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Al-Marj is located about 100 km from Benghazi, and it has a rugged mountainous terrain. The grid extensionmakesit impractical in some places, and off-grid electricity the best option. In this study, the feasibility of a small hybrid electrical Micro-hydro/Photovoltaic/battery supply system for road lighting was analyzed. The reservoir's water potential is analyzed as well as meteorological data collected from NASA. The initial reservoir flow data was taken for accurate hydropower modeling located at the start of the slope by measuring the total height with the help of GPS (Geographical Positioning System) and flow data obtained from the Ministry of Water and Irrigation. The average flow rate is 0.46 m3/s. HOMER software was used to model and analyze data for a hybrid power generation system. The study included the electrical load for lighting the road with 100 poles powered by solar energy with a LED lamp. From the HOMER program result, 30 KW PV and 0.9 KW Hydro was selected. It has an initial energy cost of $0.032 per kilowatt-hour, much lower than the network system capital cost for such particular site. For a distance of more than three kilometers, the government network can use no lighting.

On-grid and off-grid renewable energy sources have emerged as a more efficient way to meet large-scale urban and rural needs. The integration of renewables into the utilities/island mode as well as into the techno-economic feasibility studies determines the financial viability, technical and operational viability of the project. This study was conducted to integrate renewables and techno-economic feasibility analysis using the utility grid-connected and islanded mode to meet the demand for community and commercial loads with these strategic objectives. Three main analytical tasks have been performed, including: optimization, simulation, and sensitivity analysis. We selected four sensitivity cases and further divided each sensitivity case into four sensitivity loops, including different combinations of renewable energy sources, e.g., photovoltaics, wind turbines, battery banks, power grids, converters, and diesel generators. The following parameters were used to conduct a techno-economic feasibility analysis: capital costs, replacement costs, operating and maintenance costs, salvage costs, fuel costs, real-time commercial/community load, and climate data. The best results of sensitivity case-2 were selected for the techno-economic feasibility analysis of the remaining four sensitivity cases. The islanded mode net present and annualized sensitivity loop-1 costs in sensitivity case-2 were noted at $1381080.42 and $106832.62, respectively. Net present and annualized sensitivity loop-1 costs in sensitivity case-2 of the grid-connected utility mode were noted at $512792.1 and $39,666.71, respectively. The proposed utility grid-connected mode cost is two times lower than that of the islanded mode, so we have chosen sensitive case-2 for hourly, seasonal, and annual electrical load flow and techno-economic feasibility analysis to meet the community and commercial load demand.

Climate change mitigation requires developing zero-carbon, highly renewable energy systems, which require technologies to capture excess renewable electricity such as energy storage. Endeavoring to capture all available excess renewable electricity, however, may require large energy storage capacities and costs. This study therefore investigates how much curtailment is cost-optimal to allow in developing fully decarbonized electricity systems, using compliance with California's Senate Bill 100 goal as a representative case study. We combine electric grid dispatch modeling with an optimization approach for selecting the composition of energy storage technologies to capture excess renewable electricity to minimize overall system costs. We found that overbuilding cheap wind and solar and allowing the curtailment of excess renewable electricity equivalent to 25–43% of the total annual electric load resulted in the lowest cumulative systemwide cost for a fully decarbonized electricity system of about $1.8 trillion, spent between 2020 and 2045. Allowing no renewable curtailment results in significant battery requirements and a cumulative systemwide cost of $5.2 trillion spent between 2020 and 2045. Therefore, allowing some curtailment reduced the cost of building a fully decarbonized electricity system by a factor of 3 when the portfolio of technologies to capture and manage excess renewable electricity is carefully chosen.

During the COVID-19 pandemic, the energy consumption profile has been changed in several facilities as a response to the lockdown and other precautionary measures. Lockdown has reduced the electrical load consumption in commercial buildings. Accordingly, the energy cost savings have been reflected in the energy bills. However, such savings were not due to improvement of energy performance and equipment efficiency, but because of changes that occurred in the operating conditions and parameters. As a result, it was complicated to identify the real energy savings in the energy retrofit projects during this period. For the energy conservation and refurbishment projects, a challenge was faced to calculate an accurate energy savings amount and to track the energy pattern and performance during these lockdown months. This study discusses the energy behavior of retrofitted buildings that follows the energy performance contracting. It shows the improvement of energy consumption for one year for 30 studied buildings. It discusses the strategy to follow in the calculation of the energy savings during a full lockdown period. This article addresses the approach to be taken into consideration and its consequences to monitor the energy profile as per the International Performance Measurement and Verification Protocol.

This paper seeks to address an application using a small PEM fuel cell system (Heliocentris FC50), this one uses a solar hydrogen fuel in order to power a known electric load, hence, a series of experiments have been conducted in LAGE Laboratory at Ouargla University, that the effect of operating temperature on (I-V) fuel cell characteristics have been investigated, another tests were conducted to determine the relation between the hydrogen input flow and the available output power from the fuel cell, hence the photovoltaic-hydrogen system components are properly sized in order to fulfil the daily cycle energy needs, a computer program also has been developed to size system components in order to match the load of the site in the most effective way. Consequently, one scenario configuration is proposed, which covered the right balance and no power supply interruption to the proposed load.

Domestic rooftop photovoltaic (PV) systems are typically installed without energy storage and power generated in excess of the building electric load must be exported to the grid or curtailed. In this paper the use of excess PV electricity for water heating is investigated, with the hot water storage tank acting as a low-cost thermal battery. A 3.6 kWp PV system installed on a domestic house with a 315 L storage hot water heater is analysed. It is shown that advanced control of the water heater is critical to make effective use of local intermittent PV power. Without appropriate controls the excess PV power can only reduce the grid energy consumption of an electric storage water heater by approximately 13%. However, with appropriate control, savings of purchased grid electricity above 80% can be achieved for electric storage water heaters. Similarly, using a heat pump, the consumption of purchased grid electricity for water heating can be reduced by close to 90% by making use of excess PV power. The use of excess PV electricity for water heating purposes can also significantly reduce export PV electricity, thereby also assisting the voltage and frequency stabilisation of the electrical distribution network.

