Load Demand Patterns Research Articles

Optimizing microgrid efficiency: Coordinating commercial and residential demand patterns with shared battery energy storage

The optimization of energy systems within a multi-microgrid framework, enriched by shared Battery Energy Storage Systems (BESS), has emerged as a compelling avenue for enhancing the efficiency of distributed energy networks. In response to the increasing integration of BESS in modern energy systems, this study investigates the implications of incorporating BESS within connected residential-commercial Microgrids (MGs). Unlike previous studies that primarily focused on cost and reliability, this research fills a significant gap in the literature by investigating the optimization of load demands patterns. Specifically, we explore the impact of shared BESS on load demand patterns in commercial-residential MGs. The research introduces two innovative critical load metrics, peak-to-average ratio (PAR) and demand profile smoothness (DPS), to assess the influence of BESS on demand profiles. In addition, the study explores the integration of a Dynamic Thermal Rating (DTR) system, compared to traditional fixed thermal rating systems, to further optimize the performance and efficiency of connected residential-commercial MGs enriched by shared BESS. Three distinct case studies, each comprising a commercial MG (shopping mall, hotel, and office building) paired with a residential MG, were considered. Utilizing a Firefly Algorithm (FA) for optimization, the study determines the optimized BESS capacity for minimum total cost. The results highlight that the implementation of shared BESS, especially in collaboration between commercial and residential MGs, significantly reduces imported energy from the main grid, enhancing MG flexibility and resilience. While the economic benefits of shared BESS may not be substantial (up to 3.25 % cost reduction), the study underscores its contribution to more balanced and smoother load demand curves, with improvements in PAR (up to 15.63 %) and DPS (up to26.05 %). Moreover, considering DTR for transmission lines, instead of fixed thermal rating, improves the PAR and DPS up to 28.52 % and 41.06 % respectively. The findings of this study highlight the importance of considering load demand patterns in the design and operation of MGs and underscore the multifaceted benefits of shared BESS beyond economic considerations.

The capacity planning method for a hydro-wind-PV-battery complementary system considering the characteristics of multi-energy integration into power grid

The hydro-wind-PV-battery complementary operation has the potential to increase the integration of renewable energy sources into power grid. Nevertheless, the determination of the optimal capacity configuration mode and size for a hydro-wind-PV-battery complementary system (HWPBS) remains a persistent challenge. This study proposes a capacity planning framework for the HWPBS considering the characteristics of multi-energy integration to power grid. First, the centralized (CCP) and distributed (DCP) configuration patterns considering the ways of multi-energy integration into power grid are proposed to explore the optimal capacity configuration mode. Second, considering the randomness of incoming water of hydropower stations, a scenario analysis technique is proposed to obtain the typical inflow scenarios. Then, with the objectives of maximizing expected renewable generation and minimizing expected power abandonment and load loss, the capacity configuration models for the HWPBS under the CCP and DCP are developed. A load reconstruction method is proposed to reduce the computational burden. The parallelogram method is implemented to linearize the hydropower production model. Additionally, a set of indicators are proposed to evaluate the economy and reliability of the HWPBS. The proposed framework is applied to the Yalong River Basin. The results show that: the wind-PV configuration capacity is affected by load demand, battery storage and configuration patterns. The load demand process with better correlation to wind-PV output is advantageous for integrating wind and solar resources. Battery storage can effectively reduce power abandonment and load loss, and the configuration pattern has a significant impact on the final capacity of wind-PV power.

