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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]

In this paper, the role of a Battery Energy Storage System (BESS) in the United Kingdom (UK) electricity market is investigated. Such device is selected since research works related to the topic demonstrate that BESSs help facing challenges caused by renewable energy sources increasing penetration in the power systems' field. Indeed, in the next future, BESSs will assume a relevant role thanks to their fast response time. In the present paper, an optimization model is implemented to encode the UK market rules and output the optimal set of offers on day-ahead and intraday markets, dynamic frequency response services and imbalance settlement. The market rules are formalised in a mathematical model considering market temporal sequencing and BESS technical limitations. The tool also models the state of energy management, necessary since BESSs are energy limited assets. Objective function and constraints are linearized to define a Mixed Integer Linear Programming problem to reduce the computational burden and to integrate the model in the Energy Management System previously developed at University of Genoa. Specific tests are performed including and excluding ancillary services to evaluate the related advantages. The obtained outcomes reveal that ancillary services are the most convenient options within all the implemented scenarios. [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]

The overall operational costs of home energy systems (HES) has been considered as an objective function (OF) by many articles. This paper shows that considering the energy cost as OF can lead to a decrease in the load elasticity against the electricity price, which can lead to demand response programs failure. So the distribution system operator (DSO) must stop this decline. A new index is introduced to calculate the responsiveness of the PV-integrated HES, based on which a new OF for home energy management system is proposed. This index shows the load elasticity against the electricity price. The relation between energy cost and load responsiveness index is investigated, and several scenarios are analyzed to demonstrate the applicability of the proposed approach in real cases. The proposed method is implemented to a PV- and ESS- integrated HES. The results show the relation between the responsiveness of the loads and the operational costs of home. A sensitivity analysis is added to the paper to show the robustness of the proposed control scheme. • Considering the energy cost leads to a decrease in load elasticity against price. • A new index is proposed to calculate the responsiveness of HEMS, based on a new OF. • Home loads responsiveness index relation with the energy supply cost is discussed. • Roles of ESS to improving the responsiveness of residential loads are investigated. • ESS roles to reduce negative effect of PV in increasing PAR of HES are investigated. [ABSTRACT FROM AUTHOR]

Power-to-ammonia (P2A) technology has attracted more and more attention since ammonia is recognized as a natural zero-carbon fuel. In this context, this paper constructs a renewable energy powered multi-energy hub (MEH) system which integrates with a thermo-electrochemical effect based P2A facility. Subsequently, the energy management of proposed MEH system is casted to a multi-agent coordinated optimization problem, which aims to minimize operating cost and carbon dioxide emissions while satisfying constraints. Then, a novel multi-agent deep reinforcement learning method called CommNet is applied to solve this problem to obtain the optimal coordinated energy management strategy of each energy hub by achieving the distributed computation of global information. Finally, the simulation results show that the proposed method can achieve better performance on reducing operating cost and carbon emissions than other benchmark methods. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IES) with renewable energy systems (RES), carbon capture systems (CCS) and energy storage systems (ESS) are considered efficient in supporting the low-carbon energy supply with both economic and environmental benefits. Effective energy management is required to ensure the economical, environmental and reliable operation of the IES. However, the optimal IES operation is considered a non-trivial task due to the renewable generation uncertainty and the optimization of multiple contradictory objectives (e.g. economic, environmental and risk costs). This paper aims to provide a multi-level optimization model for the real-time optimal IES operation consisting of RES, ESS and CCS. This work quantifies the uncertainty by the Conditional Value at Risk (CVaR) theory in the optimization model. The uncertainty is further reduced by improving the operation strategy through a model predictive control (MPC)-based method. Also, the multi-objective optimization model is adopted to minimize the economic cost, carbon dioxide emissions (CDE) and primary energy consumption (PEC) for optimal energy scheduling in the intra-day stage. Based on the result of the intra-day stage, the feedback correction model is applied to adjust the schedule to balance the difference between the forecasting and actual values. Numerical results show that the proposed solution can provide the trade-off between economical and environmental performance. Through ablation experiments, the proposed method with feedback correction can carry out demand response with lower costs, CDE and PEC. The proposed solution is further confirmed with outperformed performance compared with single-objective optimization methods and other stochastic optimization methods. In addition, a robustness analysis is conducted to quantify the benefits of RES, ESS and CCS in IES. [ABSTRACT FROM AUTHOR]

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]

Over the last two decades, there has been a significant interest in developing solutions to improve the efficiency of power systems, such as optimal energy consumption and management. The demand response programs (DRP) have long been one of the most effective strategies to encourage consumers to change their consumption behavior. In this paper, a novel hybrid DRP has been implemented by combining the time of use (TOU) and incentive-based (IB) DRP methods. To meet the load demand, a PV-WT-BESS hybrid renewable energy system (HRES) has been proposed in this work. Further, to analyze the impact of hybrid DRP on the capacity allocation of different system components, optimal sizing has been performed in two scenarios, one with the actual load and the second with the load after considering the hybrid DRP. The optimization has been carried out by using an improved search space reduction (ISSR) algorithm. To demonstrate the effectiveness of the ISSR algorithm, the obtained results are compared with other algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), teaching-learning based algorithm (TLBO), seagull optimization algorithm (SOA), and sine cosine algorithm (SCA). Finally, a novel energy management strategy (EMS) is also proposed in this work on the supply side of the HRES, which intelligently uses the variations in the grid tariff to charge the BESS when the tariff is low. To demonstrate the proposed DRP and EMS-based sizing, a case study has been performed for a grid-connected PV-WT-BESS for the location of Kanyakumari, India. From the results obtained, it has been concluded that the hybrid DRP reduces the peak demand up to 5%, which reduces the levelized cost of energy (LCE) by 22%. Furthermore, the proposed novel EMS reduces the LCE further by 5%. The overall cost reduction of up to 25% has been achieved by using both the proposed hybrid DRP and EMS, while satisfying all necessary constraints. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper proposes an energy management system (EMS) for nanogrids to balance the power supply and forecasted demand in consideration of forecasting errors arising from high instantaneous demand. The proposed EMS employs a power-balancing optimization process for forecasted demand and a reference power modulation strategy for forecasting errors. This power-balancing optimization utilizes nanogrid sources, such as photovoltaics, fuel cells, and batteries, to meet forecasted demand and a supercapacitor charging process to overcome issues with a low energy density. The proposed reference power modulation strategy is utilized to allocate power from a hybrid energy storage system consisting of a battery and supercapacitor in order to compensate for forecasting errors. In addition, this proposed strategy considers battery and supercapacitor constraints such as the power changing rate and total power limitations. The power-balancing optimization process also operates at faster sampling rate than the reference power modulation process in order to improve the computational efficiency. The performance of the proposed EMS is evaluated using real data obtained from the Korea Electric Power Exchange. • This paper proposes a cascaded structure energy management system (EMS) for nanogrid to balance power for both forecasted demand and forecast errors including high instantaneous demand. • The proposed EMS comprises a power balancing optimization process for forecasted demand and reference power modulation strategy for the forecast errors. The power balancing optimization utilizes nanogrid sources, such as PV, fuel cell, and battery, to meet forecasted demand as well as a supercapacitor charging process for addressing low energy density characteristics. • The proposed reference power modulation strategy is utilized to allocate power from the HESS comprising a battery and supercapacitor, in order to compensate forecast errors. In addition, this proposed strategy is performed considering battery and supercapacitor constraints such as their power changing rate, total power limitation. [ABSTRACT FROM AUTHOR]