A model of integration between thermal CSP and PV technologies is discussed, showing the complementarity of the two technologies for electricity production. In the model, a CSP system with thermal storage is coupled with a PV system without storage: the PV field is expected to supply most of the electricity needed during the day and the CSP is expected to supply energy especially in the night, exploiting the storage. Excess of electric production due to PV is degraded to thermal energy and recovered in the heat storage system to integrate the thermal cycle, in order to keep the system as stand-alone as possible and to stabilize the connection with the power grid. Such a model is a non-compact hybrid PV-CSP system, i.e. the two solar fields operate independently (sharing the storage). Detailed annual simulations with realistic irradiance sequences and load profiles are presented and discussed. Results show that the solar energy can cover the 67% of the electricity needed by a Mediterranean community – despite the strong seasonal variation of the solar irradiation – and more than 90% of a constant electric load (24 hours / 7 days, e.g. an industrial district) in locations with low seasonal change of overall solar irradiation, such as a semi-arid sub-Saharan site.

The electrical load forecasting is a fundamental technique for consumer load prediction for utilities. The accurate load forecasting is crucial to design Demand Response (DR) programs in the paradigm of smart grids. Artificial Neural Network (ANN) based techniques have been widely used in recent years and applied to predict the electric load with high accuracy to participate in DR programs for commercial, industrial and residential consumers. This research work is focused on the use and comparison of two ANN-based load forecasting techniques, i.e. Feed-Forward Neural Network (FFNN) and Echo State Network (ESN), on a dataset related to commercial buildings, in view of a possible DR program application. The results of both models are compared based on the load forecasting accuracy through experimental measurements and suitably defined metrics.

Owing to the lack of direct measurement on the slagging extent of the waterwall, random or empirical soot-blowing strategies practiced in many power plants can result in untimely or excessive soot-blowing operations. In this research, a dynamic slagging monitoring model was established based on the heat balance principle and GA-BP (genetic algorithm and backpropagation) neural networks. A soot-blowing optimization strategy was formulated by adopting the model of the maximum net heat profit and setting the accumulated system heat loss as the assessment variable. The applicability of the proposed monitoring model and optimization strategy was evaluated for the waterwall in a 650MWe coal-fired utility boiler. The monitoring results have verified that the change of system heat loss is in line with the actual slagging trend and the influence of the electric load change on the monitoring results is weakened greatly. The optimization results have shown that activating all soot blowers of the waterwall in every soot-blowing operation can achieve the higher net heat profit per unit time and the shorter duration for each pair of soot blowers. Using the optimized soot-blowing strategy can also realize the dynamic adjustment of the moment and the duration of soot-blowing, and improve the heat transfer performance of the waterwall remarkably.

Intermittency from photovoltaic systems can negatively impact electricity grid stability due to increased power ramp rates and temporal misalignment between generation and load. Distributed systems, especially rooftop residential systems, present a range of orientations and spatial separation that when aggregated introduce temporal diversity. If these features reduce negative impacts, they should be weighed against increased costs per delivered energy compared to larger commercial/industrial systems. We compare power production data from 60 distributed residential PV systems spread across a large municipality with medium and large commercial rooftop systems located centrally in the municipality. Power ramp rates and power output variability were calculated. The aggregated distributed residential system ramp rates never exceed 13% per 5 min, while the medium/large commercial systems experienced ramp rates up to 61% and 68% per 5 min. In aggregate the residential output variability is 3 times lower than the commercial sites. Consistent with previous literature the output variability decreases as the number of systems grows. There are however diminishing returns due to the limited geographic area, with a relative output variability for the municipality approaching 20% of the variability of a single system. Residential data was scaled up to represent 10% of electrical load and resulted in 10% more electricity grid ramp rates exceeding ±5 MW per 5 min compared to baseline. Finally, a comparison between pyranometer and photovoltaic system ramps rates shows the latter are less severe, likely because pyranometers are single points sources.

The electrical load temporal resolution on the simulated photovoltaic system electrical energy flows and self-consumption are investigated in order to determine the analysis uncertainty. The renewable energy system under investigation includes a grid-connected photovoltaic system with a capacity of 0.55, 1.1, 1.65, 2.2 and 2.75 kWp. Results show that the load temporal resolution influences the energy flow in the modelled system even for a single household load and a simple photovoltaic system without batteries. It has been found that the household load profile characteristic impact the required data temporal resolution. For high oscillating power, the temporal resolution for the energy consumption of 1 min should be considered, however, if power fluctuations are not high and rapid, a sampling rate of 15 min or even 60 min in most cases is appropriate. The most sensitive parameter for analysis is self-consumption which decreases at high load resolution and increases at low load resolution and may affect instantaneous analysis uncertainty substantially. On average the yearly estimated absolute and relative errors for energy self-consumption in the analysed configuration is 67.33 kWh and 13% at 1.65 kWp photovoltaic system and they decrease with system capacity increase. Finally, two mathematical models are developed and validated for estimation of the relative and absolute errors generated due to the load resolution.

Due to increasing energy demand, limited fossil fuels and increasing greenhouse gasses people is in need for alternative energy sources to have a sustainable world. The objective of this study is to look for alternative solutions and design a hybrid energy system to meet any energy needs of a single family house including both utility and transportation. The system is designed and optimized using HOMER software. According to the optimization studies, levelized cost of electricity and hydrogen production was found to be 0.685$/kWh and 6.85$/kg, respectively and the cost of hydrogen which is half of its market price is very attractive. To project possible future costs in advance, sensitivity analysis was carried out and the results show that when the main components’ price decays to the half, both costs of energy will be reduced by 26.4%. This implies that further decrease on the components’ cost would bring the cost of energy to the level of energy produced by fossil fuels or even lower. Hydrogen would also be produced with much lower and tempting price. It is important to note that energy used by residential electrical load and fuel cell electric car in this study was generated by sole renewable energies and the system consumes zero fossil fuels, thus emitting no greenhouse gasses. The study considering both utility and transportation simultaneously is believed to be the first on a small scale and to attract the interest of everyone.