Stress Level Of Pregnant Women During The Pandemic Covid-19

PlagueCorona Virus Disease 2019 (Covid-19) makes everyone stressed and anxious because the transmission is very fast. Pregnant women during the Covid 19 pandemic do not rule out the possibility of experiencing stress due to the fear that something will happen to their pregnancy. Pregnancy with high stress levels can certainly have a negative impact on the mother and fetus. Stress is the body's non-specific response to every load demand and general reaction patterns and adaptations. Proper handling of pregnant women by the family can reduce the level of stress experienced by pregnant women. The purpose of this study was to determine the Stress Level of Pregnant Women During the Covid-19 Pandemic in Kalianyar Village, Ngronggot District, Nganjuk Regency.
 Descriptive research design, population and sample of 25 respondents with a sampling technique "total sampling”. The research was conducted on December 15-26 2020, the research variable Stress Levels for Pregnant Women during the Covid-19 Pandemic as a questionnaire research instrument, was analyzed by percentage and interpreted quantitatively.
 The research results obtained from 25 respondents, almost all respondents experienced moderate stress as many as 20 respondents (80%). A small portion of the respondents experienced severe stress as many as 5 respondents (20%).
 This is influenced by several factors, namely: age, education, occupation, history of pregnancy, gestational age, information about Covid-19, sources of information about Covid-19, and history of pregnancy examinations (ANC). It is hoped that pregnant women can control stress, get support from their families, always implement health protocols and think positively during pregnancy during the Covid-19 pandemic.

A Novel Dynamic Load Scheduling and Peak Shaving Control Scheme in Community Home Energy Management System Based Microgrids

Load scheduling and peak demand shaving are two critical aspects of the utility grid operation that help both the grid operators as well as end-users. This paper proposes a two-stage community home energy management system for microgrids. The first stage deals with the dynamic clustered community load scheduling scheme. Comparatively flatter power demand was attained using particle swarm optimization (PSO) incorporating user-defined constraints. The new arising or remaining peaks as a consequence of consumer constraints are catered to in the second stage. The second stage proposes a rule-based peak shaving management method for the photovoltaic (PV) systems that are connected to the grid and the battery energy storage systems. The proposed technique determines the dynamic demand and feed-in limits based on the estimations of the upcoming day’s load demand and PV power profiles. Also, the study presents an optimal rule-based management technique for peak shaving of utility grid power that sets the charge/discharge day ahead schedules of the battery. For peak energy minimization, PSO is used to calculate the optimal inputs needed for implementing the appropriate rule-based management strategy. MATLAB is used to test the proposed method for different PV power and load demand patterns, thus, achieving an average improvement of 8.5%.

Analysis of the Impact of Particulate Matter on Net Load and Behind-the-Meter PV Decoupling

With the increasing penetration of the photovoltaic (PV) generator, uncertainty surrounding the power system has increased simultaneously. The uncertainty of PV generation output has an impact on the load demand forecast due to the presence of behind-the-meter (BtM) PV generation. As it is hard to assess the amount of BtM PV generation, the load demand pattern can be distorted depending on the solar irradiation level. In several literature works, the influence of the load demand pattern from BtM PV generation is modeled using environmental data sets such as the level of solar irradiation, temperature, and past load demand data. The particulate matter is a severe meteorological event in several countries that can reduce the level of solar irradiation on the surface. The accuracy of the forecast model for PV generation and load demand can be exacerbated if the impact of the particulate matter is not properly considered. In this paper, the impact of particulate matter to load demand patterns is analyzed for the power system with high penetration of BtM PV generation. Actual meteorological data are gathered for the analysis and correlations between parameters are built using Gaussian process regression.

Minimizing pricing policies based on user load profiles and residential demand responses in smart grids

This paper considers the time of use (TOU) pricing scheme to propose a consumer aware pricing policy (CAPP), where each customer receives a separate electricity pricing signal. These pricing signals are obtained based on individualized load demand patterns to optimally manage the flexible load demand. The main objective of CAPP is to reduce the peaks in overall system demand in such a way that the pricing signals remain non-discriminatory. To achieve this goal, firstly the mathematical model of CAPP comprising TOU electricity price, and its variation based on consumption patterns is formulated. Secondly, the proposed CAPP model is further extended by integrating renewable energy and storage sources to overcome the possible creation of rebound peaks due to scheduling. This objective is achieved by implementing a control variable modeling the upper bound of the low tariff area. Thirdly, the cost minimization optimization problem is solved by using a Genetic Algorithm (GA) with the objective of the fair price distribution. Numerical and simulation results are obtained to validate the proposed model in terms of convergence, optimality, and cost reduction objective function. Results reveal that each customer receives a separate electricity price signal based on his demand pattern without affecting the utility/retailer revenue. Furthermore, the total cost results are also compared with and without TOU & CAPP schemes to further validate the nondiscrimination in electricity price.

