Electric Energy Management Research Articles

A Potential Controller for Smart Electrical Energy Management System

Integrated energy utilization has been recognized as a productive way towards better energy management, besides increasing Renewable Energy (RE) penetration. Thus, the combination of RE integrated with the Battery Energy Storage System (BESS) has been recognized as the primary solution where it is necessary to have a controller to interface the system efficiently. Hence, a smart electrical energy management system controller is designed and developed based on load leveling and peak shaving applications for real-time AC power management in this work. The main function of the controller is to continuously monitor and maintain the load demand and to produce a leveled or shaved load profile that will be seen at the grid network by controlling the battery operation. The testing results concluded that the controller able to perform both the energy applications. Overall, a dual function controller based on energy applications to maintain consumer load demand usage more securely and reliably, so that the utility bill is reduced and the battery lifetime is prolonged simultaneously is achieved in this work.

Stochastic electrical and thermal energy management of energy hubs integrated with demand response programs and renewable energy: A prioritized multi-objective framework

Energy hubs (EH) are known as multi-carrier systems that integrate multiple energy resources to enable greater flexibility in the energy provision. In this study, a multi-objective decision-making framework is proposed to determine the optimal scheduling of EHs. The proposed model considers the total cost of the EH, emissions, power losses, and average reserve of EH, simultaneously. These objectives are prioritized based on the EH preference that can be different for each EH. In this strategy, the cost of the EH has the highest priority and is considered as the main objective. The emission, system losses, and system reserve simultaneously have been considered as secondary objectives. According to the prioritization made among objectives, a lexicography optimization is performed in which cost minimization is considered in the first step, and the secondary objectives are evaluated in the second step of optimization. The intermittency nature of the electrical and thermal loads, renewable generation, and market prices are applied to the model by stochastic techniques. The proposed multi-objective model has been tested on the non-real benchmark system (standard IEEE 5-bus test system). The simulation results show that the proposed model improves the reserve capacity, emission, and system losses.

Price-Response Matrices Design Methodology for Electrical Energy Management Systems Based on DC Bus Signalling

Prosumers’ electrical installations (PEIs), as nanogrids and low-voltage microgrids, have gained importance in recent years following the development of standards such as the IEC 60364-8 series. In these systems, all distributed energy resources (DERs) are usually integrated using dc bus coupling. The IEC 60364-8-3 predicts an electrical energy management system (EEMS) for power-sharing. The overall research framework of this paper is the nanogrid power management, where complex algorithms are required, as well as the conventional state machines and hierarchical controls. However, the addition of new DERs in such systems is not straightforward due to the complicated parameter settings for energy usage optimization. A different control strategy, named price-based power management, has been conceived to make the EEMS scalable to include new sources and simplify parameterization. Since it is analogous to economic markets, most users understand the concepts and feel comfortable tuning parameters according to their own cost/benefits goals. This paper proposes a price-based power management algorithm for EEMS to automatically design the price-response matrices (PRMs). The PRMs are a way to organize power management, considering new DERs and variable price of energy. The main contribution is the methodology to design the PRMs. Experimental results are carried out to demonstrate the effectiveness of the proposed strategy. The results were obtained with a 1.5 kW prototype composed of a PV generator, battery energy storage, loads, and grid connection.

Hybrid deep neural model for hourly solar irradiance forecasting

Owing to integrating photovoltaic solar systems into power networks, accurate prediction of solar irradiance plays an increasingly significant role in electric energy planning and management. However, the existing hybrid models ignore the influence of other factors except for the irradiance time series and adopt a single branch independent network structure, which may lead to decrease prediction accuracy. In this paper, a novel multivariate hybrid deep neural model named WPD–CNN–LSTM-MLP for 1-h-ahead solar irradiance forecasting is proposed. The novel WPD–CNN–LSTM-MLP model is based on a sophisticated multi-branch hybrid structure with multi-variable inputs, which the multi-branch hybrid structure combines wavelet packet decomposition (WPD), convolutional neural network (CNN), long short-term memory (LSTM) networks, and multi-layer perceptron network (MLP), and the multi-variable inputs include hourly solar irradiance and three climate variables, namely: temperature, relative humidity, and wind speed and their combination. The new model extracts the inherent characteristics of multi-layer inputs sufficiently, overcomes the shortcomings of traditional models, and achieves more accurate forecasting results. The performance of the model is verified by actual data from Denver, Clark, and Folsom, the United States. Comparative studies of traditional individual back propagation neural network, support vector machine, recurrent neural network, LSTM, the climatology–persistence reference forecasts method and the proposed LSTM-MLP model, CNN-LSTM-MLP model, and WPD–CNN–LSTM model reveal that the proposed WPD–CNN–LSTM-MLP deep learning model has better prediction accuracy in hourly irradiance forecasting.