Focusing on the issue of significant randomness and low latitude of short-term household electrical load data, this paper proposes a novel short-term load multi-step forecasting method based on long-and short-term time series network (LSTNet). Firstly, the time sliding window method is used to sample massive historical load data to construct feature maps as input. Secondly, convolutional neural network (CNN) and long short-term memory (LSTM) are used to capture temporal short-term local information and long-term related information respectively, and autoregressive (AR) models are used as linear components. Then, the models will be evaluated using a scheme called walk-forward validation, and the average absolute percentage error (MAPE) and root mean square error (RMSE) are used as accuracy evaluation indicators. Finally, the four-year electric load data of a family in Paris, France is used to verify the proposed method and comprehensively compare the proposed method with the three most popular load forecasting algorithms. The experimental results show that in the prediction results for the next week, the MAPE and RMSE of the prediction method proposed in the paper are smaller than those of other algorithms, which can more effectively express the time series relationship of household short-term load and have higher prediction accuracy.

This study aims to design a renewable energy system that can meet the desired electrical load of households with low energy cost, high renewable energy fraction and low CO2 emissions. Photovoltaic solar power systems used to electrify typical households in Iraq were investigated through simulation and optimisation. One-minute resolution simulations and optimisations were performed to determine the performance and net present cost of two photovoltaic power system configurations, namely (i) off-grid and (ii) on-grid solar photovoltaic power systems. Results show that the two systems exhibit excellent performance, but the on-grid photovoltaic power system requires cheaper cost compared with the off-grid photovoltaic power system. The total energy generated from the off-grid photovoltaic power system meets the desired electrical load of households and recharges the batteries, whereas the excess electricity from the on-grid photovoltaic power system feeds the grid. The two designed systems are environmentally friendly and economically viable. The total net present cost of the off-grid solution is $6,244, and its energy cost is $0.196/kWh. By contrast, the total net present cost of the on-grid system is $6,115, with energy cost equal to $0.183/kWh. The obtained results confirm the suitability of photovoltaic power systems for electrifying single households in addition to feeding the national grid.

A nonlinear developed model for various PZT wind energy harvesters with arbitrary dimensions and location of electroceramics has been prepared. This model can describe unimorph and bimorph harvesters with different cantilever cross-sections attached to a tip body with different shapes. The system model has been validated with results reported in the literature. A set of parametric studies has been conducted to examine the influences of the cantilever shape in addition to the shape of the tip body. Furthermore, the impacts of load resistance and wind speed on the output power have been analyzed. The effects of number, position, and dimensions of piezoceramics on the harvester performance have also been investigated. It is found that by increasing the total area of electroceramics (including the length, width, or the number of PZT patches), the peak of the wind critical speed and the middle resistance range where the instability speed is very sensitive to the electrical load shifts toward the lower resistances. Moreover, several instrumental points to design efficient and affordable vibrating wind harvesters at low and high flows have been obtained.

The significance of electrical load forecasting on the electrical distribution network has become eminent with the entry of energy sector and electrical market. This paper of research comprehends a model for forecasting the electrical load distribution. This paper highlights the forecasting on the electrical distribution system by taking into account the load ambiguities. Support Vector Machine (SVM), which is a machine learning algorithm is the prediction or forecasting model utilized in the proposed paper for forecasting the electrical load distribution. The ambiguities that impact the electrical load distribution are; cooling loads, indoor heat gains and cooling parameters are taken into account during the prediction. The demand for electrical load is impacted by many factors starting from the climatic conditions to the economic-socio influences. The proposed load prediction model is reliable, beneficial and an enhancing model for the conservation of electrical energy in the distribution network.

The renewable energy sector in a developing country can play a vital role in socio-economic development. With proper utilization of renewable energy resources (RESs) such as photovoltaic (PV) energy system, wind energy system, hydrogen fuel cell (HFC) energy system, etc. may be accepted for renewable electricity generation for urban or rural populations. In this present work, the techno-economic analysis was carried out for the viability of a direct current (DC) based standalone hybrid PV/HFC energy system with hydrogen storage. The PV panel used for the study is monocrystalline silicon (mono-Si) with 13.5% efficiency and the study site considered for analysis the hybrid energy system is Madanapalle (13° 33′ N and 78° 30′ E), Andhra Pradesh, India. The connected electrical load to the hybrid PV/HFC system assumed to be 14 kW. The optimal size obtained for the energy system is 80 kW PV system, 20 kW fuel cell, 50 kW electrolyzer and hydrogen tank of 20 Kg. It is found that the cost of energy (COE) of considered hybrid energy system obtained is 0.161$/kWh.

This research work proposes to refine the hybrid electrical power network to supply a single residence's electrical load located in Coimbatore, Tamilnadu. The load shape of the household is considered by the pattern of energy usage used and supplied for different electrical uses by the PV/Wind/Diesel Generator (DG) set. The simulation and optimization of the scheme is carried out by the Hybrid Optimization System for Electric Renewable (HOMER) model from the National Renewable Energy Laboratory (NREL). The various analyses were discussed like Net Present Value (NPV), Energy Expense, Energy Output, Usage, Excess Energy generated by individual components of the system and the pollution produced.

With the development of smart power distribution networks, the demand of smart grid user-side management is increasingly urgent. To improve the accuracy of short-term electric load forecasting for individual users, this study proposes a short-term power load forecasting model based on K-means and FCM–BP. Firstly, by analyzing the users’ electricity consumption features, K-means is applied to group users into two clusters. Secondly, for users with strong correlation at adjacent moments, local similar data is filtered out with the help of improved Fuzzy C-Mean clustering (FCM), integrating the load value of the adjacent moments into new input features. For users with weak correlation at adjacent moments, the local similar daily data are utilized as features. Finally, the feather vectors are used as input data for BP Neural Network, which is utilized to forecast the short-term load. Experimental results show that the clustering method proposed concurs with the characteristics of users’ electricity consumption behavior. With the same forecasting method, the accuracy of clustering-based load forecasting is higher than that of non-clustering load forecasting. Compared with traditional BP, RBF and GRNN Neural Network, this model has higher forecasting accuracy.

Due to the increasing realization of environmental protection and energy structure optimization, the scale of electric vehicles (EV) is gradually expanding. However, the scale-up of EV brings new challenges about the security and economics of power system. The use of vehicle-to-grid (V2G) can not only reduce the electrical load, but also provide additional benefits for EV owners. This paper presents a value analysis model for EV aggregators to participate in ancillary services by using V2G in Shanghai and Chengdu. The potential regulatory capacity and economic benefits of V2G are analyzed based on the various demands and charge prices in different area. The results show that V2G can obviously mitigate the load pressure and save charge cost for EV owners both in Shanghai and Chengdu.

