Electric Load Management Research Articles

Design of a Power System Supervisory Control with Linear Optimization for Electrical Load Management in an Aircraft On-Board DC Microgrid

In modern aircraft, energy supply management has become a critical matter, since many aboard electrical loads have to be supplied, especially those related to flight safety. However, at the same time, the size and weight of electrical generators must be limited because of their on-board installation. In this paper, the Mixed Integrated Linear Programming (MILP) methodology has been used to formulate the Supervisor definition of the direct current (DC) microgrid (MG) on-board system with an extension for the programmable loads. Due to the problem of dimension increase, two methods have been presented and tested to perform optimal energy management (EM) aboard an aircraft: the Branch and Bound (B&B) and the Linear Regression Approximation (LRA). Finally, numerical simulations and results have been provided to validate the proposed optimization methodologies, according to the dimensions and the complexity of the problem.

Development of an Internet of Things based Electrical Load Management System

This paper presents an interoperable automation system that uses the Internet of Things technology for remotely controlling and monitoring electrical appliances connected to the power supply system. Transducers located inside the socket outlets collect vital information regarding the status of the electrical network. This information includes the AC consumption, appliance status, and information from the RFID tags. The sensor and actuators are integrated with a small microcontroller known as NodeMCU. This microcontroller is equipped with an onboard Wi-Fi transceiver thus data is processed and reliably transmitted from each socket outlet to a single board computer, known as the Raspberry Pi. The received information is processed and displayed on a standard web interface hosted by the LINUX based computer. The web interface has a variety of interactive widgets enabling the user to ‘talk’ to the appliances in real-time. Communication among the IoT devices is handled through a lightweight application layer protocol known as the MQTT protocol. Another unique feature of the presented system is its ability to identify various power quality issues that frequently occur in most electrical networks. The automation system contributes to the conservation of energy by increasing the awareness of the consumer on their daily electrical energy usage.

Hourly electric load forecasting for buildings using hybrid intelligent modelling

Because of the rapidly increasing total electric load of buildings, effective electric load management should be achieved quickly. This can be realized via electric load forecasting. In this study, a novel clustering-based hybrid prediction model is proposed to predict the 24-daily electric load of buildings. In this study, fuzzy c-means (FCM) clustering, ensemble empirical mode decomposition (EEMD), and some intelligent prediction algorithms are combined. FCM is used to extract the daily data exhibiting similar features, whereas EEMD is used for breaking down the optimal prediction algorithm is selected for each component, and the prediction results are integrated. When compared with the remaining conventional prediction models based on real data, the proposed hybrid model exhibits higher prediction accuracy.

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.

Strategically plug-in electrical vehicle load management in radial distribution system (A case study)

Objectives: To present a Plug in electric vehicle load model to integrate its maximum loading and distributed generations (DGs) units by maintaining maximum voltage profile value and lesser active and reactive power losses drop in the distribution system. Methods/Statistical analysis: This study comprises in two phases, in first phase IEEE 33-bus radial distribution system with standard data available is simulated using ETAP software. In the second phase, different PEV load model scenario and distributed generation units will be designed and integrated. At each scenario, the voltage profile, active and reactive power losses are observed and strategy is proposed in the manuscript. Findings: The test system gives convincing results even with adoption of new generation and plug-in electrical vehicles load without violating system performance. Novelty/Applications: The proposed study is best suited for practical application of distribution system such as introduction of DG units (i.e.solar PV) and plug-in electrical vehicle load with proper loading. Additionally, this study uses backward-forward sweep method to manage PEVs load with optimum size of DG units; it improves system losses and minimum per unit bus voltage within acceptable limits, unlike other studies. Result of this study allows to use smaller size of DG units with minimum cost. Keywords: Plug-in electric vehicle; radial distribution system; recharging stations; load management; distributed generation

Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks

Industrial forecasting has entered an era of unprecedented growth in the size and complexity of data which require new modeling methodologies. While many new general purpose machine learning approaches have emerged, they remain poorly understand and irreconcilable with more traditional statistical modeling approaches. We present a general class of exponential smoothed recurrent neural networks (RNNs) which are well suited to modeling non-stationary dynamical systems arising in industrial applications such as electricity load management and financial risk and trading. In particular, we analyze their capacity to characterize the non-linear partial autocorrelation structure of time series and directly capture dynamic effects such as seasonality and regime changes. Application of exponentially smoothed RNNs to electricity load forecasting, weather data and financial time series, such as minute level Bitcoin prices and CME futures tick data, highlight the efficacy of exponential smoothing for multi-step time series forecasting. The results also suggest that popular, but more complicated neural network architectures originally designed for speech processing, such as LSTMs and GRUs, are likely over-engineered for industrial forecasting and light-weight exponentially smoothed architectures capture the salient features while being superior and more robust than simple RNNs.

Ensuring the Reliability and Efficiency of the Power Industry in the Agricultural Sector of the Republic of Belarus in Modern Conditions

The necessity of improving the power supply system in the agricultural sector is substantiated by the growing share of first-category loads that require targeted reliability. Improving values of the technical and economic indicators of centralized energy generation and its transmission through main and distribution grids cannot ensure high reliability of consumers of the first category, the number of which is constantly growing in agriculture. It is noted that if the power system in outage of the generating source of the high power supply is provided by inputting the emergency reserve, then at emergency switching-off phase of the section of the mains, power supply of the consumers powered by the schemes for both radial and looped distribution network may be absent for a long period of time (several hours). The ways and methods of improving the power supply system, such as integration of a single power system with distributed generation sources, ensuring their parallel operation, load management using complex tariffs for electric energy that take into account the form of load schedules, modernization of technological processes taking into account their automation and improvement of technical and economic indicators, are considered. Taking into account the increase in electricity consumption for heating and hot water supply, we consider the option of eliminating cross-subsidization, which stimulates the interest of consumers in saving electricity and reducing the fee for electricity consumption by changing the operating modes of equipment and devices. The analysis of promising energy-saving measures in agricultural power engineering demonstrated that routine measures are being supplemented by other measures induced by the fact that power electronics and microprocessor technology have significantly advanced in their development in the agricultural energy sector over the past decades, for example, for frequency control of asynchronous motors equipped by a short-circuited rotor that has high values of energy and dynamic indicators. Since there are a number of technologies in agriculture where electric energy is used without the electric drive units application and each of the technological processes has its own requirements for the reliability and quality of electric energy, the scheme of mutual redundancy from adjacent substations using distributed generation sources and technical means of automated energy accounting, control and management of electrical loads, is considered.

Cloud based Electric Vehicle Load Management at DC Charging Stations

DC distribution system is getting wider acceptance around the world due to the reduction of conversion stages between AC and DC and the increased popularity of DC loads. Solar PV is a genuine source of DC which can directly power the DC loads like electric vehicle (EV) battery for charging. Increased deployment of Electric vehicles need more charging stations, preferably powered with renewable DC sources or DC microgrid. The intermittent nature of solar power necessitates a backup support from the AC grid. An attempt is made to develop a load management system at a DC charging station powered from renewable sources. The proposed load management system can manage the various DC loads at the charging station based on the power rating of the charging equipment, available solar power and the availability of the backup AC grid. The entire system is automated and enabled with IoT in order to receive the available solar power data through a cloud based communication system. Identification of grid failure, instantaneous load changes and communication infrastructure through cloud for updating the generation profiles are the key concerns of the proposed system.

