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Electric load forecasting plays a significant role in electric power systems for numerous applications, in terms of specific time horizons like Demand Side Management (DSM), grid stability, optimal operations, and Longterm strategic planning. However, inaccurate forecasts minimize the power supply safety, affecting the social and economic activities, security, and national defense. Therefore, Temporal Convolutional Attention-based Long Short- Term Memory (TCA-LSTM) is proposed for accurately forecasting electric load using Deep Learning (DL). By including an attention mechanism in an LSTM approach, the proposed technique focuses more on parameters with greater weights. Initially, the power load dataset is employed to evaluate the proposed approach. The obtained data is then pre-processed by utilizing the min-max normalization to reduce the impact of outliers. Then, the Fast Fourier Transform (FFT) technique is performed to extract the dominant amplitude-frequency. At last, the TCA-LSTM is used to accurately forecast the electric load. The proposed TCA-LSTM achieves a better Mean Square Error (MSE) of 0.002, 0.0074, and 1.5047 respectively in Godishala, Warangal, and Vijayawada, compared to the existing techniques namely, Regression Tree (RT), Principle Component Analysis with Recurrent Neural Network (PCA-RNN), and Artificial Neural Network (ANN). [ABSTRACT FROM AUTHOR]

Electrical disaggregation, also known as non-intrusive load monitoring (NILM) or non-intrusive appliance load monitoring (NIALM), attempts to recognize the energy consumption of single electrical appliances from the aggregated signal. This capability unlocks several applications, such as giving feedback to users regarding their energy consumption patterns or helping distribution system operators (DSOs) to recognize loads which could be shifted to stabilize the electrical grid. The project “SmartNIALMeter” brought together universities, companies and DSOs and involved the collection of a large data corpus comprising 20 buildings with a total of 100 electrical appliances for a period of up to two years at a sampling interval of five seconds. The variability of the loads, including heat pumps and a charging station for electric vehicles, and the presence of single-phase and three-phase devices make this dataset suitable for several investigations. The total consumption was collected through smart meters and each appliance's consumption was measured with a dedicated sensor, providing sub-metering for all loads. The dataset can be used to tackle several open research questions, for example to investigate new NILM algorithms able to learn with a limited amount of sub-metered data.

Closed-loop control of commercial building heating, ventilating, and air conditioning (HVAC) for demand response requires measurements used as feedback to the controllers. Demand response effectiveness is usually measured as a power deviation from baseline, but the building automation system (BAS) does not usually collect power measurements, and whole-building electric meters typically measure power at intervals of 15 min, which may be too slow for some types of demand response. Demand response strategies are sometimes focused on components of building HVAC systems, e.g., the response of supply/return fans to temperature set-point changes, but these components are usually not submetered. Fan power can be estimated from physics-based models leveraging BAS data, e.g., airflow measurements; but our ability to effectively close the loop on these estimates is not clear. In this paper, we introduce a massive dataset that contains both submetered fan power data and BAS data for several building HVAC systems during typical operation and demand response events. Through a case study we show that models leveraging BAS data alone do not provide accurate estimates of fan power during event transients, making it unlikely that closed-loop control of commercial building HVAC components for demand response would be effective using BAS data alone. This demonstrates the value of submetering HVAC components. More broadly, our dataset will enable future research bridging the gap between building control and power systems research. [ABSTRACT FROM AUTHOR]

Crack problems in nano-sized elastic structures under the Joule heating are analyzed by the finite element method (FEM). The variational principle is applied for derivation of coupled electrothermomechanical governing equations. The temperature field is described by classical Fourier′s law and it is not influenced by mechanical field. Transient heat conduction is considered since it is the slowest process among all other coupled fields here. Quasi-static conditions are considered for mechanical and electrical fields. Due to higher derivatives in governing equations the conventional FEM cannot be applied. Therefore, the mixed FEM with the C0 continuous interpolation for displacements and strains is developed. The influence of the material characteristic length on variation of crack opening displacements in finite nano-sized structures under an electric current load is investigated. [ABSTRACT FROM AUTHOR]

Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and behavior. There are many promising machine learning techniques in the literature, but black box models lack explainability and therefore confidence in the models’ robustness can’t be achieved without thorough testing on data sets with varying and representative statistical properties. Therefore this work adopts and builds on some of the highest-performing load forecasting tools in the literature, which are Long Short-Term Memory recurrent networks, Empirical Mode Decomposition for feature engineering, and k-means clustering for outlier detection, and tests a combined methodology on seven different load data sets from six different load sectors. Forecast test set results are benchmarked against a seasonal naive model and SARIMA. The resultant skill scores range from −6.3% to 73%, indicating that the methodology adopted is often but not exclusively effective relative to the benchmarks.

The Nigerian Universities rely on weak and unreliable fossil-based electric grids with diesel engine generators (DEG) as a backup. However, there is a potential to light up the campuses using power systems derived from primary renewable power systems (RPS) like wind turbine (WT) and solar photovoltaic (PV), that can be on or off-grid to improve the energy mix and duration reliably. This study presents the comparative analysis of the optimal hybrid grid and off-grid systems (OGS & OOGS) for serving the demand load of university buildings in four climatic regions of Nigeria. HOMER Pro is used to design and select the systems based on minimal net present cost (NPC) and cost of electricity (COE). The impact of a minimal renewable fraction of 95% on the optimal system architecture (OSA) and COE is studied for both grid and off-grid modes. Also, sensitivity analysis of the impact of key variables on performance for the sites is carried out. It is found that the OGS in the four regions is PV/Converter (Conv), while for the OOGS, it is PV/WT/DEG/battery (BB)/Conv except in Port Harcourt (PH), where it is PV/DEG/BB/Conv. The COE for the OGS in the Savana and monsoon climes of Enugu and PH are 10 and 19% more than that in the warm-semi arid climate zones of Maiduguri and Kano, which is approximately 0.09 $/kWh. The COE ($/kWh) for the OOGS is 0.21 in Maiduguri, 0.245 in Kano, 0.275 in Enugu and 0.338 in PH. An obligatory 95% RF changes the architecture and increases COE in all the locations except Maiduguri, with a slightly improved COE but higher NPC like other locations. It is established that the suggested hybrid system is beneficial and feasible for supplying more reliable and clean energy to educational buildings in different Nigerian locations.

Over the past few decades, models have been developed to accurately predict electrical charges. Long-term electricity forecast is the expansion of electrical equipment company management in the future. The short-term forecast for fuel and unit maintenance provides the information needed to systematically manage the unit's day-to-day operations and commitments. In this paper, we present the best in-class estimation method (OELF) to overcome micro grid problems. The proposed OELF system uses a hybrid convolutional neural network (CNN) and improved whale (IWO) to meet demand and facilitate economic growth. The main purpose of the CNN-IWO algorithm is to calculate the maximum demand for the micro grid and optimize the controllable load capacity for each project test. By investing in materials that reduce the performance of the micro grid, we can adjust the load on the micro grid and increase the controllable load. Therefore, OELF system for expanding micro grid expansion must carefully design cost control and load control strategies. The result showed that the performance of proposed OELF system is very effective in terms of mean MAPE and mean RMSE. The results clearly shows the average mean MAPE of proposed OELF system is 7.24%, 6.02% and 8.27% lower than the existing fuzzy based system in terms of 2 days, 1 days and 1 h ahead precision. The average mean RMSE of proposed OELF system is 9.37%, 8.34% and 5.41% lower than the existing fuzzy based system in terms of 2 days, 1 days and 1 h ahead precision. [ABSTRACT FROM AUTHOR]