In this article the research results of application of back-to-back HVDC system in the Tomsk electric power system are presented. Historically, power transmission between the South to the North parts of the Tomsk electric power system is realized via the long double-circuit 220 kV overhead transmission line. However, due to electrical load increasing and growing complexity of the grid, as well as the length (about 800 km) and low transfer capacity of this transit, it cannot be used as a reliable link for parallel operation of these parts, as a consequence, there is an operational section at this transit. To solve this task, the back-to-back HVDC system is proposed to provide an asynchronous link for parallel operation of these parts. Therefore, appropriate studies and calculations were performed, in particular the optimal location and sizing of back-to-back HVDC system were defined. Extra researches of back-to-back HVDC’s impact on emergency scenarios of Tomsk electric power system power are conducted. It is shown, that its installation decreases the level of short-circuit current and oscillations in electric power system, and as a result increases the stability of load operation, especially motor load.

A numerical method was developed for optimising solar–hydrogen energy system to supply renewable energy for typical household connected with the grid. The considered case study involved household located in Diyala Governorate, Iraq. The solar–hydrogen energy system was designed to meet the desired electrical load and increase the renewable energy fraction using optimum fuel cell capacity. The simulation process was conducted by MATLAB based on the experimental data for electrical load, solar radiation and ambient temperature at a 1-min time-step resolution. Results demonstrated that the optimum fuel cell capacity was approximately 2.25 kW at 1.8 kW photovoltaic power system based on the average of the daily energy consumption of 6.8 kWh. The yearly renewable energy fraction increased from 31.82% to 95.82% due to the integration of the photovoltaic system with a 2.25 kW fuel cell used as a robust energy storage unit. In addition, the energy supply, which is the economic aspect for the optimum system, levelised electricity cost by approximately $0.195/kWh. The obtained results showed that the proposed numerical analysis methodology offers a distinctive property that can be used effectively to optimise hybrid renewable energy systems.

The frequency of power outages being experienced in Sub-Saharan Africa mean that traditional methods of electricity demand forecasting which rely on directly observed demand data are inadequate for use in projections. Nevertheless, accurate forecasting methods are urgently required to ensure efficient power system operations and expansion planning. To address this gap, we develop a novel method to estimate unsuppressed electricity demand for developing countries. This follows a bottom-up approach based on socioeconomic data and a time-use database developed from a householder survey, which are used to generate household profiles using a Markov Chain approach. These profiles are then converted into electrical load time series by a series of appliance models, using reanalysis weather data to accurately represent ambient conditions for the generation of cooling demand profiles. We apply our method to a Nigerian case study, obtaining the first time series of unsuppressed residential electricity demand for the country using the first Time-of-Use Survey (TUS) for Nigerian households. We validate our model outputs using the results of a small-scale residential metering trial, which yielded a correlation coefficient of 0.97, RMSE of 0.04, and percentage error of 6% between measured and model data. This evidences that our method is a credible and practical tool for electrical demand studies in developing countries. Using the model, the forecasted domestic demand for Abuja Electricity Distribution Company ranges between 345 and 575 MW, while that of Nigeria ranges between 3829 and 6605 MW.

This study provides a framework to assess the economic viability of introducing tidal energy in a remote community to be used as a rapid screening tool by communities or project developers. Tidal projects life-cycle costs are modelled using the capabilities of HOMER Pro. Communities are described by the local price of diesel fuel (as a metric of the cost of electricity), their electrical load scale, and the local tidal current resource. A sequence of HOMER Pro models, exploring a range of these parameters and tidal turbine rated power combinations, were performed in pursuit of a simple relationship with economic viability. A general structure emerged which suggested three conditions informing the economic viability of introducing tidal energy in a remote community. These conditions were found to also be useful for determining the economic “break-in” scale for a tidal stream turbine in a given remote community. As validation, a case study involving ten remote communities along the British Columbia coast was carried out using HOMER Pro. For all ten communities, the conditions either (a) predicted correctly that the community is not economically viable, or (b) predicted correctly that the community is economically viable and the minimum turbine scale.

A significant portion of all energy generated is used to heat and cool buildings. Some of that energy can be saved by using a temperature controller with access to an accurate forecast of a building’s internal temperature. These forecasts depend on information gathered from sensors, including temperature, humidity, sunlight, and the electrical load of cooking and laundry appliances. Using publicly available data from two homes with a wide variety of sensors, we forecast internal temperature by modelling it as a linear function of recent sensor values. These models are built using techniques that improve upon standard least squares regression: forward stepwise, ridge and lasso regressions, using cross-validation. With lasso regression, we accurately forecast internal temperature every quarter hour over the next 48 h within 1 . 8 ° C in both houses. We also forecast temperature changes over each quarter-hour for the next two days, within 0 . 05 ° C , which significantly improves on previous forecasts of temperature changes using the same data. We propose a business model for forecasting as a service, where guarantees of consistent accuracy are important for attracting clients and saving energy.

In the present research, the performance of a prototype tidal turbine is evaluated via towing tank tests. The examined model is a three-bladed horizontal axis with a 1.15 m diameter. Previously, we studied the hydrodynamic behavior of this turbine and calculated the hydrodynamic coefficients using a numerical approach. Thus, this work concentrates on overwhelming the challenges, from manufacturing and assembling to data acquisition and power storage. The turbine is tested under various conditions namely: pitch angle, electrical load, carriage speed, and immersion depth to improve efficiency. Our experiment indicated the turbine performance to be the best being close to the surface of the water at different flow speeds. The optimum electrical load was found to be 20 Ω adding 16–40% to efficiency. By fixing the optimum load and tip immersion depth, the maximum efficiency of the whole rotor achieved around 0.265 at less than 0.56 m/s as towing speed while at the speed of 1.1 m/s, the maximum output power was more than 120 W having the pitch of the blades fixed at 20.4°.

