Variable Electricity Demand Research Articles

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications.

Economic feasibility of integrating solar energy into water utility operations in Ciudad Juárez, Mexico

We perform an economic feasibility analysis for installing grid-connected solar arrays with batteries to power ten water supply, distribution, and treatment stations in Ciudad Juárez in tandem with the existing local grid. This article covers detailed results from the four stations that represent the topographical and geographical variability of the city and the variable electricity demand across sites. Using data from the city’s water utility and calculations from the HOMER and RETScreen models, we find contrasting results. The HOMER model finds that solar arrays with or without batteries are economically viable while RETScreen reaches the opposite conclusion. One key reason for this is the two models’ differing predictions on the amount of electricity the proposed solar arrays would supply. Further, having reliable battery storage is paramount to overcoming grid outages and the intermittent availability of solar power to ensure the success of solar projects. Global scientific consensus recognizes the need for more renewable energy. With a sunny-climate and its status as a manufacturing hub on the US-Mexico border, Ciudad Juárez has the potential to lead the way forward in promoting solar energy. The comparative results of the HOMER and RETScreen models provide a more complete understanding of the feasibility of solar power for Ciudad Juárez’s water infrastructure as well as vital insight into the factors that shape the success of solar projects.

Seasonal variation in electricity demand of solar-powered net-zero energy housing in temperate climates

Net-zero energy buildings produce enough renewable energy to meet their own annual energy requirements and have been proposed as an important method of reducing operational greenhouse gas emissions in the building sector. However, self-generation via building-integrated solar PV in cold and temperate climates is anti-correlated with increasingly-electrified heating demand and could increase seasonal variability in net electricity demand to the point that it poses challenges for future electricity systems. In this paper we explore scenarios of future large-scale uptake of electrically-heated, solar-PV net-zero energy residential buildings in New Zealand and quantify the seasonal variation of the resulting net electricity demand. A scenario of large-scale uptake of very high efficiency buildings leads to reductions in annual demand of −30% and seasonal variation of −50% in 2050 compared to a base case of current building standards. In comparison, a scenario with self-generation via solar PV without changes to the building standard reduces annual demand by −30% but increases seasonal variation by +40%. In a scenario where very high building standards are combined with solar PV, annual demand decreases by −65% and seasonal variation by only −4%. From a policy perspective, whether large-scale solar PV self-generation should be supported (in addition to very-high efficiency buildings) depends on an economic trade-off between the value of distributed solar generation (including any carbon emission reductions) vs the electricity system cost of seasonal variation. For New Zealand, given the low cost of renewable electricity from a variety of alternative sources, scenarios of large-scale uptake of solar-powered net-zero energy buildings are not favourable from an electricity system perspective.

An Adaptive Strategy for Medium-Term Electricity Consumption Forecasting for Highly Unpredictable Scenarios: Case Study Quito, Ecuador during the Two First Years of COVID-19

This research focuses its efforts on the prediction of medium-term electricity consumption for scenarios of highly variable electricity demand. Numerous approaches are used to predict electricity demand, among which the use of time series (ARMA, ARIMA) and the use of machine learning techniques, such as artificial neural networks, are the most covered in the literature review. All these approaches evaluate the prediction error when comparing the generated models with the data that fed the model, but they do not compare these values with the actual data of electricity demand once these are obtained, in addition, these techniques present high error values when there are unexpected changes in the trend of electricity consumption. This work proposes a methodology to generate an adaptive model for unexpected changes in electricity demand through the use of optimization in conjunction with SARIMA time series. The proposed case study is the electricity consumption in Quito, Ecuador to predict the electricity demand in the years 2019 and 2020, which are particularly challenging due to atypical electricity consumption attributed to COVID-19. The results show that the proposed model is capable of following the trend of electricity demand, adapting itself to sudden changes and obtaining an average error of 2.5% which is lower than the average error of 5.43% when using a non-adaptive approach (more than 50% or error improvement).