Due to the inherent uncertainty and the mismatch between renewable energy output and load demand, renewable energy-based system optimization is increasingly important in China. Therefore, electricity demand response (EDR) is critical to the stability and efficiency of an integrated renewable energy system (IRES). The customers' demand response under the time-of-use (TOU) mechanism is the key to the entire process. However, how to optimize integrated renewable energy system based on accurately identifying the electricity demand response participation of customers is one of the main issues at this stage. To solve the problem, this paper presents a novel approach for integrated renewable energy system optimization considering electricity demand response management and multistage energy storage systems from the perspective of Fujian Province, China. To enable large-scale renewable energy integration, the original output is processed using the Hampel identifier (HI)-Savitzky-Golay filter (SGF) technology. A machine learning (ML)-based identification model is developed for EDR to determine whether the customers are participating or not. Driven by the time-of-use mechanism, customers can make dynamic adjustments based on the Logistics function. After that, an integrated renewable energy system model optimization strategy is proposed based on satisfying customers' electricity demand response. Throughout the optimization process, the multistage energy storage system plays a vital role in the residual fluctuation absorption for renewable energy filtering, the dynamic adjustment process of the demand response, and the integrated renewable energy system optimization. Through extensive case studies and discussions, it is demonstrated that random forest (RF) and support vector machine (SVM) reach 80 % identification accuracy with a 20 % testing ratio. According to the optimization approach proposed in this paper, the minimum operation cost can be calculated to be 9.336*106 CNY. The methodology presented in this paper establishes a special relationship between electricity demand response identification and dynamic adjustments, and provides an optimization model for Fujian Province, China, which is a reliable reference for future work. [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]

The ever-increasing participation of new energy sources, storage systems and controllable loads to electricity markets has introduced more complexity in the operation of power plants, microgrids (MGs) and networks. To face these issues, it is often necessary to design Distributed Energy Systems to optimize the power exchange. Usually, MG operation is managed by an Energy Management System (EMS) dealing with large amounts of parameters to be taken into account and that may drive to problems difficult to be faced and requiring long computational timings. In this direction, this paper describes the Mixed Integer Linear Programming MATLAB Based Energy Management System (MB-EMS) developed at the University of Genoa, aimed at optimizing the operating costs of a generic polygeneration system, considering different electrical and thermal production units, energy storage systems, non-programmable loads and flexible loads. Many important technical details are accounted: the duration and costs related to maintenance interventions and CO 2 emissions costs are considered for all production units as well as constraints and incentives related to the High-Efficiency Cogeneration systems. Constraints related to the minimum and maximum State-Of-Charge of energy storage systems, plus their deterioration, with the consequent optimization of their utilization, are also modelled. Specific test cases for all functionalities are presented. Moreover, a test related to a real MG performed over a time horizon of one year with a time step of 1 h is performed and solved in a short computational time. The linearization of constraints reduces the computational effort, avoiding the need of clustering or averaging parameters for similar production units. Consequently, calculations are rapidly performed, also when dealing with long term planning problems (a realistic test case with time horizon of one year and time step of 15 min can be simulated in some seconds). In this sense, the developed tool can be used both in real time operation and in long term planning problems. • MILP EMS to optimize the operating costs of a generic polygeneration system. • Production units, energy storage systems, non-programmable loads and flexible loads. • Test on a real MG over one year solved in a short computational time. • The tool can be used in real time operation and in long term planning problems. [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]

Under the background of carbon neutrality, the traditional coal chemical industry needs to upgrade the industrial park to reduce the carbon emission in the production process. Therefore, this paper studies a method to transform the coal chemical industry park by using renewable energy to produce hydrogen, and proposes an energy management scheme coupled with renewable energy to produce hydrogen in the coal chemical industry park. Firstly, based on time of use electricity price, renewable energy generation and load, an energy management strategy aiming at maximizing daily income is proposed to obtain the optimal energy management scheme with the highest daily income. Then, based on this strategy, the annual revenue maximization optimization model considering the investment and operating costs of photovoltaic, hydrogen production, hydrogen storage and electricity storage is established, and the optimal capacity configuration of different equipment in the system is obtained. The simulation model is established on the MATLAB-YALMIP platform, and GUROBI is used to solve the optimization model. The proposed scheme can save about 76,800 tons of standard coal and reduce carbon emissions by 181,247.95 tons per year. In addition, a system that only considers photovoltaic and electricity storage coupling is also established as a comparison, and the net profit of the system under this scheme is reduced by 36.47 % compared with the proposed PV-storage-hydrogen system, which highlights the economic advantages of the proposed photovoltaic, electricity storage and green hydrogen coupling system. [ABSTRACT FROM AUTHOR]

This paper presents a tool that allows to perform an ex-ante estimation of the profitability to participate to the electric energy and service markets for a microgrid or a polygeneration power plant. The tool is the evolution of an Energy Management System (EMS) previously developed by the University of Genoa and Renantis Solutions Srl. Constraints are added to the original EMS to model the Ancillary Service Market (ASM) and Day Ahead Market (DAM) mechanisms in the framework of a planning tool that allows to get estimates of the microgrid/plant costs and revenues over a long-time horizon. As the market outcome strongly depends on the Transmission System Operator (TSO) acceptance/rejection of bids and offers, a statistical approach is proposed. The optimization process is split in two steps: the first one optimally dispatches the energy units and proposes DAM offers and ASM bid and offers over a specified time horizon. Then, a Monte Carlo method is launched; at each extraction the TSO acceptance/rejection of each ASM bid and offer is simulated and there is a second step of optimization in which the energy sources are optimally re-dispatched to meet the bidding program and so to avoid imbalance power exchange that would increase the energy cost. The cost/revenue is thus computed at each extraction. The result is the probability density function of the costs/revenues that allows to estimate the profitability of the participation to the electricity markets. One test performed on a real microgrid shows that the computational time needed to complete one iteration related to a time horizon of one month is approximately 20 s. Moreover, the comparison of the outcomes with two independent launches to define the set of bids and offers to be accepted in ASM demonstrates that the statistical analysis allows to avoid misleading conclusions on the costs/revenues. Consequently, the fact that the developed EMS allows to perform a statistical analysis in a reasonable time enables the possibility of comparing multiple scenarios and gives an indication related to the range of cost/revenues in which the microgrid/plant may incur. Finally, the computational efficiency of the proposed approach allows to conclude that the developed EMS is appropriate to perform a long-time horizon (of the order of magnitude of years) analysis and, at the same time, allows to model all the involved components with a high level of details. [ABSTRACT FROM AUTHOR]