Benefit Evaluation of PV Orientation for Individual Residential Consumers

Photovoltaic (PV) installations located in the northern hemisphere must be oriented to the south in order to obtain maximal annual yield. This is mainly driven by the remuneration mechanisms which incentivize maximal energy production to a certain extent. Nowadays, such support mechanisms are declining or even phased out in many countries. Hence, self-consuming the produced energy is getting more viable. In order to match better the load demand pattern, the azimuth angle of a PV installation could be changed or oriented towards multiple directions. This article investigates the benefits of PV installations facing other directions than the south. Therefore, the Hay & Davies transposition model has been used to calculate the in-plane irradiance, as it is found in the literature to be the most accurate for non-south faced PV installations. In order to determine the benefit, a large dataset of real measured consumption profiles has been used and then divided according to their annual consumption. Large consumers with an oversized east/west-oriented PV installation especially take advantage. The self-sufficiency index (SSI) is found to increase with almost 0.94 percent points, while the self-consumption index (SCI) increases with 6.46 percent points. The peak reduction is assessed by calculating the annual moving average of the month peaks. It is found that this moving average month peak reduction is marginal. Lastly, the reduction in storage capacity is found to be not that significant, although in terms of battery utilization it is found that the number of discharge cycles is reduced with 6%.

Prediction of electricity consumption demand pattern for 2018 in Ogun State, Nigeria

This study uses probabilistic load forecast technique to predict the load demand pattern in Ogun State for year 2018. Energy consumption data for Ogun State for year 2016 and 2017 was obtained from the regional headquarter of the Ibadan Electricity Distribution Company (IBEDC), Abeokuta. The results of the study show that the energy consumption in Ogun State has the probability tendency of rising above 98,469.40 MWHR by 2.68%. Similarly, it was also established that the probability of energy consumed in the state rising below 46,494.68 MWHR within the next few months will be 5.98%. The probability that energy consumption in year 2018 will fall between 98,469.40 MWHR and 46,494.68 MWHR is 91.84%. Energy consumption in year 2018 will mostly fall between 63,500 MWHR – 86,000 MWHR. The result also indicated that energy consumption in 2018, has the highest probability of falling between 72,500 MWHR and 77,000 MWHR by 15.34%. It is unlikely it falls between 45,000 MWHR to 50,000 MWHR and 95,000 MWHR to 99,500 MWHR, with both range having their percentage probabilities at 0.19% and 2.99% respectively. Result of this study is useful to IBEDC for their operational planning and control activities. Keywords: energy consumption forecast, probabilistic load forecast (PLF), deterministic forecast

Short Term Load Forecasting based on BPL Neural Network with Weather Factors

This paper presents the development of Short Term Load Forecasting (STLF) model using Artificial Neural Network (ANN). STLF is required for electric power planning and electricity market planning. The proposed model predicts the load demand of Connecticut in the U.S. using hourly historical electric load and weather data. For improving the load prediction accuracy, we consider two main issues that are seasons and weather factors. Each season has different load demand patterns, thus the weather factors are differently applied in each season. The proposed model uses the composited weather factor which consists of temperature and dew point. The temperature and dew point weather factors are selected through the correlation coefficient to obtain the meaningful data among the weather factors. The selected weather factors adjust the level of the pitch which is the predicted load demand of one day ahead. The proposed model improves the forecasting accuracy both in summer and winter.