This paper assesses energetically and economically the power-to-hydrogen concept by exploring the excess power resulting from the mismatch between the photovoltaic (PV) generation and the electric demand of a medium-size commercial structure located in Morocco. The variability in the building electric load is considered and the power flows from the PV field to the building are predicted using Artificial Neural Networks for a time-resolution of 15 min. A MATLAB code was implemented to estimate the instantaneous hydrogen production based on a semi-empirical mathematical formulation of an Alkaline type electrolyzer with a nominal capacity of 15 kW. These combined approaches are for the first time adopted to evaluate the feasibility of integrated PV hydrogen systems under the Moroccan context. Using a set of 5 electrolyzers coupled to the 104 kWp currently installed solar PV field, it was possible to generate about 18,622 Nm3/year of hydrogen by exploring the PV excess power. The overall efficiency of the integrated system ranged from 9.5% (in March) to 10.1% (in May). Such an approach allowed enhancing the effective efficiency and capacity factor to values of 9.873% and 26.87%, respectively compared to 6.325% and 10.163% for the base case scenario without hydrogen systems. From an economic perspective, it was found that the integrated PV-hydrogen plant engendered levelized cost of electricity and hydrogen of 12.56 c$/kWh and 21.55 $/kg, respectively.

The paper deals with the primary current frequency regulation in the energy system (ES) as well as the basic requirements for NPP power units (PUs) under the conditions of involvement into the primary regulation. According to these requirements, NPP operation is related to the unloading and corresponding decrease in efficiency. It has been shown in this connection that combining NPP with a hydrogen complex (HEC) allows excluding the inefficient unloading mode. This enables the steam turbine and reactor equipment to operate in the basic mode at nominal power level. Besides, the conditions for producing and storing hydrogen and oxygen during the day as well as additionally during the night-time of off-peak electrical load may be created, which allows using them for generating peak power. In the paper, the systemic economic benefit as a result of involving NPP combined with HEC into primary regulation of the current frequency in ES with allowance for expending resources for the main equipment has been estimated. In this regard, the paper gives grounds for cyclic loadings of HEC main equipment including metal tanks for storing hydrogen and oxygen, compressors, hydrogen-oxygen combustion chamber (HOCC) for hydrogen-steam superheating of the actuating medium in the NPP steam turbine cycle. Methodological grounds for evaluating the equipment endurance under cyclic loading with involving into the primary frequency regulation by the criterion of the fatigue crack growth rate are described. It has been shown that in terms of HEC equipment, the highest load intensity occurs in HOCC due to high thermal stresses. Also, the systemic economic benefit has been estimated, and the impact of the equipment deterioration under conditions of cyclic loading has been demonstrated. It is shown that when combining NPP PUs with HEC, the efficiency of primary regulation may significantly depend on such factors as the cost of equipment exposed to cyclic loads, the frequency and intensity of cyclic loads, and the relation between the peak electricity tariff and the cost price of electric energy produced by NPP. It has been shown on the basis of novel methods for estimating the efficiency of involving NPP with HEC into the primary frequency regulation with allowance for damage to the equipment, that the use of HEC may provide a considerable economic benefit, as compared to the version of NPP unloading when involving directly into frequency regulation.

Since temperature variables are used in many load forecasting models, the quality of historical temperature data is crucial to the forecast accuracy. The raw data collected by local weather stations and archived by government agencies often include many missing values and incorrect readings, and thus cannot be used directly by load forecasters. As a result, many power companies today purchase data from commercial weather service vendors. Such quality-controlled data may still have many defects, but many load forecasters have been using them in full faith. This paper proposes a novel temperature anomaly detection methodology that makes use of the local load information collected by power companies. The effectiveness of the proposed method is demonstrated through two public datasets: one from the Global Energy Forecasting Competition 2014 and the other from ISO New England. The results show that the accuracy of the final load forecasts can be enhanced by removing the detected observations from the original input data.

Demand forecasting is and has been for years a topic of great interest in the electricity sector, being the temperature one of its major drivers. Indeed, one of the challenges when modelling the load is to choose the right weather station, or set of stations, for a given load time series. However, only a few research papers have been devoted to this topic. This paper reviews the most relevant methods that were applied during the Global Energy Forecasting Competition of 2014 (GEFCom2014) and presents a new approach to weather station selection, based on Genetic Algorithms (GA), which allows finding the best set of stations for any demand forecasting model, and outperforms the results of existing methods. Furthermore its performance has also been tested using GEFCom2012 data, providing significant error improvements. Finally, the possibility of combining the weather stations selected by the proposed GA using the BFGS algorithm is briefly tested, providing promising results.

A technico-economic analysis based on integrated modeling, simulation, and optimization approach is used in this study to design an off grid hybrid solar PV/Fuel Cell power system. The main objective is to optimize the design and develop dispatch control strategies of the standalone hybrid renewable power system to meet the desired electric load of a residential community located in a desert region. The effects of temperature and dust accumulation on the solar PV panels on the design and performance of the hybrid power system in a desert region is investigated. The goal of the proposed off-grid hybrid renewable energy system is to increase the penetration of renewable energy in the energy mix, reduce the greenhouse gas emissions from fossil fuel combustion, and lower the cost of energy from the power systems. Simulation, modeling, optimization and dispatch control strategies were used in this study to determine the performance and the cost of the proposed hybrid renewable power system. The simulation results show that the distributed power generation using solar PV and Fuel Cell energy systems integrated with an electrolyzer for hydrogen production and using cycle charging dispatch control strategy (the fuel cell will operate to meet the AC primary load and the surplus of electrical power is used to run the electrolyzer) offers the best performance. The hybrid power system was designed to meet the energy demand of 4500 kWh/day of the residential community (150 houses). The total power production from the distributed hybrid energy system was 52% from the solar PV, and 48% from the fuel cell. From the total electricity generated from the photovoltaic hydrogen fuel cell hybrid system, 80.70% is used to meet all the AC load of the residential community with negligible unmet AC primary load (0.08%), 14.08% is the input DC power for the electrolyzer for hydrogen production, 3.30% are the losses in the DC/AC inverter, and 1.84% is the excess power (dumped energy). The proposed off-grid hybrid renewable power system has 40.2% renewable fraction, is economically viable with a levelized cost of energy of 145 $/MWh and is environmentally friendly (zero carbon dioxide emissions during the electricity generation from the solar PV and Fuel Cell hybrid power system).