Two-Stage Optimal Scheduling Strategy for Large-Scale Electric Vehicles

This paper proposes a two-stage scheduling strategy for large-scale electric vehicles to reduce the adverse impact of the uncontrolled charging of the electric vehicles on the grid. Based on the statistical data of private car travel, the uncontrolled charging demand of individual electric vehicles and their aggregation are simulated. In the first stage, the electric vehicles and thermal power units are jointly scheduled. To minimize the total cost and standard deviation of the total load curve, the charging and discharging load guiding curve of the electric vehicles and the optimal output plans of the thermal power units in each period of the scheduled day are formulated. In the second stage, the electric vehicle load management and control centre formulates specific charging and discharging plans for the users through rolling optimization to follow the guiding load curve. The cost of vehicle discharge compensation is considered to improve the willingness of users to participate in scheduling and the user satisfaction. To avoid the “dimension disaster” caused by the centralized dispatching of large numbers of electric vehicles, the K-means clustering algorithm is used to divide the vehicles into different groups. Next, each group is scheduled as a unit, and the model is solved by using the particle swarm optimization algorithm. By comparing the optimization results of different scenarios, the feasibility and effectiveness of the proposed strategy are verified.

Predicting sectoral electricity consumption based on complex network analysis

High-frequency and unit-level consumption data collected by smart meters makes accurate and short-term predictions of sectoral electricity demand possible. To facilitate electricity market pricing, load management and demand response, models handling such high-dimensional data-sets are expected to realize effective variable selection, accurate prediction and, in the meantime, retain the economic mechanisms as much as possible. This paper attempts to propose a complex network based on a variable selection model that retains the causality relationships among the most relevant sectors and can achieve prediction accuracy that is comparable to other data-driven models. A dataset containing 266,000 industrial and commercial firms in Shanghai is employed to develop a complex network relying on Granger causality and correlation coefficients. Dominant nodes are selected based on a Planar Maximally Filtered Graph algorithm and then serve as explanatory variables in the linear regression model. Further comparison with LASSO, PCA and Ridge regression shows that this model can successfully realize dimension reduction but maintain significant economic mechanisms, and achieving unbiased estimation and acceptable accuracy.

The Effect of Electricity Load Management on the Operations of Small and Medium Enterprises: A Case Study of La-Nkwantanang Madina Municipality

Ghana has been experiencing frequent power cuts over the last ten years. The need to expand and diversify its generation capacity in order to improve supply security is key for development. This study explored the effects of electricity load management on the operation of small-scale enterprises, and examined the effectiveness of these enterprises’ coping mechanisms. A cross–sectional survey design was adopted as well as the mixed approach research design. A total of 152 small scale traders were sampled from Madina in La-Nkwantanang Municipality in the Greater Accra Region. The study depicted that the recent load management is having adverse effect on the profitability of many SMEs owing to the coping mechanism measures such as retrenchment of workers, working at night, reduction in workers’ salaries and use of generators they have adopted to lessen the impact of the frequent load management reported less effectiveness in resolving the challenges posed by load management. This paper advocate that, government should invest in solar energy to be a substitute power supply to the country during the dry season where there is constant sunlight which obviously will also reduce pressure on electricity plants. Broken and unbroken but old plants can be fixed or replaced by VRA and ECG to enable them work effectively as well as explore other energy technologies such as the thermal energy and the wind energy to restore constant power supply. Keywords: electricity load management, small and medium enterprises DOI : 10.7176/RJFA/10-2-08

Load management possibilities of electric vehicles when expansion planning of electric power systems

Electrical load management is undoubtedly an integral and paramount component of power industry functioning. Electric and hydrogen vehicles are not an exotic vehicle today and the most perspective for solving this problem. The paper studies the impact of advanced environmentally compatible vehicles on electric power system expansion. The potential of their use as load-controlled consumers is assessed. The results of study in the area of electric power system expansion optimization considering this potential for electricity consumption control are presented.

