently being deployed on a large scale to meet the requirements of increased energy demand, mitigate the environmental pollutants, and achieve socio-economic benefits for sustainable development. The integration of such distributed energy sources into utility grid paves the way for microgrids. The microgrid concept is introduced to have a self-sustained system consisting of distributed energy resources that can operate in an islanded mode during grid failures. In microgrid, an energy management system is essential for optimal use of these distributed energy resources in intelligent, secure, reliable, and coordinated ways. Therefore, this review paper presents a comparative and critical analysis on decision making strategies and their solution methods for microgrid energy management systems. To manage the volatility and intermittency of renewable energy resources and load demand, various uncertainty quantification methods are summarized. A comparative analysis on communication technologies is also discussed for cost-effective implementation of microgrid energy management systems. Finally, insights into future directions and real world applications are provided. [ABSTRACT FROM AUTHOR]

The objective of this study is to trace and quantify the sources of rebound effect for different energy sources in China. This paper decomposes the economy-wide rebound effect into 135 production sector-level rebound effects and five final demand components. The sector-level rebound is further decomposed into output effect and substitution effect. A two-stage decomposition method and a static computable general equilibrium model are developed to achieve this. Five types of energy-specific efficiency improvements are introduced, respectively, in all the production sectors. Results show that improving efficiency of using coal leads to the smallest macro-level rebound. For rebound decomposition, four findings are generalized. First, final consumption shows larger impact on the rebound of secondary energy sources than on primary energy sources. Second, production sectors that are big user of the efficiency-exposed energy source are expected to be key rebound contributor. Third, energy-producing sectors make negative rebound contribution. Results also show that, substitution effect is the predominant mechanism that triggers sector-level rebound. Policy implications are also discussed. [ABSTRACT FROM AUTHOR]

The increase of environmental concerns, scarcity of fossil fuel resources, uncontrolled growth of demand, along with the development of efficient multi-generation systems have made the restructuring of current energy systems inevitable. Future energy systems will be in the form of sustainable multi-energy systems. The optimal operation of such systems requires an integrated energy management system for optimal planning, control and management. Energy hub is a new and promising concept for optimal management of systems with multiple energy carriers. Energy hub has a large potential for realization of energy system models and moving towards sustainable multi-energy systems. This paper provides a comprehensive overview of the concepts and different applications of energy hubs in various energy consumption sectors including residential, commercial, industrial, agricultural, and the integration of these systems. The potential role of energy hub as an integrated energy management system to solve the main challenges in these consumption sectors is evaluated. This study focuses on the benefits earned by integration of the options such as demand side management, distributed energy resources, renewable energy resources, multi-generation systems, storage systems as well as using the smart technologies by introducing the concept of smart energy hubs. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energies into the smart grid, satisfying load and avoiding energy shortage have become a challenging issue for energy providers, owing to the intermittent nature of renewable energies. The need to reduce energy shortage and pollutant gas emissions has led to a large-scale development of energy management programs such as demand side management (DSM) to control the peak energy demand on a smart grid. The implementation of DSM programs makes the operation planning of smart grids more difficult with an additional objective of customer stakeholder. The operation plan should satisfy all the conflicting objectives of stakeholders (i.e., minimization of the total cost, minimization of the GHG emissions, and maximization of customer satisfaction). In this paper, we present a novel multi-objective optimization framework for energy management in the smart grid to significantly reduce the peak load demand and reshape the load profile. The proposed framework is comprised of four main components: (1) a forecasting model that predicts the 24-h-ahead energy load, (2) a load shifting DSM program that reduces the energy load during peak demand, (3) a piecewise linear approximation method that linearizes the non-linear objective functions and constraints, and (4) a δ -constraint multi-objective optimization method that efficiently finds the Pareto frontier solutions. The capabilities of the proposed framework demonstrated on a synthetic smart grid case study with 50 buildings. The results reveal that the proposed framework has successfully met the desired load curve while obtaining a significantly larger Pareto frontier solution set (with more non-dominated solutions) in less computational time. [ABSTRACT FROM AUTHOR]

This paper presents a study coming from an academic and industry partnership with the goals of exploring potential uses for UAS in distribution system operation and maintenance, establishing baseline costs and capabilities via equipment field tests, and simulating the cost benefits of increased maintenance. Existing UAS technology is shown to be capable of providing situational awareness for disaster response as well as increasing the number of maintenance inspections and speeding them up. [ABSTRACT FROM AUTHOR]

