Future Electricity Demand Research Articles

Future of Renewable Energy Use in Pakistan in the Context of Agrifood-Energy Nexus

Pakistan is facing a serious energy supply problem due to lack of energy that results in a shortfall of power supply from 6–10 hours/day, especially outside the big cities. The energy demand is expected to increase in every sector such as industrial, agricultural, household and transport in the years ahead due to population growth, economic development and modern lifestyle. Agricultural sector plays a vital role in the country’s economy contributing around 20% share in GDP. At present the direct energy consumption in the agricultural sector is 2%. To avoid a worse situation in the future years renewable energy resource potential exploitation is needed. This requires also proper planning and implementation of energy policies. In this work present and future electrical energy consumption, demand and supply gap of the country is presented. A sustainable agricultural productive system has become a major priority to maintain food security for the increasing population. Both direct and indirect electrical and energy consumption has emerged in agrifood sector with farm machinery, production, packing and transport of fertilizers, food processing and transport, crop production, animal product production, poultry production, etc. In addition to this excel based model is developed to integrate the renewable energy into the agrifood – energy nexus supply chain and to fulfil the current and future energy demand of the sector.

How might controlled fusion fit into the emerging low-carbon energy system of the mid-twenty-first century?

We examine the characteristics that fusion-based generation technologies will need to have if they are to compete in the emerging low-carbon energy system of the mid-twenty-first century. It is likely that the majority of future electric energy demand will be provided by the lowest marginal cost energy technology-which in many regions will be stochastically varying renewable solar and wind electric generation coupled to systems that provide up to a few days of energy storage. Firm low-carbon or zero-carbon resources based on gas-fired turbines with carbon capture, advanced fission reactors, hydroelectric and perhaps engineered geothermal systems will then be used to provide the balance of load in a highly dynamic system operating in competitive markets governed by merit-order pricing mechanisms that select the lowest-cost supplies to meet demand. These firm sources will have overnight capital costs in the range of a few $/Watt, be capable of cycling down to a fraction of their maximum power output, operate profitably at low utilization fraction, and have a suitable unit size of order 100 MWe. If controlled fusion using either magnetic confinement or inertial confinement approaches is to have any chance of providing a material contribution to future electrical energy needs, it must demonstrate these key qualities and at the same time prove robust safety characteristics that avoid the perceived dread risk that plagues nuclear fission power, avoid the generation of long-lived radioactive waste and demonstrate highly reliable operations. This article is part of a discussion meeting issue 'Prospects for high gain inertial fusion energy (part 1)'.

Electrical Load Forecasting: A methodological overview

Electric load forecasting process plays an extensive role in forecasting future electric load demand and peak load by understanding the previous data. Several researchers proved that, the presence of load forecasting error leads to an increase in operating costs. Thus Accurate electric load forecast is needed for power system security and reliability. It also improves energy efficiency, revenues for the electrical companies and reliable operation of a power system.In recent times, there are significant proliferations in the implementation of forecasting techniques. This survey aids readers to summarize and compare the latest predominant researches on electric load forecasting. Besides, it presents the most relevant studies on load forecasting over the last decade and discusses the different methods that are used in load prediction as well as the future directions in this field.

Demand response through automated air conditioning in commercial buildings\u2014a data-driven approach

Building operation faces great challenges in electricity cost control as prices on electricity markets become increasingly volatile. Simultaneously, building operators could nowadays be empowered with information and communication technology that dynamically integrates relevant information sources, predicts future electricity prices and demand, and uses smart control to enable electricity cost savings. In particular, data-driven decision support systems would allow the utilization of temporal flexibilities in electricity consumption by shifting load to times of lower electricity prices. To contribute to this development, we propose a simple, general, and forward-looking demand response (DR) approach that can be part of future data-driven decision support systems in the domain of building electricity management. For the special use case of building air conditioning systems, our DR approach decides in periodic increments whether to exercise air conditioning in regard to future electricity prices and demand. The decision is made based on an ex-ante estimation by comparing the total expected electricity costs for all possible activation periods. For the prediction of future electricity prices, we draw on existing work and refine a prediction method for our purpose. To determine future electricity demand, we analyze historical data and derive data-driven dependencies. We embed the DR approach into a four-step framework and demonstrate its validity, utility and quality within an evaluation using real-world data from two public buildings in the US. Thereby, we address a real-world business case and find significant cost savings potential when using our DR approach.