In this paper, the dynamic and static characterization of commercial metal hydride tanks is developed based on black box modelling. The objective is to determine the thermodynamic parameters of the hydride, such as enthalpy and entropy, and then to perform an energy management of this hydrogen storage system coupled to a fuel cell generator. Therefore, for this study, a specific test bench was developed around two hydride tanks. The hydrogen temperature and pressure were measured as a function of time and used to evaluate the thermal energy produced and consumed, as well as the amount of hydrogen, stored and released, during the absorption and desorption process respectively. The analysis of the results allowed the determination of a number of parameters such as the maximum reversible hydrogen storage capacity, which was 100 [g] for each tank with a relative capacity of 0.8 [%], the equilibrium pressure between 1 and 4 [bar] for both processes (absorption/desorption) as well as the evaluation of the hysteresis. The black-box approach makes this characterization method suitable for any solid-state hydrogen storage system, regardless of metal hydride type, geometrical shape, and tank size. [ABSTRACT FROM AUTHOR]

This paper proposes a new stochastic optimization framework for day-ahead (DA) energy management of a renewable-based isolated rural microgrid. Non-dispatchable distributed generations (DGs) including wind and photovoltaic (PV) power plants have been operated by the microgrid in order to meet consumers' demand. The main challenge in this condition is to keep balance between generation and demand at all times. In order to cope with this challenge, a pumped-storage unit and an incentive-based demand response program have been used in this work. Moreover, the accurate model of pumped-storage unit, responsive demand and distribution network and also the inherent uncertainty of wind and PV power generation have been considered in this paper. Finally, the effectiveness of the proposed optimization framework has been evaluated through extensive numerical studies. The results show that the pumped-storage unit optimal scheduling and demand response implementation, significantly improve economic and technical performance indexes. [ABSTRACT FROM AUTHOR]

The uncertainty of the renewable energy output and load demand poses significant challenges to the efficiency and stability of energy community operation. To reduce the adverse effects of uncertainty factors, this paper proposes a dual time-scale energy management method for a distributed energy community. In the day-ahead scheduling, an adaptive robust optimization model is proposed. This model uses the Dirichlet process mixture model (DPMM) to establish the fuzzy set of uncertainty for solar power generation and load demand. The column-and-constraint generation algorithm (C&CG) to solve the optimization model. In the intra-day scheduling, a rolling optimization method based on model predictive control (MPC) is combined with short-term source-load forecasting, which ensures consistency between the intra-day and day-ahead scheduling plans and real-time supply-demand balance. Experiments show that the robustness of energy management in the proposed method is enhanced significantly in various scenarios. At the same time, the operating costs of energy community are also reduced with different degrees of conservatism. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed energy management for active distribution network (ADN) considering aggregated office buildings. It aims to dispatch the flexible resources of office buildings to actively participate in the optimization of the ADN. Firstly, by considering the comfortable indoor temperature range and travel habits of workers, an optimal operation model for the office building with integrated heating ventilation and air conditioning (HVAC) systems and electric vehicle (EV) charging piles is constructed. Secondly, based on the piecewise linearization and second-order cone relaxation (SOCR), an ADN optimization model with aggregated office buildings is formulated and converted into a mixed-integer second-order cone programming (MISOCP) model. Then, considering the private information protection, the alternating direction method of multipliers (ADMM) is used to solve the ADN optimization model to realize the distributed energy management of the ADN with aggregated office buildings. Finally, the scheduling results of the ADN optimization model under different comfortable indoor temperatures and control methods of EVs are analyzed in the winter heating scenario. Numerical results show that the proposed method can increase the penetration of distributed photovoltaic (PV) generation and decrease losses of the distribution network while ensuring the temperature and travel demands of workers in office buildings. The information privacy of each district can also be protected with the proposed method. [ABSTRACT FROM AUTHOR]

The concept of a microgrid (MG) has been introduced to integrate the conventional generators, different renewable energy resources and energy storage systems (ESS) to meet the specific load demand. However, the intermittent nature of renewable energy resources produces a variable output, which drives an imbalance between power generation and demand in MG. The ESS is utilized to makes a balance between power generation and demand. When several renewable energy resources and ESS are available in MG as energy resources, then an energy management system (EMS) is required that can handle the stochastic nature of renewable energy resources, schedule the power of renewable energy resources and ESS for managing the power flow among MG resources and main grid while ensuing cost-effective operation. Therefore, this paper proposed an optimum EMS that aims to minimize the overall operating cost of grid-connected MG along with the short-term forecasting of PV power and load demand. The proposed EMS consists of four modules: forecasting, scheduling, data acquisition (DAQ), and human–machine interface (HMI) modules. An improved hybrid forecasting approach that combines a 3-level stationary wavelet transform (SWT) and grey wolf optimization-based least-square support vector machine (GWO-LSSVM) is proposed in the forecasting module to achieve day-ahead forecasting of PV power and load demand. In the scheduling module, the weighted salp swarm algorithm-based scheduling is applied to achieve the optimum power flow of grid-connected MG. Then, the DAQ and HMI module is used to monitor, analyze, and modified the input variables of the forecasting and scheduling module. The MATLAB/Simulink environment is then used to simulate the proposed EMS for grid-connected MG. Finally, numerical results demonstrate the efficiency of the proposed EMS for grid-connected MG with commercial load demand over the existing competitive approaches. [ABSTRACT FROM AUTHOR]

The battery-grid integration (BGI) is the crux of microgrid systems that not only performs the energy management but also coordinates distributed energy resources and loads. The prevailing BGIs are either individual power converters based, which cause low efficiency and high cost, or multiport converter (MPC) based, which involve a higher component count and/or the lower voltage limit restriction in the DC port. With the increased penetration of distributed energy resources, the demand for MPCs with a wider buck-boost voltage gain capability is unavoidable. They can be used to interface low voltage sources like fuel cells, photovoltaic panels, batteries, etc. To perform the basic functionalities in BGI converters, like the power transfer from the storage to the grid and vice versa at different working conditions, the bidirectional capability in DC and AC ports is mandatory. This paper proposes a high voltage gain, bidirectional buck-boost port based hybrid MPC, called as an energy cushion multiport converter (ECMPC), for the integration of batteries with the single-phase AC microgrid (SPACMG). The ECMPC has three ports, among them one is the bidirectional AC port for the grid, the second one is the bidirectional DC port for the battery, and the last one is a DC output port for the common usage. The modes of operation with an appropriate switching sequence is detailed and a control scheme is also suggested. The performed comprehensive simulation analysis guides the component selection and the design of the converter. The critical comparison of the suggested converter with the prevailing single-phase bidirectional DC-AC converters being employed in the BGI is also presented. The theoretical study is supported by a laboratory corroboration. The system is managed for a minimal energy wastage through anew suggested minimal wastage energy management system (MWEMS). The system in the entirety is implemented and controlled by an ALTERA field-programmable gate array, ALTERA QUARTUS II 12.0SP2 processor. • An energy cushion multiport converter is designed for microgrid-battery interface. • It is a combination of a high gain chopper and a bidirectional multilevel inverter. • The circuit does bi-directional power flow at different battery-grid conditions. • The multiport converter is suitable for interfacing all types of storage systems. • A minimal wastage energy management system is suggested for reduced energy wastage. [ABSTRACT FROM AUTHOR]