Integrating construction and demolition waste impact categories into building energy optimization through a conceptual sustainability-oriented model

Achieving energy-optimized buildings have long been amongst top priorities in building sector and increasingly led designers towards implementing new materials and design practices. Despite being beneficial at the first glance, over-implementation of these options can impose unavoidable burdens on environment, economy, and society, specifically regarding their construction and demolition waste (C&DW) disposal. To remedy this issue, simultaneous incorporation of all C&DW impact categories into energy-optimized designs is the ideal comprehensive solution. However, such approach concludes in resolving a complex multi-objective optimization problem with several objectives. Therefore, the present study proposed a conceptual sustainability-oriented model that benefits from a novel accumulated negative sustainability indicator (ANSI) with a strong potential of justifying all C&DW impact categories into one quantitative indicator. ANSI can be applied in conjunction with other energy-related indicators in an optimization structure and result in design solutions that not only are energy-efficient but also have minimum adverse impacts on sustainability in post-demolition era. A case-study under different scenarios has been performed to elucidate and evaluate the applicability and validity of the proposed model. The results showed its remarkable potential in performing as a strong alternative for the outlined complex multi-objective optimization with 9% variation in electricity demand. Comparing this model with the regular lifecycle analysis (LCA) shows a remarkable superiority in fulfilling sustainability for periods after the building's existence by over 60%.

Impact of electricity tariffs and energy management strategies on PV/Battery microgrid performances

Microgrids are considered as the future of the electricity production and networks; they often use photovoltaic (PV) production and battery storage system for supplying electricity to various loads. These microgrids need to be managed and controlled in order to operate efficiently. Five energy management (EMS) strategies more or less complex in their implementation have been developed. The most advanced rule-based control strategy integrates a PV production forecasting and a battery degradation into the decision process, thus improving the microgrid operation. An analysis of the influence of the electricity price profile on energy exchanges planning in the microgrid is realized. Four price profiles are used: peak and off-peak electricity prices, constant pricing, dynamic electricity pricing that reflects variations in electricity demand and production and a virtual electricity tariff considering the specificities of non-interconnected area as Corsica. The electricity tariff has an impact on the repartition of the energy flows and on the financial gain which justify more or less the utilization of such a microgrid. The influence of the battery size and the limitation of the electricity exchange power with the main grid were observed and depend substantially on the implemented EMS strategy.

Numerical study on the effect of guide vane opening and draft tube outlet pressure on cavitation in Kaplan turbine

variations in electricity demand and site conditions like mass flow rate and net head, force the turbines to operate under off-design conditions. This increases cyclic loading in stationary and rotating components and severe cavitation in low head turbines like Kaplan turbine. The present analysis aims to examine the influence of various operating conditions on hydraulic efficiency and cavitation characteristics using ANSYS CFX. The stages of cavitation development in the runner are extensively studied for different operating conditions. The key point of present work is the critical analysis of various flow parameters for cavitating and non-cavitating conditions. The value of critical cavitation factor is found to increase from 0.34 to 0.61 with an increase in guide vane opening from 35º to 60º. The blade loading and flow parameters are found to be affected due to changes in guide vane opening and cavitation. The computed results are validated with the experimental model testing results and found to bear close comparison.

Seasonal Variation in Electricity Demand of Solar Powered Net-Zero Energy Housing in Temperate Climates

Seasonal Variation in Electricity Demand of Solar Powered Net-Zero Energy Housing in Temperate Climates

Impact Study of Temperature on the Time Series Electricity Demand of Urban Nepal for Short-Term Load Forecasting

Short-term electricity demand forecasting is one of the best ways to understand the changing characteristics of demand that helps to make important decisions regarding load flow analysis, preventing imbalance in generation planning, demand management, and load scheduling, all of which are actions for the reliability and quality of that power system. The variation in electricity demand depends upon various parameters, such as the effect of the temperature, social activities, holidays, the working environment, and so on. The selection of improper forecasting methods and data can lead to huge variations and mislead the power system operators. This paper presents a study of electricity demand and its relation to the previous day’s lags and temperature by examining the case of a consumer distribution center in urban Nepal. The effect of the temperature on load, load variation on weekends and weekdays, and the effect of load lags on the load demand are thoroughly discussed. Based on the analysis conducted on the data, short-term load forecasting is conducted for weekdays and weekends by using the previous day’s demand and temperature data for the whole year. Using the conventional time series model as a benchmark, an ANN model is developed to track the effect of the temperature and similar day patterns. The results show that the time series models with feedforward neural networks (FF-ANNs), in terms of the mean absolute percentage error (MAPE), performed better by 0.34% on a weekday and by 8.04% on a weekend.