Performances and economic analysis of small photovoltaic–electricity energy storage system for residential applications

Currently, the need to address the issues arising from the uncontrolled growth of photovoltaic installations, such as intermittence and unpredictability of the generation that cause loss of balance in the grid, becomes unavoidable. Promising solutions for minimizing grid injection are the combination of photovoltaic generation with electricity energy storage and load management, the latter commonly known as Demand Side Management. These strategies together with incentives for self-consumption or energy independence from the network will allow facilitating the integration of the always-increasing generation of renewable energy. In Europe, the usage of residential energy grid-interactive energy storage systems for buffering of surplus photovoltaic generation is becoming a field of growing interest and market activity, as a consequence of the less attractive photovoltaic feed-in-tariffs in the near future and incentives to promote self-consumption. This study aims to evaluate the energy exchange with the grid and the rate of self-consumption of combined photovoltaic–electricity energy storage systems dedicated to residential and small commercial prosumers. More specifically, several combinations of sizes of photovoltaic plant, annual household consumptions and electricity energy storage capacity were evaluated. This analysis aims to identify which arrangement among photovoltaic power, electricity consumption and battery capacity allows reaching the highest ratio of self-sufficiency and consequently minimizing the energy exchanged with the grid. Moreover, the financial analysis of the photovoltaic–electricity energy storage system has been performed for verifying the economic viability of the photovoltaic–electricity energy storage systems under the Italian current market and economic circumstances.

A novel approach for plug-in electric vehicle planning and electricity load management in presence of a clean disruptive technology

Plug-in electric vehicles within a decarbonized electricity sector offer a remedy for pollution mitigation in the transportation sector. In this paper, a decomposed planning model for optimal transition to electric vehicle in the deep decarbonization pathway project considering electricity grid constraints is developed. A bi-level charging/discharging control for plug-in electric vehicles is proposed to increase the penetration of electric vehicles in a planning horizon. Moreover, an integrated model of electricity and transportation sectors is developed in to investigate the impact of autonomous electric vehicles as a clean disruptive technology for electric vehicles. The proposed planning model is able to determine whether the existing and future electricity grid infrastructure can meet the growing electrified transportation load in the near future or not. Hence, a Bayesian decision-based conjoint method for developing a heterogeneous transportation demand model is applied. The proposed planning model is carried out on Ontario's grid and a six-bus test system to corroborate the capability of the proposed planning model. According to the obtained results in Ontario's grid, penetration of plug-in electric vehicles is increased to 30% by 2025 while the total CO2 emission is decreased by 28% compared to the case with no electric vehicle charging/discharging management and disruptive technology.

Portuguese Plan for Promoting Efficiency of Electricity End-Use: Policy, Methodology and Consumer Participation

The Portuguese Electricity Demand-Side Efficiency Promotion Plan (PPEC) is a voluntary financial mechanism, under which several entities, among them electric utilities, may submit proposals of measures aiming at the reduction of electricity consumption or load management. It is one of the alternative options followed by the Portuguese government to the Energy Efficiency Obligations (EEO) stated in Article 7 of the EU Energy Efficiency Directive. A brief review is presented of the state of the implementation of Article 7 in EU. PPEC is one of the schemes that provide financial support to the implementation of measures whose results contribute to the commitments made under the Portuguese National Energy Efficiency Action Plan (NEEAP), the framework under which the alternatives to the EEO were designed. In the first edition of the PPEC, only three energy services were addressed, while, in the most recent PPEC edition, the sixth, measures addressed nine energy services. In addition, the co-funding by participating consumers and other agents has increased, raising the investment in energy efficiency from actors other than the program administrator. PPEC, although a voluntary mechanism, has proven to be a very competitive one, involving an increasing number of economic agents, measures and addressed energy services.

Thermal and Electrical Load Management in Smart Home Based on Demand Response and Renewable Energy Resources

Thermal and Electrical Load Management in Smart Home Based on Demand Response and Renewable Energy Resources

Electric load management in spindle run-up and run-down for multi-spindle machine tools via optimal power-torque trajectories and peak load synchronization

Electrical energy consumed during machine tool spindle run-up and run-down has a non-negligible impact on overall spindle energy performance. A model-based efficiency analysis of a common spindle unit architecture, with a separate induction motor, is conducted to investigate different loss sources. The identified model is exploited to define optimized motor torque trajectories for spindle run-up/run-down, exploiting the idle time typically available on the fastest machining modules in a transfer machine tool. Study reveals that savings can be achieved, both in terms of peak power and energy losses, by adopting optimal values for the acceleration rate and power limit. To reduce power peaks at the machine level, a unit synchronization algorithm is proposed which avoids simultaneous acceleration/deceleration of multiple spindles. The overall cycle time is preserved by prioritizing the execution of the slowest processing unit. The proposed methodologies have been applied to a three spindles flexible transfer machine tool, estimating the corresponding gains in terms of energy losses and power peak reduction.