Technical potential of on-site wind powered hydrogen producing refuelling stations in the Netherlands

This study assesses the technical potential of wind turbines to be installed next to existing fuelling stations in order to produce hydrogen. Hydrogen will be used for Fuel Cell Vehicle refuelling and feed-in existing local gas grids. The suitable fuelling stations are selected through a GIS assessment applying buffer zones and taking into account risks associated with wind turbine installation next to built-up areas, critical infrastructures and ecological networks. It was found that 4.6% of existing fuelling stations are suitable. Further, a hydrogen production potential assessment was made using weather station datasets, land cover data and was expressed as potential future Fuel Cell Electric Vehicle demand coverage. It was found that for a 30% FCEV drivetrain scenario, these stations can produce 2.3% of this demand. Finally, a case study was made for the proximity of those stations in existing gas distribution grids.

Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study

Predicting future energy demand will allow for better planning and operation of electricity providers. Suppliers will have an idea of what they need to prepare for, thereby preventing over and under-production. This can save money and make the energy industry more efficient. We applied a multiple regression model and three Convolutional Neural Networks (CNNs) in order to predict Florida’s future electricity use. The multiple regression model was a time series model that included all the variables and employed a regression equation. The univariant CNN only accounts for the energy consumption variable. The multichannel network takes into account all the time series variables. The multihead network created a CNN model for each of the variables and then combined them through concatenation. For all of the models, the dataset was split up into training and testing data so the predictions could be compared to the actual values in order to avoid overfitting and to provide an unbiased estimate of model accuracy. Historical data from January 2010 to December 2017 were used. The results for the multiple regression model concluded that the variables month, Cooling Degree Days, Heating Degree Days and GDP were significant in predicting future electricity demand. Other multiple regression models were formulated that utilized other variables that were correlated to the variables in the best-selected model. These variables included: number of visitors to the state, population, number of consumers and number of households. For the CNNs, the univariant predictions had more diverse and higher Root Mean Squared Error (RMSE) values compared to the multichannel and multihead network. The multichannel network performed the best out of the three CNNs. In summary, the multichannel model was found to be the best at predicting future electricity demand out of all the models considered, including the regression model based on the datasets employed.

Optimal Planning for Satisfying Future Electricity Demands Involving Simultaneously Economic, Emissions, and Water Concerns

The main problem related to the electricity generation planning is to ensure the total satisfaction of the demanded power along a period of time considering the population growth. Since the power demand is function of the growing population, there is a fast depletion of the environment caused mainly by the harmful emissions produced and the big amounts of water consumed in large power generation plants. Therefore, when a power supply system is planning, it is important to evaluate the negative outcomes that could be generated in the environment and to balance them with the economic performance of the system. In this work is presented an optimization approach for the strategic planning of electricity generation, the model seeks the optimal selection of technologies for the new power generation capacity (clean and conventional) which will satisfy the power demand in different scenarios in a specific period of time. The proposed multi-objective model is solved using the e-constraint method for determining a trade-off between economic, harmful emissions (CO2, NOx, SO2 and particle emissions), and water consumption through a set of Pareto diagrams. The power demand and the resources available for electricity generation in Mexico are presented as a case study, where it is possible to reduce up to 38.4% of emissions and 41.58% of water consumed, by minimizing these two factors, which has a significant environmental impact.

A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons

With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly.

The energy-climate-health nexus in energy planning: A case study in Brazil

Global greenhouse gas emissions from energy production were approximately 40% higher in 2017 than in 2000 (International Energy Agency, 2018), and ambient particulate matter – one of the byproducts from fossil fuel combustion of most concern for public health – is now the fifth largest contributor to global disease (Cohen et al., 2017). Achieving the climate targets of the Paris Agreement and the Sustainable Development Goals requires better accounting for climate and health costs in energy planning. This paper quantifies trade-offs between selected energy infrastructure, climate, and health costs when meeting future electricity demand by increasing the share of renewable energy, with a focus on variable renewable energy (VRE; here: wind and solar photovoltaic power). Using a spatially and temporally resolved approach, we analyzed three scenarios for year 2030 for Northeast Brazil, characterized by 30%, 45%, and 70% of VRE (the latter corresponds to 100% renewable energy). We find that accounting for the health impacts from electricity generation is sufficient to economically justify deep decarbonization of Northeast Brazil's power sector. Full decarbonization is economically justified when the carbon price exceeds $20/tonne CO2, which is less than Brazil's country-level social cost of carbon and only 4.8% of the global social cost of carbon. Our study shows that regional climate and health costs from electricity generation alone can be greater than the additional infrastructure costs of decarbonization. Our results highlight how systematically accounting for health and climate costs in energy planning would economically justify the decarbonization of energy systems.