With the penetration of multiple distributed energy sources, demand side management (DSM) of the regional integrated energy system (RIES) becomes more complicated in the energy market. Real-time pricing (RTP) is an effective method for DSM, which can flexibly guide the supply and demand sides to adjust their behavior to participate in demand response (DR). In this paper, a hierarchical energy system is studied including multiple RIESs with multiple energy dispatch and supplement. To maximize the social welfare, a bilevel programming model is developed, in which the upper level aims at maximizing the profits of the supplier, and the lower level aims at maximizing the RIESs' welfare. Then, the proposed bilevel model is transformed into a mixed integer quadratic programming model using duality theory and Karush-Kuhn-Tucker conditions. Furthermore, the RTP strategy is obtained, and the optimal energy scheme of RIES is given in the solution. Compared simulations in different scenarios, the total social welfare is increased by about 14.12%, the peak-to-valley difference of power load and carbon emissions are reduced by 16.99% and 5.7% respectively after DR. The results show that the proposed bilevel model under the RTP is conducive to social economy and environment. • A hierarchical market framework is studied between the main grid and multiple RIESs. • A bilevel programming model is proposed to integrate the RTP and energy dispatch in the RIES. • Besides the operating cost, users' welfare is adopted in the model. • The RTP mechanism based on IDR is designed to balance the supply and demand. • The optimal energy dispatch strategy of the RIES is obtained by using the KKT conditions to transform the bilevel model. [ABSTRACT FROM AUTHOR]

The peak period of an energy-generating wave is one of the most important parameters that describe the spectral shape of the oceanic wave, as this indicates the duration for which the waves prevail with respect to their maximum extractable energy. In this paper, a half-hourly peak wave energy period (T P) forecast model is constructed using a suite of statistically significant lagged inputs based on the partial auto-correlation function with an extreme learning machine model developed and its predictive utility is benchmarked against deep learning models, i.e., convolutional neural network (CNN/CovNet) and recurrent neural network (RNN) models and other traditional M5tree, Conditional Maximization based Multiple Linear Regression (MLR-ECM) and MLR models. The objective model (ELM) vs. the comparison models (CNN, RNN, M5tree, MLR-ECM, and MLR) were trained and validated independently on the test dataset obtained from coastal zones of eastern Australia that have a high potential for implementation of wave energy generation systems. The outcomes ascertain that the ELM model can generate significantly accurate predictions of the half-hourly peak wave energy period, providing a good level of accuracy relative to deep learning models in selected coastal study zones. The study establishes the practical usefulness of the ELM model as being a noteworthy methodology for the applications in renewable and sustainable energy resource management systems. • A real-time peak wave energy period forecasting model is constructed using ELM model. • The advance ELM is benchmarked against a deep learning and other traditional models. • The advance ELM generates better predictions than deep learning models in this region. • ELM model provides significant energy management implications for ocean waves. [ABSTRACT FROM AUTHOR]

Accurate short-term forecasting of photovoltaic (PV) power is indispensable for controlling and designing smart energy management systems for microgrids. In this paper, different kinds of deep learning neural networks (DLNN) for short-term output PV power forecasting have been developed and compared: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (BiGRU), One-Dimension Convolutional Neural Network (CNN 1D), as well as other hybrid configurations such as CNN 1D -LSTM and CNN 1D -GRU. A database of the PV power produced by the microgrid installed at the University of Trieste (Italy) is used to train and comparatively test the neural networks. The performance has been evaluated over four different time horizons (1 min, 5 min, 30 min and 60 min), for one-Step and multi-step ahead. The results show that the investigated DLNNs provide very good accuracy, particularly in the case of 1 min time horizon with one-step ahead (correlation coefficient is close to 1), while for the case of multi-step ahead (up to 8 steps ahead) the results are found to be acceptable (correlation coefficient ranges between 96.9% and 98%). • Deep NNs for short-term output photovoltaic power forecasting have been developed. • One-Step and multi-step photovoltaic power forecasting are presented and deeply analyzed. • The new advanced deep NN algorithms lead to acceptable accuracy in the case of cloudy days. • Multi-Step forecasting of photovoltaic power remains an open challenge. [ABSTRACT FROM AUTHOR]

Energy management is one of the main challenges in Microgrids (MGs) applied to Smart Buildings (SBs). Hence, more studies are indispensable to consider both modeling and operating aspects to utilize the upcoming results of the system for the different applications. This paper presents a novel energy management architecture model based on complete Supervisory Control and Data Acquisition (SCADA) system duties in an educational building with an MG Laboratory (Lab) testbed, which is named LAMBDA at the Electrical and Energy Engineering Department of the Sapienza University of Rome. The LAMBDA MG Lab simulates in a small scale a SB and is connected with the DIAEE electrical network. LAMBDA MG is composed of a Photovoltaic generator (PV), a Battery Energy Storage System (BESS), a smart switchboard (SW), and different classified loads (critical, essential, and normal) some of which are manageable and controllable (lighting, air conditioning, smart plugs operating into the LAB). The aim of the LAMBDA implementation is making the DIAEE smart for energy saving purposes. In the LAMBDA Lab, the communication architecture consists in a complex of master/slave units and actuators carried out by two main international standards, Modbus (industrial serial standard for electrical and technical monitoring systems) and Konnex (an open standard for commercial and domestic building automation). Making the electrical department smart causes to reduce the required power from the main grid. Hence, to achieve the aims, results have been investigated in two modes. Initially, the real-time mode based on the SCADA system, which reveals real daily power consumption and production of different sources and loads. Next, the simulation part is assigned to shows the behavior of the main grid, loads and BESS charging and discharging based on energy management system. Finally, the proposed model has been examined in different scenarios and evaluated from the economic aspect. • Optimal Energy Management in a smart building microgrid. • Hybrid PV-Battery for energy balance. • Economic aspects of local generation for buildings. • Minimization of exchanged power between the microgrid and the main grid. • Integration of SCADA system based on Modbus and KNX protocols. [ABSTRACT FROM AUTHOR]

–This paper deals with the optimal sizing of a hybrid photovoltaic-battery storage system for home energy management considering reliability against grid outages and demand response. To that end, a novel optimization framework is developed which aims at minimizing the electricity bill while the reliability of the system is ensured for certain common outages. In order to ensure the accuracy of the results, a large amount of characteristic outages along with demand, solar irradiance and temperature profiles are generated from real data. Clustering techniques are used for reducing this data to those most characteristics profiles and manage with the unpredictable behaviour of the outage events. Demand response is incorporated via different incentives like tariffs based on time of use and real time pricing, along with the optimal scheduling of different typical deferrable appliances. A case study on a smart-prosumer environment serves to illustrate the capabilities of the developed approach as providing sufficient guidelines for its universal applicability. Different cases studies are simulated considering different battery technologies and electricity tariffs for comparison. Various aspects related with the reliability against grid outages are also analysed like its impact on the project cost or the influence of demand response strategies. • A novel methodology for optimal planning photovoltaic-battery systems. • Grid outages are characterized via scenarios generated from historical data. • The response to demand incorporates different price-based incentives. • Optimal programming of typical deferred devices. • Several case study on a smart prosumer environment are analysed. [ABSTRACT FROM AUTHOR]