Variation in Electricity Demand and Thermal Environment by Demand Response Control

Variation in Electricity Demand and Thermal Environment by Demand Response Control

Power outages, climate events and renewable energy: Reviewing energy storage policy and regulatory options for Australia

The incidence of harsh climate change and weather events is having profound impacts on energy networks. In recent times, Australia has suffered several network failures and power outages that have placed human lives and community infrastructure at extreme risk. Subsequently, a diverse array of energy storage systems have been commended as a potential solution for electricity network performance and resilience issues. At this critical juncture, this review suggests that an assemblage of currently lacking targeted energy storage systems and supply development policies will be required to grow and advance future domestic energy storage assets and infrastructure; while also providing a mechanism to address changing climatic conditions, variations in electricity demand and associated demand response, and a reduced dependency on coal-fired power generation. Importantly, the recognition of energy storage systems services value across the energy supply chain will be a critical component of future systems growth. In addition, while some specific regulatory changes to create a new energy capacity market will be required, the majority of electricity market rules and procedures will need to be maintained to allow entry of energy storage systems in scheduled and semi-scheduled modes. The review also identified the requirement for strong governance that supports continued renewable energy and energy storage investment, intergovernmental cooperation, and technical network upgrades. Ideally, the implementation of these policy options and regulations should result in a robust and resilient power grid, reduced emissions and economically efficient energy storage systems rollouts.

Towards Asymptotic Costs for Lithium-Ion Batteries

Finding low-cost energy storage solutions is critical to minimizing the curtailment of renewable electricity generation and matching diurnal variations in electricity demand and production. Continuum electrochemical modeling has shown that minimizing cell hardware and maximizing electrode thickness for low-cost cathode materials (LMO) can help to reduce cell costs to below $100/kWh. We demonstrate the relationships between electrode thickness, porosity, rate and cost in order to design sufficiently thick intercalation cathodes that meet the minimum rate capabilities for low-cost long-duration storage applications.

A Comparative Study of Stochastic Models for Forecasting Electricity Generation and Consumption in Nigeria

With energy serious shortage of the Nigerian Power Sector owing to industry deregulation, abrupt variations in electricity demand, and increasing population density, Nigeria's economic development has been restricted. Thus, it is significant to balance the relationship between power generation and consumption, and further stabilize the two in a reasonable scope. To achieve balance, an accurate model to fit and predict electricity generation and consumption in Nigeria is required. This study, therefore, proposes a comparative study on stochastic modeling; (Harvey model, Autoregressive model, and Markov chain model) for forecasting electricity generation and consumption in Nigeria. The data gathered were analyzed and the model parameters were estimated using the maximum likelihood estimation technique. The comparative performance revealed that the Markov chain model best-predicted electricity generation than the Harvey and Autoregressive models. Also, for electricity consumption, results showed that the Harvey model predicted best than the Markov and Autoregressive models for electricity consumption. Thus, the Markov and Harvey model used to forecast electricity generation and consumption in Nigeria for the next 20 years (2018 to 2037) did not only reveal that electricity generation and consumption will continue to increase from 3,692.11 mln kW/h to 18,250.67 mln kW/h and from 2,961.10 mln kW/h to 127,071.30 mln kW/h respectively but also indicates high accuracy and the reference value of these models.

An empirical analysis of domestic electricity load profiles: Who consumes how much and when?