Evolutionary Approaches to Fog Node Placement in LV Distribution Networks

Smart grids merge intelligent computing technologies and electrical grid networks for better monitoring, control and management of electrical energy and facilities. The maturity of cloud computing has been the major driving factor for its adoption in smart grid deployments. Despite the elasticity of cloud resources, centrality and long distances to remote data centers cause high latency, high bandwidth consumptions and unstable connectivity, which are undesirable for IoT-based smart grid applications. Fog computing as an extension of cloud computing services to the edges of the network overcomes these challenges and perfectly suit the distributed nature of the low voltage (LV) electrical distribution networks as part of smart grid. The pressing issues with the adoption of fog computing for smart grid applications are finding the best placement plan for fog node locations in LV distribution networks to enhance monitoring and control. The main goal of this work is to present a mathematical model to address the aforementioned issues focusing on minimizing deployment cost and network delay. In addressing this multi-objective problem, a new algorithm, namely Future Search Particle Swarm Non-dominated Sorting Genetic Algorithm (FPNSGA), is proposed based on the combination of the best features of the NSGA-II, SMPSO, and a recently formed algorithm, Future Search. The effectiveness of the algorithm is evaluated based on the benchmarking technique (Weighted Sum approach), the convergence and diversification of the solutions using HV indicators and CPU time. The results from simulations show that the proposed mechanism is very competitive and outperforms other fog planning network schemes.

Misery and Economic Growth Nexus in Nigeria; Implications for Electrical Energy Management

At first glance, misery seems unquantifiable but has been established to be an aggregation of unemployment and inflation. Nigeria is in a pitiable condition as she is ranked the 6th most miserable country in the world. This study aims to ascertain the effect of economic growth on Misery in Nigeria, that is, to determine whether economic growth rate has strengthened or weakened the misery of Nigerians. This study adopts Autoregressive Distributed Lag (ARDL) model because it considers policy lags of economic phenomena and allows combined order of integrations. The study finds an inverse nexus between economic growth and misery. Hence, recommendations were made in form of measures to ensure the need for economic growth to increase at faster and higher pace to combat high misery levels in Nigeria.

Short-term load forecasting of industrial customers based on SVMD and XGBoost

The electricity consumption by industrial customers in the society accounts for a significant proportion of the total electrical energy. Thus, it is of great significance for demand-side electrical energy management to develop an accurate method for short-term load forecasting for industrial customers. Unlike traditional load forecasting on system-level, the load forecasting of individual industrial customer is more challenging due to its significant volatility and uncertainty. We propose an adaptive decomposition method based on VMD and SampEn (SVMD) to decompose the raw load data into a trend series and a set of fluctuation sub-series, and then establish the corresponding prediction model (line regression model for the trend series and XGBoost regression model for each fluctuation sub-series). The hyper-parameters of XGBoost are optimized by bayesian optimization algorithm (BOA). Furthermore, relevant factors that affect the electricity consumption behavior of industrial customers are considered in order to further improve the accuracy of the hybrid method. The proposed method is tested in multiple scenarios with different industrial customers of China and Irish. The results show that the proposed model has significantly improved performance over the contrast models in state-of-the-art load forecasting.

Degradation adaptive energy management strategy using fuel cell state-of-health for fuel economy improvement of hybrid electric vehicle

Most studies on fuel cell hybrid electric vehicle energy management have focused on fuel economy. However, it is also important to consider the rapid degradation of the fuel cell. Therefore, a degradation-adaptive energy management strategy is proposed in this paper. The strategy can adaptively change the power distribution between different power sources using the fuel cell state-of-health. First, a novel degradation model is established for the fuel cell. The degradation model combines the polarisation curves of the fuel cell system under different state-of-health conditions and fuel cell efficiency models. An unbalanced degradation of the fuel cell at different current densities is shown in the degradation model. The proposed strategy is modified from an instantaneous optimisation energy management strategy by including state-of-health data. Accordingly, it is possible to provide optimised control based on the decrease in efficiency, thereby taking advantage of the unbalanced degradation. The proposed strategy can adaptively adjust the power distribution during degradation to get a higher energy efficiency over entire lifetime of fuel cell. The proposed strategy is adaptive to different degradation rates and consumes a small amount of computing resources, which ensure the feasibility of real-world implication. The performance of the proposed strategy is compared with that of the original strategy via simulation. The proposed strategy can optimise the fuel economy by 1.52–2.06% and 2.26–2.90% for a half and seriously degraded fuel cell, respectively. The results reveal that the proposed strategy provide an effective approach to improving the fuel economy of degraded fuel cell hybrid electric vehicles.