Analysis of the Effects of Electrification of the Road Transport Sector on the Possible Penetration of Nuclear Fusion in the Long-Term European Energy Mix

The European Roadmap towards the production of electricity from nuclear fusion foresees the potential availability of nuclear fusion power plants (NFPPs) in the second half of this century. The possible penetration of that technology, typically addressed by using the global energy system EUROFusion TIMES Model (ETM), will depend, among other aspects, on its costs compared to those of the other available technologies for electricity production, and on the future electricity demand. This paper focuses on the ongoing electrification process of the transport sector, with special attention devoted to road transport. A survey on the present and forthcoming technologies, as foreseen by several manufacturers and other models, and an international vehicle database are taken into account to develop the new road transport module, then implemented and harmonized inside ETM. Following three different storylines, the computed results are presented in terms of the evolution of the road transport demand in the next decades, fleet composition and CO 2 emissions. The ETM results are in line with many other studies. On one hand, they highlight, for the European road transport energy consumption pattern, the need for dramatic changes in the transport market, if the most ambitious environmental goals are to be pursued. On the other hand, the results also show that NFPP adoption on a commercial scale could be justified within the current projection of the investment costs, if the deep penetration of electricity in the road transport sector also occurs.

Climate Change Effects on Hydropower in Mozambique

The impact of climate change on the production of hydropower in Mozambique is reviewed and regression analysis is applied to evaluate future climate scenarios. The results show that climate change will cause increased variability of precipitation and create flooding that can damage infrastructure such as hydropower dams. Climate change can also cause drought that will decrease surface water and reduce hydroelectric generation in Mozambique. Electricity generation is to a major extent performed through large-scale hydropower in Mozambique. To fulfill the sustainable development goals (SDGs) and an increased demand for electricity, several large and many small hydropower projects are planned and were built in the country. The economic lifetime of a hydropower plant is typically 100 years, meaning that the hydrologic regimes for the plants should be evaluated for at least this period. Climate change effects are rarely included in present feasibility studies. Economic implications associated with climate change phenomena are higher in Mozambique than in neighboring countries as its future electricity demand to a large extent is forecasted to be met by hydropower. The large hydropower potential in Mozambique should as well be considered when investing in new power plants in southern Africa.

Managing Power Demand from Air Conditioning Benefits Solar PV in India Scenarios for 2040

An Indian electricity system with very high shares of solar photovoltaics seems to be a plausible future given the ever-falling solar photovoltaic (PV) costs, recent Indian auction prices, and governmental support schemes. However, the variability of solar PV electricity, i.e., the seasonal, daily, and other weather-induced variations, could create an economic barrier. In this paper, we analyzed a strategy to overcome this barrier with demand-side management (DSM) by lending flexibility to the rapidly increasing electricity demand for air conditioning through either precooling or chilled water storage. With an open-source power sector model, we estimated the endogenous investments into and the hourly dispatching of these demand-side options for a broad range of potential PV shares in the Indian power system in 2040. We found that both options reduce the challenges of variability by shifting electricity demand from the evening peak to midday, thereby reducing the temporal mismatch of demand and solar PV supply profiles. This increases the economic value of solar PV, especially at shares above 40%, the level at which the economic value roughly doubles through demand flexibility. Consequently, DSM increases the competitive and cost-optimal solar PV generation share from 33–45% (without DSM) to ~45–60% (with DSM). These insights are transferable to most countries with high solar irradiation in warm climate zones, which amounts to a major share of future electricity demand. This suggests that technologies, which give flexibility to air conditioning demand, can be an important contribution toward enabling a solar-centered global electricity supply.