Meeting the challenges of the energy sector relies on data – in particular sharing it internally and externally with a wide range of partners. Unfortunately, this valuable data often cannot be obtained from real objects due to location specifics or privacy concerns, although accurate, open-source data are a priority to provide researchers and energy experts with the information needed to accelerate the energy transition. In recent years, many studies have focused on the development of energy communities, using different methods to create data for case studies; however, these methods are often too broad and do not correlate with conditions in real locations. This work aims to identify the challenges associated with creating realistic datasets for energy community studies, as well as highlight the methods of defining input data, considering the factors that make energy community studies a very complex task, and discuss the flaws of commonly used methods. [ABSTRACT FROM AUTHOR]

The Nigerian Universities rely on weak and unreliable fossil-based electric grids with diesel engine generators (DEG) as a backup. However, there is a potential to light up the campuses using power systems derived from primary renewable power systems (RPS) like wind turbine (WT) and solar photovoltaic (PV), that can be on or off-grid to improve the energy mix and duration reliably. This study presents the comparative analysis of the optimal hybrid grid and off-grid systems (OGS & OOGS) for serving the demand load of university buildings in four climatic regions of Nigeria. HOMER Pro is used to design and select the systems based on minimal net present cost (NPC) and cost of electricity (COE). The impact of a minimal renewable fraction of 95% on the optimal system architecture (OSA) and COE is studied for both grid and off-grid modes. Also, sensitivity analysis of the impact of key variables on performance for the sites is carried out. It is found that the OGS in the four regions is PV/Converter (Conv), while for the OOGS, it is PV/WT/DEG/battery (BB)/Conv except in Port Harcourt (PH), where it is PV/DEG/BB/Conv. The COE for the OGS in the Savana and monsoon climes of Enugu and PH are 10 and 19% more than that in the warm-semi arid climate zones of Maiduguri and Kano, which is approximately 0.09 $/kWh. The COE ($/kWh) for the OOGS is 0.21 in Maiduguri, 0.245 in Kano, 0.275 in Enugu and 0.338 in PH. An obligatory 95% RF changes the architecture and increases COE in all the locations except Maiduguri, with a slightly improved COE but higher NPC like other locations. It is established that the suggested hybrid system is beneficial and feasible for supplying more reliable and clean energy to educational buildings in different Nigerian locations. [ABSTRACT FROM AUTHOR]

Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and behavior. There are many promising machine learning techniques in the literature, but black box models lack explainability and therefore confidence in the models' robustness can't be achieved without thorough testing on data sets with varying and representative statistical properties. Therefore this work adopts and builds on some of the highest-performing load forecasting tools in the literature, which are Long Short-Term Memory recurrent networks, Empirical Mode Decomposition for feature engineering, and k-means clustering for outlier detection, and tests a combined methodology on seven different load data sets from six different load sectors. Forecast test set results are benchmarked against a seasonal naive model and SARIMA. The resultant skill scores range from −6.3% to 73%, indicating that the methodology adopted is often but not exclusively effective relative to the benchmarks. [ABSTRACT FROM AUTHOR]

Copyright of Contaduría y Administración is the property of Facultad de Contaduria y Administracion-Universidad Nacional Autonoma de Mexico and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Parallel operation by multiple power converters is typical in microgrids. The communication method for parallel control mainly applies MODBUS RTU (Remote Terminal Unit), and the control performance largely depends on the communication speed. This paper proposes a new communication method that enables fast and accurate communication between master and slave systems combined in parallel using the PWM and capture functions of DSP used as the power converter controller. In the proposed communication method, the transmitting side uses the PWM function of DSP to generate a pulse waveform whose pulse width changes according to the reference command and transmits it to the receiving side. Suppose the capture function of the receiving side DSP is set to the time difference rising and falling edge mode. The set event occurs when the capture signal is detected and initializes the counter value to zero. At this time, it detects the counter signal and receives data. We explain the proposed communication method’s principle, characteristics, and noise-canceling method and evaluate the accuracy and stability through data transmission and reception experiments. Finally, three 10kW single-phase electronic loads are connected to the R, S, and T phases to form a three-phase electronic load, and a master and two slaves are designated. The validity of the proposed communication method is verified through the current control response according to the command.