The processes and logistics of the mining industry have continually undergone transformations in order to deal with rigorous regulations, economic considerations, safety and the social burden regarding environmental conditions (i.e. every sustainability pillar). In underground mines, the use of diesel-powered machinery has been increased in the past decades. The combustion of diesel fuel produces a great quantity of pollutants (i.e. gas emissions, particulate matter, etc.). Thus, a constant ventilation is required to satisfy the regulations on health and safety of miners. One of the innovative transformations that underground mining processes have adopted is the use of electrical Load Haul Dumps (LHDs) to reduce the in-mine emissions and the ventilation burden and to improve in-mine working conditions, among other benefits. Electric mobile LHDs could be further improved by replacing battery-based energy supplies with hydrogen fuel cells. The present work focuses on the techno-economic assessment of modifying electrical LHDs by incorporating different processes and equipment (i.e. fuel cell stacks, storage tanks, DC/DC converters). The base case considers the modification of the whole mining fleet of diesel-based LHDs. As a result, a positive Net Present Value (for the project time span under consideration) and a payback period of 7.78 years were observed. The sensitivity analysis showed that considerable modifications of the current states of diesel price are required so that modifications are not feasible (diesel prices of 0.53 €/l). A tight electricity cost of 70 €/MWh was obtained as a breaking point (considerably safe for several industrial conditions).

Most stand-alone integrated energy systems (IES) with renewable energy can only meet the demand of electrical load, not both electrical load and thermal load. Those studies on combined heat and power cogeneration systems mainly focus on the optimal scheduling of each source, ignoring the difference in dynamic response between electrical and thermal processes. In fact, the different response speeds of electrical and thermal objects will bring in challenges to control. In order to specify and solve the issue, this paper proposes a detailed mechanism model of a standard IES. A model predictive control (MPC) controller is designed and tuned based on the state-space form of the system. The control simulation results imply the feasibility of the MPC controller in coordinating both electricity and heat.

The current study deals with a potential solution for the replacement of fossil fuel based energy resources with a sustainable solar energy resource. Electrical energy demand of a small community is investigated where a floating photovoltaic system and integrated hydrogen production unit are employed. Data are taken from Mumcular Dam located in Aegean Region of Turkey. PvSyst software is used for the simulation purposes. Furthermore, the obtained results are analyzed in the HOMER Pro Software. Photovoltaic (PV) electricity provides the required load and excess electricity to be used in the electrolyzer and to produce hydrogen. Saving lands by preventing their usage in conventional PV farms, saving the water due to reducing evaporation, and compensating the intermittent availability of solar energy are among the obtained results of the study for the considered scenario. Stored hydrogen is used to compensate the electric load through generating electricity by fuel cell. Floating PV (FPV) system decreases the water evaporation of water resources due to 3010 m2 shading area. FPV and Hydrogen Systems provides %99.43 of the electricity demand without any grid connection or fossil fuel usage, where 60.30 MWh/year of 211.94 MWh/year produced electricity is consumed by electric load at $0.6124/kWh levelized cost of electricity (LCOE).

This paper presents a novel online load forecasting using supervised Machine Learning (ML) algorithms in Internet of Things (IOT) environment. Short Term Load Forecasting (STLF) is an essential aspect for smart grid operations such as power dispatch and load management. IOT is an emerging Technology breaching into every segment of science and engineering. This work presents the possibility of STLF online with accurate prediction models by using ML algorithms. Electrical load consumption data and weather data at a research Lab, JNTUH, Hyderabad is used to train ML algorithms in order to implement STLF. ML algorithms based forecasting models are developed using MATLAB code through cloud computing. Online forecasting is more sophisticated and effective because of its ability to use recent data logs for training and forecasting online. Online forecasting is useful in Online Home Energy Management Systems (OHEMS) for effective energy management. ML algorithms such as Linear Regression (LR), Support Vector Machines (SVM) for regression, Ensemble Bagged (EB) regression, Ensemble Boosted (EBo) regression, Gaussian Process Regression (GPR) and Fine Tree (FT) regression are implemented on the cloud to forecast the power consumption. Performance parameters such as RMSE, MSE and MAE are derived to evaluate the effectiveness of the ML algorithms implemented. Cost effective Arduino Uno, Node MCU/ESP8266, PZEM 004T and DHT 11 sensors are used to fabricate the hardware model in order to acquire the load data for the proposed load forecasting approach. Best suited ML algorithm is suggested for the proposed online forecasting with supporting results.

In recent years, due to the digitization of the electrical devices electric load demand has been increased. Power security and stabilization are the other two challenges which necessitate the need of electrical energy storage. Along with the technological constraints, economical and environmental issues are the other challenges in the development of energy storage technologies. Fast response and high energy density features are the two key points due to which Superconducting Magnetic Energy Storage (SMES) Devices can work efficiently while stabilizing the power grid. Two types of geometrical combinations have been utilized in the expansion of SMES devices till today; solenoidal and toroidal. It has been found that the solenoidal arrangement is easier to fabricate and provides efficient approach to handle stresses produced by Lorentz forces. This work represents the development of 2.5MJ SMES solenoidal Magnet using 2G (SuperPower, YBCO having Tc=90K @ 0T) HTS tape. The effect of the operating current (passing through a single superconducting tape) has been evaluated. A reference magnetic flux of 3.5T has been considered in designing of HTS SMES. It has been found that the parallel fields for solenoidal magnet are large as compared to perpendicular fields which results into large magnitudes of Lorentz forces acting on HTS tape which may leading to structural instabilities and can cause failure. It has been concluded that higher currents can be used in order to lower down the total length of the superconductor as it can reduce the overall cost of the device

Short-term power load forecasting plays a significant role in power system security management. The prediction model in this paper is the grey wolf optimization algorithm to optimize kernel extreme learning machine (GWO-KELM). First, the Kalman filter is used to reduce the noise for the random noise interference existing in the power load data. Then determine the input and output of the prediction model. In this paper, the ELM model of three different kernel functions is used for comparative experiments, and the mean absolute percentage error is used as the evaluation model index. It is concluded from the experimental results that the GWO-KELM model used in this article has the advantages of high prediction accuracy and strong generalization ability, so it is practicable to apply the model to short-term electric load forecasting.