A hybrid economic indices based short-term load forecasting system

The bankruptcy event of Lehman Brothers and the corresponding global economic recession in 2008 and 2009, influenced the electricity load demand patterns for which traditional load forecasting approaches were not able to effectively predict. To overcome this problem, this paper proposes a new hybrid economic indices based short-term load forecasting (HEI-STLF) system. In which business indicators, such as the leading index or the coincide index, each combined with stock index as hybrid economic indices influencing factors for the support vector regression (SVR) model, to respond to the economic dynamics and reduce its impact on forecasting accuracy. The Taiwan island-wide electricity load demands from 2008 to 2011 are used as the case study for performance testing with different combinations of the Taiwan business indicator and the Taiwan Stock Exchange Capitalization-Weighted Stock Index (TAIEX). The results show that the proposed HEI-STLF system with hybrid economic indices of an annualized six-month rate of change of composite leading index and a 90days moving average of TAIEX, achieves the best forecasting performance. Compared to the traditional SVR load forecasting approach, it improves the forecasting accuracy in the best condition by 30.39% in the period when the load demands are affected by the global economic recession.

Multi-Objective Optimal Energy Consumption Scheduling in Smart Grids

A major source of inefficiency in power grids is the underutilization of generation capacity. This is mainly because load demand during peak hours is much larger than that during off-peak hours. Moreover, extra generation capacity is needed to maintain a security margin above peak load demand. As load demand keeps increasing and two-way communications are enabled by smart meters (SMs), demand response (DR) has been proposed as an alternative to installing new power plants in smart grids. DR makes use of real-time schemes to allow users to modify their load demand patterns according to their energy consumption costs. In particular, when load demand is high, energy consumption cost will be high and users may decide to postpone certain amount of their consumption needs. This strategy may effectively reduce the peak load demand and increases the off-peak demand, and hence could increase existing generation capacity utilization and reduce the need to install extra generation plants. In this paper, we consider a third-party managing the energy consumption of a group of users, and formulate the load scheduling problem as a constrained multi-objective optimization problem (CMOP). The optimization objectives are to minimize energy consumption cost and to maximize a certain utility, which can be conflicting and non-commensurable. We then develop two evolutionary algorithms (EAs) to obtain the Pareto-front solutions and the e-Pareto front solutions to the CMOP, respectively, which are validated by extensive simulation results.

평일과 주말의 특성이 결합된 연휴전 평일에 대한 단기 전력수요예측

The accuracy of load forecast is very important from the viewpoint of economical power system operation. In general, the weekdays` load demand pattern has the continuous time series characteristics. Therefore, the conventional methods expose stable performance for weekdays. In case of special days or weekends, the load demand pattern has the discontinuous time series characteristics, so forecasting error is relatively high. Especially, weekdays near the thanksgiving day and lunar new year`s day have the mixed load profile characteristics of both weekdays and weekends. Therefore, it is difficult to forecast these days by using the existing algorithms. In this study, a new load forecasting method is proposed in order to enhance the accuracy of the forecast result considering the characteristics of weekdays and weekends. The proposed method was tested with these days during last decades, which shows that the suggested method considerably improves the accuracy of the load forecast results.

Thermal analysis and performance optimization of a solar hot water plant with economic evaluation

The main objective of this study is to optimize the long-term performance of an existing active-indirect solar hot water plant (SHWP), which supplies hot water at 65°C for use in a flight kitchen, using a micro genetic algorithm in conjunction with a relatively detailed model of each component in the plant and solar radiation model based on the measured data. The performance of SHWP at Changi International Airport Services (CIASs), Singapore, is studied for better payback period using the monthly average hourly diffuse and beam radiations and ambient temperature data. The data input for solar radiation model is obtained from the Singapore Meteorological Service (SMS), and these data have been compared with long-term average data of NASA (surface meteorology and solar energy or SSE). The comparison shows a good agreement between the predicted and measured hourly-averaged, horizontal global radiation.The SHWP at CIAS, which comprises 1200m2 of evacuated-tube collectors, 50m3 water storage tanks and a gas-fired auxiliary boiler, is first analyzed using a baseline configuration, i.e., (i) the local solar insolation input, (ii) a coolant flow rate through the headers of collector based on ASHRAE standards, (iii) a thermal load demand pattern amounting to 100m3/day, and (iv) the augmentation of water temperature by auxiliary when the supply temperature from solar tank drops below the set point. A comparison between the baseline configuration and the measured performance of CIAS plant gives reasonably good validation of the simulation code. Optimization is further carried out for the following parameters, namely; (i) total collector area of the plant, (ii) storage volume, and (iii) three daily thermal demands. These studies are performed for both the CIAS plant and a slightly modified plant where the hot water supply to the load is adjusted constant at times when the water temperature from tank may exceed the set temperature. It is found that the latter configuration has better thermal and economic performances over the conventional design.