With the increased share of renewables in power generation, end users play a key role in keeping the demand at levels that better match variable supply, maintaining lower overall system costs, and reducing carbon dioxide emissions. To increase the potential for demand-side flexibility, a deeper understanding of domestic electricity load profiles is needed. Informed by customer grouping based on similar consumption patterns and drivers, targeted interventions can be better designed to time-shift peak loads and reduce overall demand. Thus, the objectives of this study are (i) to segment households in relation to their electricity load patterns using K-means clustering and (ii) to investigate household characteristics that have an influence on electricity load patterns by employing multinomial probit regression. This study uses hourly electricity consumption for 2017, combined with population-based register data for a large sample of Danish households. The results indicate that four distinct Danish household groups are characterized by different timing and magnitudes of electricity consumption, which are influenced by specific sociodemographics and dwelling characteristics. Similarities between the groups emerge with respect to the evening peak consumption, seasonal variation in electricity demand, and weekend morning demand ramp-up. Challenges and opportunities for domestic load profiling in the power industry and policymaking are discussed.

Investigation of an innovative PV/T-ORC system using amorphous silicon cells and evacuated flat plate solar collectors

Solar-driven organic Rankine cycle (s-ORC) power generation is a promising technology with thermal storage for flexible operation to meet domestic variable electricity demand. A satisfactory efficiency of this technology can be obtained only at medium-to-high temperature, for which conventional flat plate and evacuated tube solar collectors are not suitable while solar concentrators cannot efficiently utilize diffuse solar radiation. Evacuated flat plate (EFP) collectors have recently been developed for efficient solar heat collection in the temperature range from 100 to 200°C, suitable for the ORC system. At present, the cost of EFP collectors is relatively high and will lead to a long payback period of the s-ORC system. To increase the annual power yield and reduce the payback time, inexpensive amorphous silicon (a-Si) solar cells are proposed to be integrated into the EFP collectors. It is the first time to put forward such photovoltaics/thermal (PV/T) design combining a-Si cells and EFP collectors. Compared with polycrystalline silicon cells (poly-Si), a-Si cells may have a higher electrical efficiency at a higher operating temperature due to the thermal annealing effect and are expected to have a long lifetime without encapsulation in the vacuum environment provided by the EFP collectors. In this study, the a-Si PV/T-ORC system using EFP collectors is investigated. Transient performance analysis of a-Si PV/T-ORC is given for the weather data of two selected days. A comparison is also made with a stand-alone poly-Si PV system, poly-Si PV/T-ORC system and s-ORC system with EFP collectors alone, respectively. The results indicate that for a typical day in July, the a-Si PV/T-ORC system has the highest daily power output of 0.822 kWh/m2, 102.3% more than the s-ORC system, 23.8% more than the stand-alone poly-Si PV system and 12% more than the poly-Si PV/T-ORC system, respectively.

A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea

Renewable and sustainable energy systems and policies have globally been promoted to transition from fossil fuel sources to environmentally friendly renewable energy sources such as wind power, photovoltaic energy, and fuel cells. Wind and solar energy sources are erratic and difficult to implement in renewable energy systems, therefore, circumspection is needed to implement such renewable energy systems and policies. Accordingly, this study develops an energy forecasting model with renewable energy technologies on which policy can be based, using the Korean energy policy as a case study. Deep learning-based models forecast fluctuating variation in electricity demand and generation, which are necessary in renewable energy system but not possible with conventional models. The gated recurrent unit shows the best prediction performance among the forecasting models evaluated, and is therefore selected as the base model to evaluate four different renewable energy scenarios. The scenarios are evaluated according to economic-environmental cost assessment. The optimal scenario uses an integrated gasification combined cycle, onshore and offshore wind farms, photovoltaic power stations, and fuel cell plants; in particular, this scenario shows the lowest economic-environmental costs, generates stable electricity for demand, and achieves a policy with 100% renewable energy. The optimal scenario is assessed by considering its strengths, weaknesses, opportunities, and threats analysis while also considering techno-economic-environmental domestic and global energy circumstances.