Energy Development: Choice and Implementation of Strategic Decisions

The problems that have accumulated in the Russian energy sector are systemic in nature and require serious scientific analysis. The transition of this country to a market economy was accompanied by a sharp drop in electricity consumption and the commissioning of new electricity generating capacities and, as a result, led to the degradation of domestic power engineering and the collapse of the innovative system in the energy sector. Launched in 2010, the mechanism for attracting investment in the industry on the basis of capacity supply agreements allowed by 2019 to commission about 30 GW of new electricity generating capacities—two times more than in the previous 20 years. However, miscalculations in assessing the prospects for the country’s economic development and, consequently, future electricity demand have led to a large excess of capacities, mainly those that are old with low efficiency. Their maintenance requires serious costs that are transferred to the price of electricity. The causes of and remedies for the current situation are analyzed. It is argued that the currently discussed expensive program of modernization of the domestic electric power industry will not be able to overcome the accumulated problems. It is necessary to change approaches to the implementation of the program. Attention is drawn to the need to strengthen the role of the state in the preparation for and implementation of optimal strategic decisions on the scientific and technological development of the Russian electric power industry and the reconstruction of an innovative system in it. It is necessary to improve the mechanisms of state support for the creation and organization of production, to introduce new domestic equipment, and to include the active involvement of business circles in this process.

Optimum Renewable Energy Investment Planning in Terms of Current Deficit: Turkey Model

In this study, a methodology was suggested for wind and solar energy investment plans through linear optimization model for the countries with an energy-based current deficit problem. The originality of the study is that it is a renewable energy investment model based on the functioning of the balance of payments for current deficit reduction, which has not previously been encountered in the literature. While creating the model, without causing external economic imbalance, certain parameters were taken into consideration such as profit transfers for the foreign direct investments, interest payments for the domestic investments, import rates for the wind and solar energy systems, energy electric power production values, electric power load balance, electricity transmission infrastructure, CO2 emission, future electric power demand projection, and import source rates in the electric power production. It was proven that the model, for the 2019–2030 period in Turkey, not only is an opportunity for decreasing the current deficit but also ensures reaching the CO2 emission reduction target. Additionally, through the investments in wind and solar energy, it was calculated that fossil-based electric power production will decrease by 80%, and a CO2 reduction will be provided, which is equivalent of 100 million tonnes GWh natural gas. As a more general result, an optimization model was created which provides a solution for countries coping with energy-based current deficit in economic terms, energy-based air pollution in environmental terms, and renewable energy technology insufficiency.

Staying cool: The development of India's pioneering energy efficiency policy for chillers

AbstractChillers are the key energy‐consuming piece of equipment in most large space cooling systems. They can account for more than 40% of total energy consumption in any typical commercial building and in specific industrial building installations depending on the product and process. With climate change driving rapidly rising demand for commercial and industrial cooling, especially in emerging economies, energy efficiency policies for chillers are crucial to moderate future electricity demand and thereby minimize greenhouse gas emissions. However, most developing and emerging economies lack energy efficiency policies for this significant and growing cooling equipment sector. India recently announced new energy efficiency policies for chillers, projected to avoid 28 TWh of electricity consumption and 23 million tons of CO2 emissions cumulatively through 2030. This article presents a case study of the unique challenges and solutions required to design a new energy efficiency policy for chillers in India. First, the energy consumed in chiller systems is sensitive to building occupancy, load changes, seasonal variations, operation and maintenance, and climatic conditions. In order to address these sensitivities, the chiller testing standard in India factors local weather and temperature conditions into energy performance ratings to account for these sensitivities. Further, every large chiller is a customized product, therefore developing a viable policy and implementation process required collaboration with international and national experts in standardization, manufacturers, building consultants, and testing laboratories.This article is categorized under:Energy Efficiency > Economics and PolicySolar Energy: Thermal ‐‐> Climate and Environment

Site Selection of Solar PV Power Plant at Bathinda

In this paper, the technical and economical feasibility study of the photovoltaic (PV) power plant at Bathinda city in the Punjab state of INDIA has been carried out. For this, solar irradiance of this location has been used to assess the annual solar energy potential of the selected site PV plant. The complete work has been carried out using PVsyst simulation software. This study has been carried out to feed the electrical energy generation deficit and the increasing future electrical energy demand of Punjab State. At present, Punjab State Power Corporation Limited (PSPCL) is paying ₹ 9.04 per kWh to the private players and the other states to procure electrical energy to meet the consumer load demand. In this work, it has been found that the actual cost to the company generating electrical power using PV is around ₹ 3 per kWh after including the capital cost, loan interests, depreciation, running charges and maintenance costs. So, the proposed PV generation setup definitely will prove to be beneficial for profit-making proposition for the company supplying electrical energy to PSPCL. Besides, reduction in carbon and GHG emissions with this proposed generation has also been evaluated, which will save the environment from global warming. This is due to the fact that most of the electrical power generation in the Punjab state of India is through thermal power.