The cost load regulation and management of electrical energy by directions of impact on the components of total price

The issues of optimizing regional pricing are especially acute for the Kaliningrad Region due to its exclave features. At the same time, the cost of energy resources has become one of the main issues in making managerial decisions. Recently, the so-called Technoparks have become one of the new forms of organizing the production process in a certain branch of industry, or a process that is at the junction of several branches. There are a variety of descriptions of the indisputable advantages of this work format for a specific technological process, however, the possibilities in the field of reducing the costs of consumed energy resources that arise with such a local siting of production are not discussed that often. According to the authors of the article, based on the structure of the tariff, it is possible to classify methods of reducing the cost price by the impact on the components of the final cost of energy supply services. It should be noted that the classification sign of saving methods will be precisely the component of the tariff, while the methods themselves can be aimed both at reducing the price expression of each component of the tariff and at the volume of services for this component. The authors have also identified regional features of the pricing processes in the energy industry.

Analysis of CO2 reduction with micro CHP facility: Renewable energies and Stirling engine

The Cogeneration laboratory is a research facility in the University of Málaga (UMA) that allows for the behavioural study of a renewable energy installation combining solar resources and micro-CHP. Energy generation in the system is provided by a 3 kWp photovoltaic array, two solar thermal connectors and a Whispergen EU1 Stirling micro-CHP unit. Energy storage in the facility is provided by water tank and lithium-ion battery. This laboratory is managed through a programmable Mitsubishi PLC that permits the simulation of different thermal and electrical load profiles, as well as the mode of operation. The electrical energy management is controlled by the solar inverter. Environmental data, are measured using a top of the line weather station.The system’s real time status is logged through the programmable PLC. All this data is transferred and analysed in a purpose-built MATLAB-based software, where power and energy balances are conducted, efficiencies are calculated, and a CO2 emissions evaluation is studied.The CO2 emissions analysis is carried to evaluate the carbon dioxide emissions generated by the facility when the electrical and thermal demand are provided by the joint solar and micro-CHP system. These emissions come from the burning of natural gas in the micro-CHP Stirling engine, and the usage of electricity from the grid. With the current mode of operation, a reduction of up to 70% in CO2 emissions has been achieved, with an energy generation that exceeds the demand.

Water evaporation model of a catalyst converter in the context of hybrid electric vehicles energy and pollutants management

The energy and pollutants management of Hybrid Electric Vehicles (HEV) consists in determining at each instant the optimal torque split between the Internal Combustion Engine (ICE) and the Electric Motor (EM). This torque choice aims to minimize both fuel consumption and pollutants emissions. A critical piece of information in this context is the value of the Three-Way Catalyst Converter (3WCC) conversion efficiency. This value is highly dependent on the 3WCC temperature; it is hence of major importance to have an accurate estimation of it. Although the literature is abundant about simple 0D thermal models of 3WCC, it is tempting to implement a more accurate and consequently complex model. In particular, both moisture from the exhaust gases during engine start and from ambient air during cooling phases lead to the formation of a liquid water film on the monolith’s exchange surface, causing a strong temperature plateau during the warm-up phase. The water evaporation causing a delay in the 3WCC temperature increase, it could lead to a serious degradation of the pollutants emissions if not taken into account. This paper proposes hence a 1D thermal model including water phase changing that allows a 72% decrease in the 3WCC temperature estimation error during the warm up phase. This accuracy gain, occurring in a phase where the converter temperature has not reached its full efficiency, provides a 1-2% reduction in CO, HC and NO x emissions.

Analysis of Wind-wave Effect on Short-term FOWT Power Loss

The paper analyses the effects of wind misalignment, wave oscillatory movement and wind-wave misalignment onto the performance of floating offshore wind turbines (FOWT) and how they affect the power generation. The study focuses on short term prediction to match network requirements for electric energy management avoiding overloads or consumers demand mismatching. The research studies individual as well as combined effects to determine the reduction in power generation due to these effects. The predictive model is based on meteorological data for different marine stations where wind farms are either located or considered for feasible installations. The results of the modelling predict the wind misalignment is more important than wave motion effects. Power loss due to wind-wave misalignment increases with wave height and with misalignment angle. The effects are negligible for wave heights of 0.4 and 1.25 m, with almost null effects for wave height of 2.5 m., with a power reduction less than 4%. The effects, however, are significant for wave heights of 4 and 6 m, especially in this latter case, where power loss varies from 11% for wind-wave misalignment angle of 15° to more than 96% for an angle of 45°. Experimental results match theoretical predictions within 96% accuracy.