Optimal management for solar energy systems requires quality data to build accurate models for predicting the behavior of solar radiation. Solar irradiance and environmental data are provided by satellite and in-situ measurements. It is usual that satellite measurements present high temporal resolution with limited spatial resolution, and in-situ measurements provide high accuracy but significant missing data. This paper proposes a methodology based on machine learning algorithms that: i) takes the best of both data sources to obtain an improved spatio-temporal resolution, known as site-adaptation; and ii) provides highly accurate forecasting solar-radiation models based on deep learning on the improved data. Through a study case with real data, we show the benefits of using the proposed methodology based on machine and deep learning techniques to integrate data from different sources and to construct precise solar radiation forecasting models in regions where solar energy systems are required. Results show that machine learning models for site-adaptation performed up to 38% better than traditional methods. • Site-adaptation models of solar radiation with machine learning. • Machine learning and deep learning for solar radiation forecasting. • Improvement of satellite data and ground data. • Improvement of spatial-temporal resolution of a database. [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]

PHPS is a mature energy storage technology, leading to more efficient energy management and further integration of Renewable Energy Sources (RES). This paper investigates the implementation of such a system at Kastraki, Greece, combined with a second upstream reservoir with a total capacity of 10 hm3 and evaluates its efficiency. The position and the geometric characteristics of the second reservoir are determined with ArcGIS. When excess of energy exists, water is pumped from Kastraki reservoir and stored in the upper one. Otherwise, the stored water is used for energy production. The simulation is performed for 40 years with a monthly time step. Furthermore, another simulation for a typical day in July is conducted, when water and energy needs are increased. The system is evaluated in terms of covering the water supply and energy needs, as well as in terms of efficient energy management. Several results can be obtained, as follows: i) the reliability of the PHPS system for the coverage of monthly water supply and irrigation needs is 82.84 %. ii) The coverage of water supply needs for a typical day in July is 99.99 %. iii) The average monthly coverage of energy needs amounts to 83.86 %. [ABSTRACT FROM AUTHOR]

In this paper, a new design and flexible energy management strategy are presented for microgrids. The proposed intelligent energy management system (IEMS) achieves effective integration between the resilient microcontroller, chosen for its rapid response speed and its capability to perform multiple operations simultaneously, and the optimization techniques to enhance the power quality. The IEMS is designed using the FPGA board, chosen for its flexibility and capability to handle multiple and complex operations simultaneously. The experimental testing of the IEMS demonstrates a significant level of effectiveness in managing energy. To enhance system performance and ensure cost-effective reliability, advanced optimization techniques are employed. This study deals with a complex multi-objective optimization problem involving the limitations of energy generation, load demand, and a hydrogen-battery hybrid energy storage system. The moth-flame optimization (MFO) algorithm is chosen to solve this optimization problem due to its rapid convergence rate and accuracy. The effectiveness of the MFO algorithm is assessed by comparing it with several new algorithms. The obtained results show the robust performance of the IEMS and its high responsiveness to dynamic operational scenarios. It can observe, gather, and analyze data in real-time. It achieves a remarkable 1.287 % reduction in operating costs within a short timeframe. [ABSTRACT FROM AUTHOR]

PHPS is a mature energy storage technology, leading to more efficient energy management and further integration of Renewable Energy Sources (RES). This paper investigates the implementation of such a system at Kastraki, Greece, combined with a second upstream reservoir with a total capacity of 10 hm3 and evaluates its efficiency. The position and the geometric characteristics of the second reservoir are determined with ArcGIS. When excess of energy exists, water is pumped from Kastraki reservoir and stored in the upper one. Otherwise, the stored water is used for energy production. The simulation is performed for 40 years with a monthly time step. Furthermore, another simulation for a typical day in July is conducted, when water and energy needs are increased. The system is evaluated in terms of covering the water supply and energy needs, as well as in terms of efficient energy management. Several results can be obtained, as follows: i) the reliability of the PHPS system for the coverage of monthly water supply and irrigation needs is 82.84 %. ii) The coverage of water supply needs for a typical day in July is 99.99 %. iii) The average monthly coverage of energy needs amounts to 83.86 %. [ABSTRACT FROM AUTHOR]

In this paper, a new design and flexible energy management strategy are presented for microgrids. The proposed intelligent energy management system (IEMS) achieves effective integration between the resilient microcontroller, chosen for its rapid response speed and its capability to perform multiple operations simultaneously, and the optimization techniques to enhance the power quality. The IEMS is designed using the FPGA board, chosen for its flexibility and capability to handle multiple and complex operations simultaneously. The experimental testing of the IEMS demonstrates a significant level of effectiveness in managing energy. To enhance system performance and ensure cost-effective reliability, advanced optimization techniques are employed. This study deals with a complex multi-objective optimization problem involving the limitations of energy generation, load demand, and a hydrogen-battery hybrid energy storage system. The moth-flame optimization (MFO) algorithm is chosen to solve this optimization problem due to its rapid convergence rate and accuracy. The effectiveness of the MFO algorithm is assessed by comparing it with several new algorithms. The obtained results show the robust performance of the IEMS and its high responsiveness to dynamic operational scenarios. It can observe, gather, and analyze data in real-time. It achieves a remarkable 1.287 % reduction in operating costs within a short timeframe. [ABSTRACT FROM AUTHOR]

Home energy management systems are essential for the optimization of resources in complex demand scheduling problems that require energy efficiency in homes. This can be achieved through the use of Renewable Electricity Sources (RES), for cleaner and more sustainable energy generation, as well as participation in Demand Response (DR) programs, for lower energy prices. The problem becomes more complex when it is considered a community of households. This paper aims to address individual and community household participation in DR programs and RES sharing while considering constraints imposed on the operation schedule of appliances, through load shifting optimization. For that, a Genetic Algorithm is proposed, implemented, and validated, which focuses on minimizing energy costs. It takes into account dynamic pricing, distributed generation, and household community energy sharing. Using real household workload data, two case studies are presented, one for the cost optimization of an individual household and another for a photovoltaic energy-sharing household community containing twenty houses. Both case studies represent five days of scheduling, where each house can have up to five appliances able to shift. The business as usual costs are 16.74 EUR and 269.99 EUR for the individual and community case studies, respectively. Results show improvements of up to 24.3% (12.67 EUR in the optimized schedule, 4.07 EUR in savings) for the individual case study and 11.8% (238.21 EUR in the optimized schedule, 31.78 EUR in savings) for the household community. In a community, households can expect cost reductions of 1.5%–26.8% when compared to individual scheduling. [ABSTRACT FROM AUTHOR]

—As a result of the increasing demand for different energies with technological development, and with the rapid development of energy hubs (EHs), issues such as computation speed, big data and information privacy, robustness, etc. are becoming increasingly vital. Also, the problem of communication packet loss leads to an incompatibility between the generated and consumed energy. In response to the above problems, this paper proposes an Internet of things-Fog-Cloud distributed consensus-based energy management algorithm to optimally solve the energy mismatch problem in an EH environment considering communication information loss and uncertainties of some EH elements. A MATLAB/Simulink-based model of a hypothetical EH system is modified to support real-time communication. Two case studies are applied to explain the performance and effectiveness of the proposed algorithm under different packet loss scenarios. The system's sensitivity was studied depending on the number of iterations and the uncertainties of the renewable energy sources, energy prices, and load demands. Also, a comparison was made between the results obtained by the proposed method and the optimization results using the genetic algorithm and particle swarm optimization techniques. The results prove the applicability and feasibility of the proposed method for the demand management system in EHs. The results show that the proposed algorithm decreases the daily system operating cost from 372.421 to 369.941 $ and the total CO 2 emissions cost from 4848.5 to 4103.1 $. In addition, the daily energy mismatch between the generated and consumed values decreases from 579.85 to 0 kWh in the electrical system and from 435.14 to 0.073 kWh th in the heating system. Based on the previous results, the proposed algorithm of EHs contributes significantly to the development of sustainable cities. [ABSTRACT FROM AUTHOR]