Renewable energy production has been surging around the world in recent years. To mitigate the increasing uncertainty and intermittency of the renewable generation, proactive demand response algorithms and programs are proposed and developed to further improve the utilization of load flexibility and increase the efficiency of power system operation. One of the biggest challenges to efficient control and operation of demand response resources is how to forecast the baseline electricity consumption and estimate the load impact from demand response resources accurately. In this paper, we propose a mixed effect segmented regression model and a new robust estimate for forecasting the baseline electricity consumption in Southern California, USA, by combining the ideas of random effect regression model, segmented regression model, and the least trimmed squares estimate. Since the log-likelihood of the considered model is not differentiable at breakpoints, we propose a new backfitting algorithm to estimate the unknown parameters. The estimation performance of the new estimation procedure has been demonstrated with both simulation studies and the real data application for the electric load baseline forecasting in Southern California.

Aiming at the nonlinear characteristics of short-term electric load series, a short-term electric load forecasting model with improved Extreme Learning Machine (ELM) is proposed. This model is based on the data feature analysis of Ensemble Empirical Mode Decomposition (EEMD) and Long Short-Term Memory (LSTM). EEMD is used to decompose the load sequence into several regular modal components to reduce the error caused by the randomness of the sequence. LSTM and ELM are used to predict the high-frequency and low-frequency components respectively. After using the improved Extreme Learning Machine (ELM), the prediction results of each component are solved. Then the optimal results of power load forecasting are obtained. The results show that the proposed model can judge the overall change trend of the electric load and predict the details of the local change. The average absolute percentage error is 2.24% comparing to 4.78% of the best experiment result of single model, and it has better generality.

Summary: In this paper, a new technique is suggested for best electric load forecasting of South Korea. The proposed study is based on an improved metaheuristic methodology. This paper proposes a new improved version of Support Vector Machine for Regression (SVR) base on the proposed algorithm. The algorithm is based on a modified version of Crow Search Algorithm (ACSA), where, by combining by the SVR, generates an efficient classifier. Although, different drawbacks of using original CSA are stated, the proposed ACSA method provides a proper modification for resolving of these shortcomings. The forecasting model has been then performed to a historical data from South Korea to indicate the algorithm efficiency and the results are compared with some other methods, including SVR‐CGA, SVR‐CAS, ANN, and simple Regression to show the suggested method performance. Final results show that MAPD, MSE, and RMSE values of the test data with lower values for South, North, Center, and East, provided better results based on the proposed SVR‐ACSA based on different values of center and kernel of the SVR. Experimental results on the case study shows that the minimum MAPD has been added which is happened in Northern regional is 1.1062, 1.1336, 1.1065, 1.02, 2.23, 1.96, and 1.49 for SVR‐ACSA, SVR‐CGA, SVR‐CAS, ANN, Regression, LSTM‐RNN, and ELM which indicates the method's higher efficiency. [ABSTRACT FROM AUTHOR]

Based on the performed researches the method of decomposition of graphs of total electric loading of power system with application of a method of Hilbert-Huang is improved. This approach allows obtaining a homogeneous basic com-ponent of electrical load and temperature component, which has a close correlation with air temperature, which im-proves the accuracy of short-term forecasting. The results of testing the developed mathematical model are given. Ref. 9, figure, table.

Accurate forecasting of an electric load is vital in the effective management of a power system, especially in flourishing regions. A new hybrid model called logarithmic spiral firefly algorithm-support vector regression (LS-FA-SVR) is proposed to promote the performance of electric load forecasting. The new hybrid model is acquired by combining the support vector regression, firefly algorithm, and logarithmic spiral. Half-hourly electric load from five main regions (NSW, QLD, SA, TAS, and VIC) of Australia are used to train and test the proposed model. By comparing the model results with observed data on the basis of the root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE), the performance of the proposed hybrid model is the most outstanding among all the considered benchmark models. Hence, the results of this study show that the hybrid model LS-FA-SVR is preferable and can be applied successfully because of its high accuracy.