The special volume on green and sustainable chemistry and engineering has fourteen papers that were considered relevant to the present day issues and discussion, such as adequate use of raw materials and efficient energy, besides considering renewable sources for materials and energy; and changing economical canons towards circular economy. Businesses, governments and Society are facing a number of challenges to tread the sustainability path and provide wellbeing for future generations. This special volume relevance provides discussions and contributions to foster that desirable future. Chemicals are ubiquitous in everyday activities. Their widespread presence provides benefits to societies’ wellbeing, but can have some deleterious effects. To counteract such effect, green engineering and sustainable assessment in industrial processes have been gathering momentum in the last thirty years. Green chemistry, green engineering, eco-efficiency, and sustainability are becoming a necessity for assessing and managing products and processes in the chemical industry. This special volume presents fourteen articles related to sustainable resource and energy use (five articles), circular economy (one article), cleaner production and sustainable process assessment (five article), and innovation in chemical products (three articles). Green and sustainable chemistry, as well as sustainable chemical engineering and renewable energy sources are required to foster and consolidate a transition towards more sustainable societies. This special volume present current trends in chemistry and chemical engineering, such as sustainable resource and energy use, circular economy, cleaner production and sustainable process assessment, and innovation in chemical products. This special volume provides insights in this direction and complementing other efforts towards such transition. [ABSTRACT FROM AUTHOR]

In the wake of today’s highly dynamic and competitive energy markets, optimal dispatching of energy sources requires effective demand responsiveness. Suppliers have adopted a dynamic pricing strategy in efforts to control the downstream demand. This method however requires consumer awareness, flexibility, and timely responsiveness. While residential activities are more flexible and schedulable, larger commercial consumers remain an obstacle due to the impacts on industrial performance. This paper combines methods from quadratic, stochastic, and evolutionary programming with multi-objective optimization and continuous simulation, to propose a two-stage discrete-continuous multi-objective load optimization (DiCoMoLoOp) autonomous approach for industrial consumer demand response (DR). Stage 1 defines discrete-event load shifting targets. Accordingly, controllable loads are continuously optimized in stage 2 while considering the consumer’s utility. Utility functions, which measure the loads’ time value to the consumer, are derived and weights are assigned through an analytical hierarchy process (AHP). The method is demonstrated for an industrial building model using real data. The proposed method integrates with building energy management system and solves in real-time with autonomous and instantaneous load shifting in the hour-ahead energy price (HAP) market. The simulation shows the occasional existence of multiple load management options on the Pareto frontier. Finally, the computed savings, based on the simulation analysis with real consumption, climate, and price data, ranged from modest to considerable amounts depending on the consumer’s solution preference. [ABSTRACT FROM AUTHOR]

Green smart cities that rely completely on sustainable renewable energy sources are being constructed to attract investors, diversify countries' economies, and move away from dependence on fossil fuels. The main renewable energy sources used in such cities today include solar, wind, and—to a lesser extent—biomass. However, these sources are stochastic in nature, and the amount of energy they produce is not entirely controllable. This results in difficulty maintaining the balance between generation and demand in the power grid. Recent technological advances in smart grids, microgrids, virtual power plants, energy hubs, and energy interconnection technologies offer a wide range of plausible solutions to this problem. Such advances motivated the development of a complementary technology known as the energy warehouse (EW). An EW is a controlled and managed heterogeneous massive modular energy storage and wheeling system. It provides links between microgrids and bulk power systems (BPSs). After describing the structure of an EW and its energy management system and illustrating example EW operating scenarios, this paper uses the concept of bankruptcy to explain the business side of an EW. For the first time, this principle is used to address cases where the EW falls short of its obligations to the connected microgrids and BPSs. This treatment of EW contributes to a better understanding of its operation and opens further opportunities for contributions. Furthermore, our study confirms that a properly sized and located EW promises significant technical and economic benefits. These including load leveling and energy time-shift, as well as the potential to eliminate the typical transmission infrastructure, thereby reducing losses, mitigating congestion, and increasing efficiency. [ABSTRACT FROM AUTHOR]