Intensity of solar irradiance directly affects solar power generation and this makes solar irradiance forecasting a vital process in energy management systems. Existing forecasting systems show positive solar irradiance forecasting performance, but most of them are not accurate in real-life especially when there are fast-moving clouds, causing highly fluctuating solar irradiance profile, which is difficult to predict. Moreover, the requirement to pre-train Artificial Intelligence-based forecasting system has made solar irradiance forecasting impractical as long-hour weather profiles need to be collected prior to deployment. This paper proposes a new artificial intelligent algorithm namely the Regression Enhanced Incremental Self-organising Neural Network (RE-SOINN) for accurate (even for highly fluctuating profile) and adaptive solar irradiance forecasting. This algorithm works by learning the time-series solar irradiance data incrementally and predicting it in real-time. It is novel in terms of enabling the learning of data from discrete (as in the conventional) to continuous using the regression method. The proposed algorithm further improves the prediction accuracy by decomposing the input data into two components (low and high frequency components) before feeding into the RE-SOINNs. Results showed that the proposed algorithm achieves higher accuracy compared to the Persistence model, Exponential Smoothing Model and Artificial Neural Networks. • Intermittent solar irradiance nature complicates forecasting process. • Our AI model predicts hourly trends using timestamp and historical data only. • Data are decomposed based on trending significance for more accurate prediction. • Incremental Learning allows our model to adapt to changes in climate. • Proposed model outperforms ANN, Exponential Smoothing and Persistence Model. [ABSTRACT FROM AUTHOR]

Energy storage is widely used in energy flexible buildings, which have great potential for relieving the power imbalance of electrical grids. However, most of the existing energy storage systems are designed for short-term storage, and only a few systems reported for long-term or multi-time scale storage. This paper introduces a new type of multi-timescale cold storage system consisted of a heat pipe-based natural ice storage subsystem and a dual-operation chiller for buildings to enhance their energy flexibility. The proposed system operated in different modes to provide the seasonal cold storage, nighttime chilled water storage, and urgent demand response services, which can be used for relieving the power imbalance in the timescale of the long-term, short-term and real-time respectively. The working principle, system configuration, operation modes, and the implementation of the proposed system for multi-timescale power management, are presented. A case study has been conducted in a building in Beijing to demonstrate the application and effectiveness. Results show that building load factors are greatly improved seasonally (from 19.5% to 49.5%) and daily (from 55.7% to 72.2%), and the power consumption is also considerably decreased (41.2%) during the demand response (DR) event. • A cross-timescale cold storage system is proposed for energy flexible buildings. • Three energy management strategies applied for energy flexibility enhancement. • The system can be used to relieve the power imbalance in different timescales. • Load factor improved from 19.5% to 49.5% seasonally and from 55.7% to 72.2% daily. • The power consumption is considerably decreased (41.2%) during the DR event. [ABSTRACT FROM AUTHOR]

The present paper concerns the assessment of biomass by-products from the urban greenery maintenance with an energy perspective. Usually pruning is not organized by a consolidated planning practice, mainly because of lack of information. After estimating the quantity of wood pruning by-products by means of dendrometric equations and the availability of information on current greenery management, an innovative GIS-based method was implemented and tested on Milan, in northern Italy. The main outcome is the quantification of the energy potential in terms of available wood biomass, displayed on a geo-referred urban map. Moreover, the outcomes of the research could support the administrations in improving the management of these materials otherwise wasted and therefore in converting public expenses for maintenance into public profits available for further investments. On the energy supply side, the GIS database could support the local matching between available renewable sources and energy supply systems in operation (i.e. local district heating and cogenerative systems), providing a low-range and cost-effective renewable resource, also enhancing circular economy at the local scale. • A novel method for an effective urban greenery management is proposed and analysed. • Dendrometric equations and a GIS were combined for pruning by-products estimation. • The urban greenery of Milan was considered as case study. • A biomass PE potential between 26 and 76 GWh was assessed for the case study. • The use of pruning by-products in a biomass district heating plant is evaluated. [ABSTRACT FROM AUTHOR]

This paper focuses on a novel energy management approach, namely Building to Vehicle to Building, in which electric vehicles act as vector devices for renewable energy exchanges among buildings. The main goal behind this concept is to benefit from the potentiality of electric vehicles toward the achievement of the zero-energy target extended to a buildings cluster level, by exploiting renewable generation on- and off-site. To this aim, a dynamic simulation tool is developed to assess the energy and economic performance of the proposed V2B2 scheme applied to a cluster of multiple users made of non-residential buildings and electric vehicles with bidirectional charging, used as a back-up power supply for increasing self-consumption of energy produced on-site by PV panels integrated in one of the buildings, to be also exploited off-site by other users. A case study analysis is conducted for a sample cluster of 3 building sand 3 electric vehicles, located in a Mediterranean city (Naples, Italy) with the aim at conducting the proof of concept. Simulation results show that the proposed V2B2 scheme enhances the match between the on-site renewable generation and the whole system demand, i.e. buildings and electric vehicles' needs, by reducing the grid operation and boosting the system economic convenience. The proposed energy management scheme represents an example of novel aggregator energy and business model which will play a crucial role in the next generation of smart cities and communities. • Building to Vehicle to Building concept for multiple users and electric vehicles. • Development of a dynamic simulation tool for the energy and economic analysis. • Increasing the share of self-consumed PV production at a virtual micro-grid level. • Electricity delivered from the grid reduced up to 11.4% and economic saving of 8.1%. [ABSTRACT FROM AUTHOR]

This paper proposes a genetic algorithm-based method for sizing the energy storage system (ESS) in microgrids. The main goal of the proposed method is to find the energy and power capacities of the storage system that minimizes the operating cost of the microgrid. The energy management strategy (EMS) used in this paper is based on a fuzzy expert system which is responsible for setting the power output of the ESS. The design of the EMS is carried out by means of a genetic algorithm that is used to set the fuzzy rules and membership functions of the expert system. Given that the size of the storage system has a major influence on the energy management strategy, in this paper the EMS and ESS capacities are jointly optimized. In addition, the proposed method uses an aging model to predict the lifetime of the ESS. In this way it is possible to determine the cost associated with energy storage in a more precise manner. The unit commitment problem, which is crucial for the proper operation of the microgrid, has been also considered in the present work. The suggested sizing methodology has been validated in two case studies. [ABSTRACT FROM AUTHOR]