The manuscript analyses the management of low and ultra-low-temperature district heating systems (DHS) coupled with centralised and decentralised heat pumps. Operative conditions are defined in order to satisfy the heating needs without overloading the electric grid. The results are achieved by dynamic simulations, based on a real DHS located in southern Switzerland. At the building level, the heating needs are estimated considering real data and simultaneous energy simulations. Two DHS configurations, alternatives to the existing one, are simulated and suitable parameters for the management of the DHS are selected. The global performance of the two DHS is evaluated by KPIs also including the flexibility and the impact on the electric peak due to heat pumps. The achieved results are discussed providing suggestions for the stakeholders involved in DHS management for an optimal matching of the electric grid and thermal networks towards a reduction of the peak power. The rule-based control strategies defined allow the expected electric peak shaving and load levelling, conversely, the yearly energy consumptions are lightly increased and have to be further investigated. The outcomes demonstrate a global better performance of the ultra-low temperature DHS in terms of response to the applied control strategies and of energy savings. [ABSTRACT FROM AUTHOR]

Electrical disaggregation, also known as non-intrusive load monitoring (NILM) or non-intrusive appliance load monitoring (NIALM), attempts to recognize the energy consumption of single electrical appliances from the aggregated signal. This capability unlocks several applications, such as giving feedback to users regarding their energy consumption patterns or helping distribution system operators (DSOs) to recognize loads which could be shifted to stabilize the electrical grid. The project "SmartNIALMeter" brought together universities, companies and DSOs and involved the collection of a large data corpus comprising 20 buildings with a total of 100 electrical appliances for a period of up to two years at a sampling interval of five seconds. The variability of the loads, including heat pumps and a charging station for electric vehicles, and the presence of single-phase and three-phase devices make this dataset suitable for several investigations. The total consumption was collected through smart meters and each appliance's consumption was measured with a dedicated sensor, providing sub-metering for all loads. The dataset can be used to tackle several open research questions, for example to investigate new NILM algorithms able to learn with a limited amount of sub-metered data., (© 2024 The Authors.)

Copyright of B&H Electrical Engineering is the property of Sciendo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The location of a new substation is a key factor in the expansion of electrical distribution systems. This location is strategic from the point of view of the costs associated with energy supply; therefore, a holistic and integral planning of sub-transmission and primary distribution subsystems requires the development of suitable optimization methods to support the decision process. Although future electric load growth is a critical factor to define capacity and location of new substations, other technical, environmental, soil characteristics, risk, social, and administrative criteria that influence the final location are also crucial. A multicriteria decision analysis based on geographic information system is proposed in this paper to combine those criteria taking into account decision makers' preferences and physical restrictions on land use. The main contributions of this paper are the identification of the criteria and the analysis of service areas in existing substations to impose constraints on the problem. A spatial heat map that facilitates the visual interpretation of the spatial relations of the criteria is produced based on a suitability score. The proposed method was evaluated in the service area of an Ecuadorian distribution energy utility. It was found that the two more important criteria are the electric load density and the distance to subtransmission network with weights of 44% and 23%, respectively. The proposed analysis is able to identify ideal locations for new substations, which can be used by the planner to find the best long-term network expansion alternative. [ABSTRACT FROM AUTHOR]

The article concerns design of electric system with dispersed generation, more precisely, functional location of energy generation units — transmission losses relationship. Electrical load centers are the optimal locations of energy generation units, but time-varying loads shift them and transmission losses increase. The purpose of the article is development of an algorithm and the program of power supplies dynamic redistribution’s for the electric system with dispersed generation that reduce transmission losses. Power supplies dynamic redistribution’s is achieved by the creation of a virtual electric system with the possibility of redistribution over electrical load centers of energy generation units. Virtual electric system is organized on the basis of cellular communication and central computer (smart grid). The algorithm is realized in the computer-aided design MathCAD. Using this program, the calculation of the electric system of an industrial microdistrict of the city with distributed generation was performed. As a result of the calculations, cartograms of electrical loads are constructed and the loss of electric energy in distribution lines is estimated. The calculation results show a significant reduction in losses in distribution lines when using distributed generation with dynamic redistribution of power sources.