Real-time electric load management for DC zonal all-electric ship power systems

All-electric ship power systems have limited generation and include a large portion of nonlinear loads and dynamic loads relative to the total power capacity. In DC zonal ship power systems, power converter constraints and motor load voltage constraints need to be satisfied in operational real time. Sudden load changes may cause significant frequency oscillations on the AC side of the system. The load dynamics and priorities also need to be considered. Thus, individual loads in the DC zonal system need to be optimally regulated in operational real time to improve the power quality and system level stability. In this paper, a heterogeneous multi-agent system (MAS) framework was developed for a DC zonal system of an all-electric ship power system to achieve dynamic generation and load balancing while satisfying operational constraints as well as considering load priorities. The developed method integrates system dynamics and various operational constraints into MAS and controls individual loads using decentralized cooperative controller. Simulation results on a two-zone notional all-electric ship power system simulated in PSCAD software show the viability and performance of the proposed technique.

Automated energy management in distributed electricity systems: An EEPOS approach

ABSTRACTThe increasing capacity of distributed electricity generation brings new challenges in maintaining a high security and quality of electricity supply. New techniques are required for grid support and power balance. The highest potential for these techniques is to be found on the part of the electricity distribution grid.This article addresses this potential and presents the EEPOS project’s approach to the automated management of flexible electrical loads in neighborhoods. The management goals are (i) maximum utilization of distributed generation in the local grid, (ii) peak load shaving/congestion management, and (iii) reduction of electricity distribution losses. Contribution to the power balance is considered by applying two-tariff pricing for electricity.The presented approach to energy management is tested in a hypothetical sensitivity analysis of a distribution feeder with 10 households and 10 photovoltaic (PV) plants with an average daily consumption of electricity of 4.54 kWh per household and a peak PV panel output of 0.38 kW per plant. Energy management shows efficient performance at relatively low capacities of flexible load. At a flexible load capacity of 2.5% (of the average daily electricity consumption), PV generation surplus is compensated by 34–100% depending on solar irradiance. Peak load is reduced by 30% on average. The article also presents the load shifting effect on electricity distribution losses and electricity costs for the grid user.

Forecasting Building Energy Demands With a Coupled Weather-Building Energy Model in a Dense Urban Environment

Major new metropolitan centers experience challenges during management of peak electrical loads, typically occurring during extreme summer events. These peak loads expose the reliability of the electrical grid on the production and transmission side, while customers may incur considerable charges from increased metered peak demand, failing to meet demand response program obligations, or both. These challenges create a need for analytical tools that can inform building managers and utilities about near future conditions so they are better able to avoid peak demand charges and reduce building operational costs. In this article, we report on a tool and methodology to forecast peak loads at the city scale using New York City (NYC) as a test case. The city of New York experiences peak electric demand loads that reach up to 11 GW during the summertime, and are projected to increase to over 12 GW by 2025, as reported by the New York Independent System Operator (NYISO). The energy forecast is based on the Weather Research and Forecast (WRF) model version 3.5, coupled with a multilayer building energy model (BEM). Urban morphology parameters are assimilated from the New York Primary Land Use Tax-Lot Output (PLUTO), while the weather component of the model is initialized daily from the North American Mesoscale (NAM) model. A city-scale analysis is centered in the summer months of June–July 2015 which included an extreme heat event (i.e., heat wave). The 24-h city-scale weather and energy forecasts show good agreement with the archived data from both weather stations records and energy records by NYISO. This work also presents an exploration of space cooling savings from the use of white roofs as an application of the city-scale energy demand model.