The peer-to-peer (P2P) energy management in the buildings is modeled and studied by this paper. In the proposed P2P home energy management, the buildings share their energy resources with each other to supply their demands. Three individual buildings are considered, two buildings are equipped with solar panels and the other building is equipped with hydrogen storage unit. The buildings are connected to each other by power lines for P2P operation. The uncertainty of loads and solar energy are incorporated and the model minimizes the cost of solar unit, power lines, fuel-cell, electrolyzer, and storage tank. The optimization problem finds optimal power of solar system, power lines, fuel-cell, electrolyzer; optimal capacity of hydrogen storage tank; and optimal operation pattern (charging-discharging regime) of hydrogen storage system. The results demonstrate that P2P operation helps the buildings to benefit from their energy resources. • Peer-to-peer home energy management is presented to minimize the costs. • Buildings are equipped with solar units and hydrogen storage system. • Buildings share their energy resources with each other as peer-to-peer operation. • Uncertainty of load and solar energy is handled by stochastic programming. • Optimal sizing and operation are found for hydrogen storage and solar systems. [ABSTRACT FROM AUTHOR]

The combination of solar array and rechargeable battery is the main trend of energy system for scientific balloon station-keeping, when the performances of station-keeping strategies are evaluated, energy problem should be considered. In this paper, the performances of air ballast system and double balloon system are evaluated with the consideration of energy management strategy. The theoretical model consisting of thermal model, solar array power model, energy consumption model and energy evaluation model is proposed. The energy management strategy containing solar array and lithium battery is designed. Based on the theoretical model and energy management strategy, a MATLAB program is developed. The feasibility is verified by comparison analysis. The energy harvesting characteristics, effects of the station-keeping duration and effects of the station-keeping region radius are analyzed carefully. The results show that the double balloon is superior than air ballast system from the perspective of energy. The air ballast system requires more energy than double balloon system and the energy difference increases over time. Air ballast system lithium battery capacity requirement is higher than double balloon system, the total energy consumption and the energy differences decrease with expected radius increasing. It would be helpful in selecting station-keeping strategies for scientific balloon mission. • Station-keeping strategies are evaluated. • Calculation model and program are proposed. • Energy speaking, double balloon system is superior. [ABSTRACT FROM AUTHOR]

In this paper, the efficient salp swarm algorithm (ESSA) is proposed to solve the energy management (EM) with emission problem of renewable micro-grid (MG) including storage devices. Because of the uncertainties in the renewable energy sources (RESs), load demand and market prices, the probabilistic approach based on (2m + 1) point estimate method and ESSA is employed to solve the probabilistic EM problem. The proposed ESSA can be derived by introducing two modifications on the conventional salp swarm algorithm (SSA) to improve the balance between exploration and exploitation, speed up the convergence and avoiding the stuck in local optima of the SSA. The ESSA is employed to solve the deterministic and probabilistic EM with emission problem. Where the multi-objective optimization problem of cost and emission functions is transferred into a single objective function to minimize the total operating cost of the MG. The proposed ESSA is evaluated using a typical grid-connected MG with energy storage devices and compared with other methods. The results verify the superiority of the ESSA to solve the EM problem of the MG over other methods. • The probabilistic approach based on ESSA and point estimate method is proposed. • The proposed method is used to solve the deterministic and probabilistic EM of MG. • The EM problem considers minimization of cost and emission as a single problem. • The proposed method has been evaluated using a typical MG with different scenarios. • Results of all cases show the superiority of the proposed method over other methods. [ABSTRACT FROM AUTHOR]

In this paper, the efficient salp swarm algorithm (ESSA) is proposed to solve the energy management (EM) with emission problem of renewable micro-grid (MG) including storage devices. Because of the uncertainties in the renewable energy sources (RESs), load demand and market prices, the probabilistic approach based on (2m + 1) point estimate method and ESSA is employed to solve the probabilistic EM problem. The proposed ESSA can be derived by introducing two modifications on the conventional salp swarm algorithm (SSA) to improve the balance between exploration and exploitation, speed up the convergence and avoiding the stuck in local optima of the SSA. The ESSA is employed to solve the deterministic and probabilistic EM with emission problem. Where the multi-objective optimization problem of cost and emission functions is transferred into a single objective function to minimize the total operating cost of the MG. The proposed ESSA is evaluated using a typical grid-connected MG with energy storage devices and compared with other methods. The results verify the superiority of the ESSA to solve the EM problem of the MG over other methods. • The probabilistic approach based on ESSA and point estimate method is proposed. • The proposed method is used to solve the deterministic and probabilistic EM of MG. • The EM problem considers minimization of cost and emission as a single problem. • The proposed method has been evaluated using a typical MG with different scenarios. • Results of all cases show the superiority of the proposed method over other methods. [ABSTRACT FROM AUTHOR]

Health centers and hospitals can be categorized as one of the major consumers of electrical energy in building sectors. Due to their competitive environment, they need to decrease their costs, including energy costs. On the other hand, environmental problems, lack of fossil fuels, and high energy consumption lead to using alternative energy generation methods like renewable energy sources (RESs). In this paper, we consider that the hospital can produce part of its energy from RESs for cost reduction and we implement demand dispatch energy program for using RESs efficiently. The challenge is that the main goal of hospital is providing health services not energy cost reduction. Therefore, we present a bi-objective formulation for using RESs in hospitals in a way to minimize costs and dissatisfaction by scheduling the activities of the hospital by considering hospital's specific constraints and limitations. With the help of the proposed model, hospitals will decrease energy costs while maintaining comfort of patients and surgeons at the same time. The model is solved using real data of a hospital in Iran, and sensitivity analysis on different parameters is done. The proposed model will cause reduction in energy cost of the hospital by implementing demand dispatch program for using RESs in the hospital. • Health centers are considered as a new type of energy consumers. • Wind turbines and solar panels are used for energy generation in hospitals. • Activities are scheduled in a way to minimize the cost and the dissatisfaction of patients and surgeons. • The limitations of hospitals are considered in the mathematical model. • The proposed model reduces energy use in peak hours and shifts the loads to time periods with renewable energy generation. [ABSTRACT FROM AUTHOR]

Energy management is required to enable autonomous photovoltaic-powered membrane (PV-membrane) desalination systems to make the optimal use of solar energy. In this paper, a novel charge controller based on pre-set voltage sensing thresholds was designed to optimise the energy from PV panels and supercapacitors (SCs). The control algorithms were established from the data derivations with high-temporal-resolution (1s) solar irradiance (SI) source, allowing for resilient system operation under variable conditions. The impacts of ramp rates, in both SI and PV output voltage (V PV) on the system, were systematically investigated. Under a worst-case scenario, with a rapid ramp rate of ΔV PV = 2 V/s, the charge controller enabled the SCs to bridge the power gap to 6 min 20 s, permitting an additional 10 L of permeate water produced. The state-of-charge of the SCs varied from 11 to 86%, regardless of the magnitude of the ramp rate. The combination of the voltage thresholds (V pump_on = 160 V and V pump_off = 90 V) was determined to result in optimum system performance, realising a high permeate production at low specific energy consumption. It is concluded that the proposed charge controller is an effective method to enhance system resilience under worst-case solar conditions. Image 1 • Autonomous photovoltaic-powered reverse osmosis membrane desalination system. • Charge controller designed for energy management of PV-membrane system. • Control algorithms test based on high-temporal-resolution (1s) solar data. • Supercapacitors buffering for six minutes during rapid PV voltage ramp rates. • Pre-set voltage thresholds for controller determined to optimise system performance. [ABSTRACT FROM AUTHOR]