Typically, peak demand charges account for a considerable part of the operating costs of public electric vehicle charging stations. An intelligent control of the charging processes can help to reduce the peak load and the corresponding fees. This can be additionally supported by the use of a dynamic pricing scheme, which encourages customers to provide as much flexibility as possible. The present work proposes a framework for the setting of dynamic price offers for different charging deadlines and the scheduling of charging processes with the objectives of maximizing the charging station operator’s daily profit and reducing the peak of the electrical load. In a simulation study based on a use case typical for Germany, it is shown that the proposed approach can increase the charging station operator’s yearly profit by several thousand euros compared to a pricing and scheduling scheme without consideration of the peak load. Furthermore, an approach for increasing the scalability of the employed optimization of price offers is proposed and evaluated.

This paper aims to address two critical questions pertaining to sustainable manufacturing operations. First, how to model and design a multi-facility, production-logistics system under product demand and energy supply uncertainty? Second, is it economically viable to achieve net-zero energy operations through renewable microgrid integration? To answer both questions, we present a joint production scheduling and microgrid sizing model to decarbonize the manufacturing, transportation and warehousing operations by harnessing onsite wind and solar energy. The integrated planning model is solved as a two-stage optimization program: first, scheduling the production to meet the uncertain demand; second, sizing and siting the microgrid systems to meet the electric load of multiple facilities. The preliminary results show that net-zero energy operation is feasible and affordable in locations where the capacity factor of wind and solar generation exceeds 0.25 and 0.45, respectively. Sensitivity analysis shows that battery system combined with solar photovoltaics is cost-effective despite the high capital cost of storage devices. Two managerial insights are also derived. First, integrating onsite renewable power is the ultimate key to realizing net-zero energy industrial operations. Second, grid-connected microgrid generation with net metering results in lower capacity investment than island operation.

The study describes a stand-alone photovoltaic system in which the storage is realized using electrolytic hydrogen, converted into electricity in fuel cells. The aim of the research is the optimization of the sizing of the system elements chain (photovoltaic generator, electrolyzer, tank, fuel cell) with respect to the electric load to fulfil. A positive annual balance between hydrogen production and consumption must be guaranteed; furthermore, energy production surplus that cannot be stored or converted into hydrogen due to batteries or tanks capacity limits must be avoided. The energetic analysis and that of hydrogen production and consumption have been carried out on an hourly basis using the HOMER software. The study shows that, being the load active in the evening and the system disconnected from the grid, excess energy cannot be exploited unless large tanks are used, if high gas pressures are to be avoided. Consequently, the system use in public areas or residential buildings, where visual impact generated by tanks is hardly acceptable and safety rules do not allow high gas pressures, is advisable only in grid-connected configurations. Such problems are by far reduced when a marked self consumption is present.

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.

A high voltage DC power supply for the cathode of the 8.56 GHz CW klystron has been set up for electron cyclotron heating (ECH) in steady state tokamak operation on QUEST spherical tokamak. The power supply is equipped with an IGBT array and a reactor for fast shutoff of the voltage in 10 μs, where the influx of electric energy at the short circuit is limited to 5 J. AC switches also have been installed in the three-phase power lines. High voltage relays are useful to save electric energy consumption. Fast three-phase AC switching by IGBT-stack is applicable to reduce the electric load of the components of the klystron power supply.

The inclusion of thermal storage into a residential heat pump system provides an opportunity for electrical load shifting in order to reduce peak electricity demand. To assess this potential, a detailed numerical system model for a ground-source heat pump coupled with thermal storage was developed for residential heating applications. Water-based thermal storage and hybrid storage containing water and phase change materials (PCMs) were considered. The amount of thermal buffering needed to shift the heat pump operation to off peak electricity periods was numerically assessed for a house of 180 m2 floor area in Toronto, Ontario, Canada. A very cold day ambient profile was considered as the extreme case to reach the study conclusions. Results indicated that total electrical load shift to off-peak hours was achieved by a 2.5 m3 water tank or a 1 m3 hybrid tank containing 50% PCM by volume. This implies around 65% reduction in storage volume without compromising the space heating capability. This is attributed to the higher storage capacity offered by the hybrid system when the temperature operating range is limited by heat pump operation. The influence of operating temperature ranges and packing ratio was presented. Lower operating ranges and higher packing ratios lead to better thermal buffering of the hybrid system at smaller storage volumes. However, careful choice of the PCM encapsulation geometry is needed to guarantee complete melting/solidification during the charging/discharging period.

Micro-grids (MGs) are practical solutions for integrating distributed energy resources (DERs) in order to supply electrical and heat demands. In this study, a stochastic model for optimal management of combined heat and power (CHP)-based MGs considering economic, environmental and reliability aspects have been proposed. Two sources of uncertainty including forecast errors of electrical load demand and wind power are considered in scenario generation process using roulette wheel mechanism. Availabilities of units are taken into account to model reliability using capacity outage probability table (COPT). For solving such non-linear, non-convex and complicated stochastic problem, exchange market algorithm (EMA) and the weighted sum method are employed for combining three conflicting objectives and solving multi-objective problem as a single objective problem. Fuzzy satisfying method is applied to choose best compromise solution. The main goal of the stochastic dynamic reliable economic emission dispatch (SDREED) problem is to determine output of each generating unit so that fuel cost and amount of emission are minimized while the electrical demand is provided by more reliable units and operational constraints are met. The output of stochastic problem gives better sight for generation scheduling of MGs. The obtained results show the effectiveness and ability of the proposed method in solving SDREED.