Energy Management Systems for Residential Buildings With Electric Vehicles and Distributed Energy Resources

This paper proposes two electric energy management systems (EMSs) in the context of a grid-connected residential neighbourhood with electric vehicles (EVs), battery storage, and solar photovoltaic (PV) generation. The EMSs were developed to minimize the cost of electricity whilst having no impact on routine individual energy needs and travel patterns. The EMSs were evaluated using common sets of real data with the aim to compare the effectiveness of a centralized EMS with decentralized EMS. The models also accounted for the battery capacity degradation and the associated costs. Simulation studies and numerical analyses were presented to validate the effectiveness of the proposed EMSs considering a high-density residential building in Sydney, Australia. The simulation results indicate that the centralized EMS is more effective compared to the decentralized EMS in terms of cost savings. It is also observed that the energy management strategies significantly reduce the energy drawn from the grid compared to un-optimized energy management schemes.

Considerations to Approach Membrane Biofouling in Microbial Fuel Cells

Among bioelectrochemical systems, those referred to as microbial fuel cells (MFCs) are widely implemented for wastewater management and simultaneous recovery of electrical energy. MFCs are fundamentally assisted by bacterial populations, mostly mixed cultures to be more exact that, after oxidizing the substrate, are capable of facilitating the passage of electrons to an electron acceptor, usually the anode. However, certain undesired bacterial strains often colonize not only the electrode but the membrane separator over time and cause severe biofouling. The membrane is an architectural element of many MFCs and determines the efficiency of the system. In this paper, this issue is overviewed briefly and some considerations concerning how to approach the problem are presented.

Development and scaled-up simulation of an automated electrical energy management system for passageway illumination

Abstract Buildings account for much of the world’s energy consumption; thus, it is important to develop ways, not just to harness more of the energy in the universe, but also to make system of devices in buildings more energy-efficient. Occupancy-based illumination is an emerging trend in energy management, but research in this field has mostly been focused on illumination of rooms and workspaces in building interiors. To the best of the authors’ knowledge, there is virtually no literature on the energy optimisation of depersonalised spaces such as passageways. This paper explores the development and evaluation of a controller-based model for illumination of passageways in buildings, where relatively-large number of depersonalized luminaires are usually found and automation might prove especially beneficial. After development, testing and scaled-up simulations, results show that occupancy-based illumination of exterior corridors and semi-covered passages in buildings is feasible, economical, has significant energy savings, and is worthy of consideration for modern buildings.

A healthcare evaluation system based on automated weighted indicators with cross-indicators based learning approach in terms of energy management and cybersecurity

ObjectiveHospital performance evaluation is vital in terms of managing hospitals and informing patients about hospital possibilities. Also, it plays a key role in planning essential issues such as electrical energy management and cybersecurity in hospitals. In addition to being able to make this measurement objectively with the help of various indicators, it can become very complicated with the participation of subjective expert thoughts in the process. MethodAs a result of budget cuts in health expenditures worldwide, the necessity of using hospital resources most efficiently emerges. The most effective way to do this is to determine the evaluation criteria effectively. Machine learning (ML) is the current method to determine these criteria, determined by consulting with experts in the past. ML methods, which can remain utterly objective concerning all indicators, offer fair and reliable results quickly and automatically. Based on this idea, this study provides an automated healthcare system evaluation framework by automatically assigning weights to specific indicators. First, the ability of hands to be used as input and output is measured. ResultsAs a result of this measurement, indicators are divided into only input group (group A) and both input and output group (group B). In the second step, the total effect of each input on the output is calculated by using the indicators in group B as output sequentially using the random forest of the regression tree model. ConclusionFinally, the total effect of each indicator on the healthcare system is determined. Thus, the whole system is evaluated objectively instead of a subjective evaluation based on a single output.