A probabilistic transmission pricing methodology considering transmission reliability margins

This paper posits on a reasonable quantification of the intrinsic reliability offered by an existing transmission network structure for a given set of power transactions. We rely on the concept of transmission reliability margins in the lines, which act as safety nets to protect system security in the face of aleatory uncertainties in the availability of transmission lines and epistemic uncertainty in accounting for the load demand patterns. This is proposed to be used as an equitable basis to charge the users of transmission network, generation and load consumption entities alike, over and above the usage charges levied according to the ‘extent-of-use’ normative. Using the tandem combination of deterministic and probabilistic load flow studies in conjunction with power flow tracing procedures, we propose a step-by-step procedure to arrive at a novel transmission pricing methodology. A six-bus Roy Billinton Test System (RBTS) is employed to illustrate the conceptual feasibility and the computational procedure.

Long-term operation optimization of integrated generation systems by fuzzy logic-based management

Abstract The aim of this paper is to implement a smart control strategy to improve the long-term energy performance of stand alone electric generation systems integrated by renewable energy systems (photovoltaic and wind energy conversion systems). The paper describes a fuzzy logic application to optimize the energy management (EM) of integrated generation systems (IGS) and shows its effectiveness in exploiting the qualitative information, on an optimal operation of diesel groups (DGs) and of renewable generating units (available from manufacturers) as well as on the operating experience of IGSs supplied by highly variable and site-specific renewable energy sources and coupled with different load demand patterns. As a case study, a rule-based operation planning strategy has been implemented to a small size experimental IGS prototype in order to minimize both fuel consumption and load energy not supplied. The simulation results prove that a fuzzy-based EM, taking into account the fuzziness of predicted wind and solar resources as well as user load demand, assures better energy performance on long-term operations than conventional approaches.

Impact of distributed generation on distribution investment deferral

The amount of distributed generation (DG) is increasing worldwide, and it is foreseen that in the future it will play an important role in electrical energy systems. DG is located in distribution networks close to consumers or even in the consumers' side of the meter. Therefore, the net demand to be supplied through transmission and distribution networks may decrease, allowing to postpone reinforcement of existing networks. This paper proposes a method to assess the impact of DG on distribution networks investment deferral in the long-term. Due to the randomness of the variables that have an impact on such matter (load demand patterns, DG hourly energy production, DG availability, etc.), a probabilistic approach using a Monte Carlo simulation is adopted. Several scenarios characterized by different DG penetration and concentration levels, and DG technology mixes, are analyzed. Results show that, once initial network reinforcements for DG connection have been accomplished, in the medium and long-term DG can defer feeder and/or transformer reinforcements.

Passive load following method for PEFC system with reformer and its efficiency improvement evaluated by using the concept of exergy

The Metal-Hydride Intermediate-Buffer (MIB) method, which enables the load following operation of the PEFC system with reformer, has been proposed. Feasibility of this system has already been confirmed experimentally and the result shows that the metal hydride is not poisoned with impurities in reformed gas. It is expected that the MIB method improves the efficiency of PEFC systems especially in low load. Efficiency improvement in low load of the PEFC system for residential use is important because the average electric power consumption in general residences is about 10 to 20% of the peak power. In this paper, the PEFC system efficiency using the MIB method is evaluated by the concept of exergy. The exergy efficiency of this system has been calculated and compared with the conventional system as a function of load factor. The result shows that the efficiency of our system is higher especially at lower load factor, but slightly lower at the rated condition than that of the conventional system. The obtained relation between the load factor and the exergy efficiency has been adapted to load demand patterns for one year measured at a residence. The results show that maximum exergy efficiency of the system with the MIB method is 4.5% higher than that of the conventional system, and the system efficiency with the MIB method is higher than the conventional system above 900 W of rated power. The PEFC system for residential use is being investigated mainly for a 1-kW-class generator. It has been clarified that the PEFC system with the MIB method has an ability to improve the efficiency at 1 kW rated power and passive load following function that the conventional systems never have. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 152(4): 17–26, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20148