Based on the research, the article presents three algorithms that allow to select from the overall electrical load (OEL) of the power system technological and temperature components in each hour of the daily schedule, which provides greater accuracy of short-term forecasting (STF) OEL of the power system. Calculations by three algorithms were performed according to Kyivenerho. The analysis of reading temperature sensors on four sources from the point of view of possibility of their application at STF is carried out. [ABSTRACT FROM AUTHOR]

The manuscript analyses the management of low and ultra-low-temperature district heating systems (DHS) coupled with centralised and decentralised heat pumps. Operative conditions are defined in order to satisfy the heating needs without overloading the electric grid. The results are achieved by dynamic simulations, based on a real DHS located in southern Switzerland. At the building level, the heating needs are estimated considering real data and simultaneous energy simulations. Two DHS configurations, alternatives to the existing one, are simulated and suitable parameters for the management of the DHS are selected. The global performance of the two DHS is evaluated by KPIs also including the flexibility and the impact on the electric peak due to heat pumps. The achieved results are discussed providing suggestions for the stakeholders involved in DHS management for an optimal matching of the electric grid and thermal networks towards a reduction of the peak power. The rule-based control strategies defined allow the expected electric peak shaving and load levelling, conversely, the yearly energy consumptions are lightly increased and have to be further investigated. The outcomes demonstrate a global better performance of the ultra-low temperature DHS in terms of response to the applied control strategies and of energy savings.

In this paper, the fracture problem of a functionally graded piezoelectric material strip (FGPM strip) containing a crack perpendicular to the interface between the FGPM strip and a homogeneous layer under an electric load is considered. Material properties are assumed to be exponentially dependent on the distance from the interface. The superposition technique is used to solve the governing equations. The stresses induced by the electric load in the un-cracked laminate are calculated, and the obtained normal stress is used as the crack surface tractions with opposite sign to formulate the mixed boundary value problem. By using the Fourier transforms, the electro-mechanical fracture problem is reduced to a singular integral equation, which is solved numerically. The stress intensity factors of the internal crack and the edge crack are computed and presented for the various values of the nonhomogeneous and geometric parameters.

Accurate short-term load forecasting can ensure the safe operation of the grid. Decomposing load data into smooth components by decomposition algorithms is a common approach to address data volatility. However, each component of the decomposition must be modeled separately for prediction, which leads to overly complex models. To solve this problem, a VMD-WSLSTM load prediction model based on Shapley values is proposed in this paper. First, the Shapley value is used to select the optimal set of special features, and then the VMD decomposition method is used to decompose the original load into several smooth components. Finally, WSLSTM is used to predict each component. Unlike the traditional LSTM model, WSLSTM can simplify the prediction model and extract common features among the components by sharing the parameters among the components. In order to verify the effectiveness of the proposed model, several control groups were used for experiments. The results show that the proposed method has higher prediction accuracy and training speed compared with traditional prediction methods.

The basis for achieving high quality of electricity are three components: high-quality power generation, uninterrupted transmission and distribution over reliable networks. In conditions of commensurability of the power of electric motors with the power supply and with a considerable extension of the cable network, algorithms for automated design of the power supply system are needed. This article describes an approach based on the application of the Dijkstra algorithm, which allows to reduce costs in the construction of the distribution network and provide the necessary voltage level at the motor terminals or minimum energy losses in the cable network. The main and arbitrary structure of a power supply network with an electric load in terms of the theory of the genetic algorithm is described, on the basis of which it is possible to create an effective means of determining rational configurations of a power supply network with an electric load.