In the Smart Grid context, the involvement of end-users is a key element for the implementation of demand response, as a way to enhance the energy efficiency of the electricity infrastructure also enabling to cope with the intermittency of renewable energy sources. Although the participation of end-users may result in higher complexity of system management, it may have a positive impact on mitigating the volatility of electricity prices. End-users may also be a key component in the provision of ancillary services using demand side resources to offer the system operator additional means to enhance system flexibility, robust planning, constraint management and operation scheduling, contributing to the balance between load and supply under a load follows supply paradigm. Demand response is seen as an effective and reliable strategy for the successful integration of renewable energy sources, in a perspective of integrated energy resource management, handling the demand curve using load flexibility whenever the system requires it. This embodies the possibility of changing/controlling the load profile by optimally time deferring the use of some equipment. This paper focuses on the role of energy management systems aggregators, both concerning actual practice in industry as well as research, which can be seen as relevant players contributing to that endeavour. A review of recent literature and projects is made, putting in perspective the role of energy management systems aggregators in the Smart Grid context, in association with demand response programs and technologies, involving the participation of end-users in the provision of ancillary services. The aim is recognizing trends, opportunities, challenges and potential barriers regarding the creation of energy management systems aggregators to improve overall system performance, characterizing the services provided by aggregators and identifying potential research gaps. [ABSTRACT FROM AUTHOR]

The development of smart grids is a strategic goal at both national and international levels and has been funded by many research programs. At the same time, an increasing interest is rising about local energy systems using renewable energy sources (RES). In this paper, the creation of a monitoring and managing procedure of an electricity micro-smart grid in a small agro-food enterprise is presented. Scopes of the procedure are both the minimization of the energy exchange between the local grid and the public utility grid and the optimization of the exploitation of renewable sources. To achieve that, it was necessary to match energy demand and supply in as short as possible time steps, trying to create a self-sufficient small district. The two objectives above can also generate financial savings due to the reduction of the electricity purchase from the grid. The agro-industrial test site is a prosumer (both a producer and a consumer of energy) and it was equipped with wireless networks of smart meters and devices, monitoring generators and loads, a data acquisition tool and a user interface that shows the monitoring results and suggests the optimization strategies of the smart grid to be undertaken. [ABSTRACT FROM AUTHOR]

Although electric power networks and district heating networks are physically coupled, they are not operated in a coordinated manner. With increasing penetration of renewable energy sources, a coordinated market-based operation of the two networks can yield significant advantages, as reduced need for grid reinforcements, by optimizing the power flows in the coupled systems. Transactive control has been developed as a promising approach based on market and control mechanisms to coordinate supply and demand in energy systems, which when applied to power systems is being referred to as transactive energy. However, this approach has not been fully investigated in the context of market-based operation of coupled electric power and district heating networks. Therefore, this paper proposes a transactive control approach to coordinate flexible producers and consumers while taking into account the operational aspects of both networks, for the benefit of all participants and considering their privacy. A nonlinear model predictive control approach is applied in this work to maximize the social welfare of both networks, taking into account system operational limits, while reducing losses and considering system dynamics and forecasted power supply and demand of inflexible producers and consumers. A subtle approximation of the operational optimization problem is used to enable the practical application of the proposed approach in real time. The presented technique is implemented, tested, and demonstrated in a realistic test system, illustrating its benefits. • Transactive control design for coupled electric power and district heating networks. • Development and application of an accurate and computationally efficient system model. • Technical and economic optimal system operation considering participant privacy. [ABSTRACT FROM AUTHOR]

Educational sector is growing drastically as a result of upward trend of population. These schools require tremendous quantities of energy units to be generated and consumed. As a result, this will impact negatively on the environment by increasing the carbon emissions and depletion of non-renewable energy resources. Moreover, the harsh climate condition contributes substantially to increase the energy consumption in various weather zones in the world. Therefore, several energy efficiency plans have been developed to improve the energy performance of the schools facilities globally. Hence, this paper discusses the energy management program as a sustainable strategy that applicable on the schools. This program is designed for the schools to improve the energy efficiency, indoor environmental quality and adopting the best management practices during the operating stages. This research has developed a customized energy management program for one of the schools in UAE. It has concluded the findings by using computerized hourly thermal modeling. The energy model has evaluated the proposed energy conservation measures and the best practices that were generated by the energy management program steps. This case shows that the energy performance could be improved substantially by 35%. Thus, the energy use index of the audited school that adopted the energy management program has reduced from 438 kWh/m 2 /year to 285 kWh/m 2 /year. The proposed energy management program has been articulated in this study as a consecutive and linear progression. This methodology presents several steps to mitigate the carbon emission by addressing the sustainability dimensions; economic, environment and social aspects. This study discusses the steps and the work flow of developing the energy management program and the anticipated outcomes for the school's buildings. The program considers practical ECMs along with the students and teachers participation and interaction. Furthermore, it highlights the success criteria that assure the sustainability of the energy efficiency at the operating life cycle of school's facilities. [ABSTRACT FROM AUTHOR]