Modeling of transient operation of steam superheater in CFB boiler

Currently, a need to balance power grids and compensate the variable electricity demand caused by a large share of electricity produced from renewable energy sources is observed. In countries where the coal-fired power plants are the main energy sources, the operation in the flexible regime is required. The flexible operation is characterized by frequent load changes, work outside the nominal operating conditions, and the more frequently start-ups, shutdowns, and partial load changes. Circulating Fluidized Bed combustion technology gives boiler and plant operators greater flexibility in burning a wide range of coal and other fuels, keeping high efficiency and reduced pollution. The unit load changes are realized by changing the amount of produced steam and load of the boiler. Paper presents the results of numerical modeling of superheater operation in Circulating Fluidized Bed (CFB) boiler. The superheater is characterized by the complex shape of tube cross-section. During the superheater calculations, the temperature distribution of the material in complex cross-section need to be determined. To perform numerical analysis and calculate the superheater operation in the transient state, the mathematical model of superheater was created. The use parameters dependent on temperature and pressure allowed to develop a universal superheater model. To verify the numerical model, the temperature distributions at steady state were compared with the results obtained using CFD software, confirming good results accuracy. Finally, the numerical analysis of the transient operation of steam superheater was performed. The calculation was carried out for three different partial loads and for changing steam temperature at the superheater inlet. Steam and tubes material temperature changes during transient flexible boiler operation were presented. The developed model can be used to perform calculations in the full range of boiler load, both during steady and transient boiler operation. Equations describing the physical processes occurring inside the superheater makes the model universal for a given type of a boiler.

A comparative study of time series analysis for forecasting energy demand in Nigeria

The complexity of the Nigerian Power Sector due to industry deregulation and abrupt variations in electricity demand and the ever increasing population density requires urgent research attention and industry action. Getting an accurate model to fit the energy demand pattern has become imperative and the need for an appropriate model cannot be overemphasized. This article therefore presents a comparative study on time series modelling of average and peak load forecasting in Nigeria. Data from the National Control Center (NCC), Oshogbo was harvested and analyzed using Harvey, Autoregressive, Moving Average and Exponential Smoothing Time Series Models while using R-Statistics for model simulation. The comparative performance showed that the Harvey Model best predicted load demand with least values of 111.0188 Root Mean Squared Error, (RMSE), 9.1095 Mean Absolute Error (MAE), 2.3579 Mean Absolute Percentage Error (MAPE) and 0.0156 Theil Inequality Coefficient (TIC) for average load and 117.4345 RMSE, 10.183 MAE, 17.01 Mean Absolute Percentage Error (MAPE), and 0.015 TIC for peak load. http://dx.doi.org/10.4314/njt.v38i2.24

Peak shaving and valley filling potential of energy management system in high-rise residential building

In recent years, China has recognized rapidly increasing High-rise Residential Building (HRB) constructions due to the high rate of urbanization. The intensive and variable electricity demand in HRBs exerts large pressure on grid. Pairing Energy Management System (EMS) with PV storage system provides a clean and efficient way to utilize local renewable resources. By dispatching shiftable loads and storage resources, EMS could effectively reshape the electricity net demand profiles and match customer demand and PV generation. In this paper, a Multi-Agent System (MAS) framework is employed to investigate the peak shaving and valley filling potential of EMS in a HRB which is equipped with PV storage system. The effects of EMS on shiftable loads and PV storage resources are analyzed. The simulation results reveal the feasibility of the proposed approach to effectively flatten the HRB electricity demand and net demand profiles. With the help of EMS, the peak-to-valley ratio of demand profiles and net demand profile are reduced significantly.

A Framework for Expansion Planning of Data Centers in Electricity and Data Networks Under Uncertainty

This paper presents the expansion planning for data centers and data routes in the data and electricity networks considering the uncertainties in the planning horizon to ensure an acceptable rate of service to the requests received from the end-users in the data network. The objective is to determine the location and capacity of the data centers as well as the required data routes while considering the imposed constraints in the electricity and data networks. The installation cost of data centers and data routes, as well as the expected operation cost of the data centers, are minimized. The proposed problem addressed the uncertainties in the expansion planning of the electricity networks including the availability of renewable generation resources, the variations in electricity demand, the availability of generation and transmission components in the electricity network, and the uncertainties in the number of requests received by the user groups in the data network. The problem is formulated as a mixed integer linear programming problem and Bender decomposition and electricity price signals are used to capture the interaction among the data and electricity networks. The presented case study shows the effectiveness of the proposed approach.