This study proposes a framework to analyse the concept of power to hydrogen (P2H) for fuelling the next generation of aircraft. The impact of introducing new P2H loads is investigated from different aspects namely, cost, carbon emission, and wind curtailment. The newly introduced electric load is calculated based on the idea of replacing the busiest international flight route in the Europe, Dublin‐London Heathrow, by hydrogen fuel‐powered aircraft as a high potential candidate for the next generation of air travel systems to cope with the ambitious targets set in Europe Flight Path 2050 by the Advisory Council for Aeronautics Research in Europe. The simulation is performed on a representative Irish transmission network to demonstrate the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

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 kW p 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. • Managing excess PV energy via the power-o-hydrogen concept. • ANNs are used to predict AC power from PV field. • Variable building load with sub-hourly values is considered. • Excess PV power can generate a yearly hydrogen production of 18 622 Nm3. • Integrated PV-hydrogen system improved considerably the effective capacity factor. [ABSTRACT FROM AUTHOR]

With the development of economy and the growth of industrial demands, the peak–valley difference of electric load is ever increasing, calling for the deployment of energy storage units. Advanced‐adiabatic compressed air energy storage (AA‐CAES) is a promising large‐scale energy storage technology and exhibits various advantages in fast response, long service time, low environmental impact and so on. It also has the potential in combined heat‐and‐power production because heat is a by‐product when air is compressed. This study presents a novel AA‐CAES‐based energy hub and its mathematical formulation considering the pressure behaviours and mass flow rate variations of AA‐CAES, and further envisions its business model for the transaction with a power distribution system and a heating system under time‐of‐use price. A bilevel game‐theoretical model is developed to capture the interaction between the two infrastructures through the integrated demand response of the energy hub and investigate the equilibrium state at which none of the stakeholders would like to alter their strategy unilaterally. Results show the interdependence of electricity and heat prices as well as the economic impact of energy hub performance. [ABSTRACT FROM AUTHOR]

Nowadays we observe rather rapid growth of energy accumulators market. There are prerequisites to their extensive application in Belarus. In spite of technology development problems pertaining to optimization of electric power and their operation under conditions of specific systems “generator – accumulator – consumer” (GAC) have not obtained proper consideration. At the same time tuning and optimization of the GAC system may provide competitive advantages to various accumulating systems because application of accumulator batteries in non-optimal charge – discharge conditions reduces its operating resource. Optimization of the GAC system may include utilization of hybrid accumulator systems together with heterogeneous chemical and mechanical accumulators, tuning of system controller parameters etc. Research papers present a great number of empirical and analytical methods for calculation of electric loads. These methods use the following parameters as initial data: time-averaged values of actual electric power consumption, averaged apartment electric loads, empirical and statistical form coefficients, coefficients of maximum electric load for a group of uniform consumers. However such models do not meet the requirements of detailed simulation of relatively small system operation when the simulation must correspond to non-stationary, non-averaged, stochastic load nature. The paper provides a simple approach to the detailed simulation of electric loads in regard to small projects such as multi-unit apartment building or small agricultural farm. The model is formulated both in physical and algorithmic terms that make it possible to be easily realized in any programming environment. The paper presents convergence of integral electric power consumption, which is set by the model, to statistically averaged parameters. Autocorrelation function has been calculated in the paper that shows two scales for autocorrelation of simulated load diagrams. The paper contains of an analysis on Fourier spectra for generated electric load diagrams. The model of electric load may be used separately or as a component of a general model in the GAC system. It may also be applied for tuning of automatic systems for control and accounting of power resources and various research purposes.

Model of main structure of power-supply is considered for research of states of working collections of induction motors. Model consists of separate electromechanic modules, which can contain different amount of motors. Modules are connected in different points to main cable, laid from transformer before remoted module. On base of this model is developed software program, intended for finding of optimum parameters of structure of network of power-supply with use the genetic algorithm.