Optimally Automated Home Management for Smart Grid System Using Sensor Networks: Gaza Strip as a Case Study

Smart Grid (SG) allows real-time management of electric loads via the integration of Information and Communication Technology (ICT) concepts. Therefore, Home Energy Management Systems (HEMS) have been recently developed to smartly manage the electric loads in smart homes. In this paper, the Automated Home Management (AHM) system is introduced to intelligently control and schedule the electric loads. Hardware and software implementations are achieved to remotely control, monitor, measure and manage the electricity. Hardware components include a microcontroller, electric current sensor, electric voltage sensor, and Ethernet module. The Internet of Things (IoT) platform using Message Queuing Telemetry Transport (MQTT) protocol is embraced to connect the AHM system at homes with the data center at the operator of electricity. Thus, two electric energy management algorithms are introduced. Firstly, the Power Limit Management (PLM) algorithm is proposed to control the electric loads according to the available electrical energy. Secondly, Smart Electrical Task Scheduling (SETS) algorithm is developed to schedule the electric loads, so that the electricity daily cost, and the user comfort are improved. Simulation results show that the introduced SETS and PLM algorithms save energy consumption, and daily electrical cost with reasonable user comfort. Unlike other approaches, the proposed SETS uses a weighting parameter that can tune the level of comfort and daily price. When the weighting parameter is equal to half, user dissatisfaction is improved by 91.5% compared by the case of using a weighting parameter of unity. Furthermore, daily price is improved by 11.4% compared by the case of using a weighting parameter of zero.

Enhanced Q-learning for real-time hybrid electric vehicle energy management with deterministic rule

Power allocation plays an important and challenging role in fuel cell and supercapacitor hybrid electric vehicle because it influences the fuel economy significantly. We present a novel Q-learning strategy with deterministic rule for real-time hybrid electric vehicle energy management between the fuel cell and the supercapacitor. The Q-learning controller (agent) observes the state of charge of the supercapacitor, provides the energy split coefficient satisfying the power demand, and obtains the corresponding rewards of these actions. By processing the accumulated experience, the agent learns an optimal energy control policy by iterative learning and maintains the best Q-table with minimal fuel consumption. To enhance the adaptability to different driving cycles, the deterministic rule is utilized as a complement to the control policy so that the hybrid electric vehicle can achieve better real-time power allocation. Simulation experiments have been carried out using MATLAB and Advanced Vehicle Simulator, and the results prove that the proposed method minimizes the fuel consumption while ensuring less and current fluctuations of the fuel cell.

A novel cascade approach to control variables optimisation for advanced series-parallel hybrid electric vehicle power-train

In recent years, road vehicles are being increasingly equipped with hybrid electric power-trains in order to provide significant gains in fuel economy and reductions in greenhouse gases emissions. Since hybrid power-trains consist of two or more different energy sources, many of their variants are present nowadays which leads to many open questions in terms of hybrid electric power-train structure selection, components sizing and energy management control, which all have influence on the power-train purchase cost and efficiency. The control variables optimisation is crucial in order to find the set of optimal control rules for different power-train operating regimes which would yield the minimum possible fuel consumption. Among different control variable optimisation methods, the dynamic programming approach is usually used in literature, because of its unique feature to find the global optimum solution with a certain degree of precision. However, this optimisation method also requires significant computing power and its application is limited to low-order systems. Having this in mind, this paper evaluates the benefits of innovative cascade approach to hybrid electric vehicle control variable optimisation wherein dynamic programming is combined with a gradient-based optimisation algorithm in a systematic and a straightforward way in order to significantly reduce the optimisation execution time and also to increase the precision of the globally-optimal result.

POET Structured energy management and efficiency improvement of a grid-integrated electric vehicle energy Charging Stations

Sustainable energy supply plays a pivotal role in the successful deployment of electric vehicle charging station (EVCS). The EVCS’s daily operation is governed by the availability of resources to meet the energy requirements without any load rejection. Developing countries have now turned to renewable energy powered vehicles to reduce their dependency on fossil fuels. They have been successful in their attempts to deploy EVCS in their respective countries whilst constantly working on the improvement of this infrastructure. However, the same cannot be uttered in South Africa. The county has limited power-generating capacity, which limits it capability of fully sustaining the existing load. An additional burden on the already ailing grid could have dire consequences on the local grid. Load shedding is imposed on customers as a temporary solution in order to avoid imminent blackouts. Conversely the ever increasing fossil fuel prices threatens the livelihood of an ordinary citizen. As an increase in this resources leads to an increase in transportation cost and food prices. To combat this problems, an efficient, sustainable and cost effective electric vehicle charging station that will be beneficial to the grid as well as the environment is necessary. POET (performance, operation, equipment and technology) energy efficiency improvement and management framework is utilized as an effort to analyse and initiate a feasibility and sustainability of the system to be implemented, by holistically looking at the performance, operation, equipment as well as technology. The afore mention POET energy efficiency improvement and management will look into the two of the three processes, namely targeting and planning in fourth level: An engineering level as an attempt to improve the efficiency and economical nobility of the system. A review in to the impact of technological advancement, of the proposed grid-integrated hybrid peer to peer electric vehicle energy sharing at a Charging Station in South Africa will be conducted.