Many existing energy system models rely on input data available at country-level, or at the level of administrative divisions. However, there is usually no correlation between the distribution of data such as solar radiation, wind speed, and electrical load on one hand, and the administrative divisions on the other hand. The goal of the research is to measure the impact of the shape of model regions on the results of system optimization models. A novel clustering methodology for high-resolution data is presented and applied to define new regions for an energy system model which optimizes expansion planning and unit commitment. The new model regions take into account the bottlenecks in the transmission system and their effect on the expansion of renewable energy sources. We compare the obtained energy mixes, new capacities, and curtailment levels against a model using administrative divisions. The results show discrepancies between the models in the case of a high share of variable renewable energy, and quantify the impact of the distribution of load, wind and solar resources on energy system models. Possible applications of the new model regions are discussed to emphasize their utility for modelers and policy-makers. Keywords: Spatial clustering, Energy system model, Expansion planning, Optimization, GIS

In order to promote the environmental conservation and energy efficiency, sufficient attention has been paid to biomass gasification-based combined cooling, heating and power (BGBCCHP) systems. Accurate evaluation of BGBCCHP systems is helpful to improving its performance. The maximum generalized entropy principle was introduced to reveal the essential characteristics of BGBCCHP systems. The BGBCCHP system was regarded as an open and evolutionary information network. Annual total cost, annual primary energy consumption, annual carbon dioxide emissions and exergy efficiency were used as evaluation criteria. Reasonable weights were conferred to the criteria so as to make the system stable. Moreover, an evaluation model based on the maximum generalized entropy principle was established to determine the eigenvalues of the BGBCCHP system. Self-organizing feature map network was used to simulate the evaluation model of the BGBCCHP system. A case study based on an office building in Beijing, China, was carried out. Three operating strategies (following the thermal load, following the electric load and following the hybrid electric-thermal load) were considered in the evaluation of the BGBCCHP system. Sensitivity analysis was carried out to investigate the impact of economic and technical parameters on the evaluation results. Results indicated that the evaluation model based on the maximum generalized entropy principle could be used to evaluate the BGBCCHP system operated under different operating strategies. The BGBCCHP system operated following the electric load achieved the maximum eigenvalue, which showed that its best integrated performance. On the contrary, the BGBCCHP system operated following the thermal load achieved the minimum eigenvalue, which showed that its worst integrated performance. The impact of sensitivity factors on the eigenvalues of the BGBCCHP system was different, which showed that the integrated performance of the BGBCCHP system was closely related to such factors.

A comprehensive thermoeconomic analysis is presented for a novel integrated solar hydrogen energy system for standalone operation. The proposed system includes a solar PVT module (photovoltaic thermal), a FC (Fuel cell) and a battery to meet the electrical load demand and domestic hot water over a year. The PVT component works as a primary energy source converting solar energy into electricity and heat. The excess electrical energy and hot water produced by PVT are consumed for producing hydrogen, which can be stored. The generated hydrogen is fed to the fuel cell to produce electricity and water to satisfy the demand. The proposed system is convenient for different seasons of the year because in all time, produced power satisfy the demand. The first and second laws of thermodynamics are used to evaluate the performance of each component and the overall system. Economic assessment of this system is also conducted considering the net present cost, and the system performance is optimized based on this parameter. The overall electrical efficiency of the system is obtained as 9% and the levelized cost of electricity is determined as $ 0.286/kWh. For a steady operation of system, integrating a battery system is convenient when solar energy is not available for a short term. When there is a longer-term shortage of solar radiation, up to 8 days, the electricity can be supplied by utilizing the hydrogen storage system.

Smart grid and microgrid technology based on energy storage systems (ESS) and renewable energy are attracting significant attention in addressing the challenges associated with climate change and energy crises. In particular, building an accurate short-term load forecasting (STLF) model for energy management systems (EMS) is a key factor in the successful formulation of an appropriate energy management strategy. Recent recurrent neural network (RNN)-based models have demonstrated favorable performance in electric load forecasting. However, when forecasting electric load at a specific time, existing RNN-based forecasting models neither use a predicted future hidden state vector nor the fully available past information. Therefore, once a hidden state vector has been incorrectly generated at a specific prediction time, it cannot be corrected for enhanced forecasting of the following prediction times. To address these problems, we propose a recurrent inception convolution neural network (RICNN) that combines RNN and 1-dimensional CNN (1-D CNN). We use the 1-D convolution inception module to calibrate the prediction time and the hidden state vector values calculated from nearby time steps. By doing so, the inception module generates an optimized network via the prediction time generated in the RNN and the nearby hidden state vectors. The proposed RICNN model has been verified in terms of the power usage data of three large distribution complexes in South Korea. Experimental results demonstrate that the RICNN model outperforms the benchmarked multi-layer perception, RNN, and 1-D CNN in daily electric load forecasting (48-time steps with an interval of 30 min).

Using different types of renewable energy sources considering their uncertainties causes numerous challenges for minimizing the operation cost and maximizing the reliability of system. Hence, stochastic programming is an essential tool to consider the system uncertainties. This paper presents a day-ahead energy management system to decrease the operation cost and increase the reliability of a Microgrid considering a number of challenges for supporting electrical and thermal loads. In the proposed method, micro-CHP units, renewable energy sources, auxiliary boiler and energy storage system are all responsible for supplying the electrical and thermal loads. The problem is formulated as a multi-objective optimization problem. Moreover, the influence of considering the electrical energy storage system as a non-ideal battery with charge/discharge efficiency less than 1 is investigated. Also, demand response programs are provided based on load shifting contracts to consumers. A scenario-based approach is used to cover the uncertainties of renewable energy sources, market price and electrical load. Besides, this paper considers both islanding and grid-connected modes of Microgrid and investigates the influence of demand side management on operation cost and reliability in both modes. The capability of the proposed algorithm is analyzed by simulation results of a 3-feeder Microgrid.

Energy harvesters, especially nonlinear systems with broad bandwidth, have made it possible for devices in IoT (Internet of Things) to extract energy from the ambient environment. However, due to the multistability feature of the nonlinear system, high and low energy orbits coexist in the system. It is critical to control the multistability so as to obtain high energy output. This paper proposes the load perturbation method to attain the high-energy orbit of nonlinear energy harvesters. Based on the electromechanical model, investigating the electrical load effects on the system shows that varying load results in different system states, which suggests that the load perturbation may trigger the transformation of system states. Therefore, a load perturbation by disconnecting the electrical load is employed to attain the high-energy orbit. Simulation and experiments reveal that the load perturbation can stimulate high-energy orbit oscillation for both monostable and bistable systems. The power output can be amplified for multifold times. The perturbation requires only one switch without additional mechanical structures. Furthermore, this method consumes little energy so that no external electrical energy source is needed. The load perturbation method opens opportunities for wide applications of nonlinear energy harvesters.