This paper presents a magnetically coupled hybrid renewable energy system (RES) for residential applications. The proposed system integrates the energies of a set of PV panels, a fuel cell stack, and a battery using a multi-winding magnetic link to supply a residential load. It can operate in multiple grid-connected and off-grid operation modes. An energy management unit including an off-line dynamic programming-based optimization stage and a real-time rule-based controller is designed to optimally control the power flow in the system according to the provided energy plan. The system is designed according to the required standards of the grid-connected residential RES. Different sections of the proposed system including steady-state operation, control techniques, energy management method and hardware design are studied in brief. A prototype of the proposed system is developed and experimentally tested for an energy management scenario considering both sunny and cloudy profiles of the PV generation. The energy distribution and cost analysis approved the benefits of the proposed system for residential consumers. • Combining dynamic programming based optimization and real-time control techniques. • A multi-port multi-operation mode magnetically coupled renewable energy system. • An optimized energy management technique using real-time and forecasted data. • Design of residential renewable energy system according to the recent standards. • Providing energy distribution and energy cost analysis of the system. [ABSTRACT FROM AUTHOR]

In this paper, a new type of wind turbine that is called INVELOX has been used. INVELOX has many advantages such as six times more power generation than previous types, work at low speed, inconsiderable maintenance and investment costs, and reduce the environmental effects of previous wind turbines. Moreover, other renewable and nonrenewable generators are used in the energy management and scheduling of the microgrid. The test case is a microgrid with selling and buying energy capability in which the cost and pollution are considered as the objective functions. In the following, Uncertainties of wind speed, solar radiation and electrical-thermal loads are investigated and a multi-objective stochastic mixed integer linear programming is solved in the first scenario. Then, in the second scenario, the effects of fuel cost uncertainty on generation units and objective functions have been studied. The Epsilon constraints method and fuzzy satisfying are utilized to solve the problem and choose the best solution, respectively. By using of INVELOX turbines, total cost and pollution of the microgrid in both deterministic and stochastic planning are reduced from 192.68 $ to 97.23 $ and 249.28 $ to 126.38 $, as well 3334.76 Kg to 3302.7 and 3925.63 to 3910.2 Kg respectively. • Introducing INVELOX Turbine and considering different scenarios with/without it. • Multi-objective, deterministic and stochastic EM with uncertain parameters. • Using Epsilon constraints optimization method and fuzzy satisfying approach. • Considering Total cost and environmental pollution as objective functions. • Highly decreasing total cost and pollution with INVELOX Turbines. [ABSTRACT FROM AUTHOR]

Recent developments in heating systems have witnessed a significant increase of heat pumps with a highly temperature-dependent efficiency. Optimal real-time operation of these heating systems with predictive control requires a thorough understanding and modeling of the internal temperature distribution of the associated thermal energy storage. At the same time, the thermal energy storage models need to be sufficiently simple to ensure computational tractability in real-time predictive control. Therefore, this article presents a stratified thermal energy storage model with constant layer volume and variable temperature suitable for real-time predictive control. The model employs a novel formulation with quadratic or simpler constraints which enable high accuracy at low computation burden. The proposed model is validated experimentally and compared with other models available in literature. The results show that the proposed stratified thermal energy storage model represents the real-world behavior of a thermal energy storage with great accuracy, while reducing the required computational burden as compared to other models for real-time operation and control. A case study further demonstrates that the increased accuracy of the proposed new model leads to cost and energy savings for the operator. [Display omitted] • The paper proposes a predictive control model of a stratified thermal energy storage. • It is integrated into an optimization model for Energy Management Systems (EMS). • Experimental validation shows high accuracy and good computation time. • A case study demonstrates the applicability of the proposed model in EMS. [ABSTRACT FROM AUTHOR]

The integration of a large number of photovoltaic systems into the electricity grid presents significant technical challenges. To address the issue of excess power injection into the grid, various standards recommend curtailment of PV systems, which results in penalizing renewable energy. This paper proposes a solution to the problem without resorting to curtailment. The key innovation is to optimize the energy injected into the grid, achieving a delicate balance between benefits for prosumers and the grid operator, while also avoiding PV curtailment. The results obtained underscore substantial advantages for prosumers when contrasted with commercial solutions that resort to curtailing PV power, while ensuring the alignment of interests with network providers. The proposed energy management system is based on a multi-objective linear programming algorithm and aims to increase energy injection into the grid during peak regional demand while simultaneously considering the electricity price profile. The study utilizes digital twin technology to establish a dependable framework for validating proposed algorithms and comparing simulated results with experimental ones. By doing so, this process guarantees the accuracy of simulation results and allows for informed decision-making regarding the system under investigation. [ABSTRACT FROM AUTHOR]

Hybrid systems have emerged as a reliable solution to meet the increasing demand loads in various fields and address the electricity shortage in remote areas. Consequently, research efforts have been directed towards determining the optimal sizing of hybrid system components to cater to different areas' demand loads. Effective energy management of hybrid micro-grid components is essential to ensure the delivery of high-quality and cost-effective power supply globally. Various optimization techniques have been employed to achieve this objective and determine optimal solutions. This paper proposes an improved version of the Flow Direction Algorithm (FDA), named mFDA, to determine the optimal sizing of a standalone system comprising solar panels, a diesel generator (DG), and a battery bank to meet the demand load in Luxor, Egypt. The primary objective of this research is to reduce the cost of energy (COE) of the system, the net present cost (NPC) of the proposed hybrid power system, the probability of a power supply loss (LPSP), and the size of a microgrid's components. The proposed methodology aims to provide an effective solution to optimize the hybrid system's performance and energy management, which can be applied in other regions as well. To achieve economic energy with high quality and effective sizing with the lowest annual cost in the shortest amount of time, Results from the proposed mFDA are evaluated with those from the original (FDA), Grey Wolf Optimizer (GWO), Gorilla Troops Optimizer (GTO), a new Optimization technique based on COOT Bird Natural Life Model (COOT), and Smell Agent Optimization (SAO) algorithm. The MATLAB program's simulation results indicate that the proposed modified mFDA algorithm performs better than other algorithms when it comes to designing hybrid microgrid component sizing. After 44 iterations, the mFDA approach demonstrates stable convergence characteristics and achieves a minimum Cost of Energy (COE) of approximately 0.2658555/kWh, a Low Probability of Sustainable Power (LPSP) of 0.06555, and a Net Present Cost (NPC) of 7,767,179. The original FDA method closely follows, attaining a COE of about 0.2658555/kWh, LPSP of 0.06555, and NPC of 7,767,179. • Effective energy management of hybrid micro-grid components. • Improved approach using modified Flow Direction Algorithm (mFDA). • Primary objective: reduce cost of energy, net present cost, LPSP, and size. • mFDA achieves best design of hybrid microgrid components in shorter time. • Proposed approach helps achieve economic energy with low annual cost. [ABSTRACT FROM AUTHOR]