Future electricity demand and flexibility potential of private households

In the upcoming years, a disruptive revolution of the electricity demand in terms of load patterns and annual consumption is expected. Electricity from renewable energy sources can be used to decarbonise further sectors like the mobility sector. These new appliances, like electric vehicles, significantly increase and reshape the electricity demand of private households while efficiency gains slightly reduce the electricity consumption. Furthermore, the ongoing digitalisation allows an efficient controlling of the flexibility of these appliances. These developments make detailed investigations of the future electricity demand and flexibility potentials of private households inevitable. For this purpose, a Bottom-Up simulation method has been developed and is presented in this study. Using time use data of household members, activity profiles are derived based on a time-dependent Markov-chain model. Based on these activity profiles, different submodels for the simulation of electric appliances, lighting, heating and electric vehicles are used. The investigations in this contribution show the influence of new appliances like electric vehicles on the electricity demand of private households in terms of peak load and energy demand. Furthermore, the results allow a better estimation of the flexibility potential of appliances like washing machines based on the simultaneity of their usage.

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

Load forecasting is a pivotal part of the power utility companies. To provide load-shedding free and uninterrupted power to the consumer, decision-makers in the utility sector must forecast the future demand for electricity with a minimum error percentage. Load prediction with less percentage of error can save millions of dollars to the utility companies. There are numerous Machine Learning (ML) techniques to amicably forecast electricity demand, among which the hybrid models show the best result. Two or more than two predictive models are amalgamated to design a hybrid model, each of which provides improved performances by the merit of individual algorithms. This paper reviews the current state-of-the-art of electric load forecasting technologies and presents recent works pertaining to the combination of different ML algorithms into two or more methods for the construction of hybrid models. A comprehensive study of each single and multiple load forecasting model is performed with an in-depth analysis of their advantages, disadvantages, and functions. A comparison between their performance in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are developed with pertinent literature of several models to aid the researchers with the selection of suitable models for load prediction.

Hybrid short-term forecasting of the electric demand of supply fans using machine learning

This paper presents the development and application of multi-step-ahead short-term forecasting models targeting supply fans installed in an institutional building. The models applied in this work consist of an artificial neural network (ANN) applied in order to forecast the future supply air flow rate of the fans (black-box approach), and a physical model coupled with the ANN applied in order to forecast the future electric demand of the supply fans (hybrid grey-box approach). The forecasting models use measurement data obtained at 15-min intervals in order to forecast the target variables over the next 6 h. The architecture of the ANN was found through an automated search in the training data set. The paper compares the results of selected ANN models with those from other machine learning techniques (support vector regression and ensemble methods) along with a simple forecasting approach. The results of this study show a better forecasting performance when compared with the results from other publications: the CV(RMSE) is 1.8–3.4% for the air flow rate, and 4.8–7.3% for the electric demand for all new models. The results demonstrate that automating the hyperparameter search of the ANN architecture can help alleviate the difficulty of manual parameter setting and achieve a high performing model.

Least‐cost generation expansion planning using whale optimization algorithm incorporating emission reduction and renewable energy sources

Ever-increasing energy demand and technology development are continuously pursuing the attention of power system planners to design an optimum strategy for supplying future electrical demand. Generation expansion planning turns out to be a crucial step for an efficient energy management system in a modern power grid. In this paper, a novel whale optimization algorithm with penalty factor approach (WOA-PFA) framework has been developed to solve a nonlinear, discrete, dynamic, highly complex, heavily constrained GEP problem. Reliability evaluation is accomplished using equivalent energy function approach. A virtual mapping procedure is utilized to add computational strength to the WOA-PFA framework. Emission reduction is performed by utilizing total emission reduction constraint. The results are organized in four case studies starting with base power plants and adding wind, solar, and solar with storage facility in succession for the rest of case studies. Simulation results prove that the proposed WOA-PFA shows promising results in terms of the least cost and computational time as compared with the techniques presented in the literature.