Passive Load Following Method for PEFC System with Reformer and its Efficiency Improvement Evaluated by Using the Concept of Exergy

The Metal Hydride Intermediate-Buffer (MIB) method, which enables the load following operation of the PEFC system with reformer, has been proposed. Feasibility of this system has been already confirmed by the experiment and the result shows that metal hydride is not poisoned with impurities in reformed gas. It is expected that the MIB method improves the efficiency of PEFC system especially in low load. Efficiency improvement in low load of the PEFC system for residential use is important because the average electric power consumption in general residences is about 10-20% of the peak power. In this paper the efficiency of PEFC system with MIB method is evaluated by using the concept of exergy. The exergy efficiency of this system has been calculated and compared with the conventional system as a function of load factor. The result shows that efficiency of our system is higher especially in lower load factor, but slightly lower at the rated condition than that of the conventional. The obtained relation between the load factor and the exergy efficiency has been adapted to load demand patterns for one year measured at a residence. The results show that maximum exergy efficiency of the system with the MIB method is 4.5% higher than that of the conventional system, and the system efficiency with the MIB method is higher than the conventional system above 900W of rated power. The PEFC system for residential use is being investigated mainly about 1kW class generator. It has been clarified that the PEFC system with the MIB method has an ability to improve the efficiency at 1kW rated power and the passive load following function that the conventional systems never have.

A multiobjective-optimal neuro-fuzzy extension to power plant co-ordinated control

This paper presents an overall control scheme for wide-range multiobjective-optimal operation of a fossil-fuel power unit. The scheme provides the means to accommodate operating scenarios characterized by multiple operating requirements, and to attain optimal operation along any arbitrary unit load-demand pattern. The proposed strategy builds upon current co-ordinated control schemes, which typically account for simultaneous control of power and steam pressure. First, the scope of co-ordination over the internal processes of the unit is extended by including the control of the drum water level to achieve balanced overall plant operation at all loads. Then, a supervisory reference governor and a neuro-fuzzy feedforward control path are added to complement the already existing multiloop feedback control configuration. The reference governor implements a set-point scheduler based on unit load demand to set-point mappings, which are designed by solving a multiple objective optimization problem. The feedforward control path approximates the nonlinear multivariable inverse static behaviour of the power unit at the optimal operating conditions specified by the set-point mappings through a set of multi-input-single-output fuzzy inference systems, which are designed using a neuro-fuzzy learning paradigm. With this approach, the plant is driven by an arbitrary unit load-demand pattern from which the reference governor specifies the multiobjective-optimal operating conditions for the plant through set-point trajectories; the feedforward control path provides control signals to achieve wide-range manoeuvrability and the feedback control path compensates for uncertainties and disturbances, along and around the commanded set-point trajectories, respectively. Simulation results demonstrate the feasibility of the proposed control scheme.

A neuro-fuzzy approach for modelling electricity demand in Victoria

Neuro-fuzzy systems have attracted growing interest of researchers in various scientific and engineering areas due to the increasing need of intelligent systems. This paper evaluates the use of two popular soft computing techniques and conventional statistical approach based on Box–Jenkins autoregressive integrated moving average (ARIMA) model to predict electricity demand in the State of Victoria, Australia. The soft computing methods considered are an evolving fuzzy neural network (EFuNN) and an artificial neural network (ANN) trained using scaled conjugate gradient algorithm (CGA) and backpropagation (BP) algorithm. The forecast accuracy is compared with the forecasts used by Victorian Power Exchange (VPX) and the actual energy demand. To evaluate, we considered load demand patterns for 10 consecutive months taken every 30 min for training the different prediction models. Test results show that the neuro-fuzzy system performed better than neural networks, ARIMA model and the VPX forecasts. © 2001 Elsevier Science B.V. All rights reserved.